Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems.

BACKGROUND AND HYPOTHESIS: Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. STUDY DESIGN: Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. STUDY RESULTS: PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). CONCLUSIONS: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis.

Validation of an ICD-code-based case definition for psychotic illness across three health systems.

Background and Hypothesis: Early detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. Study Design: Using EHRs at three health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into five higher-order groups. 1,133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. Study Results: PPVs across all diagnostic groups and hospital systems exceeded 70%: Massachusetts General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). Conclusions: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the development of risk prediction models designed to predict or detect undiagnosed psychosis.

Patient Perspectives of the Hospital Discharge Process: A Qualitative Study.

Care transitions after hospitalization require communication across care teams, patients, and caregivers. As part of a quality improvement initiative, we conducted qualitative interviews with a diverse group of 53 patients who were recently discharged from a hospitalization within a safety net hospital to explore how patient preferences were included in the hospital discharge process and differences in the hospital discharge experience by race/ethnicity. Four themes emerged from participants regarding desired characteristics of interactions with the discharge team: (1) to feel heard, (2) inclusion in decision-making, (3) to be adequately prepared to care for themselves at home through bedside teaching, (4) and to have a clear and updated discharge timeline. Additionally, participants identified patient-level factors the discharge planning team should consider, including the social context, family involvement, health literacy, and linguistic barriers. Lastly, participants identified provider characteristics, such as a caring and empathetic bedside manner, that they found valuable in the discharge process. Our findings highlight the need for shared decision-making in the discharge planning process to improve both patient safety and satisfaction.

Long-Term Trends in Psychiatric Emergency Services Delivered by the Boston Emergency Services Team.

Rising psychiatric emergency department (ED) presentations pose significant financial and administrative burdens to hospitals. Alternative psychiatric emergency services programs have the potential to alleviate this strain by diverting non-emergent mental health issues from EDs. This study explores one such program, the Boston Emergency Services Team (BEST), a multi-channel psychiatric emergency services provider intended for the publicly insured and uninsured population. BEST provides evaluation and treatment for psychiatric crises through specialized psychiatric EDs, a 24/7 hotline, psychiatric urgent care centers, and mobile crisis units. This retrospective review examines the sociodemographic and clinical characteristics of 225,198 BEST encounters (2005-2016). Of note, the proportion of encounters taking place in ED settings decreased significantly from 70 to 58% across the study period. Findings suggest that multi-focal, psychiatric emergency programs like BEST have the potential to reduce the burden of emergency mental health presentations and improve patient diversion to appropriate psychiatric care.