Another area where AI can be hugely helpful is in presenting data and helping patients make sense of it. After all, they are confronted with the same challenge as their doctors: quantities of specialized information that can be life saving, or just the opposite.  The patient’s informed consent is required in every aspect of care – from their contributing biological samples preparing for complex surgeries, to accepting a given course of treatment. How do we empower patients to make truly informed decisions, while allowing developers access to the streams of data? 


Respect for a patient’s individual autonomy is an established principle in modern medicine. In the past half century, the concept of autonomy has promoted patients from passive recipients of care to partners in planning their own treatments.

The notion of patient empowerment is reflected by developments in regulation and guidance. Think of phrases such as “patient led care,” “patient engagement” and “shared treatment decision-making”. 

The new strategy of patient involvement in health services is emerging in conjunction with initiatives for deepening inter-professional collaboration, all with the further aim of providing team-based care. Policy-makers envision some non-physician providers’ expanding scope of practice going hand-in-hand with this increased focus on patient-centered care. Can AI help deepen the trust between patients and the professionals in their care circle?

Research shows that high-performing teams are not built piecemeal. They achieve superior levels of cooperation because their members trust one another. Trust and a strong sense of group identity build confidence in their effectiveness as a team. In other words, such teams possess high levels of group EI (emotional intelligence).

The right amount of information, shared at the right time, can promote significant improvement in patients’ ability to choose the right treatments.

In a randomized controlled trial at two hospitals in Boston researchers studied the impact of a video-based decisions support tool for patients in the hospital. Decisions about cardiopulmonary resuscitation (CPR) and intubation are core parts of advance care planning, particularly for seriously ill, hospitalized patients. However, these discussions are often avoided. Seriously ill patients who viewed a video about CPR and intubation were less likely to want these treatments. Better informed about their options, they gave orders to forgo CPR/ intubation, and discussed their preferences with providers. This study brings into question what represents Informed Consent in the emerging world of AI and Big data? (El-Jawahri, et al., 2015)

Anonymizing data is a recognized pre-condition for its collection. It allows access to health data without compromising the patient’s right to privacy or security. Sayo, founder of Self-Care Catalysts, makes a compelling case for codifying trust between the givers and the gatherers of data: patients who give their health data should have greater access to it themselves. They should be able to track it, and see how their data is being used. There should also be an available, clearly delineated process of opting out of data sharing. (Nuffield Council on Bioethics, 2015)
Researchers in the United Kingdom have tested the concept of Dynamic Consent. (Spencer, K. et al., 2016 ). Here, information technology is used to determine just what patients are consenting to share. A succession of digital interface screens is presented to the patient. Information and choices about data gathering, and its potential uses, are delineated. Patients are thus enabled to tailor consent according to their own preferences. 
However, this concept is bound to come into conflict with the demand from AI for access to large pools of patient data. This is useful in training AI’s predictive function, which is not always related to a specific patient’s care. These secondary uses of identifiable patients’ data, (eg. to develop commercial products,) represent unchartered waters for public payers, patient advocates and commercial vendors. Google’s company, DeepMind, launched several projects with large health systems that bring out important questions about the commercial exploitation of individuals’ data and publicly funded health systems. These systems are fertile ground for “schooling” AI programs owned by powerful global corporations. (, 2016)
There is great potential to be realized by combining data with analytics, and technology with expertise. Many ills can be more efficiently cured, once value is understood and paid for. However, a patient centric, value-defining framework – one capable of informing data-sharing in the new healthcare sphere – does not yet exist. 

The Office of the National Coordinator for Health Information Technology, United States Government Health and Human Services (ONC) has identified patient-generated health data (PGHD) as an important issue for advancing patient engagement. It has initiated a series of activities to gain more information about PGHD’s value, and the various approaches to its implementation. The PGHD policy framework project is integral to Stage 3 of the Meaningful Use Rule, and the U.S. administration’s Precision Medicine Initiative. (, 2016)


AuthorVeronika Litinski