About

Carri W. Chan is the Cain Brothers and Company Professor of Healthcare Management in the Division of Decision, Risk, and Operations at Columbia Business School, where she also serves as the Faculty Director of the Healthcare and Pharmaceutical Management Program. Her primary research interests focus on data-driven modeling of complex stochastic systems, dynamic optimization, and queueing theory, with specific applications in healthcare operations management. Professor Chan's work integrates empirical methods with mathematical modeling to develop evidence-based strategies aimed at improving healthcare delivery. She has collaborated extensively with healthcare providers and administrators at prominent health systems including Columbia University Irving Medical Center, Geisinger Health Systems, Montefiore Medical Center, Northern California Kaiser Permanente, and Weill Cornell Medical Center. She earned her PhD in Electrical Engineering from Stanford University and completed her undergraduate studies at MIT. In her role as Faculty Director of the Healthcare and Pharmaceutical Management Program, she oversees the curriculum and extracurricular activities designed to support students and alumni pursuing careers in the healthcare industry.

Research topics

• Computer Science
• Medicine
• Computer network
• Operations research
• Operations management
• Anesthesia
• Mathematics
• Process management
• Economics
• Emergency medicine
• Nursing
• Marketing
• Surgery
• Business
• Intensive care medicine
• Internal medicine
• Engineering

Selected publications

• Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance

  arXiv (Cornell University) · 2026-04-15

  preprintOpen access1st authorCorresponding

  AI tools increasingly guide targeted interventions in healthcare, education, and recruiting. Algorithms score individuals, trigger outreach to those above a threshold (e.g., high-risk or high-value), and encourage them to request service; then providers deliver service to those who request. Standard practice sets the threshold and selects the algorithm to maximize predictive accuracy, assuming that better predictions yield better outcomes. We show that this approach is suboptimal when limited service capacity and probabilistic behavioral responses influence who receives service. In such settings, the optimal score threshold must balance two effects: ensuring all capacity is filled (utilization) and ensuring high-value individuals are served despite competition between requests (cannibalization). We characterize the optimal threshold and prove that policies based solely on predictive accuracy are generally suboptimal. Further, because optimal thresholds vary with service capacity, algorithm selection metrics like AUC, which weight all thresholds equally, are misaligned with operational performance. We introduce a new metric--Operational AUC (OpAUC)--and show it leads to optimal algorithm selection. Finally, we conduct a case study on sepsis early warning data and illustrate the magnitude of improvement that can be achieved from improved threshold and algorithm selection.

  PublisherDOI

• Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance

  ArXiv.org · 2026-04-15

  articleOpen access1st authorCorresponding

  AI tools increasingly guide targeted interventions in healthcare, education, and recruiting. Algorithms score individuals, trigger outreach to those above a threshold (e.g., high-risk or high-value), and encourage them to request service; then providers deliver service to those who request. Standard practice sets the threshold and selects the algorithm to maximize predictive accuracy, assuming that better predictions yield better outcomes. We show that this approach is suboptimal when limited service capacity and probabilistic behavioral responses influence who receives service. In such settings, the optimal score threshold must balance two effects: ensuring all capacity is filled (utilization) and ensuring high-value individuals are served despite competition between requests (cannibalization). We characterize the optimal threshold and prove that policies based solely on predictive accuracy are generally suboptimal. Further, because optimal thresholds vary with service capacity, algorithm selection metrics like AUC, which weight all thresholds equally, are misaligned with operational performance. We introduce a new metric--Operational AUC (OpAUC)--and show it leads to optimal algorithm selection. Finally, we conduct a case study on sepsis early warning data and illustrate the magnitude of improvement that can be achieved from improved threshold and algorithm selection.

  PublisherOA PDF

• Implementing a prediction driven framework for emergency department nurse staffing to optimize real time decisions

  npj Health Systems · 2025-05-08 · 3 citations

  articleOpen access

  Abstract This study implemented and evaluated a prediction-driven nurse staffing framework in a large adult emergency department. The framework leveraged a two-stage prediction model that forecasted patient volume and guided staffing decisions. Using a pre-post study design, we compared patient throughput (measured by door-to-evaluation time, active treatment time, boarding time, length of stay, and left-without-being-seen rate) and cost outcomes (measured as hourly nurse staffing costs) before and after implementation. The model achieved an RMSE of 11.261 and MAPE of 13.414% at the base stage, and an RMSE of 9.973 and MAPE of 12.126% at the surge stage. The framework reduced hourly staffing costs by $162.04 without negatively affecting throughput. Reducing one nurse per hour from the recommended level increased wait times by two minutes, with an additional 2.3-min increase when staffing dropped below 20% of recommendations. These findings highlight the potential of prediction-driven staffing to reduce costs while maintaining patient throughput.

