The Future of Patients as the Audience for Pathways Rather Than Providers


J Clin Pathways. 2018;4(7):44-47. doi:10.25270/JCP.2018.09.00034

Received April 3, 2018; Accepted July 26, 2018


Deontics Ltd., London, UK


Dr Guy Wood-Gush


220 Screenworks

22 Highbury Grove

London N5 2ER, UK


Dr Wood-Gush is chief executive officer and a founder of Deontics. Dr Patkar is chief medical officer and a founder of Deontics.

Key Words

Abstract: Due to technologic and societal shifts, it seems clear that patients will be much more involved in their care going forward. Health care providers and decision-makers should consider these shifting cultural and technologic dynamics in their care delivery strategies. There is much discussion needed about how providers can involve patients in their own care so that it remains safe, appropriate, and adequate. The creation of evidence-based pathways for use by patients would be the natural next step to help patients find the best, most-evidence based information rather than potentially unvetted information they find online. We discuss how providers and clinical pathway developers can evolve with these technologic and societal changes for the benefit of all stakeholders. 

Historically, because of an almost complete asymmetry in knowledge and impenetrable medical jargon, medical decisions about patients’ health have been almost completely made by doctors. While well-intentioned physicians might have taken time to explain a decision and its consequences to a patient, the reality is that only a few patients would have a firm understanding of their treatment options and the relevant pros and cons. But a number of new trends are converging to enable a more informed and active patient group and general public. Many believe1-4 there is a revolution on its way, as envisaged by Eric Topol in his famous book, The Patient Will See You Now: The Future of Medicine Is in Your Hands.5

Society has changed as the internet has transformed the ease of accessing information; all doctors are familiar with the Google phenomenon leading to self-diagnoses and an increasingly curious and interested patient population that no longer takes the physician’s advice as necessarily being the best. The very different standards of care in different regions (eg, depending on whether a patient lives in rural vs urban area, distanced from a specialty center) have been well documented as have the very high rates of medical errors occurring. Indeed, prior research has estimated that medical errors may be associated with over 400,000 deaths each year in the United States.6 Patients are increasingly aware of these dynamics and are searching out the best possible treatments, even if it means considerable travel.

Simultaneously, entrepreneurs looking to create new tech opportunities have found a large market for consumer-facing apps delivering a profusion of wellness solutions relating to everything from exercise to nutrition. These apps are directly engaging a growing online community of health-conscious, often young, consumers in managing their own wellness. Now, even more medically orientated apps delivering heavyweight medical advice—often providing relatively easy access to real physicians online—are beginning to add a more medically focused dimension, as we will discuss below.

Following the increased ease of access to information through the internet and the development of patient-facing technology, the third axis contributing to the shift to a more informed and active patient group is the emergence of value-based care. As provider organizations around the world come under increasing financial and competitive demands, combined with the influences above, there is a continuing pressure to drive expertise and health management into the community and frequently into the patients’ own homes, reducing lengths of stay and admissions to acute care. Of course, the cheapest care, where it can be efficiently managed, is patient self-care. In relation to this demand for more value in care, the importance of patient-centered care and patient-related outcomes have become increasingly important.

There is much discussion needed about how providers can involve patients in their own care so that it remains safe, appropriate, and adequate. As patients are increasingly utilizing technology as a means to educate themselves in medical care—looking up their diseases, medical research, treatment options—the creation of evidence-based pathways for use by patients would be the natural next step to help patients find the best, most evidence-based information rather than potentially unvetted information they find online. In order to deploy evidence-based pathways knowledge to patients online, a number of technical capabilities will need to be successfully addressed.

Pathways in Executable Formats

First, there has to be a capability to digitize the clinical knowledge embodied in a pathway. Some may think that this is merely providing access to electronic versions of clinical guidelines online. However, in reality, this does not optimize this process any better than handing out generic leaflets about a particular disease to a patient, as a patient is unlikely to have the skill set to relate the relevant information to their own condition. 

The more sophisticated strategy would be to provide a patient-user-friendly pathways digital platform that can be integrated with individual patient data to automatically generate diagnostic and treatment recommendations. This, at the moment, is more easily achieved in diagnosis, where decision trees, triage, machine learning, and predictive analytics can be directly brought to bear, rather than in treatment, where the vast variation in individual patients and the relative lack of defined probabilistic advice can make it difficult to apply such modalities comprehensively and broadly to highly sophisticated decision support. 

An example of diagnostic machine learning technology is Google Deep Mind, which has been deployed at hospitals in London to identify patients at high risk of suffering acute kidney injury and to help categorize ophthalmic scan results to aid in diagnosis of retinal disease.7,8

While decision-tree models are widely used in clinical decision support (CDS) technologies and available within most electronic medical record systems, these generally are limited to situation action alerts and order sets that do not function well for less linear and more advanced treatment advice. However, decision trees are deployed to great effect in, for example, most commercially available oncology pathway systems, where the contingencies and algorithmic logic are well established and linear in nature. 

Only a few companies, relatively, are currently providing generic, highly sophisticated, treatment-focused CDS, Deontics, Elsevier, and IBM Watson being examples.

The take-home message here is that the technologies to digitize clinical pathways and to deploy them over the internet already exist and will improve, providing patients with the capabilities to understand the pros and cons of the specific treatment options, to avoid unnecessary treatment, and, above all, to clarify issues affecting their own safety.

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