Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department.
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| Titel: | Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department. |
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| Autoren: | Leon-Justel A; Clinical Biochemistry, Hospital Universitario Virgen Macarena, Sevilla, Spain.; Institute of Biomedicine of Seville (IBIS), Sevilla, Spain.; Superior Council for Scientific Research (CSIC), Sevilla, Spain.; Universidad de Loyola Andalucia - Campus de Sevilla, Universidad Loyola (Andalucia), Sevilla, Spain., Jimenez-Barragan M; Clinical Biochemistry, Hospital Universitario Virgen Macarena, Sevilla, Spain., Navarro-Bustos C; Emergency Department, Hospital Universitario Virgen Macarena, Sevilla, Spain., Martin-Perez S; Clinical Biochemistry, Hospital Universitario Virgen Macarena, Sevilla, Spain., Garrido-Castilla JM; Emergency Department, Hospital Universitario Virgen Macarena, Sevilla, Spain., Morales-Barroso IM; Emergency Department, Hospital Universitario Virgen Macarena, Sevilla, Spain., Oltra-Hostalet F; Emergency Department, Hospital Universitario Virgen Macarena, Sevilla, Spain., Fernandez-Gallardo MF; Emergency Department, Hospital Universitario Virgen Macarena, Sevilla, Spain., Diaz-Luque A; Clinical Biochemistry, Hospital Universitario Virgen Macarena, Sevilla, Spain., Eugenio-Pizarro A; Emergency Department, Hospital Universitario Virgen Macarena, Sevilla, Spain., Luque-Cid A; Clinical Biochemistry, Hospital Universitario Virgen Macarena, Sevilla, Spain., Sanchez-Mora C; Clinical Biochemistry, Hospital Universitario Virgen Macarena, Sevilla, Spain. |
| Quelle: | Journal of medical economics [J Med Econ] 2025 Dec; Vol. 28 (1), pp. 871-884. Date of Electronic Publication: 2025 Jun 07. |
| Publikationsart: | Journal Article; Validation Study |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Taylor & Francis Country of Publication: England NLM ID: 9892255 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1941-837X (Electronic) Linking ISSN: 13696998 NLM ISO Abbreviation: J Med Econ Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2015- : Abingdon, Oxford : Taylor & Francis Original Publication: Richmond, Surrey : Brookwood Medical, 1998- |
| MeSH-Schlagworte: | Emergency Service, Hospital*/organization & administration , Emergency Service, Hospital*/statistics & numerical data , Point-of-Care Testing*/statistics & numerical data , Data Science*, Humans ; Length of Stay/statistics & numerical data ; Workflow ; Triage ; Time Factors ; Female ; Male ; Data Analytics |
| Abstract: | Aims: 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. Materials and Methods: 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. Results: 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. Limitations: 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. Conclusions: 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. |
| Contributed Indexing: | Keywords: C; C6; C67; C69; Overcrowding; advanced data analytics model; emergency department; point-of-care; simulation |
| Entry Date(s): | Date Created: 20250528 Date Completed: 20250607 Latest Revision: 20250607 |
| Update Code: | 20250608 |
| DOI: | 10.1080/13696998.2025.2508659 |
| PMID: | 40434437 |
| Datenbank: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstract: | Aims: 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.<br />Materials and Methods: 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.<br />Results: 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.<br />Limitations: 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.<br />Conclusions: 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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| ISSN: | 1941-837X |
| DOI: | 10.1080/13696998.2025.2508659 |