Introduction
Human health outcomes are often products of the interplay between several biological and environmental factors. Based on this relationship, scientists have often strived to learn a lot of information surrounding the physiological, anatomical and biological issues underpinning human health outcomes with mixed outcomes (Blau, Brown, Mahanta, & Amir, 2016). The concept of personalized medicine has helped to improve the process because it helps them to understanding how each of the selective factors, highlighted above, affect human health outcomes and how they possibly intersect to determine human health outcomes (Blau et al., 2016). The concept of personal medicine hails from a common philosophy in the health practice (shared by philosophers, such as Hippocrates) which says, “It is far more important to know what person the disease has than what disease the person has” (Management Association Information Resources, 2016, p. 298).
In the last decade, there has been a range of new medical products that have come to the market, courtesy of advances in personal medicine. However, these developments have not been homogenously distributed in different fields of medicine because some health sectors have embraced the concept better than others (Management Association Information Resources, 2016). For example, many researchers agree that the oncology field has benefitted the most from advances in personalized medicine (Schleidgen, Klingler, Bertram, Rogowski, & Marckmann, 2013). In fact, in the last three years alone, the Federal Drug Administration (FDA) has approved four cancer drugs that have been developed from the concept (U.S. Department of Health and Human Services, 2013). These drugs have specifically been targeted at patients who have developed certain types of tumours, but who have specific genetic characteristics. Recently, the FDA also approved specific therapies for some patients who suffer from specific cystic fibrosis (U.S. Department of Health and Human Services, 2013). Most of these patients have genetic mutations that cause the disease. New technologies that stem from personalized medicine have also allowed medical researchers to use 3-D printing to treat critically ill infants (Schleidgen et al., 2013). For example, this technology has been used to create a bioresorbable tracheal splint to treat this group of patients.
Each of the aforementioned examples explains the contributions of personalized medicine in the health field. Generally, this field of medicine advocates for the development of precision drugs to suit individual characteristics or preferences of different patient cohorts. However, in as much as developments in personalized medicine are more concentrated today than in the past, the concept is not new.
According to Rehm, Hynes, and Funke (2016), the concept of personal medicine has existed for more than 100 years and it was not until the 19th century that scientists started to appreciate its relevance in the field of medicine. Researchers in the fields of chemistry and microscopy spearheaded this new development (Management Association Information Resources, 2016). They were among the first researchers to use personal medicine to investigate the underlying causes of different diseases affecting human societies during the 19th century (U.S. Department of Health and Human Services, 2013). Since then, advances in health and medicine have aided the dissipation of different tenets of the concept to the wider medical practice, thereby making it granular over time. For example, developments in the pharmaceutical and medical industries led to the spread of the concept in different fields of medicine through developments in imaging technology and data mining skills (Albanese et al., 2013). In the middle of the 20th century, researchers started investigating how different people respond to different drugs, including how they metabolize them and how they help them to cope with the diseases affecting them (U.S. Department of Health and Human Services, 2013). These improvements emerged as a foundation for developments in the pharmacogenetics sector. Based on such developments, the transformation of the personalized medicine industry, from an idea into practice, was concentrated in the 20th century. Relative to this development, the U.S. Department of Health and Human Services (2013) says, “Rapid developments in genomics, together with advances in a number of other areas, such as computational biology, medical imaging, and regenerative medicine, are creating the possibility for scientists to develop tools to truly personalize diagnosis and treatment” (p. 5).
Although there have been many developments made in personalized medicine, researchers still agree that they have a long way to go in understanding why patients respond to differently to varied drugs and treatments (Blau et al., 2016). This problem partly comes from the inability of clinical practitioners to predict (accurately) the outcome of different treatment methods to specific groups of patients. Coupled with the availability of other treatment options and the lack of genetic biomarkers for every patient that would allow them to predict their reactions to different therapies, these methods are often less than optimal (Management Association Information Resources, 2016). For example, current practice dictates that a patient suffering from high blood pressure would be subjected to only one line of treatment, without the proper understanding of whether this treatment is ideal for his/her individual preferences or not. In fact, currently, most doctors select a treatment method based on general information about the patient. The personalized approach focuses on assigning treatment plans to these groups of patients, based on specific information about their genetic makeup (Personalized Medicine Coalition, 2014). Comparatively, current practice dictates that if the patient fails to respond to one line of treatment, the doctor switches to another. Generally, this treatment plan is based on trial and error. Some negative outcomes associated with it may be adverse drug responses, or even patient dissatisfaction, or complaints, about the services offered (Personalized Medicine Coalition, 2014). In extreme cases, patients may refuse to follow the laid down treatment regimens and lead to the worsening of health outcomes (U.S. Department of Health and Human Services, 2013).
Personalized medicine strives to eliminate these challenges by streamlining the medical decision-making processes by making it more receptive to individual health profiles. In this regard, the concept helps practitioners to understand which treatments would work for a specific cluster of patients and which ones would cause adverse side effects, or outcomes, for the same group of patients. This contribution explains why some observers consider personalized medicine as an art that helps medical practitioners to “provide patients with the right medicine, at the right dosage and at the right time” (U.S. Department of Health and Human Services, 2013, p. 6).
Indeed, medical researchers have always observed that, although people may suffer from the same diseases or conditions, treatments may work differently for different groups of people (Management Association Information Resources, 2016). Advances in technology, in different fields of medicine, such as genomics, and regenerative medicine, have created the opportunity to provide personalized medicine because they have allowed medical experts to treat and monitor their patients more precisely and effectively (Buriani et al., 2012). In other words, what is new is the promise that personalized medicine will develop targeted therapeutic tools for understanding who will respond better, or worse, to a specific medical treatment. Similarly, the concept promises that medical practitioners would be able to better assess the health risks that specific groups of patients could suffer from, based on their genetic biomarkers (Rehm et al., 2016). The increased use of personalized medicine in the last decade is proof of the power of science in advancing medical practice. However, these developments do not understate the complexity associated with understanding human health and diseases. In this review, we explore the different aspects of personalized medicine through a review of how bench research could improve bedside utility. This paper is divided into five chapters that explore the role of certification and surveillance in personal medicine, adherence to the disruption caused by the concept, what works and what does not work (from bench to bedside), the future technology trends in personalized medicine and the infrastructure needed to support the concept in the healthcare practice.
Assuring a Solid Infrastructure
Many health care institutions often find it difficult to implement the concept of personalized medicine without a proper or solid infrastructure to support the process (Blau et al., 2016). Concisely, there are many challenges associated with the implementation of this concept and they make it difficult for medical practitioners to implement personal medicine without considering the structures needed to make the concept workable in their places of work. Scaling challenges and the challenge of managing huge populations are some issues that highlight the need to have a solid infrastructure when implementing personalized medicine (Personalized Medicine Coalition, 2014). The process of developing the right infrastructure to meet the needs of personalized medicine requires an evaluation of existing technology, optimization of the clinical trial network, and an understanding of the support that scientists need in identifying new biomarkers that would help to provide personalized care to different patient groups (Halim, 2015). Similarly, there needs to be well thought out strategies that would help stakeholders to understand the clinical utility of personalized trials, as a standard procedure for choosing the best treatment methods to give patients (Moore, 2015).
Karlson, Boutin, Hoffnagle, and Allen (2016) draw our attention to the need for a solid information technology (IT) infrastructure as a core area of implementing the personal medicine concept because it would help to increase collaboration across different facets of the healthcare practice. This collaboration could lead to the development of a joint infrastructure for collecting and storing biological information and data regarding different cohorts of patients. In addition, there is a need to have a nationwide technological infrastructure for accommodating both treatment and research data in key areas of personal medicine (U.S. Department of Health and Human Services, 2013). Indeed, some nations have built this infrastructure and connected it with existing local and central health infrastructures within their jurisdictions (Management Association Information Resources, 2016). Such infrastructural networks are often notable within their health care systems.
Some institutions have developed such IT infrastructures, with high precision, as is seen from the works of the Massachusetts General Hospital, which has built an IT infrastructure for personalized medicine (Weiss & Shin, 2016). Through a program called the Partners Personalized Medicine, the hospital developed this unique infrastructure, which contains four key tenets. The first one is a laboratory for molecular medicine. In this facility, the hospital undertakes genetic testing for different patients around the world. The main aim of developing this unit was to bridge the gap between research and clinical medicine within the facility (Weiss & Shin, 2016). Having been in existence for more than 13 years, the facility has evolved to maintain a specialization in germ line mutation testing. The second tenet of the hospital’s IT infrastructure is the translational genomics core (TGC), which performs next generation sequencing (Weiss & Shin, 2016). Genotyping and gene expression analysis are other activities that go on within this unit. It is available for research to all partner investigators and even those who are not partners in the hospital.
Through this open-policy advantage, the healthcare facility provides a platform for collaboration to different types of researchers in the area. The third component of the IT infrastructure is the partner’s bio-bank, which contains gene samples of more than 50,000 patients (Weiss & Shin, 2016). The samples include DNA, Plasma and Serum samples for different groups of patients from different partners. In this regard, this component of the IT infrastructure acts as a bank for storing biological samples for research and analysis. Closely related to this component of the institution’s IT network is the bio-bank portal, which acts as a platform for researchers to investigate the different biological samples available (Ishikawa, 2012). In this platform, they bring together different phenotypes, genotypes and samples of biological specimen from different patient groups for analysis. Collectively, this information technology infrastructure is broad and encompasses the input of different health personnel, including clinicians, patients and researchers. Their collective contribution promotes enhanced research and optimal patient care. Generally, this example shows how the IT infrastructure is important in the implementation of personalized medicine because it supports different functions associated with the concept (Weiss & Shin, 2016).
The development of a solid infrastructure to support personalized medicine has not only occurred at an institutional level, but regionally as well. This is because countries have collaborated to improve the quality of their medical research on personalized medicine through the development of joint infrastructural platforms. For example, according to Sun (2016), Asian countries have pooled their resources to develop joint infrastructure projects that would support the implementation of personalized medicine. For example, the International Cancer Genome Consortium is one such platform that has been developed by these countries to aid in cancer research and assist in the development of cancer treatment methods within the wider framework of personalized medicine (Sun, 2016; American Society of Clinical Oncology, 2017). China, India, Japan, and Singapore are four Asian countries that are part of the initiative as well (Sun, 2016). However, the list of nations funding the project is longer because Saudi Arabia and South Korea are equally part of the nations funding the initiative (Sun, 2016).
Assadi and Nabipour (2014a) say the danger associated with the adoption of personalized medicine rests in the socioeconomic inequalities that persist throughout the world. In other words, developed or wealthy countries are in a better position to manage the challenges associated with the concept, thereby providing the ground to increase health inequalities around the world. The high cost of processing and storing data is only one challenge associated with this inequality. The others are the high costs associated with research and development as well as the technical skills that are required in the process, which some of these countries may lack. Assadi and Nabipour (2014a) say that, compounding this problem is akin to the concept of the “tragedy of the commons,” where the interests of a few individuals may trample over the interests of the majority. However, Noble Laurette Elinor Ostrom says that this problem should not be alarming because it is not common among human societies in the first place (Assadi & Nabipour, 2014a). He bases his view on the fact that human beings have often found solutions to their problems by developing trust between one another. Through clear regulatory frameworks, human societies have learned to work well with each other, considering trust and respect becomes the key currency of cooperation. However, there is a strong need to understand the role that institutional diversity may play in making human societies prosper even more.
The diversity of human societies should be reflected in the diverse applications of personalized medicine. Creating different tiers of health care systems is one way of doing so because having only one-tier of health care may compromise the need to have an equitable and efficient resource distribution method in the health care industry (Assadi & Nabipour, 2014a). A nested regulation system should promote this kind of framework because there needs to be a neutral ground where science and ethics, within the wider framework of personalized medicine meet. In other words, all stakeholders in the health care sector should be accountable for their actions because no person should be above another, or try to undercut what another party is doing.
In the last few decades, the infrastructure supporting personalized medicine has started to evolve because institutions and health agencies are starting to focus on building their educational and legal infrastructures, which are supposed to complement the implementation of personalized medicine (Hood & Flores, 2012). For example, at an institutional level, different medical schools have started to develop training programs that strive to educate people about the benefits of personalized medicine and how they could use the concept in their practice (U.S. Department of Health and Human Services, 2013). The initiative has also spread to intergovernmental levels where federal and state authorities are introducing new programs to educate practicing physicians about personalized medicine and how they could incorporate it in their practice (U.S. Department of Health and Human Services, 2013). Health-based institutions, such as the Genetic Nursing Credentialing Commission, which gives practicing nurses a licence to practice personalized medicine (their certification mostly focuses on genetics), have spearheaded such initiatives (Sweet & Michaelis, 2012). Different medical centres around the world have also taken the initiative of branding themselves “centres of personalized medicine,” based on these developments. Hospitals have also not been left behind because they have changed their policies to accommodate personalized medicine as a core area of their practice (Management Association Information Resources, 2016). These policies have mostly been used to guide treatment decisions from the beginning to the end of a patient’s care.
The involvement of the national governments in enabling the development of the legal infrastructure needed in the implementation of personalized medicine comes from the fact that they recognize the efficiency and cost-reduction advantages the concept introduces to the healthcare practice (Personalized Medicine Coalition, 2014). Different governments have reported different paces in enabling the development of this legal infrastructure. For example, in America, the Obama administration introduced the Genomics and Personalized Medicine Act and the American Recovery and Reinvestment Act, which were aimed at complementing the existing legal infrastructure for personalized medicine (U.S. Department of Health and Human Services, 2013). Both legal instruments were pivotal in deploying more than $19 billion in federal resources to the upgrading of the country’s electronic information platform to support personalized medicine (Sweet & Michaelis, 2012). Particularly, this investment helped in the effective and efficient use of genetic testing data for cancer management. The outcome was a reduction in the cost of health care (Sweet & Michaelis, 2012). Such infrastructural developments also help in the improvement of communication networks between basic researchers and clinical researchers because both sets of stakeholders could communicate more efficiently and effectively on the improved electronic platform. The same initiatives have also been associated with a reduction in the misuse of genetic information gathered and stored in the personalized medicine bio-data (Hood & Flores, 2012).
