Review - Using the American College of Surgeons’ National Surgical Quality Improvement Program (ACS-NSQIP) Database to Teach Data Exploration, Statistical Analysis, and Clinical Inference to Residents and Medical Students

Sep 21, 2026

Large clinical registries support teaching data-driven decision making for trainees, and the American College of Surgeons’ National Surgical Quality Improvement Program (ACS-NSQIP) provides an unusually granular surgical dataset with national benchmarking. The work develops an educator framework that uses ACS-NSQIP for data exploration, statistical analysis, and clinical inference through a modular curriculum aligned to core competencies. Eight sequential teaching modules cover dataset acquisition, governance, software use, example exercises, assessment approaches, and common pitfalls. The result enables statistical literacy and supports medical trainees’ data-analysis and publishing capability in a clinically meaningful context.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Background

Objective

Methods

Results

Conclusions

Introduction

Overview of the ACSNSQIP Database

Structure and Content

Data Quality

Access

Strengths & Limitations

Educational Rationale

Variables Contained in the NSQIP

Review Using the American College of Surgeons’ National Surgical Quality Improvement Program (ACS-NSQIP) Database to Teach Data Exploration, Statistical Analysis, and Clinical Inference to Residents and Medical Students Thor S. Stead, M. D.1, Latha Ganti, M. D., M.S., M. B.A., F.A. C. E. P.2,3, Yuchen Hua, Sc. M.4 1 Department of Anesthesiology, Weill Cornell Medicine/New York-Presbyterian Hospital, New York, NY, USA, 2 Medical Education, The Warren Alpert Medical School of Brown University, Providence, RI, USA, 3 Department of Research, Orlando College of Osteopathic Medicine, Orlando, FL, USA, 4 Data Science Institute, Brown University, Providence, RI, USA Keywords: NSQIP, data science, ACGME, statistics, quality improvement, program director, research, teaching [https://doi.org/10.62186/001c.137943](https://doi.org/10.62186/001c.137943) Academic Medicine & Surgery Background Large, rigorously collected clinical registries can ground the teaching of data‐driven decision making to trainees. The American College of Surgeons’ National Surgical Quality Improvement Program (ACS-NSQIP) is one of the world’s most granular surgical datasets. Objective To provide educators with a step by step framework for using ACS-NSQIP as an educational tool for data exploration, statistical analysis, and clinical inference. Methods We synthesized published experience with NSQIP based curricula, user guides, and quality improvement (QI) initiatives, and distilled best practices into a modular curriculum mapped to core competencies. Results We outline eight sequential teaching modules—from dataset acquisition to QI implementation—supported by example exercises, assessment strategies, and guidance on governance, software, and common pitfalls. Conclusions ACS-NSQIP affords an authentic, scalable platform to teach modern quantitative skills ina clinically meaningful context, fostering both statistical literacy and a platform to expand both the data analysis skills and publishing acumen of medical trainees. INTRODUCTION Quantitative literacy is now central to competent clinical practice and research.1 Residency program directors and medical school educators therefore seek experiential resources that integrate biostatistics with real-world patient outcomes.2 The American College of Surgeons’ National Surgical Quality Improvement Program (ACSNSQIP)—spanning more than 11 million operations and >200 peri-operative variables—offers such a resource, with validated data abstraction and national benchmarking.3 Beyond research, NSQIP data underpin resident Quality-InTraining Initiative (QITI) curricula that measurably reduce morbidity and improve performance metrics.4 OVERVIEW OF THE ACSNSQIP DATABASE • Structure and Content: Annual Participant Use Files (PUFs) include patient demographics, 30-day outcomes, and procedure-targeted modules (e.g., colectomy, mastectomy) . The 2023 PUF, for example, captures 994,313 cases across 676 sites with HIPAA-compliant de-identification.5 • Data Quality: NSQIP employs trained Surgical Clinical Reviewers, inter-rater reliability audits, and riskadjusted modelling, yielding validated complication rates superior to administrative claims data.6 • Access: PUFs are freely available to participating institutions; external investigators may apply through the ACS Research Data Center. Institutional NSQIP Table 1. Examples by which the NSQIP can be used to fulfill ACGME competencies.8 | Competency | NSQIP-Driven Learning Outcome | | --- | --- | | Patient Care & QI | Use risk-adjusted reports to identify modifiable complications and design interventions. | | Medical Knowledge | Apply epidemiologic principles to interpret surgical outcomes. | | Systems-Based Practice | Benchmark institutional performance against national norms. | | Practice-Based Learning | Conduct iterative data queries, hypothesis testing, and self-assessment. | datasets offer richer variable depth but require sitesp

FAQs

What dataset features support educational use?

ACS-NSQIP includes annual participant use files with patient demographics and 30-day outcomes, uses trained clinical reviewers with risk-adjusted modeling for validated complication rates, and offers benchmarks for institutional comparison.

Sep 21, 2026

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