By Manish Sood, CEO of Reltio
The pharmaceutical industry is embracing a more patient-centric approach throughout the drug development and commercialization value chain. As patients become more involved in their care and evaluate options available to them, pharma organizations are working on new care models to work more closely with the patients and not just with physicians, healthcare organizations and payers.
2019 will see an increased focus on patient experience during clinical trials as well as post-commercial launch. Pharma companies will strengthen their commitment towards personalized medicine and business models such as pay-for-performance and get more involved in care by collaborating with providers, payers, and pharmacy benefit managers (PBMs) to ensure adherence.
Pharma organizations will leverage advanced analytics and machine learning to learn more about their customers, including patients, and use the insights to deliver better health outcomes. Machine learning will be used not only to deliver personalized care but also to improve the data quality required to deliver insights for personal care. The technology will also be used to counter pharma’s exposure to fraud and compliance risk.
A focus on patient experience will force the payers, pharma, and providers to come together and provide a holistic approach towards overall wellness. When these entities work together, they can develop much more effective treatment programs with better results. This will require pharma to use capabilities such as Data as a Service to share and exchange data in a compliant way to improve collaboration and expedite the launch of treatments and wellness programs.
Getting there requires a well-thought-out data strategy that helps bring together all patient data in a compliant way and uncover relationships between patients, physicians, caregivers, hospitals, payers, plans and prescriptions. In 2019, new data management technologies will help pharma organizations create a reliable patient data foundation followed by the use of advanced analytics and machine learning on reliable data to identify the next best action for timely and compliant patient engagement in order to achieve the best health outcomes.