Lessons Learned from Implementing a Pilot RCT of Transitional Care Model for Individuals with Serious Mental Illness

Archives of Psychiatric Nursing (Impact Factor: 1.03). 08/2014; DOI: 10.1016/j.apnu.2014.03.005

ABSTRACT We adapted an evidence-based Transitional Care Model for older adults being released from acute care hospitals for patients with serious mental illness and medical co-morbidities being discharged from two psychiatric units of an acute care hospital (TCare) and evaluated implementation issues. An advisory group (AG) of community stakeholders assessed barriers and facilitators of a 90-day T-Care intervention delivered by a psychiatric nurse practitioner (NP) in the context of conducting a pilot randomized controlled trial. Minutes of AG and case narratives by NP of 20 intervention participants were content analyzed. Patients with immediate and pressing physical health problems were most receptive and actively utilized the service. Provider barriers consisted of communication and privacy issues making it difficult to contact patients in mental health facilities. In contrast, the NP was accepted and valued in the physical health arena. Psychosocial needs and relationship issues were demanding and we recommend a team approach for TCare with the addition of a social worker, peer provider, and consulting psychiatrist for severely mentally ill patients being released from an acute physical health hospitalization.

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    ABSTRACT: OBJECTIVE: People with multiple and persistent mental and physical health problems have high rates of transition failures when transferring from a hospital level of care to home. The transitional care model (TCM) is evidence-based and demonstrated to improve posthospital outcomes for elderly with physical health conditions, but it has not been studied in the population with serious mental illness. METHOD: Using a randomized controlled design, 40 inpatients from two general hospital psychiatric units were recruited and randomly assigned to an intervention group (n = 20) that received the TCM intervention that was delivered by a psychiatric nurse practitioner for 90 days posthospitalization, or a control group (n = 20) that received usual care. Outcomes were as follows: service utilization, health-related quality of life, and continuity of care. RESULTS: The intervention group showed higher medical and psychiatric rehospitalization than the control group (p = .054). Emergency room use was lower for intervention group but not statistically significant. Continuity of care with primary care appointments were significantly higher for the intervention group (p = .023). The intervention group's general health improved but was not statistically significant compared with controls. CONCLUSIONS: A transitional care intervention is recommended; however, the model needs to be modified from a single nurse to a multidisciplinary team with expertise from a psychiatric nurse practitioner, a social worker, and a peer support specialist. A team approach can best manage the complex physical/mental health conditions and complicated social needs of the population with serious mental illness.
    Journal of the American Psychiatric Nurses Association 09/2014; 20(5):315-27. DOI:10.1177/1078390314552190
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    ABSTRACT: Purpose Explore whether agent-based modeling and simulation can help healthcare administrators discover interventions that increase population wellness and quality of care while, simultaneously, decreasing costs. Since important dynamics often lie in the social determinants outside the health facilities that provide services, this study thus models the problem at three levels (individuals, organizations, and society). Methods The study explores the utility of translating an existing (prize winning) software for modeling complex societal systems and agent's daily life activities (like a Sim City style of software), into a desired decision support system. A case study tests if the 3 levels of system modeling approach is feasible, valid, and useful. The case study involves an urban population with serious mental health and Philadelphia's Medicaid population (n = 527,056), in particular. Results Section 3 explains the models using data from the case study and thereby establishes feasibility of the approach for modeling a real system. The models were trained and tuned using national epidemiologic datasets and various domain expert inputs. To avoid co-mingling of training and testing data, the simulations were then run and compared (Sect. 4.1) to an analysis of 250,000 Philadelphia patient hospital admissions for the year 2010 in terms of re-hospitalization rate, number of doctor visits, and days in hospital. Based on the Student T-test, deviations between simulated vs. real world outcomes are not statistically significant. Validity is thus established for the 2008 to 2010 timeframe. We computed models of various types of interventions that were ineffective as well as 4 categories of interventions (eg, reduced per-nurse caseload, increased check-ins and stays, etc.) that result in improvement in well-being and cost. Conclusions The 3 level approach appears to be useful to help health administrators sort through system complexities to find effective interventions at lower costs.
    Artificial Intelligence in Medicine 09/2014; DOI:10.1016/j.artmed.2014.08.006 · 1.36 Impact Factor


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Jan 29, 2015