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Roadmap for Transforming Preoperative Assessment to Preoperative Optimization

Aronson, Solomon MD, MBA, FASA, FACC, FCCP, FAHA, FASE*,†; Murray, Sutton MS*; Martin, Gavin MBChB, MMCi*; Blitz, Jeanna MD*; Crittenden, Timothy RN; Lipkin, Mike E. MD, MBA§; Mantyh, Christopher R. MD; Lagoo-Deenadayalan, Sandhya A. MD, PhD; Flanagan, Ellen M. MD*; Attarian, David E. MD, FACS, FAOA; Mathew, Joseph P. MD, MSc, MBA, FASE*; Kirk, Allan D. MD, PhD, FACS#; Perioperative Enhancement Team (POET)

Author Information
doi: 10.1213/ANE.0000000000004571


See Articles, p 803, p 804, p 808

Although improvements in surgical outcomes can be attributed to changes in care delivery across the entire perioperative continuum, assigning the discrete contribution of preoperative patient preparation to improvements in surgical outcome is complex and easily conflated. This uncertainty is common, often subtle, and frequently confronted when deciding the optimal timing of elective surgery.

Because conventional preoperative assessment is based on accepting and adapting to the condition of the patient, it occurs in close proximity to the surgical date. Consequently, when patients are seen in a preoperative clinic, it is often too late to effectively address comorbid conditions and modifiable risks. This practice compromises the important preoperative goal of not only identifying but also directly mitigating risks from comorbid medical conditions, which impact intraoperative management and adversely affect patients’ postoperative outcomes.

Approximately 50 million surgical procedures are performed annually in the United States, with overall good outcomes but highly variable results.1–5 Perioperative complications, albeit uncommon, are increasingly attributed to nonsurgical patient disease and comorbidity (Figure 1). Nevertheless, proactive preoperative management of comorbidity—especially subtle comorbid conditions—remains a challenge in most models of care.6

Figure 1.
Figure 1.:
Perioperative risk predictors of postoperative adverse outcomes categorized by health care, patient, and socioeconomic characteristics. BP indicates blood pressure; COPD, chronic obstructive lung disease; GDFT, goal-directed fluid therapy; HR, heart rate; OSA, obstructive sleep apnea.

The preanesthesia clinic (PAC) model is commonly used for presurgical preparation. In this setting, patients typically have an in-person clinic visit, a phone screen encounter, or a chart review only assessment. The visit type is typically protocolized and determined by criteria set by the respective surgical service.7 Depending on the encounter type, an appointment includes a presurgery risk review, medication review, presurgical history, and possibly a limited physical examination. It also allows for signing the anesthesia consent, reviewing the advance directive, and completing copayments and other financial matters. The scheduling of a PAC visit often occurs just before the scheduled surgical date, largely because the posted surgery date is prioritized. Little opportunity thus exists to effectively manage modifiable comorbid medical conditions, present in up to 20% of patients scheduled for surgery, thereby forfeiting an opportunity to positively impact surgical outcome and cost.8–14

Surgical care coordination should determine a patient’s readiness for surgery and recovery, whereby effort and time needed to meaningfully impact readiness are weighed and balanced with immediacy of surgery. We describe here a strategic case, business case, and operational roadmap to transition presurgical assessment to more reliably reduce surgery-associated risk, thereby enhancing value-based surgical care and perioperative population health management.


We recognized that, with traditional PAC care, when a patient is seen before an established surgery date, it is typically too late to effectively modify their operative risk without a disruption of patient, family, and surgeon expectations. We also acknowledged limited understanding of the contribution of modifiable preoperative comorbid medical conditions to surgical outcomes, which often poses a dilemma when deciding the optimal timing of elective surgery.

Our institutional perioperative enhancement team (POET) is a multidisciplinary group and learning health unit that delivers current and future best clinical practices while remaining committed to scientific discovery. Most recently, POET has implemented a Preoperative Anesthesia and Surgical Screening (PASS) Clinic to screen patients and to more proactively and efficiently manage modifiable risks at the time a patient’s surgical candidacy is first considered. This transformative model enables improved and early access to health care for complex patients. It also enhances perioperative patient tracking, risk stratification, disposition, and resource planning.

The PASS Clinic functions as a protocol-driven clinical care corridor whereby prescriptive preoperative screening and evaluation occur. When referred to the PASS Clinic, the patient’s readiness for surgery is determined by a team of specifically trained and focused providers. After evaluation, if further optimization of a modifiable condition is warranted, then referral to an optimization program is made.15 Importantly, the surgical team is involved in defining procedural urgency and setting the upper limit on the available time window for preoperative optimization. Including this parameter has been vital for a meaningful dialogue between surgeons and anesthesiologists about how best to balance mitigating modifiable risk factors versus the immediacy of the surgery.

Figure 2.
Figure 2.:
Schematic diagram of the journey from surgical declaration until surgery with implementation of the PASS clinic and POET preoperative screening and optimization steps and ideal timelines. PACU indicates postanesthesia care unit; PASS, perioperative anesthesia and surgical screening; POET, perioperative enhancement team; OSA, obstructive sleep apnea; PCN, penicillin; SNF, skilled nursing facility.

Needed patient optimization services are delivered via integrated, but in some cases, independent preoperative optimization clinics or POET optimization clinics, which are staffed by physicians and/or advanced practice providers (APPs). POET optimization clinics (Figure 2) support the management of chronic medical conditions in perioperative patients.



Implementation of the PASS Clinic required collaboration among physicians, nurses, allied health practitioners, finance and information technology experts, and administrative partners—all aligned for care redesign, business modeling, and operational project management.

While the PASS Clinic was being implemented, the existing PAC remained operational within the same footprint. Stable day-to-day operations for over 40,000 cases per year required a commitment to maintain concomitant parallel processes by many disciplines. Multiple interdependent efforts were synchronized at the institutional level. Critically, everyone involved in PASS implementation focused on socializing the concept, conducting numerous visits and presentations to individual surgical groups, and meeting frequently with the providers in the clinic. This continuous feedback and a demonstrated willingness to modify plans to address real or perceived concerns were essential and integral to the transformation.

Making the Business Case

The business case for PASS was developed, vetted, presented, and ultimately approved by health system leadership. This effort was initiated by the Department of Anesthesiology and endorsed by all the departments of surgery. It required hospital finance, nursing leadership, and administration support.

The value proposition for the PASS Clinic was defined using calculation of its direct contribution margin, as well as projected cost avoidance for the system. Development of a financial model for the PASS Clinic and each of its associated preoperative optimization programs was guided by an analysis of expected patient volumes, capacity cost rate (including personal cost with flexible skill options), and time variable assumptions. Volume assumptions, capacity cost rate, and operations expressed as times spent for specific activities, together with time-driven activity-based costing,16 provided a projected contribution margin. Other factors included cost avoidance and system readiness for value-based care contracting.

Work Flow, Space Planning, and Information Technology

Once the business plan was accepted, a workflow study and capacity analysis defined new positions and novel scopes of work. Some optimization programs, such as the anemia clinic17 and diabetes clinic,18 were already established. Each new hire for any new clinic was closely based on space availability, job posting, expected recruiting lead time, credentialing, on-boarding, and training (as needed) to correspond to official opening of the clinic itself. The ability to perform as an advertised preoperative patient optimization service and to enable charge capture when appropriate were incorporated into the staff hiring and space planning.

Space planning further included the redistribution, reassignment, and/or building of space to accommodate dedicated and flex rooms for a perioperative sleep apnea clinic, nutrition clinic, smoking cessation clinic, and a room for evaluation and consultation with telehealth capabilities from other remote PASS Clinic sites. Workflows and space assignments were reorganized, and heretofore hospital-based employees (eg, phone screeners) were reassigned to work from home with secure computer terminals to free up space for other purposes.

Growing pains occurred in all domains but were mitigated by an overarching and concomitant cultural development program known as the “1 Duke Periop” initiative across all perioperative platforms, which emphasized frontline problem solving, collegial engagement, respect, teamwork, and ownership.

Significant institutional information (IT) technology resources were committed to the project. There was an early identified need for multidimensional scheduling template development, data tracking systems, dashboard development, and electronic health record (EHR) integration.

Patient Triaging

A key initial operational hinge point was methodically determining which patients were phone screen eligible versus which needed an in-person clinic visit. A smart logic was developed that included fixed and dynamic elements based on a question algorithm for determining phone screen eligibility. An algorithm and automatic evaluation of existing patient data points and decision rules were developed to determine a patient’s status on key points (ie, cardiac history, ability to lie flat, presence of pain) without the need for chart review. When combined, these points calculated a background score used to determine eligibility for a phone screen or an in-person visit. The score is based on discrete data in the system, and it has numerous applications, for example, a status board or patient list report shown as an icon of a phone or a visit; a chart/navigator section shown as a phrase “patient is eligible for phone screen”; and a report filter listing only patients who are eligible for in-person visit. In the absence of these needed algorithm data points (eg, new patients to the hospital system), it was necessary for clinic schedulers to promptly determine a patient’s appointment eligibility. This was accomplished by asking the patient a series of yes/no screening questions, the results of which determined the appropriate visit type.

To decompress front-end decision support and triage workload, a category of phone screen ineligible procedures was created. This “high-risk surgery” category streamlined the screening and scheduling process by eliminating an unnecessary step of subjecting a patient to a decision tree for determination of phone screen eligibility. There was consensus that a designation of “high-risk surgery” based on the intrinsic risk of surgery itself should drive the need for an in-person evaluation.

We determined the most commonly performed procedures at our institution, and from that list, we curated the 100 highest-risk procedures to bypass phone screen consideration and be directly scheduled for an in-person visit. Surgical specialists were also engaged to determine consensus regarding appropriateness for a phone screen versus in-person PASS screen. We triage patients for phone screen who have multiple procedures and who do not require formal reassessment for 30 days unless an acute event has occurred. We inquire about any such changes with defined phone screen return visits. Beyond 30 days, we advocate for patient phone screen reevaluation to ensure medical stability. High-risk surgical procedures were based on standard “high-risk” surgery criteria.19–21

Patient Tracking and Scheduling Tools

Tracking tools were developed so that the status of a patient’s readiness for surgery (Table 1), including PASS clinic scheduling and any referral(s), was available for surgeons’ offices. We built out specific channels (through-points) for each of the POET optimization clinics. This allowed for improved scheduling efficiency, as well as a common way for various groups to communicate with each other regarding overall status or specific issues for a POET optimization pathway.

Table 1. - Conditions That Are Evaluated in the PASS Clinic Along With Threshold Criteria to Trigger a POET Referral
Preoperative Risk Factor
Opioid risk: this scores 13 rules that make the opioid risk tool from patient Hx information.
• Male patient and
 Personal Hx of ETOH abuse
 Family Hx of ETOH abuse
 Family Hx of drug abuse (illicit/illegal)
• Female patient and
 Personal Hx of ETOH abuse
 Family Hx of ETOH abuse
 Family Hx of drug abuse (illicit/illegal)
• Family Hx of prescription drug abuse
• Personal Hx of illicit/illegal drug abuse
• Personal Hx of prescription drug abuse
• Age between 16 and 45 y
• Hx/problem list: depression/major depression
• Hx/problem list: ADD, OCD, bipolar, or schizophrenia
• Hx of preadolescent sexual abuse
Anemia: this scores last laboratory value for Hgb (<12).
Endocrine: this scores last laboratory values for either/both Hgb A1C (>7.5) or blood glucose (>160).
Tobacco: this scores smoking/tobacco status in social Hx.
POSH: this scores patient age (>80) or age >65 y with any of the following
• Dementia or cognitive decline
• Visual impairment worse than 20/70 binocular with correction
• Frailty
Pain: this scores questionnaire sent to the patient at the time of surgical clinic appointment.
• Current drug abuse (prescription or illegal)
• Do you think your pain is terrible/won’t get any better?
STOP-BANG: this scores 4 questions.
• Age >50
• Neck circumference >42 cm
• Patient is male
• BMI >35
Epworth sleepiness scale: this scores values from the flowsheet.
DASI: this scores values from the flowsheet.
PCN allergy: this score self-reported PCN allergy, as well as PCN allergy confirmation from the Allergy Clinic.a
PONS: this scores 7 different rules.
• BMI <18.5
• Unintentional weight loss of >10% in last 6 mo
• Answer to question regarding “Eating less than half of normal diet in past week?”
• Albumin value <3 mg/dL
• Vit D value <20
• Hgb A1C >7.5
Abbreviations: ADD, anxiety/depressive disorder; BMI, body mass index; DASI, Duke activity status index; ETOH, alcohol; Hgb, hemoglobin; Hx, history; OCD, obsessive compulsive disorder; PASS, preoperative anesthesia and surgical screening; PCN, penicillin; POET, perioperative enhancement team; PONS, perioperative nutrition screen; POSH, Perioperative Optimization for Senior Health; STOP-BANG, Snoring, Tiredness, Observed apnea, blood Pressure, Body mass index, Age, Neck circumference, and Gender; Vit, vitamin.
aScore green if no allergy listed, red if allergy confirmed via Allergy Clinic, and orange if self-reported allergy.

There was process uniformity so PASS Clinic and optimization program providers did not need to use one way for one clinic and another way for others. Because each POET optimization clinic has its own channel, which was discretely saved and tracked, it was easier to display the individual status of the optimization episodes on status boards and reports or in chart and navigator sections.

A scheduling access system facilitated open scheduling of patients into optimization clinics via unique, discrete visit types and creation of scheduling templates. Because visit type specificity was so important to the function of our channels, we worked with our scheduling template group to create discrete POET-specific appointment visit types—instead of simply “new patient” or “return patient.” This allowed for schedule fast tracking and optimal function for our channels.

Defining Elective Surgery

To balance the goal of performing cases in a timely fashion with the time needed to optimize patients, an initial definition of elective surgery was refined to better meet consumer expectations. A new system defining elective surgery guardrails (surgical-directed willingness to wait for optimization) was agreed on to inform PASS Clinic providers about the best-case scenario for acceptance of time to postpone surgery for optimization to occur. These new elective strata were defined as none, <2 weeks, 2 weeks, 4 weeks, 6 weeks, 8 weeks, 12 weeks, and 24 weeks. It was understood that anything <2 weeks (albeit elective) was not sufficient time to effectively initiate an optimization program and was managed accordingly. These guardrails were implemented expecting that, over time, empiric data will be forthcoming to allow their modification.

PASS Clinic Patient–Nurse Navigator and Patient Service Access Team

A PASS Clinic patient–nurse navigator position was created to assist patients with scheduling and coordination of care. A patient tracking dashboard was developed for the nurse navigator and other designated key stakeholders. Workbench reporting was used to track operational and individual APP and attending physician continuous quality improvement. A multiprovider dashboard for the patient service access (PSA) team and APPs to monitor patient profiles on the day of clinic and subsequent status were developed. A pamphlet called “the PASSport” was created and distributed to patients to help guide and communicate follow-up patient appointments. The patient was assigned a patient navigator and given the option of meeting with a financial care coordinator.

Redesign of Preanesthesia Testing

A redesign of the preanesthesia testing (PAT) documentation was developed with smart logic and data elements, which included a patient identification feature that alerted day-of-surgery anesthesiology providers about patient-specific best practice advisories (BPAs) and alerts for intraoperative and postoperative care protocols. The diabetes mellitus (DM) BPA, for example, alerts anesthesia providers on the day of surgery, and up to 4 days before scheduled surgery, that the patient was referred to and managed preoperatively by endocrinology in the preoperative diabetic clinic for improved glucose control. The BPA recommends that the attending anesthesiologist on the day of surgery use a specific set of orders and provides a hyperlink to POET guidelines for perioperative management (eg, DM, obstructive sleep apnea). Included in the orders are laboratory testing, an order for appropriate postoperative consults, and a hyperlink to a detailed map of the suggested management plan for patients, preoperatively, intraoperatively, and postoperatively.

Tracking Clinical and Process Outcomes

Table 2. - Outcome Data Fields That Are Captured and Tracked Include (a) Process Outcomes, (b) Health Outcomes, (c) Health Economic Outcomes, and (d) Productivity Outcomes
Process outcomes
• Hemoglobin
• Transfusion
• Hemoglobin A1C
• Blood glucose
• Albumin
• Vitamin D
• Carbon monoxide
• Morphine dose equivalent
Health outcomes
• Surgical site infection up to 1 y postoperative
• Acute kidney injury up to 1 y postoperative
• Myocardial infarction up to 1 y postoperative
• Atrial fibrillation up to 1 y postoperative
• Smoking abstinence up to 1 y postoperative
• Stroke up to 1 y postoperative
• Cognitive dysfunction up to 1 y postoperative
• Indexed mortality up to 1 y postoperative
• False- versus true-positive PCN allergy designation
Health economic outcomes
• Case mix index
• Indexed length of stay
• ED visits at 30, 60, and 90 d
• Hospital readmission at 30, 60, and 90 d
Managerial efficiency and productivity outcomes
• Time to PASS clinic appointment (phone screen and in-person) after scheduled from surgery request
• Time to POET clinic appointment after referral from PASS clinic
• Number and type of POET referrals per day, week, month
Abbreviations: BMI, body mass index; ED, emergency department; PASS, preoperative anesthesia and surgical screening; PCN, penicillin; POET, perioperative enhancement team.

Patient identifiers are used to collect essential data fields, which are autopopulated from the electronic medical record (EMR) databases and linked to process outcomes (eg, hemoglobin, hemoglobin A1C, albumin), health outcomes (eg, surgical site infection [SSI], acute kidney injury [AKI], myocardial infarction [MI]), health economic outcomes (eg, length of stay, readmission), and managerial efficiency and productivity outcomes (Table 2). In addition, a perioperative patient-reported outcomes tool and dissemination process are under development.

Promoting Awareness of the Program

The PASS Clinic and its multiple optimization clinics were advertised and described in educational brochures. Patient portals and websites were updated to introduce the concept. Letters were drafted for communicating back to the primary care providers when an appointment was recommended. Systematic meet and greet and question and answer (Q&A) sessions were conducted throughout the hospital and system. Finally, local and national media were notified.


The PASS Clinic launched at Duke University Hospital on September 4, 2018, with a primary commitment to patients and surgeons to efficiently determine readiness for surgery as soon as surgery was being considered. Patient access was critical for meeting expectations of the PASS Clinic because of the time-sensitive nature of this preoperative ambulatory clinic. It was appreciated early on that, while guidelines and evidence for best practice are known, transformation of practice is difficult. Our guiding principle, therefore, was “between data and change is reality”—with reality understood to be emotion, trust, culture, and importantly making it easy to do.

Soon after launch, we developed systems to monitor PASS Clinic scheduling bottlenecks and enhance efficiency in patient scheduling. Before launch, it was understood that patients referred to the PASS Clinic within 7 days of surgery were unlikely candidates for optimization. Since launch, the average lead time (excluding patients scheduled within a 7-day period before surgery) for a PASS Clinic appointment before the requested surgery date has been 45 days. Approximately 30% of patients currently scheduled in the PASS clinic are within a 7-day window before their requested surgery date.

Ambulatory Scheduling Corridor

Figure 3.
Figure 3.:
The weekly average number of referrals from the PASS clinic by POET optimization clinic. POET indicates perioperative optimization team; PASS, perioperative anesthesia and surgical screening.

At launch, a newly created ambulatory scheduling corridor for patients to be evaluated in the PASS Clinic without a definite, preassigned surgery date has averaged 15% of the total PASS Clinic appointments per week and continues to grow. Within this ambulatory PASS Clinic scheduling corridor, there is 95% patient compliance for attending their preoperative evaluation appointment. More than 40% of the patients referred to the PASS Clinic through this ambulatory scheduling corridor are subsequently referred to POET optimization clinics (Figure 3). The remaining patients evaluated in the PASS Clinic (ie, those with specific requested surgery dates) have increased 3.5% week over week since launch. The percentage of patients subsequently referred to POET optimization programs from this corridor has ranged from 15% to 20%.

