Diagnostic accuracy in Family Medicine residents using a clinical decision support system (DXplain): a randomized-controlled trial

Martínez-Franco, Adrián Israel; Sánchez‐Mendiola, Melchor; Mazón-Ramírez, Juan J.; Hernández-Torres, Isaías; Rivero-López, Carlos A.; Spicer, Troy; Martínez-González, Adrián

doi:10.1515/dx-2017-0045

Cited by 34 publications

(36 citation statements)

References 29 publications

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“…While software cannot replace MDD, it can increase application of IPF guidelines in the community and allow referral of the most complex cases to regional centers for MDD. Many software platforms have been proposed to facilitate clinical decision making, demonstrating successful reducing costs and improving patient outcomes [3].…”

Section: Discussionmentioning

confidence: 99%

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Diagnosis of idiopathic pulmonary fibrosis by virtual means using “IPFdatabase”- a new software

Bennett

Mazzei

Collins

et al. 2019

Respiratory Medicine

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Background: The diagnostic algorithm for idiopathic pulmonary fibrosis (IPF) guidelines has some shortcomings. The aim of the present study was to develop a novel software, "IPFdatabase", that could readily apply the diagnostic criteria per IPF guidelines and make a 'virtual' diagnosis of IPF. Methods: Software was developed as a step-by-step compilation of necessary information according to guidelines to enable a diagnosis of IPF. Software accuracy was validated primarily by comparing software diagnoses to those previously made at a Center for Interstitial Lung Diseases. Results: Clinical validation on 98 patients (68 male, age 61.0 ± 8.5 years), revealed high software accuracy for IPF diagnosis when compared to historical diagnoses (sensitivity 95.5%, specificity 96.2%; positive predictive value 95.5%, negative predictive value 96.2%). A general radiologist and a general pathologist reviewed relevant data with and without the new software: interobserver agreement increased when they used the IPFdatabase (kappa 0.18 to 0.64 for radiology, 0.13 to 0.59 for pathology). Conclusion: IPFdatabase is a useful diagnostic tool for typical cases of IPF, and potentially restricts the need for MDDs to atypical and complex cases. We propose this web-designed software for instant accurate diagnosis of IPF by virtual means and for educational purposes; the software is readily accessed with mobile apps, allows incorporation of updated version of guidelines, can be utilized for gathering data useful for future studies and give physicians rapid feedback in daily practice.

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Section: Discussionmentioning

confidence: 99%

“…Many software platforms has been proposed to facilitate clinical decision making demonstrateing successful in boosting physicians' diagnostic confidence, reducing costs and improving patient outcomes https://doi.org/10.1016/j.rmed.2018.12.011 Received 16 August 2018; Received in revised form 24 December 2018; Accepted 26 December 2018 [3].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of idiopathic pulmonary fibrosis by virtual means using “IPFdatabase”- a new software

Bennett

Mazzei

Collins

et al. 2019

Respiratory Medicine

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“…DXplain is an electronic reference based DDSS that provides probable diagnosis based on clinical manifestations. 50 In a randomized control trial involving 87 family medicine residents, those randomized to use the system showed significantly higher accuracy (84% vs. 74%) on a validated diagnosis test involving 30 clinical cases. 50 Given the known incidence of diagnostic errors, particularly in primary care, 51 there is a lot of hope for CDSS and IT solutions to bring improvements to diagnosis.…”

Section: Clinical Managementmentioning

confidence: 97%

“…50 In a randomized control trial involving 87 family medicine residents, those randomized to use the system showed significantly higher accuracy (84% vs. 74%) on a validated diagnosis test involving 30 clinical cases. 50 Given the known incidence of diagnostic errors, particularly in primary care, 51 there is a lot of hope for CDSS and IT solutions to bring improvements to diagnosis. 52 We are now seeing diagnostic systems being developed with non-knowledge-based techniques like machine learning, which may pave the way for more accurate diagnosis.…”

Section: Clinical Managementmentioning

confidence: 97%

An overview of clinical decision support systems: benefits, risks, and strategies for success

et al. 2020

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Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

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“…ontology with high transparency but the performance is often not as high (Cestnik et al, 1987;Miller, 2016;Martinez-Franco et al, 2018). Due to the high stake consequences of making medical diagnostic decisions it is important that the proposed DST solutions meet all proposed guidelines.…”

Section: Decision Support Tools' Requirements For Intelligent Roboticsmentioning

confidence: 99%

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

Čeněk

York

et al. 2018

Front. Robot. AI

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In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.

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Diagnostic accuracy in Family Medicine residents using a clinical decision support system (DXplain): a randomized-controlled trial

Cited by 34 publications

References 29 publications

Diagnosis of idiopathic pulmonary fibrosis by virtual means using “IPFdatabase”- a new software

Diagnosis of idiopathic pulmonary fibrosis by virtual means using “IPFdatabase”- a new software

An overview of clinical decision support systems: benefits, risks, and strategies for success

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

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