39
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Do ‘virtual wards’ reduce rates of unplanned hospital admissions, and at what cost? A research protocol using propensity matched controls

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          This retrospective study will assess the extent to which multidisciplinary case management in the form of virtual wards (VWs) leads to changes in the use of health care and social care by patients at high risk of future unplanned hospital admission. VWs use the staffing, systems and daily routines of a hospital ward to deliver coordinated care to patients in their own homes. Admission to a VW is offered to patients identified by a predictive risk model as being at high risk of unplanned hospital admission in the coming 12 months.

          Study design and data collection methods

          We will compare the health care and social care use of VW patients to that of matched controls. Controls will be drawn from (a) national, and (b) local, individual-level pseudonymous routine data. The costs of setting up and running a VW will be determined from the perspectives of both health and social care organizations using a combination of administrative data, interviews and diaries.

          Methods of analysis

          Using propensity score matching and prognostic matching, we will create matched comparator groups to estimate the effect size of virtual wards in reducing unplanned hospital admissions.

          Conclusions

          This study will allow us to determine relative to matched comparator groups: whether VWs reduce the use of emergency hospital care; the impact, if any, of VWs on the uptake of primary care, community health services and council-funded social care; and the potential costs and savings of VWs from the perspectives of the national health service (NHS) and local authorities.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: not found
          • Article: not found

          Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Five laws for integrating medical and social services: lessons from the United States and the United Kingdom.

            W Leutz (1999)
            Because persons with disabilities (PWDs) use health and social services extensively, both the United States and the United Kingdom have begun to integrate care across systems. Initiatives in these two countries are examined within the context of the reality that personal needs and use of systems differ by age and by type and severity of disability. The lessons derived from this scrutiny are presented in the form of five "laws" of integration. These laws identify three levels of integration, point to alternative roles for physicians, outline resource requirements, highlight friction from differing medical and social paradigms, and urge policy makers and administrators to consider carefully who would be most appropriately selected to design, oversee, and administer integration initiatives. Both users and caregivers must be involved in planning to ensure that all three levels of integration are attended to and that the borders between medical and other systems are clarified.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.

              To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of "reference" conditions for which improved management may help to prevent future admissions. Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were "flagged" incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly "flagged" patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a "business case" has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.
                Bookmark

                Author and article information

                Contributors
                Role: Senior Fellow,
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                Role: Head of Research,
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                Role: Associate Professor of Economics,
                University of Auckland, Owen G Glenn Building, 12 Grafton Road, Auckland, New Zealand
                Role: Senior Research Analyst,
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                Role: Senior Research Analyst,
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                Role: Associate Professor of Health Policy and Public Service,
                New York University, Robert F. Wagner Graduate School of Public Service, 295 Lafayette Street, 2nd Floor, New York, NY 10012-9604, USA
                Role: Director,
                The Nuffield Trust, 59 New Cavendish Street, 59 New Cavendish Street, London W1G 7LP, UK
                Journal
                Int J Integr Care
                IJIC
                International Journal of Integrated Care
                Igitur publishing (Utrecht, The Netherlands )
                1568-4156
                Apr-Jun 2011
                30 June 2011
                : 11
                : e079
                Affiliations
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                University of Auckland, Owen G Glenn Building, 12 Grafton Road, Auckland, New Zealand
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK
                New York University, Robert F. Wagner Graduate School of Public Service, 295 Lafayette Street, 2nd Floor, New York, NY 10012-9604, USA
                The Nuffield Trust, 59 New Cavendish Street, 59 New Cavendish Street, London W1G 7LP, UK
                Author notes
                Correspondence to: Dr. Geraint H. Lewis, The Nuffield Trust, 59 New Cavendish Street, London W1G 7LP, UK, Phone: +020 7631 8450, Fax: +020 7631 8451, E-mail: geraint.lewis@ 123456nuffieldtrust.org.uk
                Article
                ijic2011079
                3178802
                21949489
                c99c8d20-b3d7-4189-b1be-a1c810e7594c
                Copyright 2011, International Journal of Integrated Care (IJIC)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Categories
                Research and Theory

                Health & Social care
                delivery of health care,integrated,evaluation studies,clinical protocols
                Health & Social care
                delivery of health care, integrated, evaluation studies, clinical protocols

                Comments

                Comment on this article