POET Optimization Programs

Thus far, >5000 referrals from the PASS Clinic to POET optimization programs have occurred. The preoperative anemia clinic, preoperative diabetes clinic, preoperative penicillin allergy testing clinic, preoperative nutrition clinic, preoperative pain clinic, and preoperative smoking cessation clinics are fully operational. An interface with the previously existing Perioperative Optimization for Senior Health (POSH) clinic has been established for coordinated geriatric care. Among the PASS patients (all inclusive) who are referred to a POET optimization clinic, approximately half of these patients are referred to more than one POET optimization program.

Some POET optimization clinics are physically embedded within the PASS clinic footprint (nutrition, anemia, and smoking cessation). Other POET programs (diabetes, pain, allergy testing, senior health) are independent and physically separated from the PASS Clinic but are linked via open scheduling templates with PASS. A few preoperative optimization clinics utilize telemedicine capabilities (smoking cessation, nutrition, diabetes), whereas others have been launched since PASS (obstructive sleep apnea) and/or planned for future developed (stress management, coagulation, fitness, prehabilitation).

For each optimization program, adherence to POET care redesign principles is followed, including assessment of clinical need, evidence-based process reengineering, establishment of risk threshold criteria, workflow and throughput analysis, personal and space planning, financial planning, and business case modeling. We maintain that, importantly, this model enables critical perioperative outcomes investigation22,23 regarding the impact of preoperative modification of existing medical comorbid conditions. The clinic has increasingly served as a common place and point for clinical research coordinators to engage with patients and integrate clinical trial enrollment and workflow with the clinical care.

Remaining Hurdles

After 9 months, there remain hurdles to overcome before full potential of the PASS Clinic model can be realized and assessed. While some of these hurdles are logistical (eg, need for faster access, need for space) and some cultural (eg, variance in uptake), knowledge and educational gaps also exist that are being addressed. Since launch, for example, our system to help better define elective surgery with inclusion of guardrail definitions to determine surgical-directed willingness to wait until optimization has resulted in the selection of “none” in 85% of case requests. That said, all stakeholders acknowledge that evaluation of postimplementation data will help guide future appropriate adjustments to elective guardrail definitions. We continue to track and prospectively collect data on impact to reduce uptake variance. We expect the ultimate structure of optimization windows to evolve over a period of several years.

The hurdles encountered are being collaboratively addressed among our administrators, surgeons, and anesthesiologists. Support for the PASS concepts has been reasonably easy to acquire, as most agree that patients should be in the best condition possible given the temporal constraints of the procedure involved. A key to initial cultural acceptance has been the anticipation that it will evolve, and at most decision points, the process has been designed to allow for an opt-out approach for surgeons whose schedules are challenging to adjust. The understanding and expectation of operational plasticity have eased anxiety over rigid program implementation.


Knowing that patients with poorly managed chronic medical conditions pose greater risk for adverse postoperative outcomes is not surprising. Indeed, the association between most comorbid medical conditions and perioperative outcomes has been established and should inform decision-making to better identify patients who may benefit from optimization.

The more interesting questions are whether poorly managed chronic medical conditions, those which empirically lead to increased morbidity, can be meaningfully modified before surgery, and what the most effective approach is to optimize patients with poorly managed chronic medical disease before surgery. Can we develop processes that promote optimal surgical outcomes and provide health benefits long after surgical intervention?

The timescale for rigorous assessment of PASS extends well beyond this initial implementation phase. However, this initially successful coordinated organizational effort will markedly improve the ability to define the magnitude of any improved outcomes. Moreover, important decisions regarding weather to increase access to resource-intensive multidisciplinary optimization programs are dependent on the answers to these questions.

Several challenges lie ahead. It is important to understand how well this process impacts individual patient surgical outcomes. POET and PASS patient data are being tracked and linked to case mix index; indexed mortality; indexed length of stay; emergency department visits; readmission at 30, 60, 90 days; and postoperative MI, SSI, and AKI up to 1 year.

