Integrating machine learning predictions for perioperative risk management: towards an empirical design of a flexible-standardized risk assessment tool
Open accessAbstract
Background Surgical patients are complex, vulnerable, and prone to postoperative complications that can potentially be mitigated with quality perioperative risk assessment and management. Several institutions have incorporated machine learning (ML) into their patient care to improve awareness and support clinician decision-making along the perioperative spectrum. Recent research suggests that ML risk prediction can support perioperative patient risk monitoring and management across several situations, including the operating room (OR) to intensive care unit (ICU) handoffs. Objectives Our study objectives were threefold: (1) evaluate whether ML-generated postoperative predictions are concordant with clinician-generated risk rankings for acute kidney injury, delirium, pneumonia, deep vein thrombosis, and pulmonary embolism, and establish their associated risk factors; (2) ascertain clinician end-user suggestions to improve adoption of ML-generated risks and their integration into the perioperative workflow; and (3) develop a user-friendly visualization format for a tool to display ML-generated risks and risk factors to support postoperative care planning, for example, within the context of OR-ICU handoffs. Methods Graphical user interfaces for postoperative risk prediction models were assessed for end-user usability through cognitive walkthroughs and interviews with anesthesiologists, surgeons, certified registered nurse anesthetists, registered nurses, and critical care physicians. Thematic analysis relying on an explanation design framework was used to identify feedback and suggestions for improvement. Results 17 clinicians participated in the evaluation. ML estimates of complication risks aligned with clinicians' independent rankings, and related displays were perceived as valuable for decision-making and care planning for postoperative care. During OR-ICU handoffs, the tool could speed up report preparation and remind clinicians to address patient-specific complications, thus providing more tailored care information. Suggestions for improvement centered on electronic tool delivery; methods to build trust in ML models; modifiable risks and risk mitigation strategies; and additional patient information based on individual preferences (e.g., surgical procedure). Conclusions ML estimates of postoperative complication risks can provide anticipatory guidance, potentially increasing the efficiency of care planning. We have offered an ML visualization framework for designing future ML-augmented tools and anticipate the development of tools that recommend specific actions to the user based on ML model output.
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Integration with electronic health records was common, particularly in more mature tools.
“Integration with electronic health records (EHRs) was common, particularly in more mature tools [23,24,26–34,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
The theme perceived usefulness was identified in 13 papers.
“The theme perceived usefulness was identified in 13 papers, where participants reported the how the systems impacted their work and associated tasks [23,24,26,28–33,35–38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Participants commonly reported that XAI tools support task completion.
“Across these studies participants commonly reported that the XAI tools support task completion [23,26,28,29,31,33].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
XAI systems were perceived as useful for teamwork by coordinating care or improving communication between teams.
“XAI systems were also perceived as useful for teamwork, [23,30] by coordinating care or improving communication between teams, for example “…improving the quality of information transmission at handoff” [23].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Education and training was a theme that arose in 9 studies.
“Education and training was a theme that arose in 9 studies, where participants described their need for, or experience of, learning how to use the XAI system [23,25,26,28–30,32,35,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Participants in several studies described the role of the model's performance in influencing trust.
“Participants in several studies described the role of the model’s performance in influencing trust [23,25,31–33,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Some health professionals had limited time to interpret model explanations and make a decision.
“Considering context and users was also reported, with some health professionals having limited time to interpret model explanations and make a decision [23,25,27,32].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Aligning the XAI tool with existing systems such as embedding it into the hospital electronic health record was important.
“Similarly aligning the XAI tool with existing systems such as embedding it into the hospital electronic health record was important [23,26,35].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Health professionals positively perceived, and were more likely to use, XAI systems that fit into their existing workflow.
“Health professionals positively perceived, and were more likely to use, XAI systems that fit into their existing workflow [23–26,30,31,33,35].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Perceptions on the model's performance were reported in 13 studies.
“Perceptions on the model’s performance was reported in 13 studies [23–26,29–37].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Perceptions regarding efficiency or saving time were reported in six studies.
“Perceptions regarding efficiency or saving time were reported in six studies [23,28–30,32,33].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
In two studies of XAI prototypes, participants perceived that further training or practice using the tool would have increased their understanding.
