An approach to predicting patient experience through machine learning and social network analysis. (26th October 2020)

• Record Type: Journal Article
• Authors: Bari, Vitej; Hirsch, Jamie S; Narvaez, Joseph; Sardinia, Robert; Bock, Kevin R; Oppenheim, Michael I; Meytlis, Marsha
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 12(2020)
• Page Start: 1834
• Page End: 1843
• Publication Date: 2020-10-26
• Subjects: patient experience -- electronic medical record -- physician-patient relations -- machine learning -- social network analysis; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa194
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Improving the patient experience has become an essential component of any healthcare system's performance metrics portfolio. In this study, we developed a machine learning model to predict a patient's response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey's "Doctor Communications" domain questions while simultaneously identifying most impactful providers in a network. Materials and Methods: This is an observational study of patients admitted to a single tertiary care hospital between 2016 and 2020. Using machine learning algorithms, electronic health record data were used to predict patient responses to Hospital Consumer Assessment of Healthcare Providers and Systems survey questions in the doctor domain, and patients who are at risk for responding negatively were identified. Model performance was assessed by area under receiver-operating characteristic curve. Social network analysis metrics were also used to identify providers most impactful to patient experience. Results: Using a random forest algorithm, patients' responses to the following 3 questions were predicted: "During this hospital stay how often did doctors. 1) treat you with courtesy and respect? 2) explain things in a way that you could understand? 3) listen carefully to you?" with areas under the receiver-operating characteristic curve of 0.876, 0.819, and 0.819, respectively. Social network analysis found that doctors with higher centrality appear to have an outsized influence on patient experience, as measured by rank in the random forest model in the doctor domain. Conclusions: A machine learning algorithm identified patients at risk of a negative experience. Furthermore, a doctor social network framework provides metrics for identifying those providers that are most influential on the patient experience.