ArticleApplied clinical informatics2016
Feasibility of Population Health Analytics and Data Visualization for Decision Support in the Infectious Diseases Domain: A pilot study.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Observational
- Article
- Exploring the Role of Technology in the Implementation of Social Accountability in Medical Education: A Brief Review.Journal of pharmacy & bioallied sciences · 2024Review
- Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Scoping review: the empowerment of Alzheimer's Disease caregivers with mHealth applications.NPJ digital medicine · 2021Article
- Social dynamics of a population-level dashboard for antimicrobial stewardship: A qualitative analysis.American journal of infection control · 2021Article
- Pharmacogenomics cascade testing (PhaCT): a novel approach for preemptive pharmacogenomics testing to optimize medication therapy.The pharmacogenomics journal · 2021Review
- Artificial Intelligence-Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study.JMIR research protocols · 2020Article
- Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design.JMIR research protocols · 2019Article
- Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design.JMIR mHealth and uHealth · 2019Article
- Visualizing Infection Surveillance Data for Policymaking Using Open Source Dashboarding.Applied clinical informatics · 2019Article
- Public Health, Population Health, and Epidemiology Informatics: Recent Research and Trends in the United States.Yearbook of medical informatics · 2017Review
- Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition.Proceedings. IEEE International Conference on Healthcare Informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectiveBig data or population-based information has the potential to reduce uncertainty in medicine by informing clinicians about individual patient care. The objectives of this study were: 1) to explore the feasibility of extracting and displaying population-based information from an actual clinical population's database records, 2) to explore specific design features for improving population display, 3) to explore perceptions of population information displays, and 4) to explore the impact of population information display on cognitive outcomes.
methodsWe used the Veteran's Affairs (VA) database to identify similar complex patients based on a similar complex patient case. Study outcomes measures were 1) preferences for population information display 2) time looking at the population display, 3) time to read the chart, and 4) appropriateness of plans with pre- and post-presentation of population data. Finally, we redesigned the population information display based on our findings from this study.
resultsThe qualitative data analysis for preferences of population information display resulted in four themes: 1) trusting the big/population data can be an issue, 2) embedded analytics is necessary to explore patient similarities, 3) need for tools to control the view (overview, zoom and filter), and 4) different presentations of the population display can be beneficial to improve the display. We found that appropriateness of plans was at 60% for both groups (t9=-1.9; p=0.08), and overall time looking at the population information display was 2.3 minutes versus 3.6 minutes with experts processing information faster than non-experts (t8= -2.3, p=0.04).
conclusionA population database has great potential for reducing complexity and uncertainty in medicine to improve clinical care. The preferences identified for the population information display will guide future health information technology system designers for better and more intuitive display.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.