Following several recent, highly publicized violent incidents involving individuals with psychiatric illnesses in New York City (NYC), the New York State (NYS) Office of Mental Health began a policy of using administrative data to identify apparent interruptions in care and escalating need for services among targeted cohorts of the most vulnerable individuals disabled by psychiatric illnesses in NYC, with the overall aim of facilitating timely interventions by the public mental health system. In concert with NYC, NYS has allocated $13 million to create borough-based Care Monitoring Teams charged with responding to """"""""clinical alerts"""""""" issued when a pattern of service use (or non-use) in Medicaid claims suggests a problem in care for high- need individuals. This policy initiative represents a marked shift for the State of New York, and has the potential to transform the way that all public mental health authorities use claims data. The proposed project will describe the impact of this critical NYS/NYC policy change on access to and cost, quality and outcomes of care provided to the neediest population of individuals with serious mental illness. This application proposes to use pooled Medicaid claims data from NYS and Pennsylvania (PA) to examine the impact of the """"""""clinical alerts"""""""" generated by Medicaid claims data on continuity of care and use of psychiatric emergency services in NYC. We hypothesize that this new intervention will reduce the likelihood of the service patterns associated with """"""""clinical alerts"""""""", and also increase the use of outpatient care following the issuance of those alerts. We will look at service use data from Medicaid over a 4 year period (from 2 years prior to 2 years after the intervention begins), and will use data from matched targeted cohorts in PA and Upstate New York (UNY) to control for temporal trends.
The specific aims of the proposed project are:
Aim #1 : To evaluate whether clinical alerts reduce gaps in using mental health and substance abuse services.
Aim #2 : To evaluate whether clinical alerts reduce gaps in filling psychotropic medications.
Aim #3 : To evaluate whether clinical alerts reduce use of emergency room and inpatient services.
Aim #4 : To evaluate the impact of clinical alerts on cost of services. In sum, this application describes a time-critical opportunity to empirically and prospectively study a major new policy initiative in a large public mental health system. The proposed project will study a model for using secondary data to identify service patterns that may predict increased risk, triggering interventions to preempt further risk, and using personalized information in a very diverse setting. Results from this study will inform public mental health authorities in PA and UNY (the comparison conditions) as well as nationwide, of the benefit of using claims data to generate clinical alerts.
All states face challenges in improving care for individuals with serious mental illness who are frequent users of high intensity mental health services such as psychiatric emergency room and psychiatric hospitalization yet not engaged in outpatient treatment. New York State has embarked on a project to use administrative data (e.g., Medicaid claims), to flag high need individuals with service patterns suggesting the need for prompt clinical intervention. An evaluation of the impact of this initiative would be of great policy interest to all state mental health authorities and criminal justice systems, as well as mental health advocates everywhere who have long requested greater oversight of care for individuals at high risk of detrimental disengagement from the mental health system.
|Smith, Thomas E; Stein, Bradley D; Donahue, Sheila A et al. (2014) Reengagement of high-need individuals with serious mental illness after discontinuation of services. Psychiatr Serv 65:1378-80|
|Smith, Thomas E; Appel, Anita; Donahue, Sheila A et al. (2014) Determining engagement in services for high-need individuals with serious mental illness. Adm Policy Ment Health 41:588-97|
|Stein, Bradley D; Pangilinan, Maria; Sorbero, Mark J et al. (2014) Using claims data to generate clinical flags predicting short-term risk of continued psychiatric hospitalizations. Psychiatr Serv 65:1341-6|