University Hospitals Cleveland Medical Center: Managing Capacity in Neurology

Joel  Goh; Robert S. Huckman; Nikhil  Sahni

Case | HBS Case Collection | March 2018

University Hospitals Cleveland Medical Center: Managing Capacity in Neurology

by Joel Goh, Robert S. Huckman and Nikhil Sahni

Abstract

In December 2014, Dr. Anthony Furlan, chair of the Department of Neurology at University Hospitals Cleveland Medical Center (UH), faced a mandate from the hospital’s executive leadership team. Specifically, all UH departments were directed to take steps within six months to reduce the waiting time for outpatient appointments—measured as the time to first available outpatient appointment—to no more than 15 days. For Furlan and his colleagues in neurology, achieving this target was a significant challenge, as the department’s current time to first available appointment was 93 days. The case considers several alternatives for reducing waiting time in outpatient neurology without increasing the total clinical staff. The case allows students to evaluate opportunities for expanding the effective capacity of a complex service operation and to understand the tradeoffs between customer service and labor utilization.

Keywords: health care; Hospitals; capacity planning; scheduling; Health Care and Treatment; Service Operations; Performance Capacity; Health Industry; North America; United States; Ohio; Cleveland;

Language: English

Format: Print

12 pages

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Citation:

Goh, Joel, Robert S. Huckman, and Nikhil Sahni. "University Hospitals Cleveland Medical Center: Managing Capacity in Neurology." Harvard Business School Case 618-062, March 2018.

Article | Management Science | September 2018

Discretionary Task Ordering: Queue Management in Radiological Services

Maria Ibanez, Jonathan R. Clark, Robert S. Huckman and Bradley R. Staats

Work-scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks (batching) and those tasks they expect to complete faster (shortest expected processing time). Moreover, they exercise more discretion as they accumulate experience. Exploiting random assignment of tasks to doctors’ queues, instrumental variable models reveal that these deviations erode productivity. This productivity decline lessens as doctors learn from experience. Prioritizing the shortest tasks is particularly detrimental to productivity. Actively grouping similar tasks also reduces productivity, in stark contrast to productivity gains from exogenous grouping, indicating deviation costs outweigh benefits from repetition. By analyzing task completion times, our work highlights the tradeoffs between the time required to exercise discretion and the potential gains from doing so, which has implications for how discretion over scheduling should be delegated.

Keywords: discretion; scheduling; queue; healthcare; learning; experience; Decentralization; operations; service operations; service delivery; Performance; Performance Effectiveness; Performance Efficiency; Performance improvement; Performance Productivity; Decisions; time management; Cost vs Benefits; Health Industry; Operations; Service Operations; Service Delivery; Performance; Performance Effectiveness; Performance Efficiency; Performance Improvement; Performance Productivity; Decisions; Time Management; Cost vs Benefits; Health Industry;

Citation:

Ibanez, Maria, Jonathan R. Clark, Robert S. Huckman, and Bradley R. Staats. "Discretionary Task Ordering: Queue Management in Radiological Services." Management Science 64, no. 9 (September 2018): 4389–4407. (Working paper available here. Winner of the 2017 Best Paper Competition of the POMS College of Healthcare Operations Management. Featured in Forbes, Quartz, and Inc.) View Details

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Cost-effectiveness studies of medical innovations often suffer from data inadequacy. When Markov chains are used as a modeling framework for such studies, this data inadequacy can manifest itself as imprecision in the elements of the transition matrix. In this paper, we study how to compute maximal and minimal values for the discounted value of the chain (with respect to a vector of state-wise costs or rewards) as these uncertain transition parameters jointly vary within a given uncertainty set. We show that these problems are computationally tractable if the uncertainty set has a row-wise structure. Conversely, we prove that the row-wise structure is necessary for tractability. Without it, the problems become computationally intractable (strongly NP-hard). We apply our model to assess the cost effectiveness of fecal immunochemical testing (FIT), a new screening method for colorectal cancer. Our results show that despite the large uncertainty in FIT's performance, it could be cost-effective relative to the prevailing screening method of colonoscopy.

In 2015, the Department of Veterans Affairs (VA) ran the largest healthcare system in the United States, with over 1,700 sites of care that served nearly 9 million veterans. One year earlier, a scandal had erupted over a cover-up of the excessive wait times veterans faced to get medical appointments in some VA facilities. The fallout led to the resignation of the Secretary of Veterans Affairs, a criminal investigation by the Federal Bureau of Investigation, and the eventual replacement of 14 of VA’s top 17 leaders. This case documents the efforts of the new leadership team to improve access, regain trust, and transform the organization to address the broadening medical needs of the nation's growing veteran population.

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University Hospitals Cleveland Medical Center: Managing Capacity in Neurology

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