Using RWE Across the Drug Product Lifecycle

In addition to providing insights about comparative effectiveness and pharmacovigilance, drug makers are being spurred to consider real world evidence (RWE) in a broader context. With the price tag for developing a new drug hitting unprecedented levels, companies have started using RWE to cut waste, curb costs and maximize the benefits of their marketing spend.

Similar to the relentless rise in healthcare expenditures, the cost of drug development has increased sharply in recent years. By the time a drug achieves market approval; the process has been underway for more than a decade and has cost as much as a billion dollars. A 2014 study from the Tufts Centre for the Study of Drug Development found that it takes, on average, $2.6 billion to develop a new prescription medicine that gains market approval when factoring in out of pocket costs and time costs. Compared to a similar study from 2003, the price tag for developing a prescription drug has increased 145% over the last decade, even after inflation was taken into account.

Organizations that can compress clinical development timelines by learning earlier on which drugs are effective on which patient groups can reduce the number of drugs that are abandoned late in the development process. This can save millions of dollars and months of misplaced effort. In addition, drug manufacturers are also discovering that RWE can deliver value at multiple points along the drug development lifecycle. RWE is emerging as a valuable tool to recruit for clinical trials, improve product launches, target the right prescribers and patients, and support ongoing access through creative pricing and reimbursement mechanisms. It has been estimated that by applying RWE in a systematic way a top-ten pharmaceutical company could realize $1 billion in value.

The IMS Health report, RWE Market Impact on Medicines, provides more than 100 non-safety case studies where RWE has been used to influence product decisions in the U.S. and other western nations.

Many of these examples are prospective studies where a drug product was able to achieve reimbursement by having the manufacturer compensate payers for patients who did not respond to therapy or did not meet benchmarks. In other cases, studies that used RWE to show a product’s effectiveness allowed for formulary status and premium pricing to be restored. Another report noted a case of a biopharmaceutical company that was able to surpass expectations for a new product’s uptake by more than 10%. Despite the presence of a long-established competitor in that market, the company was able to use RWE to focus their detailing efforts on physicians with relevant patients who met the target treatment profile.

Examples like these illustrate the promise of RWE, but it is still early days for its use in widespread decision-making. Even organizations that have implemented RWE platforms or data warehouses have yet to realize the full potential of RWE. One reason is that much more can be done in linking together different datasets. There are many sources of data that exist across the healthcare landscape. EMRs provide a wealth of information, including patient demographics, medical conditions, vital signs, lab results, and treatment information. Individual claims databases, prescription databases and hospitalization records add to the overall picture. The real promise of RWE will be realized by having a comprehensive view of the patient experience. This will only happen when data gathered at the various stages of care can be connected.

The linking of disparate datasets can be facilitated by creating de-identified data that retains a high level of granularity or specificity. Data masking approaches that broadly redact data are insufficient for this purpose; a risk-based approach to data de-identification is required.

Maintaining privacy requires the removal or perturbing of identifying variables within the dataset so that a person cannot be re-identified from their data. However, not all forms of de- identification are concerned with the delivery of high-quality information for analysis. To provide value for decision-making, the data used in RWE analysis must retain as much granularity as possible.

Techniques like masking can drastically reduce the quality of data since they will often completely remove important variables, such as dates or zip codes. While masking appears to offer a simple solution to removing identifiers, it is best avoided as a blanket approach to de-identify data. Other methodologies are available that allow de- identification to be done in a way that protects individual privacy while maintaining the quality of the data. The use of a risk-based approach to de- identification, like the Expert Determination method described in the HIPAA Privacy Rule, is recommended to anonymize data for RWE. With risk-based de-identification, attributes like dates can be generalized or aggregated rather than eliminated. Other techniques available with risk- based de-identification, like date shifting, allow chronological information and durations to be retained. All of this enables better information to be retained for use in subsequent analysis, providing richer and more accurate findings.

 

This is the second installment in our RWE series. Next week: Building a De-Identification Pipeline to Support RWE.

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