The Challenge of Harnessing Data within Healthcare Liability Captives
February 21, 2019
Breaking down claims data silos to bring various data sets together in healthcare systems requires a systematic approach, according to a panel of experts who recently spoke at the World Captive Forum.
Robert Hanscom, vice president of business analytics at Coverys, said that there is much talk and commotion surrounding "big data." With big data, he said there is a need to have well-organized, actionable data where everyone can work together.
At the same time, according to Michael Maglaras, principal at Michael Maglaras & Company, healthcare organizations and their captive insurers will be increasingly involved with lawsuits related to societal issues, the kind of issues that are currently saturating media headlines where the resulting claims have nothing to do with clinical outcomes. For instance, as our nation faces the opioid crisis, what effect will lawsuits against "Big Pharma" have on acute care facilities? Or how will captive insurers be affected by the sexual misconduct era in which we live?
Actionable Claims Data
Mr. Hanscom said that captive insurers will need to take medical malpractice data out of the media headlines ("the headlines") to identify "root cause indicators" and "root causation factors" to generate actionable data. However, he said the fact is that the headlines provide just one small data set, where the bigger data is what happens in the day-to-day healthcare environment and the subsequent centralization of this data.
Medical malpractice data should not be merely "one drop" but instead a starting point for thinking about related issues, according to Mr. Hanscom. For instance, the net of related issues includes adverse events and root cause analysis cases (a subset of adverse events). There are also patient complaints and "human" interpretation of outputs that are derived from information intelligence, he explained.
Concerning medical malpractice claims, Mr. Hanscom said big data encompasses countrywide medical malpractice data that is currently fragmented, where different parties hold tightly onto their own data—even within organizations.
He advised that often, serious issues are discovered by accident and that such issues should not merely present themselves "just because someone asked." Instead, he said there should be a systemic approach to bringing various data sets together.
Furthermore, there is also data that informs population management, said Mr. Hanscom. The issue with this, he explained, is that, again, most healthcare systems view this claims data in fragments. What we need to consider is how to link these data sets together to go further in informing healthcare organizations.
Mr. Maglaras emphasized that when a medical error happens at the practitioner/nurse level that costs lives and makes the headlines, you can bet that a healthcare organization's captive board of directors will ask how it happened and how it can be fixed. According to Mr. Maglaras, the way it gets fixed is because a piece of captive claim information percolates up in ways that affect people's pockets.
In an example, he described a routine situation that happened almost 20 years ago where a patient who required a blood transfusion died because she was given the wrong blood type. At that organization's captive board meeting, a decision was made that affected how things were done on the ground level.
Bringing the example forward to the realm of "big data," Mr. Hanscom said that this type of information is called "signal data." He said that since the medical malpractice data set is small, it often reflects the "worst of the worst." Therefore, in Mr. Maglaras's example above, once a case like this emerges, this "type" of information should be communicated back to the organization and the healthcare system as a "signal" and then examined to determine if it is a systemic issue that could happen again, according to Mr. Hanscom.
He added, signal data can be strong, weak, or moderate. When there is a strong data signal, he said, "It should always be posed back to the healthcare system [to ask questions that ultimately] assess whether or not the vulnerability is still there. That's really the goal. That's what makes this data actionable."
Mr. Maglaras emphasized that since the early 1980s (even as far back as the late 1970s), this data has existed on the balance sheets of captive insurance companies who have literally been capturing this kind of data for decades. However, we are not using this data, he said.
"What do Slovenia, and Cuba, and Italy, and France, and Costa Rica know about delivering babies that we don't?" he asked. "Our maternal and fetal death rate is higher than theirs. We have the data, it's in captives, we're just not using it," according to Mr. Maglaras.
Breaking Down Silos
When asked about the obstacles to breaking down silos and how to address the issues surrounding medical malpractice claims data fragmentation, Mr. Hanscom advised that tackling some of the challenges simply involves observing how medical malpractice claims data is collected and "paid attention to." He believes this involves the following.
- Getting claims files well organized, starting with a shared taxonomy that is used to code claims data.
- Performing a root cause analysis on claims files that identifies causation factors, regardless of the claim's outcome, in order to describe why a claim was brought in the first place.
- Once a systematic approach is applied to multiple years of claims, an organization can start to harness "claims intelligence."
A centralized environment with one taxonomy allows for a bridge between data sets. Using the same codes for the same types of cases would give healthcare systems a centralized view of the data, he explained.
Mr. Maglaras emphasized, "You want to go into a healthcare system and get a high quality of care, and that healthcare system has access to data to improve [your] care through a captive, and they are still not using it."
Pictured above at the 2019 World Captive Forum are Robert Hanscom, vice president of business analytics at Coverys (left), and Michael Maglaras, principal at Michael Maglaras & Company (right).
February 21, 2019