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      The influence of context on the effectiveness of hospital quality improvement strategies: a review of systematic reviews

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      Author(s): , , , , , , , on behalf of the DUQuE Consortium
      Publication date (Electronic):
      Journal: BMC Health Services Research
      Publisher: BioMed Central

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          Abstract

          Background

          It is now widely accepted that the mixed effect and success rates of strategies to improve quality and safety in health care are in part due to the different contexts in which the interventions are planned and implemented. The objectives of this study were to (i) describe the reporting of contextual factors in the literature on the effectiveness of quality improvement strategies, (ii) assess the relationship between effectiveness and contextual factors, and (iii) analyse the importance of contextual factors.

          Methods

          We conducted an umbrella review of systematic reviews searching the following databases: PubMed, Cochrane Database of Systematic Reviews, Embase and CINAHL. The search focused on quality improvement strategies included in the Cochrane Effective Practice and Organisation of Care Group taxonomy. We extracted data on quality improvement effectiveness and context factors. The latter were categorized according to the Model for Understanding Success in Quality tool.

          Results

          We included 56 systematic reviews in this study of which only 35 described contextual factors related with the effectiveness of quality improvement interventions. The most frequently reported contextual factors were: quality improvement team ( n = 12), quality improvement support and capacity ( n = 11), organization ( n = 9), micro-system ( n = 8), and external environment ( n = 4). Overall, context factors were poorly reported. Where they were reported, they seem to explain differences in quality improvement effectiveness; however, publication bias may contribute to the observed differences.

          Conclusions

          Contextual factors may influence the effectiveness of quality improvement interventions, in particular at the level of the clinical micro-system. Future research on the implementation and effectiveness of quality improvement interventions should emphasize formative evaluation to elicit information on context factors and report on them in a more systematic way in order to better appreciate their relative importance.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12913-015-0906-0) contains supplementary material, which is available to authorized users.

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          Most cited references63

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          Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

          Amit X Garg, Neill K. J. Adhikari, Heather McDonald … (2005)
          Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials. To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit. We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information. We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes. One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001). Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.
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            Systematic review: impact of health information technology on quality, efficiency, and costs of medical care.

            Basit Chaudhry, Jerome K Wang, Shinyi Wu … (2006)
            Experts consider health information technology key to improving efficiency and quality of health care. To systematically review evidence on the effect of health information technology on quality, efficiency, and costs of health care. The authors systematically searched the English-language literature indexed in MEDLINE (1995 to January 2004), the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database. We also added studies identified by experts up to April 2005. Descriptive and comparative studies and systematic reviews of health information technology. Two reviewers independently extracted information on system capabilities, design, effects on quality, system acquisition, implementation context, and costs. 257 studies met the inclusion criteria. Most studies addressed decision support systems or electronic health records. Approximately 25% of the studies were from 4 academic institutions that implemented internally developed systems; only 9 studies evaluated multifunctional, commercially developed systems. Three major benefits on quality were demonstrated: increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors. The primary domain of improvement was preventive health. The major efficiency benefit shown was decreased utilization of care. Data on another efficiency measure, time utilization, were mixed. Empirical cost data were limited. Available quantitative research was limited and was done by a small number of institutions. Systems were heterogeneous and sometimes incompletely described. Available financial and contextual data were limited. Four benchmark institutions have demonstrated the efficacy of health information technologies in improving quality and efficiency. Whether and how other institutions can achieve similar benefits, and at what costs, are unclear.
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              Effect of clinical decision-support systems: a systematic review.

              Tiffani Bright, Anthony Wong, Ravi Dhurjati … (2012)
              Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Agency for Healthcare Research and Quality.
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                Author and article information

                Contributors
                Dionne S. Kringos: d.s.kringos@amc.uva.nl
                Rosa Sunol: rsunol@fadq.org
                Cordula Wagner: c.wagner@nivel.nl
                Russell Mannion: r.mannion@bham.ac.uk
                Philippe Michel: philippe.michel@chu-lyon.fr
                Niek S. Klazinga: n.s.klazinga@amc.uva.nl
                Oliver Groene: oliver.groene@lshtm.ac.uk
                Journal
                Journal ID (nlm-ta): BMC Health Serv Res
                Journal ID (iso-abbrev): BMC Health Serv Res
                Title: BMC Health Services Research
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1472-6963
                Publication date (Electronic): 22 July 2015
                Publication date PMC-release: 22 July 2015
                Publication date Collection: 2015
                Volume: 15
                Electronic Location Identifier: 277
                Affiliations
                [ ]Department of Public Health, Academic Medical Center (AMC) - University of Amsterdam, PO Box 22660, 1100 DD Amsterdam, The Netherlands
                [ ]Avedis Donabedian Research Institute, University Autonomous of Barcelona, C/Provenza 293, Pral. 08037 Barcelona, Spain
                [ ]NIVEL, Netherlands Institute for Health Services Research, PO Box 1568, 3500 BN Utrecht, The Netherlands
                [ ]Health Services Management Centre, University of Birmingham, Birmingham, B15 2RT UK
                [ ]Quality and Safety Department, Lyon University, Hospital Network, Lyon, France
                [ ]Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH UK
                [ ]Avedis Donabedian Research Institute (FAD), Universitat Autonoma de Barcelona, ᅟ, Spain
                [ ]Red de investigación en servicios de salud en enfermedades crónicas REDISSEC, ᅟ, Spain
                Article
                Publisher ID: 906
                DOI: 10.1186/s12913-015-0906-0
                PMC ID: 4508989
                PubMed ID: 26199147
                SO-VID: 1ae16d6b-7ead-48b7-8675-d58b0f374121
                Copyright © © Kringos et al. 2015
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 8 October 2014
                Date accepted : 5 June 2015
                Categories
                Subject: Research Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2015

                ScienceOpen disciplines: Health & Social care
                Data availability:
                ScienceOpen disciplines: Health & Social care

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