Systematische risikoanalyse der medizinischen leistungsprozesse durch detaillierte mitarbeiterbefragungen eine effektive basis zur optimierung der patientensicherheit

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Stephan B. Sobottka - , Department of Neurosurgery, TUD Dresden University of Technology (Author)
  • Maria Eberlein-Gonska - , TUD Dresden University of Technology (Author)
  • Gabriele Schackert - , TUD Dresden University of Technology (Author)
  • Armin Töpfer - , TUD Dresden University of Technology (Author)

Abstract

Due to the knowledge gap that exists between patients and health care staff the quality of medical treatment usually cannot be assessed securely by patients. For an optimization of safety in treatment related processes of medical care, the medical staff needs to be actively involved in preventive and proactive quality management. Using voluntary, confidential and non punitive systematic employee surveys, vulnerable topics and areas in patient care revealing preventable risks can be identified at an early stage. Preventive measures to continuously optimize treatment quality can be defined by creating a risk portfolio and a priority list of vulnerable topics. Whereas critical incident reporting systems are suitable for continuous risk assessment by detecting safety relevant single events, employee surveys permit to conduct a systematic risk analysis of all treatment related processes of patient care at any given point in time.

Translated title of the contribution
A systemic risk analysis of hospital management processes by medical employees an effective basis for improving patient safety

Details

Original languageGerman
Pages (from-to)228-236
Number of pages9
JournalZeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen
Volume103
Issue number4
Publication statusPublished - 2009
Peer-reviewedYes

External IDs

PubMed 19545085

Keywords

Sustainable Development Goals

Keywords

  • Patient safety, Quality management, Risk analysis, Rrisk management