Literature DB >> 31440796

[Experiences of an Initiative Qualitätsmedizin reviewer].

G Popken1.   

Abstract

BACKGROUND: The quality of medical care is on a high level in hospitals, yet variations in quality as well as room for improvement can often be identified. This potential can be made visible by active error management. The Initiative Qualitätsmedizin (IQM) carries out a quality measurement based on routine data. Furthermore, it commits to the transparency of the results by publication. Other means for quality improvement include peer review.
METHODS: Peer reviews serve to clarify statistical abnormalities with the applied quality indicators without using reprisals. Reviews take place following accepted analysis criteria and are subject to explicit rules concerning the process. The peer teams are comprised of members from several providers. Each review is ended with a summarized record including a proposed solution as well as a time frame. It is essential that all participants are satisfied after finishing the peer review. The subsequent implementation is the responsibility of the head of medicine.
CONCLUSION: It is a challenge, especially in the field of medicine, to change long-standing learned and practised processes. To bring one's daily actions to the attention by others publicly might yet be another and even bigger challenge. The willingness to undergo such a process and to accept the resulting criticism is being experienced and accepted very differently.

Entities:  

Keywords:  Error management; Quality indicator; Quality management; Quality of health care; Standard operating procedures

Mesh:

Year:  2019        PMID: 31440796     DOI: 10.1007/s00120-019-1021-3

Source DB:  PubMed          Journal:  Urologe A        ISSN: 0340-2592            Impact factor:   0.639


  19 in total

1.  Hospital mortality league tables.

Authors:  Bobbie Jacobson; Jenny Mindell; Martin McKee
Journal:  BMJ       Date:  2003-04-12

2.  Mortality control charts for comparing performance of surgical units: validation study using hospital mortality data.

Authors:  Paris P Tekkis; Peter McCulloch; Adrian C Steger; Irving S Benjamin; Jan D Poloniecki
Journal:  BMJ       Date:  2003-04-12

3.  Using administrative data to predict important health outcomes. Entry to hospital, nursing home, and death.

Authors:  N P Roos; L L Roos; J Mossey; B Havens
Journal:  Med Care       Date:  1988-03       Impact factor: 2.983

4.  Explaining variations in hospital death rates. Randomness, severity of illness, quality of care.

Authors:  R E Park; R H Brook; J Kosecoff; J Keesey; L Rubenstein; E Keeler; K L Kahn; W H Rogers; M R Chassin
Journal:  JAMA       Date:  1990-07-25       Impact factor: 56.272

5.  Use of claims data systems to evaluate health care outcomes. Mortality and reoperation following prostatectomy.

Authors:  J E Wennberg; N Roos; L Sola; A Schori; R Jaffe
Journal:  JAMA       Date:  1987-02-20       Impact factor: 56.272

6.  A population-based approach to monitoring adverse outcomes of medical care.

Authors:  N P Roos; C D Black; L L Roos; R B Tate; K C Carriere
Journal:  Med Care       Date:  1995-02       Impact factor: 2.983

7.  Using Medicare claims for outcomes research.

Authors:  J B Mitchell; T Bubolz; J E Paul; C L Pashos; J J Escarce; L H Muhlbaier; J M Wiesman; W W Young; R S Epstein; J C Javitt
Journal:  Med Care       Date:  1994-07       Impact factor: 2.983

8.  Judging hospitals by severity-adjusted mortality rates: the case of CABG surgery.

Authors:  B Landon; L I Iezzoni; A S Ash; M Shwartz; J Daley; J S Hughes; Y D Mackiernan
Journal:  Inquiry       Date:  1996       Impact factor: 1.730

9.  Surgeon volume compared to hospital volume as a predictor of outcome following primary colon cancer resection.

Authors:  Deborah Schrag; Katherine S Panageas; Elyn Riedel; Lillian Hsieh; Peter B Bach; Jose G Guillem; Colin B Begg
Journal:  J Surg Oncol       Date:  2003-06       Impact factor: 3.454

10.  Measuring hospital mortality rates: are 30-day data enough? Ischemic Heart Disease Patient Outcomes Research Team.

Authors:  D W Garnick; E R DeLong; H S Luft
Journal:  Health Serv Res       Date:  1995-02       Impact factor: 3.402

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