Editor’s Note. This post is authored by Eugene Litvak PhD, Arnold Milstein MD, MPH and Mark Smith MD, MBA. Photos and bios for Milstein and Smith are above. Litvak is President and CEO of the Institute for Healthcare Optimization and also an Adjunct Professor in Operations Management in the Department of Health Policy and Management at the Harvard School of Public Health. Prior to his current position he was director of the Program for the Management of Variability in Health Care Delivery at the Boston University Health Policy Institute and Professor at the BU School of Management. His work focuses on operations management in health care delivery organizations. He was a member of the Institute of Medicine Committee “The Future of Emergency Care in the United States Health System“, and is a member of the “Hospitals in Pursuit of Excellence National Leadership Council” of the American Hospital Association.
A recurring theme in efforts to correct U.S. health system performance deficits is that the growing complexity of care delivery requires borrowing systematic methods of performance management from engineering science. The 2001 “Crossing the Quality Chasm” report and the IOM’s 2005 joint report with the National Academy of Engineering elaborated on the uses of engineering tools in the design and management of health care delivery processes, distinct from their use in the design of medical devices and other more tangible health industry inputs.
Catalyzed by performance improvement initiatives of many quality-focused organizations, foundations, researchers and operations improvement consultants, the use of engineering tools that improve health care delivery processes have gained a small beachhead in the U.S. health care system. Substantial quality and efficiency gains from wider applications of service engineering science have been described by several leading health systems in prominent national health policy fora such as MedPAC and IOM public meetings. Components of engineering science that have been usefully applied to healthcare are Queuing Theory — the study of waiting times in their dynamics — and Variability Methodology, which involves identifying, classifying and quantifying different types of variability in patient flow.
An equally prominent theme in recent efforts to improve U.S. health care are value-based provider payment methods. For example, the Patient Protection and Affordable Care Act contains provisions to reward more “efficient” hospitals. Can Queuing Theory and Variability Methodology also be applied to improve provider payment methods?
Use of Engineering Science to Improve the Fairness of Provider Payment
Knowledge from the operations management component of engineering science demonstrates that a hospital’s size and the percentage of its admissions that are scheduled determine its lowest attainable cost per high-quality treatment. (See Litvak E. Optimizing Patient Flow by Managing its Variability. In Berman S. (ed.): Front Office to Front Line: Essential Issues for Health Care Leaders. Oakbrook Terrace, IL: Joint Commission Resources, 2005, pp. 91-111) Accordingly, failing to adjust Medicare’s DRG-based hospital prospective payment system or the wider hospital payment bundles envisioned in the new health reform law inadvertently imposes unintended handicaps on smaller hospitals or hospitals with a higher ratio of urgent or emergent admissions to scheduled admissions.
For example, imagine two hospitals – a smaller hospital with 5 medical intensive care unit (M-ICU) beds and a hospital twice its size with 10 M-ICU beds. To simplify this illustration, assume also that patient acuity levels, the pattern of patient arrivals, and a 2.5 day average ICU length are identical, and that demand for the two ICUs is proportional to the hospital’s size (1 patient/day to the small hospital’s ICU and 2 patients/day to the large hospital’s ICU). If each ICU is full and one patient requiring ICU admission is treated in each hospital’s Emergency Department (ED), will both hospitals incur the same cost burden during the period that the patient remains in the ED while awaiting an ICU bed? Queuing Theory, a well-demonstrated component of operations management, demonstrates that the patient waiting for a bed in the larger hospital with the proportionally larger ICU would on the average wait much less: 0.43 hours vs. 3.12 hours in the ED of the hospital with the smaller ICU. This comparison is summarized in Table 1.
Half The Size And Patient Volume Lead To 7 Times Longer ED Wait
ED wait for M-ICU bed
|Smaller Hospital||100||5||2.5||1||3.12 hrs|
|Larger Hospital||200||10||2.5||2||.43 hrs|
This more than 7-fold difference in time translates into a 7-fold predictable difference in ED monitoring costs between the large and small hospital. It is also much greater than the 2x difference in the ICUs’ sizes. Even if the two hospitals are equally well managed, the lowest attainable cost per admission is lower in the hospital with the larger ICU. Today, Medicare’s DRG-based payment system does not make allowances for the smaller hospital’s structurally imposed higher ED monitoring costs. New episode-based hospital payment systems should make such allowances, absent evidence of other patient benefit from care in larger hospitals.
Engineering knowledge also enables an understanding of why another factor uncontrollable by hospitals – the annual percentage of admissions that are emergent or urgent – affects a hospital’s lowest attainable cost per admission. For example, if 95 percent of a hospital’s admissions are scheduled admissions and 5 percent are emergencies that arrive randomly, Queuing Theory demonstrates that it will be able to safely manage a 90 percent or higher bed occupancy. However, if only 5 percent of its admissions were scheduled, the hospital could not operate safely at more than 80 percent occupancy and will thus incur a higher cost per admission. Indeed, any attempt to increase its bed occupancy above 80 percent would lead to higher patient morbidity and mortality due to delays, periodic nurse overloading and consequent medical errors. In summary, engineering science informs designers of hospital payment reform that both its size and its pattern of patient arrival governs a hospital’s lowest safely-attainable cost per admission.
There is widespread agreement that per capita health care expenditure growth in excess of GDP growth, particularly in Medicare, is unsustainable. Given the large percentage of US health care costs consumed by hospital inpatient care, payer pressure on hospital margins is inevitable and already embedded in the Affordable Care Act.
Insights from engineering science offer health care payers an important opportunity to fine-tune DRG-based payments and wider hospital payment bundles likely to be implemented by the new CMS Innovation Center. As Medicare and other payers renew their effort after health reform to gear payment to what is required by an “efficient hospital,” payment fairness depends on the use of this science.
Engineering science also offers a major short-term opportunity for hospitals to lower their operating cost substantially and improve quality of care. The application of Queuing Theory and Variability Methodology to “smooth” hospital admissions likely represents the single largest single opportunity for surgeons and hospital leaders to improve the affordability of their care and, thereby, of U.S. health insurance. Early hospital adopters such as Cincinnati Children’s Hospital (CCH) have been able to increase hospital throughput capability by more than 15 percent without increasing staff or capital spending , thereby avoiding construction by CCH of 100 new beds and capital expenditures of over $1 million per bed. If the experience of early hospital adopters proves generalizable, it will reduce U.S. hospital cost per admission by ~15 percent. Since hospitalizations, including outpatient procedures, consume about 30 percent of US health care spending, this single improvement would reduce the cost of U.S. health insurance by roughly 4-5 percent if hospitals pass savings through to insurers and insurers, in turn, to insurance buyers. It is also likely to reduce hospital mortality by sparing hospital nursing units preventable bulges in the number of new patients they must admit in a single day.
The Affordable Care Act did not require major sacrifices by hospitals and physicians. Americans now need hospital, surgeon, and physician leaders to deploy Variability Methodology and other operation management methods such as Queuing Theory quickly to smooth their daily rate of admissions, and then learn how they can use other engineering tools to improve the value of U.S. hospitals to society. In return, hospital payers should use the same engineering tools to improve the fairness of current and new bundled hospital payment methods.