One of the many nuances that has always escaped me is the direct (and indirect) relationship between Comparative Effectiveness Research (CER) Studies and Pharma/LS industry. Why do CER studies mean to Pharma and what are some of its implications in how Pharma stakeholders conducts its business?
CER is the conduct and synthesis of research comparing the benefits and harms of different interventions and strategies to prevent, diagnose, treat, and monitor health conditions…CER can provide patients, providers, payers, and other stakeholders with the information to improve decision-making about treatments, coverage options, and other issues affecting health care quality and outcomes.
Comparative Effectiveness Research is not a novel concept. However, with recent changes in policies (American Recovery and Reinvestment Act in 2009 and PPACA in 2010), significant attention has been directed to the need for Patient-Centered Outcomes Research.
In 2009, $1.1 billion of President Barack Obama’s stimulus package was earmarked for CER.
The passage of PPACA, in turn, established the Patient-Centered Outcomes Research Institute (PCORI), which oversees and sponsors CER in the U.S. and provides health care decision-makers with current and relevant data that can enable them to make more informed decisions.
Since it first began approving research awards in 2012, PCORI has committed a total of $549 million in support for comparative clinical effectiveness research (CER) and related projects.
Combined, NIH and AHRQ had nearly $675 million in funding for CER in 2014.
Current Health Care Decision-Making Environment
Recent survey conducted by NPC reported the following findings based on 122 key deicison-makers in the space:
- Research Standards: There is a growing movement toward widely agreed-upon research standards (49% consensus)
- Research Priorities: Many felt that research priorities somewhat/adequately reflected treatment choices in 2015 (41% consensus)
- Transparency: Slightly less than half of respondents felt that there is no or little transparency in evidence evaluation
- Treatment Assessments: Value of treatments remains narrowly focused on only clinical effectiveness (58% consensus)
- Integrated Purchasing of Health Services: Nearly 2/3 of respondents feel that purchasing of health services trends toward a siloed view
- Outcome-Based Contracting: Most felt there is little to no outcomes-based contracting (70% consensus)
- Completeness of Comparative Effectiveness Evidence Base: There is not enough evidence available to answer treatment questions (67% consensus)
- Use of Real-World Evidence: Real-world evidence is limited in decision-making (44% consensus)
How Do CER Studies Relate to Pharma / Life Sciences Stakeholders?
This is the central question I originally embarked on to dig deeper. After reviewing multiple pieces of the literature available, I had come across an opinion piece by Michael Abrams, managing partner at Numerof & Associates, Inc., who had succinctly articulated the linkage:
The ultimate impact of each of these changes for pharma will be a redefinition of value. To fully identify the potential value of products, manufacturers will need to look beyond product attributes, such as physician ease of use. Instead, they will need to evaluate new products on how they potentially improve current treatment regimens for the condition addressed, save costs and improve outcomes. For comparative purposes, determining the potential of a new drug to take the place of multiple drugs in a current therapeutic regimen could be important. Demonstrating equivalent efficacy (vs. superiority) may be sufficient—and of interest to payers—if the product has an improved dosing frequency (and improved patient compliance) or some other benefit, such as reducing the number of medications required for treatment or the number of physician visits.
Source: Pharmaceutical Commerce
In short, the changes in reimbursement scheme will require for most stakeholders in HC, pharma included, to fully consider the direct impact of products on patient outcome. This shift necessitates an infrastructure for: 1) normalized data lake containing anonymized patient population data, 2) a closet full of tool kits for the positioning, processing, and evaluation of data, and 3) a virtual environment safe and efficient enough for conducting (1) and (2).
This brings us, once again, for the need for deep analytics in the Life Sciences industry that leverages Real World Evidence.