A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study. (July 2020)

• Record Type: Journal Article
• Authors: Burgermaster, Marissa; Son, Jung H.; Davidson, Patricia G.; Smaldone, Arlene M.; Kuperman, Gilad; Feller, Daniel J.; Burt, Katherine Gardner; Levine, Matthew E.; Albers, David J.; Weng, Chunhua; Mamykina, Lena
• Is Part Of: International journal of medical informatics. Volume 139(2020)
• Publication Date: 2020-07
• Subjects: Patient-generated health data -- Knowledge representation -- Expert system -- Suggestion system -- Personalized nutrition; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104158
• Languages: English
• ISSNs: 1386-5056

Abstract

Graphical abstract: Highlights: Sparse self-monitoring data present challenges for machine learning methods. Qualitative modeling of dietitian reasoning over patient data resulted in a knowledge model. To dietitians, the knowledge model represented how recommendations from patient data are made. Synthesizing patient data and clinical knowledge may improve personalized decision-making. Abstract: Introduction: Self-monitoring technologies produce patient-generated data that could be leveraged to personalize nutritional goal setting to improve population health; however, most computational approaches are limited when applied to individual-level personalization with sparse and irregular self-monitoring data. We applied informatics methods from expert suggestion systems to a challenging clinical problem: generating personalized nutrition goals from patient-generated diet and blood glucose data. Materials and methods: We applied qualitative process coding and decision tree modeling to understand how registered dietitians translate patient-generated data into recommendations for dietary self-management of diabetes (i.e., knowledge model). We encoded this process in a set of functions that take diet and blood glucose data as an input and output diet recommendations (i.e., inference engine). Dietitians assessed face validity. Using four patient datasets, we compared our inference engine's output to clinical narratives and gold standards developed by expert clinicians. Results: To dietitians, the knowledge model represented how recommendations from patient data are made. Inference engine recommendations were 63 % consistent with the gold standard (range = 42 %–75 %) and 74 % consistent with narrative clinical observations (range = 63 %–83 %). Discussion: Qualitative modeling and automating how dietitians reason over patient data resulted in a knowledge model representing clinical knowledge. However, our knowledge model was less consistent with gold standard than narrative clinical recommendations, raising questions about how best to evaluate approaches that integrate patient-generated data with expert knowledge. Conclusion: New informatics approaches that integrate data-driven methods with expert decision making for personalized goal setting, such as the knowledge base and inference engine presented here, demonstrate the potential to extend the reach of patient-generated data by synthesizing it with clinical knowledge. However, important questions remain about the strengths and weaknesses of computer algorithms developed to discern signal from patient-generated data compared to human experts.