Effect of a Postdischarge Virtual Ward on Readmission or Death for High-Risk Patients

Dhalla, Irfan A.; O’Brien, Tara; Morra, Dante; Thorpe, Kevin E.; Wong, Brian M.; Mehta, Rajin; Frost, David; Abrams, Howard; Ko, Francoise; Rooyen, Patrick Van; Bell, Chaim M.; Gruneir, Andrea; Lewis, Geraint; Daub, Stacey; Anderson, Geoff; Hawker, Gillian; Rochon, Paula A.; Laupacis, Andreas

doi:10.1001/jama.2014.11492

Cited by 120 publications

(159 citation statements)

References 29 publications

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“…53 Three studies were undertaken in North America, one in Canada 45 and two in the USA (yielding three papers). 47,50,51 A summary of studies is presented in Table 5.…”

Section: Settingsmentioning

confidence: 99%

“…45 All papers were published from 2010 onwards with reported data collection or coverage between 2005 and 2014. A, no or few flaws, the study credibility, transferability, dependability and confirmability is high; B, some flaws, unlikely to affect the credibility, transferability, dependability and/or confirmability of the study; C, some flaws which may affect the credibility, transferability, dependability and/or confirmability of the study.…”

Section: Settingsmentioning

confidence: 99%

“…Study designs comprised three randomised controlled trials, 45,47,50 three cohort studies, 44,48,56 one cross-sectional study 55 and one further observational study. 46 Three wholly qualitative studies were included, 27,53,54 with qualitative methods also featuring in two others.…”

Section: Study Designsmentioning

confidence: 99%

“…48,49,52,54,55 PARR uses data on prior hospitalisations to predict risk of rehospitalisation and, hence, calculates risk only for those patients with a previous admission. The combined predictive model was reported in three studies, 48,49,52 with single references to the Elder Risk Assessment (ERA), 50 Case Smart Suite Germany, 46 Length of stay, Acuity, Comorbidities, ED visits in previous 6 months (LACE), 45 Nairn Case Finder, 44 High Impact User Manager 52 and Adjusted Clinical Groups (ACGs). 53 An unnamed tool developed by the Senior Care Action Network Health Plan was used for the Levine et al study.…”

Section: Risk Tools Identifiedmentioning

confidence: 99%

“…47 All except two tools were reported as predicting the likelihood of an emergency hospital admission (or re-admission in the case of the PARR tool), within 12 months. The LACE tool predicts risk within 30 days 45 and the ERA tool 51 predicts risk within 24 months. The time span for the ACG tool used in Sauto Arce et al's qualitative study 53 was not provided.…”

Section: Risk Tools Identifiedmentioning

confidence: 99%

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Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Snooks

Bailey-Jones

Burge-Jones

et al. 2018

Health Serv Deliv Res

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Background With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim To evaluate the introduction of predictive risk stratification in primary care. Objectives To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM. Design Randomised stepped-wedge trial with economic and qualitative components. Setting Abertawe Bro Morgannwg University Health Board, south Wales. Participants Patients registered with 32 participating general practices. Intervention PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support. Main outcome measures Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs. Data sources Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews. Results Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets. Limitations In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative. Conclusions Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients. Future research (1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners. Trial and study registration Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874. Funding The National Institute for Health Research Health Services Delivery and Research programme.

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“…53 Three studies were undertaken in North America, one in Canada 45 and two in the USA (yielding three papers). 47,50,51 A summary of studies is presented in Table 5.…”

Section: Settingsmentioning

confidence: 99%

Section: Settingsmentioning

confidence: 99%

Section: Study Designsmentioning

confidence: 99%

Section: Risk Tools Identifiedmentioning

confidence: 99%

Section: Risk Tools Identifiedmentioning

confidence: 99%

See 3 more Smart Citations

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Snooks

Bailey-Jones

Burge-Jones

et al. 2018

Health Serv Deliv Res

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Managing Posthospital Care Transitions for Older Adults

Boling

2014

JAMA

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In this issue of JAMA, Dhalla and colleagues 1 report findings from a randomized trial comparing the effect of usual care vs a "virtual ward" model of posthospital care management for older adults on reducing the primary end point of 30-day hospital readmissions. The virtual ward focused on care coordination by telephone or e-mail contact as well as clinic or home visits for several weeks following hospital discharge. With Medicare hospital reimbursement increasingly tied to 30day readmission rates, this study has potential clinical and financial implications.The study by Dhalla et al was well designed and aimed to test the model described, had successful randomization, targeted a representative group of older patients at high risk of readmission or death, and enrolled approximately 960 patients per group, sufficient to detect reduced hospitalization. The intervention was intended to be scalable using existing hospital resources without substantial investment, specialized training, or role-specific expertise, which was cited as a potential weakness of some published models. The average intervention lasted 35 days with multiple telephone or e-mail contacts (mean, 2.3 per patient), clinic visits (mean, 0.5), and home visits (mean, 2.8) by care coordinators (mean, 1.6), physicians (mean, 0.5), and pharmacists (mean, 0.75). One physician, in an alternating role, was the medical authority on the care team in which the core clinical personnel were care coordinators. It appears, based on the numbers of patients and teams, that a virtual ward had a momentary census of about 30 patients, modestly larger than a typical inpatient service. Despite the coordination of care and direct care provided by the interprofessional team for several weeks after hospital discharge, there were no statistically significant differences between the virtual ward group vs the usual care group in the primary outcome of hospital admission or death within 30 days of discharge (21.2% in the virtual ward group; 24.6% in the usual care group) or in the secondary outcomes of nursing home admission or emergency department visits at 30 days, or in any of these outcomes at 90 days, 6 months, or 1 year.How should these findings reported by Dhalla et al be interpreted in the context of the literature and clinical circumstance of complex patients being discharged from hospitals? Previous smaller randomized clinical trials have reported reductions in rehospitalization for specific groups of patients. In a study by Coleman et al 2 that enrolled 370 per group in 2002 and had nurse practitioners as key agents in managing post-hospital care, 30-day readmissions in the intervention group (8.3%) were re-

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International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions

et al. 2016

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Effect of a Postdischarge Virtual Ward on Readmission or Death for High-Risk Patients

Abstract: clinicaltrials.gov Identifier: NCT01108172.

Cited by 120 publications

References 29 publications

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Managing Posthospital Care Transitions for Older Adults

International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions

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