Impact of published clinical outcomes data: case study in NHS hospital trusts

Citation
R. Mannion et M. Goddard, Impact of published clinical outcomes data: case study in NHS hospital trusts, BR MED J, 323(7307), 2001, pp. 260-263
Citations number
7
Categorie Soggetti
General & Internal Medicine","Medical Research General Topics
Journal title
BRITISH MEDICAL JOURNAL
ISSN journal
0959535X → ACNP
Volume
323
Issue
7307
Year of publication
2001
Pages
260 - 263
Database
ISI
SICI code
0959-535X(20010804)323:7307<260:IOPCOD>2.0.ZU;2-J
Abstract
Objective To examine the impact of the publication of clinical outcomes dat a on NHS Trusts in Scotland to inform the development of similar schemes el sewhere. Design Case studies including semistructured interviews and a review of bac kground statistics. Setting Eight Scottish NHS acute trusts. Participants 48 trust staff comprising chief executives, medical directors, stroke consultants, breast cancer consultants, nurse managers, and junior doctors. Main outcome measures Staff views on the benefits and drawbacks of clinical outcome indicators provided by the clinical resource and audit group (CRAG ) and perceptions of the impact of these data on clinical practice and cont inuous improvement of quality. Results The CRAG indicators had a low profile in the trusts and were rarely cited as informing internal quality improvement or used externally to iden tify best practice. The indicators were mainly used to support applications for further funding and service development. The poor effect was attributa ble to a lack of professional belief in the indicators, arising from percei ved problems around quality of data and time lag between collection and pre sentation of data; limited dissemination; weak incentives to take action; a predilection for process rather than outcome indicators; and a belief that informal information is often more useful than quantitative data in the as sessment of clinical performance. Conclusions Those responsible for developing clinical indicator programmes should develop robust datasets. They should also encourage a working enviro nment and incentives such that these data are used to improve continuously.