An Analytics Approach To Designing Patient Centered Medical Home

Recently the patient centered medical home (PCMH) model has become a popular team based approach focused on delivering more streamlined care to patients. In current practices of medical homes, a clinical based prediction frame is recommended because it can help match the portfolio capacity of PCMH teams with the actual load generated by a set of patients. Without such balances in clinical supply and demand, issues such as excessive under and over utilization of physicians, long waiting time for receiving the appropriate treatment, and non continuity of care will eliminate many advantages of the medical home strategy. In this research, we formulate the problem into two phases. At the first phase we proposed a multivariate version of multilevel structured additive regression (STAR) models which involves a set of health care responses defined at the lowest level of the hierarchy, a set of patient factors to account for individual heterogeneity, and a set of higher level effects to capture heterogeneity and dependence between patients within the same medical home team and facility. We show how a special class of such models can equivalently be represented and estimated in a structural equation-modeling framework. A Bayesian variable selection with spike and slab prior structure is then developed that allows including or dropping single effects as well as grouped coefficients representing particular model terms. We use a simple parameter expansion to improve mixing and convergence properties of Markov chain Monte Carlo simulation. A detailed analysis of the VHA medical home data is presented to demonstrate the performance and applicability of our method. In addition, by extending the hierarchical generalized linear model to include multivariate responses, we develop a clinical workload prediction model for care portfolio demands in a Bayesian framework. The model allows for heterogeneous variances and unstructured covariance matrices for nested random effects that arise through complex hierarchical care systems. We show that using a multivariate approach substantially enhances the precision of workload predictions at both primary and non primary care levels. We also demonstrate that care demands depend not only on patient demographics but also on other utilization factors, such as length of stay. Our analyses of a recent data from Veteran Health Administration further indicate that risk adjustment for patient health conditions can considerably improve the prediction power of the model. For the second phase, with the help of the model developed in first phase, we are able to estimate the annual workload demand portfolio for each patient with given attributes. Together with the healthcare service supply data, and based on the principles of balancing supply and demand, we developed stochastic optimization models to allocate patients to all PCMH teams in order to make balance between supply and demand in healthcare system. We proposed different stochastic models and two solution approaches such as Progressive Hedging and L shaped Benders Decomposition. We described the application of the two mentioned algorithms and finally we compared the performance of the two methods

Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs

Reducing potentially preventable readmissions has been identified as an important issue for decreasing Medicare costs and improving quality of care provided by hospitals. Based on previous research by medical professionals, preventable readmissions are caused by such factors as flawed patient discharging process, inadequate follow-ups after discharging, and noncompliance of patients on discharging and follow up instructions. It is also found that the risk of preventable readmission also may relate to some patient's characteristics, such as age, health condition, diagnosis, and even treatment specialty. In this study, using both general demographic information and individual past history of readmission records, we develop a risk prediction model based on hierarchical nonlinear mixed effect framework to extract significant prognostic factors associated with patient risk of 30-day readmission. The effectiveness of our proposed approach is validated based on a real dataset from four VA facilities in the State of Michigan. Simultaneously explaining both patient and population based variations of readmission process, such an accurate model can be used to recognize patients with high likelihood of discharging non-compliances, and then targeted post-care actions can be designed to reduce further rehospitalization.Comment: 6 pages, to be submitted in IEEE CASE 201