Donald Lee

Associate Professor of Information Systems & Operations Management Emory University, Goizueta Business School

  • Atlanta GA

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Emory University, Goizueta Business School

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Biography

Professor Lee's research develops rigorous data science techniques for improving the delivery of health care. His work is recognized by R01 funding from the National Institutes of Health, one of the nation's preeminent grants for medical research. On the applied front, he has extensive experience designing data-driven tools for problems ranging from healthcare financial planning to real-time warning systems for adverse medical events. On the methodological front, his research has resolved foundational questions in causal inference and in survival machine learning. His work has appeared in leading journals in management, statistical machine learning, and healthcare.

Professor Lee also holds a joint appointment in the Department of Biostatistics & Bioinformatics at the Rollins School of Public Health. Prior to joining Emory, he served as an associate professor at Yale and held appointments in the School of Management and in the Department of Statistics & Data Science.

Education

Stanford University

MS/PhD (Statistics/Operations Research)

Cambridge University

BA/MA/MMath (Mathematics)

Areas of Expertise

Healthcare Operations
Medical outcomes evaluation
Statistical machine learning
Causal Inference

Research Spotlight

6 min

Hiring More Nurses Generates Revenue for Hospitals

Underfunding is driving an acute shortage of trained nurses in hospitals and care facilities in the United States. It is the worst such shortage in more than four decades. One estimate from the American Hospital Association puts the deficit north of one million. Meanwhile, a recent survey by recruitment specialist AMN Healthcare suggests that 900,000 more nurses will drop out of the workforce by 2027. American nurses are quitting in droves, thanks to low pay and burnout as understaffing increases individual workload. This is bad news for patient outcomes. Nurses are estimated to have eight times more routine contact with patients than physicians. They shoulder the bulk of all responsibility in terms of diagnostic data collection, treatment plans, and clinical reporting. As a result, understaffing is linked to a slew of serious problems, among them increased wait times for patients in care, post-operative infections, readmission rates, and patient mortality—all of which are on the rise across the U.S. Tackling this crisis is challenging because of how nursing services are reimbursed. Most hospitals operate a payment system where services are paid for separately. Physician services are billed as separate line items, making them a revenue generator for the hospitals that employ them. But under Medicare, nursing services are charged as part of a fixed room and board fee, meaning that hospitals charge the same fee regardless of how many nurses are employed in the patient’s care. In this model, nurses end up on the other side of hospitals’ balance sheets: a labor expense rather than a source of income. For beleaguered administrators looking to sustain quality of care while minimizing costs (and maximizing profits), hiring and retaining nursing staff has arguably become something of a zero-sum game in the U.S. The Hidden Costs of Nurse Understaffing But might the balance sheet in fact be skewed in some way? Could there be potential financial losses attached to nurse understaffing that administrators should factor into their hiring and remuneration decisions? Research by Goizueta Professors Diwas KC and Donald Lee, as well as recent Goizueta PhD graduates Hao Ding 24PhD (Auburn University) and Sokol Tushe 23PhD (Muma College of Business), would suggest there are. Their new peer-reviewed publicationfinds that increasing a single nurse’s workload by just one patient creates a 17% service slowdown for all other patients under that nurse’s care. Looking at the data another way, having one additional nurse on duty during the busiest shift (typically between 7am and 7pm) speeds up emergency department work and frees up capacity to treat more patients such that hospitals could be looking at a major increase in revenue. The researchers calculate that this productivity gain could equate to a net increase of $470,000 per 10,000 patient visits—and savings to the tune of $160,000 in lost earnings for the same number of patients as wait times are reduced. “A lot of the debate around nursing in the U.S. has focused on the loss of quality in care, which is hugely important,” says Diwas KC. But looking at the crisis through a productivity lens means we’re also able to understand the very real economic value that nurses bring too: the revenue increases that come with capacity gains. Diwas KC, Goizueta Foundation Term Professor of Information Systems & Operations Management “Our findings challenge the predominant thinking around nursing as a cost,” adds Lee. “What we see is that investing in nursing