“In two studies of XAI prototypes, participants perceived if they received further training or practice using the tool it would have increased their understanding [23,32].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Impact on workflow arose as a theme in 9 papers.
“Impact in workflow related to how XAI systems aligned with and influenced work processes, and arose as a theme in 9 papers [23–27,30,31,33,35].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
A number of studies reported trust could be earned over time as model performance was proven.
“A number of studies also reported trust could be earned over time as model performance was proven [23,25,31–33,36], for example, an electrophysiologist said “I just think I should see that it confirms our decisions in enough cases - then I would feel comfortable about colleagues leaning on it” [33].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Perceptions of the XAI model's data quality impacted participants' trust and use of tools, and trust in outputs was reduced when XAI systems used hospital data that participants knew was unreliable.
“Similarly, perceptions of the XAI model’s data quality impacted participants trust and use of tools and trust in outputs were reduced when XAI system’s used hospital data that participants knew was unreliable [23,25,26,29,32,33,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
XAI model predictions were reportedly more likely to be accepted when they aligned with health professionals' knowledge or with the patient context.
“XAI model predictions were reportedly more likely to be accepted when they aligned with health professionals’ knowledge, or with the patient context [23,25,27–30,32–36]”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Participants in Abraham et al.'s study reported risk thresholds shown in the bar graph explanation appeared arbitrary and should be based on prior empirical studies.
“For example, participants in Abraham et. al.’s [23] study reported risk thresholds shown in the bar graph explanation appeared arbitrary and should be based on prior empirical studies.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Eight studies reported participants desired actionable information from XAI systems.
“Eight studies in this review reported participants desired actionable information from the XAI systems [23–25,28,30,32,36,37].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Several studies reported trust and acceptance of model outputs were facilitated by providing evidence for the model effectiveness on patients and/or evidence of the output.
“Several studies reported trust and acceptance of model outputs were facilitated by providing evidence for the model effectiveness on patients and/or evidence of the output [23,29,30,33,35,36].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Colour coding was viewed as a helpful visual cue for conveying risk severity.
“Colour coding was viewed as a helpful visual cue for conveying risk severity.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Five studies specifically described using SHAP.
“Five studies [23,25,27,28,34] specifically described using SHapley Additive exPlanations (SHAP)”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Healthcare professionals preferred grouping features and ordering them by their contribution to predicted risk to allow easy and quick interpretation.
“For presentation of features, healthcare professionals preferred grouping features and ordering them by their contribution to predicted risk to allow easy and quick interpretation.”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Health professionals in three studies reported XAI explanations could help communicate risks or escalations in care to patients.
“Health professionals in three studies reported XAI explanations could help communicate risks or escalations in care to patients [23,35,37].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
In three studies, participants reported wanting to see variables that could be altered through interventions in feature-based explanations.
“With regard to feature based explanations, participants in three studies reported wanting to see the variables that could be altered through interventions [23,25,28,32].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Health professionals' perceptions involving design and usability were found in 14 papers.
“Health professionals’ perceptions involving design and usability were found in 14 papers [23–34,37,38].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Two studies described their systems using general ML terminology without referencing explainability or interpretability.
“while two studies described their systems using general ML terminology without referencing explainability or interpretability [23,30].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
Health professionals valued the presentation of contextual information such as patient results alongside predictions and explanations.
“Health professionals valued the presentation of contextual information such as patient results alongside predictions and explanations”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [23]
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 [65]
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- Authors
- Abraham, J., Bartek, B., Meng, A., Ryan King, C., Xue, B., Lu, C., & Avidan, M. S.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Abraham, Joanna
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 137
- Pages
- 104270
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W4311761926
- Cited by
- 50
- References
- 47
- Citations by year
- 2026: 18 · 2025: 15 · 2024: 11 · 2023: 6
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- machine learning, perioperative risk assessment, postoperative complication prediction, clinical decision support, risk visualization, end-user usability, risk factors, trust in AI, risk mitigation, postoperative care planning, clinical workflow integration, explanation design, acute kidney injury, delirium, deep vein thrombosis, pulmonary embolism, pneumonia
- MeSH terms
- Machine Learning, Critical Care, Patient Care, Humans, Risk Assessment, Surgeons
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