Background: Observational and experimental studies of the diagnostic task have demonstrated the importance of the first hypotheses that come to mind for accurate diagnosis. A prototype decision support system (DSS) designed to support GPs' first impressions has been integrated with a commercial electronic health record (EHR) system.

Aim: To evaluate the prototype DSS in a high-fidelity simulation.

Design And Setting: Within-participant design: 34 GPs consulted with six standardised patients (actors) using their usual EHR. On a different day, GPs used the EHR with the integrated DSS to consult with six other patients, matched for difficulty and counterbalanced.

Method: Entering the reason for encounter triggered the DSS, which provided a patient-specific list of potential diagnoses, and supported coding of symptoms during the consultation. At each consultation, GPs recorded their diagnosis and management. At the end, they completed a usability questionnaire. The actors completed a satisfaction questionnaire after each consultation.

Results: There was an 8-9% absolute improvement in diagnostic accuracy when the DSS was used. This improvement was significant (odds ratio [OR] 1.41, 95% confidence interval [CI] = 1.13 to 1.77, <0.01). There was no associated increase of investigations ordered or consultation length. GPs coded significantly more data when using the DSS (mean 12.35 with the DSS versus 1.64 without), and were generally satisfied with its usability. Patient satisfaction ratings were the same for consultations with and without the DSS.

Conclusion: The DSS prototype was successfully employed in simulated consultations of high fidelity, with no measurable influences on patient satisfaction. The substantially increased data coding can operate as motivation for future DSS adoption.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325662PMC
http://dx.doi.org/10.3399/bjgp16X688417DOI Listing

Publication Analysis

Top Keywords

diagnostic accuracy
8
decision support
8
support system
8
dss
5
gps
4
accuracy gps
4
gps early-intervention
4
early-intervention decision
4
system high-fidelity
4
high-fidelity simulation
4

Similar Publications

Harnessing Raman spectroscopy and multimodal imaging of cartilage for osteoarthritis diagnosis.

Sci Rep

December 2024

School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Life Sciences Building 85, University Road, Highfield, Southampton, SO17 1BJ, UK.

Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need.

View Article and Find Full Text PDF

The monocyte-to-Apolipoprotein A1 ratio (MAR) emerges as a potentially valuable inflammatory biomarker indicative of metabolic dysfunction-associated fatty liver disease (MASLD). Accordingly, this investigation primarily aims to assess the correlation between MAR and MASLD risk. A cohort comprising 957 individuals diagnosed with type 2 diabetes mellitus (T2DM) participated in this study.

View Article and Find Full Text PDF

Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of backscattered light from tissues and provides valuable insights into the birefringence properties of biological tissues. Contrastive unpaired translation (CUT) was used in this study to generate a synthetic PS-OCT image from a single OCT image. The challenges related to extensive data requirements relying on labeled datasets using only pixel-wise correlations that make it difficult to efficiently regenerate the periodic patterns observed in PS-OCT images were addressed.

View Article and Find Full Text PDF

Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.

View Article and Find Full Text PDF

Utility of a novel scoring system for difficulty of pure laparoscopic hepatectomy for intrahepatic cholangiocarcinoma.

Sci Rep

December 2024

Department of General Surgery, Cancer center, Division of Hepatobiliary and Pancreatic Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, 310014, Hangzhou, Zhejiang Province, China.

Despite the growing adoption of laparoscopic hepatectomy (LH) for intrahepatic cholangiocarcinoma (ICC), there is no scoring system available designed to evaluate its surgical complexity. This paper aims to introduce a novel difficulty scoring system (DSS), designated as the Wei-DSS, exclusively tailored to assess the surgical difficulty of pure LH for ICC. We retrospectively collected clinical data from ICC patients who underwent pure LH at our institution, spanning from November 2018 to May 2024.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!