Sheila A. Donahue

A Psychometric Analysis of Project Liberty's Adult Enhanced Services Referral Tool

OBJECTIVES Project Liberty was the first federally funded crisis counseling program to offer evidence-informed treatments to crisis counseling recipients in need of more intensive clinical intervention. The Adult Enhanced Services Referral Tool was developed as a screening instrument for making and monitoring referrals to enhanced services. This study aimed to examine how well the tool functioned for identifying persons who would perceive a need for professional treatment. METHODS A one-page tool was created that assessed demographic characteristics, risk categories, and psychological reactions to the focal event, September 11, 2001. Psychosocial reactions were assessed by the 12-item SPRINT-E, which is an expanded version of the Short Post-Traumatic Stress Disorder Rating Interview (SPRINT). The SPRINT-E was embedded in the Adult Enhanced Services Referral Tool. Data were collected from 788 clients who received crisis counseling between June and October 2003. RESULTS The SPRINT-E is a unidimensional measure of distress and dysfunction. Internal consistency was excellent for the total sample (alpha=.93) and subsamples. Among the 543 clients offered referral, 71 percent accepted. Among those offered referral, the number of intense reactions (score of 4, quite a bit, or 5, very much) was by far the strongest predictor of referral acceptance. CONCLUSIONS The SPRINT-E was successfully integrated into the crisis counseling program and provided an apparently successful, empirical basis for referral from counseling to professional treatment. Results of the brief psychological assessment provided a stronger basis for referral to treatment than membership in a risk category (for example, family member of deceased) alone.

Utilization of acute inpatient psychiatric services: “Heavy users” in New York State

The severely and persistently mentally ill (SPMI) population are often forced to rely on emergency/acute inpatient services. The authors analyze Medicaid expenditures and state hospital utilization data to identify and describe the characteristics of “heavy users” of these services. Also described is New York States intensive case management (ICM) initiative targeted at this “heavy user” group. It is postulated that ICM will alter the utilization patterns of participants from over-reliance on the most costly services and more effectively and efficiently serve the SPMI population, specifically the “heavy user” subpopulation.The severely and persistently mentally ill (SPMI) population are often forced to rely on emergency/acute inpatient services. The authors analyze Medicaid expenditures and state hospital utilization data to identify and describe the characteristics of “heavy users” of these services. Also described is New York States intensive case management (ICM) initiative targeted at this “heavy user” group. It is postulated that ICM will alter the utilization patterns of participants from over-reliance on the most costly services and more effectively and efficiently serve the SPMI population, specifically the “heavy user” subpopulation.

Development and Use of a Transition Readiness Scale to Help Manage ACT Team Capacity

OBJECTIVE This article describes the creation, validation, and use of an assertive community treatment (ACT) Transition Readiness Scale (TRS) to identify clients who may be ready to transition from ACT services. Scale development was prompted by concerns over long stays on ACT teams and the resulting impact on access. METHODS Data were extracted from a centralized clinical reporting system for all 1,365 persons enrolled for at least 12 months as of August 2008 in the 42 ACT teams in New York City, including 382 clients of eight of those ACT teams. Data in seven domains deemed relevant to transition readiness were used to calculate readiness scores for each client. An algorithm assigned clients to one of three categories: consider for transition, readiness unclear, and not ready. RESULTS Via the TRS algorithm, of the 1,365 clients, 192 (14%) were assigned to the consider-for-transition group, 382 (28%) to the unclear group, and 791 (58%) to the not ready group. Clinicians on the eight ACT teams categorized 15% of their current clients in the consider-for-transition group, whereas the TRS algorithm classified 18% in this category. Overall, the TRS agreed with the category assigned by ACT team clinicians in 69% of cases. CONCLUSIONS The TRS may provide ACT teams and program administrators with a tool to identify clients who may be ready to transition to less intensive services, thereby opening scarce slots. Because ACT cases are complex, data summaries can offer useful syntheses of information, particularly when data from several assessments are used to summarize a clients trajectory.