  PublisherOA PDFDOI

• The Impact of Surgeon Daily Workload and Its Implications for Operating Room Scheduling

  Production and Operations Management · 2025-11-06 · 1 citations

  articleOpen access

  In many service systems, an individual server’s workload can have a substantial impact on service time and quality. Such effects are particularly important in healthcare systems which often operate under resource and time constraints. In much of the literature, this effect of workload has been primarily considered at the system and instantaneous level rather than the individual and cumulative level. In this study, we investigate this relationship in the context of cardiac surgery, that is, how surgery duration and patient outcomes are affected by the individual surgeon’s daily workload. Using a detailed data set of more than 5,600 cardiac operations in a large hospital, we quantify how individual surgeon daily workload (the number of operations performed by the focal surgeon) affects surgery duration and patient outcomes. To handle the endogeneity of surgeon daily workload, we construct instrumental variables using operational factors of the cardiac surgery department, including the regular surgery schedule of surgeons. We find that high daily workload for the focal surgeon is associated with longer surgery duration as well as post-surgery length-of-stay in the intensive care unit and hospital. These results highlight the potential negative impact of high individual surgeon workload. We develop a surgical scheduling model that incorporates the estimated impact of surgeon daily workload. We solve the model by mixed-integer quadratic programing and show that our proposed schedule can substantially reduce total operating room (OR) time and post-surgery length-of-stay. Our results suggest that hospitals should take into account the effects of individual surgeon daily workload when managing their ORs. Specifically, they can substantially improve patient flow and patient outcomes by smoothing individual surgeon’s workload across days.

  PublisherOA PDFDOI

• Rapid Response Teams for Proactive Sepsis Treatment

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Waiting Online vs. In Person: An Empirical Study on Outpatient Clinic Visit Incompletion

  Manufacturing & Service Operations Management · 2025-07-25 · 5 citations

  article

  Problem definition: The adoption of online services, such as telemedicine, has increased rapidly over the last few years. To better manage online services and effectively integrate them with in-person services, we need to better understand customer behaviors under the two service modalities. Utilizing data from two large internal medicine outpatient clinics, we take an empirical approach to study service incompletion, which can be because of either patient no-show or leaving without being seen. Methodology/results: We focus on estimating the causal effect of whether the provider has cleared prior appointments—used as a proxy of intraday delay—on service incompletion for in-person and telemedicine appointments, respectively. When providers have not cleared prior appointments, patients may have to wait, making them more likely to leave without being seen, leading to a higher service incompletion rate. We introduce a multivariate probit model with instrumental variables to handle estimation challenges because of endogeneity, sample selection, and measurement error. We also conduct a numerical analysis of the intraday sequencing rule when having both telemedicine and in-person patients. Our estimation results show that intraday delay increases the telemedicine service incompletion rate by 7.40%, but it does not have a significant effect on the in-person service incompletion rate. Managerial implications: Our study suggests that telemedicine patients may leave without being seen, whereas in-person patients are not sensitive to intraday delay. More importantly, failing to properly distinguish between incompletions caused by intraday delays and those resulting from no-shows can lead to highly inferior patient sequencing decisions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0365 .

  PublisherDOI

• Prediction-Driven Surge Planning with Application to Emergency Department Nurse Staffing

  Management Science · 2024-05-24 · 17 citations

  article

  Determining emergency department (ED) nurse staffing decisions to balance quality of service and staffing costs can be extremely challenging, especially when there is a high level of uncertainty in patient demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by using demand forecasts. In this work, we study a two-stage prediction-driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) nurse staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus system stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Last, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and nurse staffing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework has the potential to reduce annual staffing costs by 10%–16% ($2 M–$3 M) while guaranteeing timely access to care. This paper was accepted by David Simchi-Levi, healthcare management. Funding: J. Dong was partially supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-1944209]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02781 .

