[Detection of adverse events in thyroid and parathyroid surgery using trigger tool and Minimum Basic Data Set (MBDS)]
R Kaibel Val1, P Ruiz López2, A I Pérez Zapata3
1Clínica Hospital Povisa, Vigo, España.
Journal of Healthcare Quality Research
|October 29, 2020
Summary
The trigger tool is more effective than the Minimum Basic Data Set (MBDS) for detecting adverse events (AE) in surgical patients with thyroid and parathyroid disease. Most identified AEs were preventable and of low severity.
Area of Science:
- Healthcare Quality Improvement
- Patient Safety Research
- Surgical Outcomes Analysis
Background:
- Adverse events (AE) in hospitalized patients can impact outcomes and increase healthcare costs.
- Accurate detection of AEs is crucial for implementing effective patient safety interventions.
- Existing methods for AE detection have varying sensitivities and specificities.
Purpose of the Study:
- To compare the efficacy of the trigger tool and the Minimum Basic Data Set (MBDS) in identifying adverse events (AEs).
- To assess AE detection in hospitalized surgical patients with thyroid and parathyroid disease.
Main Methods:
- A retrospective, cross-sectional observational study analyzed data from May 2014 to April 2015.
- Patients undergoing thyroidectomy and parathyroidectomy were included.
- Adverse events were identified using a trigger tool and the MBDS through systematic clinical documentation review.
Main Results:
- 203 AEs were identified in 251 patients; 90.04% were detected by the trigger tool versus 10.34% by MBDS.
- The trigger tool demonstrated higher sensitivity (91.27%) compared to MBDS (13.49%).
- MBDS showed higher specificity (100%) and positive predictive value (100%) than the trigger tool.
Conclusions:
- The trigger tool is significantly more sensitive for detecting adverse events in this patient population.
- The majority of detected AEs were of low severity and considered preventable.
- Specific triggers like "antiemetic administration" and "calcium administration" showed high predictive power for AE detection.
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