Covid-19 triage in the emergency department 2.0 : how analytics and AI transform a human-made algorithm for the prediction of clinical pathways
© 2023. The Author(s)..
The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
|---|---|
| Enthalten in: |
Health care management science - 26(2023), 3 vom: 10. Sept., Seite 412-429 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Bartenschlager, Christina C [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 08.09.2023 Date Revised 21.09.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1007/s10729-023-09647-2 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM35929345X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM35929345X | ||
| 003 | DE-627 | ||
| 005 | 20231226080549.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10729-023-09647-2 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1197.xml |
| 035 | |a (DE-627)NLM35929345X | ||
| 035 | |a (NLM)37428304 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Bartenschlager, Christina C |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Covid-19 triage in the emergency department 2.0 |b how analytics and AI transform a human-made algorithm for the prediction of clinical pathways |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 08.09.2023 | ||
| 500 | |a Date Revised 21.09.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results | ||
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Clinical decision making | |
| 650 | 4 | |a Covid-19 triage | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Predictive analytics | |
| 700 | 1 | |a Grieger, Milena |e verfasserin |4 aut | |
| 700 | 1 | |a Erber, Johanna |e verfasserin |4 aut | |
| 700 | 1 | |a Neidel, Tobias |e verfasserin |4 aut | |
| 700 | 1 | |a Borgmann, Stefan |e verfasserin |4 aut | |
| 700 | 1 | |a Vehreschild, Jörg J |e verfasserin |4 aut | |
| 700 | 1 | |a Steinbrecher, Markus |e verfasserin |4 aut | |
| 700 | 1 | |a Rieg, Siegbert |e verfasserin |4 aut | |
| 700 | 1 | |a Stecher, Melanie |e verfasserin |4 aut | |
| 700 | 1 | |a Dhillon, Christine |e verfasserin |4 aut | |
| 700 | 1 | |a Ruethrich, Maria M |e verfasserin |4 aut | |
| 700 | 1 | |a Jakob, Carolin E M |e verfasserin |4 aut | |
| 700 | 1 | |a Hower, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a Heller, Axel R |e verfasserin |4 aut | |
| 700 | 1 | |a Vehreschild, Maria |e verfasserin |4 aut | |
| 700 | 1 | |a Wyen, Christoph |e verfasserin |4 aut | |
| 700 | 1 | |a Messmann, Helmut |e verfasserin |4 aut | |
| 700 | 1 | |a Piepel, Christiane |e verfasserin |4 aut | |
| 700 | 1 | |a Brunner, Jens O |e verfasserin |4 aut | |
| 700 | 1 | |a Hanses, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Römmele, Christoph |e verfasserin |4 aut | |
| 700 | 0 | |a LEOSS study group |e verfasserin |4 aut | |
| 700 | 1 | |a Spinner, Christoph |e investigator |4 oth | |
| 700 | 1 | |a Ruethrich, Maria Madeleine |e investigator |4 oth | |
| 700 | 1 | |a Lanznaster, Julia |e investigator |4 oth | |
| 700 | 1 | |a Römmele, Christoph |e investigator |4 oth | |
| 700 | 1 | |a Wille, Kai |e investigator |4 oth | |
| 700 | 1 | |a Tometten, Lukas |e investigator |4 oth | |
| 700 | 1 | |a Dolff, Sebastian |e investigator |4 oth | |
| 700 | 1 | |a von Bergwelt-Baildon, Michael |e investigator |4 oth | |
| 700 | 1 | |a Merle, Uta |e investigator |4 oth | |
| 700 | 1 | |a Rothfuss, Katja |e investigator |4 oth | |
| 700 | 1 | |a Isberner, Nora |e investigator |4 oth | |
| 700 | 1 | |a Jung, Norma |e investigator |4 oth | |
| 700 | 1 | |a Göpel, Siri |e investigator |4 oth | |
| 700 | 1 | |a Vom Dahl, Juergen |e investigator |4 oth | |
| 700 | 1 | |a Degenhardt, Christian |e investigator |4 oth | |
| 700 | 1 | |a Strauss, Richard |e investigator |4 oth | |
| 700 | 1 | |a Gruener, Beate |e investigator |4 oth | |
| 700 | 1 | |a Eberwein, Lukas |e investigator |4 oth | |
| 700 | 1 | |a Hellwig, Kerstin |e investigator |4 oth | |
| 700 | 1 | |a Rauschning, Dominic |e investigator |4 oth | |
| 700 | 1 | |a Neufang, Mark |e investigator |4 oth | |
| 700 | 1 | |a Westhoff, Timm |e investigator |4 oth | |
| 700 | 1 | |a Raichle, Claudia |e investigator |4 oth | |
| 700 | 1 | |a Akova, Murat |e investigator |4 oth | |
| 700 | 1 | |a Jensen, Bjoern-Erik |e investigator |4 oth | |
| 700 | 1 | |a Schubert, Joerg |e investigator |4 oth | |
| 700 | 1 | |a Grunwald, Stephan |e investigator |4 oth | |
| 700 | 1 | |a Friedrichs, Anette |e investigator |4 oth | |
| 700 | 1 | |a Trauth, Janina |e investigator |4 oth | |
| 700 | 1 | |a de With, Katja |e investigator |4 oth | |
| 700 | 1 | |a Guggemos, Wolfgang |e investigator |4 oth | |
| 700 | 1 | |a Kielstein, Jan |e investigator |4 oth | |
| 700 | 1 | |a Heigener, David |e investigator |4 oth | |
| 700 | 1 | |a Markart, Philipp |e investigator |4 oth | |
| 700 | 1 | |a Bals, Robert |e investigator |4 oth | |
| 700 | 1 | |a Stieglitz, Sven |e investigator |4 oth | |
| 700 | 1 | |a Voigt, Ingo |e investigator |4 oth | |
| 700 | 1 | |a Taubel, Jorg |e investigator |4 oth | |
| 700 | 1 | |a Milovanovic, Milena |e investigator |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Health care management science |d 1997 |g 26(2023), 3 vom: 10. Sept., Seite 412-429 |w (DE-627)NLM097535176 |x 1572-9389 |7 nnns |
| 773 | 1 | 8 | |g volume:26 |g year:2023 |g number:3 |g day:10 |g month:09 |g pages:412-429 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s10729-023-09647-2 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 26 |j 2023 |e 3 |b 10 |c 09 |h 412-429 | ||