The key incident monitoring and management system – history and role in quality improvement
| Autor: | Tony Badrick, Stephanie Gay, Mark Mackay, Ken Sikaris |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
030213 general clinical medicine
medicine.medical_specialty Quality management Quality Assurance Health Care Clinical Biochemistry Pre-Analytical Phase 030204 cardiovascular system & hematology History 21st Century Risk Assessment 03 medical and health sciences Patient safety 0302 clinical medicine medicine Humans Risk management Quality Indicators Health Care Risk Management Medical Errors business.industry Incidence (epidemiology) Biochemistry (medical) General Medicine History 20th Century Haemolysis Quality Improvement Harm Emergency medicine Patient Safety Laboratories Risk assessment business Quality assurance |
| Zdroj: | Clinical Chemistry and Laboratory Medicine (CCLM). 56:264-272 |
| ISSN: | 1437-4331 1434-6621 |
| DOI: | 10.1515/cclm-2017-0219 |
| Popis: | Background: The determination of reliable, practical Quality Indicators (QIs) from presentation of the patient with a pathology request form through to the clinician receiving the report (the Total Testing Process or TTP) is a key step in identifying areas where improvement is necessary in laboratories. Methods: The Australasian QIs programme Key Incident Monitoring and Management System (KIMMS) began in 2008. It records incidents (process defects) and episodes (occasions at which incidents may occur) to calculate incident rates. KIMMS also uses the Failure Mode Effects Analysis (FMEA) to assign quantified risk to each incident type. The system defines risk as incident frequency multiplied by both a harm rating (on a 1–10 scale) and detection difficulty score (also a 1–10 scale). Results: Between 2008 and 2016, laboratories participating rose from 22 to 69. Episodes rose from 13.2 to 43.4 million; incidents rose from 114,082 to 756,432. We attribute the rise in incident rate from 0.86% to 1.75% to increased monitoring. Haemolysis shows the highest incidence (22.6% of total incidents) and the highest risk (26.68% of total risk). “Sample is suspected to be from the wrong patient” has the second lowest frequency, but receives the highest harm rating (10/10) and detection difficulty score (10/10), so it is calculated to be the 8th highest risk (2.92%). Similarly, retracted (incorrect) reports QI has the 10th highest frequency (3.9%) but the harm/difficulty calculation confers the second highest risk (11.17%). Conclusions: TTP incident rates are generally low (less than 2% of observed episodes), however, incident risks, their frequencies multiplied by both ratings of harm and discovery difficulty scores, concentrate improvement attention and resources on the monitored incident types most important to manage. |
| Databáze: | OpenAIRE |
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