Literature Collection

IMPORTANCE: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3 % of the whole population. The ability to conduct risk screening of SMIs at large scale could inform early prevention and treatment. OBJECTIVE: A scalable machine learning based tool was developed to conduct population-level risk screening for SMIs, including schizophrenia, schizoaffective disorders, psychosis, and bipolar disorders, using 1) healthcare insurance claims and 2) electronic health records (EHRs). DESIGN, SETTING AND PARTICIPANTS: Data from beneficiaries from a nationwide commercial healthcare insurer with 77.4 million members and data from patients from EHRs from eight academic hospitals based in the U.S. were used. First, the predictive models were constructed and tested using data in case-control cohorts from insurance claims or EHR data. Second, performance of the predictive models across data sources was analyzed. Third, as an illustrative application, the models were further trained to predict risks of SMIs among 18-year old young adults and individuals with substance associated conditions. MAIN OUTCOMES AND MEASURES: Machine learning-based predictive models for SMIs in the general population were built based on insurance claims and EHR. RESULTS: A total of 301,221 patients with SMIs and 2,439,890 control individuals were retrieved from the nationwide health insurance claim database in the U.S. A total of 59,319 patients with SMIs and 297,993 control individuals were retrieved from EHRs spanning eight different hospitals from a major integrated healthcare system in Massachusetts, U.S. The obtained predictive models for SMIs achieved AUCROC of 0.76, specificity of 79.1 % and sensitivity of 61.9 % on an independent test set of an all-age case-control cohort from insurance claim data, and AUCROC of 0.83, specificity of 85.1 % and sensitivity of 66.4 % using EHR data. The fine-tuned models for specific use case scenarios outperformed two rule based benchmark methods when predicting 12-month risk of SMIs among 18-year old young adults but had inferior performance to benchmark methods when predicting SMIs among individuals with substance associated conditions in claims data. CONCLUSION: Performance of our SMI prediction models constructed using health insurance claims or EHR data suggest feasibility of using real world healthcare data for large scale screening of SMIs in the general population. In addition, our analysis showed cross data source generalizability of machine learning models trained on real world healthcare data. Models constructed from insurance claims appear to be transferable to EHR cohorts and vice versa.

PURPOSE: Best practice regarding screening for cancer-related distress includes timely follow-up with psychosocial services to address identified needs. Cancer centers frequently struggle to identify distress via systematized, low-burden workflows and link patients to high-quality, evidenced-based care. Models of psychological and psychiatric consultation can address several known challenges of attending to patient and provider need and can be designed with varying resources and levels of integration. Consultation can be offered in inpatient and outpatient settings and function independently or within existing supportive care departments. METHODS: This review summarizes four models of consultation including 1) inpatient psychological consultation, 2) outpatient psychological consultation, 3) integrated and tiered psychiatric consultation, and 4) integration of behavioral health providers into subspecialty teams. We present data on utilization of each model, as well as patient clinical outcomes and satisfaction measures and provider satisfaction. RESULTS: Consultation models are utilized and offer an effective approach to optimizing timely and accessible care. Utilizing this model of care between July 2020 and June 2021, we managed more than 1200 inpatient referrals for consultation and responded to more than 1600 outpatients with positive distress screens. Programs should consider strengths and limitations of implementing consultation models, with an emphasis on available staffing and institutional investment in supportive care for cancer survivors.