Human-Centered AI For Better Health Outcomes

Original article was published by David Yakobovitch on Artificial Intelligence on Medium


Human-Centered AI For Better Health Outcomes

Health Technology 2020

Health care has come a long way and with the integration of technologies including artificial intelligence, machine learning¹ and natural language processing, clinicians derive insights from data with better outcomes. At the same time, the adoption of technology in healthcare has faced challenges with health care providers unable to harness the power of technology to address patient problems.

Human-centered design is one framework gaining traction in the health care world, with physicians using frameworks to understand patient problems and address them. The human-centered model has the interest of patients and enables collaboration and communication. Simply put, human-centered design² brings together all stakeholders including patients, clinicians and technology to offer seamless experiences across the board.

Photo by National Cancer Institute on Unsplash

Problem-solving is the biggest hurdle facing health care today and this is where human-centered design steps in. From a human-centered design perspective, we go from purpose to means with electronic health records³ a good example. EHR systems are sometimes poorly designed from the user perspective where they unintentionally serve as a data repository for clinical information.

The most impactful applications of AI in healthcare will be created by integrating AI deep into the user interfaces and workflows of hospitals.

Overview

Healthcare organizations are often customers of health IT and have a different set of duties, goals, and stakeholders they must satisfy. Understanding your stakeholders is important if you want to improve the quality of care. Problems and pain points exist in all organizations, and the best place to start is by understanding the problems faced by clinicians and patients.

If we are to make strides with AI in healthcare⁴, these are the questions we should be focusing more on as an industry. Technology can save lives but it is an enabler, not a solution by itself. True innovation is born in the interplay between people and technology. This requires a thoroughly human-centric approach to AI.

Executing Human-Centered Design in Health Care

The following are some approaches health organizations can use to adopt human-centered design in their organizations:

-Problem analysis

-Feedback system

-Stakeholder collaboration

-Framing ideas

-Developing frameworks

-Prototyping

-Testing

AI and a Patient-Centric Model

Burdened by clerical work and inefficient systems, clinicians now spend more time with machines and with reporting than directly with their patients. People with health trackers often do not know what to do with the numbers they are given. These examples serve as a reminder that as an industry, we should design solutions around people’s needs, not around what’s technologically possible.

Nurses want to focus their time and energy on patients. Patients with chronic diseases and healthy people alike want to be empowered in taking control of their own health. Only if we design for those needs, will people fully embrace AI-enabled solutions in their daily work and lives. This is a very fundamental notion, which is also deeply ingrained in competitive health organizations.

Where should AI focus Lie?

Any AI-enabled solution should be designed as a natural and helpful extension for people, like a car navigation system that supports you in finding the best route to your destination. AI will augment healthcare⁶ providers, patients, and healthy people alike.

Photo by Jair Lázaro on Unsplash

Adaptive Intelligence

The concept of #adaptiveintelligence takes this a step further, by adding contextual awareness. This means a system can learn and adapt to the skills and preferences of the person that uses it, and to the situation. The analogy with car navigation comes to mind again: The system may offer you personalized advice, depending on your preferred routes.

Quality health care solutions help radiologists to speed up their workflows by recording and reproducing their hanging protocols in a consistent manner.

Thinking beyond Data in AI

Clinical staff and patients need to be involved from the start in the development of such solutions. Clinicians can ask the right questions, validate machine-based recommendations, and interpret them in a clinical context where people’s health is at stake.

Value comes from actionable insights that are deployed wisely, and that leads to better outcomes at a lower cost. That is the real gold, and we need human knowledge to mine it. Nurses need to be the ones making the final decisions, in order to drive the best patient outcomes. Approaches that combine data and human knowledge will be the most powerful.

From a patient perspective, impactful AI-enabled solutions will be about more than just presenting data to people. AI allows us to develop solutions that adapt to such needs, providing personalized coaching to help people adhere to treatment plans. A smart #algorithm⁷ by itself will not do the trick. Understanding human psychology is just as important.

We all know how difficult it is to change behavior. Again, this is where the expertise of healthcare professionals comes in. By combining #datascience with behavioral science, we can support people to take control of their own health.

Better Patient Experiences

Getting AI to work effectively truly is a collaborative effort, and that is what makes it so exciting. More and more, #datascientists, designers, and clinicians in hospitals are working on innovations together.

Together, we can only make these efforts succeed if we center them around people. Coupled with the emergence of connected medical and personal health devices, AI and data science offer amazing capabilities.

We have an unprecedented opportunity to help solve one of the biggest challenges in the world: providing high-quality care and good quality of life to all, at an affordable cost. By taking a people-centric approach, we can put advanced technology to a wonderful use ̶ helping the human touch to triumph across the health continuum.

What human-centered design offers are a means to solve problems by recognizing the cause of a problem and understanding the ‘why’? When we focus on answering the right questions and speaking to the right people, we can implement lasting effects for all involved parties. As healthcare becomes more adept at understanding and implementing human-centered design for all, we are getting closer to achieving healthcare that is better for everyone, especially the patients.

Does human-centered design make sense in the current health care context? Share your comments below to contribute to the discussion on Human-Centered AI For Better Health Outcomes

Works Cited

¹Machine Learning, ²Human Centered Design, ³Electronic Health Records, ⁴AI in Healthcare, ⁵Problem Analysis, ⁶Augment Healthcare, ⁷Smart Algorithm,

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