The New Alphabet Soup of Market Access and Reimbursement: Part 3 of 3
As I have been glued to the television over the past many days watching the devastating Sandy coverage, I realize how each person and town has been affected so differently. I highly respect the efforts and jobs of the National Guard, FEMA and local police/fire fighters . . . each response required is unique based on what each person/town is dealing with from a recovery perspective.
This coincides with today’s discussion on Comparative Effectiveness Research. It’s true that some medical treatments may not work for everyone and that some treatments may work better for some versus others. This research that Jamie is discussing below can help identify the treatments that may work best for you. Just like specific recovery efforts that may work better for one person/location affected by Hurricane Sandy versus another.
And on that note…
Once more, let’s welcome back, Jamie Banks, PhD! As a “refresher,” Jamie is part of our team here at McCormick LifeScience Consultants as a health care consultant and strategist with experience in health economics, outcomes research, health policy, value assessment, trial design, technology valuation, and communications/publications. Questions? Need help? Email her @ email@example.com.
Jamie . . . have at it!
We’re in the home stretch of our 3-part series on the new vocabulary of market access and reimbursement. In the first 2 legs we covered PROs (Patient Reported Outcomes; September 2012 issue) and HEOR (Health Economics and Outcomes Research; October 2012 issue). To this alphabet soup, we are now adding more acronyms related to our last and very important topic: CER – an essential tool for value-oriented decision-makers.
What Exactly is CER?
It’s Comparative Effectiveness Research! Not be confused with Cost-Effectiveness Research, which may use the same acronym. Not surprisingly, CER is about comparing active interventions in relevant populations and settings of care through designs such as:
- Systematic reviews
- Retrospective analyses
- Non-experimental prospective studies
- Experimental prospective studies
CER is Not New…
…but has become increasingly important in demonstrating value to clinicians, payers and other decision-makers throughout product life cycle.
Under the American Recovery and Reinvestment Act (aka ARRA 2009), CER received $1.1 billion in federal funding for a mandate:
” …to improve health outcomes by developing and disseminating evidence-based information to patients, clinicians, and other decision-makers… about which interventions are most effective for which patients under specific circumstances.”
With all the studies and data at hand, you might think clinicians and payers should have all the information they need to make sound decisions. But, guess what? They don’t.
In fact, in most cases, clinicians lack evidence on which to base medical decisions regarding care and treatment. Payers lack evidence on the value of interventions in the diverse populations in which they may be used. (Would you pay for something whose value you could not ascertain?)
What About Randomized Clinical Trials (RCTs)?
With their rigorous designs, stringent inclusion/exclusion criteria, RCTs can tell us about efficacy and safety in this population within the limited time course of the trial, but not about its broader effectiveness and safety in routine settings of care. And, if the comparison is against an intervention that is not mainstream or is a placebo, the information gained from the clinical trial may not be useful or practical for decision-making.
Let’s take as an example, a new (hypothetical) antihypertensive agent. Clinical trials show better control of blood pressure on average than usual care in the patients in the trial who are between 18-54 years of age without renal or hepatic impairment and not on other drugs that may affect the action of the new drug. Well, that’s terrific; we know the drug can work in that population. But what about for patients who are elderly, are renally impaired, and/or have other serious co-morbid conditions, such as diabetes? And is the intervention safe in these patients beyond the time course of the RCT?
Clearly, other types of data sources are needed to provide “real world” information for decision-makers.
Enter, Electronic Data
By now, we have probably all encountered the term “electronic health/medical record” (EHR or EMR in acronym-speak) and patient registries. With large electronic databases containing detailed clinical information on a full range of patients in diverse care settings, CER can be conducted much more efficiently and economically than is possible for RCTs.
CER is therefore, the Perfect Solution, Right?
While CER can improve our understanding on the relative effectiveness and safety of interventions in diverse patients and settings of care, its results have the same limitation as clinical trials and other population-based studies; that is:
The results are statistical averages that do not apply to any one patient.
More personalized, patient-centered care will need to rely on Big Data and their disease models. But that’s another story…
- It is important to recognize the importance of CER. The federal government has a mandate and funding to conduct CER initiatives; health plans and payers will look to CER to assess the value of competing interventions in actual patient populations.
- Understand the differences between RCTs and CER. A regulatory RCT is experimental and conducted under conditions of stringent patient selection, restricted time frames, defined endpoints and structured clinical care; the generalizability of results is limited. CER focuses on broader evaluations of effectiveness and safety across the range of patients treated in routine settings of care.
- Don’t confuse CER as an abbreviation for Comparative Effectiveness Research with CER as an abbreviation for Cost-Effectiveness Research (seen occasionally). Examine the context in which “CER” is quoted to discern the meaning.
- Understand that, like other population-based research results, CER results are statistical averages that do not apply to any one patient.