Using analytics and partnerships to break through healthcare operations barriers: a conversation with Patrick McGill, MD, EVP, Chief Transformation Officer, Community Health Network

At LeanTaaS’ Transform Virtual Hospital Operations Summit on June 7-8, 2022, Patrick McGill, MD, at that time Community Health Network’s Chief Transform Officer, gave a fireside chat exploring in depth how his health system’s collaborative and learning-driven culture led to data transformation both in operations and in care delivery. View the full talk, with LeanTaaS COO Sanjeev Agrawal, here.

A version of this Q&A originally appeared on Becker’s Hospital Review.

Q1: Can you talk about the circumstances at Community Health Network now, the healthcare operations challenges you face as an organization, and your key goals? How does your role as Chief Transformation Officer fit in?

Dr. McGill: Community Health Network is an integrated healthcare delivery system based in Indianapolis. We have five main hospitals, four smaller specialty hospitals, and 1,300 employee providers across all specialties.

CHN was founded about 60 years ago but solidified in the 1980s and 90s as we brought on a large number of practices, hospitals, and other groups. Caregivers in these groups have always had autonomy, which strengthens our functioning as a multidisciplinary organization. But it has also led to variation and opertional inefficiencies. Physicians and leaders had their own approaches and “ways of doing things”, for instance scheduling OR blocks, placing referrals, or sending patients through the ED. Standardizing these approaches to healthcare operations for efficiency’s sake has been a long-time priority.

As we emerge from the pandemic we’re also grappling, like many health systems, with high labor costs and patients re-entering the system for treatments and diagnoses after delays. One key goal is to be consistently data-driven in our operations, to ensure we deliver care efficiently. We’re also focused on leveraging our healthcare data in future decisions: CHN is a $3 billion organization, and it is simply not possible to run it, as we had to in the recent past, 45 days in the rear.

We aim to use data in everything we do and be smart around the investments we make in people, technology, and expansion. In my role as Chief Transformation Officer, I lead efforts to promote healthcare data literacy and optimization across the organization, so all our caregivers can obtain and leverage actionable, real-time data in their day-to-day jobs. This way we can take an informed approach to decision-making, improving operational efficiency, and strengthening our delivery of care.

Q2: How long have you been on the journey to healthcare data optimization at CHN? How have you prioritized what steps to take, and what are some key wins you have had in the process?

We started this journey six or eight years ago. We have been live on Epic since 2021, but we have since wanted to refine and develop our analytics capabilities within the organization. We began by educating our caregivers on waste and variation, helping identify where their own pain points and frictions contributed to these issues. We adopted analytics tools that showed cost or outcome variation and where we could improve. Clinicians experienced firsthand analytics-driven scheduling and revenue processes relieved them of time-consuming operational tasks and helped them deliver care at the top of their license. This experience kept them continually engaged in data-driven improvement projects.

Having a foundation of standardized improvement methodology, then layering on data analytics, supported us in navigating the pandemic: as capacity, safety regulations, and care needs changed rapidly, we could identify exactly what problems needed to be solved and the most efficient way to solve them.

Overall, we’ve found that starting small and choosing specific manageable projects, whose successes naturally engage our stakeholders, helped build a self-perpetuating, data-driven culture. I always maintain that our journey to data analytics optimization at CHN is a marathon, not a sprint. We need to still be able to run when we reach the finish line. That’s what we wake up every day trying to do.

Q3: CHN turned to both internal and external resources when developing its data analytics capabilities for better healthcare operations and therefore healthcare capacity management. Why did you decide to seek partnerships, and what did you look for in the partners you chose? 

Dr. McGill: Our EHR is essential to delivering care as it captures all patient touchpoints. But it is simply not equipped with the level of healthcare data analytics needed to accommodate the operational realities of patients and providers – needed equipment, appointment or surgery times, working hours – efficiently and cost effectively. The EHR also focuses on the past and has little ability to address the present or the future. We extended our analytics capabilities to address these gaps.

At CHN, we recognize that there is no problem we’re trying to solve that someone else either isn’t working on or hasn’t solved already. Therefore, we value partnerships, and prioritize those that support learning and innovation from each other’s experience. All 630 people who report to me, for instance, are considered internal partners who are empowered to solve any variation or friction they see in our healthcare operations.

To this end, we developed our Center for Analytics Excellence in 2018, partnering our IT functions, including the traditional EHR, with our internal analytics teams, which worked with clinicians and stakeholders to frame their problems through an analytics lens and source the information they needed to solve them. Through this internal partnership we built a self-service application that can close 40% of our analytics requests.

We only invest in external solutions when we are fully unable to address problems internally, and when we understand the solution to be truly worth the investment. In this case, we needed a technology partner who could deliver analytics powerful enough to produce optimal operational recommendations for the present and the future. We also still prioritized this partner’s willingness to learn, grow, and problem solve alongside our organization. We determined that healthcare AI expert LeanTaaS would fit our criteria, helping us become a better health system as we deployed their iQueue solutions to improve our operating room schedules, as LeanTaaS leveraged our insights and concerns to become a stronger technology partner. In the roughly two months since going live, partnership has been a success, as iQueue for Operating Rooms has powered our surgical operations, including an additional 1,250 hours of OR time beyond allocated blocks being requested and approved.

Q4. What’s your advice to health systems on a similar data transformation journey?

Dr. McGill: All health systems are unique and should consider their own makeup and needs when they approach their data analytics journeys. But universally, data transformation fails when systems try to “boil the ocean” and accomplish all their goals at the same time. Systems should instead begin with specific and achievable first steps, ones whose results have immediate impact on stakeholders and make their jobs easier and more rewarding.

Fostering mutually beneficial partnerships across the organization, and outside it, is also key. At CHN we maintain a “servant leader” approach, recognizing our ability to learn from each other and always keeping our ability to help each other as our focus. This perspective reveals the most fruitful partnerships and enables them to flourish and produce results.

Promising early steps combined with a learning-centered mindset supports a self-perpetuating culture of innovation, as stakeholders stay engaged with the process, break down silos to collaborate across the organization, and become willing to move past “the way things have always been done”. This is the environment needed to deploy a data analytics program successfully and improve healthcare operations as well.

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