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