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Accepted for/Published in: JMIR Formative Research

Date Submitted: Feb 24, 2021
Date Accepted: Dec 14, 2021

The final, peer-reviewed published version of this preprint can be found here:

Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study

Scheder-Bieschin J, Blümke B, de Buijzer E, Cotte F, Echterdiek F, Nacsa J, Ondresik M, Ott M, Paul G, Schilling T, Schmitt A, Wicks P, Gilbert S

Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study

JMIR Form Res 2022;6(2):e28199

DOI: 10.2196/28199

PMID: 35129452

PMCID: 8861871

Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study

  • 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

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.

ClinicalTrial:

German Clinical Trials Register DRKS00024115; https://drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00024115


 Citation

Please cite as:

Scheder-Bieschin J, Blümke B, de Buijzer E, Cotte F, Echterdiek F, Nacsa J, Ondresik M, Ott M, Paul G, Schilling T, Schmitt A, Wicks P, Gilbert S

Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study

JMIR Form Res 2022;6(2):e28199

DOI: 10.2196/28199

PMID: 35129452

PMCID: 8861871

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