The Impact of Incarceration on Health and Health Care Utilization: a System Perspective.

BACKGROUND: Previously incarcerated individuals report high rates of chronic disease and reduced health care access. We characterized the impact of recent incarceration in jail or prison on chronic disease burden and health care utilization. METHODS AND FINDINGS: Incarceration data over 10 years were matched to health system data and patients were classified by recent incarceration status. Each cohort was stratified by gender and neighborhood socioeconomic status for utilization analysis. Main outcomes were chronic disease incidence and health care utilization. Incarceration had a significant but small effect on chronic disease incidence. Incarceration had a moderate to large effect on emergency department and behavioral health utilization, with additional differences seen by gender and socioeconomic status. CONCLUSION: Incarceration's impact on quantity and type of health care utilization varies with socioeconomic status and gender. Future work should evaluate the impact of length or number of cycles of incarceration on health or health care utilization.

A continual prediction model for inpatient acute kidney injury.

Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. But it is preventable and potentially reversible with early diagnosis and management. Therefore, several machine learning based predictive models have been built to predict AKI in advance from electronic health records (EHR) data. These models to predict inpatient AKI were always built to make predictions at a particular time, for example, 24 or 48 h from admission. However, hospital stays can be several days long and AKI can develop any time within a few hours. To optimally predict AKI before it develops at any time during a hospital stay, we present a novel framework in which AKI is continually predicted automatically from EHR data over the entire hospital stay. The continual model predicts AKI every time a patient's AKI-relevant variable changes in the EHR. Thus, the model not only is independent of a particular time for making predictions, it can also leverage the latest values of all the AKI-relevant patient variables for making predictions. A method to comprehensively evaluate the overall performance of a continual prediction model is also introduced, and we experimentally show using a large dataset of hospital stays that the continual prediction model out-performs all one-time prediction models in predicting AKI.