Abstract
BACKGROUND: Globally, the incidence and mortality of chronic liver disease are escalating. Early detection of liver disease remains a challenge, often occurring at symptomatic stages when preventative measures are less effective. The Chronic Liver Disease score (CLivD) is a predictive risk model developed using Finnish health care data, aiming to forecast an individual's risk of developing chronic liver disease in subsequent years. The Kanta Service is a national electronic health record system in Finland that stores comprehensive health care data including patient medical histories, prescriptions, and laboratory results, to facilitate health care delivery and research. OBJECTIVE: This study aimed to evaluate the feasibility of implementing an automatic CLivD score with the current Kanta platform and identify and suggest improvements for Kanta that would enable accurate automatic risk detection. METHODS: In this study, a real-world data repository (Kanta) was used as a data source for "The ClivD score" risk calculation model. Our dataset consisted of 96,200 individuals' whole medical history from Kanta. For real-world data use, we designed processes to handle missing input in the calculation process. RESULTS: We found that Kanta currently lacks many CLivD risk model input parameters in the structured format required to calculate precise risk scores. However, the risk scores can be improved by using the unstructured text in patient reports and by approximating variables by using other health data-like diagnosis information. Using structured data, we were able to identify only 33 out of 51,275 individuals in the "low risk" category and 308 out of 51,275 individuals (<1%) in the "moderate risk" category. By adding diagnosis information approximation and free text use, we were able to identify 18,895 out of 51,275 (37%) individuals in the "low risk" category and 2125 out of 51,275 (4%) individuals in the "moderate risk" category. In both cases, we were not able to identify any individuals in the "high-risk" category because of the missing waist-hip ratio measurement. We evaluated 3 scenarios to improve the coverage of waist-hip ratio data in Kanta and these yielded the most substantial improvement in prediction accuracy. CONCLUSIONS: We conclude that the current structured Kanta data is not enough for precise risk calculation for CLivD or other diseases where obesity, smoking, and alcohol use are important risk factors. Our simulations show up to 14% improvement in risk detection when adding support for missing input variables. Kanta shows the potential for implementing nationwide automated risk detection models that could result in improved disease prevention and public health.