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Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

ABSTRACT: BACKGROUND:The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS:Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ("unaided"), versus after use of the DDSS ("aided"). RESULTS:The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p?

SUBMITTER: Segal MM

PROVIDER: S-EPMC5155385 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

Segal Michael M MM Athreya Balu B Son Mary Beth F MB Tirosh Irit I Hausmann Jonathan S JS Ang Elizabeth Y N EY Zurakowski David D Feldman Lynn K LK Sundel Robert P RP

Pediatric rheumatology online journal 20161213 1

<h4>Background</h4>The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists.<h4>Methods</h4>Using vignettes of actual clinical cases, clinician testers generated a differential diagnos ...[more]

PMID: 27964737

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Project description:ImportanceAppendicitis is the most common pediatric surgical emergency. Efforts to improve efficiency and quality of care have increased reliance on computed tomography (CT) and ultrasonography (US) in children with suspected appendicitis.ObjectiveTo evaluate the effectiveness of an electronic health record-linked clinical decision support intervention, AppyCDS, on diagnostic imaging, health care costs, and safety outcomes for patients with suspected appendicitis.Design, setting, and participantsIn this parallel, cluster randomized trial, 17 community-based general emergency departments (EDs) in California, Minnesota, and Wisconsin were randomized to the AppyCDS intervention group or usual care (UC) group. Patients were aged 5 to 20 years, presenting for an ED visit with right-sided or diffuse abdominal pain lasting 5 days or less. We excluded pregnant patients, those with a prior appendectomy, those with selected comorbidities, and those with traumatic injuries. The trial was conducted from October 2016 to July 2019.InterventionsAppyCDS prompted data entry at the point of care to estimate appendicitis risk using the pediatric appendicitis risk calculator (pARC). Based on pARC estimates, AppyCDS recommended next steps in care.Main outcomes and measuresPrimary outcomes were CT, US, or any imaging (CT or US) during the index ED visit. Safety outcomes were perforations, negative appendectomies, and missed appendicitis. Costs were a secondary outcome. Ratio of ratios (RORs) for primary and safety outcomes and differences by group in cost were used to evaluate effectiveness of the clinical decision support tool.ResultsWe enrolled 3161 patients at intervention EDs and 2779 patients at UC EDs. The mean age of patients was 11.9 (4.6) years and 2614 (44.0%) were boys or young men. RORs for CT (0.94; 95% CI, 0.75-1.19), US (0.98; 95% CI, 0.84-1.14), and any imaging (0.96; 95% CI, 0.86-1.07) did not differ by study group. In an exploratory analysis conducted in 1 health system, AppyCDS was associated with a reduction in any imaging (ROR, 0.82; 95% CI, 0.73- 0.93) for patients with pARC score of 15% or less and a reduction in CT (ROR, 0.58; 95% CI, 0.45-0.74) for patients with a pARC score of 16% to 50%. Perforations, negative appendectomies, and cases of missed appendicitis by study phase did not differ significantly by study group. Costs did not differ overall by study group.Conclusions and relevanceIn this study, AppyCDS was not associated with overall reductions in diagnostic imaging; exploratory analysis revealed more appropriate use of imaging in patients with a low pARC score.Trial registrationClinicalTrials.gov Identifier: NCT02633735.

Project description:BackgroundThis project engages patients and physicians in the development of Decision Boxes, short clinical topic summaries covering medical questions that have no single best answer. Decision Boxes aim to prepare the clinician to communicate the risks and benefits of the available options to the patient so they can make an informed decision together.MethodsSeven researchers (including four practicing family physicians) selected 10 clinical topics relevant to primary care practice through a Delphi survey. We then developed two one-page prototypes on two of these topics: prostate cancer screening with the prostate-specific antigen test, and prenatal screening for trisomy 21 with the serum integrated test. We presented the prototypes to purposeful samples of family physicians distributed in two focus groups, and patients distributed in four focus groups. We used the User Experience Honeycomb to explore barriers and facilitators to the communication design used in Decision Boxes. All discussions were transcribed, and three researchers proceeded to thematic content analysis of the transcriptions. The coding scheme was first developed from the Honeycomb's seven themes (valuable, usable, credible, useful, desirable, accessible, and findable), and included new themes suggested by the data. Prototypes were modified in light of our findings.ResultsThree rounds were necessary for a majority of researchers to select 10 clinical topics. Fifteen physicians and 33 patients participated in the focus groups. Following analyses, three sections were added to the Decision Boxes: introduction, patient counseling, and references. The information was spread to two pages to try to make the Decision Boxes less busy and improve users' first impression. To try to improve credibility, we gave more visibility to the research institutions involved in development. A statement on the boxes' purpose and a flow chart representing the shared decision-making process were added with the intent of clarifying the tool's purpose. Information about the risks and benefits according to risk levels was added to the Decision Boxes, to try to ease the adaptation of the information to individual patients.ConclusionResults will guide the development of the eight remaining Decision Boxes. A future study will evaluate the effect of Decision Boxes on the integration of evidence-based and shared decision making principles in clinical practice.

Project description:ImportanceAn increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer).ObjectivesTo conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation.Evidence reviewA search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed.FindingsA total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I.Conclusions and relevanceThis systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.

Project description:With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature. Supplementary Information The online version contains supplementary material available at 10.1007/s12599-022-00745-z.

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Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

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Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

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