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RISE

Surgical Quality at the Intersection Between Education and Accountability – Overview for Trainees and Surgeons in Practice

Molly Kobritz, MD; Zhenni Xie, MD; Anthony Antonacci, MD, FACS; Vihas Patel, MD, FACS

June 6, 2023

Key Learning Objectives

  • Problem-based learning and improvement (PBLI) and systems-based practice (SBP) are core competencies for both residents and attendings.

  • Participation in quality improvement (QI) is required for both hospital credentialing as well as ABS continuous certification, and is supported by resources provided by the AHRQ and the ACS.

  • Methodologies to reduce cognitive bias, improve identification of adverse events, and identify actionable areas of improvement may augment the Morbidity & Mortality (M&M) conference to increase the intersection of medical education and accountability.

Intended audience: surgical educators, surgical quality officers, surgery residents, junior and senior faculty surgeons 

Objectives: After reading this manuscript, the reader will be able to: 

  • Describe national initiatives pertinent to education in surgical quality, including the Agency for Healthcare Research and Quality (AHRQ), the American College of Surgeons National Surgical Quality Improvement Program® (ACS NSQIP) and the ACS Quality Verification Program (QVP). 
  • Identify methods to improve upon Morbidity and Mortality (M&M) review to enhance educational and quality improvement opportunities. 
  • Discuss the role for cognitive de-biasing in surgical quality improvement processes. 

Introduction

Surgical quality is achieved through a collaborative and iterative improvement process that encompasses recognition, mitigation, rescue from and ultimately prevention of adverse events. Trainees and novice faculty are often at the nexus of this complex interplay between education, professionalism, patient outcomes, malpractice, and reimbursement. Participation in surgical quality is often time consuming for faculty and can erode residents’ participation in patient care activities, particularly if risk mitigation and compensation are prioritized over teaching. It is imperative to educate our residents and faculty about PBLI, SBP and the resources available to integrate surgical quality into continuing education and practice at large. We aim to provide an overview of: 

  • National initiatives pertinent to education in surgical quality 
  • The role of M&M in quality improvement and surgical education 
  • Reducing cognitive bias in these processes 

Agency for Healthcare Research and Quality (AHRQ) 

The AHRQ was established by the Affordable Care Act in 2010 as a federal agency tasked with improving the quality and safety of America’s healthcare systems. The agency uses a three-prong approach which consists of: 

  • Safety and quality research 
  • Educational materials on how to catalyze system-wide improvements in care  
  • Outcomes assessments using data 

In the last 2 decades, there has been a pivot from fee-for-service to value-based care.  Quality mandates implemented by the AHRQ have impacted surgical practices by restricting reimbursement for hospital acquired conditions and aligning payments to hospitals’ outcome measures such as 30-day readmissions and rates of medical harm.1 These “carrot and stick” policies have seen success. From 2010 to 2014, there were 2.1 million fewer hospital-acquired conditions, 87,000 fewer patients dying in hospitals and a $20 billion reduction in health care costs.2  

In addition to reimbursement mandates, the AHRQ publishes safety toolkits for implementation in surgical teams: Toolkit to Promote Safe Surgery and Toolkit to Improve Safety in Ambulatory Care Centers.3,4 These toolkits encompass technical and adaptive strategies to improve clinical and cultural approaches to patient safety, which are broadly applicable to surgical teams across settings. The cultural elements promoted include leadership, engagement, teamwork, and multidisciplinary communication. These skills are critical to the development of safe surgical culture, and trainees and faculty alike should be aware of these AHRQ resources. Furthermore, the AHRQ provides organizational templates and other in-depth resources for any surgeon embarking on a surgical quality improvement program (SQIP).  

American College of Surgeons National Surgical Quality Improvement Program® (ACS NSQIP) and Quality Verification Program (ACS QVP) 

The first step of any effective SQIP is to identify the area in need of improvement. Data collection and tracking is essential. Historically, data used in QI were re-purposed coding, billing, and insurance claims data, commonly referred to as “administrative” data. Although generally easier to obtain, these data are not easily translated into actionable QI initiatives.  Instead, the ACS established the ACS NSQIP to collect in-depth and trusted data to help surgeons better understand their quality of care and opportunities for improvement. ACS NSQIP tracks patients for 30 days post-operatively and compiles data directly from patients’ charts, which outperforms administratively coded data in identification of surgical complications. Furthermore, the NSQIP database is risk-adjusted and case-mix adjusted, allowing surgeons and hospitals to compare and interpret complication rates appropriately.5  

The ACS has recently established the Quality Verification Program (ACS QVP) which builds upon ACS NSQIP data to help build hospital-based surgical quality programs. The ACS QVP is built upon 12 standards, which cultivate quality at the system-, hospital- and specialty-levels (Table 1).6  

