Herb Fillmore (Director of Research at Treo Solutions) was looking at the HEDIS data set again. He figured that on average there would be very little variation in process measures because we all pretty much agree on them (things like "percent of patients appropriately referred for colo-rectal cancer screening"). He figured that process measures should be pretty easy to nail and quality improvement ought to focus on the greater expected variation in hitting outcome measure targets (things like "Percent of patients with diabetes with HbA1c less than 8.").
He was surprised that the variation in process measures exceeded that of the outcome measures. He drilled into the process measures he found process measures with very low variation (prescribing the right asthma meds for kids, ordering tests for patients with diabetes, etc.) and others with high variation (chlamydia screening, alcohol and drug dependency interventions, etc). [Take a look at Herb's blog post on this.]
What struck me as I reviewed his bifurcated list was the different each process meant to a busy office. Prescribing a med is fast. So is ordering a blood test. Grab the paper, fill out the form, off you go. Most of the low variation process measures fell into what I'd call a 'low work impact' category.
Achieving high rates of delivery of care on these lower work burden process measures rests on implementation of reliable gap analysis, embedding the gap analysis and intervention into the work flow and training the office staff, coupled with reliable and regular measurement. Once past the initial learning curve these processes are pretty simple.
On the other hand, the highly variable process measures struck me as leading to potentially significant workflow burden. Screening for alcohol or chemical dependency might not eat up a lot of time, but working the bureaucratic hurdles to get someone into treatment is a huge burden for lots of different reasons. Even after the initial learning curve in a practice, each event could significantly ramp up the work flow.
If we want to tackle more the more complex issues we need a payment and policy environment aligned with the work. This is especially true when we focus on the entire population of care in a practice or group: rather than credit quality for achieving high scores on a handful of metrics that may only touch a fraction of the population we must address the underlying practice functions that support or impede patient outcomes: access to care, effective communication (continuity and support of self-efficacy), etc.
These underlying issues impact the entire population served in the practice and thus have the greatest potential impact on overall quality and cost of care. HEDIS provides no insight into these underlying issues so we must look to more innovative approaches like the patient experience data one can obtain through Dartmouth's HowsYourHealth.org engine.