Coordinated Specialty Care Discharge, Transition, and Step-Down Policies, Practices, and Concerns: Staff and Client Perspectives

Jones, Nev; Gius, Becky K.; Daley, Tamara C.; George, Preethy; Rosenblatt, Abram; Shern, David L.

doi:10.1176/appi.ps.201900514

Cited by 20 publications

(26 citation statements)

References 51 publications

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“…To make a decision about the correct post-discharge care, a treating clinician will have to take into consideration the individual's wishes for further treatment, their likely need for further intensive mental health treatment, and the current availability and capacity of secondary care services [6][7][8] . In addition, the next care provider may often have its own eligibility criteria or diverging opinions about the need for continued specialist mental health input 9,10 .…”

Section: Clinical Decision Makingmentioning

confidence: 99%

“…While some of these relapses and readmissions may be due to reasons beyond the control of mental health services, they may contribute to this risk through underestimation of the individuals' need for specialist mental health care, leading to an individual's deterioration and eventual relapse. Both clinicians 6,7 and individuals themselves 8 share the concern that those who have maintained good mental health during EIP with its specialist, multidisciplinary support, can relapse after EIP treatment has ended. Transition periods in psychiatry (and throughout medicine) are historically and habitually a point of high risk 13,14 .…”

Section: Clinical Decision Makingmentioning

confidence: 99%

“…Transition periods in psychiatry (and throughout medicine) are historically and habitually a point of high risk 13,14 . Good practice discharge guidelines for EIP exist 15 , but the eligibility criteria for access to treatment can differ by region and by service capacity at the time of discharge 6 .…”

Section: Clinical Decision Makingmentioning

confidence: 99%

“…We timed our prognostic tool measurement to coincide with a pre-determined medical review prior to the transfer of care for the move from EIP services to the next care provider. Finally, our prognosis tool focuses on a clinical decision with an outcome that is associated with considerable uncertainty, has significant implications for both individuals and primary and secondary care services, and has been highlighted by clinicians and people with psychosis as an area of clinical need 6 .…”

Section: Applying Prediction Models To Clinical Decision Makingmentioning

confidence: 99%

“…The transition from EIP to the next stage of treatment, whether that be treatment by a primary care provider or by a different mental health team is a critical moment in an individual's journey following treatment for psychosis. Individuals can have conflicting feelings about their needs for further treatment, and their readiness for the inevitable change in the care they receive, thus clinicians' communication and planning with individuals about their discharge can be influential in smoothing this process [6][7][8] . Sensitive use of the tool could act in discussions as an indicator of future need for care.…”

Section: Use Of the Model In Clinical Practicementioning

confidence: 99%

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Development and external validation of an admission risk prediction model after treatment from early intervention in psychosis services

et al. 2021

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Early Intervention in psychosis (EIP) teams are the gold standard treatment for first-episode psychosis (FEP). EIP is time-limited and clinicians are required to make difficult aftercare decisions that require weighing up individuals’ wishes for treatment, risk of relapse, and health service capacity. Reliable decision-making tools could assist with appropriate resource allocation and better care. We aimed to develop and externally validate a readmission risk tool for application at the point of EIP discharge. All persons from EIP caseloads in two NHS Trusts were eligible for the study. We excluded those who moved out of the area or were only seen for assessment. We developed a model to predict the risk of hospital admission within a year of ending EIP treatment in one Trust and externally validated it in another. There were n = 831 participants in the development dataset and n = 1393 in the external validation dataset, with 79 (9.5%) and 162 (11.6%) admissions to inpatient hospital, respectively. Discrimination was AUC = 0.76 (95% CI 0.75; 0.77) in the development dataset and AUC = 0.70 (95% CI 0.66; 0.75) in the external dataset. Calibration plots in external validation suggested an underestimation of risk in the lower predicted probabilities and slight overestimation at predicted probabilities in the 0.1–0.2 range (calibration slope = 0.86, 95% CI 0.68; 1.05). Recalibration improved performance at lower predicted probabilities but underestimated risk at the highest range of predicted probabilities (calibration slope = 1.00, 95% CI 0.79; 1.21). We showed that a tool for predicting admission risk using routine data has good performance and could assist clinical decision-making. Refinement of the model, testing its implementation and further external validation are needed.

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