ArticleVascular health and risk management2010
Computerized history-taking as a tool to manage dyslipidemia.
Article in Vascular health and risk management, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Computerized Self-Reported Medical History Taking to Support Early Rule Out of Major Adverse Cardiac Events in Patients With Acute Chest Pain: Post Hoc Analysis of the CLEOS-CPDS Prospective Cohort Study.Journal of medical Internet research · 2026Observational
- Performance of computerized self-reported medical history taking and HEAR score for safe early rule-out of cardiac events in acute chest pain patients: the CLEOS-CPDS prospective cohort study.European heart journal. Digital health · 2025Article
- Are medical history data fit for risk stratification of patients with chest pain in emergency care? Comparing data collected from patients using computerized history taking with data documented by physicians in the electronic health record in the CLEOS-CPDS prospective cohort study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Use of Self-Reported Computerized Medical History Taking for Acute Chest Pain in the Emergency Department - the Clinical Expert Operating System Chest Pain Danderyd Study (CLEOS-CPDS): Prospective Cohort Study.Journal of medical Internet research · 2021Article
- Article
- Improving the Primary Care Consultation for Diabetes and Depression Through Digital Medical Interview Assistant Systems: Narrative Review.Journal of medical Internet research · 2020Review
- Article
- Medical diagnosis as a linguistic game.BMC medical informatics and decision making · 2017Article
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundValidated guidelines to manage low-density lipoprotein (LDL)-cholesterol are utilized inconsistently or not at all even though their application lowers the incidence of coronary events. New approaches are needed, therefore, to implement these guidelines in everyday practice. METHODS AND
resultsWe compared an automated method for applying The National Cholesterol Education Panel (NCEP) guidelines with results from routine care for managing LDL-cholesterol. The automated method comprised computerized history-taking and analysis of historical data without physician input. Results from routine care were determined for 213 unselected patients and compared with results from interviews of the same 213 patients by a computerized history-taking program. Data extracted from hospital charts showed that routine care typically did not collect sufficient information to stratify risk and assign treatment targets for LDL-cholesterol and that there were inconsistencies in identifying patients with normal or elevated levels of LDL-cholesterol in relation to risk. The computerized interview program outperformed routine care in collecting historical data relevant to stratifying risk, assigning treatment targets, and clarifying the presence of hypercholesterolemia relative to risk.
conclusionsComputerized history-taking coupled with automated analysis of the clinical data can outperform routine medical care in applying NCEP guidelines for stratifying risk and identifying patients with hypercholesterolemia in relation to risk.
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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.