ArticleComputers in biology and medicine2015
Clinicians' evaluation of computer-assisted medication summarization of electronic medical records.
Article in Computers in biology and medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- What maximizes the effectiveness and implementation of technology-based interventions to support healthcare professional practice? A systematic literature review.BMC medical informatics and decision making · 2018Pooled it
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
- Involving Health Care Professionals in the Development of Electronic Health Records: Scoping Review.JMIR human factors · 2023Article
- A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study.Journal of medical Internet research · 2022Article
- Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review.JMIR medical informatics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Each year thousands of patients die of avoidable medication errors. When a patient is admitted to, transferred within, or discharged from a clinical facility, clinicians should review previous medication orders, current orders and future plans for care, and reconcile differences if there are any. If medication reconciliation is not accurate and systematic, medication errors such as omissions, duplications, dosing errors, or drug interactions may occur and cause harm. Computer-assisted medication applications showed promise as an intervention to reduce medication summarization inaccuracies and thus avoidable medication errors. In this study, a computer-assisted medication summarization application, designed to abstract and represent multi-source time-oriented medication data, was introduced to assist clinicians with their medication reconciliation processes. An evaluation study was carried out to assess clinical usefulness and analyze potential impact of such application. Both quantitative and qualitative methods were applied to measure clinicians' performance efficiency and inaccuracy in medication summarization process with and without the intervention of computer-assisted medication application. Clinicians' feedback indicated the feasibility of integrating such a medication summarization tool into clinical practice workflow as a complementary addition to existing electronic health record systems. The result of the study showed potential to improve efficiency and reduce inaccuracy in clinician performance of medication summarization, which could in turn improve care efficiency, quality of care, and patient safety.
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.