In digital health time, 10 years might as well be 50.
A decade ago, the industry was picking up speed for a digital health boom. Since then, the industry has seen health systems acquiring lots of shiny new tech, then adapting to the COVID-19 pandemic with even more (crucial and timely) tech solutions.
Adnan Iqbal, Luma Health
In the years following the pandemic, health systems have downsized and consolidated their large suites of digital health tools, keeping only the ones that serve as true platforms and deliver high ROI. Yet still, the market has continued to grow more crowded, making it a herculean task to find those ROI-driving tools with thousands of options to choose from.
But there’s a new digital health boom coming–or rather, here. AI is ushering in a new digital health era, as health systems are seeking solutions that can alleviate growing clinician and staff shortages, unsustainable call volumes, and other manual tasks. We’re also at the peak of the AI hype cycle, and health systems are feeling the pressure to evaluate a booming field of AI entrants.
As health systems navigate a new digital health boom, how can we take the lessons learned from the previous decade to determine what is truly bringing value?
More Options Don’t Create More Impact
As the digital health market has boomed around us, I’ve heard from hundreds of health systems that many of their newly implemented technologies didn’t deliver, creating more work.
Patient reminder tools can confuse patients and create more phone calls. Referrals and questionnaires often need to be reviewed and transcribed into the electronic health record. Fancy apps – and even the EHR patient portal – often aren’t being used by patients who don’t have the patience for another download or login.
These health systems recognized that new technology comes with lofty promises, but each new entrant into the health IT stack requires more maintenance – of integrations, the solution itself, and its workflow within the rest of the tech stack. Many health systems significantly downsized their digital health tools in the past several years, taking an “EHR-first” approach. Now, health systems have cut out the vendors that require extra work and don’t provide patient, staff, and business impact.
AI’s Potential – and Potential Pitfalls
AI presents an exciting new opportunity for health systems overloaded with manual tasks that simply can’t be solved by hiring more staff. In addition to the great work being done to parse clinical documentation and alleviate physician burden, some smart applications of AI for staff and patient benefits include:
- Giving patients access to key data points from their record. Patients are often calling their providers to ask for simple information that AI can provide from the EHR. Empowering patients to self-serve with AI avoids dedicating limited staff time to simply finding and relaying information, and allows staff to work on higher-value tasks.
- Allowing patients to take action for tasks that can be done via self-service – when the patient wants, on whichever device they prefer – like requesting an Rx refill, rescheduling an appointment, or getting directions.
- Parsing structured data that would otherwise require tedious double-documentation, like reviewing faxed Rx refill requests.
However, AI must be evaluated with the same rigor used for now-common digital tools like patient texting. AI solutions must not create more work with subpar EHR integration, additional digital dead ends, or a lack of transparency that produces additional staff work. And of course, any use of clinical information must be rigorously evaluated to ensure it doesn’t negatively impact patient care.
A Practical Approach to AI
Today, health systems don’t have the staff, budget, or time resources to implement new technology for the sake of innovation or to keep up with the hype cycle. They have high standards for technology – it must deliver tangible impact with measurable ROI, not just vibes.
The same questions they use to evaluate non-AI vendors can be used to cut through the AI noise and find the solutions that will provide ROI. These questions might include:
- What problem will this solution solve? How will we measure its effectiveness?
- Can the solution be applied to other problems down the road?
- Does the potential benefit of the solution outweigh the effort of implementation and ongoing maintenance (especially if a new integration is required)?
- Will the vendor work with us to customize or create solutions for our unique use cases?
As health systems look to the expanding field of AI-based health IT, evaluating new technologies with the lessons learned from the last decade of technology development will help ensure good investments.
Byline: Adnan Iqbal, Co-Founder and CEO at Luma Health
Adnan Iqbal is the co-founder & CEO of Luma Health, the market-leading Patient Success company solving the biggest challenge in healthcare – getting a patient in front of the right provider and to the best healthcare outcome quickly.Luma Health currently serves 100M+ unique patients, 200,000+ healthcare providers, and 600+ health systems and clinics across the US. The company has raised $160M+ in capital from leading technology investors including FTV Capital, US Venture Partners, Texas Medical Center, InHealth Ventures, and the Stanford-StartX Fund.Adnan holds a BS in Environmental Biology from the University of California, Berkeley and graduate degrees from the Stanford Graduate School of Business and the Institute of Biotechnology at the University of Cambridge.
