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Artificial Intelligence Integrated in Healthcare

Introduction

Artificial intelligence is one of the innovations that promise to have a significant impact on healthcare delivery. AI refers to the simulation of human performed tasks by machines such as computer systems and robots. AI helps in data analysis through electronic health records, diagnosis, and disease management (Bresnick, 2018). Proper use of AI techniques in a healthcare setting can help to minimize errors in data analysis and enhance the quality of care received by patients.

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Purpose

This paper aims to talk about AI as an innovative idea that can be integrated into healthcare. The paper will detail some of the strategies used in executing artificial intelligence in health care. It will also discuss the risks and benefits that are likely to arise from using AI in healthcare. This will help to inform on strategies that can be used to minimize possible risks. The paper will highlight the facilitators of healthcare institutions applying AI and the barriers that are likely to be faced in the process.

Problem

There exists a human resource crisis in healthcare institutions that have a significant impact on the quality of care delivered to patients. The human resource crisis in healthcare has resulted from a shortage of healthcare providers, burnout among professionals, and increasing demand for chronic care (O’Donnell, 2019). This has led to challenges in healthcare delivery and the quality of care offered to patients, impacting their quality of life.

Problem Analysis

According to Miseda et al. (2017), there is a shortage of nurses and doctors, especially in rural areas and developing nations. Only thirty-eight percent of global nurses and twenty-five percent of doctors provide their services in rural areas. Burnout is also a contribution to the shortage of human resources experienced in healthcare. Burnout affects job turnover and the possibility of a healthcare provider quitting their work (Reith, 2018). A significant number of healthcare professionals experience burnout from their work duties, and the risk is likely to be great if the problem is not adequately addressed. The rise in the number of people with chronic diseases and conditions has enhanced the demand for chronic care, thus putting pressure on healthcare providers.

Results of Analysis

The analysis carried out above indicates that the quality of care delivered to patients is likely to be low, which might affect their quality of life. There is a shortage of healthcare providers, which is likely to increase within the coming years. This leaves a significant number of patients unattended, which can affect their quality of life. Healthcare professionals also experience burnout due to the pressure they face when handling their day-to-day activities. Burnout affects how they deliver healthcare which can impact patients’ quality of life. The increase in the number of patients suffering from chronic illnesses has also increased pressure on healthcare providers and has a likelihood of causing burnout.

Recommendations

To eliminate the human resource crisis in healthcare, it will be necessary for healthcare institutions to integrate AI into their operations. AI will help handle some of the tasks specified for healthcare providers, such as analysis of patient data, and detecting medical issues. According to Miseda et al. (2017), deep learning algorithms can be used to diagnose conditions related to cardiology, dermatology, and oncology. Using AI in healthcare will help to reduce the burden on healthcare providers and enhance the quality of care received by patients.

Strategies for Implementation

AI can be implemented in healthcare to manage patient data stored in electronic health records. Using AI in electronic health records helps the medical provider to retrieve and analyze patient data easier for them to make informed decisions. This will help to ensure that treatments specified for patients are accurate and appropriate for their medical conditions (Lin et al., 2020). AI in electronic health records will help handle complex data that cannot be analyzed manually and help to reduce the time used in the process.

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Healthcare institutions can also implement AI in their operations to enhance the early detection of medical problems. AI can be used to detect cancer in its early stages by relying on mammograms. AI helps in the review and translation of mammograms, increasing the speed by thirty times compared to when the process is done manually. AI also enhances the accuracy of such processes compared to relying on humans who are prone to errors.

Risks of Implementation

One of the risks of implementing artificial intelligence in healthcare institutions is injuries and errors. According to Walter (2019), artificial intelligence systems can lead to errors that cause patient injuries or other medical problems, such as when a wrong drug is prescribed to a patient. A disadvantage with this is that patients are likely to react differently to errors that result from digital tools compared to human errors. Also, if a problem affects artificial intelligence in a healthcare institution, it is likely to affect a huge number of patients at once.

