The ideal balanced mental health service system presupposes that planners can determine the need for various required services. The history of deinstitutionalization has shown that one of the most difficult such determinations involves the number of needed psychiatric beds for various localities. Historically, such assessments have been made on the basis of waiting and vacancy lists, expert estimates, or social indicator approaches that do not take into account local conditions. Specifically, this study aims to generate benchmarks or estimated rates of needed psychiatric beds for the 50 U.S. states by employing a predictive analytics methodology that uses nonlinear regression. Data used were secured primarily from the U.S. Census' American Community Survey and from the Substance Abuse and Mental Health Administration. Key predictors used were indicators of community mental health (CMH) service coverage, mental health disability in the adult population, longevity from birth, and the percentage of the 15+ who were married in 2018. The model was then used to calculate predicted bed rates based on the 'what-if' assumption of an optimal level of CMH service availability. The final model revealed an overall rate of needed beds of 34.9 per 100,000 population, or between 28.1 and 41.7. In total, 32% of the states provide inpatient psychiatric care at a level less than the estimated need; 28% at a level in excess of the need; with the remainder at a level within 95% confidence limits of the estimated need. These projections are in the low range of prior estimates, ranging from 33.8 to 64.1 since the 1980s. The study demonstrates the possibility of using predictive analytics to generate individualized estimates for a variety of service modalities for a range of localities.
Keywords: benchmarks; inpatient care; needs assessment; predictive analytics; psychiatric beds; psychiatric deinstitutionalization.