Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
This study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and ED pati...
| Autores: | , , , , , , , , , , , |
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| Tipo de recurso: | conjunto de datos |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/401736 |
| Acceso en línea: | http://hdl.handle.net/10261/401736 https://digital.csic.es/handle/10261/401734 |
| Access Level: | acceso abierto |
| Palabra clave: | C C6 C67 C69 Overcrowding Advanced data analytics model Emergency department Point-of-care Simulation |
| Sumario: | This study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and ED patients’ flow. We built a discrete event model simulation (DEMS) to represent workflow of a Spanish ED. Historical data from ED, published evidence and expert estimates were used to support the model. Different scenarios of progressive utilization of POCT in patients’ care triaged as Emergency Severity Index (ESI) level 3 were compared to standard-of-care (SoC) in terms of time-to-first medical intervention (TFMI), time-to-disposition decision (TDD), total length of stay (LoS) and patient workflow. In POCT maximum utilization scenario (60% of ESI-3 patients), time savings reached 27.44, 14.58 and 13.96 min of TFMI, 55.77, 13.64 and 13.97 min of TDD and 89.60, 18.55 and 13.98 min of LoS (ESI-3, 4 and 5 patients, respectively). Statistically significant reductions were found for all time outcomes in every POCT scenario for ESI-3, 4 and 5 patients. Internal validation didn’t show differences between model results and real data. Simplifications were made due to theoretical nature of computer-simulation models. Some input data and assumptions regarding individual process times were derived from interviews. Theoretical distributions were assumed; other activities outside the ED were considered as a disruption to the system; finally, findings reflect experience of a single ED. Advanced data analytics has become a useful tool in analyzing lots of processes. Our study showed that advanced data analytics has become an exceptional tool in clinical laboratories and exemplifies how POCT incorporation in ED for care of ESI-3 patients reduces physicians’ workload and waiting times of ESI-3, 4 and 5 patients, thus optimizing the patients’ medical journey. |
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