Dataset Information

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects.

ABSTRACT:

Background

The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on "what good looks like" might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans.

Methods

We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusion based on literature review and the study team's experience; (2) a two-round modified Delphi exercise with a panel of experts to establish consensus on the importance of these features; and (3) a small in-person consensus group meeting to finalise the list of features.

Results

A list of 104 candidate questions was generated. A panel of 19 experts in the Delphi reviewed these questions and produced consensus on retaining 46 questions in the first round and on a further 22 in the second round. Thematic analysis of open text responses from the panellists suggested a number of areas of debate that were explicitly considered by the consensus group. The exercise yielded 74 questions (71% of 104) on which there was consensus in five categories of measurement relating to: design, data collection and management, analysis, action, and embedding.

Conclusions

This study offers a consensus-based view on the features of a good measurement plan for a QI project in healthcare. The results may be of use to QI teams, funders and evaluators, but are likely to require further development and testing to ensure feasibility and usefulness.

SUBMITTER: Woodcock T

PROVIDER: S-EPMC6961316 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects.

Woodcock Thomas T Adeleke Yewande Y Goeschel Christine C Pronovost Peter P Dixon-Woods Mary M

BMC medical research methodology 20200114 1

<h4>Background</h4>The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on "what good looks like" might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans.<h4>Methods</h4>We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusi ...[more]

PMID: 31937262

Similar Datasets

Project description:OBJECTIVES:Healthcare is a complex system, so quality improvement will commonly lead to unintended consequences which are rarely evaluated. In previous qualitative work, we proposed a framework for considering the range of these potential consequences, in terms of their desirability and the extent to which they were predictable or expected during planning. This paper elaborates on the previous findings, using consensus methods to examine what consequences should be identified, why and how to prioritise, evaluate and interpret all identified consequences, and what stakeholders should be involved throughout this process. DESIGN:Two-round modified Delphi consensus study. SETTING AND PARTICIPANTS:Both rounds were completed by 60 panellists from an academic, clinical or management background and experience in designing, implementing or evaluating quality improvement programmes. RESULTS:Panellists agreed that trade-offs (expected undesirable consequences) and unpleasant surprises (unexpected undesirable consequences) should be actively considered. Measurement of harmful consequences for patients, and those with high workload or financial impact was prioritised, and their evaluation could also involve the use of qualitative methods. Clinical teams were agreed as important to involve at all stages, from identifying potential consequences, prioritising which of those to systematically evaluate, undertaking appropriate evaluation and interpreting the findings. Patients were necessary in identifying consequences, managers in identifying and prioritising, and improvement advisors in interpreting the data. CONCLUSION:There was consensus that a balanced approach to considering all the consequences of improvement can be achieved by carefully considering predictable trade-offs from the outset and deliberately pausing after implementation to identify any unexpected surprises and make an informed decision as to whether quantitative or qualitative evaluation is needed and feasible. Stakeholders' roles in in the process of identifying, prioritising, evaluating and interpreting potential consequences should be explicitly addressed within planning and revisited during and after implementation.

Project description:BackgroundThe COVID-19 pandemic has highlighted the importance of mental wellbeing. The identification and implementation of quality measures can improve health outcomes and patient experience. The objective was to identify and define a core set of valid and relevant pediatric mental health quality measures that will support health system evaluation and quality improvement in British Columbia, Canada.MethodsThe study consisted of four phases. First, a comprehensive database search identified valid pediatric quality measures focused on mental health and substance use (MH/SU). Second, the identified quality measures were mapped to focus areas, which were then prioritized by two stakeholder groups consisting of 26 members. Third, up to two representative measures for each prioritized focus area were pre-selected by an expert panel (n = 9). And fourth, a three-step modified Delphi approach was employed to (1) assess each quality measure on a 7-point Likert scale against three relevance criteria (representative of a quality problem, value to intended audience and actionable), (2) discuss the results, and (3) select and rank the most relevant measures. Forty-eight stakeholders were invited to participate; of those 24 completed the round 1 survey, 21 participated in the round 2 discussion and 18 voted in the round 3 selection and ranking survey. For round 1, consensus was determined when at least 70% of the response rates were within the range of five to seven. For round 3, Kendall's coefficient of concordance W was used as an estimator of inter-rater reliability.ResultsOne-hundred pediatric mental health quality measures were identified in the database search. Of those, 37 were mapped to ten focus areas. Pre-selection resulted in 19 representative measures moving forward to the Delphi study. Eleven measures met the consensus thresholds and were brought forward to the round 2 discussion. Round 3 ranking showed moderate to strong raters' agreement (Kendall's W = 0.595; p < 0.01) and resulted in the following five highest-ranked measures: level of satisfaction after discharge from inpatient admission due to MH/SU, number of patients experiencing seclusion or restraint, length of time from eating disorder referral to assessment, number of ED visits due to MH/SU, and number of readmissions to ED.ConclusionThe selected core set of valid and relevant pediatric quality measures will support sustainable system change in British Columbia. The five top-ranked measures will be refined and tested for data collection feasibility before being implemented in the province.

Dataset Information

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects.

Background

Methods

Results

Conclusions

Publications

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets