|Source||Rutgers Center for State Health Policy (2014)|
|Year of Publication||2014|
|Authors||Chakravarty, S.; J. C. Cantor; J. T. Walkup, and J. Tong|
|City||New Brunswick, NJ|
|Selection||Grey literature; Financing & sustainability|
This report informs strategies to develop initiatives aimed at improving population health and decreasing avoidable hospitalizations and costs in New Jersey. Focusing on the role of behavioral health (BH) conditions in potentially avoidable hospital use and cost, this report builds on a series of publications supported by The Nicholson Foundation that examined opportunities provided by the Medicaid ACO Demonstration Program to improve health and lower costs in low-income New Jersey communities (Chakravarty, Cantor, and Tong 2014; Chakravarty et al. 2013). We examine the presence of BH conditions among hospital patients that can exacerbate the adverse effects of chronic medical conditions leading to avoidable inpatient (IP) hospitalizations and Emergency Department (ED) visits. Specifically, we examine the presence of BH conditions including severe mental illness (SMI) among patients who are hospital high-users (4+ IP stays or 6+ ED visits over 2008-2011), and among avoidable/preventable IP hospitalizations and ED visits that can be prevented with adequate ambulatory care in the community. We use an enhanced version of New Jersey uniform billing hospital discharge dataset enabling us to follow patient utilization over time and identify high users of hospital resources. A higher prevalence of BH among hospital high-users, and avoidable hospitalizations would inform targeting of mental health and substance abuse services among these high-use, complex patients.
|Additional||More about this reference (PDF - 3.95 MB)|
|Note||This grey literature reference is included in the Academy's Literature Collection in keeping with our mission to gather all sources of information on integration. Grey literature is comprised of materials that are not made available through traditional publishing avenues. Often, the information from unpublished resources can be limited and the risk of bias cannot be determined.|