AI as a Catalyst for Transformation in Healthcare Operations

Anyone who has been part of leadership in healthcare will tell you that change management is hard. It’s not that care teams don’t want to improve outcomes for their patients—of course they do, that’s why they got into healthcare—it’s that they’re already overburdened and on the receiving end of what seems like a never-ending cycle of performance improvement projects.

As much as we have all soaked up the mantra of continuous improvement, we also have a limited capacity for constant change. It’s exhausting, particularly when almost two-thirds of all change initiatives in healthcare fail, according to an article published in the National Library of Medicine.

But change we must. To be successful in a market increasingly characterized by sicker patients and smaller margins, operational leaders are tasked with helping their organization meet ambitious goals for growth, throughput, robotic asset utilization, and more—yet that work must be done without disrupting patients, staff, or daily workflows.

Adding complexity to the already formidable challenge of change management is the ongoing nursing shortage—a situation that is predicted to extend well beyond 2030.

“There’s not a single outlook on the [healthcare] workforce that’s positive,” says Everett Haley, MHA, BSN, RN, Director of Nursing at OhioHealth’s Riverside Methodist Hospital. “Right now, every healthcare institution in the country is looking for ways to automate, ways to simplify, and ways to take the burden off of care teams.”

With recent advances in AI and generative AI, there is new focus on how hospitals can adopt this rapidly expanding technology to reduce staff burnout and improve operational efficiency.  That same technology promises to harness the vast amount of data that is generated every day within hospitals. Much of that data has been around since the introduction of the EHR, but the absence of robust models has meant that data has not been easily interpreted by hospital executives or actionable for frontline providers.

“We’ve always had lots of data,” says Haley. “But we’ve always lacked analytics, because analytics is more complex and strong data scientists are not easy to find, recruit, and retain.”

So how can hospital leaders deliver true step-change improvements while dealing with limited analytics insights and staffing pressures? With the power of AI.

Practical applications of AI in hospital change management

Here are a few ways leading health systems are using AI to deliver measurable improvements in hospital operations.

Identifying areas for operational improvement

Remember that mountain of data? AI tools can quickly analyze it and identify parts of your organization that could benefit most from process improvements. Take Length of Stay (LOS) for example. LOS is a very complex metric to improve as there are so many different processes that contribute to it. However, AI can enable hospitals to click deeper, and deeper, and deeper to truly understand the root causes of excess days so you can focus improvement efforts there. For instance, a hospital system could determine that MRI turnaround times on weekends are contributing 0.15 days to overall LOS.  By comparing the variable impact of suboptimal processes, executive leaders can make more informed decisions about how and when to invest resources - for example, by hiring weekend evening MRI techs, or by implementing an AI-powered flow prioritization algorithm to make sure discharge dependent studies get optimally sequenced.

Optimizing Operating Room (OR) utilization

Surgery is a huge revenue center for most hospitals, so much so that it often funds the rest of a hospital’s mission. Despite the importance of surgical programs, outdated scheduling tools, manual processes, and a lack of insights about a hospital’s specific market and growth opportunities limit revenue potential. Too often, operating rooms sit empty, expensive robotics are underutilized, while patients wait longer than necessary for surgery. Software tools that use AI, machine learning, and predictive analytics can help hospital systems boost utilization and increase revenue in the surgical space. Qventus, as an example, has been delivering meaningful, tangible outcomes for its customers—on average delivering 10x ROI.

Decreasing administrative burden on care teams

The Health Management Academy recently noted that 41% of nurse managers work 51+ hours per week. What’s more is that up to 50% of that time is spent on below-license, administrative tasks. Across care settings, clinical teams are overburdened with these manual tasks, from wading through faxed medical records to coordinating care via phone and email, to keeping up with endless chart notes. AI-powered tools can help nurses and other clinical professionals work at top-of-licence by automating manual, below-license tasks and freeing up more time for meaningful clinical care.

Automating discharge planning

Discharge planning is another operational area where many hospital systems want to improve. Better discharge planning leads to higher throughput and additional bed capacity, decreasing ED boarding, and ultimately helping hospitals serve more patients in their communities. AI tools powered by machine learning models can auto-populate EDD and dispositions directly into the EHR on the first morning after admission to help care teams align early on a discharge plan. Then, these models can continue to pressure test the discharge plan throughout the patient’s stay, identifying opportunities for earlier discharge, and discharges to lower levels of care.  At the same time, ancillary team action can be optimally sequenced by AI to best support patient flow, assuring patients are seen in the order that best contributes to discharge success.

AI in action

OhioHealth is a nationally-recognized, not-for-profit health system with a network of 15 hospitals, plus ambulatory and other care sites, serving patients across a 50-county area. They implemented Qventus’ Inpatient Solution to improve process efficiency and reduce length of stay. The solution uses AI to detect gaps in care plans and optimally sequence care steps to best support patient flow. Its real-time data and accurate predictions allow for reduced time spent on manual work, enhanced patient outcomes from timely discharges, and smoother transitions to post-hospital care. In less than six months since launch, Qventus’ AI-powered solution has helped OhioHealth save 8,554 excess days (annualized), save $3.32 million (annualized), create 23 daily bed capacity, and increase discharge volume by 4.4%.

“With Qventus' Inpatient Solution we’ve reduced the length of stay for patients significantly, resulting not only in significant financial savings but also increased access to care,” said Scott Estep, System Vice President, Nursing Operations & Capacity Management at Ohio Health.

Making AI work for you

In the evolving landscape of healthcare, managing change has always been hard, and the ongoing challenges of overburdened staff and limited resources that have made it even harder, are not going away. The rise of AI-powered tools offers a transformative opportunity for hospital leaders to address these challenges by optimizing operations, increasing capacity, and enhancing patient care. These tools analyze vast amounts of hospital data, deliver actionable insights, and enable more efficient resource allocation. As hospitals face increasing complexity, AI stands out as a critical enabler for strategic growth and operational excellence.