April 4, 2019
The United States spent $3.5 trillion on healthcare in 2017 and is predicted to spend nearly $6 trillion in 2027. To bend this cost curve and improve overall health outcomes, many decision makers in payer and provider networks are increasingly requiring the life sciences industry to demonstrate both the clinical and economic value of new therapies, medical devices, and procedures.
New features and benefits alone are no longer sufficient for market acceptance, especially for offerings generally perceived to be commoditized (e.g., self-monitoring of blood glucose devices for people living with diabetes). New offerings must show concrete evidence that they provide increased clinical benefit and additional economic value beyond what is available today. For years, the life sciences industry has considered clinical trials to be the gold standard of evidence generation.
However, clinical trials may be limited by geographically or demographically narrow patient populations, restrictive physician or hospital experiences, or inconsistent data capture over time. Many clinical trials also fail to examine economic end points or collect data regarding the economic value of a particular procedure or technology. Alternatively, many leaders in the health sciences industry are leveraging publicly available data sources to quickly and effectively demonstrate the health economics value of new therapies, medical devices, and procedures.
Sources of publicly available healthcare data tend to be compiled from billing data that a healthcare system generates to obtain reimbursement for examinations or procedures performed. The billing data tends to include information on the medical condition of the patient, the procedure performed, and the cost of that procedure. The billing data can also include information on the length of the patient's hospital stay, as well as prescription drug use. Alternatively, some healthcare data are sourced from electronic medical records that also collect pain and function data. All of these data are de-identified to protect patient privacy.
Clinical, analytical, and marketing leaders in the life sciences industry can leverage these data sources for multiple purposes. Publicly available data can help research and analytics teams conduct market research and understand how different therapies, procedures, and technologies are being used in the market for benchmarking purposes. Our team at Exponent recently examined a data set to evaluate the potential effect that professional practice guideline changes had on the use of an existing therapy that our client provided. Our analysis indicated that the new guidelines coincided with a market shift toward the use of a costly surgical procedure instead. Our client used this analysis to begin an informed dialogue with healthcare professionals and payers regarding the clinical benefits of their therapy and whether those benefits outweighed the cost.
Clinical teams can also use publicly available data to help stratify patients into low, moderate, and high-risk tiers. This information can help physicians identify patients who require better education pre-surgery and take appropriate steps to modify their risk factors. For example, if the data shows that obesity is associated with a higher risk of complications after a specific type of surgery, physicians may elect to delay surgery to proactively address the health condition and minimize the patient's post-surgery risk. This information can also help attorneys better understand the complication rates and risk factors associated with different therapies, as well as those of alternative therapies that may come up in a case. For example, our team evaluated the patient risk factors following an orthopedic procedure. This analysis helped support expert opinions for why a particular patient's risk factors may have played a role in his specific health outcome.
In addition, the life sciences industry may be able to use publicly available healthcare data to conduct post-market surveillance on product performance in the real world. This data may quantitatively complement traditional customer feedback channels and supplement existing clinical trial data with a broader patient perspective. If the data is of sufficient quality, it can also help augment the risk/benefit profile of a product and support FDA expanded indications for product use. For example, our team at Exponent recently studied the risk of patients dying after a spine fracture. Data showed that certain therapies were associated with lower risk of patient mortality. This data was ultimately cited and used to support a product labeling expansion of the potential clinical benefits of one of those therapies. Our team at Exponent has also published on the complication rates following the use of different types of medical devices.1,2,3 This allows comparison of a specific therapy's performance against a real-world assessment of the broader therapy category to better understand what the patient experience is in a general population.
How Exponent Can Help
Exponent's multidisciplinary team of biomedical engineers, statisticians, and health economists has a keen understanding of the applications and limitations of publicly available healthcare data. Coupled with our strong publication track record and diverse clinician partnerships, we can help clients effectively evaluate and communicate the clinical and economic benefits of therapies, medical devices, and procedures.
Malkani AL, Himschoot KJ, Ong KL, Lau EC, Baykal D, Dimar JR, Glassman SD, Berry DJ. Does Timing of Primary Total Hip Arthroplasty Prior to or After Lumbar Spine Fusion Have an Effect on Dislocation and Revision Rates? J Arthoplasty 2019; Jan 14
Ong KL, Auerbach JD, Lau E, Schmier J, Ochoa JA. Perioperative Outcomes, Complications, and Costs Associated with Lumbar Spinal Fusion in Older Patients with Spinal Stenosis and Spondylolisthesis. Neurosurg Focus 2014; 36(6): E5.
Henzman C, Ong K, Lau E, Seligson D, Roberts CS, Malkani AL. Complication Risk After Treatment of Intertrochanteric Hip Fractures in the Medicare Population. Orthopedics 2015; 38(9):e799-805.