The development of the health infrastructure supporting personalized medicine is not only improving communication among different researchers, but also integrating different sub-domains of several fields associated with the concept. For example, the National Cancer Institute, in America, has benefitted from these infrastructural developments by integrating the activities of different clinical and research laboratories (Burock, Meunier, and Lacombe, 2013). The Biometrics Normative Grid Initiative, which began in 2004, has also benefitted from the same development by maintaining an integrated cycle of medical discovery, which has been merged with clinical application, thereby making the process more efficient and effective (Hood & Flores, 2012). The American Centre for Disease Control and Prevention has intervened to regulate the process by assessing the ethical, social, and legal implications of practicing personalized medicine, especially as it relates to genetic tests (Burock et al., 2013).
However, major changes in the creation of a solid infrastructure for the deployment of personalized medicine cannot occur without the involvement of insurance agencies. Their involvement in the development of the necessary infrastructure is becoming increasingly important, especially after studies have shown that personalized medicine helps to reduce treatment costs (Management Association Information Resources, 2016). Indeed, as explained by Sweet and Michaelis (2012), “a better understanding of the genetic basis for disease risk can help some people tailor their diet, environment and lifestyle to reduce their preventable risk of diseases for which their genetic susceptibility is greatest, avoiding the cost of treatment” (p. 29). Furthermore, by improving health care decision-making processes, insurance companies are attracted to the fact that personalized medicine could help to reduce the costs associated with ineffective treatments (Management Association Information Resources, 2016). The same cost reduction measure could occur through the reduction of treatment costs associated with adverse side effects. Other players in the heath sector, such as the American Association of Health Plans and the United Health and Kaiser Permanente, have also acknowledged the cost reduction advantages associated with personalized medicine (Albanese et al., 2013). Individual insurers, such as Aetna have also acknowledged the same finding.
As part of their risk management procedures, some insurance companies are also paying for pre-symptomatic genetic tests to assess their level of risk before committing to any insurance plan. The CDC is at the forefront in trying to help these firms get ahead of the personalized medicine trend by making it easy for them to get approval for tests and to make easy payments while doing so (U.S. Department of Health and Human Services, 2013). Accessibility is one aspect of insurance that is identified by medical researchers to be of critical importance in personalized medicine because they say it is vital for all patients to access precision medicine (Management Association Information Resources, 2016). Through the increased investments in payment structures and insurance payment processes, patients should be easily billed.
The provision of a decision support structure is also central in creating a solid infrastructure for the deployment of personal medicine because the concept increases the medical options available for most patients (Burock et al., 2013). Consequently, patients need to make the right decisions when evaluating these options. Without a proper decision support infrastructure, they would be unable to do so effectively. Since personalized medicine also demands advanced data analysis methods, Aronson et al. (2016 say there is a need to develop a strong infrastructure that would support such a decision-making structure.
Boutin et al. (2016) say that countries also need to develop sound infrastructures with innovative facilities that would accommodate the different processes, such as genomics, associated with precision medicine. As part of the same infrastructure, there needs to be sober efforts to include advanced analytical tools and digital performance tools that would not only have a high storage capacity but also have high processing capabilities to enhance service delivery (Burock et al., 2013). The need to have a qualified and capable management team to oversee the implementation of this infrastructure is also important because without it, it would be difficult to extract the value that could be extracted from personalized medicine (Management Association Information Resources, 2016). Such professionals may include mathematicians, computer scientists, biologists and the likes (Management Association Information Resources, 2016). At the same time, countries and hospital agencies should also make parallel investments in security management to protect the privacy of patients who are part of the research process. Institutional support should also be a central theme in the creation of a solid infrastructure for the deployment of the personalized medicine concept because without the commitment of health institutions, it would be difficult to regulate the quality of findings or health outcomes that emerge from the process (U.S. Department of Health and Human Services, 2013).
Comprehensively, assuring a solid infrastructure for personalized medicine is important because the concept always comes with a large number of patients who require personalized medicine services. As seen in this chapter, different researchers, such as Tsai et al. (2016) and Albanese et al. (2013), have emphasized the importance of doing so. They say investments in infrastructure could lead to cost-effective assays across different levels of care. Based on their assertions, having a solid infrastructure to support personalized medicine is akin to setting the stage for the implementation of the concept.
Role of Certification and Surveillance
Although many medical professionals know the benefits of personalized medicine, the regulatory framework surrounding its implementation and adoption has not been straightforward. Different regulatory aspects of personalized medicine have been keen to regulate what works, develop new knowledge to understand patient needs, and understand the sources of this knowledge (Tsai et al., 2016). The nature of these regulatory frameworks often dictate the pace of adopting personalized medicine and how people would accept its use in the short-term and in the long-term. In this chapter, the policies we highlight here are regulations surrounding personalized medicine tests, surveillance, and certification standards that are of paramount importance to our understanding of personalized medicine. Different jurisdictions have unique surveillance standards and regulations surrounding the concept. The table below presents an overview of FDA’s policies governing personalized medicine tests in America.
The FDA has developed different regulations for undertaking laboratory tests to ascertain the gene makeup of different patients that would eventually lead to better administration of drugs and dosage, or improved decision-making processes surround health dilemmas (Personalized Medicine Coalition, 2014).
Different authorities have surveyed personalized medicine processes at different levels, but laboratory testing has received most of the coverage (Tsai et al., 2016). Diagnostic testing has often happened within two large clusters that include laboratory-developed tests and diagnostic kits which contain regents and materials needed to undertake the tests. The FDA regulates most of the products used to undertake these tests (Personalized Medicine Coalition, 2014). Few types of equipment used in personalized medicine are often classified as “medical devices” because most of them are considered laboratory developed testing devices (Tsai et al., 2016). Nonetheless, many jurisdictions around the world often exercise regulatory discretion when surveying laboratory-developed tests. For example, the FDA often exercises risk-based oversight methods on regulating the kinds of equipment and tools used to undertake laboratory developed tests (Personalized Medicine Coalition, 2014). They often use existing Acts to do so, but some observers have questioned their jurisdiction in regulating this field of personalized medicine (Weiss & Shin, 2016). These concerns have often emerged after other health-based agencies have claimed jurisdiction over the same process. For example, the Centre for Medicare and Medicaid Services (CMS) has also claimed that it should regulate laboratory-developed tests and all the equipment associated with it (Blau et al., 2016). This claim has been made from the understanding that such laboratory testing methods are subject to rules outlined in the Clinical Laboratory Improvement Amendment (CLIA) (Blau et al., 2016).
CLIA is a certification requirement of laboratory testing facilities in America (Schleidgen et al., 2013). Usually, the duty of enforcing these compliance standards is undertaken by state agencies. Some accredited organisations are also in a position to certify laboratory-testing facilities in the country (Schleidgen et al., 2013). The College of American Pathologists is a leading accredited institution that could offer CLIA certification to these testing facilities (Personalized Medicine Coalition, 2014). The same is true for other organizations, such as COLA (Personalized Medicine Coalition, 2014). Before certification is granted, these institutions are often required to ensure that the organisations seeking it conform to the stringent certification requirements outlined in the CLIA guidebook. However, the presence of additional requirements for different CLIA certification may cause variations in certification standards (Blau et al., 2016).
The developments in personalized medicine research have also elicited concerns among different stakeholders regarding the safety of the processes (Weiss & Shin, 2016). This concern particularly manifests in the context that misinterpretations may occur when using test findings. Some negative outcomes that may stem from such concerns may be misdiagnosis, making improper medical decisions or undertaking a preventive surgical procedure that is not needed in the first place. Based on some of these outcomes, some observers have often said there should be more oversight undertaken by authorities, such as the FDA, to manage the process. For example, Smoller et al. (2016) say that molecular tests undertaken in the context of personalized medicine should be subject to FDA approval. Based on the seriousness of these effects and their associated long-term effects on human health, the FDA has shown its intention to institute tiered regulation requirements for all parties to follow (Weiss & Shin, 2016). Studies that are more rigorous will also be subjected to elaborate regulations to make sure that their findings are safe to use. The National Institute of Health has also taken a dominant approach in creating a testing registry that would guarantee the transparency of molecular tests undertaken by researchers who specialize in personalized medicine (Smoller et al., 2016). According to Gainer et al. (2016), the testing registry includes more than 16,000 tests in molecular testing.
Regulatory bodies, which oversee the activities of medical practitioners in the field of personalized medicine, are also facing new dilemmas regarding their activities because personalized medicine introduces new requirements in their regulation and certification standards. For example, safety is one controversial subject that has eluded most of these regulatory bodies because personalized medicine has introduced individualistic treatment methods, which show that what could be safe for one person may not be safe for another (Smoller et al., 2016). Generally, O’Donnell (2013) describes this issue as the inability of existing regulatory structures and principles to comply with personalized approaches to medical treatments. Such concerns have arisen in the past, as was the case in the use of the drug bucindolol, which was discontinued in 2001 because it was established that it did not bring improvements in the health of patients suffering from heart conditions (Fiuzat et al., 2013). However, interest in its use was rekindled years later when it was established that patients who had two genetic variants regulating heart function responded well to the treatment (Gainer et al., 2016). Thus, differences in genetic profiles among different clusters of patients create a regulatory nightmare for many oversight agencies because what could be safe for one group of patients may be unsafe for another.
Regulatory concerns have also emerged from the issue of ascertaining the biology of complicated diseases, such as cancer. For example, molecular tests have revealed that different people suffering from the same type of cancer may have different types of biological compositions (Verma, 2014). In this regard, it becomes difficult to use the biological composition of the disease for the greater purpose of developing interventions that would benefit a greater percentage of people (Fiuzat et al., 2013). For instance, if we examine lung cancer alone, we find that it may have more than 80 mutations, which would naturally make it difficult for researchers to select one as the basis for the development of precision medicine. Furthermore, there are several incidences where one type of mutation may only occur in one type of patient. In this regard, it becomes difficult for regulatory authorities to conduct clinical trials that only have one person as the target population. Such randomized clinical trials are often referred to as n-of-1 clinical trials that also prove to be a regulatory nightmare for regulatory agencies when they want to ascertain the safety, or ethical integrity, of the associated treatments (Fiuzat et al., 2013). This dilemma is partly regarded as a reason why some observers consider n-of-1 trials as platforms for providing only anecdotal evidences (Verma, 2014). However, as Burton, Cole, and Lucassen (2012) observe, such trials are geared towards providing individualized care plans that are suited to only the affected individuals, which coincidentally happens to be the same basis for the implementation of the personalized medicine concept.
Adherence to the Disruption
Personalized medicine is often regarded a disruption in the way health care practitioners have traditionally provided their services. As opposed to adopting the general approach of dispensing medicine and making health care decisions, based on general knowledge about diseases and patient groups, personalized medicine demands that health care practitioners take more care in providing health services that would specifically appeal to the needs and biological makeup of specific groups of patients. The success of personalized medicine depends on the willingness by all health stakeholders to make the concept a success. In this chapter, we explain the needs and methodologies required to make two key health stakeholders to adhere to the implementation of personalized medicine – patients and medical professionals. We also highlight how personalized medicine affects the routine of patients’ lives.
Getting Patients to Adhere
Getting patients to adhere to the steps involved in the implementation of personalized medicine is critical in the successful implementation of the concept. Non-adherence would happen if the patients do not initiate their treatment plans or fail to stick to existing treatment methodologies. Previous researchers have pointed out that non-adherence to health care plans often leads to significant resource wastages (Verma, 2014). In fact, according to Damato and Heimann (2013), such a problem could lead to 2.5% in gross domestic product (GDP) wastages annually. Other studies have shown that the non-adherence to treatment plans is responsible for 13% of all health care costs (U.S. Department of Health and Human Services, 2013).
Without the commitment of patients to adhere to this mode of treatment, their medical conditions may worsen. In this regard, ensuring patients adhere to their treatment plans is a priority for many health care service providers. Several research studies have failed to provide empirical evidence that would explain why patients fail to adhere to their health care plans, but generally, their analysis point towards the existence of a large group of uninformed patients who are most prone to this problem (Gainer et al., 2016). These research findings come from the background of research studies, which have shown that many health researchers have failed to address the specific patient-centred issues that could influence adherence levels (Gainer et al., 2016). An economist from a Chicago-based university, Tomas Philopson argues that, in addition to the lack of adequate knowledge among patients, irrationality is also responsible for the lack of patient adherence to health care plans (Burton et al., 2012). However, it is important to note that there is insufficient evidence to support the idea that lack of knowledge and irrationality are solely responsible for poor patient adherence to health care plans.
Denford, Frost, Dieppe, Cooper, and Britten (2014) present a contrary analysis by saying that the strict adherence to ineffective health care plans is also a huge problem in the health sector. In response to this issue, Horgan et al. (2014) say that health care service providers need to be aware of the fact that patients are more rational than previously thought because nobody minds their welfare more than the patients themselves. Although non-adherence is a problem in personal medicine, Jakka and Rossbach (2013) say that the concept itself should be a solution to the problem. In other words, he says that the failure to comply with treatment plans could be easily solved by the introduction of personalized medicine.
Getting patients to adhere to their treatment plans demands that health care service practitioners educate them about the potential advantages associated with precision medicine (Denford et al., 2014). While doing so, the patients are also bound to reciprocate positively through an increased commitment to their care plans, if they realize that their health is improving. This is often the objective for most medical practitioners because personal medicine, if applied well, should lead to better health outcomes, compared to traditional types of medicine. When patients learn that precision medicine increases their safety profile and reduces their health care costs, they should naturally adhere to the treatment plans. Similarly, if patients are aware of the fact that personalized medicine provides them with optimal treatment plans, they are bound to stick to the treatment plans because they know they would benefit from doing so (Denford et al., 2014).
Researchers have also shown that trust has a huge role to play in increasing the adherence of patients to treatment plans (Jakka & Rossbach, 2013; Horgan et al., 2014). This finding comes from several research studies, which have highlighted discrimination and privacy issues as significant obstacles to the realization of patient buy-in in personalized medicine. For example, studies by Louca (2012) and Mathieu et al. (2012) have shown that race-based personalized medicine have often been shunned by some patients who believe the treatment method could be a ploy to bring racial discrimination in the health practice. Such concerns have led to the poor adherence to treatment plans and the emergence of suspicion among practitioners and patients (Jakka & Rossbach, 2013; Horgan et al., 2014). Again, the solution is to develop trust between the two parties.
Although developing trust between patients and their health care service providers is highlighted as one way to improve patient adherence, generally, it should be understood that recognizing the barriers to the adoption of personalized medicine among patients is a successful strategy of making sure that patients adhere to their treatment plans as well (Jakka & Rossbach, 2013; Horgan et al., 2014). Generally, as Egan and Philipson (2016) report, health care service providers are often aware about the population-wide effects of treatment methods, but patients are often concerned about the individualized effects of such treatment plans. This statement shows the potential for proponents of personalized medicine to increase the compliance of patients to treatment plans.
Getting Medical Professionals to Adhere
The adherence of medical professionals to personal medicine is as important as the adherence of patients to their treatment plans, within the framework of personal medicine. Generally, many physicians believe in the advantage that personal medicine brings to their practice and to communities. However, there are concerns that some of them may not fully adhere to the principles of the concept because of regulatory issues and workplace problems (Management Association Information Resources, 2016). In fact, many physicians are only completing the transition into electronic medical records, with little results to show on how such a development in the medical field has eased their work duties, or made things easier for them. It is from this view that Vizirianakis (2014) says many physicians are still working more and seeing their patients less, regardless of the advancements in technology and the improvements in their practice. Based on this assertion, there are concerns that some medical professionals may fail to be on-board regarding the implementation of personalized medicine (Personalized Medicine Coalition, 2014).
In other words, some of them may be cynical about how personalized medicine would be of benefit to them. Some of the tangible issues highlighted by these medical professionals include complex privacy laws and tedious insurance paperwork that needs to be completed when offering this kind of treatment (Personalized Medicine Coalition, 2014). The refocus on value-based pricing is also problematic for some medical practitioners who see it as an additional input in the health care system (U.S. Department of Health and Human Services, 2013). Many of them believe that these additions often create more lines of bureaucracies, which could possibly make their work harder than it already is. Based on these issues, there is real concern among medical practitioners that personalized medicine could only mean “more of the same.” As such, some medical practitioners have a real dilemma regarding specific issues surrounding the concept, such as whether they would be required to ask or verify additional details about patients when they fall sick and whether the additional inputs associated with personal medicine would yield greater returns for them and their patients (Personalized Medicine Coalition, 2014).
Since personalized medicine demands a new decision-making framework for patients and their health care service providers, Moch et al. (2012) say that medical practitioners should be provided with an electronic decision support system that would allow them to make the best medical choices for their patients. In their view, the researchers believe that the presence of these electronic decision-making tools would eliminate the perception that personalized medicine complicates existing medical procedures (Moch et al., 2012). The electronic tools would also allow the medical practitioners to stay abreast with ongoing research in the field of personalized medicine. This fact comes from the understanding that it is difficult for medical practitioners to synthesise all the information associated with personalized medicine, as a prerequisite for good decision-making (Nabipour & Assadi, 2014). With these technology-aided tools, it would be easy for medical practitioners to choose the safest and most efficient treatment methods for their patients. They would also help them to administer and monitor treatment methods effectively.
The use of computer-aided techniques in helping medical practitioners to adopt personalized medicine has been highlighted in the works of Flockhart and Dawes who say that the use of these techniques is instrumental in improving clinical decision support systems (CDSS) (Nabipour & Assadi, 2014). They also claim that the same technique would help physicians to interpret genetic data. Although many medical practitioners understand that genetics could significantly influence how people react to certain types of medications, many researchers, such as Payne and Annemans (2013) agree that they have not received proper training in pharmacogenics. Their lack of training in this field could prove to be a barrier in securing their commitment to adhere to the principles of personalized medicine, or in making sure they are enthusiastic about its adoption. Nonetheless, going back to the use of computer-aided techniques in making medical practitioners feel that personalized medicine does not make their work more difficult, Rogowski et al. (2015) say it is important for physicians who understand pharmacogenics to take a leading role in developing clinical decision support systems for others to follow.
Through their leadership, they could easily reach a consensus regarding recommended prescription practices that other medical practitioners could rely on to make diagnoses, or prescribe specific medications. Indeed, as Schleidgen and Marckmann (2013) point out, this system would identify a specific set of biomarkers that would be used to make a computer algorithm that would allow medical practitioners to make informed decisions, as expected within the wider context of personalized medicine. The algorithm could be integrated with a patient’s EMR system, thereby allowing medical practitioners to ascertain whether a specific drug could be useful to the patient, or not. Referring to this advancement, Cullis (2015) says that, “Such support systems will become more sophisticated and more predictive as more data, including whole-genome sequences or other proteome and microbiome data are added to the digital version of you” (p. 78).
The use of technology in personalized medicine is bound to increase adherence by medical practitioners to the principles of personalized medicine because there is a general misinformed view among some of them that personalized medicine would make their work difficult (Nabipour & Assadi, 2014). However, as seen through the works of several researchers, such as Cullis (2015) and Rogowski et al. (2015), which have been highlighted in this paper, the use of computerized decision-making systems is bound to offer a convincing argument among medical practitioners that personalized medicine would not only make their work easier, but also increase the efficiency of their decision-making processes.
Ideally, within the larger context of personal medicine, doctors should be able to not only view the physical condition of their patients, but also take them through their individual risk factors to allow them to make the best choices about their health (Zimmern & Khoury, 2012). Stated differently, medical practitioners of the future would be like health coaches who will give their followers the latest tools of interpreting their health and identifying biological markers of their health that could be jeopardizing their wellbeing and helping them reverse the negative trend. This new role of medical practitioners would be fundamentally different from what many people share with their doctors today.
Affecting the Routine in Patients Life
The popularity of personalized medicine comes with many social and ethical issues that need to be addressed to secure the support of all stakeholders needed to make the process happen. Most of these ethical and social issues are bound to affect the routine in patients’ lives. To explain how such issues could affect normal lives, we could use the example of Louise Brown who helped to deliver the first test-tube baby in 1978 (Zimmern & Khoury, 2012). Although his experiment was a major milestone in the area of human fertility, it brought new questions regarding how people conceive and bring new life on earth. For example, legal issues regarding the status of the fertilized eggs and the position of surrogate mothers in children’s’ lives are some of the new issues that the experiment brought to light and that changed how we perceive, or process, fertility issues (Cullis, 2015). As Tsimberidou, Ringborg and Schilsky (2013) observe, most of these issues are bound to increase in complexity and even worsen in complication, as advances in medicine bring new probabilities to how the medical field operates.
In the context of personalized medicine, the routine of patients could be affected by the need to get adequate genetic information from them. For example, to get reliable information about genotype and phenotypes, which are instrumental in making the right diagnoses regarding medical treatments, researchers have to sample two groups of patients – healthy and unhealthy patients (Cullis, 2015). In fact, as Rubin (2014) points out, researchers need a sample from different groups of patients with different ancestral backgrounds to collect valid data. Concisely, past research studies that have delved into this issue have had to sample the genetic profile of hundreds of people (Cullis, 2015). Thereafter, they had to compare their genetic data with those who have different genetic profiles (usually between people who suffer from one condition and those who do not suffer from the same condition) to get a reliable genetic gap that would explain the presence of a disease. Indeed, as Olsen and Jorgensen (2014) point out, this is the only reliable way to identify a faulty gene. The biggest disruption to patients’ lives is the requirement that they have to avail their genetic information for research. Some people may view such a process as suspicious because genetic information could be used to discriminate on them, or to get biological information about them (that was not previously sought) (Rubin, 2014). For example, the police can use such information to solve crimes, or to apprehend criminals, thereby causing suspicion among some members of the public who may feel that such information could be used for other purposes that were not intended for in the first place.
One area of key concern among researchers in this field is the dilemma they often experience when they bump into incidental findings regarding a patients’ data. Indeed, when conducting genetic texts, medical practitioners could discover certain information about the patients, without necessarily looking for it (Ehlerding & Cai, 2016). For example, they could discover that the parents of a patient (or one of the parents) are not biologically related to a child. They could also discover that a patient suffers from a certain genetic predisposition to a chronic disease. These issues are important to know, but the dilemma arises when the medical practitioners have to battle with the decision of whether to inform their respondents about their new discoveries, or not (Mason-Suares, Sweetser, Lindeman, & Morton, 2016).
Granted, if they do so, they could significantly disrupt the lives of their patients, or cause significant turmoil in their personal lives, especially concerning how they would relate with others, or even how they view their lives in the first place (mostly if they discover a terminal illness, or a predisposition to a certain disease). However, within this analysis, it is also important to point out that these disruptions may traverse both ways (negatively and positively) because there are instances whereby if patients are informed about their predisposition to a certain genetic condition, they may take the appropriate preventive measures to decrease their predisposition to the same conditions (Ehlerding & Cai, 2016).
Personalized medicine could also disrupt patients’ lives in the sense that the willingness of one member of a family to provide genetic information about themselves for medical purposes could also be a point of concern for other family members who would also be exposed to the same finding because they share the same genetic profile. This concern draws our attention to the need to determine the extent of family involvement in the decision of one member to disclose their genetic information to other parties. Medical practitioners also face a dilemma regarding whether they should use some leftover genetic samples for other experiments other than the ones they were initially intended for (Ehlerding & Cai, 2016).
Several societal issues have taken the same form of debate that have often characterized the ethical issues surrounding personalized medicine because some people have expressed their concerns about the disruptions created by personalized medicine in the context of how societies have traditionally worked (Mason-Suares et al., 2016). For example, there have been high incidences of abortion reported among mothers who discover that their unborn children have a high risk of suffering from Down’s syndrome (Mason-Suares et al., 2016). Such reports are often recorded in cases where expectant mothers discover this probability through prenatal genetic screening. The same outcome has been observed among expectant mothers who discover that their unborn children may suffer from Tay-Sachs disease (Mason-Suares et al., 2016). Advances in the area of personalized medicine have contributed to this trend because a simple blood test could reveal such biological information, which these expectant mothers act on, albeit irrationally. In fact, Draghia-Akli (2012) reveals that more than 90% of expectant mothers in Europe who discover such a condition often abort their unborn babies. American statistics also show the same trend, but only slightly lower (Draghia-Akli, 2012). The implication of such developments is the possible decline in the incidence of Down’s syndrome. The possibility that the disease may disappear in totality is also a real one.
Since blood tests can also reveal the gender of an unborn baby, societal disruption could also occur in societies that often prefer one gender to another. Cases of abortion upon the discovery of the gender of a baby are also common among such societies (Rubin, 2014). Such is the case of China, where the preference for boys significantly outweighs that of girls. Therefore, it is no wonder that in 2020, experts predict that there would be 40 million more men than women in the socialist nation (Rubin, 2014). Nonetheless, most of the issues highlighted in this chapter are being addressed at different levels of policy that span across public, legal, and social fields. However, our attempt at explaining how personalized medicine could influence the lives of patients and medical practitioners is an indicator of the influence that molecular research is making in-roads in different aspects of medicine and public life.
From Bench to Bedside: What works and what does not?
Vital Signs Monitoring Made Easy
It is important to assess a patient’s vital signs because they outline the performance of the body’s main functions. There are four main vital signs which medical practitioners have to monitor in patients – “respiration rate, blood pressure, body temperature and pulse rate” (John Hopkins Medicine, 2017, p. 1). Here, it is important to point out that some medical literatures say that blood pressure is not necessarily a vital sign, but most medical practitioners often measure it together with the other three vital signs mentioned (John Hopkins Medicine, 2017). Generally, these vital signs are integral in making diagnosis about different medical conditions because as Buchanan, Wordsworth, and Schuh (2013) demonstrate, they are important in detecting, or monitoring, medical issues.
The process of monitoring vital signs often involves using multimodal monitors that are often attached to patients in different levels of care. Personalized medicine is set to change how medical practitioners monitor vital signs because, unlike past approaches that rely on general standards of monitoring vital signs, personalized medicine allows medical practitioners to provide personalized and automatic detection of vital sign changes, which are easily communicated to the physician, virtually and regardless of their physical locations (John Hopkins Medicine, 2017). This development is regarded as a critical inclusion in primary care settings where the early detection of vital sign changes is required, at least as an indicator of health deterioration, to make early interventions before a patient’s health status deterioriates (Buchanan et al., 2013). This requirement has been highlighted in studies that show the need for early monitoring of vital sign changes among patients that suffer from chronic conditions, such as congestive heart failure (John Hopkins Medicine, 2017). Therefore, the vital sign monitoring technology employed in personalized medicine is applicable in medical emergency scenarios. When they exceed the individualized threshold, it becomes easy for medical practitioners to mobilize the health resources closest to the patient to provide primary care on time.
