Program Evaluation and Resource Center, VHA Office of Mental Health and Suicide Prevention, Menlo Park, California, United States of America.
Center for Innovation to Implementation, VHA Palo Alto Health Care System, Menlo Park, California, United States of America.
PLoS One. 2021 Aug 16;16(8):e0256268. doi: 10.1371/journal.pone.0256268. eCollection 2021.
Healthcare systems monitor and improve mental health treatment quality, access, continuity and satisfaction through use of population-based and efficiency-based staffing models, the former focused on staffing ratios and the latter, staff productivity. Preliminary evidence suggests that both high staffing ratios and moderate-to-high staff productivity are important for ensuring a full continuum of mental health services to indicated populations.
METHODS & FINDINGS: With an information-theoretic approach, we conducted a longitudinal investigation of mental health staffing, productivity and treatment at the largest integrated healthcare system in American, the Veterans Health Administration (VHA). VHA facilities (N = 140) served as the unit of measure, with mental health treatment quality, access, continuity and satisfaction predicted by facility staffing and productivity in longitudinal mixed models. An information-theoretic approach: (a) entails the development of a comprehensive set of plausible models that are fit, ranked and weighted to quantitatively assess the relative support for each, and (b) accounts for model uncertainty while identifying best-fit model(s) that include important and exclude unimportant explanatory variables. In best-fit models, higher staffing was the strongest and most consistent predictor of better treatment quality, access, continuity and satisfaction. Higher staff productivity was often, but not always associated with better treatment quality, access, continuity and satisfaction. Results were further nuanced by differential prediction of treatment by between- and within-facility predictor effects and variable interactions.
A population-based mental health staffing ratio and an efficiency-based productivity value are important longitudinal predictors of mental health treatment quality, access, continuity and satisfaction. Our longitudinal design and use of mixed regression models and an information-theoretic approach addresses multiple limitations of prior studies and strengthen our results. Results are discussed in terms of the provision of mental health treatment by healthcare systems, and analytical modeling of treatment quality, access, continuity and satisfaction.
医疗保健系统通过使用基于人群和基于效率的人员配备模型来监测和改善心理健康治疗质量、可及性、连续性和满意度，前者侧重于人员配备比例，后者侧重于员工生产力。初步证据表明，高人员配备比例和中高水平的员工生产力对于确保向目标人群提供全面的心理健康服务连续性都很重要。
我们采用信息论方法，对美国最大的综合性医疗保健系统——退伍军人事务部（VA）的心理健康人员配备、生产力和治疗进行了纵向研究。VA 设施（N=140）作为测量单位，通过纵向混合模型，用设施人员配备和生产力预测心理健康治疗质量、可及性、连续性和满意度。信息论方法：（a）需要开发一套全面的合理模型，这些模型经过拟合、排名和加权，以定量评估每个模型的相对支持度，（b）在识别包含重要解释变量和排除不重要解释变量的最佳拟合模型的同时，考虑模型不确定性。在最佳拟合模型中，更高的人员配备是更好的治疗质量、可及性、连续性和满意度的最强和最一致的预测因素。更高的员工生产力通常，但并非总是与更好的治疗质量、可及性、连续性和满意度相关。通过设施间和设施内预测因子效应和变量交互作用的差异预测，结果进一步细化。
基于人群的心理健康人员配备比例和基于效率的生产力值是心理健康治疗质量、可及性、连续性和满意度的重要纵向预测因素。我们的纵向设计和使用混合回归模型以及信息论方法解决了先前研究的多个局限性，并增强了我们的结果。结果从医疗保健系统提供心理健康治疗的角度以及治疗质量、可及性、连续性和满意度的分析模型进行了讨论。
