scholarly journals CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

2017 ◽  
Author(s):  
Nicola Whiffin ◽  
Roddy Walsh ◽  
Risha Govind ◽  
Matthew Edwards ◽  
Mian Ahmad ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practice ◽  
Decision Support Tool ◽  
Specific Information ◽  
Multiple Sources ◽  
Support Tool ◽  
User Input ◽  
Best Practice Guidelines ◽  
Interactive Interface ◽  
Genome Interpretation

ABSTRACTPurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support varian interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fisher’s P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

scholarly journals SPI-Hub™: a gateway to scholarly publishing information

2020 ◽  
Vol 108 (2) ◽  
Author(s):  
Taneya Y. Koonce ◽  
Mallory N. Blasingame ◽  
Jerry Zhao ◽  
Annette M. Williams ◽  
Jing Su ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Publishing Industry ◽  
Decision Support Tool ◽  
Health Sciences ◽  
Scholarly Publishing ◽  
Multiple Sources ◽  
Support Tool ◽  
Data Framework ◽  
Single Location

Background: Advances in the health sciences rely on sharing research and data through publication. As information professionals are often asked to contribute their knowledge to assist clinicians and researchers in selecting journals for publication, the authors recognized an opportunity to build a decision support tool, SPI-Hub: Scholarly Publishing Information Hub™, to capture the team’s collective publishing industry knowledge, while carefully retaining the quality of service.Case Presentation: SPI-Hub’s decision support functionality relies on a data framework that describes journal publication policies and practices through a newly designed metadata structure, the Knowledge Management Journal Record™. Metadata fields are populated through a semi-automated process that uses custom programming to access content from multiple sources. Each record includes 25 metadata fields representing best publishing practices. Currently, the database includes more than 24,000 health sciences journal records. To correctly capture the resources needed for both completion and future maintenance of the project, the team conducted an internal study to assess time requirements for completing records through different stages of automation.Conclusions: The journal decision support tool, SPI-Hub, provides an opportunity to assess publication practices by compiling data from a variety of sources in a single location. Automated and semi-automated approaches have effectively reduced the time needed for data collection. Through a comprehensive knowledge management framework and the incorporation of multiple quality points specific to each journal, SPI-Hub provides prospective users with both recommendations for publication and holistic assessment of the trustworthiness of journals in which to publish research and acquire trusted knowledge.

scholarly journals 2344. Evaluation of a Best Practice Alert (BPA) to Reduce Inappropriate Testing for Clostridium difficile Infection (CDI) Within a Multi-Hospital System

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S805-S806
Author(s):  
Ryan K Dare ◽  
Claire E Bewley ◽  
Amanda J Novack ◽  
Jared M Heiles ◽  
Larissa K Chin
Keyword(s):  
Decision Support ◽  
Best Practice ◽  
Decision Support Tool ◽  
Medication Administration ◽  
Nucleic Acid Amplification ◽  
Or Education ◽  
Hospital System ◽  
Support Tool ◽  
Best Practice Alert ◽  
Hospital Acquired

Abstract Background Hospital-acquired CDI contribute to significant morbidity, mortality, and cost burden in hospitalized patients. Clinical practice guidelines recommend strict testing criteria when employing nucleic acid amplification testing alone as to not test asymptomatic carriers. A BPA within the electronic medical record (EMR) may assist with this screening. Methods At our 9-hospital system, we created a BPA to help identify patients who may not meet criteria for CDI testing. Initial BPA (January 2018) asked if patient had 3 or more stools (yes/no) and if laxatives were administered in the last 48 hours (yes/no). An expanded BPA was updated to pull medication administration records for use of laxatives in the prior 48 hours (August 2018) and notified providers of recent C. difficile testing in the past 7 days (January 2019). C. difficile orders from March 2017 (historical), March 2018 (intervention 1), and March 2019 (intervention 2) were evaluated to assess impact of these interventions. Results C. difficile testing during 30,621 (historical), 31,299 (intervention 1), and 31,960 (intervention 2) patient-days were evaluated. Rates of C. difficile orders and infections are reported in the table. Ratio of positive C. difficile specimens to tested specimens were similar between the historical arm (51 of 402; 12.7%) and both intervention 1 (42 of 271; 15.5%) and intervention 2 (45 of 316; 14.2%) arms (P = 0.3 and P = 0.5, respectively). Intervention 1 and intervention 2 arms were similar in all metrics. Statistical analysis was performed using Stata, v.14.2. Conclusion Implementation of a decision support tool to assist with C. difficile testing significantly decreased order rates in both the initial and expanded BPA intervention arms. Compared with historical rates, incidence of CDI decreased in both intervention arms though these were not statistically significant. Similarly, ratio of positive specimens to specimens tested increased in both intervention arms, though not significant, indicating a trend toward improved patient selection. To improve appropriate CDI testing, further oversight and/or education is needed to accompany implementation of an EMR decision support tool, such as BPAs. Disclosures All authors: No reported disclosures.

Electronic Intervention to Improve Structured Cancer Stage Data Capture

2016 ◽  
Vol 12 (10) ◽  
pp. e949-e956 ◽  
Author(s):  
Michael Cecchini ◽  
Kim Framski ◽  
Patricia Lazette ◽  
Teresita Vega ◽  
Michael Strait ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practice ◽  
Decision Support Tool ◽  
Cancer Staging ◽  
Free Text ◽  
Cancer Stage ◽  
Support Tool ◽  
Clinical Notes ◽  
Practice Advisory ◽  
Electronic Intervention

Purpose: Cancer staging is critical for prognostication, treatment planning, and determining clinical trial eligibility. Electronic health records (EHRs) have structured staging modules, but physician use is inconsistent. Typically, stage is entered as unstructured free text in clinical notes and cannot easily be used for reporting. Methods: We created an Epic Best Practice Advisory (BPA) decision support tool that requires physicians to enter cancer stage in a structured module. If certain conditions are met, the BPA is triggered as a hard stop, and the physician cannot chart until staging is complete or a reason for not staging is selected. We used Plan, Do, Study, Act methodology to inform the intervention and compared preexisting staging rates to rates at 4, 8, and 12 months postintervention. Results: For 12 months before BPA implementation, 1,480 of 5,222 (28%) patients had cancer stage structured within the Epic problem list. From 1 to 4 months after the BPA 2,057 of 1,788 (115%) cases were staged in Epic. In the 5- to 8-month period after the BPA, 1,057 of 1,893 (56%) cases were staged, and 9 to 12 months after the BPA 1,082 of 1,817 (60%) were staged. Conclusion: Electronic decision support improves the rate of structured cancer staging at our institution. The staging rates between 56% and 60% for the 5- to 8-month and 9- to 12-month periods likely reflect accurate postintervention staging rates, whereas the initial 115% rate for 1 to 4 months is inflated by providers staging cancers diagnosed before the BPA.

scholarly journals Decision Support Tool for Screening of Tuberculosis Exposed Individuals Seeking Care at a Public Academic Health System

2020 ◽  
Vol 41 (S1) ◽  
pp. s184-s184
Author(s):  
Stephanie Cobb ◽  
Stephanie Nguyen ◽  
Deepa Raj ◽  
Dena Taherzadeh ◽  
Pranavi Sreeramoju
Keyword(s):  
Decision Support ◽  
Health System ◽  
Screening Test ◽  
Best Practice ◽  
Decision Support Tool ◽  
Safety Net ◽  
Contact Tracing ◽  
Homeless Shelter ◽  
Support Tool ◽  
Local Public Health

Background:Mycobacterium tuberculosis (TB) is one of the leading causes of morbidity and mortality worldwide. At our health system, 50–100 patients are diagnosed with tuberculosis every year. One risk factor for TB is residence within a homeless shelter. In response to an increased number of cases in local homeless shelters, the health department sought assistance with contact tracing of individuals potentially exposed to tuberculosis. We report the results of contact tracing performed at our health system. Methods: The setting is a 770-bed, safety-net, academic hospital with community clinics and a correctional health center. Name, date of birth, and social security number of contacts potentially exposed during February 2009 to July 2013 were programmed into the electronic medical records to create a decision support tool upon entering the health system. The best practice alert (BPA) informed physicians of the exposure and offered a link to a screening test, T-spot.TB, and a link to an information sheet. This intervention was implemented from July 2013 to July 2015. After excluding patients with active TB, data on the magnitude of exposure in each homeless shelter and screening test results were analyzed with ANOVA using SPSS v 26 software. Results: Of the 8,649 identified exposed contacts, 2,118 entered our health system. Of those for whom the BPA was triggered, 1,117 had a T-spot.TB done, with 313 positive results and 57 borderline results. Table 1 shows that shelter 3 was correlated with a positive T-spot.TB. Conclusions: The BPA, which prompted physicians to evaluate an individual for TB, was effective at capturing high-risk, exposed individuals. Clinical decision support tools enabled our safety-net health system to respond effectively to a local public health need.Funding: NoneDisclosures: None

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool
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