There have been different types of technological advancements made in the health care sector to allow people to monitor specific vital signs, without necessarily having to seek the input of doctors. These remote monitoring devices allow patients to send information about their vital signs, virtually to their health care service providers who in turn store and analyse them with the goal of detecting when medical interventions need to be carried out (Zimmern & Khoury, 2012). Patients can also store such medical data (about their vital signs) virtually and gain access to them whenever they want, regardless of their physical locations. The beauty of personalized medicine, aided by the increased reliability of remote monitoring devices, rests in the ability of medical practitioners to give personalized feedback to patients regarding what their vital signs mean, or how changes in their vital signs affect their treatment therapies (Zimmern & Khoury, 2012).
Advances in technology are also pushing the trend for making vital sign monitoring easier because many technological companies are developing new devices for this purpose. For example, companies are developing wearable devices to monitor vital signs at any point in time and at any location. Coupled with new computer systems embedded at different points of a patient’s primary environment, such devices are bound to provide the continuous monitoring of different vital signs. The Kinect sensor is one such device, which has been marked as a progressive device for meeting purpose (Zimmern & Khoury, 2012). Other devices that serve the same purpose include Xbox, which monitors the heart rate, based on different physiological movements of the face and of the entire body (Tsai et al., 2016). Most of these changes are too small to notice by the human senses. Some of these devices have the ability to detect whether a person’s eyes are open, or shut, and can even detect information about a person’s mood, and even whether they are having a heart attack, or not (based on physiological changes). Using virtual platforms, these devices could notify medical personnel about ongoing physiological changes regarding a patient and whether urgent care is needed.
Many researchers agree that monitoring vital signs to make accurate diagnosis could be a tricky affair especially because studies show that many doctors often make wrong diagnosis 15% of the time, even after monitoring vital signs (Cullis, 2015). However, personalized medicine is set to change this outcome because it will increase the accuracy of diagnosis tests, which rely on molecular information. Indeed, many molecular biomarkers for determining health outcomes are measurable. Genetic features and the varied levels of certain proteins are known to increase the accuracy of diagnosis by providing the accurate risk levels for certain diseases (Tsai et al., 2016). These technologies have demonstrated their success in diagnosing inherited diseases among different classes of patients. For example, Egalite, Groisman, and Godard (2014) demonstrate its efficacy in predicting the risk of Gaucher’s diseases, which often occurs because of a protein mutation in a defective gene.
Pharmacogenics is also another area of personalized medicine that makes it easy to monitor the vital signs for predicting the reactions a patient would experience by taking certain drugs (Cullis, 2015). This field of medicine often assesses the relationship between a specific genotype and the effects of a certain drug type. This way, medical practitioners can know in advance whether a patient would have an adverse drug reaction, or not. This area of personalized medicine is important because taking certain types of drugs, with a gene makeup that would have an adverse reaction to the same type of drug could be disastrous for most patients (Egalite et al., 2014).
Many drugs already have specific genetic biomarkers that would help medical practitioners to understand the kind of reaction a patient would have by taking them (Egalite et al., 2014). In fact, according to the FDA, more than 120 drugs have known genetic biomarkers that would help health practitioners to know whether they would cause an adverse drug reaction to specific genetic typologies, or not (Personalized Medicine Coalition, 2014). Nonetheless, existing evidence shows that personalized medicine has made monitoring vital signs easy.
Changes in Therapy Protocols from Long-Term Data
Traditionally, medical practitioners have often relied on generalized information to prescribe specific therapies to specific groups of patients. By simply asking patients to describe their physical health and carrying out targeted tests, medical practitioners have often relied on generalized information to recommend specific therapies. From the nature of these tests, the process of recommending treatment therapies often relies on short-term data because medical practitioners often use the current information they are given by the patients to make specific diagnoses and to recommend specific treatment plans (Blau et al., 2016). As highlighted in this review, personalized medicine seeks to change this mode of operation by using molecular testing methods to recommend targeted therapies, as opposed to generalized therapies that may, or may not, work.
These tests often lead to the development of a succinct database that outlines the genetic profile of the patient, which could be useful to researchers when they want to recommend specific therapies. The data could also be useful in providing a long-term view of a patient’s health. Such developments imply a change in therapy protocols because experts would no longer rely on short-term information to make treatment decisions; instead, they would rely on a long-term understanding of a patient’s health to arrive at this decision. There are many advantages associated with this type of therapy protocol. For example, medical practitioners would be able to gain a broader understanding of a patient’s health and develop health interventions that appeal to this broader health picture. This approach would be unlike past methodologies, which rely on a short-term assessment of a patient’s health data.
It is easy to miss important details about a patient’s information using such short-term data because medical practitioners are unable to make sense of how their treatment plans fit within a patient’s health structure. Similarly, they are unable to comprehend how a patient would react to the recommended therapies, or even whether they would work on the patient in the first place.
As highlighted in this report, personalized medicine depends on the effective use of long-term data to assess the risk levels that different patients have regarding their propensity to get certain diseases. The same data is useful in the selection of the best therapy protocols to follow in the treatment of select diseases. As highlighted by Siebert et al. (2015), personalized medicine helps medical practitioners to develop customized therapies for specific groups of patients. This is in line with the goal of the concept, which is to identify the best drug for treating each group of patients. Since its proper implementation would lead to minimal side effects, it is pertinent to note that the genomic revolution in precision medicine is quickly highlighting the important role of gene make-up in identifying the best treatment method to use. The increased popularity of genome-wide studies in the field of personal medicine has created a wide and long-term oriented data pool of individualized gene profiles of different patients (Blau et al., 2016). However, the challenge that is emerging from the creation of this pool of data is determining which ones have the highest therapeutic importance. A related concern is identifying the right therapeutic drugs to use even after identifying the right therapy protocols to follow, after the analysis of long-term data.
No other area of medicine has demonstrated the need for targeted therapy other than cancer treatment (Tsimberidou et al., 2013). Targeted medicine is based on the administration of drug treatments and is aimed at controlling the spread of cancer cells. Personalized medicine has helped medical practitioners to choose the right treatment therapies for patients suffering from different types of cancers because it has helped them to identify which proteins in the cancer cells need to be targeted to yield the best health outcomes (Tsimberidou et al., 2013). Personalized medicine has also helped them to identify which genes, within the cancer cells, that need to be targeted to stop their spread.
Personalized medicine is instrumental in choosing the right treatment therapies for different classes of patients suffering from different types of cancers because not all tumours can be targeted the same way. In other words, not everyone will respond positively to one type of therapeutic treatment. For example, as described by the American Society of Clinical Oncology (2017), most cancer patients have a gene mutation called KRAS, which often causes the spread of cancerous cells in the body. Certain therapies are often ineffective for patients suffering from this gene mutation (American Society of Clinical Oncology, 2017). For example, cetuximab and panitumumab therapies are ineffective in these types of patients. Through this analysis alone, testing for KRAS alone could help the doctor to recommend the best type of treatment (American Society of Clinical Oncology, 2017).
Patient Data Analysis Made Easy
Analyzing patients’ data is often a critical step in knowing the history of one’s illness, or generally knowing the health status of such a person. From this information, it is easy to make the right medical decisions from a background of having adequate knowledge. Traditionally, it was difficult for medical practitioners to give the best diagnosis, or prescribe the right treatment method, because of the lack of sufficient patient data (American Society of Clinical Oncology, 2017). Consequently, they had to work with what they had. In other words, they often relied on a narrow view of the patient’s health (mostly based on a present illness or disease). The importance of making sure that medical practitioners had the right information to make the right diagnosis and treatment created the need for preserving medical data. The introduction of electronic medical records further improved this process because it made it easier to retrieve such information and (more importantly) allowed medical practitioners to analyse it using highly specialized techniques.
Generally, personalized medicine has created a proliferation of research data in different fields of omics, such as “genomics, transcriptomics, proteomics, epigenomic, metagenomics, metabolomics, nutriomics” (American Society of Clinical Oncology, 2017, p. 33). The development and accumulation of medical data in these key areas of personal medicine research has led to the emergence of systems biology, which is increasing our understanding of how biological mechanisms could be used to predict human health outcomes through precision medicine. The same process has led to an emergence of a new data analysis process in the area of personalized medicine, which has been characterized, by the process of integrating information from different medical fields (Werner, Mills, & Ram, 2014). It has also helped to explain complex biological interactions that help to predict the adverse reactions of patients and the right therapies appropriate for different patient groups (Werner et al., 2014). These changes in data analysis approaches have led to a redefinition of the same process because there has been a complete refocus on holism, as opposed to reductionism, which was associated with traditional data analysis approaches.
The changes brought about by improved data analysis methods in precision medicine come from the reliance on experimental and computational approaches to data analysis (Sweet & Michaelis, 2012). Traditional medical data analysis procedures are inferior to this new method of data analysis because they mostly rely on observational epidemiology. In this regard, they are not sufficiently equipped to accommodate the underlying patterns explaining the multifaceted and heterogeneous disorders that are needed to prevent, or treat, certain diseases. Here, it is important to point out the importance of analysing medical issues from multiple perspectives.
There are several technological tools used in data analysis that have made the process easier to compete in personalized medicine (Sweet & Michaelis, 2012). PGx is one such tool, which is useful in predicting drug responses (Werner et al., 2014). It uses biomarkers to do so and only applies genotypes, and not phenotypes, when assessing the response a patient would have to a specific drug type (Werner et al., 2014). In this analysis, phenotypes are often products of the intersection between genotypes and environmental factors, such as lifestyle factors and dietary issues. The metabolic profiling often takes into account different factors associated with a patient, such as their physiological state and their genetic polymorphism (Werner et al., 2014). Coupled with understanding the influence of environmental factors on the same issue, it is easy for medical practitioners to understand the kind of reactions a patient would have after being subjected to a specific treatment plan.
The pharmaceutical industry is blazing the trail in terms of understanding how big data analysis could be instrumental in personalized medicine. Key researchers in the field say that there is a lot of data today concerning medical research than they ever envisioned (Werner et al., 2014). In fact, according to Dizon, Politi, and Back (2013), it is possible to generate a lot of medical research today, which is equivalent to the data that was available from the start of humankind to the year 2003. Since such amounts of data could be generated in one day, it becomes easy to understand how data analysis is instrumental in making people understand the challenges of personalized medicine. The challenge is not only confined to the personalized medicine concept because the wider life-sciences industry often encounters this challenge as well (Dizon et al., 2013).
The increase in data pertaining to the personalized medicine sector has partly been fuelled by developments in genome sequencing technologies and the sharing of clinical trial data (U.S. Department of Health and Human Services, 2013). The incorporation of electronic medical records is also another catalyst in the data management processes of personalized medicine (Personalized Medicine Coalition, 2014). Collectively, these tools have led to the rise in the number of patient registries. However, advancements in social media networks have also allowed for increased data sharing. The use of medical data systems has further aided in this trend because most researchers have paired them with advanced analytical tools to make sense of the data (Personalized Medicine Coalition, 2014). Based on the explosion of such data, observers expect more targeted therapies to improve the efficacy of personalized medicine (Dizon et al., 2013).
In a different context of data analysis, drug manufacturers have always tested their drugs on a selective group of patients and observed how they reacted to it. However, through advancements in technology and the potential of using tools and strategies available in personalized medicine, drug manufacturers would find it easier to use biomarkers to understand the reaction of patients to specific treatment therapies (Wang, Zhang, & Sun, 2013). Through these data analysis methods, the drug manufacturers would have better tools to undertake more targeted clinical trials and improve the efficacy of their operations. Through the same strategy, if they want to undertake their own independent tests, they are in a better position to recruit only those respondents who will respond to the drug and exclude those that would have limiting reactions (Dizon et al., 2013).
The potential that exists in this data analysis method is firmly rooted in the fact that personalized medicine could potentially redefine how manufacturers test their drugs (Personalized Medicine Coalition, 2014). This development could have an implication of reducing the time and cost of bringing new drugs to the market. Using biomarkers to conduct such tests is one way to go about the issue (Dizon et al., 2013). Nonetheless, these developments should be done in recognition of the fact that the interest of the drug companies and the patients should be secured. By doing so, there will be a higher validation of biomarkers (Wang et al., 2013). Similarly, patients would have access to safer and more efficient treatment methods.
As greater volumes of data are generated daily, researchers should make sure they keep reviewing this information and evaluate whether the intended outcomes of the drugs are being assessed, or not. Researchers who focus on companion diagnosis tests should be more wary of this fact because their research needs a careful review of the drugs and associated treatment processes. It is also necessary for these researchers to observe these techniques if they want to increase the coverage of their research findings, or to get proper reimbursements for their expenditure (Faulkner et al., 2012). Some researchers are already ahead of this trend because they have created proprietary data networks that would allow them to evaluate the outcomes of their research processes, viz-a-viz their expectations (Faulkner et al., 2012). For example, one researcher, cited in Cho, Jeon, and Kim (2012), who evaluated breast cancer recurrence among women after analysing the health outcomes of more than 925 patients consistently compared the real outcomes versus the expected outcomes and established that there were significant costs savings that accrued from the use of genomic testing. Comprehensively, although it is important to acknowledge the growing volume of research data on personalized medicine, it is important to recognise that having access to the concept alone is not enough. In other words, health practitioners should be able to synthesise the data effectively and come up with the most effective and accurate findings.
