ArticleJournal of the American Medical Informatics Association : JAMIA2014
Paving the COWpath: data-driven design of pediatric order sets.
Article in Journal of the American Medical Informatics Association : JAMIA, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- AI with agency: a vision for adaptive, efficient, and ethical healthcare.Frontiers in digital health · 2025Article
- Fractured systems: a literature review of OR/MS methods applied to orthopaedic care settings and treatments.Health systems (Basingstoke, England) · 2024Review
- Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning.Journal of biomedical informatics · 2023Article
- The Effect of Implementation of Guideline Order Bundles Into a General Admission Order Set on Clinical Practice Guideline Adoption: Quasi-Experimental Study.JMIR medical informatics · 2023Article
- A Data-Driven Algorithm to Recommend Initial Clinical Workup for Outpatient Specialty Referral: Algorithm Development and Validation Using Electronic Health Record Data and Expert Surveys.JMIR medical informatics · 2022Article
- Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals.Applied clinical informatics · 2022Article
- Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The influence of integrating clinical practice guideline order bundles into a general admission order set on guideline adoption.JAMIA open · 2021Article
- Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review.JMIR medical informatics · 2020Review
- Predicting Inpatient Medication Orders From Electronic Health Record Data.Clinical pharmacology and therapeutics · 2020Article
- The bird's-eye view: A data-driven approach to understanding patient journeys from claims data.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- When order sets do not align with clinician workflow: assessing practice patterns in the electronic health record.BMJ quality & safety · 2019Article
- Predicting emergency department orders with multilabel machine learning techniques and simulating effects on length of stay.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Clinical pathway modelling: a literature review.Health systems (Basingstoke, England) · 2019Review
- Neural Networks for Clinical Order Decision Support.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.Journal of biomedical informatics · 2018Article
- Inpatient Clinical Order Patterns Machine-Learned From Teaching Versus Attending-Only Medical Services.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.International journal of medical informatics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEvidence indicates that users incur significant physical and cognitive costs in the use of order sets, a core feature of computerized provider order entry systems. This paper develops data-driven approaches for automating the construction of order sets that match closely with user preferences and workflow while minimizing physical and cognitive workload. MATERIALS AND
methodsWe developed and tested optimization-based models embedded with clustering techniques using physical and cognitive click cost criteria. By judiciously learning from users' actual actions, our methods identify items for constituting order sets that are relevant according to historical ordering data and grouped on the basis of order similarity and ordering time. We evaluated performance of the methods using 47,099 orders from the year 2011 for asthma, appendectomy and pneumonia management in a pediatric inpatient setting.
resultsIn comparison with existing order sets, those developed using the new approach significantly reduce the physical and cognitive workload associated with usage by 14-52%. This approach is also capable of accommodating variations in clinical conditions that affect order set usage and development. DISCUSSION: There is a critical need to investigate the cognitive complexity imposed on users by complex clinical information systems, and to design their features according to 'human factors' best practices. Optimizing order set generation using cognitive cost criteria introduces a new approach that can potentially improve ordering efficiency, reduce unintended variations in order placement, and enhance patient safety.
conclusionsWe demonstrate that data-driven methods offer a promising approach for designing order sets that are generalizable, data-driven, condition-based, and up to date with current best practices.
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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.