Progress in modern medicine has resulted in the amount of information related to the health of patients to grow exponentially. This capacity of data has grown beyond the capabilities of manual input of individual physicians. As a result, health information technology was designed to work with corroboration with electronic health records (EHR) to manage and process the complex network of collected data. However, these systems are often experiencing ineffective design, low rates of adoption, and a lack of standards. A properly developed and comprehensive database can be used to increase the quality of care delivery and manage patient outcomes. Healthcare (hospital) associated infections (HAI) are a critical clinically-based patient problem that could be addressed through competent database management by creating an effective system of reporting and surveillance used to identify at-risk patients.
Patient Problem
Healthcare-associated infections are a clinically-based problem that presents a considerable threat to patients’ safety. These infections result in increased morbidity and mortality rates as well as increased costs of healthcare. Common infections include central line-associated bloodstream infections, catheter-associated urinary tract infections, and surgical site infections. Statistics indicate that more than 700,000 infections may be reported annually, and targeted measures can decrease rates by as much as 70% (CDC, 2016). One of the primary strategies to eradicate HAIs is to identify risk factors and act for prevention rather than a reactionary basis. Surveillance is a comprehensive mechanism for calculating patient outcomes and care processes by analyzing substantial amounts of data. This information is available to the healthcare staff to aid in decision making and treatment to improve outcomes. Manual surveillance is copious and increases the possibility of human error as HAI cases have to be identified by chart review and drawing conclusions based on experience. A database able to combine necessary data and analyze relevant connections can automate and optimize the process of identification, surveillance, and prevention of healthcare-associated infections.
In order to detect instances of hospital-acquired infections, surveillance systems can use a wide array of patient data available in the electronic health records. This may include clinical, pharmaceutical, microbiological, and administrative information. As the adoption of EHR increases and expands, the data can increase the effectiveness of HAI detection systems to address potential threats. An electronic surveillance system database lack variability as the show consistency in collecting necessary parameters. Therefore, electronic surveillance has better efficiency than manual observation, also showing more than 60% saved time and more than 40% reduction in chart reviews (De Bruin, Seeling & Schuh, 2014). The more data sources provided by EHR, the more accurate and sensitive a HAI detection system can be.
EHR Data
A wide array of data points derived from electronic health records can be used for surveillance. Below is a list of the most commonly used data sources for surveillance systems.
The simplest of these indicators seek to use ICD procedure and discharge codes and the presence of a device that may be used to determine the risk of potential infection. These are structured, allowing to select an input based on the already determined code. However, these have been found to have low sensitivity in determining HAI. Recently there has been an introduction of physician narratives which are unstructured, consisting of notes relevant to the patient and their condition. These are classified as medico-administrative indicators which are least commonly used as the sole determinant of HAI risk and maintain an irregularly low sensitivity of 20-60% (De Bruin et al., 2014).
More complex clinical and laboratory-based data consists of data about patient vital signs and results from biochemistry and microbiology laboratory analyses. Microbiology remains the most common indicator of HAI. It is structured data based on whether the cultures are positive or negative. However, a challenge arises during assessment since not all infections have positive blood cultures, origins of pathogens may be unclear, and the designated depth of infection is nuanced. Systems using this data achieve both sensitivity and specificity of 91% (De Bruin et al., 2014). Indicators in this category and laboratory results are quantifiable, more reliable, and easier for database management systems to analyze.
Data Relationships (150)
The database should be structured based on a multi-stage system which is able to take basic considerations to determine risk factors for HAI. The data structure should be progressive forming relationships.
This database structure helps to capture the numerator and denominator for each level covering all types of infections. For example, the numerator being a positive blood culture result which is easily captured electronically with the denominator being a comprehensive assessment of a patient. Many of these data points can be gathered from a patient’s electronic health record as it is entered upon evaluation or finalized laboratory results. Additional data can be used to add detail to the database, such as the number of days a patient has had the device. As definitions are modified over time, the database can be modified to become more autonomous in determining threats of HAI (Woeltje et al., 2014).
The functional relationship between all levels of the database is cause and effect. If a particular risk factor is met, it moves to the next level to more specifically identify the potential threat. The database can be used to optimize and ensure efficiency for the surveillance process. It would semi-autonomously consider the most common indicators of HAIs and other critical signs established by the National Health Safety Network which can be used to avoid time-consuming and expensive manual processes of surveillance for prevention purposes (Shepard et al., 2014). The interconnectivity with various components of EHR will provide the necessary data sources for the algorithm to become more sensitive and accurate over time with the possibility of modern machine learning technology.
Conclusion
Electronic detection systems using database management from collected EHR patient data can be used to address a clinically-based problem of healthcare-associated infections. The issue is one of the leading causes of morbidity and mortality in hospital settings, requiring enhancement of recognition processes. The database can use a wide array of data about a patient to determine a risk-assessment. As more data becomes available, the system becomes more sensitive to be used for non-manual infection control.
References
CDC. (2016). HAI data and statistics. Web.
De Bruin, J. S., Seeling, W., & Schuh, C. (2014). Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: A systematic review. Journal of the American Medical Informatics Association, 21(5), 942–951. Web.
Shepard, J., Hadhazy, E., Frederick, J., Nicol, S., Gade, P., Cardon, A.,… Madison, S. (2014). Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting. American Journal of Infection Control, 42(3). Web.
Woeltje, K. F., Lin, M.Y., Klompas, M., Wright, M.O., Zuccotti, G., & Trick, W.E. (2014). Data requirements for electronic surveillance of healthcare-associated infections. Infection Control and Hospital Epidemiology, 35(9), 1083-1091. Web.