A new type of data analysis, under the concept of personalized medicine is making in-roads in the ability of experts to predict adverse drug reactions patients would have when they undergo specific therapies – pharmacometabolomics (Cho et al., 2012). This data analysis method often combines the results found in metabolic profiling and assesses it with metabolite assessment tools to get a better understanding of how patients would react to certain medications (Cho et al., 2012). Some observers have highlighted the success associated with metabolomics and associated it with the technological advancements or progresses made in personalized medicine (Faulkner et al., 2012). One advantage of pharmacometabolomics over other similar technologies is its ability to accurately represent the phenotype of an organism better than other technologies could (Faulkner et al., 2012). This ability allows it to provide not only a fair but accurate biological impact of specific therapies. Usually, this method involves the analysis of bio-fluids at two stages – pre-dose and post-dose to investigate how a patient reacts to a specific therapy (Cho et al., 2012). In the process, medical practitioners could get surrogate markers that they could use for other treatments. This data analysis method is also useful in providing drug metabolite information for a specific group of patients, thereby allowing medical practitioners to understand how they would respond to specific therapies (Cho et al., 2012).
Relative to concerns expressed by some observers that personalized medicine could cause a widening of health care gap between poor and rich countries, it is critical to point out that there is a lag in the ability of health care practitioners to link the process of generating data versus integrating it in their health care practice (Egalite et al., 2014). Using developments that have emerged from the area of genome studies (coupled with the advancements in high-throughput technologies), it is becoming increasingly easy for health care practitioners to perform different measures within a relatively short time. This process has increased their access to data regarding different biometric information about patients, such as their “DNA (genomics), transcribed RNA from genes over time (transcriptomics), DNA methylation and protein profiles of specific tissues and cells (epigenomics and proteomics), metabolites (metabolomics), among other types of omics data” (Alyass, Turcotte, & Meyre, 2015, p. 35).
The ease through which personalized medicine has helped revolutionize data analysis has also infiltrated how medical practitioners carry out their work. For example, trained experts who have traditionally interpreted histopathological and radiological images manually now have to resort to use computational means to do so because it is easier that way (Alyass et al., 2015). This development partly explains some of the challenges that medical practitioners envision in the adoption of personalized medicine because some of them may see it as a threat to their work. Indeed, most of they may deem it as being a strong catalyst to rendering their services obsolete (Alyass et al., 2015). However, some informed scholars who argue that personalized medicine would only come in to make their work easier and not necessarily negate their contribution to their practice have squashed this fear (U.S. Department of Health and Human Services, 2013). We have partly explored such issues in the section that investigated how stakeholders could increase the adherence of medical practitioners to personalized medicine. Nonetheless, there is a consensus that personalized medicine will ease the data analysis processes that are associated with the concept and with the general medical practice (U.S. Department of Health and Human Services, 2013).
Business models that are firmly focused on the concept of returns on investments have created the need to develop further technological tools to improve the data analysis process, cognizant of the fact that such tools need to hasten the process of generating omics data and make the process more efficient (Personalized Medicine Coalition, 2014). Business interests in the practice also highlight the need to make such processes more affordable than they already are (Personalized Medicine Coalition, 2014). Based on these advances, Alyass et al. (2015) say that omics data generation is bound to become easier and more affordable for many. A colleague of theirs also add that omics platforms are bound to become more affordable because data generation would no longer be a problem for countries that could manage the challenges associated with data analysis within the realm of personalized medicine (Draghia-Akli, 2012).
The idea underlying the use of omics data in personalized medicine was to get enough data to help researchers to truly understand the underlying mechanisms of different diseases and to provide medical researchers with the information they need to develop adequate preventive strategies for preventing or treating specific health issues (Thrall, 2015; U.S. Department of Health and Human Services, 2013). The main idea was to provide a specific framework for translating the data obtained in unique omic profiles into individualised, or specific, health data that could be easily comprehended or used. However, researchers have experienced some limitations in their ability to use available molecular mechanisms to understand the complex relationships that would often characterize different progressions of diseases (Management Association Information Resources, 2016). In this regard, the ability of some researchers to regulate these complex relationships continues to be limited.
Biological relationships in the healthcare practice are already complex enough and coupled with environmental influences; it becomes increasingly difficult to comprehend how existing disease networks operate. It is even more complex to understand how the systems underlying these networks operate without the proper analysis of the complex relationships underlying the aforementioned biological processes. Relative to this observation, Rosland, Heisler, and Piette (2012) say that most of the issues that were previously associated with data generation in laboratory testing procedures have transferred to the management of data. The diagram below shows the interrelationship between data integration, analysis and interpretation within the process of generating data in the first place, which emerges from different omics profiles, such as genomics, proteomics, epigenomics, ,metagenomics, metabolomics, nutriomics, and transcriptomics.
Collectively, the data generation and data analysis processes assist clinical practitioners to understand the complex disease networks that provide important data, which is useful in predicting and formulating patient-specific interactions (Alyass et al., 2015). The end process is the provision of care delivery or patient education, which could easily happen through clinical care innovations.
Based on the above diagram, we find that if we were to concentrate on data integration and analysis alone, significant investments need to be made in the areas of systems biology, bioinformatics, biomathematics, and biostatistics, if stakeholders in the medical sector want to truly benefit from the success of personalized medicine. It is also crucial to point out that developments in these sectors are the only sure way of speeding the process of personalized medicine adoption (Alyass et al., 2015). Traditional laboratory testing processes and facilities cannot handle the challenges that come from having huge volumes of omics data (Thrall, 2015; U.S. Department of Health and Human Services, 2013). Consequently, stakeholders in the health sector need to invest more in informatics support to expand the storage capacity and computational resources needed to make personalized medicine a success. These investments need technical inputs because they should be directed towards developing laboratory-hosted servers, which are not only expensive to install and maintain, but also dynamic enough to accommodate the workflow requirements of different processes associated with personalized medicine (Management Association Information Resources, 2016). The failure to make such investments could lead to sub-optimal performance.
Eilat-Tsanani, Tabenkin, Shental, Elmalah, and Steinmetz (2013) say that the potential of meeting the computational requirements of personalized medicine rests in cloud computing. They say that this facility could easily handle the demand requirements of multiple computers and allow medical practitioners to access them anywhere they want (Eilat-Tsanani et al., 2013). Alyass et al. (2015) give several examples of places which have adopted this type of cloud computing service as
“EasyGenomics cloud in Beijing Genomics Institute (BGI), and “Embassy” clouds as part of ELIXIR project in collaboration with multiple European countries (UK, Sweden, Switzerland, Czech Republic, Estonia, Norway, the Netherlands, and Denmark)” (p. 35).
Relative to the above views, the focus on data analysis, within the realm of personalized medicine, is based on the development of cloud-based tools. Through such efforts, there have been several tools developed that meet this criterion. For example, the graphics-processing unit has been developed with this focus in mind (Alyass et al., 2015). The goal has been to promote general purpose computing. The main advantage associated with such tool kits is their fast computation power (Alyass et al., 2015). In fact, as Goldhirsch et al. (2013) outline, they have been faster by around one or two magnitudes, compared to past data processing techniques, such as the central processing unit. These developments have been designed with the sole purpose of managing the growing volume of data associated with personalized medicine (Alyass et al., 2015). To demonstrate the efficacy associated with the advanced technological toolkits of personalized medicine, Alyass et al. (2015) give the example of the MUMmerGPU toolkit. They say, “processes queries in parallel on a graphics card, achieves more than a 10-fold speedup over a CPU version of the sequence alignment kernel, and outperforms the CPU version of MUMmer by 3.5-fold in total application time when aligning reads” (Alyass et al., 2015, p. 38).
Although these developments are poised to increase the speed and efficiency of data analysis, Goldhirsch et al. (2013) say there needs to be a lot of work done to develop new algorithms that would complement the data analysis process. This observation is made because of the heterogeneous framework associated with omics data. The nature of this data often creates problems associated with communications and synchronizations of the data analysis process (Katrib, Hsu, Bui, & Xing, 2016). Nonetheless, there are specific trade-offs to be made when it comes to developing these new algorithms. They could be realized through a trade off between cost cutting measures and synchronization efforts, or between cost-cutting measures and communication (Management Association Information Resources, 2016). Furthermore, there is a challenge in developing algorithms that could be effectively secured, or relied upon. Additionally, there are many cases where software developers have made errors in programming, thereby jeopardizing the integrity of the data analysis procession (Goldhirsch et al., 2013). These errors may have led to wrongfully mapped SNPs, or other problems associated with wrongful programming (Goldhirsch et al., 2013). Based on the potential of such mistakes to affect the credibility of biological data used in personalized medicine, there is a need to make sure that the research platforms used to develop research data are reliable. More importantly, this step should be done before the use of omics data.
ToolShed is one software tool highlighted by Ahn (2016) as a reliable instrument for making sure that the bioinformatics tools used in personalized medicine research are reliable. Other tools, such as Bioimbus, and Bioconductor are also publicly available to researchers to achieve the same goal (Alyass et al., 2015). The consistency in asking important questions, such as how to make the data more reliable, is improving the usefulness of bioinformatic tools in the health care practice (Alyass et al., 2015). Comparatively, lightweight programming environments, based on the cloud-computing platform are set to help medical researchers to understand different biological networks that underpin the success of personalized medicine (Rosenstein et al., 2014). The diagram below shows how a cloud-based data analysis structure could help to ease the process of data analysis within the wider framework of personalized medicine.
Although the above diagram is elaborate, it is important to point out that more programs that are effective need to be developed to synthesize omics data (Alyass et al., 2015). At the same time, the above diagram shows us the importance of developing a solid infrastructure for the implementation of personalized medicine. This issue is partly explained in earlier sections of this review because there is a strong need to develop a strong infrastructural framework that would support the data analysis process associated with omics data, which is the bedrock of the implementation of personalized medicine (Shajahan-Haq, Cheema, & Clarke, 2015). The development of such an infrastructure highlights the interdisciplinary cloud-based model that often underpins data analysis processes in many health care systems that adopt the personalized medicine model (Shajahan-Haq et al., 2015). The interchange between modelling and software experts within such a structure is integral in not only the analysis of omics data, but also in interpreting it to understand how it fits within the general objective of the medical researchers.
Generally, personalized medicine requires the use of effective data analysis methods that would portray biological systems using omics data (Moch et al., 2012). However, the process of doing so requires the incorporation of mathematical techniques and complex statistical processes that would help to expound on the causal links between different biological networks that should explain the entire concept of personalized medicine. The use of computerized data analysis techniques may appear as a complex endeavour, but it should also be appreciated for the opportunities it creates to the wider health care field. Although there are many causal links associated with thousands of biomarkers associated with precision medicine, the causal links associated with the different markers should not be disregarded as trivial. These biological relationships could easily be defined through non-linear interactions and through the jointed relationships of different biological markers. These relationships may make it difficult to understand the right signals to observe and to comprehend those that simply occur because of the randomness of test trials. These relationships may be further compounded by the fact that different genes have different functions within selected cells. This observation may add to the variability that already exists across different cohorts of people. Based on these observations, it is important to appreciate the complexity of biological data and the associated difficulty it poses to researchers when they want to extract important data from the complex relationships that characterize existing biological networks. Stemming from these challenges, it is pertinent for researchers to appreciate the importance of computation and experimental techniques in data analysis that would ultimately help medical practitioners to make the best out of a bad/complex situation.
At this point, it is also important to point out that the high dimensionality of the biological data associated with personalized medicine is a challenge for many researchers when trying to interpret omics data. This challenge is often exacerbated by the presence of small samples, viz-a-viz a large number of measurement techniques (Pin, Fredolini, & Petricoin, 2013). Indeed, the commonality associated with the use of thousands of measurement techniques when analysing biological data in personalized medicine may pose a challenge when trying to understand the importance of such data, or when interpreting it (Shajahan-Haq et al., 2015). Here, biological, or data assessment models, often become less reliable with an increase in dimensionality (Nabipour & Assadi, 2016b). The complexity often comes about because of increased model complexity and bias variance because of fixed sample sizes (Pin et al., 2013). Based on this assertion, it is important to also point out that different factors often affect the prediction advantage associated with many data analysis techniques in precision medicine. Some of these factors include model over-fitting, and the presence of large standard errors (Pin et al., 2013). Estimate instability and local convergence are other issues that may also affect the precision advantage of data analysis techniques (Pin et al., 2013). The caveat to this analysis stems from the ability of researchers to make repeated tests using the same variables. Using different data analysis tools, such as mean and variances, it is easy to establish the distribution of variables in the data analysis process (Oskouie & Taheri, 2015).
It is often difficult to rely on the estimates given within a given data sample, especially if the estimates are significantly different from the true distribution criterion (Pin et al., 2013). It is also difficult to detect these deviations when there is an increase in the number of measurements affixed to a small or static sample number (Oskouie & Taheri, 2015). Analyzing single or integrative omics data therefore emerges as a tricky issue because if we were to include the errors associated with chance alone, the entire data analysis process could be problematic. This problem has prompted different researchers to suggest the need for conducting multiple testing processes, at least to minimise the possibility of encountering false positives in the data analysis process (Oskouie & Taheri, 2015). Another strategy for reducing this type of error is reducing the dimensionality attached to the issue under investigation. Different researchers, including Altaf-Ul-Amin, Afendi, Kiboi, and Kanaya (2014) have supported this solution. Comprehensively, dimensionality could affect the data analysis process because it captures the differences or influences of variance and bias when testing different variables.
