Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
| Autor: | Rebecca C. Steorts, Curtis Lambert, Armando Bedoya, Yueqi Shen, Benjamin A. Goldstein, Cara O'Brien, Matthew Phelan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
clinical decision support
medicine.medical_specialty Clinical decision support system Article law.invention 03 medical and health sciences 0302 clinical medicine law Medicine In patient 030212 general & internal medicine lcsh:R5-920 Framingham Risk Score business.industry Health Policy Public Health Environmental and Occupational Health 030208 emergency & critical care medicine Early warning score predictive models Intensive care unit Confidence interval 3. Good health Identification (information) electronic health records Emergency medicine Work flow business lcsh:Medicine (General) |
| Zdroj: | MDM Policy & Practice, Vol 5 (2020) MDM Policy & Practice |
| ISSN: | 2381-4683 |
| Popis: | Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014–2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79–0.83) versus 0.740 (95% confidence interval = 0.72–0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients’ clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|