Sorry, no pun intended.  Not suggesting you inhale, but rather take a step back before you get all excited about a single court case.

Finally, there’s an actual real live bona fide true factual documented report of medical marijuana (MM) paid for by work comp.  To date, there’s been much speculation, a lot of pixels published, and way too much hot air – but now we have our first – and as far as I can tell only – time a work comp payer has been told to pay for MM.

Let the floodgates open.

Or perhaps not.

Our industry loves to hyperventilate endlessly on newly-emerging-highly-unlikely-but-really-scary cost drivers.  Medical marijuana, obesity, an aging population have had their day in the virtual sun, and although the latter two are worth discussing, the reality is the research indicates they just aren’t that much of a factor.

But now, a court in New Mexico has said an insurer has to pay for marijuana.  Thanks to the pros at WorkCompCentral, the case, Vialpando v. Ben’s Automotive Service and Redwood Fire & Casualty, is now on the tip of everyone’s tongue.  The claimant’s attorney is telling insurers to contract with marijuana dispensaries (!), others are opining the “floodgates” will open, and friend Mark Pew is advising employers to implement drug-free workplaces.

Before we spend a lot of time and energy on this, let’s consider the real world.

There is little credible research indicating medical marijuana is an effective treatment for pain.  Yes, there is some evidence that in relatively small populations MM has been beneficial, however they aren’t large enough, nor objective enough, to provide convincing proof.  Notably, it is difficult for researchers to study a Schedule I drug, and that has undoubtedly hampered the process.  Despite that limitation, over 40 studies have been conducted, and according to Medscape; “The majority of the studies showed an improvement in pain relief in comparison to a placebo or to other traditional pain medications. About a quarter of the studies showed no improvement.”

When one digs into the research, there’s quite a bit of variation in the study design, the pain type, duration, measurement methodology and assessment process making it difficult if not impossible to come up with an overall sense of efficacy or effectiveness. Some research even indicates THC can increase sensitivity to some types of pain.

THC – the most studied active ingredient in marijuana – has been studied extensively, with mixed results.  In a relativecly-small study of advanced-stage cancer patients, some subjects become highly agitated and anxious after ingesting THC orally.  Others reacted differently, and to varying degrees.

All that said, I could not locate any large-scale, double-blind randomized control studies addressing the effect of MM on pain.  The studies tend to be small, deal with discrete groups of volunteers (hey, want to get paid to get high?), and focus on one specific type of pain (neuropathic the most common, where the effect of MM seems to be pretty significant).

Does MM offer a possibly-better-alternative to opioids?  Absolutely.  Does it come with its own set of problems?  Absolutely: about 10 percent of users may become “addicted”; others experience very high anxiety levels; it certainly can be diverted.

What does this mean for you?

Until and unless there is credible research and a clear understanding of the risks and potential benefits of medical marijuana, we won’t see widespread – or even very little – use in workers’ comp.  Schedule I status, problematic side effects, social stigma, and legal issues are all major barriers – and will remain so.

Okay, let’s get back to real issues.

BTW, kudos to WCC; the article is comprehensive, well-researched, and provides a solid background on the overall issue as well as an explanation of the legal situation in New Mexico and other states.

There’s so much excitement about big data and the potential for it in work comp that it would be all to easy to forget some basics.

The law of small numbers chief among them.

Work comp accounts for a tad more than one percent of US medical spend – $30 billion in comp medical vs $2.8 trillion in total medical spend in 2012.

Many docs treat just a couple of work comp claims a year, and those who do handle a lot of WC claims see a wide range of injuries: knees, ankles, backs, shoulders, hands damaged by cuts, sprains strains and severe trauma.  When looking to compare providers – or procedures for that matter – researchers need enough data points to develop a statistically-valid sample set.  In most cases, no single provider has enough claims to enable clear-cut evaluation.  And, if they do, there aren’t any other providers in their service area with the necessary volume, making comparisons nigh-on-impossible.

The issue is statistical validity and statistical accuracy. Simply put, is the measurement procedure capable of measuring what it is supposed to measure. Without enough data, there just isn’t enough information to accurately assess performance.

That’s not to say researchers can’t do very meaningful and helpful analyses; the one just published on opioid prescribing by physicians dispensing docs to work comp claimants is a perfect example; the ongoing research by CWCI, WCRI, and NCCI provide plenty of additional examples.

The problem occurs when consultants, payers or managed care firms try to make definitive statements about individual providers based on inadequate data.  In my experience, provider rankings are often – but not always – based on little more than reimbursement or “savings” figures, and in no way account for “quality” measured by return to work, disability duration, cost-per-claim.  There isn’t enough data to case-mix adjust, not enough data to make comparisons, or really “rate” docs.

I would note that some payers, most often state funds, and some managed care firms, notably MedRisk (HSA consulting client) have a wealth of data and can (and do) make valid comparisons.

What does this mean for you?

Beware of rankings, ratings, and comparisons of individual providers.  Unless the underlying data is robust.