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

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 nonstacking 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 that 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 subconcept records were reported. HistoriView handles large patient data preserving original information in a reasonable time.

Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative.

Estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) incidence, also known as Long COVID, have varied across studies and changed over time. We estimated PASC incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). Overall, 7% of children and 8.5%-26.4% of adults developed PASC, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 4% in children and ranged from 4-7% among adults, representing a lower-bound incidence estimation based on two control groups - contemporary COVID-19 negative and historical patients (2019). Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating Long COVID remains a public health priority. Examining temporal patterns and risk factors of PASC incidence informs our understanding of etiology and can improve prevention and management.