The ease at which advances made in personalized medicine have helped to improve the analysis of patients’ data is highlighted in an article by the University of Arkansas for Medical Sciences (2015) which shows how big data analytical tools is driving the revolution in personalized medicine. Many programs are hinged on this relationship. For example, one program called the Precision Medicine Initiative (cited in Yoshida, Nishiumi, & Azuma, 2015, p. 6) highlights the role of big data analytics in personalized medicine. The program has a budget of $130 million and is intended to analyse the genetic data relating to 1,000,000 people to establish how they would react to a certain drug (Yoshida et al., 2015). Supported by the National Institute of Health, this program intends to recruit 1,000,000 participants in less than four years and categorise them as one cohort (Yoshida et al., 2015). Afterwards, the resulting data will be available to the public as one large cohort.
The resulting data will also lead to the accumulation of big data because the program intends to record a lot of information about the participants, including their lifestyle patterns, history of health, and even the kind of environmental exposures that affect their wellbeing (Yoshida et al., 2015). The big data that will be developed from these assessments would allow medical practitioners to track patient’s responses to treatment therapies, based on genetic markers (Kichko, Marschall, & Flessa, 2016). The data would also help identify the specific groups of people who would be at risk of developing certain diseases (Yoshida et al., 2015).
Based on the nature of the cohort and their response to specific drugs, such data could also serve as the basis for undertaking smaller and controlled clinical trials. For instance, if a researcher intended to investigate a cohort of 100 patients who are undergoing a specific therapy, it would take him a long time to get the sample. However, by simply using the available cohort described above, he would be in a position to undertake his study without much hassle. Thus, the ability to use big data analytical methods in personalized medicine allows researchers to identify their desired research sample in a short time (Nabipour & Assadi, 2015). According to the National Health Initiative, it intends to use the data available and link it with mobile apps, which would allow it to track the health changes of their respondents and possibly intervene when there is a need to do so, to improve the health outcomes of their respondents (Thrall, 2015). Relative to this assertion, Wagner (2015) says, “Advances in mobile devices, cheaper genome sequencing, databases, big data, and electronic health records make knowledge and health goals possible” (p. 2).
Although many people agree that personalized medicine would completely revolutionize how medical practitioners analyse their data, the change would not come as fast as many people would like to think. Relative to this assertion, Wu, Rice, and Wang (2012) say, “In parallel to an escalating two-tiered health system at the global level, a similar two-tiered phenomenon is observed with regard to our ability to generate and analyse omics data that may delay even further the transition to personalized medicine” (p. 55). However, Ciardiello et al. (2014) say generating, analysing and storing such data is still expensive, despite the advancements in technology, meaning that personalized medicine could be a confine of countries that can afford to manage the requirements of data analysis and processing, at least in the short-term.
At this point, it is important to restate that the concept of personalized medicine is firmly rooted in the understanding that diseases are often heterogeneous in terms of their causes and how their victims respond to medications (Yadav et al., 2016). In other words, people would have unique responses to diseases and therefore, they should have similarly unique treatment methods. These facts mean that proponents of personalized medicine have to device unique strategies that would pinpoint the root causes of specific diseases, as opposed to merely relying on the manifestation of unique symptoms, as the basis for choosing specific treatment methods. The consensus among many researchers in this field is that there needs to be a deliberate effort to mine data effectively to understand the unique structures of different diseases affecting human populations (Thrall, 2015; U.S. Department of Health and Human Services, 2013).
This step should naturally hasten the process of identifying biomarkers that would be used to assess unique disease patterns. Some pharmaceutical companies have adopted an innovative strategy of forging public-private partnerships to extract the true value of big data analysis in personalized medicine because they understand its potential in improving healthcare dynamics (Lenz, Fensch, & Sollano, 2012). There is little doubt that these methods are going to create a patient-centric focus in health care treatment. Nonetheless, Duburs, Neibecker, and Žarković (2012) say the good news is that many technological advancements in personal medicine are generating more data than what medical practitioners actually need. Based on this assertion, Beger (2013) says that the major problem associated with this development is that there would be more data generated than can be sufficiently processed. However, historical trends do not support this observation because the possibility of a novel idea springing up is always real.
Future Technology Trends
It is difficult to comprehend the impact of technology on personalized medicine without understanding the economics underlying the concept. Here, it is important to point out that many researchers still do not understand the economic value of specific therapies associated with precision medicine. A recent study by Jonsson (2013) highlights this fact because it shows a lack of empirical results to link clinical assessments and their economic values within the wider scope of personalized medicine. This gap in literature also manifests through the numerous studies that have failed to mention the economic values associated with personalized tests. Berman et al. (2012) say this omission is wrong because pioneers in the medical technology sector should first see the economic value of their input before they can fully commit to fanning the next wave of future technology trends. Nonetheless, understanding technology trends is a critical step in supporting personalized medicine.
Although the economic costs associated with personalized medicine remain unclear, there are real concerns that the concept may exacerbate health inequalities between developing and developed countries. These concerns arise because, as shown in the data analysis section, personalized medicine is an expensive affair and only those countries that can afford to pay for all the processes associated with it are bound to be the biggest beneficiaries. Those that cannot do so are bound to lag behind in this regard.
Of critical importance in this analysis is the opportunity to deploy current technological tools to extract big data from large samples of biological data. This process allows researchers to get information about previously unknown factors about diseases and use the same information to improve human health outcomes (Lenz et al., 2012). Most of these unknown factors are highlighted as biomarkers or targets for drug intervention to yield the desired outcome. Therefore, the big data is instrumental in personalizing medicine. However, researchers are still grappling with the issue of how to leverage this advantage to increase efficiency in data analysis.
Advances in technology within the greater realm of personalized medicine have also happened through a digital revolution that has helped medical practitioners and stakeholders in the health sector to use available medical data to create new knowledge. Indeed, as Bengoechea (2012) observers, the explosion of the digital medical space has been concurrent with significant changes in the health care sector. For example, characterizing this trend has been an emergence of digital resources for both ends of care delivery – physicians and patients (Penet, Krishnamachary, Chen, Jin, & Bhujwalla, 2014). WebMD.com and Healthfinder.gov are some digital web resources that have provided patients and medical practitioners with important knowledge surrounding different aspects of their health and the health care practice in general. MedlinePlus.gov and MerckSource.com are other digital platforms that have provided stakeholders in the health sector with the same type of information. These web resources have marked the new wave of personalized health resources, which are becoming increasingly useful to people who would have not otherwise gained access to such resources (Nabipour & Assadi, 2016a). The personalized nature of such information has also supported the precision medicine movement because people could easily understand how their health could be improved through the available digital platforms. At the same time, they are able to understand the specificity involved with certain illnesses by getting the individualized experiences of different groups of people, especially because they have access to several discussion groups within these web platforms.
Although experts in the field of personalized medicine have generally accepted the use of sequencing technologies in the practice, there has been some little effort made to understand how new technological advancements in this field are bound to impact the personalized medicine sector. For example, there have been new developments made in next-generation sequencing technology, but they are poorly understood, or poorly investigated, by experts in this field (Nabipour & Assadi, 2016a). These gaps in testing still exist despite the importance of advancing new tests in genetic polymorphisms, which is a critical area in personalized medicine. However, at this point of analysis, it is critical to note that most technological advancements have been concentrated in the area of diagnosis. For example, there have been new portable devices that have recently entered the market, after vigorous testing, to establish their safety and reliability. Measuring vital signs is one area we have discussed in this paper, which has greatly benefitted from such advancements in technology. Observers estimate that these advances in technology are bound to improve patient management and enhance the speed of getting data for a thorough analysis (Kircher, Hricak, & Larson, 2012).
The aforementioned data shows that although patients often troop into hospitals to get health services, medical practitioners often have limited data pertaining to individualized patient information to give individualized treatment methods, partly because of inadequate technology, or the failure to get available technology. However, ongoing developments in the technological sector are bound to affect different areas of personalized medicine, thereby paving the way for the mitigating of challenges affecting the adoption of personalized medicine in the first place. Data analysis is one area that is bound to be significantly affected by developments in technology because many researchers, such as Collins and Varmus (2015) have pointed out that technology is bound to improve correlations and data science. Those who have supported this view draw comparisons to how technological advancements have greatly changed commerce, mostly through e-commerce (Nabipour & Assadi, 2016a). Sites like Amazon have greatly benefitted from this trend through the development of algorithms that allow them to match people’s buying interests and their possible desirable products.
Djekidel (2012) says that, in some way, their ability to do so circles around the concept of “personalization” because such companies have been able to match individualized interests with customized products that appeal to those interests in the first place. This is the same model applied by medical researchers in the area of personalized medicine because they strive to match treatment methods with individualized genetic profiles. Indeed, the algorithms used in commerce to achieve the same goal are similar to the medical algorithms used by health researchers to promote precision medicine (Nabipour & Assadi, 2016a). Using the same technology, researchers have been able to match specific patients with other patients of similar genetic profiles to get the best treatment methods that would match with their biomarkers (Ishikawa, 2012).
The potential of new technology to improve coordination efforts in personalized medicine rests in data analysis and data mining efforts surrounding the practice. For example, two researchers, Nigam Shaw and Russ Altman have used new technology in data mining efforts to identify rare side effects associated with specific types of drugs and the types of populations that suffer the highest risk of being affected by them (Ishikawa, 2012). By understanding a patient’s biological makeup and comprehending how their genetic profiles would affect their response to specific treatments, medical practitioners are in a better position to understand how specific treatment methods would affect their patients, or to find out which therapies are appropriate for specific groups of patients.
Advances in new technology within the personalized medicine field are also bound to affect advancements in clinical utility of genomics data because new technologies have made it easier to obtain new sequencing patterns, which are integral to the speedy and efficient use of medical data (Herold et al., 2016). Existing bottlenecks in regulatory practices and the use of DNA data are slowing the process down, but health stakeholders are working to eliminate them for the betterment of their practice. Nonetheless, many examples that show how advancements in technology have helped improve the personalized medicine practice exist. For example, Assadi and Nabipour (2014b) demonstrates how improved sequencing has helped medical researchers to better detect and manage breast cancer because clinical utility tests that involve BRCA1 and BRCA2 sequencing have helped to prevent the occurrence of breast cancer among patients who have been proved to be of high risk (Ishikawa, 2012). The same sequencing tests have been used to minimize the incidence of fibrosis because clinical tests that have identified the CFTR gene among patients have helped determine the risk of developing the disease (Ishikawa, 2012).
Advances in technology within the bioinformatics space and the data analysis field will also help in the adoption of personalized medicine because, as highlighted in the section explaining data analysis in personalized medicine (in this review), we found that data analysis is one of the biggest hurdles countries have to overcome when implementing personalized medicine. Third party players in the technology industry have risen up to the challenge and are providing solutions to some of these problems by providing new technological platforms, such as cloud computing services, that should help medical researchers to undertake their data analysis and reviews (Chen & Snyder, 2012). This is a clear departure from their traditional role in the medical practice, which was mostly focused on instrumentation. Most of these new technologies will be critical in shaping the new age of personalized medicine because they will eliminate some of the bottlenecks associated with its adoption.
Advances in technology are also creating a new trend in the personalized medicine field – datafication of tissues. This development comes from the fact that most developments in the personalized medicine area have come from the use of DNA tissue to extract important data that would be useful in the medical decision-making process. The current trend in medical research indicates a new age where diagnoses would be made from multiple sources of data (Ishikawa, 2012). This new age of information would happen if researchers realize that they could get more advanced medical information (besides DNA) from diagnosis processes, such as image processing and not the traditional DNA testing only. The ability to generate quantifiable information through image processing has been termed by many researchers as part of phenomics. Relative to this assertion, Ishikawa (2012) says that, “Although genomic data can give clues to the ideal therapy, tissue images typically are more highly correlated to stage and presentation of disease, making the correlation of both types of data essential to the future of personalized medicine” (p. 3).
Telemedicine is also set to be part of the next generation of technological development that would herald the new wave of personalized medicine. The same technology could overlap with developments in new biosensors that could help medical practitioners to improve the area of preventive medicine. Already, there has been adequate evidence to show that these developments have been used in vital sign monitoring (Hood, Balling, & Auffray, 2012). In a recent TedMed seminar, one of the speakers was able to demonstrate how he could monitor his vital signs remotely, using a cell phone (Ishikawa, 2012). Such a development changes the way we think about personalized medicine because although we used to see it as a strategy of developing personalized treatment, many researchers are now seeing it as the new way of allowing physicians to monitor their patients remotely. The same development also allows them to make customized treatments remotely and on the go.
Recent technological changes have heralded a new period where medical devices are small, but similar changes are likely to allow medical practitioners to implant some of these devices in a patient’s body to monitor vital signs, or to undertake other medical functions. Based on such facts, it is possible to imagine a world where physicians could easily monitor the glucose levels of their patients remotely and evaluate whether their prescribed treatment methods have crossed the prescribed threshold levels, or not.
Advances in technology could also affect precision medicine through the development of engineering cells using 3D technology (Gupte & Hamilton, 2016). Such technological advancements could also allow medical practitioners to print organs for further analysis. As organ transplants become more common and the number of patients requiring organ treatment becomes even bigger, the thought of aiding the organ transplanting process by allowing medical researchers to develop organ imprints using 3D technology, from a patient’s cells, could be real. Ishikawa (2012) supports this assertion by saying there is nothing more personal than the idea of “printing” one’s organ based on the cell structure of a patient. Regenerative medicine is one area of practice that is already showing signs of this type of progress because some pioneer researchers, such as Tony Atala, have already “printed” 3-D kidneys to facilitate improvements in renal medicine (Gupte & Hamilton, 2016).
Some observers say that most of these 3-D printed organs should serve as models to help researchers understand how patients would react to specific drugs (Ishikawa, 2012). Additionally, some of them say that 3-D printing technology should help medical researchers to develop custom organs from patient’s cells and use them as replacement when the original ones malfunction (Ghasemi, Nabipour, Omrani, Alipour, & Assadi, 2016). Although technological developments, such as DNA sequencing exist, most of the proposals highlighted in this paper (as possible developments in the personalized medicine field) are still in the “proof of concept” phase. Nonetheless, if we examine the role of personalized medicine in the future of health care, we find that technological developments will be at the centre of the progress.
