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Life Sciences Companies, Partners Play Key Roles in Enabling Personalized Medicine
In today's market there are many masters to please when bringing a new therapy to market. Jeff Elton covers some of the key bases you'll need to hit, and offers advice on the best possible approach.
Historically, clinical efficacy and safety criteria were vital in helping life sciences companies determine which therapies to develop and their relative priority. But now, in addition to those criteria, providers, payers, regulators and patients want to see that a therapy offers value, both to the healthcare delivery system and quality of life outcomes before supporting it and subsequently justifying reimbursement. Increasingly life sciences companies are using value-based approaches to guide new product development efforts from discovery through commercialization.
Clinical trial data - as well as data found in electronic medical records (EMRs) that relate to a patient's lifestyle, medical and prescription history, outcomes, or to specific genetic characteristics of the patient or disease - can help life sciences companies and their partners target a therapy. And in turn this data may provide the highest value to patients and the healthcare system. As they increase in use and the amount of longitudinal data also increases, EMR systems offer important patient data and can be used to model and validate ‘value.’ Because these in-depth data are generally organized around a patient and collected within defined care management processes, they give an optimum perspective on process and outcomes.
Determining Responsibility in a Team Effort
As a therapeutic is developed, launched and commercialized, life sciences companies; provider entities; pharmacies; third-party companies, such as self-insured employers; software and service providers; and others are finding ways to work together to add value to that therapeutic in a real clinical context. Each partner has different responsibilities in gathering, integrating, and interpreting data. All of this is centered around the patient and care management process, so that a therapy's highest value potential can be realized. Partners must harness each other's insight and expertise. They must jointly assemble the data, insights, and analytics needed to achieve personalization, and work around process and operating models to deliver the greatest benefit most efficiently.
"Increasingly the provision of high quality, highly effective, and efficiently delivered care involves multiple partners."
Integrating data is a system-wide issue and requires specific actions of all partners. Life sciences companies must:
- Clearly understand drivers of value in the specific therapeutic and treatment area.
- Understand the value delivered on an absolute and relative basis for the provider system and patients.
- Develop data and information exchanges around care management processes that include disease mechanism expertise and insights into different patient stratification approaches for the highest value.
- Define risk and value sharing reimbursement approaches that align incentives across providers, other risk bearers and life science companies.
Increasingly the provision of high quality, highly effective, and efficiently delivered care involves multiple partners each bringing a different basis of value. The health systems and providersare responsible for the overall care management process, making the decision to engage others in fulfilling that responsibility, and holding the "formal record" of electronic health data in the EMR. Pharmacy benefit managers and health payersmayhave responsibility for tracking, fulfilling, and gaining adherence to pharmaceutical therapy. They also provide guidelines that indicate whether clinical decisions are acceptable within evidence-based guidelines, and therefore are reimbursable. Increasingly, care providers are managing a portion of the risk.
Labs services see test results and transmit them to care delivery entities for inclusion in the EMR. Employers may have incentive models and other oversight processes to control clinical quality and costs.
There is not a long history of life sciences companies becoming directly involved in the provision of care. But the fullest realization of value means that life sciences companies must offer their intellectual assets and know-how, including patient stratification, clinical trial design and other data analytics. They must bring their in-depth, decades-long knowledge of a disease state to bear in disease management processes for the benefit of all.
Becoming Value Managers
Life sciences companies must be visible on the real value they are creating by becoming value managers. Life sciences companies must help in determining value with new approaches for mining patient-focused data and by also becoming proactive in keeping costs low. For instance, life science companies, providers, and employers may take a more active role by improving a patient's adherence to their therapeutic treatment. The more adherent type of patient may require fewer in-person health visits and avoid or delay expensive disease-state complications, minimizing costs to the health care system, improving their productivity to the employer, and increasing their quality of life.
Often, the only way a partner knows if it is delivering value is to collect data in an actual clinical context, define exactly what is value, and evaluate the value delivered by alternative approaches and therapeutic interventions. The first step in doing this is to bring the data together in a data model that allows the team to understand the issues from a process perspective. EMR’s themselves do not necessarily align patient data around a single or broadly agreed to disease management model.
Also needed are analytics capabilities, based on a language set of a particular disease state, thereby making it possible to interrogate the data and other available information. Fundamentally, data are being constructed in ways that make it possible to group different data types and associate them around a specific care management disease process or relative to alternative approaches, along with a variety of analytics and other capabilities.
Analytics can be predictive or retrospective, offering insights into outcomes that are the basis for calculating value. Others may predict risks or events for specific patients that can lower value realized, such as a patient being nonadherent to their treatment regimens. With this information in hand, partners can allocate more resources to higher risk patients, or to those patients for whom the benefit of a therapeutic will be highest.
While it remains early days, the capability to make such decisions may eventually become the basis for different payment or revenue models between providers, payers, and partners. Ultimately a value-based system can provide incentives and compensation for meaningful innovations.
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