Dataset Information

Interactive Visual Displays for Interpreting the Results of Clinical Trials: Formative Evaluation With Case Vignettes.

ABSTRACT:

Background

At the point of care, evidence from randomized controlled trials (RCTs) is underutilized in helping clinicians meet their information needs.

Objective

To design interactive visual displays to help clinicians interpret and compare the results of relevant RCTs for the management of a specific patient, and to conduct a formative evaluation with physicians comparing interactive visual versus narrative displays.

Methods

We followed a user-centered and iterative design process succeeded by development of information display prototypes as a Web-based application. We then used a within-subjects design with 20 participants (8 attendings and 12 residents) to evaluate the usability and problem-solving impact of the information displays. We compared subjects' perceptions of the interactive visual displays versus narrative abstracts.

Results

The resulting interactive visual displays present RCT results side-by-side according to the Population, Intervention, Comparison, and Outcome (PICO) framework. Study participants completed 19 usability tasks in 3 to 11 seconds with a success rate of 78% to 100%. Participants favored the interactive visual displays over narrative abstracts according to perceived efficiency, effectiveness, effort, user experience and preference (all P values <.001).

Conclusions

When interpreting and applying RCT findings to case vignettes, physicians preferred interactive graphical and PICO-framework-based information displays that enable direct comparison of the results from multiple RCTs compared to the traditional narrative and study-centered format. Future studies should investigate the use of interactive visual displays to support clinical decision making in care settings and their effect on clinician and patient outcomes.

SUBMITTER: Bian J

PROVIDER: S-EPMC6037946 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Interactive Visual Displays for Interpreting the Results of Clinical Trials: Formative Evaluation With Case Vignettes.

Bian Jiantao J Weir Charlene C Unni Prasad P Borbolla Damian D Reese Thomas T Wan Yik-Ki Jacob YJ Del Fiol Guilherme G

Journal of medical Internet research 20180625 6

<h4>Background</h4>At the point of care, evidence from randomized controlled trials (RCTs) is underutilized in helping clinicians meet their information needs.<h4>Objective</h4>To design interactive visual displays to help clinicians interpret and compare the results of relevant RCTs for the management of a specific patient, and to conduct a formative evaluation with physicians comparing interactive visual versus narrative displays.<h4>Methods</h4>We followed a user-centered and iterative design ...[more]

PMID: 29941416

Similar Datasets

Project description:BackgroundThe diagnostic accuracy of differential diagnoses generated by artificial intelligence chatbots, including ChatGPT models, for complex clinical vignettes derived from general internal medicine (GIM) department case reports is unknown.ObjectiveThis study aims to evaluate the accuracy of the differential diagnosis lists generated by both third-generation ChatGPT (ChatGPT-3.5) and fourth-generation ChatGPT (ChatGPT-4) by using case vignettes from case reports published by the Department of GIM of Dokkyo Medical University Hospital, Japan.MethodsWe searched PubMed for case reports. Upon identification, physicians selected diagnostic cases, determined the final diagnosis, and displayed them into clinical vignettes. Physicians typed the determined text with the clinical vignettes in the ChatGPT-3.5 and ChatGPT-4 prompts to generate the top 10 differential diagnoses. The ChatGPT models were not specially trained or further reinforced for this task. Three GIM physicians from other medical institutions created differential diagnosis lists by reading the same clinical vignettes. We measured the rate of correct diagnosis within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and the top diagnosis.ResultsIn total, 52 case reports were analyzed. The rates of correct diagnosis by ChatGPT-4 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 83% (43/52), 81% (42/52), and 60% (31/52), respectively. The rates of correct diagnosis by ChatGPT-3.5 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 73% (38/52), 65% (34/52), and 42% (22/52), respectively. The rates of correct diagnosis by ChatGPT-4 were comparable to those by physicians within the top 10 (43/52, 83% vs 39/52, 75%, respectively; P=.47) and within the top 5 (42/52, 81% vs 35/52, 67%, respectively; P=.18) differential diagnosis lists and top diagnosis (31/52, 60% vs 26/52, 50%, respectively; P=.43) although the difference was not significant. The ChatGPT models' diagnostic accuracy did not significantly vary based on open access status or the publication date (before 2011 vs 2022).ConclusionsThis study demonstrates the potential diagnostic accuracy of differential diagnosis lists generated using ChatGPT-3.5 and ChatGPT-4 for complex clinical vignettes from case reports published by the GIM department. The rate of correct diagnoses within the top 10 and top 5 differential diagnosis lists generated by ChatGPT-4 exceeds 80%. Although derived from a limited data set of case reports from a single department, our findings highlight the potential utility of ChatGPT-4 as a supplementary tool for physicians, particularly for those affiliated with the GIM department. Further investigations should explore the diagnostic accuracy of ChatGPT by using distinct case materials beyond its training data. Such efforts will provide a comprehensive insight into the role of artificial intelligence in enhancing clinical decision-making.

Dataset Information

Interactive Visual Displays for Interpreting the Results of Clinical Trials: Formative Evaluation With Case Vignettes.

Background

Objective

Methods

Results

Conclusions

Publications

Interactive Visual Displays for Interpreting the Results of Clinical Trials: Formative Evaluation With Case Vignettes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets