The nature of clinical decision-making is changing rapidly due to a combination of structural factors and advancements related to evidence generation. Technology will play an integral part in shaping how clinical decision-making evolves in the years to come.

Technology will shape clinical decision-making in three ways:

1. Clinical practice is moving from memory-based to technology-assisted
2. The increasing availability of data and evidence will enable more proactive, precise, and personalized care
3. Care teams will expand in ways that cause ripple effects on decision-making processes and autonomy

A few weeks ago, Advisory Board's Solomon Banjo, Managing Director Life Sciences, sat down with two physicians who have developed tech solutions focused on helping their peers make better decisions with their patients. Their solutions and perspectives demonstrate the potential of technology to shape the future of clinical decision-making for the better.

The two panelists were Kevin Larsen—SVP of Clinical Innovation at Optum, where he leads the clinical team in the Center for Advanced Clinical Solutions, focused on clinical decision support and provider enablement—and Art Papier, CEO of VisualDx, a company dedicated to improving health and medical decisions in the home and in the exam room.  Art has greater than 30 years-experience in researching and developing clinical decision support and patient engagement technologies.

Here are five key takeaways from the conversation.

1. Clinical practice is transitioning from memory-based to technology-assisted but there needs to be consensus on where we focus

Technology advances so quickly that there is a tendency to get carried away by the "next big thing" and think about every problem that can be addressed compared to zeroing in and iterating on what is already successful to refine it.  While both can be done simultaneously, the key is capitalizing on where technology can be most beneficial for clinicians.

Two specialties highlighted by Art were primary care and emergency medicine where providers see high volumes of complex problems and are expected to solve them during 15-minute visits. Primary care and emergency medicine are also the most common touchpoints that patients have with the health care system so these are areas where we should pinpoint for technology-assisted decision-making to help these clinicians do their jobs more efficiently.

In addition to these specialties, several other specialty areas can benefit as well by taking advantage and moving toward integration of technology in their clinical care through adoption of use in follow-up visits. This can open rooms for initial visits that need to be completed with the doctors and comes at a higher value to the hospital.

Aside from the benefits to quality of care in terms of decision-making, technology assisted approaches benefit hospital systems through cost savings, and can potentially improve scheduling, patients seen per day, and amount of time spent with the patient.

2. Clinical decision support technology should empower patients and providers

Patients are under-emphasized in clinical decision-making technology. Now that we have so much data, the focus is understandably on aggregating that data and presenting that information to clinicians at the point of care. Patients are also collecting more of their own data and have access to a lot of medical information/knowledge via sites like WebMD and Healthline, resulting in less of a knowledge disparity between patient and provider.

As shared decision-making gains prominence, future clinical decision support technology should create a continuum of knowledge between patients and providers, and employ tools that guide and teach the professional and the patient.

Advancing Digital Equity Through Literacy and Design

For example, the Swedish Rheumatology Register that enables patients to track their symptoms and share with their provider in real-time, as well as guides patients on how to self-manage their own symptoms has had great results.

In cases like this, leveraging technology to empower patients can alleviate clinician burnout. It is worth noting that the ability for patients to fully engage with such tech solutions depends on advancing digital health equity as an industry.

3. Clinical decision support technology needs to support de-centralized decision-making

As care teams expand, there is an opportunity for technology to maximize the scope of practice of pharmacists, APPs, and other clinical staff by routing decisions to the most appropriate stakeholder and alerting staff to care the patient needs that might not be the reason for their visit.

Another overlooked part of the care team is the patient's support network. Technology can help unburden physicians by making knowledge transparent and effectively articulating the right information to the right care team member. Yet, clinical decision support solutions have not capitalized on this due to the propensity to focus on the physician as the most important clinical decision-maker. Clinical decision support technology used by clinicians should align the care team so that patients are getting clear, consistent messaging and information to inform their decisions.

In the future, the most effective systems/processes for decision-making will effectively articulate which decisions should be made by which type of clinical support and clearly articulate when decisions should be automated, versus made by patients, versus made by clinicians, or other members of the care team. 

4. Clinical decision support technology should not computerize the chaos of traditional clinical practice

There are two opposing philosophies for clinical practice: speed and thoroughness. Rather than addressing this tension, we have digitized the status quo. For example, most solutions providers are trying to embed more information or support into the EHR. However, EHRs already are crowded and burdensome for physicians, and it's unclear whether the EHR is the ideal destination for clinical decision-support.

In an ideal world, technology will complement clinician expertise and enhance it, saving time for clinicians and allowing them to focus on other tasks (like treating the patients in front of them). For example, technology can be employed to reduce documentation burden, where NLP and AI can offload much of the documentation burden. When it comes to clinical decision support, we want to give providers something sleek and streamlined not a digitized version of established burdensome information and processes.

5. Amplifying technology to address health disparities requires us to innovate from a minoritarian view

While a majoritarian view is not inherently bad, advancing health equity requires designing technological solutions that work for the most vulnerable members of our population and innovating for the minority. As technology proliferates, we must be clear and careful with AI and machine learning to ensure we're not incorporating information based on biased algorithms.

Clinical decision support technology that advances health equity should be designed to actively work against bias and examine the issues of particular, narrow populations rather than a majoritarian point of view. This also means iterating on existing technologies and making improvements that promote equity in clinical care.

The future of clinical decision-making is unclear but exciting because all stakeholders can act in order to shape it. My colleagues and I have given our take and made predictions about how these contextual changes will impact how clinicians use evidence to make decisions in 2032.  The report will help life sciences leaders specifically, but other leaders think about how they should engage with other stakeholders to prepare for the future of clinical decision-making.