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Cited by 20Hospital Admissions and OutcomesPatient Safety and Medication ErrorsElectronic Health Records Systems

Ascertaining Design Requirements for Postoperative Care Transition Interventions

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Abraham, Joanna, King, Christopher R., Meng, Alicia·2021·Applied Clinical Informatics
DOI 10.1055/s-0040-1721780PMC PMC7904383
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Abstract

Background Handoffs or care transitions from the operating room (OR) to intensive care unit (ICU) are fragmented and vulnerable to communication errors. Although protocols and checklists for standardization help reduce errors, such interventions suffer from limited sustainability. An unexplored aspect is the potential role of developing personalized postoperative transition interventions using artificial intelligence (AI)-generated risks. Objectives This study was aimed to (1) identify factors affecting sustainability of handoff standardization, (2) utilize a human-centered approach to develop design ideas and prototyping requirements for a sustainable handoff intervention, and (3) explore the potential role for AI risk assessment during handoffs. Methods We conducted four design workshops with 24 participants representing OR and ICU teams at a large medical academic center. Data collection phases were (1) open-ended questions, (2) closed card sorting of handoff information elements, and (3) scenario-based design ideation and prototyping for a handoff intervention. Data were analyzed using thematic analysis. Card sorts were further tallied to characterize handoff information elements as core, flexible, or unnecessary. Results Limited protocol awareness among clinicians and lack of an interdisciplinary electronic health record (EHR)-integrated handoff intervention prevented long-term sustainability of handoff standardization. Clinicians argued for a handoff intervention comprised of core elements (included for all patients) and flexible elements (tailored by patient condition and risks). They also identified unnecessary elements that could be omitted during handoffs. Similarities and differences in handoff intervention requirements among physicians and nurses were noted; in particular, clinicians expressed divergent views on the role of AI-generated postoperative risks. Conclusion Current postoperative handoff interventions focus largely on standardization of information transfer and handoff processes. Our design approach allowed us to visualize accurate models of user expectations for effective interdisciplinary communication. Insights from this study point toward EHR-integrated, “flexibly standardized” care transition interventions that can automatically generate a patient-centered summary and risk-based report.

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  • In our work on OR to ICU handoffs, participants endorsed similar desires to integrate AI into summaries of patient data like laboratory results and vital signs and the need to focus on actionability.

    “In our work on OR to ICU handoffs,66 participants endorsed similar desires to integrate AI into summaries of patient data like laboratory results and vital signs and the need to focus on actionability.”

    From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [66]

    The Conclusion (page unavailable) proposes an AI-generated patient handoff summary, and “Flexible Elements and Machine Learning-Generated Risk Predictions” reports that most residents and nurses wanted recent vital signs included. The Discussion links patient-specific risks to anticipatory guidance and care planning. But the copy does not establish that participants endorsed AI-generated summaries specifically of laboratory results and vital signs; it also reports mixed views on whether AI-generated risks would affect care. (LLM, conf=0.84)

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  • In the authors' previous study at OR to ICU handoff, key findings were difficulty of making EHR information universally accessible, need to focus on AI with direct relevance to patient care, and general acceptance of blending AI risk prediction with current summaries of patient data into a handoff tool.

    “Key findings of that study were the difficulty of making EHR information universally accessible, the need to focus on AI with direct relevance to patient care, and general acceptance of blending AI risk prediction with current summaries of patient data into a handoff tool.”

    From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [66]

    The Conclusion (page unavailable) proposes an EHR-integrated handoff summary with AI-generated risks that is accessible to sending and receiving teams, and the Discussion emphasizes information relevant to postoperative care. But the study does not establish a general acceptance of AI risk predictions: the Abstract reports divergent views, and the “Flexible Elements and Machine Learning-Generated Risk Predictions” section says nurses and fellows did not expect the risk information to affect their care. The “Intervention Modality and Access Considerations” section notes occasional lack of computer access, rather than a general difficulty making EHR information universally accessible. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Abraham, J., King, C. R., & Meng, A.
Affiliations
Washington University in St. Louis (US)
ORCID
Venue
Applied Clinical Informatics
Publisher
Georg Thieme Verlag KG
Published
2021-01
Volume
12
Issue
01
Pages
107-115
ISSN
1869-0327 (ISSN-L)
DOI
10.1055/s-0040-1721780
PMC
PMC7904383
OpenAlex
W3130056599
Cited by
20
References
2
Citations by year
2026: 1 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 3 · 2021: 2
FWCI
3.92
Type
journal article
Language
en
Open access
Locations
  • Applied Clinical Informatics (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
  • Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
electronic health record integration, interdisciplinary communication, card sorting, clinician perspectives
MeSH terms
Artificial Intelligence, Communication, Humans, Intensive Care Units, Patient Transfer, Postoperative Care, Patient Handoff

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