ArticleJournal of pathology informatics2018
Using Heatmaps to Identify Opportunities for Optimization of Test Utilization and Care Delivery.
Article in Journal of pathology informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Article
- Comparative Effectiveness of Surgical, Endovascular, and Conservative Strategies for Unruptured Intracranial Aneurysms.Stroke (Hoboken, N.J.) · 2026Article
- Prevalence of heart failure pharmacotherapy utilisation, frailty and adverse drug events among hospitalised adults older than 75 years: a multicentre cross-sectional study.Internal medicine journal · 2025Article
- Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.Journal of medical Internet research · 2020Article
- Burden of Menstrual Pain Measured by Heatmap Visualization of Daily Patient-Reported Data in Japanese Patients Treated with Ethinylestradiol/Drospirenone: A Randomized Controlled Study.International journal of women's health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhen a provider orders a test in a pattern that is substantially different than their peers, it may indicate confusion in the test name or inappropriate use of the test, which can be elucidated by initiating dialog between clinicians and the laboratory. However, the analysis of ordering patterns can be challenging. We propose a utilization index (UI) as a means to quantify utilization patterns for individual providers and demonstrate the use of heatmaps to identify opportunities for improvement. MATERIALS AND
methodsLaboratory test orders by all providers were extracted from the laboratory information system. Providers were grouped into cohorts based on the specialty and patient population. A UI was calculated for each provider's use of each test using the following formula: (UI = [provider volume of specific test/provider volume of all tests]/[cohort volume of specific test/cohort volume of all tests]). A heatmap was generated to compare each provider to their cohort.
resultsThis method identified several hot spots and was helpful in reducing confusion and overutilization.
conclusionThe UI is a useful measure of test ordering behavior, and heatmaps provide a clear visual illustration of the utilization indices. This information can be used to identify areas for improvement and initiate meaningful dialog with providers, which will ultimately bring improvement and reduction in costs. Our method is simple and uses resources that are widely available, making this method effective convenient for many other laboratories.
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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.