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Using cloud-based, open-source technology to evaluate, improve, and rapidly disseminate community-based intervention data.

ABSTRACT: Building Uplifted Families (BUF) is a cross-sector community initiative to improve health and economic disparities in Charlotte, North Carolina. A formative evaluation strategy was used to support iterative process improvement and collaborative engagement of cross-sector partners. To address challenges with electronic data collection through REDCap Cloud, we developed the BUF Rapid Dissemination (BUF-RD) model, a multistage data governance system supplemented by open-source technologies, such as: Stage 1) data collection; Stage 2) data integration and analysis; and Stage 3) dissemination. In Stage 3, results were disseminated through an interactive dashboard developed in RStudio using RShiny and Shiny Server solutions. The BUF-RD model was successfully deployed in a 6-month beta test to reduce the time lapse between data collection and dissemination from 3 months to 2 weeks. Having up-to-date preliminary results led to improved BUF implementation, enhanced stakeholder engagement, and greater responsiveness and alignment of program resources to specific participant needs.

SUBMITTER: Mayfield CA

PROVIDER: S-EPMC7671634 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Using cloud-based, open-source technology to evaluate, improve, and rapidly disseminate community-based intervention data.

Mayfield Carlene A CA Gigler Margaret E ME Snapper Leslie L Jose Jainmary J Tynan Jackie J Scott Victoria C VC Dulin Michael M

Journal of the American Medical Informatics Association : JAMIA 20201101 11

Building Uplifted Families (BUF) is a cross-sector community initiative to improve health and economic disparities in Charlotte, North Carolina. A formative evaluation strategy was used to support iterative process improvement and collaborative engagement of cross-sector partners. To address challenges with electronic data collection through REDCap Cloud, we developed the BUF Rapid Dissemination (BUF-RD) model, a multistage data governance system supplemented by open-source technologies, such as ...[more]

PMID: 32940684

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Project description:BackgroundA typical Copy Number Variations (CNVs) detection process based on the depth of coverage in the Whole Exome Sequencing (WES) data consists of several steps: (I) calculating the depth of coverage in sequencing regions, (II) quality control, (III) normalizing the depth of coverage, (IV) calling CNVs. Previous tools performed one normalization process for each chromosome-all the coverage depths in the sequencing regions from a given chromosome were normalized in a single run.MethodsHerein, we present the new CNVind tool for calling CNVs, where the normalization process is conducted separately for each of the sequencing regions. The total number of normalizations is equal to the number of sequencing regions in the investigated dataset. For example, when analyzing a dataset composed of n sequencing regions, CNVind performs n independent depth of coverage normalizations. Before each normalization, the application selects the k most correlated sequencing regions with the depth of coverage Pearson's Correlation as distance metric. Then, the resulting subgroup of [Formula: see text] sequencing regions is normalized, the results of all n independent normalizations are combined; finally, the segmentation and CNV calling process is performed on the resultant dataset.Results and conclusionsWe used WES data from the 1000 Genomes project to evaluate the impact of independent normalization on CNV calling performance and compared the results with state-of-the-art tools: CODEX and exomeCopy. The results proved that independent normalization allows to improve the rare CNVs detection specificity significantly. For example, for the investigated dataset, we reduced the number of FP calls from over 15,000 to around 5000 while maintaining a constant number of TP calls equal to about 150 CNVs. However, independent normalization of each sequencing region is a computationally expensive process, therefore our pipeline is customized and can be easily run in the cloud computing environment, on the computer cluster, or the single CPU server. To our knowledge, the presented application is the first attempt to implement an innovative approach to independent normalization of the depth of WES data coverage.

Project description:Valid, direct observation of medical student competency in clinical settings remains challenging and limits the opportunity to promote performance-based student advancement. The rationale for direct observation is to ascertain that students have acquired the core clinical competencies needed to care for patients. Too often student observation results in highly variable evaluations which are skewed by factors other than the student's actual performance. Among the barriers to effective direct observation and assessment include the lack of effective tools and strategies for assuring that transparent standards are used for judging clinical competency in authentic clinical settings. We developed a web-based content management system under the name, Just in Time Medicine (JIT), to address many of these issues. The goals of JIT were fourfold: First, to create a self-service interface allowing faculty with average computing skills to author customizable content and criterion-based assessment tools displayable on internet enabled devices, including mobile devices; second, to create an assessment and feedback tool capable of capturing learner progress related to hundreds of clinical skills; third, to enable easy access and utilization of these tools by faculty for learner assessment in authentic clinical settings as a means of just in time faculty development; fourth, to create a permanent record of the trainees' observed skills useful for both learner and program evaluation. From July 2010 through October 2012, we implemented a JIT enabled clinical evaluation exercise (CEX) among 367 third year internal medicine students. Observers (attending physicians and residents) performed CEX assessments using JIT to guide and document their observations, record their time observing and providing feedback to the students, and their overall satisfaction. Inter-rater reliability and validity were assessed with 17 observers who viewed six videotaped student-patient encounters and by measuring the correlation between student CEX scores and their scores on subsequent standardized-patient OSCE exams. A total of 3567 CEXs were completed by 516 observers. The average number of evaluations per student was 9.7 (±1.8 SD) and the average number of CEXs completed per observer was 6.9 (±15.8 SD). Observers spent less than 10 min on 43-50% of the CEXs and 68.6% on feedback sessions. A majority of observers (92%) reported satisfaction with the CEX. Inter-rater reliability was measured at 0.69 among all observers viewing the videotapes and these ratings adequately discriminated competent from non-competent performance. The measured CEX grades correlated with subsequent student performance on an end-of-year OSCE. We conclude that the use of JIT is feasible in capturing discrete clinical performance data with a high degree of user satisfaction. Our embedded checklists had adequate inter-rater reliability and concurrent and predictive validity.

Dataset Information

Using cloud-based, open-source technology to evaluate, improve, and rapidly disseminate community-based intervention data.

Publications

Using cloud-based, open-source technology to evaluate, improve, and rapidly disseminate community-based intervention data.

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