Quality indicators for the community care of MSK conditions: An online modified-Delphi study.

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Tác giả: A Banerjee, A Braybrooke, M Brooks, R Burgess, J C Hill

Ngôn ngữ: eng

Ký hiệu phân loại: 620.0045 Engineering and allied operations

Thông tin xuất bản: Netherlands : Musculoskeletal science & practice , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 476067

BACKGROUND: Quality indicators can be used within healthcare to measure and benchmark performance. Variations in the access and quality of healthcare for musculoskeletal conditions highlights the need for a nationally agreed set of indicators. OBJECTIVES: The study's primary aim was to develop a set of care quality themes and indicators for musculoskeletal community care. METHODS: An online Delphi process was used, that included clinicians, managers, researchers, and patients. In round one, participants rated 79 indicators, across six care quality themes, for their importance to quality community musculoskeletal care. Following this, participants discussed the results of the first round in an online panel and then re-rated indicators in a second-round survey. The panel discussion focused on a) the importance rating of indicators, and b) the wording of care quality themes. Consensus for high importance was set at ≥60% in both rounds of the study. RESULTS/FINDINGS: Forty-six individuals participated in the first round of the study, with 21 participating in the second round. After the second round, six care quality themes emerged: 1) Optimising patient access and assessment, 2) Optimising patient education, self-management, and rehabilitation, 3) Optimising personalised care, 4) Optimising diagnosis, imaging, investigation, and referrals, 5) Optimising patient experience and outcomes, and 6) Population health relevant to musculoskeletal conditions. Within the six themes, 59 indicators were ranked as highly important. CONCLUSIONS: This study has developed a set of care quality themes and indicators for community musculoskeletal care. In the next stage of indicator development, real-world data will be used to validate the indicators across several constructs.
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