Aligning Patient Acuity With Resource Intensity After Major Surgery

Abstract

Objective: Develop unifying definitions and paradigms for data-driven methods to augment postoperative resource intensity decisions. Summary Background Data: Postoperative level-of-care assignments and frequency of vital sign and laboratory measurements (ie, resource intensity) should align with patient acuity. Effective, data-driven decision-support platforms could improve value of care for millions of patients annually, but their development is hindered by the lack of salient definitions and paradigms. Methods: Embase, PubMed, and Web of Science were searched for articles describing patient acuity and resource intensity after inpatient surgery. Study quality was assessed using validated tools. Thirty-five studies were included and assimilated according to PRISMA guidelines. Results: Perioperative patient acuity is accurately represented by combinations of demographic, physiologic, and hospital-system variables as input features in models that capture complex, non-linear relationships. Intraoperative physiologic data enriche these representations. Triaging high-acuity patients to low-intensity care is associated with increased risk for mortality; triaging low-acuity patients to intensive care units (ICUs) has low value and imparts harm when other, valid requests for ICU admission are denied due to resource limitations, increasing their risk for unrecognized decompensation and failure-to-rescue. Providing high-intensity care for low-acuity patients may also confer harm through unnecessary testing and subsequent treatment of incidental findings, but there is insufficient evidence to evaluate this hypothesis. Compared with data-driven models, clinicians exhibit volatile performance in predicting complications and making postoperative resource intensity decisions. Conclusion: To optimize value, postoperative resource intensity decisions should align with precise, data-driven patient acuity assessments augmented by models that accurately represent complex, non-linear relationships among risk factors.

Triggers for medical emergency team activation after non-cardiac surgery

“…The best approach is controversial, due to resource limitations, 7 mixed evidence of efficacy of ICU admission, 8,9 a lack of evidence of efficacy of HDU care, 10 and difficulties triaging patients to avoid overuse of ICU resources. 11 Ward-based care with MET rescue in the event of physiological deterioration is the most common strategy used in Australian hospitals, as MET systems reduce mortality for surgical patients. 12,13 However, recent evidence suggested that high-risk patients were more likely to die if their admission to ICU after surgery occurred following a MET call compared with immediate admission to ICU, suggesting that preventing deterioration of physiology was preferable to delayed rescue via MET call.…”

Artificial Intelligence–enabled Decision Support in Surgery

“…Human decision-making is subject to cognitive biases, errors, and preventable harm, especially for less experienced and sleep-deprived surgeons 2–7. In contrast, artificial intelligence models can rapidly and reproducibly process large quantities of data, are not susceptible to fatigue, and occasionally demonstrate superior performance in prediction and classification tasks that underlie decisions 8,9. Unlike traditional, rule-based inference engines, artificial intelligence models can discover new and complex, nonlinear associations and often outperform rule-based models 10,11.…”

Postoperative Overtriage to an Intensive Care Unit Is Associated With Low Value of Care

“…6 Despite the potential consequences of postoperative overtriage, there is no consensus definition of overtriage and there is sparse evidence associating overtriage, patient outcomes, and value of care. 7 Triage patterns reported in medical and mixed-medical surgical populations may not be generalizable to postoperative patients, who are uniquely vulnerable to hemorrhage, respiratory failure, opiate toxicity, and sepsis. [8][9][10] Most systems for classifying patient acuity would require further gains in accuracy and integration with clinical and digital workflows before real-world implementation.…”