Increasing the Impact of Quality Improvement on Health: An Expert Panel Method for Setting Institutional Priorities

Rubenstein, Lisa V.; Fink, Arlène; Yano, Elizabeth M.; Simón, Barbara; Chernof, Bruce; Robbins, Alan S.

doi:10.1016/s1070-3241(16)30170-5

Cited by 26 publications

(28 citation statements)

References 9 publications

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“…We chose expert panel 22 and key informant interview 23,24 and survey methods 25 because they are well suited to studies in which organizations, rather than individuals, are the subjects of analysis. Key informants are uniquely prepared to make useful observations that describe the impact of multiple levels of influence on organizational behavior (e.g., a service chief may have insight into the influence of the age and gender mix of the organization's patient population on the likelihood that providers value genetic testing).…”

Section: Developing a Research Planmentioning

confidence: 99%

Section: Expert Panelsmentioning

confidence: 99%

“…22 In the latter context, top-ranking organizational managers are brought together to examine existing data and institutional priorities to define a set of "products and processes that will enable the institution to move from the type of care it currently delivers toward its anticipated future. " 22 (p. 422). Historically, expert panel methodology has involved inviting experts with varying relevant backgrounds to participate in a consensus-building exercise, typically conducted in a three-round modified Delphi process involving a round 1 survey where each panelist ranks action priorities; a round 2 in-person meeting to discuss survey results and come to consensus on initial rankings; and a round 3 follow-up survey for final rankings of top-rated priorities.…”

Section: Expert Panelsmentioning

confidence: 99%

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Assessing multilevel determinants of adoption and implementation of genomic medicine: an organizational mixed-methods approach

Oishi

Marshall

Hamilton

et al. 2015

Genetics in Medicine

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Purpose: Adoption and implementation of evidence-based genetic and genomic medicine have been slow. We describe a methodology for identifying the influence of organizational factors on adoption and implementation of these services in health-care organizations. Methods:We illustrate a three-component, mixed-methods health services research approach, including expert panels, qualitative interviews with key informants, and quantitative surveys completed by key informants.Results: This research approach yielded a baseline assessment of existing genetic health-care models in the Veterans Health Administration and identified organizational barriers to and facilitators of adoption. In aggregate, the panel and key informant strategies created a communication network of relevant organizational stakeholders and a detailed foundation of organizational knowledge from which to design tools and models for implementation-level genetic/genomic translation. Conclusion:Expert panel and key informant strategies can be used to create a backdrop of stakeholder involvement and baseline organizational knowledge within which to plan translation research and to inform strategic planning and policies for adoption and implementation of genetic services in health-care organizations.

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Section: Developing a Research Planmentioning

confidence: 99%

Section: Expert Panelsmentioning

confidence: 99%

Section: Expert Panelsmentioning

confidence: 99%

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Assessing multilevel determinants of adoption and implementation of genomic medicine: an organizational mixed-methods approach

Oishi

Marshall

Hamilton

et al. 2015

Genetics in Medicine

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“…In March 2015, we convened a 1-day modified Delphi expert panel 17,32 to review our development methods and the results of applying our measures. Panelists included VA and non-VA experts in quality and performance measurement, depression, primary care/mental health integration, and program evaluation.…”

Section: Expert Panelmentioning

confidence: 99%

“…31 Major depressive affective disorder recurrent episode mild degree X X 296. 32 Major depressive affective disorder recurrent episode moderate degree X X 296. 33 Major depressive affective disorder recurrent episode severe degree without psychotic behavior X X 296.…”

Section: Appendicesmentioning

confidence: 99%

Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data

et al. 2016

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BACKGROUND:The Veterans Health Administration (VA) has invested substantially in evidence-based mental health care. Yet no electronic performance measures for assessing the level at which the population of Veterans with depression receive appropriate care have proven robust enough to support rigorous evaluation of the VA's depression initiatives. OBJECTIVE: Our objectives were to develop prototype longitudinal electronic population-based measures of depression care quality, validate the measures using expert panel judgment by VA and non-VA experts, and examine detection, follow-up and treatment rates over a decade (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). We describe our development methodology and the challenges to creating measures that capture the longitudinal course of clinical care from detection to treatment. DESIGN AND PARTICIPANTS: Data come from the National Patient Care Database and Pharmacy Benefits Management Database for primary care patients from 1999 to 2011, from nine Veteran Integrated Service Networks. MEASURES: We developed four population-based quality metrics for depression care that incorporate a 6-month look back and 1-year follow-up: detection of a new episode of depression, 84 and 180 day follow-up, and minimum appropriate treatment 1-year post detection. Expert panel techniques were used to evaluate the measure development methodology and results. Key challenges to creating valid longitudinal measures are discussed. KEY RESULTS: Over the decade, the rates for detection of new episodes of depression remained stable at 7-8 %. Follow-up at 84 and 180 days were 37 % and 45 % in 2000 and increased to 56 % and 63 % by 2010. Minimum appropriate treatment remained relatively stable over the decade (82-84 %). CONCLUSIONS: The development of valid longitudinal, population-based quality measures for depression care is a complex process with numerous challenges. If the full spectrum of care from detection to follow-up and treatment is not captured, performance measures could actually mask the clinical areas in need of quality improvement efforts.

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Continuity of Care in Children’s Mental Health: Development of a Measure

Tobon

Reid

Goffin

2013

Adm Policy Ment Health

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A new measure, Continuity of Care in Children's Mental Health (C3MH), is presented. The study involved item generation, pre-testing, pilot testing, and validation. The C3MH was administered to 364 parents recruited from 13 children's mental health agencies in Ontario, Canada. The measure includes five scales supported by confirmatory factor analysis. Scale validity was supported through analyses of relationships with established measures of satisfaction, problem severity, and therapeutic alliance, as well as through known-group differences. The results of a pilot youth-report version (N = 57) are presented. The C3MH will be a useful tool for assessing improvements in system integration.

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Increasing the Impact of Quality Improvement on Health: An Expert Panel Method for Setting Institutional Priorities

Cited by 26 publications

References 9 publications

Assessing multilevel determinants of adoption and implementation of genomic medicine: an organizational mixed-methods approach

Assessing multilevel determinants of adoption and implementation of genomic medicine: an organizational mixed-methods approach

Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data

Continuity of Care in Children’s Mental Health: Development of a Measure

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