New AAOHN Journal Article on Benchmarking Medical Absence
In the February 2005 issue of the AAOHN Journal, the Feature Article (offering CE Credit) is "Benchmarking Medical Absence: Measuring the Impact of Occupational Health Nursing". This article explores reasons occupational health nurses must benchmark, how to benchmark, and how to use the results. Industry-standard benchmarking techniques featuring the Official Disability Guidelines database are highlighted, including Grading RTW 101, Incidence and Prevalence Tracking, Outlier Rate, etc. The American Association of Occupational Health Nurses requested the article because of the importance of benchmarking. First developed in working with Dr. Charles Prezzia (Gen. Mgr. Health Svcs. & Medical Dir. of U.S. Steel), these techniques are becoming an industry-framework. In addition to improving outcomes using ODG to manage treatment and return-to-work decisions, users can also benchmark performance retrospectively, to identify best practices.
Benchmarking Medical Absence using ODG
Below are three industry-standard benchmarking techniques using Official Disability Guidelines (including "Grading RTW 101", "Outlier Percentage", and "Beating the Guideline"). These can be performed manually with manageable datasets, or the raw data from ODG can be licensed in text or Excel files to computer automate the process. Contact us to inquire about licensing the raw data (please indicate the nature of your inquiry).
and letter grades can be assigned measuring return-to-work performance. This is especially valuable when comparing different
divisions, plants, TPA’s, case managers, healthcare providers, or benefit
plans. The steps are as follows:
1. Sum up all internal closed claims durations for a given operating unit (employer, department, division, case manager, TPA, etc), capping each at 365 days (if necessary) and excluding permanent total disability claims.
2. Sum up corresponding At-Risk durations from ODG Summary Guidelines (with an ICD9 coded At-Risk date corresponding to each claim).
3. Divide the sum of the At-Risk dates minus the sum of internal claims durations by the sum of the At-Risk dates: (sum of At-Risks – sum of claims)/sum of At-Risks.
Multiply the result by 100 to get a percentage score.
Repeat for other operating units and compare based on percentage and
letter grading system below.
A few points to note:
An A grade is difficult to attain, as it should be.
Characteristics typical of A
programs include early-reporting, aggressive resource-driven case management
using evidence-based disability duration guidelines, treatment patterns based
on best available medical evidence, approved physician networks, vocational
rehabilitation, and finally, modified duty programs, which are essential to A
Small employers typically perform
better than large ones, usually because large employers tend to provide more
generous benefit structures, like STD insurance and lenient absence policies
with regards to sick leave. These
programs may be important to luring and retaining good employees, but without
adequate case management, can also act as disincentives to return-to-work
A typical medium-sized U.S.
employer would receive a C grade.
Regardless of ultimate grade, the
value of these benchmarks is to create consistency across different
conditions, allowing for comparison among internal or external claims entities
(TPAs, providers, case managers), geographic and demographic samples or
subdivisions of another nature, and time-series evaluations of new programs,
despite the likelihood of heterogeneous case mixes.
Figure 1 is a hypothetical
example for a firm with a limited number of claims (the At-Risk dates in this
example are not current/accurate – see current version of Official
Disability Guidelines for accurate data).
a term used to describe the bottom 10% of cases with respect to disability
duration, or those cases having durations greater than the At-Risk date from
the ODG Summary Guidelines (by which 90% return as a national benchmark).
This relatively small percentage of claims represents a huge proportion
of costs. Any claim to exceed its
At-Risk date should solicit a “red flag”, as it has become an outlier (and
using the methodology above - Grading RTW 101 – these claims receive a
failing grade of F).
important to monitor these claims and attempt to identify the cause of and
solution to any significant imbalance. In
cost-savings initiatives, return on investment will be greatest in reducing an
imbalance of outliers. They not
only contribute heavily to costs due to lost productivity, but they also
account for a significant imbalance of medical costs through excessive
utilization of medical services. Organizations
can measure Outlier Percentage for all cases or a select subset (by provider,
for example) as follows:
demonstrate the value of return-to-work efforts for all cases or a select
subset (by case manager, for example), as follows:
1. Sum up all internal closed claims durations, capping each at 365 days (if necessary) and excluding permanent total disability claims.
Sum up corresponding At-Risk durations from ODG Summary
Guidelines (with an ICD9 coded At-Risk date corresponding to each claim).
3. Subtract the sum of internal claims durations from the sum of At-Risk dates to show days “saved”.
4. Divide the result by number of claims to calculate average days saved per claim.
Compute dollars saved using:
(average daily wage x 5) x number of days saved.
While this may seem arbitrary,
case management is certainly critical to the return-to-work process, and while
these are not “hard savings” figures, the value of these benchmarks is to
create consistency across different conditions, allowing for comparison among
operating units, despite the likelihood of heterogeneous case mixes.
 “Beating the Guideline” is an innovative way of using a benchmark to demonstrate savings. Of course, by definition, a “guideline” should not be regularly outperformed, and that’s why the At-Risk date is used.
 Total indemnity costs have been conservatively estimated to be five times direct costs, based on industry studies (Kalina, AAOHN Journal, August 1998, and Guidotti, AMA, Occupational Health Services, 1989)
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