Table 1: ACS QVP Standards7

  1. Leadership commitment and engagement to ensure surgical quality and safety (S,H)
  2. A designated Surgical Quality Officer who is accountable for quality across all surgery departments and divisions (S,H)
  3. A Surgical Quality and Safety Committee with representation from all surgical specialties and adjunctive disciplines, which serves as a forum for surgery-wide quality activities and provides an infrastructure that fosters communication throughout the institution (S,H)
  4. A safety culture and practice of high-reliability principles that is at the core of the hospital’s mission, embedded and identifiable throughout the institution (S,H)
  5. Standardized processes and sufficient resources for collecting, analyzing, and reviewing clinically relevant data (risk-adjusted and benchmarked when possible) to monitor and identify potential surgical quality and safety issues at the hospital and individual specialty level (S,H,Sp)
  6. Continuous quality improvement using data (S,H,Sp)
  7. A standardized, documented process for formal retrospective case review to monitor adverse events, assess compliance with protocols, and identify opportunities for improvement and standardization (S,H,Sp)
  8. Standardized processes to monitor and address quality and safety issues with individual surgeon practice through a formal peer-review process (S,H)
  9. Meaningful and thorough processes for credentialing and privileging that ensure all surgeons are qualified and able to provide safe and appropriate surgical care (S,H,Sp)
  10. Standardized and team-based processes in the five phases of care (preoperative evaluation, immediate preoperative, intraoperative, postoperative, post-discharge) (S,H,Sp)
  11. Standardized, evidence-based, multidisciplinary management of specific diseases (S,Sp)
  12. Compliance with hospital-level regulatory performance metrics (S,H)

S = System Level, H = Hospital Level, Sp = Specialty Level

Morbidity and Mortality Conference  

M&M conference is a time-honored tradition in surgery, is mandated by the  Accreditation Council for Graduate Medical Education Residency Review Committee (ACGME RRC) in Surgery, and fulfills American Board of Surgery Maintenance of Certification (ABS MOC) criteria in many respects. M&M provides ongoing opportunity to critically evaluate patient care and to integrate available evidence to improve surgical practice. However, M&M conferences lack rigorous methods of case-identification, carrying the risk of biased reporting and leading to underreporting of complications as compared to nationally compiled databases.8 In order for M&M conferences to provide both education and accountability, there must be standardized methods of case selection and case discussion, as well as development of action plans to address areas of improvement identified through the M&M process.9 Furthermore, M&M conferences often fail to explicitly recognize and discuss personal experiences with error.10 Without systematic approaches, M&M carries significant risk of bias and failure to address underlying causes of adverse events. 

Addressing Cognitive Bias in Surgical Quality Efforts 

De-biasing strategies complement M&M review and enhance surgeons’ ability to critically identify adverse events and areas for improvement in both systems and individual practice. Artificial intelligence technologies may further improve surgeons’ identification of adverse events by illuminating blind spots at risk for repeat errors.11,12 Regular integration of systems audits create opportunities to directly address systems failures and personal error to both increase accountability and improve patient care.13 We recommend the use of structured reporting systems and data collection to enhance identification of trends in errors and clear targets for improvement in M&M review.14,15 Furthermore, these systems can be used as a structural framework to integrate regular assessments of cognitive bias. At our institution, cognitive bias assessments are mandatory components of each M&M critique. This requires learners to transition from intuitive to deliberative thinking and provides opportunities to incorporate debiasing strategies “in the moment” to address weaknesses at the individual level rather than at the more frequently discussed systems level (Figure 1 and Table 2).16,17 These approaches together augment M&M to create meaningful improvements in both individual and systems-based practices.  

Figure 1. Approach to Cognitive Debiasing in M&M and SQIP1
Figure 1. Approach to Cognitive Debiasing in M&M and SQIP1

Examples of Cognitive Debiasing Strategies useful in Surgical Reasoning17

  1. Consider alternative or differential diagnoses.
  2. Promote linear reasoning by using checklists, guidelines, or consultations.
  3. Replace intuition (Type 1 thinking) with analytical thinking (Type 2 thinking).
  4. Optimize conditions for favorable reasoning (modify workflow and/or time constraints).

Conclusion 

Surgeon and surgical leadership engagement in surgical quality is critical to patient safety and continuing education. Both residents and faculty must be aware of the tools available to continuously integrate surgical quality with patient care, and to capitalize on daily opportunities for improvement. 

