Mohan Giridharadas

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Technological innovation has flourished over the past two years, spurred by a rapid need for digital transformation and shifts to remote work. Although in the past healthcare has typically lagged behind other industries in technological adoption, it is imperative that hospitals and health systems move to the forefront of innovation, so they can act with agility to tackle current challenges. 

One key step these groups can take to advance modernization and better their practice is to successfully implement, execute and operationalize AI platforms across their networks.

Many healthcare leaders are hesitant to take on new technologies due to safety and accuracy concerns. Others think they cannot afford such an investment without a guaranteed reward; however, hospitals have more to lose if they do not adopt advanced technology tools. Leading health providers such as Novant Health are implementing AI to improve patient and staff safety and quality, allowing them to accomplish their technological innovation goals for better use of resources, with higher satisfaction.

Health systems today face the daunting task of combating high levels of physician and nurse burnout. According to a 

, 73% of physicians reported they currently feel burnt out, and 93% have felt burnout at some point during their careers. Retaining employees is exceedingly difficult, and losing staff means a heavier workload for those who remain. Leaders are highly cognizant of adding more to a physician’s workload and are constantly looking for ways to ease this burden.

In the day-to-day, nurses and physicians have effectively become data entry clerks and logistics staff. Oftentimes, much of nurses’ workdays are spent on regulatory documentation or tracking down needed equipment and medication. They spend only a fraction of their shift caring for patients.

Physicians and nurses are motivated to provide meaningful care and communicate with patients. AI and automation provide a means to streamline time-consuming manual processes and data entry tasks, allowing clinicians to give patients their full attention and offer the best possible care.

Consumers have become accustomed to the immediate conveniences provided by technological advancements in other industries, such as airlines, retail and banking. Healthcare is primed for similar transformation in patient experiences using AI-based tools.

These tools offer great logistics with better access and lower wait times as well as smoother in-person experiences for patients. For example, an infusion center might offer a “waitless” IV infusion experience, which allows patients to select appointment times from their phones. When they arrive at the center, patients go directly to their infusion chair and skip the waiting room entirely, improving experience and avoiding risks for patients vulnerable to infection. This autonomy and convenience also make patients more likely to keep their appointment, thus reducing the no-shows that complicate infusion scheduling. After implementing an AI-based capacity management solution, Novant Health saw significant improvements to patient experience, including a 

When hospitals advance their technology stack via AI platforms and machine learning, they also improve physicians’ ability to work with them. For example, surgeons may be juggling block time across multiple hospitals, and AI systems can help coordinate these schedules. Upon Novant Health’s adoption of AI tools, the organization saw a 

 in “splitter surgeon” volume, a clear uptick in physician loyalty due to technologically advanced scheduling.

Some predictive analytics tools alert surgeons and schedulers of unused block time, prompting them to release it rather than let an operating room go empty. Surgeons can optimize their own schedule and make the OR available for another who may need it for a last-minute operation, benefitting all parties involved.

Time is of the essence when caring for patients. Health systems using outdated systems risk wasting time as staff face significant amounts of administrative work and needed resources go underutilized. Additionally, the pandemic has compounded staff burnout and made strong performance difficult. Streamlining administrative tasks and optimizing patient scheduling allows staff to take scheduled breaks for much-needed rest and finish shifts on time and channel their efforts into patients. This improves staff and patient experience and reduces the potential for medical errors, ultimately leading to better outcomes.

Health system executives must take several important steps when they incorporate AI at their organizations. First, they must develop consensus among leadership staff. Leaders should advocate for AI in their organization by showing how it can improve safety and quality of care by proactively addressing areas of concern. The CEO and Board are likely to support solutions that deliver the safest and highest quality care for patients and staff alike, as well as those that reduce related costs and deliver higher revenue from increased care access.

It is also critical to keep the CFO informed of all AI initiatives and their outcomes, considering they will ultimately write the check. Instead of focusing purely on the return on investment, show financial leaders the return on

 to clearly indicate that the benefits of AI encompass not only improved metrics but also an overall attractive technological advancement for the organization.

Health systems often operate with the mindset that, given their limited resources, they cannot risk making investments in advanced technology. Usually, the alternative is to create an owned solution, rather than finding an outside resource. However, operating the needed analytics requires deep expertise in operations, data science, building scalable software and enabling process changes on the front line. This requires significant investment on its own and is extremely difficult for most provider organizations to assemble.