staff more than pays for itself in downstream financial benefits for hospitals. It is effectively a win-win-win for patients, nurses, and healthcare providers.” Nurse Load: the Biggest Impact on Productivity To get to these findings, the researchers analyzed a high-resolution dataset on patient flow through a large U.S. teaching hospital. They looked at the real-time workloads of physicians and nurses working in the emergency department between April 2018 and March 2019, factoring in variables such as patient demographics and severity of complaint or illness. Tracking patients from admission to triage and on to treatment, the researchers were able to tease out the impact that the number of nurses and physicians on duty had on patient throughput. Using a novel machine learning technique developed at Goizueta by Lee, they were able to identify the effect of increasing or reducing the workforce. The contrast between physicians and nursing staff is stark, says Tushe. “When you have fewer nurses on duty, capacity and patient throughput drops by an order of magnitude—far, far more than when reducing the number of doctors. Our results show that for every additional patient the nurse is responsible for, service speed falls by 17%. That compares to just 1.4% if you add one patient to the workload of an attending physician. In other words, nurses’ impact on productivity in the emergency department is more than eight times greater.” Boosting Revenue Through Reduced Wait Times Adding an additional nurse to the workforce, on the other hand, increases capacity appreciably. And as more patients are treated faster, hospitals can expect a concomitant uptick in revenue, says KC. “It’s well documented that cutting down wait time equates to more patients treated and more income. Previous research shows that reducing service time by 15 minutes per 30,000 patient visits translates to $1.4 million in extra revenue for a hospital.” In our study, we calculate that staffing one additional nurse in the 7am to 7pm emergency department shift reduces wait time by 23 minutes, so hospitals could be looking at an increase of $2.33 million per year. Diwas KC This far eclipses the costs associated with hiring one additional nurse, says Lee. “According to 2022 U.S. Bureau of Labor Statistics, the average nursing salary in the U.S. is $83,000. Fringe benefits account for an additional 50% of the base salary. The total cost of adding one nurse during the 7am to 7pm shift is $310,000 (for 2.5 full-time employees). When you do the math, it is clear. The net hospital gain is $2 million for the hospital in our study. Or $470,000 per 10,000 patient visits.” Incontrovertible Benefits to Hiring More Nurses These findings should provide compelling food for thought both to healthcare administrators and U.S. policymakers. For too long, the latter have fixated on the upstream costs, without exploring the downstream benefits of nursing services, say the researchers. Their study, the first to quantify the economic value of nurses in the U.S., asks “better questions,” argues Tushe; exploiting newly available data and analytics to reveal incontrovertible financial benefits that attach to hiring—and compensating—more nurses in American hospitals. We know that a lot of nurses are leaving the profession not just because of cuts and burnout, but also because of lower pay. We would say to administrators struggling to hire talented nurses to review current wage offers, because our analysis suggests that the economic surplus from hiring more nurses could be readily applied to retention pay rises also. Sokol Tushe 23PhD, Muma College of Business The Case for Mandated Ratios For state-level decision makers, Lee has additional words of advice. “In 2004, California mandated minimum nurse-to-patient ratios in hospitals. Since then, six more states have added some form of minimum ratio requirement. The evidence is that this has been beneficial to patient outcomes and nurse job satisfaction. Our research now adds an economic dimension to the list of benefits as well. Ipso facto, policymakers ought to consider wider adoption of minimum nurse-to-patient ratios.” However, decision makers go about tackling the shortage of nurses in the U.S., they should go about it fast and soon, says KC. “This is a healthcare crisis that is only set to become more acute in the near future. As our demographics shift and our population starts again out, demand for quality will increase. So too must the supply of care capacity. But what we are seeing is the nursing staffing situation in the U.S. moving in the opposite direction. All of this is manifesting in the emergency department. That’s where wait times are getting longer, mistakes are being made, and overworked nurses are quitting. It is creating a vicious cycle that needs to be broken.” Diwas Diwas KC is a professor of information systems & operations management and Donald Lee is an associate professor of information systems & operations management. Both experts are available to speak about this important topic simply click on either icon now to arrange an interview today.

