Optimizing the Use of Electronic Health Records to Identify High-Risk Psychosocial Determinants of Health.

BACKGROUND: Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data. However, not all clinically relevant data reside in claims and formatted data. Recently, there has been increasing interest in including patients with complex psychosocial determinants of health in care coordination programs. Psychosocial risk factors, including social determinants of health, mental health disorders, and substance abuse disorders, are less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data, and due to US Health Insurance Portability and Accountability Act (HIPAA) regulations are often not available as claims data. OBJECTIVE: The objective of our study was to develop a systematic approach using word recognition software to identifying psychosocial risk factors within any part of a patient's electronic health record (EHR). METHODS: We used QPID (Queriable Patient Inference Dossier), an ontology-driven word recognition software, to scan adult patients' EHRs to identify terms predicting a high-risk patient suitable to be followed in a care coordination program in Massachusetts, USA. Search terms identified high-risk conditions in patients known to be enrolled in a care coordination program, and were then tested against control patients. We calculated precision, recall, and balanced F-measure for the search terms. RESULTS: We identified 22 EHR-available search terms to define psychosocial high-risk status; the presence of 9 or more of these terms predicted that a patient would meet inclusion criteria for a care coordination program. Precision was .80, recall .98, and balanced F-measure .88 for the identified terms. For adult patients insured by Medicaid and enrolled in the program, a mean of 14 terms (interquartile range [IQR] 11-18) were present as identified by the search tool, ranging from 2 to 22 terms. For patients enrolled in the program but not insured by Medicaid, a mean of 6 terms (IQR 3-8) were present as identified by the search tool, ranging from 1 to 21. CONCLUSIONS: Selected informatics tools such as word recognition software can be leveraged to improve health care delivery, such as an EHR-based protocol that identifies psychosocially complex patients eligible for enrollment in a care coordination program.

Integrating mHealth and Systems Science: A Combination Approach to Prevent and Treat Chronic Health Conditions.

Chronic health conditions are a growing global health concern and account for over half of all deaths worldwide. Finding ways to decrease the burden of and resources allotted to chronic health conditions is of primary importance. Recent advances in technology and insights into modeling techniques offer promising approaches, which if combined, represent a novel direction that would further advance the prevention and treatment of chronic health conditions.