Based on the technological advancements in the personalized medicine field, we also find that the possibilities that exist in this field mostly hinge on merging personal or genetic factors together with new technological improvements to create a perfect blend of medical data that accentuates both concepts. It is also important to point out that these developments are occurring in a sector that is already complex. Leopold, Vogler, Habl, Mantel-Teeuwisse and Espin (2013) say that although different aspects of the health care sector are interactive, it may be difficult to fully comprehend the benefits of genetic testing associated with personalized medicine.
Researchers have explored the different areas of technological advancements in their respective areas of practice. For example, the Human Genome project outlined by Heydler (2013) has created a better understanding of how medical practitioners could use DNA profiling and tests to improve their practice. Most of the developments that have occurred in DNA testing have helped reduce the cost associated with the process and improve the efficiency of the same. Ishikawa (2012) also says that these advantages have been realized through improvements in handling liquids associated with genetic tests. In other words, the available technology has helped researchers to dispense the right amount of fluids when conducting genetic tests. The same technology has also helped in the miniaturization of these tests to make them more practical and useful to users (Wikoff et al., 2015). However, the automation of these clinical tests is only one part of the equation when it comes to the adoption of technology in personalized medicine. Nonetheless, generally, these advancements in technology are likely to increase the understanding and access to genetic information that medical researchers need.
Such advantages exist through the exploitation of different platforms, such as genotyping (Boutin et al., 2016). The same possibilities could be realized through single workflows, or though sequencing processes. Cloud computing, as mentioned in earlier sections of this report is also at the centre stage of these developments, especially regarding the process of updating or analysing data (Wikoff et al., 2015). The importance of cloud computing as an essential technological component of personalized medicine cannot be ignored because the volume of data generated through personalized medicine is too big and too broad to be processed or handled by simplistic data processing tools. Relative to this assertion, Ishikawa (2012) says, “Whereas generating an assay and decoding the data from a single node might take two to three days on a hospital server, cloud-based platforms can spin up 20 nodes and complete the analysis in a matter of minutes” (p. 2).
Another important driver for these changes is the need to share data among medical practitioners and medical researchers because the failure to do so could inhibit their ability to reap the full benefits of personalized medicine. The ability of technology firms to provide cloud-computing services as a platform where medical researchers could share such information and still maintain the security of their projects is a critical step in embracing personalized medicine.
Generally, the possibilities that could be realized through technological developments in the personalized medicine sector demonstrate that we live in exciting times because these developments have brought new light in the workings, or structures, of human genetics. The role of technology in this space rests in the fact that it shows us new ways of exploiting the potential of precision medicine. In this regard, it would be possible to see new developments in the sector where researchers continue to sequence genome parts that have the greatest impact on the health sector.
Conclusion
The success of personalized medicine depends on the perspectives of all health stakeholders. Indeed, the contributions, or views, of patients, clinicians, and drug manufacturers are instrumental in making this area of medicine more acceptable and successful. The same is true regarding the involvement of information technology specialists, health care providers and insurance companies alike. Based on the personalized nature of the personalized medicine concept, public interest is bound to shape the nature and trajectory of this type of treatment more than any other field of medicine. Aided by the popularity of forensic-based investigative shows, we find it hard to believe that the public would be disinterested in the growing area of personalized medicine. The growing acceptance of genetic testing is partly characterized by the growing acceptance of patients to have their genome screened for possible medical conditions and the reduction in associated costs. Since many patients are submitting their results (for these testing procedures) to common medical platforms, there is a high likelihood of new discoveries in this area of medicine being made. However, in as much as these developments are aiding the acceptance of personalized medicine, there are still existing concerns among the public that the results of these genetic tests could be used against them when they are seeking employment, or when they want to get a health insurance plan. However, part of the legal framework highlighted in this paper is trying to mitigate this concern, as seen through the Federal Genetic Information Non-Discrimination Act, which prevents health agencies or employers from using patients’ information for unsolicited purposes.
Since such ethical safeguards are in place, it may become easier for authorities to accept personalized medicine. Furthermore, as people become more educated in their health choices and on the available options surrounding preventable medicine, they could be better equipped to make healthier choices about their lives. In this regard, they would be better informed to promote their own health and wellness.
Predicting the future is often a difficult thing, but some fundamental issues about precision medicine are bound to stay the same. For example, based on the evidence gathered in this report, personal medicine is here to stay. Its spread is going to be aided with advancements in digital data analysis methods, as more people share data about their biology and medical conditions, or as more data about medical issues become available to more people and health agencies. These processes are going to lead to an improved understanding of medical issues and diseases that continue to plague modern society. In addition to providing the right diagnosis and treatment plan for specific types of diseases and specific types of people, personalized medicine will also be instrumental in maintaining healthy lifestyles. Medical practice is also bound to become more democratized, as more people gain access to complicated medical diagnoses that were previously not within their grasp or understanding.
Wellness industries are bound to be among the largest beneficiaries of the trend towards personalized medicine because of the new focus on preparedness, as opposed to the treatment of diseases. Therefore, the current format of the medical industry is bound to completely change. This change is bound to be coupled with the introduction of new ethical and social dilemmas. Four critical areas would possibly drive the next decade of developments in this critical medical research area.
One of them would be gene therapy because most research is concentrated on understanding how specific gene patterns affect the risk of getting certain diseases. Medical research in the area of brain functions would also champion the new trend in personalized medicine because many researchers are starting to understand the link between brain activity and general physiological health. This area could be the second key driver of personalized medicine in the next decade. The third key driver could come from ongoing investigations in the area of human ageing, which has also been characterized by increased research interest in the last decade. The fourth area to drive interests in personalized medicine could also come from developments in molecular level research because, as we have seen in this paper, most evidence gathered in the area of personalized medicine come from this area of research.
Based on the evidences provided in this review, it is not difficult to envision a world where medical researchers could sequence a person’s genome right from birth to death. It is also conceivable to have a situation where every aspect of a person’s health care plan is based on this type of sequencing. However, to achieve this sort of result, it is crucial to overcome some of the limitations associated with personalized medicine, which we have highlighted in this review. Concisely, as highlighted in key sections of this study, issues of utility, cost of personalized medicine, and the comprehension of the value of the concept need to be addressed.
Issues surrounding clinical utility stem from the failure of medical professionals to holistically use the information they obtain from genome sequencing. However, as highlighted in this review, pioneers in the field of precision medicine are making tremendous progress in working out treatment plans that would specifically work in certain groups of patients. This development alone is making significant changes in how medical practitioners prescribe specific types of treatment therapies. Such developments are significantly improving how medical practitioners prescribe individual treatments. In the long run, it is highly likely that pharmaceutical companies would use this information to develop better drugs for the public.
Reimbursement is another problem that should be solved if the field of personalized medicine is to pick up and redefine how medical practitioners undertake their practice. This concern is important because payers are still grappling with finding new ways of how to reimburse costs associated with genetic testing. Part of the problem is the lack of clarity regarding the return on investments associated with genetic testing. As highlighted in this review, such challenges also spill over into determining what to test for and the purposes for testing because these issues contribute to the quandary that existing payers encounter. The irony that exists in this situation is that payers would benefit from cost-reduction measures by paying for these tests. The existence of this irony partly explains why some people are willing to pay for some of these genetic tests out of pockets. Such a trend may alienate health care payers from the new trend because they continue to misunderstand the benefits associated with such tests (to themselves and to their clients).
Another area that needs to be addressed is the type of changes that would emerge in physician-patient relationships because medical practitioners and their patients are bound to develop deeper relationships within the framework of personalized medicine. Gene sequencing is a product of such conversations because it outlines the biological profiles of the patients that the health care service providers need to work on. This development should bring a new level of relationship between the medical practitioners and their patients. It should also create a new level of understanding between both parties. Observers, pundits and proponents of personalized medicine alike have shared this view because the new level of relationship between medical practitioners and patients is going to be increasingly redefined by genetic testing.
Generally, the tools needed for personalized medicine are there because there is enough data (existing or developing) that would help medical researchers to prescribe the right medications, at the right doses and at the right time – key tenets of precision medicine. The use of genetic testing to support this new era of personalized medicine is gaining traction and is moving beyond the realm of urbanized medicine to patients’ circles because at an individual level, people are starting to recognize the importance of the concept in their health. Indeed, as highlighted in the technological section of this review, advances in technology will provide medical researchers with more information about what they need to do to make personalized medicine better for their patients. Coupled with advances in technology, through the digital revolution in genomic testing and data management, collectively, these trends are set to make personalized medicine a revolutionary concept in the health sector.
References
Ahn, B. (2016). Personalized medicine based on theranostic radioiodine molecular imaging for differentiated thyroid cancer. Biomed Res Int., 6(8), 64-66.
Albanese, C., Rodriguez, O.C., VanMeter, J., Fricke, S.T., Rood, B.R., Lee, Y., Wang, S.S. (2013). Preclinical magnetic resonance imaging and systems biology in cancer research: Current applications and challenges. Am J Pathol., 182, 312–8.
Altaf-Ul-Amin, M., Afendi, F.M., Kiboi, S.K., & Kanaya, S. (2014). Systems biology in the context of big data and networks. Biomed Res Int., 4(2), 857-903.
Alyass, A., Turcotte, M., Meyre, D. (2015). From big data analysis to personalized medicine for all: Challenges and opportunities. BMC Medical Genomics, 8, 33-45.
American Society of Clinical Oncology. (2017). Understanding targeted therapy.
Aronson, S., Mahanta, L., Hien, L., Clark, E., Babb, L., Oates, M., … Lebo, M. (2016). Information technology support for clinical genetic testing within an Academic medical centre. J. Pers. Med., 6(1), 5.
Assadi, M., & Nabipour, I. (2014a). The evolving role of molecular imaging in transforming reactive to proactive (P4) medicine: predictive, preventive, personalized and participatory. J Nucl Med., 55(1), 1310.
Assadi, M., & Nabipour, I. (2014b). The future of molecular imaging in paradigm shift from reactive to proactive (P4) medicine. Nucl Med Commun., 5, 1193–6.
Beger, R. (2013). A review of applications of metabolomics in cancer. Metabolites, 3, 552–74.
Bengoechea, J. (2012). Infection systems biology: from reactive to proactive (P4) medicine. Int Microbiol.,15, 55–60.
Berman, D.M., Bosenberg, M.W., Orwant, R.L., Thurberg, B.L., Draetta, G.F., Fletcher, C.D., … Loda, M. (2012). Investigative pathology: Leading the post-genomic revolution. Lab Invest., 92, 4–8.
Blau, A., Brown, A., Mahanta, L., & Amir, S. (2016). The translational genomics core at partners personalized medicine: facilitating the transition of research towards personalized medicine. J. Pers. Med., 6(1), 10.
Boutin, N., Holzbach, A., Mahanta, L., Aldama, J., Cerretani, X., Embree, K., …Vickers, M. (2016). The information technology infrastructure for the translational genomics core and the Partners Biobank at Partners Personalized Medicine. J. Pers. Med., 6(1), 6.
Buchanan, J., Wordsworth, S., & Schuh, A. (2013). Issues surrounding the health economic evaluation of genomic technologies. Pharmacogenomics, 14, 1833–47.
Buriani, A., Garcia-Bermejo, M.L., Bosisio, E., Xu, Q., Li, H., Dong, X., … Simmonds, M.S. (2012). Omic techniques in systems biology approaches to traditional Chinese medicine research: Present and future. J Ethnopharmacol., 140, 535–44.
Burock, S., Meunier, F., & Lacombe, D. (2013). How can innovative forms of clinical research contribute to deliver affordable cancer care in an evolving health care environment? Eur J Cancer., 49, 2777–83.
Burton, H., Cole, T., & Lucassen, A. (2012). Genomic medicine: Challenges and opportunities for physicians. Clin Med., 12, 416–9.
Chen, R., & Snyder, M. (2012). Systems biology: Personalized medicine for the future? Curr Opin Pharmacol., 12, 623–8.
Cho, S.H., Jeon, J., & Kim, S.I. (2012). Personalized medicine in breast cancer: A systematic review. J Breast Cancer, 15, 265–72.
Ciardiello, F., Arnold, D., Casali, P.G., Cervantes, A., Douillard, J.Y., Eggermont, A., … Eniu, A. (2014). Delivering precision medicine in oncology today and in future-the promise and challenges of personalised cancer medicine: a position paper by the European Society for Medical Oncology (ESMO). Ann Oncol., 25, 1673–8.
Collins, F.S., & Varmus, H. (2015). A new initiative on precision medicine. N Engl J Med., 372, 793–5.
Cullis, P. (2015). The personalized medicine revolution: How diagnosing and treating disease are about to change forever. New York, NY: Greystone Books Ltd.
Damato, B., & Heimann, H. (2013). Personalized treatment of uveal melanoma. Eye., 27, 172–9.
Denford, S., Frost, J., Dieppe, P., Cooper, C., & Britten, N. (2014). Individualisation of drug treatments for patients with long-term conditions: A review of concepts. BMJ Open., 4(1), 41-45.
Dizon, D.S., Politi, M.C., & Back, A.L. (2013). The power of words: Discussing decision-making and prognosis. Am Soc Clin Oncol Educ Book. 2, 442–6.
Djekidel, M. (2012). Radiogenomics and radioproteomics. Omi J Radiol., 2, 2–4.