Donald LeeDiwas KC

4 min

Survival analysis: Forecasting lifespans of patients and products

How long will you live? Should you spring for that AppleCare+ warranty for your iPhone? When will your buddy pay you back for that lunch? For centuries, soothsayers have striven to understand the lifespan of things – be they patient longevity, product lifecycles, or even time to loan default. Nowadays, scientists have turned away from reading tea leaves and toward survival analysis – a complex data science method for predicting not only whether an event will happen (the death of a patient, the failure of a product or machine, default on a payment, and so on) but when this event is likely to occur. But it’s problematic. Until now, the tools of survival analysis have only been applicable in certain settings. This is due to the inherent heterogeneity of what is being analyzed: differences in patient lifestyles, demographics, product usage patterns, and so on. New research by Goizueta Business School’s Donald Lee, associate professor of information systems and operations management and of biostatistics and bioinformatics, has yielded a new tool that greatly extends survival analysis to broader use cases. “Historically, scientists have used classic survival analysis tools to predict the lifespan of different things in different fields, from products to patients,” Lee said. “Since the 1950s, the Kaplan-Meier estimator has been the benchmark for analyzing lifetime data, particularly in clinical trials. The next breakthrough came in the 1970s when the Cox proportional hazards model was introduced, which allows researchers to incorporate variables that can affect the predictability of things like patient mortality.” The problem with the existing survival analysis tools, Lee said, is that they make certain assumptions that can skew the predictions if the assumptions are not met. “There are very few existing tools that can incorporate variables without imposing assumptions on how they affect survival, let alone when there are a lot of variables that can also change over time. For example, two iPhones will have different lifespans depending on the temperature at which they are stored, amongst many other factors. But it’s unlikely that storing your phone at 30 degrees will halve its lifespan compared to storing it at 60 degrees. This sort of linear relationship is commonly assumed by existing tools.” Lee’s team developed a new survival methodology based on something called gradient boosting: a machine learning technique that combines decision trees to yield predictions. The method, Lee said, is totally assumption-free (or nonparametric in technical parlance) and can deal with a large number of variables that can change continuously over time, making it significantly more general than existing methods. Nothing like it has been seen until now, he noted. “Calculating the survival rate of anything is super complex because of the variables. Say you want to create an app for a smart watch that monitors the wearer’s vitals and use this information to create a real-time warning indicator for stroke. Doing this accurately is difficult for two reasons,” Lee explained. “First, a large number of variables may be relevant to stroke risk, and the variables can interact in ways that break the assumptions central to existing survival analysis methods. And second, variables like blood pressure vary over time, and it is the recent measurements that are most informative. This introduces an additional time dimension that further complicates things.” The software implementation of Lee’s method, BoXHED, overcomes both issues and allows scientists to develop real-time predictive models for conditions like stroke. The trained model can then be ported to a watch app to tell its wearer if and when they’re likely to have a stroke, a process known as inferencing in machine learning lingo. The implications, Lee said, are huge. “BoXHED now opens the door for modern applications of survival analysis. In previous research, I have looked at the design of early warning mortality indicators for patients with advanced cancer and also for patients in the ICU. These use other methods to make predictions at fixed points in time, but now they can be transformed into real-time warning indicators using BoXHED.” He cited the case of end-stage cancer patients who are often better served by hospice care than by aggressive therapy. “Accurate predictions of survival are absolutely critical for care planning. In previous analyses, we have seen that using existing predictive models to inform end-of-life care planning can potentially avert $1.9 million in medical costs and 1,600 days of unnecessary inpatient care per 1,000 patient visits in the United States. BoXHED is likely to lead to even better results.” Lee’s research paper is forthcoming in the Annals of Statistics. He has also created an open-source software implementation of BoXHED, which can radically improve the accuracy of survival analysis across a breadth of applications. The paper describing BoXHED was published in the International Conference on Machine Learning, and the latest version of the BoXHED software can be found online. If you are a journalist or looking to speak with Donald Lee – simply click on his icon now to arrange an interview or appointment today.

Donald Lee

In the News

Hiring More Nurses Generates Revenue for Hospitals

EmoryBusiness.com  online

2024-09-05

But might the balance sheet in fact be skewed in some way? Could there be potential financial losses attached to nurse understaffing that administrators should factor into their hiring and remuneration decisions?

Research by Goizueta Professors Diwas KC and Donald Lee, as well as recent Goizueta PhD graduates Hao Ding 24PhD (Auburn University) and Sokol Tushe 23PhD (Muma College of Business), would suggest there are. Their new peer-reviewed publication* finds that increasing a single nurse’s workload by just one patient creates a 17% service slowdown for all other patients under that nurse’s care.

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