ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023
A Practical Approach to Optimize Computerized Provider Order Entry Systems and Reduce Clinician Burden: Pre-Post Evaluation of Vendor-Derived "Order Friction" Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 2 citations in OpenAlex.
- Defining Documentation Burden (DocBurden) and Excessive DocBurden for All Health Professionals: A Scoping Review.Applied clinical informatics · 2024Article
- Improving Evidenced-based Outpatient Order Set Utilization in the Gastrointestinal Division of a Large Pediatric Health System.Pediatric quality & safetyArticle
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computerized provider order entry (CPOE) systems have been cited as a significant contributor to clinician burden. Vendor-derived measures and data sets have been developed to help with optimization of CPOE systems. We describe how we analyzed vendor-derived Order Friction (OF) EHR log data at our health system and propose a practical approach for optimizing CPOE systems by reducing OF. We also conducted a pre-post intervention study using OF data to evaluate the impact of defaulting the frequency of urine, stool and nasal swab tests and found that all modified orders had significantly fewer changes required per order (p<0.01). Our proposed approach is a six-step process: 1) understand the ordering process, 2) understand OF data elements contextually, 3) explore ordering user-level factors, 4) evaluate order volume and friction from different order sources, 5) optimize order-level design, 6) identify high volume alerts to evaluate for appropriateness.
Indexed as
Identifiers
38222358PMC10785931W4390889617What 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.