Do You Know Who Your Best Doctors Are?

by Karen Wolfe

In Workers’ Compensation, the medical provider network philosophy has been in place for years. Most networks were developed using the logic that all doctors are essentially the same. Rather than evaluate performance, the focus was on obtaining discounts on bills, thereby saving money.

Physician selection by adjusters and others has frequently been based on non-objective criteria. Those include familiarity, repetition, proximity, and sometimes just assumption or habit. Often the criteria is something as flimsy as, ‘We always use this doctor” or “The staff returns my calls”. The question is which doctors really are best and why?

Assumptions

The first assumption that must be debunked is that discounts save money. Doctors are smart—no argument there. So to make up the lost revenue for discounted bills, they increase the number of visits or services to the injured worker or extend the duration of claims by prolonging treatment. To uncover these behaviors, examine the data.

Recommendations

Amazingly, even doctors do not always make the best choices about other doctors. They may recommend doctors whom they know socially, professionally, or by informal reputation, but they may not know how they actually practice. They may not know a physician upcodes bills, dispenses medications, or over-prescribes Schedule II drugs. The data will reveal that information.

Referrals

Doctors may be unaware they are adding to claim complexity by referring to certain specialists. Again, familiarity and habit are often the drivers. On the other hand, duplicity among providers is fraudulent behavior and it can be uncovered by examining the data.

Clustering

Analysis of data can expose clustering of poorly performing, abusive, or fraudulent providers referring to one another. The analysis may also divulge patterns of some providers associated with certain plaintiff attorneys.

Management practices

Treating doctors influence claims and their outcomes in other ways. Management indicators unique to Workers’ Compensation such as return to work, indemnity costs, and disability ratings can be analyzed in the data to spotlight both good and poor medical performance. These outcome indicators are either directed by, or influenced by the physician and they can be uncovered through data analysis.

Clinical quality

Claims adjusters and other non-medical persons simply cannot evaluate the clinical capability of medical providers, especially doctors. Performance analysis must take place at a higher level. Evaluations for specific ICD-9 diagnoses and clinical procedures such as surgery must be made. Frequency, timing, and outcome can be examined in the data in context with diagnoses and procedural codes, thereby disclosing the excellence or incompetency of physicians.

Negative clinical outcomes that can be analyzed include, but are not limited to hospital readmissions, repeated surgery, or infection. Physicians associated with negative medical outcomes should be avoided.

Fairness

When analyzing clinical indicators for performance, care should be taken to compare only similar conditions and procedures. Without such discrimination, the results are dubious. Specificity is critical.

When using data analysis to find the best doctors and other medical providers, fairness is also important. Provider performance should be compared only with similar specialty providers for similar diagnoses and procedures. Results will not be accurate or reliable if performance analysis is not apples-to-apples.

Pushback

Medical providers may question data analysis to evaluate performance claiming they treat the more difficult cases. The data can be analyzed to determine diagnostic severity as well. Diagnostic codes in claims can be measured and scored, thereby disclosing medical severity.

Find the best practice doctors

Now is the time to step up to a much more dignified and sophisticated approach to selecting medical providers. Decisions about treating physicians must be based on fact, not assumption or habit.  Fortunately, the data can be analyzed to locate the best-in-class and expose the others.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data to score medical provider performance and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org