ArticleJournal of medical systems2022
Development and Utilization of a Diagnostic Support Tool for Asthma within the Electronic Medical Record.
Article in Journal of medical systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Towards Electronic Medical Record (EMR) utilization in developing countries: a conceptual model based on systematic literature review.BMC medical informatics and decision making · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Asthma in childhood is a common and costly chronic disease. Quality asthma care can lead to better control of asthma thus decreasing use of health services. The gold standard for pediatric asthma diagnosis and management is the National Heart, Lung and Blood Institute (NHLBI) guidelines for Diagnosis and Management of Asthma which center on precisely establishing the severity of asthma, as this precise classification delineates appropriate therapy. However, navigating these guidelines is a challenge for primary care providers that creates a barrier to providing quality care. We aim to improve precision in asthma severity classification in the community healthcare setting through the development of an electronic asthma decision support tool (eADST) incorporating NHLBI guidelines embedded within the electronic health record system. We developed an algorithm for the eADST to guide the health care provider to the appropriate classification and subsequent therapy. We engaged our health system's electronic health record informatics team and together developed and revised the tool. We launched the tool in three academic community clinics and measured precision in asthma classification in the twelve months prior to the availability of the tool and the twelve months following the launch. We found a significant improvement in precision of asthma severity classification following the launch, a necessary first step in improvement of asthma care. The next step will be to evaluate the impact of the tool on asthma outcomes.
Indexed as
Identifiers
36040523What 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.