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Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study
BACKGROUND: Establishing rapport and empathy between patients and their health care provider is important but challenging in the context of a busy and crowded emergency department (ED). OBJECTIVE: We explore the hypotheses that rapport building, documentation, and time efficiency might be improved i...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861871/ https://www.ncbi.nlm.nih.gov/pubmed/35129452 http://dx.doi.org/10.2196/28199 |
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author | Scheder-Bieschin, Justus Blümke, Bibiana de Buijzer, Erwin Cotte, Fabienne Echterdiek, Fabian Nacsa, Júlia Ondresik, Marta Ott, Matthias Paul, Gregor Schilling, Tobias Schmitt, Anne Wicks, Paul Gilbert, Stephen |
author_facet | Scheder-Bieschin, Justus Blümke, Bibiana de Buijzer, Erwin Cotte, Fabienne Echterdiek, Fabian Nacsa, Júlia Ondresik, Marta Ott, Matthias Paul, Gregor Schilling, Tobias Schmitt, Anne Wicks, Paul Gilbert, Stephen |
author_sort | Scheder-Bieschin, Justus |
collection | PubMed |
description | BACKGROUND: Establishing rapport and empathy between patients and their health care provider is important but challenging in the context of a busy and crowded emergency department (ED). OBJECTIVE: We explore the hypotheses that rapport building, documentation, and time efficiency might be improved in the ED by providing patients a digital tool that uses Bayesian reasoning–based techniques to gather relevant symptoms and history for handover to clinicians. METHODS: A 2-phase pilot evaluation was carried out in the ED of a German tertiary referral and major trauma hospital that treats an average of 120 patients daily. Phase 1 observations guided iterative improvement of the digital tool, which was then further evaluated in phase 2. All patients who were willing and able to provide consent were invited to participate, excluding those with severe injury or illness requiring immediate treatment, with traumatic injury, incapable of completing a health assessment, and aged <18 years. Over an 18-day period with 1699 patients presenting to the ED, 815 (47.96%) were eligible based on triage level. With available recruitment staff, 135 were approached, of whom 81 (60%) were included in the study. In a mixed methods evaluation, patients entered information into the tool, accessed by clinicians through a dashboard. All users completed evaluation Likert-scale questionnaires rating the tool’s performance. The feasibility of a larger trial was evaluated through rates of recruitment and questionnaire completion. RESULTS: Respondents strongly endorsed the tool for facilitating conversation (61/81, 75% of patients, 57/78, 73% of physician ratings, and 10/10, 100% of nurse ratings). Most nurses judged the tool as potentially time saving, whereas most physicians only agreed for a subset of medical specialties (eg, surgery). Patients reported high usability and understood the tool’s questions. The tool was recommended by most patients (63/81, 78%), in 53% (41/77) of physician ratings, and in 76% (61/80) of nurse ratings. Questionnaire completion rates were 100% (81/81) by patients and 96% (78/81 enrolled patients) by physicians. CONCLUSIONS: This pilot confirmed that a larger study in the setting would be feasible. The tool has clear potential to improve patient–health care provider interaction and could also contribute to ED efficiency savings. Future research and development will extend the range of patients for whom the history-taking tool has clinical utility. TRIAL REGISTRATION: German Clinical Trials Register DRKS00024115; https://drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00024115 |
format | Online Article Text |
id | pubmed-8861871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-88618712022-03-10 Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study Scheder-Bieschin, Justus Blümke, Bibiana de Buijzer, Erwin Cotte, Fabienne Echterdiek, Fabian Nacsa, Júlia Ondresik, Marta Ott, Matthias Paul, Gregor Schilling, Tobias Schmitt, Anne Wicks, Paul Gilbert, Stephen JMIR Form Res Original Paper BACKGROUND: Establishing rapport and empathy between patients and their health care provider is important but challenging in the context of a busy and crowded emergency department (ED). OBJECTIVE: We explore the hypotheses that rapport building, documentation, and time efficiency might be improved in the ED by providing patients a digital tool that uses Bayesian reasoning–based techniques to gather relevant symptoms and history for handover to clinicians. METHODS: A 2-phase pilot evaluation was carried out in the ED of a German tertiary referral and major trauma hospital that treats an average of 120 patients daily. Phase 1 observations guided iterative improvement of the digital tool, which was then further evaluated in phase 2. All patients who were willing and able to provide consent were invited to participate, excluding those with severe injury or illness requiring immediate treatment, with traumatic injury, incapable of completing a health assessment, and aged <18 years. Over an 18-day period with 1699 patients presenting to the ED, 815 (47.96%) were eligible based on triage level. With available recruitment staff, 135 were approached, of whom 81 (60%) were included in the study. In a mixed methods evaluation, patients entered information into the tool, accessed by clinicians through a dashboard. All users completed evaluation Likert-scale questionnaires rating the tool’s performance. The feasibility of a larger trial was evaluated through rates of recruitment and questionnaire completion. RESULTS: Respondents strongly endorsed the tool for facilitating conversation (61/81, 75% of patients, 57/78, 73% of physician ratings, and 10/10, 100% of nurse ratings). Most nurses judged the tool as potentially time saving, whereas most physicians only agreed for a subset of medical specialties (eg, surgery). Patients reported high usability and understood the tool’s questions. The tool was recommended by most patients (63/81, 78%), in 53% (41/77) of physician ratings, and in 76% (61/80) of nurse ratings. Questionnaire completion rates were 100% (81/81) by patients and 96% (78/81 enrolled patients) by physicians. CONCLUSIONS: This pilot confirmed that a larger study in the setting would be feasible. The tool has clear potential to improve patient–health care provider interaction and could also contribute to ED efficiency savings. Future research and development will extend the range of patients for whom the history-taking tool has clinical utility. TRIAL REGISTRATION: German Clinical Trials Register DRKS00024115; https://drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00024115 JMIR Publications 2022-02-07 /pmc/articles/PMC8861871/ /pubmed/35129452 http://dx.doi.org/10.2196/28199 Text en ©Justus Scheder-Bieschin, Bibiana Blümke, Erwin de Buijzer, Fabienne Cotte, Fabian Echterdiek, Júlia Nacsa, Marta Ondresik, Matthias Ott, Gregor Paul, Tobias Schilling, Anne Schmitt, Paul Wicks, Stephen Gilbert. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.02.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Scheder-Bieschin, Justus Blümke, Bibiana de Buijzer, Erwin Cotte, Fabienne Echterdiek, Fabian Nacsa, Júlia Ondresik, Marta Ott, Matthias Paul, Gregor Schilling, Tobias Schmitt, Anne Wicks, Paul Gilbert, Stephen Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study |
title | Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study |
title_full | Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study |
title_fullStr | Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study |
title_full_unstemmed | Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study |
title_short | Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study |
title_sort | improving emergency department patient-physician conversation through an artificial intelligence symptom-taking tool: mixed methods pilot observational study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861871/ https://www.ncbi.nlm.nih.gov/pubmed/35129452 http://dx.doi.org/10.2196/28199 |
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