For decades, physicians, patients, and policy analysts have complained about the US health care system. Costs are too high, quality is haphazard, and engaging with the system is frustrating. It is hard to shake the feeling that this will never change.
But in the past few years, a number of health care organizations have been trying to bring improvements to the US health care system. These are not radical changes, like those in political proposals for health care reform but are what I would call “practical reform.” A growing body of evidence suggests that practical reform is feasible and can have large benefits. Here are some of the key areas.
Changes in Health Systems
Successful changes at the health system level involve 4 activities. First, systems can encourage their clinicians to specialize in particular types of patients or procedures. It has long been known that volume matters for outcomes—for example, a surgeon who does 100 operations a year typically has better outcomes than one who does 20. In addition, a recent study found that specialization matters. For several common procedures, including coronary artery bypass graft surgery and valve replacement, physicians in the top quartile of specializing in a given procedure have mortality rates that are 15% to 46% lower than those in the lowest quartile of physicians specializing in that procedure.
Building on this specialization, health systems can do a better job routing patients to the right type of clinician. Imagine that a patient experiences knee pain and calls the orthopedics department to schedule an appointment. There is no guarantee that the physician available for the first visit is an expert in knees. When they are not, a visit is wasted, so the patient has to wait again for an appropriate specialist. University Hospitals in Cleveland was able to reduce waiting days in its neurology department from 95 days to less than 15 days and increase physician schedule density, in part by better matching patients to appropriate physicians.
Organizations also can use predictive analytics to determine who needs particular types of care. Think of this as the equivalent of genotyping but at the patient level. Hospital Corporation of America used its rich database on births to identify factors associated with maternal mortality. Interventions resulting from their data analysis drove down maternal mortality significantly. Other organizations are using predictive analytics to reduce rehospitalization for patients with chronic disease and improve the clinical course for people in intensive care units. One-third of hospitals and health systems currently use predictive analytics, as do 80% of health insurance companies.
Predictive analytics can also be used with a focus on costs. One of the easiest ways to save money is to avoid use of expensive institutions when they are not needed. A number of organizations have found significant savings from predicting which hospitalized patients need institutionalized postacute care and which ones can go home. Routine care can also be handled in less expensive settings. More than 60% of patients going to an emergency department in California could be moved to urgent and retail clinics, which cost about half of an office visit and one-fifth of an emergency department visit.
Finally, health systems can make data entry less costly and frustrating. The typical physician spends more than 48 minutes a day adding data for quality metrics to electronic medical records. Medical scribes can substitute for physician time, although this requires money. Passive data collection from medical devices—for example, for depression and monitoring falls—has shown early benefits. And pruning unnecessary notices in electronic medical records improves clinician satisfaction. In the future, natural language processing and artificial intelligence may be able to reduce documentation time; health systems should be considering their strategy in this area.
Changes for Payers
There are other steps that payers can take to improve care. All of the major insurers have programs in artificial intelligence. Perhaps the United States as a whole should have such a strategy. For example, according to a government policy paper, a formal goal of the United Kingdom is to use data, artificial intelligence, and innovation “to transform the prevention, early diagnosis and treatment of chronic diseases by 2030.” Working on the clinical margin makes sense for many payers because they have access to the full patient record, not just patient visits to specific clinicians and facilities. Furthermore, many insurers now own brick-and-mortar health care facilities (such as Aetna, which is part of CVS Health, with its pharmacies and walk-in clinical services), providing a natural platform for health management.
When payers cannot or choose not to be involved in care decisions, they can be more open to data sharing with other clinicians. For clinicians to manage care, they need access to the data that insurers have. In other industries, such as retailing, there is a history of data sharing that provides a model of mutual benefit (for example, data sharing between Walmart and its suppliers). Some insurance organizations are now committing to the same.
Finally, payers could move to harmonize the administrative cacophony of medical care, something that is acutely needed. Billing-related and insurance-related costs account for about 15% of medical spending, and quality assessment is also costly. Payers are starting to take steps to reduce these costs. For example, some are “gold carding” clinicians to allow them to avoid prior authorization from insurers, provided that they have a history of appropriate use or that their electronic medical record incorporates guidelines from leading organizations. Other payers are working to harmonize payment models so as to avoid excessive administrative costs and to transmit more powerful incentives to physicians.
An analysis by the consulting firm McKinsey suggests that payers, clinicians, and health systems could reduce aggregate administrative costs by 17% by adopting current best practices and by another 10% (a total of 27% of the overall administrative costs or about 4% of overall medical spending) by importing best practices from other industries, such as creating a clearinghouse with standardized components for bill submission. (Full disclosure: I was an advisor to the McKinsey study.)
All told, the benefits from practical reform could be large. The McKinsey study estimates that taking these and related steps could allow the US health care system to meet the demand for increased care over the next decade with no increase in the workforce and a savings of up to $2.3 trillion. Care quality and patient and physician experience would also improve.
None of this is to deny that political changes are needed. The current system of incomplete coverage and high out-of-pocket costs for many low-income families is not ideal. But putting all our efforts into political reform has drawbacks, including the immobilization that often accompanies major reform discussions. There is a lot that we can do while waiting for the political system to agree on a new direction. Pursuing such avenues is a worthy goal for the next phase of health reform.
About the author: David M. Cutler, PhD, is the Otto Eckstein Professor of Applied Economics in the Department of Economics and Kennedy School of Government at Harvard University and a member of the Institute of Medicine. He served on the Council of Economic Advisers and the National Economic Council during the Clinton Administration and was senior health care advisor to Barack Obama’s presidential campaign. He is a commissioner on Massachusetts’ Health Policy Commission. He is the author of the The Quality Cure (2014) and Your Money or Your Life (2004). He tweets at @cutler_econ. (Image: Ted Grudzinski/AMA)
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