Hospital Mega-Mergers Present Chance to Improve Quality, Safety

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By Mary Anne Pazanowski

Hospitals are more than ever looking to rectify a startling statistic: that about one in every three admitted hospital patients will suffer unintended injuries or illnesses as a result of preventable medical errors.

One trend, the mergers of large, multi-hospital health systems, could help accelerate the timeline for eliminating the problem by utilizing patient care quality data to reduce common adverse consequences of hospital admissions. Hospital counsel are optimistic because the resulting health systems can generate massive amounts of data that can be analyzed to improve protocols for ensuring patient safety and quality of care, hospital law experts told Bloomberg Law.

The increase in data generation scale should benefit health-care providers and payers, who annually spend billions of dollars to treat or compensate patients injured by preventable errors. It also could help improve patients’ peace of mind if they know they are less likely to become sicker during their hospital stay than when they went in.

Hospitals in merged systems should be able to learn a lot from one another’s data to improve patient care outcomes, Phil Zarone, a partner at Pittsburgh-based health-care firm Horty, Springer & Mattern, told Bloomberg Law. Zarone is a Bloomberg Law advisory board member.

Katharine Van Tassel, a health law professor at Concordia University Law School in Boise, Idaho, told Bloomberg Law she is “cautiously optimistic” that large system mergers will be good for health-care quality “in the long run” though it could require the investment of a lot of money to get there.

Health-care quality and patient safety should improve as more health systems come together to form mega-systems, given the number of patients they serve and the amount of data they can generate, the attorneys said. They must, however, ensure that the sheer amount of information doesn’t overwhelm them and that they have good information-sharing protocols in place, they said.

Improving Quality

Hospitals have been working to improve quality and reduce errors for a long time. In 2005, the U.S. Congress took a major step to try to help, passing the Patient Safety and Quality Improvement Act. The PSQIA established a voluntary reporting system, so error data can be compiled and analyzed by a Patient Safety Organization (PSO) with a view to developing recommendations for improvement. The PSQIA’s implementing regulation, the Patient Safety Rule, took effect in 2009.

The PSQIA is designed to allow hospitals to share quality lessons they have learned, Zarone said. Quality personnel overseeing multiple-hospital systems can, through a PSO, conduct ongoing evaluations, determine the best practices at each facility, and share that information with other system hospitals, he said. The law’s privilege and confidentiality provisions were designed to prevent the information from being used against the systems.

The bigger the system, the more information that potentially could be shared, Zarone added. Large systems have more resources—including more clinical experts—that could provide much more robust data and improve their clinical practice guidelines.

Zarone said it might not be easy to coordinate information-sharing among some of the biggest multiple-hospital systems—a proposed merger announced in December between Ascension Health and Providence St. Joseph Health, for example, would create a 191-hospital system spread over several states. This problem, however, could be addressed by setting up regional or state-specific PSOs, he said.

Also, multiple hospitals working together to improve quality and patient safety could bring about needed changes much more quickly and on a far larger scale than if each hospital worked independently, he said.

Real-World Data

Van Tassel agreed that larger systems can be expected to have access to a great deal more information that could be used to improve care quality. For example, they will have more “real-world data” available for analysis. Real-world data includes clinic information, such as electronic health records (EHRs), claims and billing data, as well as information from patients’ personal health monitoring devices that they share with their physicians.

More information, however, isn’t always better, Van Tassel cautioned. Observational data routinely is collected, but not validated, she said. Large health systems, therefore, need to develop better ways to improve the accuracy of the data they collect if they want to use it to improve quality, she said.

Developing a “systemized” approach to data collection can help improve its accuracy, Van Tassel said. That is, the data collected to help improve quality constantly should be under review to weed out “stale” or old information.

A very large system may be too big to quickly and continuously conduct such reviews, Van Tassel said. She added, however, that the problem could be addressed by delegating the analysis to the people who have the expertise in the areas under review.

Zarone and Van Tassel both said bigger systems likely will have large provider bases that include many experts in their fields. The clinical personnel should be looking at the protocols annually to ensure they don’t stagnate and aren’t based on bad data, she said.

Health-Care Transitions

The transition in the way health care is provided—from customary care, to evidence-based care, to personalized care—also is improving patient safety and quality of care, Van Tassel said. Mega-systems can lead the way in this area, as well.

Customary care means treatments customarily used to address certain symptoms, like prescribing aspirin for a headache. These treatments are based on doctors’ experience over the years, she said. The health-care system has evolved into one of evidence-based care, in which generally available information about a large patient population is gathered and analyzed to develop the most efficient care for the greatest number of people.

Customary and evidence-based care, however, don’t take into account “outliers,” Van Tassel said. These are people who have traits that may be out of the ordinary, like a body chemistry that breaks down medications differently than most people, such that tried-and-true or evidence-based treatments don’t work for them. Treatment protocols now are moving into personalized care, which takes those traits into consideration, Van Tassel said.

The large amounts of data potentially collected by mega-systems might help. These systems see more patients, and so are likely to care for a larger number of outliers in the aggregate. This could help them develop protocols to help these individuals get the care they need or at least recognize that there is a certain percentage of patients who require more personalized care, Van Tassel said.

Medical Error Reporting

Larger systems also may be able to help change the “name, blame, shame” system of medical error reporting that may be getting in the way of accuracy, Van Tassel said. She suggested that the systems’ leadership ought to consider adopting an anonymous reporting regime, similar to that used in the airline industry to report pilot error.

Doctors are under-reporting errors, due to fears of medical malpractice lawsuits and peer review sanctions, she said. This impacts the efficacy of a medical error analysis because the information being used to develop the solution is incomplete. “A remedy is only as good as the data on which it is based,” she said.

An anonymous reporting system wouldn’t replace traditional peer review activities, Van Tassel said, but it might help lessen the barriers to effective quality review.

To contact the reporter on this story: Mary Anne Pazanowski in Washington at

To contact the editor responsible for this story: Peyton M. Sturges at

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