This insightful article was provided to us by one of our contributors James J. Gillespie, President, Center for Healthcare Innovation and Brian Speicher, MBA Candidate, Sloan School of Management, MIT.
“To a great extent, we have lost control of the data and, in many ways, our traditional position as information providers. Patients and others now have much more information than we do. They can identify issues such as adverse side effects or inappropriate use of our products much earlier than we can. How will they use this control? Will pharma companies benefit from richer information so that we can deliver better outcomes?”
David Norton, Chairman, Johnson & Johnson Pharmaceutical Group (Progressions, Pharma 3.0)
Despite being a powerful and wealthy industry, with lots of brilliant and creative people working at all levels, through the years, the pharmaceutical industry fell behind in controlling and providing two key resources: data and information. As the comments from Mr. Norton suggest, the industry is certainly becoming more aware of this deficiency and is attempting to adopt strategies to get more fully into the data “game”.
However, given this evolutionary lag, entrepreneurial, patient-level organizations have mobilized themselves in an effort to aggregate patient experience information online through broad, accessible communication platforms. These growing communities of patients are sharing detailed accounts of their health histories and the intimacies of personal trial and error associated with the management of their disease states. For example, patient-users of the website “Patients Like Me” volunteer detailed pharmacotherapeutic data including therapeutic agents used, attempted dose ranges (both approved and off-label), side effects, therapeutic combinations, and treatment durations. The resulting pool of accumulated patient experience is remarkably transparent, and is noteworthy in that it includes multidimensional patient experiences comparably, across treatments.
To extend the thought experiment posed by Mr. Norton – How might these databases evolve into first generation comparative effectiveness engines? How might industrious patient groups organize themselves into their own observational studies or even clinical trials to evaluate treatment approaches for themselves, generating their own proprietary datasets? Emphasis has been historically placed on the desire for firms to disclose clinical trial efficacy and safety data from pre-NDA studies. What has become increasingly adopted is the perspective that the total value of the therapeutic – its effectiveness – is the true measure of value concerning the impact of a given intervention. By comparison, the NDA is merely the value proposal of the product and the weight of evidence argument has yet to be litigated. Patient groups need only decide how and when to actualize the potential of their influence in this realm, and become authentic stakeholders in the determination of “willingness to pay”.
Employing technology has become the centerpiece in most data strategies that impart this grass roots empowerment. Once the exclusive domain of resource rich large pharmaceutical manufacturers, the provision of information can be achieved by individuals or firms through the use of educational websites, social media platforms, and apps on wireless devices. These flexible channels can be constructed for both gathering data from and giving information to patients, physicians, and payers.
From a clinical perspective, data will undoubtedly play the central role in helping health care companies of every stripe better coordinate care and efficiently apply resources to improve health outcomes. For example, by applying sophisticated data mining techniques to examine big pools of disaggregated data, pharmaceutical firms can develop more medically efficacious and cost-effective clinical trials. Large health care providers can determine what treatment protocols achieve optimal patient outcomes, while sparing resources. Basic research efforts into costly areas such as pharmacogenomics can complement their development efforts through this data mining, whereby target identification can be done for pennies on the dollar when compared to other more expensive and time-consuming biomarker identification procedures.
Yet, much uncertainty remains regarding the future interaction between data/information and the pharmaceutical industry. This is not necessarily adverse. The issue is trying to determine the full upside potential of more available and flexible health care information systems and what insights may be achieved with data analyses.
There are many channels that host potentially disruptive areas of thinking and discussion. These will be the subject of future communications from CHI:
- The emerging pressure of comparative effectiveness in therapeutics and the many methodological issues of determining what is “best”,
- The stranglehold IMS Health has on prescribing data, and how an alternative source of information may be achieved,
- The battle for standard-setting in the application of the electronic health record/medical record, and the analytic power that will come from this vast accumulation of data,
- The ability to partner with data sources from evidenced based medical practices such as Intermountain Health Care to survey effectiveness of disease management,
- The impact of companies like Castlight, offering patients the ability to engage in arbitrage for their own health care services.
The next several years will be filled with emergence, alternatives, battles, partnering, and arbitrage. The key for the pharmaceutical industry will be the ability to collaboratively set an agenda within life science information (i.e., anticipating emerging issues, conducting exploratory analyses, encouraging hypothesis generation) and the creation of challenging internal and external discussions driven by analytical insights.