by Paddy Padmanabhan

How A.I. and blockchain are driving precision medicine in 2017

Jan 17, 2017
AnalyticsArtificial IntelligenceCybercrime

Recent announcements by the ONC and the FDA for partnership programs on artificial intelligence and blockchain show how these advanced technologies could transform healthcare.

The healthcare headlines this year have been dominated by the imminent repeal of the Affordable Care Act (ACA). However, against the backdrop of a long-term transition to value-based care (VBC), a handful of emerging technology initiatives are quietly making news in advancing precision medicine in healthcare.

Healthcare is concerned primarily with three things today:

  • Unlocking the value of data for insights that will drive precision medicine and population health.
  • Improving efficiencies by enabling seamless digital experiences for patients and caregivers.
  • Ensuring security and privacy in transmitting and storing personal health information.

The promise of precision medicine requires complete access to all available data about an individual. Over the past few years, digitization of health records through the implementation of EHR systems has covered the vast majority of hospitals and physician practices. Efforts to unlock value from unstructured data are already under way using natural language processing (NLP) technologies. The onward march toward VBC involves the leveraging of structured and unstructured data from all kinds of sources.

Much of the data available about patients is generated in clinical settings. A new data source called patient generated health data (PGHD) refers to data generated from patients via wearables and consumer health applications. Other data sources include sensors and smart devices that are part of the internet of things (IoT) and are capable of transmitting data wirelessly through remote monitoring systems.

Though there has been a considerable growth in consumer health apps and wearables over the past few years, much of the data is unusable or not being used for a variety of reasons. The Office of the National Coordinator for Health Information Technology (ONC) has just released a draft report on PGHD and its role in healthcare research and delivery. Consulting firm Accenture, which was contracted by the ONC to identify challenges and opportunities in the adoption of PGHD, released a white paper outlining the current state, including an expected timeline for adoption of PGHD in clinical protocols. The white paper draws on the experience of pilot projects conducted at Sutter Health, a large health system in northern California. The projects focused on patients with Type 2 diabetes and used PGHD from various devices in real time.

Another interesting example of PGHD is the effort underway at health systems such as Kaiser Permanente to incorporate personal genomic data, provided by patients, in diagnosis and treatment protocols.

The opportunity with PGHD lies in its ability to involve patients in care delivery, supported by a higher volume and velocity of medical data that can enable personalized care plans. The challenges relate to the lack of consistency and quality of data received from patients on the one hand, and the absence of adequate processes, protocols and systems to ingest and analyze the data at health systems on the other. Additional challenges pertain to data interoperability, and the PGHD effort is part of the ONC’s 10-year vision and road map for nationwide interoperability. Already, there has been steady progress on this (read my predictions for healthcare IT in 2017).

The emerging role of A.I.

Artificial intelligence (A.I.) has dominated the headlines from the beginning of this year. Research firm IDC has forecast that spending on A.I. technologies would rise from $8 billion in 2016 to $47 billion in 2020. Healthcare was identified in the report as one of the sectors likely to spend the most in cognitive or A.I. systems. The definition of A.I. runs the gamut from virtual personal assistants (bots) all the way to intelligent automation and cognitive computing using machine learning and deep learning algorithms. Applications can be found in all aspects of a healthcare enterprise, from IT operations to precision medicine.

Platforms such as IBM Watson Health have made significant progress in the use of A.I. and cognitive computing in oncology and genomics. Recently, digital health firm HealthTap, which offers a platform that provides online medical consulting, launched what could be the first A.I.-based app, named Dr. A.I. The platform provides personalized recommendations based on cognitive insights from a vast database of medical profiles and knowledge gained from millions of online consultations. A.I. is now being applied to large volumes of data from increasingly diverse sources. The results are guiding clinicians on everything from drug discovery to diagnosis and treatment.

Blockchain and information security

A serious concern in achieving the goals of interoperability and precision medicine is the unprecedented levels of data breaches (including ransomware) experienced by health plans and health systems in 2016, which is expected to continue into 2017. Recent reports have pointed to the role of foreign countries in healthcare data breaches, raising questions about the misuse of personal health information in national security.

Blockchain, an open-source technology that uses a distributed database for secure transactions, has the potential to address many of the challenges related to security and privacy with personal health information. The most visible application of the technology so far has been in bitcoin, a form of cryptocurrency that could disrupt the banking and financial system as we know it. In healthcare, there is an emerging interest in applying blockchain to personal healthcare data in the context of research, clinical trials and population health management.

Blockchain enables the gathering and integrating data from a distributed network of participants in the healthcare value chain. Every event or transaction is time-stamped and is unalterable after the fact, which makes it reliable and trustworthy. By ensuring the provenance of data, blockchain provides data integrity and establishes trust among participants. Blockchain enables participants to share data securely with other participants. It means patients can now securely share personal data with their providers, something that has been a challenge until now.

The Food and Drug Administration (FDA) has recently announced a partnership with IBM Watson Health to explore the use blockchain technology in oncology. The partnership will help integrate data from multiple sources and provide a 360-degree view of patients. The technology will enable all participants, including patients, to share data from EHR systems as well as PGHD from wearables and smart devices. The initiative is expected to demonstrate the transformative potential of blockchain technology in enabling the discovery of new drugs through evidence-based research, as well as improving health outcomes for patient populations.

The future — closer than we think

We are in the early stages of a revolution in precision medicine enabled by advanced technologies such as A.I. and blockchain. The ONC’s PGHD adoption curve indicates we are in the early adoption stage in 2017 and will enter a growth phase from 2018 onward. In a recent survey of healthcare executives by IBM’s Institute for Business Value, 16% of the respondents said they expect to have a commercial solution in 2017. The pioneering effort by these early adopters is critical for putting these technologies within reach of the broader healthcare ecosystem. Federally supported programs such as the PGHD and blockchain initiatives can accelerate this progress.

As the late Yogi Berra might have said, “It’s hard to make predictions — especially about the future.” However, the future for A.I. and blockchain in healthcare may already be here.