Privacy is also a risk associated with implementing artificial intelligence in a healthcare institution. Artificial intelligence systems face a risk of data breaches through hacking. In this case, patient data might end up in the wrong hands and be used for the wrong purposes. Data breaches can also affect the provision of healthcare by interfering with patient records. Healthcare institutions must lay out security strategies when implementing artificial intelligence.

Benefits of Implementation

Artificial intelligence can perform better than human medical providers, thus helping to enhance care delivery and its quality. Existing artificial intelligence systems can even predict an injury even before it occurs, which is not possible among human medical providers (Van Eetvelde et al., 2021). Artificial intelligence can also automate activities in a healthcare institution, such as managing electronic health records (Price, 2019). This helps to save time and resources likely to be used when relying on human labor to manage healthcare records.

Facilitators for Implementation

Existing information technology systems in healthcare institutions act as facilitators for the implementation of artificial intelligence. Artificial intelligence scans are easily integrated into existing information technology systems without interfering with how workflow practices are conducted (Strohm et al., 2020). Another facilitator to the implementation of artificial intelligence is the expectations held by healthcare providers and stakeholders about its effectiveness (Klumpp et al., 2021). In this case, healthcare providers are likely to adopt artificial intelligence without problems.

Barriers to Implementation

The lack of adequate funding is one of the barriers to the implementation of artificial intelligence in healthcare institutions. According to Strohm et al. (2020), the benefits and costs of implementing artificial intelligence vary from one department to another, thus complicating funding decisions. Some artificial intelligence systems can also be very expensive to implement and maintain in a healthcare institution. Trust issues among patients, especially the elderly, also act as barriers to implementing artificial intelligence in medical institutions (Kuan, 2019). Patients who are not aware of the benefits of artificial intelligence are likely to refuse the use of such systems when receiving medical care.

References

Bresnick, J. (2018). Top 12 Ways Artificial Intelligence Will Impact Healthcare. HealthITAnalytics. Web.

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Klumpp, M., Hintze, M., Immonen, M., Ródenas-Rigla, F., Pilati, F., Aparicio-Martínez, F., Çelebi, D., Liebig, T., Jirstrand, M., Urbann, O., Hedman, M., Lipponen, J. A., Bicciato, S., Radan, A., Valdivieso, B., Thronicke, W., Gunopulos, D., & Delgado-Gonzalo, R. (2021). Artificial intelligence for hospital health care: Application cases and answers to challenges in European hospitals. In Healthcare (Vol. 9, No. 8, p. 961). Multidisciplinary Digital Publishing Institute. Web.

Kuan, R. (2019). Adopting AI in Health Care Will Be Slow and Difficult. Harvard Business Review. Web.

Lin, W. C., Chen, J. S., Chiang, M. F., & Hribar, M. R. (2020). Applications of artificial intelligence to electronic health record data in ophthalmology. Translational vision science & technology, 9(2), 13-13. Web.

Miseda, M. H., Were, S. O., Murianki, C. A., Mutuku, M. P., & Mutwiwa, S. N. (2017). The implication of the shortage of health workforce specialists on universal health coverage in Kenya. Human resources for health, 15(1), 1-7. Web.

O’Donnell, R. (2019). The HR Challenges Shaping the Healthcare Industry. Workest. Web.

Price, N. (2019). Risks and remedies for artificial intelligence in health care. Brookings. Web.

Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P., & Moors, E. H. (2020). Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. European radiology, 30, 5525-5532. Web.

Van Eetvelde, H., Mendonça, L. D., Ley, C., Seil, R., & Tischer, T. (2021). Machine learning methods in sports injury prediction and prevention: a systematic review. Journal of experimental orthopaedics, 8(1), 1-15. Web.

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Walter, M. (2019). 6 serious risks associated with AI in healthcare. AI in Healthcare. Web.

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