ReviewInternational journal of medical informatics2010
Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study.
Review in International journal of medical informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses 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
13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Comprehensive process model of clinical information interaction in primary care: results of a "best-fit" framework synthesis.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- The impact of eHealth on the quality and safety of health care: a systematic overview.PLoS medicine · 2011Pooled it
- Timing of Insulin with Meals in the Hospital: a Systems Improvement Approach.Current diabetes reports · 2019Review
- Efficacy of CPP-ACP and CPP-ACPF for Prevention and Remineralization of White Spot Lesions in Orthodontic Patients: a Systematic Review of Randomized Controlled Clinical Trials.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2019Article
- Development of Decision Support System to Predict Neurofeedback Response in ADHD: an Artificial Neural Network Approach.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2019Article
- Understanding Unintended Consequences and Health Information Technology:. Contribution from the IMIA Organizational and Social Issues Working Group.Yearbook of medical informatics · 2016Review
- Barriers and facilitators to the use of computer-based intensive insulin therapy.International journal of medical informatics · 2011Article
- Characteristics and effects of nurse dosing over-rides on computer-based intensive insulin therapy protocol performance.Journal of the American Medical Informatics Association : JAMIA · 2011Article
- Expressing observations from electronic medical record flowsheets in an i2b2 based clinical data repository to support research and quality improvement.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
- Effects of blood glucose transcription mismatches on a computer-based intensive insulin therapy protocol.Intensive care medicine · 2010Article
- Clinical decision support with automated text processing for cervical cancer screening.Journal of the American Medical Informatics Association : JAMIAArticle
- Anticipating and addressing the unintended consequences of health IT and policy: a report from the AMIA 2009 Health Policy Meeting.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
introductionEvaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation.
resultsThis paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error. DISCUSSION: Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements.
conclusionThis paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT.
Indexed as
Identifiers
What 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.