Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.

OBJECTIVES: Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. METHODS: Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. RESULTS: The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. CONCLUSION: A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

Improving Continuity of Care via the Discharge Summary.

Discharge summaries (DCS) frequently fail to improve the continuity of care. A chart review of 188 DCS was performed to identify specific components that could be improved through health information technology. Medication reconciliations were analyzed for completeness and for medical reasoning. Documentation of pending results and follow-up details were analyzed. Patient preferences, patient goals, and the handover tone were noted. Patients were discharged on an average of 9.8 medications, only 3% of medication reconciliations were complete and medical reasoning was frequently absent. There were 358 pending results in 188 hospital discharges though only 14% were mentioned in the DCS. Documentation of clear, timely follow-up was present for less than 50% of patients. Patient preferences, patient goals, and lessons learned were rarely included. A handover tone was in only 17% of the DCS. Evaluating the DCS as a clinical handover is novel but information for safe handovers is frequently missing.

Rethinking the discharge summary: a focus on handoff communication.

The discharge summary is one of the most critical documents in medical care settings, but it is prone to systematic lapses that compromise the continuity of care. Discontinuity is fostered not only by incomplete inclusion of data (such as pending labs or medication reconciliations) but also by failure to document clinical reasoning and unfinished diagnostic workups. To correct these problems, the authors propose the Situation-Background-Assessment-Recommendations (SBAR) format for discharge summaries. SBAR is already used for handoffs the way Subjective-Objective-Assessment-Plan is for progress notes. The SBAR format supports the concise presentation of relevant information along with guidance for action. It shifts the paradigm and purpose of the discharge summary away from being a "Captain's Log" (a historical record of the events, actions taken, and their consequences during hospitalization) and towards being a handoff document (a tool for communication between health professionals aimed at ensuring continuity of care). To test SBAR as a template for discharge summaries, the authors have initiated a study to document the impact of the SBAR model on the quality of trainees' thinking in discharge summaries.