Draghia-Akli, R. (2012). Enabling personalized medicine in Europe: A look at the European Commission’s funding activities in the field of personalized medicine research. Per Med., 9, 151–5.
Duburs, G., Neibecker, D., & Žarković, N. (2012). Chemistry and personalized medicine – the research and development future of Europe. Croat Med J., 53(1), 291–3.
Egalite, N., Groisman, I.J., & Godard, B. (2014). Personalized medicine in oncology: Ethical implications for the delivery of healthcare. Per Med., 11, 659–68.
Egan, M., & Philipson, T. (2016). Health care adherence and personalized medicine. Web.
Ehlerding, E.B., & Cai, W. (2016). Harnessing the power of molecular imaging for precision medicine. J Nucl Med., 57, 171–2.
Eilat-Tsanani, S., Tabenkin, H., Shental, J., Elmalah, I., & Steinmetz, D. (2013). Patients’ perceptions of radical prostatectomy for localized prostate cancer: A qualitative study. Isr Med Assoc J., 15, 153–7.
Faulkner, E., Annemans, L., Garrison, L., Helfand, M., Holtorf, A.P., Hornberger, J.,
… Hughes, D. (2012). Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: a report of the ISPOR personalized medicine special interest group. Value Health, 15, 1162–71.
Fiuzat, M., O’Connor, C.M., Gueyffier, F., Mascette, A.M., Geller, N.L., Mebazaa, A.,
… Voors, A.A. (2013). Biomarker-guided therapies in heart failure: A forum for unified strategies. J Card Fail., 19, 592–9.
Gainer, V., Cagan, A., Castro, V., Duey, S., Ghosh, B., Goodson, A., … Wattanasin,
N. (2016). Using i2b2 to enable researchers to work with the partners biobank data and samples in the partners biobank portal at partners personalized medicine. J. Pers. Med., 6(1), 7.
Ghasemi, M., Nabipour, I., Omrani, A., Alipour, Z., & Assadi, M. (2016). Precision medicine and molecular imaging: New targeted approaches toward cancer therapeutic and diagnosis. American Journal of Nuclear Medicine and Molecular Imaging, 6(6), 310–327.
Goldhirsch, A., Winer, E.P., Coates, A.S., Gelber, R.D., Piccart-Gebhart, M., Thürlimann, B., … Senn, H.J. (2013). Personalizing the treatment of women with early breast cancer: Highlights of the St. Gallen international expert consensus on the primary therapy of early breast cancer 2013. Ann Oncol., 24(9), 2206–23.
Gupte, A.A., & Hamilton, D.J. (2016). Molecular imaging and precision medicine. Cardiology, 133, 178–80.
Halim, A.B. (2015). Some imminent but overlooked pre-analytical and analytical challenges currently facing biomarkers and companion diagnostics. Ann N Y Acad Sci., 13(6), 63–70.
Henzler, T., & Fink, C. (2015). Functional computed tomography in oncology and cardiovascular imaging: A key player in the era of precision medicine and radiogenomics. Eur J Radiol., 84, 2345–6.
Herold, C.J., Lewin, J.S., Wibmer, A.G., Thrall, J.H., Krestin, G.P, Dixon, A.K., Schoenberg, S.O. (2016). Imaging in the age of precision medicine: Summary of the proceedings of the 10th biannual symposium of the international society for strategic studies in radiology. Radiology, 279, 226–38.
Heydler, T. (2013). 5 ways technology is changing personalized medicine. Web.
Hood, L., Balling, R., & Auffray, C. (2012). Revolutionizing medicine in the 21st century through systems approaches. Biotechnol J., 7, 992–1001.
Hood, L., & Flores, M. (2012). A personal view on systems medicine and the emergence of proactive P4 medicine: Predictive, preventive, personalized and participatory. N Biotechnol., 29, 613–24.
Horgan, D., Jansen, M., Leyens, L., Lal, J.A., Sudbrak, R., Hackenitz, E., Busshoff, U. (2014). An index of barriers for the implementation of personalised medicine and pharmacogenomics in Europe. Public Health Genomics, 17, 287–98.
Ishikawa, T. (2012). Emerging new technology to advance personalized medicine. J Pharmacogenomics, 3, 113-115.
Jakka, S., & Rossbach, M. (2013). An economic perspective on personalized medicine. HUGO J., 7, 1–6.
John Hopkins Medicine. (2017). Vital signs. Web.
Jonsson, B. (2013). Technology assessment for new oncology drugs. Clin Cancer Res., 19, 6–11.
Karlson, E., Boutin, B., Hoffnagle, A., & Allen, N. (2016). Building the partner’s healthcare biobank at partners personalized medicine: Informed consent, return of research results, recruitment lessons and operational considerations. J. Pers. Med. 6(1), 10.
Katrib, A., Hsu, W., Bui, A., & Xing, Y. (2016). Radiotranscriptomics: A synergy of imaging and transcriptomics in clinical assessment. Quant Biol., 4, 1–12.
Kichko, K., Marschall, P., & Flessa, S. (2016). Personalized medicine in the U.S. and Germany: Awareness, acceptance, use and preconditions for the wide implementation into the medical standard. J Pers Med., 6(1), 221-225.
Kircher, M.F., Hricak, H., & Larson, S.M. (2012). Molecular imaging for personalized cancer care. Mol Oncol., 6, 182–95.
Lenz, C., Fensch, M., & Sollano, J.A. (2012). Implementing personalised medicine in clinical practice: Country investments and public-private partnerships in Europe. J für Pharmakologie und Therapie., 21, 3–7.
Leopold, C., Vogler, S., Habl, C., Mantel-Teeuwisse, A.K., & Espin, J. (2013). Personalised medicine as a challenge for public pricing and reimbursement authorities – A survey among 27 European countries on the example of trastuzumab. Health Policy, 113, 313–22.
Louca, S. (2012). Personalized medicine – a tailored health care system: Challenges and opportunities. Croat Med J., 53, 211–3.
Management Association Information Resources. (2016). Pharmaceutical sciences: Breakthroughs in research and practice: Breakthroughs in research and practice. New York, NY: IGI Global.
Mason-Suares, H., Sweetser, D., Lindeman, N., & Morton, C. (2016). Training the future leaders in personalized medicine. J Pers Med., 6(1), 223-8.
Mathieu, T., Bermont, L., Boyer, J.C., Versuyft, C., Evrard, A., Cuvelier, I., Couderc, R. (2012). Lexical fields of predictive and personalized medicine. Ann Biol Clin., 70, 651–8.
Moch, H., Blank, P., Dietel, M., Elmberger, G., Kerr, K., Palacios, J., … Penault-Llorca, F. (2012). Personalized cancer medicine and the future of pathology. Virchows Arch., 460, 3–8.
Moore, T. (2015). How technology is transforming personalized medicine. Web.
Nabipour, I., & Assadi, M. (2014). Infrastructures for systems medicine in Iran’s health roadmap. ISMJ, 17, 974–92.
Nabipour, I., & Assadi, M. (2015). Converging technologies: shaping the future of medicine. ISMJ, 17, 1045–67.
Nabipour, I., & Assadi, M. (2016a). Precision medicine, an approach for development of the future medicine technologies. ISMJ, 19, 167–84.
Nabipour, I., & Assadi, M. (2016b). The technological singularity and exponential medicine. ISMJ, 18, 1287–98.
O’Donnell, J.C. (2013). Personalized medicine and the role of health economics and outcomes research: Issues, applications, emerging trends, and future research. Value Health, 16(6), 1–3.
Olsen, D., & Jorgensen, J.T. (2014). Companion diagnostics for targeted cancer drugs – clinical and regulatory aspects. Front Oncol., 4,105.
Oskouie, A.A., & Taheri, S. (2015). Recent developments and application of metabolomics in cancer diseases. J Paramed Sci., 6, 116–35.
Payne, K., & Annemans, L. (2013). Reflections on market access for personalized medicine: Recommendations for Europe. Value Health., 16(6), 32–8.
Penet, M.F., Krishnamachary, B., Chen, Z., Jin, J., & Bhujwalla, Z.M. (2014). Molecular imaging of the tumor microenvironment for precision medicine and theranostics. Adv Cancer Res., 124, 235–56.
Personalized Medicine Coalition. (2014). The case for personalized medicine. Web.
Pin, E., Fredolini, C., & Petricoin, E.F. (2013). The role of proteomics in prostate cancer research: Biomarker discovery and validation. Clin Biochem., 46, 524–38.
Rehm, H.L., Hynes, E., & Funke, B. (2016). The changing landscape of molecular diagnostic testing: Implications for academic medical centres. J. Pers. Med., 6(1), 8.
Rogowski, W., Payne, K., Schnell-Inderst, P., Manca, A., Rochau, U., Jahn, B., Alagoz O. (2015). Concepts of ‘personalization’ in personalized medicine: Implications for economic evaluation. PharmacoEconomics, 33, 49–59.
Rosenstein, B.S., West, C.M., Bentzen, S.M., Alsner, J., Andreassen, C.N., Azria, D., … Barnett, G.C. (2014). Radiogenomics: Radiobiology enters the era of big data and team science. Int J Radiat Oncol Biol Phys., 89, 709–13.
Rosland, A.M., Heisler, M., & Piette, J.D. (2012). The impact of family behaviors and communication patterns on chronic illness outcomes: A systematic review. J Behav Med., 35, 221–39.
Rubin, E. (2014). Developing precision medicine in a global world. Clin Cancer Res., 20, 1419–27.
Schleidgen, S., Klingler, C., Bertram, T., Rogowski, W.H., & Marckmann, G. (2013). What is personalized medicine: Sharpening a vague term based on a systematic literature review. BMC Med Ethics, 14, 55.
Schleidgen, S., & Marckmann, G. (2013). Re-focusing the ethical discourse on personalized medicine: a qualitative interview study with stakeholders in the German healthcare system. BMC Med Ethics, 14(20), 45-8.
Shajahan-Haq, A., Cheema, M., & Clarke, R. (2015). Application of metabolomics in drug resistant breast cancer research. Metabolites, 5, 100–18.
Siebert, U., Jahn, B., Rochau, U., Schnell-Inderst, P., Kisser, A., Hunger, T., Sroczynski, G. (2015). Oncotyrol–center for personalized cancer medicine: Methods and applications of health technology assessment and outcomes research. Z Evid Fortbild Qual Gesundhwes., 109, 330–40.
Smoller, J., Karlson, E., Green, R., Kathiresan, S., MacArthur, D.G., Talkowski, M., … Weiss, S.T. (2016). An eMERGE clinical centre at partners personalized medicine. J. Pers. Med., 6(1), 5.
Sun, S. (2016). Socio-economics of personalized medicine in Asia. London, UK: Routledge.
Sweet, K., & Michaelis, R. (2012). The busy physician’s guide to genetics, genomics and personalized medicine. New York, NY: Springer Science & Business Media.
Thrall, J.H. (2015). Moreton lecture: Imaging in the age of precision medicine. J Am Coll Radiol., 12, 1106–11.
Tsai, E.A., Shakbatyan, R., Evans, J., Rossetti, P., Graham, C., Shamra, H., Lebo, M. (2016). Bioinformatics workflow for clinical whole genome sequencing at partners healthcare personalized medicine. J. Pers. Med., 6(1), 12.
Tsimberidou, A.M., Ringborg, U., & Schilsky, R.L. (2013). Strategies to overcome clinical, regulatory, and financial challenges in the implementation of personalized medicine. Am Soc Clin Oncol Educ Book., 2(3), 118–125.
University of Arkansas for Medical Sciences. (2015). Big data analysis fuels personalized medicine. Web.
U.S. Department of Health and Human Services. (2013). Paving the way for personalized medicine. Web.
Verma, M. (2014). Molecular profiling and companion diagnostics: Where is personalized medicine in cancer heading? Per Med.,11, 761–71.
Vizirianakis, I. (2014). Handbook of personalized medicine: Advances in nanotechnology, drug delivery, and therapy. New York, NY: CRC Press.
Wagner, D. (2015). 6 ways big data is driving personalized medicine revolution. Web.
Wang, X., Zhang, A., & Sun, H. (2013). Power of metabolomics in diagnosis and biomarker discovery of hepatocellular carcinoma. Hepatology, 57, 2072–7.
Weiss, S.T., & Shin, M.S. (2016). Infrastructure for personalized medicine at partners healthcare. J Pers Med., 6(1), 121-9.
Werner, H.M., Mills, G.B., & Ram, P.T. (2014). Cancer systems biology: A peek into the future of patient care? Nat Rev Clin Oncol., 11, 167–76.
Wikoff, W., Hanash, S., DeFelice, B., Miyamoto, S., Barnett, M., Zhao, Y., Goodman, G. (2015). Diacetylspermine is a novel prediagnostic serum biomarker for non-small-cell lung cancer and has additive performance with pro-surfactant protein b. J. Clin. Oncol., 33, 3880–6.
Wu, D., Rice, C.M., & Wang, X. (2012). Cancer bioinformatics: A new approach to systems clinical medicine. BMC Bioinformatics, 13(71), 55-9.
Yadav, K., Khader, S., Readhead, B., Yadav, S., Li, L., Kasarksis, A., … Tewari, A.K. (2016). Systems medicine approaches to improving understanding, treatment, and clinical management of neuroendocrine prostate cancer. Curr Pharm Des., 22, 5234–5248.
Yoshida, M., Nishiumi, S., & Azuma, T. (2015). Gastroenterological cancer diagnosis by metabolomics-discovery of pancreatic cancer biomarker. Rinsho Byori., 63, 450–6.
Zimmern, R.L., & Khoury, M.J. (2012). The impact of genomics on public health practice: The case for change. Public Health Genomics, 15,118–24.