The rehabilitation industry has move ahead to assume more responsibility than just measuring a person’s functional ability. Systems are now in place to also predict how much change should occur for a person receiving rehabilitation services. This is the first paper that I am aware of that is asking the next question: How accurate is the prediction? In my opinion, a systematic review proves to be a challenging method to answer questions. When you think about it, systematic reviews typically have quite a bit of heterogeneity which makes it difficult for clinicians to actually apply what is learned into practice. This particular publication that I reviewed did it’s best to focus on what we in the United States would term risk adjustment. I was introduced to a new term: case-mix adjustment (which as I read the paper seems to be the same as risk adjustment).

Predictive ability across US study models ranged from 18-42% and in UK models from 23-30%, demonstrating moderate to strong predictive ability across models.

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 If we use common sense, a model that only focuses on patient factors will be 100% accurate. A final treatment outcome includes far more than just patient factors. If you think of what rehabilitation looks like, there are other factors that play a role in treatment outcomes: clinician factors, patient-clinician factors and clinic factors. I’d like to think that a clinician’s belief system affects an outcome. I’d like to believe that the strength of the therapeutic alliance has a role. I’d also like to think that the way an organization structures patient experience also has a role. In my mind, if a model that only focuses on patient factors is able to accurately predict the outcome 40-50% of the time, the model is very strong. 

Because there are now more products on the market that risk adjust, I take the stand that the industry needs to demand the percentage of variation in the product’s ability to predict the clinical outcome. The next step then includes defining the power of the product’s predictive ability. As an example 0-15% variation explained = poor; 16-25% variation explained = limited; 26-30% variation explained = average; 31-42% variation explained = good; >42% variation explained = excellent. Since I am just thinking out loud with this concept, I would also propose that 26-42% would be defined as the typical industry standard to meet when providing predictive analytics for clinicians. The reason I believe this is important is because outcomes are compared in aggregated data. If the risk adjustment process is not able to adequately capture the important factors to increase the predictive accuracy, then clinicians will be unfairly compared.

The abstract is included below for you to review.

Models used for case-mix adjustment of patient reported outcome measures (PROMs) in musculoskeletal healthcare: A systematic review of the literature.

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An evaluation process with that level of error is not only unacceptable, but also reflective of irresponsibility of negotiators to ensure some level of accuracy in the process. This level of error represents far more than just an “oops.”

When the distribution of outcomes is divided into 10 equal groups, unadjusted versus risk adjusted outcomes matters. Without risk adjustment, 70% of the time, the outcome was categorized into a different group than a risk adjusted outcome.

In order to be more accurately evaluated on outcomes and quality, patient characteristics need to be considered and factored into the evaluation. Without risk adjustment that takes into consideration patient characteristics that affect outcomes, a high amount of error happens which affects a clinicians ranking of performance.

You’ll find the abstract to the recent study below.

Impact of Risk-Adjustment on Provider Ranking for Patients With Low Back Pain Receiving Physical Therapy.

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