Aligning Patient Acuity with Intensity of Care after Surgery

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ABSTRACT A key aim of this proposal is to equip the candidate with the training and resources necessary to develop expertise and experience in large-scale, multi-institutional informatics research using electronic health record data and machine learning to develop clinical decision-support tools. This proposal builds toward the candidate’s long-term career goal of becoming an independent surgeon-scientist with expertise in design and implementation of machine learning systems to augment clinical decision-making. To accomplish this goal, the candidate and mentors propose a systematic investigation of postoperative ‘patient acuity’ (i.e., risk for critical illness and death) and ‘intensity of care’ (i.e., triage destination and frequency of vital sign and laboratory measurements). After major surgery, misaligned patient acuity and intensity of care can lead to preventable harm and inappropriate resource use, affecting approximately 15 million inpatient surgeries annually in the US alone. When high-acuity patients receive low-intensity care, postoperative complications can progress to critical illness and cardiac arrest. Providing high-intensity care to low-acuity patients has low value and may cause harm through unnecessary treatments. It is difficult to address these problems systematically because there is no validated, unifying ‘intensity of care’ definition. The overall objective of this application is to understand intensity of care decision spaces in surgical patients and match them to clinical phenotypes and outcomes, leveraging this knowledge to generate precise, autonomous decision-support tools. The central hypothesis of this application is that inappropriate postoperative intensity of care is common, predictable, and associated with increased short- and long-term mortality, morbidity, and hospital costs. The rationale for this work is that integrating electronic health record data, machine learning, and clinical domain expertise offers opportunities to understand postoperative intensity of care decisions and develop decision-support tools capable of optimizing clinical outcomes and resource use. The specific aims of this proposal are to (1) develop and validate postoperative intensity of care definitions, (2) develop and validate interpretable, actionable acuity assessments that elucidate decision spaces, and (3) identify and predict postoperative intensity of care phenotypes. The proposed research is significant because it addresses a problem that affects millions of patients annually and is associated with potentially preventable harm and suboptimal resource use. The approach is innovative because the candidate and mentors are unaware of any prior attempts to classify and adjudicate postoperative intensity of care and understand the phenotypes and characteristics of patients receiving insufficient or excessive care. During the award period, the candidate will apply for an NIH-R01 investigator-initiated award for the prospective clinical implementation of an interpretable, actionable decision support tool incorporating validated intensity of care definitions and knowledge garnered from phenotype clustering, initially in a silent data collection period followed by a live period during which clinicians are provided with model outputs and clinically actionable recommendations.