How automation helps hospitals improve operating room access and grow… Leave a comment

Mudit Garg (Provided by the author)

It’s no secret that hospitals are struggling financially – and for many those struggles are likely to continue for the foreseeable future. At the same time, surgeons continue to face hurdles getting optimal time in the operating room.

Faced with escalating labor costs, staffing shortages, and fewer patient discharges, hospitals saw margins crater in 2022, the worst financial year for hospitals and health systems since the start of the COVID-19 pandemic, according to a report from Kaufman Hall. Approximately half of U.S. hospitals finished the year with a negative margin.

All aspects of patient care, from admission to treatment to discharge, have been hurt by the labor shortage. Hospitals faced increases in labor expenses last year, driven by a competitive labor market, as well as a growing reliance on contract labor. Increased lengths of stay due to a decline in discharges also hurt margins.

Backed into a financial corner with no obvious way out and no signs of labor cost growth slowing, many leading health systems have turned to artificial intelligence (AI) and automation technology to optimize OR access, grow strategic case volume, and reduce workload intensity for staff.

Hospitals that fail to embrace new technologies such as AI, machine learning, and automation are at risk of falling on the wrong side of this “digital divide” and the consequences for their finances, not to mention the health of the communities they serve, may be severe.

Automation drives greater efficiency and OR revenue

For many hospitals, solving these complex problems requires a new approach to improve operations. Driven by AI, care automation solutions enable hospitals and health systems to automate all operational activities involved in delivering care, from discharge planning to OR access to patient flow.

Improve OR scheduling

Specifically, automation technology can significantly improve OR scheduling, unlocking excess capacity to enable greater efficiency and drive higher surgical revenue, which is a crucial financial engine for hospitals.

OR teams and surgeons have long had to manually manage the complex puzzle-like game that is scheduling. As a result of the unpredictability and inefficiencies in this process, OR teams and schedulers experience a high degree of burnout. Surgeons are held back from providing care in a timely manner. Patients suffer as a result, experiencing delays in care when there is no OR time available for weeks or months, or having to frantically prepare for surgery when their procedure gets scheduled at the last minute.

Care automation technology provides intelligence and automation that reduces manual work for schedulers, decreasing their cognitive burden and burnout. At the same time, the system provides surgeons with a much easier way to access OR time. With better predictability of schedules and volumes, this improves staffing for OR teams.

Strategically grow OR cases

OR software enabled by advanced machine learning (a category of AI) and behavioral science, can intelligently automate manual scheduling processes. It can account for many variables at once: which surgeons favor Tuesday mornings or typically need longer surgery times, which ones need the robotic room, how many days of lead time each surgeon typically needs, how often a surgeon starts procedures late, and which surgeons perform the procedures that represent the highest value to the hospital. It can hold and analyze more information than even the most experienced human scheduler.

Surgeons’ schedulers can also tap into the system via a search function that works like an online travel booking site that brings up the best flight options within seconds. They enter multiple search criteria (time of day, case type, length of procedure, room type, preferred location) and receive a list of slots that are the best fit.

A better approach to discharge planning

After hospitals improve their OR scheduling processes, they often look to discharge planning as the next strategic area of investment, enabling them to free bed capacity to grow revenue.

However, in health systems and hospitals today, managing discharge planning is a manual, complex and chaotic process. This dysfunctional system results in significant variability in the care process and costly excess days that lock up critical bed capacity and resources for hospitals — while simultaneously resulting in a poor patient experience.

When patients are admitted to the hospital, there’s no discharge plan at the start. Frontline team members need to analyze hundreds of data points included in each patient’s electronic health record (EHR) to determine key elements of the discharge plan, including estimated date of discharge, barriers to discharge, and likely disposition. Doing this manually is incredibly challenging — particularly because teams often only have a matter of seconds or minutes per patient during multidisciplinary rounds.

Estimated dates of discharge are manually entered, often not until late in a patient’s stay. Disposition needs are identified days into the stay, costing days if post-acute logistics have not been started early enough. With too many orders to track, discharge barriers are often not identified until the last day, requiring teams to dig through charts, go through open orders, and manually check off barriers.

Integrating with EHRs, these solutions combine AI, behavioral science, and process redesign to hardwire discharge planning best practices. The technology automates key steps for discharge planning, predicts barriers to discharge, orchestrates their resolution, and enables leaders to effectively manage accountability.

How one hospital simplified surgical access to grow volume
Allina Health is a non-profit healthcare system based in Minneapolis, Minn., that owns or operates 12 hospital campuses and more than 90 clinics in Minnesota and western Wisconsin.

As part of a health system initiative to improve patient flow and increase capacity, Allina sought to optimize its surgical services by minimizing variability in every aspect of the process, from determining the appropriateness of surgery, to the surgeries themselves, to the operations of the post-anesthesia care unit. Hospital leadership expected that with improved consistency, Allina could increase surgical volumes overall, in particular, the volume of cases using its surgical robotic capabilities, and explore new market opportunities that its existing inefficiencies wouldn’t allow.

To accomplish these goals, the health system adopted a care automation solution that uses AI and machine learning to enable surgeons and schedulers to boost efficiency by viewing all available slots that meet their needs in a matter of seconds without logging into the EHR.

The care automation solution increases surgical access by using AI to identify and offer ideal slots to surgeons and schedulers. Further, the solution employs machine learning to predict unused blocks of OR time weeks in advance and applies behavioral science principles to prompt surgeons to release time, freeing up OR time.

Allina implemented the solution at its Northwestern Abbott location, and achieved impressive results. Specifically, the hospital added 3.5 cases per OR per month in the first four months after adoption, experienced a 36% increase in cases per surgical robot per month, and released 100+ hours of OR block time early per month. As a result of the successful initiative at one location, Allina plans to adopt the solution across its other hospitals.

For hospitals and health systems, the new financial reality is one characterized by staffing shortages, escalating costs, and razor-thin margins. As a result, the bottom line for every hospital leader is that it’s a question of when, not if, to embrace automation technology to improve OR performance.

Mudit Garg is co-founder and CEO of Qventus, which provides AI-powered software for care operations automation.


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