  PublisherDOI

• Beyond order‐based nursing workload: A retrospective cohort study in intensive care units

  Journal of Nursing Scholarship · 2024-05-12 · 3 citations

  articleOpen access

  INTRODUCTION: In order to be positioned to address the increasing strain of burnout and worsening nurse shortage, a better understanding of factors that contribute to nursing workload is required. This study aims to examine the difference between order-based and clinically perceived nursing workloads and to quantify factors that contribute to a higher clinically perceived workload. DESIGN: A retrospective cohort study was used on an observational dataset. METHODS: We combined patient flow, nurse staffing and assignment, and workload intensity data and used multivariate linear regression to analyze how various shift, patient, and nurse-level factors, beyond order-based workload, affect nurses' clinically perceived workload. RESULTS: Among 53% of our samples, the clinically perceived workload is higher than the order-based workload. Factors associated with a higher clinically perceived workload include weekend or night shifts, shifts with a higher census, patients within the first 24 h of admission, and male patients. CONCLUSIONS: The order-based workload measures tended to underestimate nurses' clinically perceived workload. We identified and quantified factors that contribute to a higher clinically perceived workload, discussed the potential mechanisms as to how these factors affect the clinically perceived workload, and proposed targeted interventions to better manage nursing workload. CLINICAL RELEVANCE: By identifying factors associated with a high clinically perceived workload, the nurse manager can provide appropriate interventions to lighten nursing workload, which may further reduce the risk of nurse burnout and shortage.

  PublisherOA PDFDOI

• Telemedicine is associated with reduced socioeconomic disparities in outpatient clinic no-show rates

  Journal of Telemedicine and Telecare · 2023-03-27 · 18 citations

  article

  Introduction The global pandemic caused by coronavirus (COVID-19) sped up the adoption of telemedicine. We aimed to assess whether factors associated with no-show differed between in-person and telemedicine visits. The focus is on understanding how social economic factors affect patient no-show for the two modalities of visits. Methods We utilized electronic health records data for outpatient internal medicine visits at a large urban academic medical center, from February 1, 2020 to December 31, 2020. A mixed-effect logistic regression was used. We performed stratified analysis for each modality of visit and a combined analysis with interaction terms between exposure variables and visit modality. Results A total of 111,725 visits for 72,603 patients were identified. Patient demographics (age, gender, race, income, partner), lead days, and primary insurance were significantly different between the two visit modalities. Our multivariable regression analyses showed that the impact of sociodemographic factors, such as Medicaid insurance (OR 1.23, p < 0.01 for in-person; OR 1.03, p = 0.57 for telemedicine; p < 0.01 for interaction), Medicare insurance (OR 1.11, p = 0.04 for in-person; OR 0.95, p = 0.32 for telemedicine; p = 0.03 for interaction) and Black race (OR 1.36, p < 0.01 for in-person; OR 1.20, p < 0.01 for telemedicine; p = 0.03 for interaction), on increased odds of no-show was less for telemedicine visits than for in-person visits. In addition, inclement weather and younger age had less impact on no-show for telemedicine visits. Discussion Our findings indicated that if adopted successfully, telemedicine had the potential to reduce no-show rate for vulnerable patient groups and reduce the disparity between patients from different socioeconomic backgrounds.

  PublisherDOI

• Use of Real-Time Information to Predict Future Arrivals in the Emergency Department

  Annals of Emergency Medicine · 2023-01-19 · 21 citations

  article
  PublisherDOI

Recent grants

• Collaborative Research: Management of Transitional Care Units to Improve Hospital Outcomes

  NSF · $150k · 2012–2016

• CAREER: Managing Patient Flows with Congestion Effects

  NSF · $400k · 2014–2020

Frequent coauthors

• Gabriel J. Escobar

  Kaiser Permanente

  26 shared

• Nicholas Bambos

  Stanford University

  24 shared

• Song‐Hee Kim

  Medical College of Wisconsin

  14 shared

• Michelle N. Gong

  Montefiore Medical Center

  11 shared

• Marcelo Olivares

  Complex Engineering System Institute

  11 shared

• Mor Armony

  New York University

  8 shared

• Hayley B. Gershengorn

  University of Miami

  8 shared

• Vivek F. Farias

  Massachusetts Institute of Technology

  7 shared