3 Methods Hospitals Can Recuperate Surgical Revenues by Leveraging AI and Automation

No department is more central than surgical services to the overall financial health of a hospital, nor more important to its reputation and its potential for growth.

Hospitals’ operating suites contain much of their advanced, automated technology, such as robots and lasers, yet far too often, hospitals still rely on an awkward combination of legacy technologies – spreadsheets, electronic health records (EHR) tools, phone calls, and faxes – to manage surgical services.

Most surgical departments lack effective automation to address these two common challenges:

  • Manual processes: Surgical scheduling is typically characterized by stressed, overstretched administrative staff at both ends: in the hospital and in surgeons’ practices. Automation should remove friction at every step and ease their burdens from end to end.
  • Inadequate analytics: Turning data into action can be difficult when systems are not specifically designed to analyze and present the data in a way that motivates users to take the needed actions. Basic data visualization and performance analytics are a given for any software, but most perioperative management software isn’t sophisticated enough to coordinate the steps that maximize scheduling lead times and strategically fill white space.

However, hospitals can surmount these challenges through the adoption of automation software, which use artificial intelligence (AI) and machine learning to identify issues, apply automation and behavioral science to orchestrate actions, then leverage statistical analytics to help manage accountability and performance.

Following are three ways that automation software tools enable hospitals to grow revenues and utilization:

Unlock operating room time

It’s only natural for people to conserve scarce resources to be sure they’re there when they need them. That’s what surgeons tend to do with operating room (OR) “block time.” Block time lets them establish predictable schedules for themselves and their patients and assures them of being able to do procedures in a timely fashion.

When they don’t need all their time, they should release it, but they often don’t. Their schedulers are too busy and may hang onto it “just in case,” and only release it when they’re sure they can’t use it, which may be too late for anyone else to use it. Typically, 30% or more of available hours go unused, and the slots that can be rebooked on short notice can’t be used for procedures that require more lead time and may represent more value to the hospital.

Software with advanced machine learning capabilities can learn the booking patterns of each block owner with great precision. With enough historical data, a genuinely intelligent machine learning model can predict up to 30 days out how likely it is that a surgeon will use a certain slot that hasn’t yet been scheduled and employ principles of behavioral science to liberate it for use by another surgeon.

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.

This intelligence allows the system to identify the optimal surgeon for a particular slot and automatically reach out to their scheduler, much as an online retailer can look at buying habits across millions of customers and recommend products with sometimes eerie accuracy. If that surgeon can’t use the slot, the scheduler clicks the appropriate button and the system moves to the next one on its list. No more hit-or-miss, no more first-come, first-serve, no more manual calling to find a case.

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. Especially for independent practices, when it’s this easy for surgeons’ schedulers to find available OR time, they’ll turn first to the hospital using this software, and save themselves the tedium of calling other facilities.

Gain market share

According to a recent survey by The Health Management Academy, 86% of health system executives say that increasing referrals and reducing leakage is important or extremely important for increasing surgical revenue. Generally, there are three avenues to accomplish these goals, depending on the characteristics of a hospital’s market, current services, and strategic plan:

  • Identify physicians who refer patients to surgeons–and which other surgeons they refer patients to. Surgeons can use this information to see where they might be losing referrals and take steps to strengthen their relationships with those referring physicians.
  • Look at the overall utilization patterns of “splitters.” What other facilities do they use, how often, and for which procedures? Armed with this information, surgical leaders can offer block time, or tweak the time they already have, to encourage surgeons to remain in their hospitals, and also find out what other factors might be making other sites more appealing.
  • Identify surgeons who do the types of procedures surgical leaders want to attract to their hospitals and see where they are performing those procedures now. This information gives administrators a starting point to analyze how to reach out to these high-value surgeons and what to do to attract them.

To obtain these capabilities, claims data from multiple clearinghouses is combined and then stripped of identifying information to protect patient privacy. Even when we don’t know the identity of individual patients, a process called “tokenization” enables records to be linked together from the same patient, creating a detailed picture of their care journey across multiple providers. Machine learning is then applied to fill any gaps and improve the precision of the data. Surgical leaders can then identify practice and referral patterns: which surgeons do what types of procedures, and which primary care physicians refer patients to those surgeons.

Manual processes and inadequate analytics frequently prevent hospitals from achieving OR revenue and utilization goals. By using automation, behavioral science, and comprehensive real-world data, hospitals can unleash the full potential of their OR enterprises and supercharge perioperative growth.

Photo: hoozone, Getty Images

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