Value-based healthcare, a 2020 outlook

By | 4 minute read | December 16, 2019

Technological advancements in the last decade have begun to re-imagine how health is measured, managed and delivered. 2020 will be no different. Today, the model for delivering care is orientated toward addressing business objectives, rather than achieving patient outcomes. We’re seeing a shift towards “value-based health,” which strives for better outcomes, quality and care for patients, expanded access for all, and significantly lower costs. In my view, A number of elements are behind this shift, and many of the technological forces are the ones that have always driven healthcare’s evolution. In 2020, we’ll apply them in new ways and to new challenges, with the goal of helping to drive optimal outcomes for stakeholders in the healthcare ecosystem.

The IBM Institute for Business Value (IBV) and IBM Watson Health recently conducted a study of healthcare payers and providers to determine what is needed to transition to an inclusive, value-based health system – integrating technology to accelerate progress and moving healthcare outside the clinic.

1. Cloud:

Adopting a cloud computing architecture offers the flexibility and scalability to run and manage a suite of analytic capabilities that support current and next-generation applications, all in a secured, environment. Ultimately, a cloud-based analytics platform could share data to potentially help doctors, patients, caregivers and clinicians make timely and effective decisions about patient therapeutics.

2. Artificial Intelligence:

AI has already been applied to use cases in healthcare such as identifying patterns in patient cohorts to build models of a particular disease’s progression, and analyzing genetic information to help determine treatment efficacy. AI can also be applied to the design and development of clinical trials, previously a labor-intensive and manual process. With a market size expected to hit $68.9 billion by 20261, optimizing the clinical trial workflows could help to reduce spending, lower costs, optimize processes. Already, AI-automated trial matching can integrate data from electronic health records, medical literature and eligibility criteria from legislative bodies and learn how to interpret the trial requirements based on patient cases. In one IBM Watson Health™ study, AI-based identification cut the time required to screen patients for clinical trial eligibility by 78 percent2.

3. The Internet of Things:

The declining cost of sensors and the growth of secure, cloud-based platforms could make a profound impact on the healthcare industry. With the patient’s consent, tracking, monitoring and measuring a patient’s health outside of the doctor’s office, in a near real-time, passive manner through activities a patient would already be doing in their daily life, could open up our ability to generate data and insights about the individual. But this isn’t a system that can be built on individual devices.  Capitalizing on this opportunity requires a robust and integrated approach which would allow multiple microservices and devices to work off of a single analytics platform

4. Blockchain:

Both IoT and AI rely on secure access to health data in order to generate insights. With healthcare data expected to double every 73 days by 20203, and extensive regulations and risks associated with sharing, data offers both challenges and opportunities for organizations. Blockchain could offer a solution. Data added to a blockchain can be shared in near real-time across a group of permissioned individuals and/or institutions. Every event or transaction is time-stamped and becomes part of the immutable record of the object, whether that’s a medicine or a patient record. The transparency afforded by blockchains moves the data from individual, siloed ownership to a jointly accessible, secured record for permissioned stakeholders. This shareable record becomes the single source of truth for a patient’s (or thing’s) history, seamlessly carried by the patient as a digital record, regardless of location or health system.

5. Quantum:

As quantum computing technology continues to advance, research scientists and early adopters are exploring the opportunities and challenges posted by this unprecedented computing power. With quantum computing, AI systems may be able to identify patterns and derive insights in systems so complex that there just has not been enough classical computer resource in the world to model them, including modeling chemical reactions, identifying compounds with similar chemical properties, and drug research and development.

  • To improve quality, workflows and patient outcomes, the healthcare industry must adopt practices and technologies designed to extract value and insights from data. IBM Watson Health is committed to help build smarter health ecosystems. This means working with you to help you achieve simpler processes, better care insights, faster breakthroughs, and improved experiences for people around the world.



  1. Grand View Research. (2019). Clinical Trials Market Size, Share & Trends Analysis Report By Phase, By Study Design, By Indication And Segment Forecasts, 2019 – 2026. Grand View Research. Retrieved from
  2. Beck J, Vinegra M, Dankwa-Mullan I, Torres A, Simmons C, Holtzen H, Urman A, Roper N, Norden A, Rammage M, Hancock S, Lim K, Rao P, Coverdill S, Roberts L, Williamson P, Howell M, Chau Q, Culver K, Sweetman R. Cognitive technology addressing optimal cancer clinical trial matching and protocol feasibility in a community cancer practice. J Clin Oncol. 2017;35 (suppl; abstr 6501). doi: 10.1200/JCO.2017.35.15_suppl.6501.
  3. Densen P. Challenges and opportunities facing medical education. Trans A Clin Climatol Asso. 2011; 122:48-58