ArticleJournal of the American Medical Informatics Association : JAMIA
A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02596087 (Improving Quality by Maintaining Accurate Problems in the Electronic Health Record), which is not on this map. Cited by 57 papers, 2 of them syntheses that pooled it.
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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.
Improving Quality by Maintaining Accurate Problems in the Electronic Health Record
Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Validity of International Classification of Diseases Codes for Identifying Neuro-Ophthalmic Disease in Large Data Sets: A Systematic Review.Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society · 2020Pooled it
- Electronic problem lists: a thematic analysis of a systematic literature review to identify aspects critical to success.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness.Journal of the American Medical Informatics Association : JAMIA · 2023Trial
- Risk factors of proteinuria in renal cell carcinoma patients treated with VEGF inhibitors: a secondary analysis of pooled clinical trial data.British journal of cancer · 2016Trial
- Design of a cluster-randomized trial of electronic health record-based tools to address overweight and obesity in primary care.Clinical trials (London, England) · 2015Trial
- Use of an electronic problem list by primary care providers and specialists.Journal of general internal medicine · 2012Trial
- Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.Journal of the American Medical Informatics Association : JAMIATrial
- How Data Analytics Can Be Leveraged to Enhance Graduate Clinical Skills Education.Journal of graduate medical education · 2025Article
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Patient-centric knowledge graphs: a survey of current methods, challenges, and applications.Frontiers in artificial intelligence · 2024Review
- Identification and assessment of classification criteria attributes for systemic lupus erythematosus in a regional medical record data network.Lupus science & medicine · 2023Article
- Observational analysis of documentation burden and data duplication in trauma patient pathways at a major trauma centre.BMJ open quality · 2023Observational
- Inaccurate recording of routinely collected data items influences identification of COVID-19 patients.International journal of medical informatics · 2022Article
- Linking Provider Specialty and Outpatient Diagnoses in Medicare Claims Data: Data Quality Implications.Applied clinical informatics · 2021Article
- Data gaps in electronic health record (EHR) systems: An audit of problem list completeness during the COVID-19 pandemic.International journal of medical informatics · 2021Article
- Impact of a problem-oriented view on clinical data retrieval.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality.BMC medical informatics and decision making · 2021Article
- Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.Journal of medical Internet research · 2021Article
- Characterizing outpatient problem list completeness and duplications in the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Rule-Based Cohort Definitions for Acute Respiratory Failure: Electronic Phenotyping Algorithm.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete.
objectiveTo develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. STUDY DESIGN AND
methodsWe identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy.
resultsSeventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone.
conclusionWe developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.
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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.