The anatomy of decision support during inpatient care provider order entry (CPOE): Empirical observations from a decade of CPOE experience at Vanderbilt
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The authors describe a pragmatic approach to the introduction of clinical decision support at the point of care, based on a decade of experience in developing and evolving Vanderbilt's inpatient "WizOrder" care provider order entry (CPOE) system. The inpatient care setting provides a unique opportunity to interject CPOE-based decision support features that restructure clinical workflows, deliver focused relevant educational materials, and influence how care is delivered to patients. From their empirical observations, the authors have developed a generic model for decision support within inpatient CPOE systems. They believe that the model's utility extends beyond Vanderbilt, because it is based on characteristics of end-user workflows and on decision support considerations that are common to a variety of inpatient settings and CPOE systems. The specific approach to implementing a given clinical decision support feature within a CPOE system should involve evaluation along three axes: what type of intervention to create (for which the authors describe 4 general categories); when to introduce the intervention into the user's workflow (for which the authors present 7 categories), and how disruptive, during use of the system, the intervention might be to end-users' workflows (for which the authors describe 6 categories). Framing decision support in this manner may help both developers and clinical end-users plan future alterations to their systems when needs for new decision support features arise.
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Secondary care studies have suggested that alerts should be presented at the point of selecting drugs rather than at the moment of printing.
“Studies undertaken in a secondary care setting have suggested the importance of presenting alerts at the point of selecting drugs (as opposed to the moment of printing), 22”
From ‘Too much, too late’: mixed methods multi-channel video recording study of computerized decision support systems and GP prescribing (2013) · cites this paper as [22]
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Clinical Decision Support Systems
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Turning Off Frequently Overridden Drug Alerts: Limited Opportunities for Doing It Safely
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A four-phase model of the evolution of clinical decision support architectures
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A framework for evaluating the appropriateness of clinical decision support alerts and responses
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Self-citationMiller, K. M., Mosby, D. L., Capan, M., Kowalski, R. L., Ratwani, R. M., Noaiseh, Y., Kraft, R., Schwartz, S., Weintraub, W. S., & Arnold, R. C. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx118 · Cited by 130 · see citing works
Supporting Patient Care in the Emergency Department with a Computerized Whiteboard System
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Self-citationReducing Unnecessary Inpatient Laboratory Testing in a Teaching Hospital
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Reducing Unnecessary Inpatient Laboratory Testing in a Teaching Hospital
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Design of decision support interventions for medication prescribing
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Computer-based Insulin Infusion Protocol Improves Glycemia Control over Manual Protocol
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New Unintended Adverse Consequences of Electronic Health Records
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Adverse Drug Events during AKI and Its Recovery
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Discovery and Clinical Decision Support for Personalized Healthcare
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The Chief Clinical Informatics Officer (CCIO)
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Randomized Trial to Improve Prescribing Safety During Pregnancy
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Identifying asthma exacerbations in a pediatric emergency department: A feasibility study
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Electronic Health Record Systems
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Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis
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Self-citationePrescribing: Reducing Costs through In-Class Therapeutic Interchange
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Data-driven approach to Early Warning Score-based alert management
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Presenting Multiple Drug Alerts in an Ambulatory Electronic Prescribing System
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Clinical Decision Support for Colon and Rectal Surgery: An Overview
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Care Provider Order Entry (CPOE): A Perspective on Factors Leading to Success or to Failure
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Designing Computerized Provider Order Entry Software in Iran
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Stakeholder perspectives of system-related errors: Types, contributing factors, and consequences
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- Authors
- Miller, R. A., Waitman, L. R., Chen, S., & Rosenbloom, S. T.
- Affiliations
- Vanderbilt University Medical Center (US)
- Corresponding author
- Miller, Randolph A.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2005-12
- Volume
- 38
- Issue
- 6
- Pages
- 469-485
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2098595796
- Cited by
- 144
- References
- 102
- Citations by year
- 2024: 5 · 2023: 1 · 2022: 3 · 2021: 4 · 2020: 6 · 2019: 2 · 2018: 5 · 2017: 8 · 2016: 11 · 2015: 7 · 2014: 7 · 2013: 13 · 2012: 11
- FWCI
- 18.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Vanderbilt University
- National Institutes of Health (5r01lm007995-06, 5r01lm006226-03)
- School of Medicine, Vanderbilt University
- U.S. National Library of Medicine (K22-LM-08576, R01-LM-007995, K22LM008576, LM06226, R01-LM-06226)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- clinical decision support, computerized physician order entry, inpatient care, point-of-care intervention, clinical workflow, workflow disruption, clinical teaching
- MeSH terms
- Admitting Department, Hospital, Decision Making, Organizational, Humans, Inpatients, Patient Education as Topic, Personnel, Hospital, Professional-Patient Relations, Tennessee
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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