We understand that access to this level of care may be challenging to expand in resource-constrained environments; however, we believe that institutional interest to best serve patients and measurement of return on investment when optimizing surgical readiness need to be appreciated in a larger context. We are working with institutional and community hospital partners to provide adaptive implementation strategies. Preoperative comorbidity and surgical care complexity portend not just postoperative mortality but also increased cost of care.24 The proposed preoperative optimization process thus also should enhance health economic outcomes.

The relationship between process outcomes (easily tracked) and health outcomes of these efforts to economic outcomes within an institution (more difficult to define) will need to be established, as will metrics of success to support and to sustain the paradigm shift.

Going forward, numerous quality measures exist to aid the assessment of the PASS approach. These include robust quality datasets from National Surgical Quality Improvement Program (NSQIP), the Vascular Quality Initiative (VQI), and the Society of Thoracic Surgery (STS). These data are well integrated into other health systems and should improvement in these accepted standards be realized, they will be strong evidence compelling other hospitals and medical centers to adopt this model.

There is also opportunity for discovery in that this protocolized approach will allow for biological indices of ongoing disease burden, unresolved inflammation, and physiologic decompensation to be assessed, potentially providing an increasingly granular and actionable measure of patient resilience and risk. Aggregated administrative, clinical, and biological data from this process should enable machine and deep neural network learning and clinical decision support tool development. Along the journey, steps toward that end will include reengineering perioperative medical care delivery (prevention, screening, and management) around bundles for patient populations; measure of outcomes (measures of process, health, economic, operation, patient experience); investment in perioperative services relative to the value they create for population health; alignment of reimbursement around value creation; and acceptance that consumers need to be involved in their health risk mitigation.

In our current volume paradigm, issues with hospital and provider incentives, as well as patient pushbacks, remain. Value is achieved with incremental upfront investments and change in culture. Should surgeons, patients, or health systems forfeit a next available slot to allow for optimization, at the cost of operating room (OR) utilization? Ultimately, models like the PASS Clinic will need to provide answers to this question in the scope of total cost of care management and easily rationalize changes in practice that positively contribute to value as we move forward.


Perioperative Enhancement Team (POET): Danielle M. Caldwell, BSN, RN (Nurse Manager, PASS Clinic), David G. A. Williams, MD (Department of Anesthesiology), Kate Ulrich, MS, BSN, RN (Duke University Hospital), and Cliff Flintom Jr (Duke University Health System).


Name: Solomon Aronson, MD, MBA, FASA, FACC, FCCP, FAHA, FASE.

Contribution: This author helped design the project, implement the study, and write the manuscript.

Name: Sutton Murray, MS.

Contribution: This author helped design the project, implement the study, and edit the manuscript.

Name: Gavin Martin, MBChB, MMCi.

Contribution: This author helped design the project, implement the study, and edit the manuscript.

Name: Jeanna Blitz, MD.

Contribution: This author helped implement the study and edit the manuscript.

Name: Timothy Crittenden, RN.

Contribution: This author helped implement the study and edit the manuscript.

Name: Mike E. Lipkin, MD, MBA.

Contribution: This author helped design the project, implement the study, and edit the manuscript.

Name: Christopher R. Mantyh, MD.

Contribution: This author helped edit the manuscript.

Name: Sandhya A. Lagoo-Deenadayalan, MD, PhD.

Contribution: This author helped design the project and edit the manuscript.

Name: Ellen M. Flanagan, MD.

Contribution: This author helped implement the study.

Name: David E. Attarian, MD, FACS, FAOA.

Contribution: This author helped design the project, implement the study, and edit the manuscript.

Name: Joseph P. Mathew, MD, MSc, MBA, FASE.

Contribution: This author helped design the project, implement the study, and edit the manuscript.

Name: Allan D. Kirk, MD, PhD, FACS.

Contribution: This author helped design the project, implement the study, and write the manuscript.

This manuscript was handled by: Thomas R. Vetter, MD, MPH.



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