Instead, hospitals and health systems must look for capable partners who bring this expertise.Novant Heath’s 

Institute of Innovation & Artificial Intelligence

 uses AI to enhance personalized patient care and is built on their collaboration with leading AI experts, helping them use leading-edge technology to connect with patients, team members and the communities they serve.

Another key to success in AI implementation is to take a phased approach with each new AI effort. For example, instead of attempting to immediately implement system-wide – which entails many compounded risks and may lead to failure without extensive planning – leaders can start by launching a new AI program at a smaller hospital or facility to run a parallel process with legacy systems for a minimum of 2 months.

Health systems can this way gather feedback from hospital staff, identify positive points and room for improvement, and reassess before implementing elsewhere. Additionally, the best way to measure success is through patient feedback, so it is necessary to have a process in place to capture patients’ thoughts on their experience with scheduling and time at the clinic. At this stage, any problems that arise can be addressed on a manageable scale. If all goes as planned and improvements are quickly noticed, implementation can continue at a broader scale.

Ultimately, the goal will be to implement successful AI platforms throughout the health system organization to bring numerous positive outcomes throughout, to all staff and patients. When considering new AI programs, viable platforms must have the ability to support an entire system, rather than a single point solution, market or service. After taking into consideration any feedback from staff and priorities of leadership, leaders will be able to take the new, innovative platform across the organization, therefore benefiting all stakeholders and putting manual, error-prone processes in the past. Platforms also will allow the health systems to grow as the AI companies add more algorithms.

executive vice president and chief medical & scientific officer

Mohan Giridharadas is founder and CEO of LeanTaaS.

Articles

Leading health providers are implementing AI to improve patient and staff safety and quality, allowing them to accomplish their technological innovation goals for better use of resources, with higher satisfaction.

Advancing Healthcare Innovation through AI Adoption

As market forces visibly disrupt healthcare (less access to cheap capital, reimbursement pressures, rising demand for services in the face of constrained supply), digitally transforming core processes is not merely an option any more. Every health system wants to get better in using digital technologies to improve the patient experience, increase access and lower cost. 

However, almost everyone also faces a few significant roadblocks in achieving the success such transformation promises. These roadblocks fall into the same well known categories all change programs encounter: technology capabilities, culture and core beliefs, process shortfalls and people related (skills and incentives).

Based on our work with over 100 of the country’s largest health systems and conversations with dozens more, we have uncovered five core beliefs that currently prevent many health systems from rapidly innovating and achieving significant operational and clinical improvement. Here are the five core beliefs and why they need to be overcome for a successful transformation:

EHRs are good at a lot of things but predicting and prescribing solutions to probabilistic events like schedule optimization and forecasting outcomes isn’t one of them. Yes, a lot of money has been spent on the EHR, and they serve a very specific and important purpose but they cannot solve all the required operational and clinical issues that create access and lower cost.

2. “Analytics = EHR reports, maybe some Excel, Tableau, and homegrown dashboards. That’s all we need.”

Analytics is a lot more than dashboards and reports. Dashboards are like scales at the doctor’s office - they “admire the historical problem.” Weighing scales report on a known problem (“You need to lose 20 lbs) but do nothing to actually solve the issue. To truly achieve digital transformation requires investing in tools that predict future events and prescribe actions to achieve a better outcome. Like Waze rerouting you along a better path when it sees bad traffic ahead.

3. “Anything we need beyond the EHR we can build internally.”

Even a company as large as GM doesn’t build Microsoft Word simply because it can afford to hire 1,000 people to try and do so. It is easy to underestimate the investments and scaling needs of useful software. Just because a skunkworks project meets the needs of a small group of users, it does not mean it can scale to meet the needs of a larger group, or solve larger problems.

4. “If I create an innovation team and invest in startups I will be able to innovate on my own.”

Innovation happens at the front lines. For it to work, 98% of your organization should be excited and engaged in innovation, not just the 2%. It’s great to have an innovation team, but do they have a budget? Are the operating teams engaged? Is everyone empowered and tasked with the goal of transformations? Is there a reward system to align goals with behavior?

5. “The Cloud is Unsafe” and so we will stay client-server to protect information.”

Amazon, Google, Microsoft have spent billions of dollars building secure cloud infrastructure. The chances that any one health system can invest as much to achieve more security behind the firewall is close to zero.

As market forces visibly disrupt healthcare (less access to cheap capital, reimbursement pressures, rising demand for services in the face of constrained supply), digitally transforming core processes is not merely an option any more. 

Mohan Giridharadas

Articles

Advancing Healthcare Innovation through AI Adoption

5 Core Beliefs Blocking Digital Transformation in Health Systems