TY - JOUR
T1 - Ascertaining Design Requirements for Postoperative Care Transition Interventions
AU - Abraham, Joanna
AU - King, Christopher R.
AU - Meng, Alicia
N1 - Funding Information:
We would like to thank our clinicians for their participation. Work included in this document was produced by machine learning risk predictions at operating room– intensive care unit handoff. This work was produced with the support of the Big Ideas Program, a BJC HealthCare and Washington University School of Medicine internal grant program, hosted by the Healthcare Innovation Laboratory and the Institute for Informatics.
Publisher Copyright:
© 2021 Thieme Medical Publishers, Inc.. All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - Continuity of care
KW - anesthesia
KW - care transition
KW - handoffs
KW - intensive and critical care
KW - machine learning
KW - requirements analysis and design
KW - surgery
UR - http://www.scopus.com/inward/record.url?scp=85101742560&partnerID=8YFLogxK
U2 - 10.1055/s-0040-1721780
DO - 10.1055/s-0040-1721780
M3 - Article
C2 - 33626584
AN - SCOPUS:85101742560
SN - 1869-0327
VL - 12
SP - 107
EP - 115
JO - Applied clinical informatics
JF - Applied clinical informatics
IS - 1
ER -