Characteristics of Project Liberty Clients That Predicted Referrals to Intensive Mental Health Services

OBJECTIVE The authors describe characteristics of Project Liberty crisis counseling recipients that predicted referral to more intensive professional mental health treatments over the two-year period after the terrorist attacks on the World Trade Center. METHODS Random-effects ordinal regression models were applied to data from 684,500 logs of Project Liberty service encounters for individual counseling sessions. RESULTS Overall, about 9 percent of individual counseling visits ended with a referral to professional mental health services. Individuals needing intensive mental health treatment continued to enter Project Liberty for two years after the World Trade Center attacks. The strongest predictor of referral was having reactions to the attack that fell into a greater number of the four domains assessed-behavioral, emotional, physical, or cognitive domains. Individuals with reactions in four domains were most likely to be referred. Those who had greater attack-related exposure were also more likely to be referred. CONCLUSIONS It is important to provide long-term access to brief counseling and triage services and to target these interventions specifically to individuals displaying greater distress or impairment and having more traumatic exposure.

Using claims data to generate clinical flags predicting short-term risk of continued psychiatric hospitalizations.

OBJECTIVE As health information technology advances, efforts to use administrative data to inform real-time treatment planning for individuals are increasing, despite few empirical studies demonstrating that such administrative data predict subsequent clinical events. Medicaid claims for individuals with frequent psychiatric hospitalizations were examined to test how well patterns of service use predict subsequent high short-term risk of continued psychiatric hospitalizations. METHODS Medicaid claims files from New York and Pennsylvania were used to identify Medicaid recipients ages 18-64 with two or more inpatient psychiatric admissions during a target year ending March 31, 2009. Definitions from a quality-improvement initiative were used to identify patterns of inpatient and outpatient service use and prescription fills suggestive of clinical concerns. Generalized estimating equations and Markov models were applied to examine claims through March 2011, to see what patterns of service use were sufficiently predictive of additional hospitalizations to be clinically useful. RESULTS A total of 11,801 individuals in New York and 1,859 in Pennsylvania identified met the cohort definition. In both Pennsylvania and New York, multiple recent hospitalizations, but not failure to use outpatient services or failure to fill medication prescriptions, were significant predictors of high risk of continued frequent hospitalizations, with odds ratios greater than 4.0. CONCLUSIONS Administrative data can be used to identify individuals at high risk of continued frequent hospitalizations. Payers and system administrators could use such information to authorize special services (such as mobile outreach) for such individuals to promote service engagement and prevent rapid rehospitalizations.

Reengagement of High-Need Individuals with Serious Mental Illness After Discontinuation of Services

OBJECTIVE The study determined rates of reengagement in services for individuals with serious mental illness who had discontinued services. METHODS As part of a quality assurance program in New York City involving continuous review of Medicaid claims and other administrative data, clinician care monitors identified 2,834 individuals with serious mental illness who were apparently in need of care but disengaged from services. The care monitors reviewed monthly updates of Medicaid claims, encouraged outreach from providers who had previously worked with identified individuals, and determined whether individuals had reengaged in services. RESULTS Reengagement rates over a 12-month follow-up period were low, particularly for individuals who had been incarcerated or for whom no service provider was available to provide outreach. CONCLUSIONS Subgroups of disengaged individuals with serious mental illness have different rates of reengagement. Active outreach by providers might benefit some, but such targeting is inefficient when the individual cannot be located.

Using staff focus groups to refine a feedback process for people who used Project Liberty.

Project Liberty provided free counseling services to those affected by the September 11th attacks. Focus groups were conducted with Project Liberty provider staff to gain feedback on their participation in the process of evaluating Project Liberty individual crisis counseling services. Focus groups provided information regarding barriers to eliciting feedback from people who used Project Liberty services that informed planning for the next phase of the evaluation. Focus groups proved to be a valuable method for collecting data from service providers across provider sites that differed geographically, culturally, ethnically, and organizationally, as well as in methods of offering services to individuals with mental health problems related to the attack on the World Trade Centers.