ArticleApplied clinical informatics2017
Automating Clinical Score Calculation within the Electronic Health Record. A Feasibility Assessment.
Article in Applied clinical informatics, 2017. 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.
- Integrating Generative AI into Nomograms for Breast Cancer Nodal Risk Predictions.Annals of surgical oncology · 2026Article
- Article
- Prediction of peri-operative mortality in care of preterm children in non-cardiac surgery.BMC anesthesiology · 2025Article
- Limitations in the use of automated mental status detection for clinical decision support.International journal of medical informatics · 2023Article
- Integration of Risk Scores and Integration Capability in Electronic Patient Records.Applied clinical informatics · 2022Article
- Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.JMIR formative research · 2022Article
- Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study.JMIR formative research · 2022Article
- Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study.Journal of medical Internet research · 2021Article
- Usage Patterns of Web-Based Stroke Calculators in Clinical Decision Support: Retrospective Analysis.JMIR medical informatics · 2021Article
- Development and Evaluation of Novel Electronic Medical Record Tools For Avoiding Bleeding After Percutaneous Coronary Intervention.Journal of the American Heart Association · 2019Article
- Stroke prophylaxis in critically-ill patients with new-onset atrial fibrillation.Journal of thrombosis and thrombolysis · 2019Article
- On the Way to Close the Loop in Information Logistics: Data from the Patient - Value for the Patient.Yearbook of medical informatics · 2018Review
- Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.BMC emergency medicine · 2018Article
- Breadcrumbs: Assessing the Feasibility of Automating Provider Documentation Using Electronic Health Record Activity.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
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
Abstract
objectivesEvidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records.
methodsWe manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings.
resultsWe identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable.
conclusionComplete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments - partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study.
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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.