Assessing Clinical Predictions of Early Rehospitalization in Schizophrenia

Olfson, Mark; Mechanic, David; Boyer, Carol A.; Hansell, Stephen; Walkup, James; Weiden, Peter J.

doi:10.1097/00005053-199912000-00003

Cited by 125 publications

(114 citation statements)

References 31 publications

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“…[33][34][35] More limited literature demonstrates that adding outpatient utilization information and psychosocial factors improve predictive models for acute care utilization. [35][36][37] Prior studies of physician's ability to predict death or acute care utilization results show mixed results; [13][14][15][16][17][18]22 however, our quantitative model, designed to predict physician-defined complexity from increasingly available data, shows promise as an additional tool in identifying and stratifying high-risk patients for population management interventions. Health care delivery systems could replicate our approach in their own context or add proxies for psychosocial complexity that are available in their data repositories to strengthen their risk-stratification approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Current quantitative methods for identifying the complex patients at highest risk for suboptimal future clinical quality and utilization outcomes rely primarily on diagnosis-based and utilization-based algorithms to predict future utilization. [13][14][15][16][17][18][19][20][21][22][23] These tools miss clinical characteristics that are not present in billing data and may not capture non-clinical contributors to patient complexity.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

et al. 2015

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BACKGROUND: Improving the ability to risk-stratify patients is critical for efficiently allocating resources within healthcare systems. OBJECTIVE: The purpose of this study was to evaluate a physician-defined complexity prediction model against outpatient Charlson score (OCS) and a commercial risk predictor (CRP). DESIGN: Using a cohort in which primary care physicians reviewed 4302 of their adult patients, we developed a predictive model for estimated physician-defined complexity (ePDC) and categorized our population using ePDC, OCS and CRP. PARTICIPANTS: 143,372 primary care patients in a practice-based research network participated in the study. MAIN MEASURES: For all patients categorized as complex in 2007 by one or more risk-stratification method, we calculated the percentage of total person time from 2008-2011 for which eligible cancer screening was incomplete, HbA1c was ≥ 9 %, and LDL was ≥ 130 mg/dl (in patients with cardiovascular disease). We also calculated the number of emergency department (ED) visits and hospital admissions per person year (ppy). KEY RESULTS: There was modest agreement among individuals classified as complex using ePDC compared with OCS (36.7 %) and CRP (39.6 %). Over 4 follow-up years, eligible ePDC-complex patients had higher proportions (p<0.001) of time with: incomplete cervical (17.8 % vs. 13.3 % for OCS; 19.4 % vs. 11.2 % for CRP), breast (21.4 % vs. 14.9 % for OCS; 22.7 % vs. 15.0 % for CRP), and colon (25.9 % vs. 18.7 % for OCS; 27.0 % vs. 18.2 % for CRP) cancer screening; HbA1c≥9 % (15.6 % vs. 8.1 % for OCS; 15.9 % vs. 6.9 % for CRP); and LDL≥130 mg/dl (12.4 % vs. 7.9 % for OCS; 11.8 % vs 9.0 % for CRP). ePDCcomplex patients had higher rates (p<0.003) of: ED visits (0.21 vs. 0.11 ppy for OCS; 0.17 vs. 0.15 ppy for CRP), and admissions in patients 45-64 and ≥ 65 years old (0.11 vs. 0.10 ppy AND 0.24 vs. 0.21 ppy for OCS). CONCLUSION: Our measure for estimated physiciandefined complexity compared favorably to commonly used risk-prediction approaches in identifying future suboptimal quality and utilization outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

et al. 2015

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“…24 One study that examined the accuracy of clinicians in predicting the readmissions of patients with schizophrenia indicated that fewer than 20% of readmissions were predicted, and even this was only achieved because of the relatively homogenous nature of the local population with mental illness. 25 Moreover, clinician assessment is limited to those patients who already have contact with a service. Therefore, using clinicians to predict risk across a large, enrolled population is relatively inefficient and inaccurate.…”

Section: Identifying High Risk Patientsmentioning

confidence: 99%

Predictive risk modelling in health: options for New Zealand and Australia

Panattoni¹,

Vaithianathan

Ashton³

et al. 2011

Aust. Health Review

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Abstract.Predictive risk models (PRMs) are case-finding tools that enable health care systems to identify patients at risk of expensive and potentially avoidable events such as emergency hospitalisation. Examples include the PARR (Patients-atRisk-of-Rehospitalisation) tool and Combined Predictive Model used by the National Health Service in England. When such models are coupled with an appropriate preventive intervention designed to avert the adverse event, they represent a useful strategy for improving the cost-effectiveness of preventive health care. This article reviews the current knowledge about PRMs and explores some of the issues surrounding the potential introduction of a PRM to a public health system. We make a particular case for New Zealand, but also consider issues that are relevant to Australia.

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“…Moreover, patients who are readmitted to inpatient psychiatric care are often found to have been recently discharged from a psychiatric hospitalization. In previous studies, 24 percent of 262 patients (11) and 38 percent of 128 patients (12) with a recent hospital discharge were readmitted within three to six months. In a study of state hospital patients in Massachusetts, Fisher and colleagues (13) found a 50 percent readmission rate within four years of discharge among 5,610 patients.…”

mentioning

confidence: 95%

Effects of Patient and Health-System Characteristics on Community Tenure of Discharged Psychiatric Inpatients

Bruffærts¹,

Sabbé²,

Demyttenaere³

2004

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6 68 85 5 O ver the past two decades, the organization and provision of mental health care have changed dramatically. The deinstitutionalization of mental health services has resulted in shorter stays in psychiatric hospitals and in larger numbers of psychiatric patients living in the community (1). Many communities have too few resources to help these patients (2). The number of psychiatric referrals to hospital emergency departments has increased, partly because of the general lack of linkage between institutional and community services (3). This trend has been observed both in the United States and in Europe (4).Previous research has provided consistent evidence that persons with previous use of psychiatric services are among the most frequent users of psychiatric emergency departments (5-10). Moreover, patients who are readmitted to inpatient psychiatric care are often found to have been recently discharged from a psychiatric hospitalization. In previous studies, 24 percent of 262 patients (11) and 38 percent of 128 patients (12) with a recent hospital discharge were readmitted within three to six months. In a study of state hospital patients in Massachusetts, Fisher and colleagues (13) found a 50 percent readmission rate within four years of discharge among 5,610 patients. Early hospital readmission has been related to the presence of mental illnesses with poor prognoses (13-15), to poor quality of patients' social networks, and to patients' difficulties with hygiene at discharge (16).Early readmissions, however, may also reflect the quality of postdischarge treatment and care. Some unplanned psychiatric referrals could be explained by the unexpected and acute progression of the patient's mental illness, but the rest may result because the mentally ill person has been discharged from the hospital too soon or because follow-up in the community is ineffective.Lower readmission rates have been associated with the existence of follow-up services. For example, McIntosh and Worley (17) implemented telephone follow-up and aftercare groups for patients who were discharged from a psychiatric hospitalization. Fifteen months after the implementation of these services, these authors found a readmission rate of

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Assessing Clinical Predictions of Early Rehospitalization in Schizophrenia

Cited by 125 publications

References 31 publications

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

Predictive risk modelling in health: options for New Zealand and Australia

Effects of Patient and Health-System Characteristics on Community Tenure of Discharged Psychiatric Inpatients

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