In orthopaedics, as in much of medicine, improvements in patient care are achieved when processes are developed to analyze outcomes. Medical professional liability (MPL) claims can serve as lagging indicators or signals of serious problems in the delivery of care. Medical liability insurer Coverys thoroughly analyzes MPL cases; the resulting data then serve as a critical guide to the vulnerabilities permeating health care.
Recently, I spoke with Coverys’ Robert Hanscom, vice president of business analytics, and Lisa Simm, manager of risk management, to learn how the insurer is helping healthcare providers decrease their risks with medical malpractice.
According to Mr. Hanscom and Ms. Simm, “The MPL industry is not content to simply report on claims trends and then throw a smattering of risk-management content at various topics; rather, they are positioning the data to be relevant and actionable.”
They do admit, however, that MPL data have limitations, the most formidable of which are:
MPL claims are a small data set that often defies statistical significance.
MPL claims are always a look to the past—sometimes the distant past.
MPL cases often occur because of a unique convergence of factors or events that may not transpire the same way ever again.
Despite these shortcomings, MPL data should not be ignored. Lessons learned from MPL data—and the signals of what to be on the alert for—should be actively integrated into the delivery of patient care. “There is a distinct likelihood that preventable adverse outcomes will continue to replay if changes in behavior do not occur. If systems, skills, and behaviors are not fundamentally changed, the probability increases that similar types of harm will once again reach the patient,” said Mr. Hanscom and Ms. Simm.
They explained that Coverys has developed a methodology for making MPL data actionable. It involves applying hypotheses or “signal intelligence” to healthcare delivery processes to determine past vulnerabilities that represent potential future risks. These signals are categorized as:
weak—based on unusual cases or rare events
moderate—based on causation factors that were known to have existed in the past and may have been rectified
strong—based on causation factors that are unresolved, with recurrence highly likely
Peer comparisons are used when possible to provide context to data. This strengthens the signal intelligence in three ways: (1) It suggests where healthcare providers may have been outliers in the past; (2) it allows entities to benefit from the signals emitting from peer organizations, even when they themselves have not experienced specific categories of claim activity; and (3) in the highly specialized fields of medicine and health care, peer review and peer comparisons are a widely accepted quality-improvement technique.
Mr. Hanscom and Ms. Simm explained that Coverys has “a team of professionals with clinical, legal, and risk-management backgrounds perform root-cause analyses on each and every claim and then select primary causation factors from a risk-management taxonomy. The goal is to aggregate the most common, deeply rooted vulnerabilities that led to past claims. To reduce variability and subjective bias from [one] analyst to [another], inter-rater reliability monitoring and testing are an integrated component of this work.”
Coverys currently has a rich, comparative database covering eight to 10 years of nearly 20,000 MPL cases. According to Mr. Hanscom and Ms. Simm, “These cases provide ‘actionable intelligence’ for all healthcare providers. This database can be queried and sorted by size of hospital or practice by state, region, clinical specialty, and causation type.”
Coverys has developed a five-step methodology to better understand the medical liability risks a physician may face.
Step one: Gather signals from a hospital or practice’s MPL profile over the past five years. Similarly, gather signals from the hospital or practice’s peers. Assign weights to these signals (e.g., factors of high-severity injury events are given greater weight than low-severity injury events). Reviewing the MPL profile of the entity as well as the MPL profiles of peers will allow for a full understanding of common vulnerabilities. These weighted factors of the signals permit providers and administrators to identify key patient safety priorities.
Step two: Determine whether factors that caused adverse outcomes in the past are currently present. This is most commonly accomplished through risk assessments, surveys, and focus groups. Confirming the presence of the risks allows for the development of responsive actions.
Step three: If the same causal factors are evident consistently, develop risk-mitigating recommendations to address problem areas, tapping into nationally accepted best practices.
Step four: Apply advanced analytics to the vulnerabilities that have remained active. For example, an MPL profile of a particular orthopaedic group practice demonstrates that variabilities in presurgical evaluations have been a factor leading to postoperative complications (which have resulted in litigation). The key question: If the variabilities remain unaddressed, what is the likelihood that more patients will experience similar preventable adverse outcomes?
Step five: As U.S. health care moves from volume- to value-based compensation models (e.g., the Medicare Access and CHIP Reauthorization Act and Merit-based Incentive Payment System), link any and all risk recommendations, as directly as possible, to quality measures that ultimately will impact the way physicians, practices, and hospitals are paid.
Understanding these five steps may help orthopaedic surgeons better understand and mitigate the risk of future MPL. The ingredients—credible data, analytics, assessment, and active knowledge of best practices—are important. As with all other medical issues that are determined by the state, it is important to understand regional risks, as the U.S. markets are individually unique.
Michael R. Marks, MD, MBA, is an orthopaedic surgeon; editor of the AAOS Now Medical Liability Committee column; senior medical director at relievant Systems; a member of the AAOS Patient Safety Committee; a trainer for the AAOS Communications Skills Mentoring Program; and a consultant with KarenZupko & Associates, Inc. He can be reached at firstname.lastname@example.org.