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In this first episode of Season 2, focusing on AI in Healthcare, our experts participate in an intensive workshop exploring how AI and data solutions can improve the healthcare delivery experience. The session brings together engineers, designers, delivery leads, and subject matter experts to tackle pressing healthcare challenges.
The workshop spotlights critical insights about post-discharge patient care:
- Patients face overwhelming challenges after leaving the hospital, including understanding healthcare provider instructions and managing new medication regimens.
- Current discharge processes often rely on antiquated paper-based systems.
- Finding specialized medical professionals remains a persistent challenge for patients.
Our team's solution framework addresses these challenges through two primary approaches:
- Post-Discharge Assistant to Aid Patients and Caretakers: An AI-powered platform that can push medication scheduling notifications and reminders, established during the hospital stay
- Real-Time Search Platform for Unexpected Patient Care: Search functionality that helps patients locate medical professionals with specific specialties in real-time, with customizable criteria for immediate care needs
Both concepts aim to make healthcare provider recommendations and data easily accessible, understandable, and trustworthy for patients. The session concludes with a strategic framework for moving forward, emphasizing the importance of evaluating use cases based on ROI and technical feasibility before proceeding to design and development phases.
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Sydnor [00:00:05] We participated in a really exciting workshop today focused on: how can we take our deep understanding of customer needs and problems in a healthcare delivery scenario and think about where AI or other data-related solutions can improve the experience for those users.
Conner [00:00:23] And so the challenge from a data perspective is, how do we make the recommendations and the data that is provided by the healthcare provider easily accessible, simple and easy to understand, and trustworthy?
Paul [00:00:38] Specifically, the example we used was a patient who has been discharged from the hospital and gone home.
Sydnor [00:00:45] They may have really antiquated hardcopy papers telling them what to do. They might have a new medication regimen. They may have physical exercises to go through. They may need to schedule follow-up visits. And that is a really overwhelming moment, they're trying to think about how they contend with where they are and what comes next.
Paul [00:01:04] There were two primary themes. One of them was really figuring out how we can leverage messaging. One of the ideas was to potentially push medication scheduling. Setting that up at the hospital so that systems can push those push notifications. An additional idea was surfacing more real-time data to patients and folks who maybe need to find medical professionals quickly. Something like Zillow, where you can do a broad-based search and narrow down results based on your specific criteria. A very similar type of functionality could exist to help you locate medical professionals with a specific specialty in real time, which I think would benefit a lot of people, especially in these moments where they may need help more quickly.
Sydnor [00:01:51] It was a great process. That was a very efficient way to go from a problem to an idea. I mean, truly, in 75 minutes we went from really hairy, thorny problems to some really interesting, compelling ideas, even sketches of those ideas. It was successful because there was a variety of perspectives in the room. We had engineers and designers and program delivery folks. And so having that richness of perspective of folks who have worked with a variety of clients, understand good processes, but bring creative thinking to the table was really exciting too.
Conner [00:02:25] Now that we've had an opportunity to learn from the business experts, and we've had an opportunity to ideate a bunch of really cool and impactful use cases, we'll prioritize them based on that perceived customer value that we gleaned from the business experts. And we're going to evaluate them based on technical feasibility. That way, before we dive into design and development, we can have a really holistic, informed view of each of those use cases.
