TY  - BOOK
AU  - Goeschel, Christine
TI  - A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects
SN  - 1471-2288
PY  - 2020///
KW  - *Delivery of Health Care/og [Organization & Administration]
KW  - *Delphi Technique
KW  - *Quality Improvement
KW  - Consensus
KW  - Female
KW  - Humans
KW  - Male
KW  - Surveys and Questionnaires
KW  - MedStar Institute for Quality and Safety
KW  - Journal Article
N1  - Available online from MWHC library: 2001 - present
N2  - 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; 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; 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
UR  - https://dx.doi.org/10.1186/s12874-019-0886-6
ER  -