The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation

Park, Heekyong; Wang, Taowei David; Wattanasin, Nich; Castro, Víctor M.; Gainer, Vivian S.; Goryachev, Sergey; Murphy, Shawn N.

doi:10.1101/2021.05.30.21257945

Cited by 1 publication

(2 citation statements)

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“…(RISC) department, we have been using a categorical data timeline for phenotype validation. We learned from experience that naively applying timelines can cause many problems and undo its benefits 36 . The complexity of healthcare data poses challenges.…”

Section: In the Mass General Brigham (Mgb) Research Information Scien...mentioning

confidence: 99%

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HistoriView: Implementation and Evaluation of a Novel Approach to Review a Patient Using a Scalable Space-Efficient Timeline without Zoom Interactions

Park,

Wang,

Wattanasin

et al. 2024

Appl Clin Inform

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Background: Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge. Objective: This study aims to develop a scalable, efficient timeline to enhance patient review for research purposes. The focus is on addressing the challenges presented by the intricate and voluminous patient data. Methods: We propose a high-throughput, space-efficient HistoriView timeline for an individual patient. For a compact overview, it uses non-stacking event representation. An overlay detection algorithm, y-shift visualization, and popup-based interaction facilitate comprehensive analysis of overlapping datasets. An i2b2 HistoriView plugin was deployed, using split query and event reduction approaches, delivering the entire history efficiently without losing information. For evaluation, 11 participants completed a usability survey and a preference survey, followed by qualitative feedback. To evaluate scalability, 100 randomly selected patients over 60 years old were tested on the plugin and were compared with a baseline visualization. Results: Most participants found HistoriView was easy to use and learn and delivered information clearly without zooming. All preferred HistoriView over a stacked timeline. They expressed satisfaction on display, ease of learning and use, and efficiency. However, challenges and suggestions for improvement were also identified. In the performance test, the largest patient had 32,630 records, which exceeds the baseline limit. HistoriView reduced it to 2,019 visual artifacts. All patients were pulled and visualized within 45.40 seconds. Visualization took less than 3 seconds for all. Discussion and Conclusion: HistoriView allows complete data exploration without exhaustive interactions in a compact overview. It is useful for dense data or iterative comparisons. However, issues in exploring sub-concept records were reported. HistoriView handles large patient data preserving original information in a reasonable time.

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Section: In the Mass General Brigham (Mgb) Research Information Scien...mentioning

confidence: 99%

“…We learned from experience that naively applying timelines can cause many problems and undo its benefits. 36 The complexity of health care data poses challenges. For example, diseases may be acute or chronic, and treatment can vary in length and intensity.…”

Section: Introductionmentioning

confidence: 99%