 

References 

  1. Liang BA, Mackey T. Quality and Safety in Medical Care: What Does the Future Hold? Arch Pathol Lab Med. 2011;135(11):1425-1431. doi:10.5858/arpa.2011-0154-OA
  2. Saving Lives and Saving Money: Hospital-Acquired Conditions Update. Accessed November 7, 2022. https://www.ahrq.gov/hai/pfp/interimhacrate2014.html
  3. Toolkit To Improve Safety in Ambulatory Surgery Centers. Content last reviewed May 2017. Agency for Healthcare Research and Quality, Rockville, MD. https://www.ahrq.gov/hai/tools/ambulatory-surgery/index.html
  4. Toolkit To Promote Safe Surgery. Content last reviewed December 2017. Agency for Healthcare Research and Quality, Rockville, MD. https://www.ahrq.gov/hai/tools/surgery/index.html
  5. Birkmeyer JD, Shahian DM, Dimick JB, et al. Blueprint for a New American College of Surgeons: National Surgical Quality Improvement Program. J Am Coll Surg. 2008;207(5):777-782. doi:10.1016/j.jamcollsurg.2008.07.018
  6. Fischer CP, Hu QL, Wescott AB, Maggard-Gibbons M, Hoyt DB, Ko CY. Evidence Review for the American College of Surgeons Quality Verification Part II: Processes for Reliable Quality Improvement. J Am Coll Surg. 2021;233(2):294-311.e1. doi:10.1016/j.jamcollsurg.2021.03.028
  7. American College of Surgeons. Optimal Resources for Surgical Quality and Safety, 2021 ACS QVP Standards.; 2021.
  8. Hutter MM, Rowell KS, Devaney LA, et al. Identification of surgical complications and deaths: an assessment of the traditional surgical morbidity and mortality conference compared with the American College of Surgeons-National Surgical Quality Improvement Program. J Am Coll Surg. 2006;203(5):618-624. doi:10.1016/j.jamcollsurg.2006.07.010
  9. Churchill KP, Murphy J, Smith N. Quality Improvement Focused Morbidity and Mortality Rounds: An Integrative Review. Cureus. 12(12):e12146. doi:10.7759/cureus.12146
  10. Pierluissi E, Fischer MA, Campbell AR, Landefeld CS. Discussion of Medical Errors in Morbidity and Mortality Conferences. JAMA. 2003;290(21):2838-2842. doi:10.1001/jama.290.21.2838
  11. Mellia JA, Basta MN, Toyoda Y, et al. Natural Language Processing in Surgery: A Systematic Review and Meta-analysis. Ann Surg. 2021;273(5):900-908. doi:10.1097/SLA.0000000000004419
  12. Kobritz M, Patel V, Rindskopf D, et al. Practice Based Learning and Improvement: Improving Morbidity and Mortality Review using Natural Language Processing. J Surg Res. 2022;283:351-356 doi:10.1016/j.jss.2022.10.075.
  13. Szostek JH, Wieland ML, Loertscher LL, et al. A systems approach to morbidity and mortality conference. Am J Med. 2010;123(7):663-668. doi:10.1016/j.amjmed.2010.03.010
  14. Antonacci AC, Lam S, Lavarias V, Homel P, Eavey RD. A Morbidity and Mortality Conference-Based Classification System for Adverse Events: Surgical Outcome Analysis: Part I. J Surg Res. 2008;147(2):172-177. doi:10.1016/j.jss.2008.02.054
  15. Antonacci AC, Lam S, Lavarias V, Homel P, Eavey RA. A Report Card System Using Error Profile Analysis and Concurrent Morbidity and Mortality Review: Surgical Outcome Analysis, Part II. J Surg Res. 2009;153(1):95-104. doi:10.1016/j.jss.2008.02.051
  16. Antonacci AC, Dechario SP, Rindskopf D, Husk G, Jarrett M. Cognitive bias and severity of harm following surgery: Plan for workflow debiasing strategy. Am J Surg. 2021;222(6):1172-1177. doi:10.1016/j.amjsurg.2021.08.035
  17. Antonacci AC, Dechario SP, Antonacci C, et al. Cognitive Bias Impact on Management of Postoperative Complications, Medical Error, and Standard of Care. J Surg Res. 2021;258:47-53. doi:10.1016/j.jss.2020.08.040
  18. Croskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf. 2013;22 Suppl 2:ii58-ii64. doi:10.1136/bmjqs-2012-001712

Authors

Molly Kobritz, MD

Resident Physician, Department of Surgery, Northwell-North Shore/LIJ
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell

Zhenni Xie, MD

Resident Physician, Department of Surgery, Northwell-North Shore/LIJ
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell

Anthony Antonacci, MD, FACS

Medical Director, Lenox Hill Hospital
Medical Board Vice President of Surgery, Lenox Hill Hospital
Associate Professor, Department of Surgery
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell

Vihas Patel, MD, FACS

Director, Surgery-Critical Care, Northwell-North Shore/LIJ
Vice Chair of Academic Affairs, Northwell-North Shore/LIJ
Associate Professor, Department of Surgery
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell

Corresponding Author:

Molly Kobritz, MD
mkobritz@northwell.edu