Mediaboss Marketing

A recent study found that two-thirds of health systems have experienced a significant decline in income, with 27% losing money in at least one of the three years studied. The drop in earnings from fiscal year (FY) 2015 to FY 2017 represented $6.8 billion, a startling 44% reduction. There was some improvement in FY 2018, with revenue growth exceeding expenses by 0.1% for the first time since 2015 but growth remains lackluster.

In order for hospitals to advance or maintain standards of care, and to grow in ways that best serve patient needs, they have to get smarter about resource utilization. Operating rooms (ORs) should be a top priority. Not only do ORs represent the majority of a hospitals margin, they are also ripe with ways to increase efficiency and improve capacity. (Read: Top 20 AI Use Cases: Artificial Intelligence in Healthcare)

Multiple factors go into OR utilization: how long procedures are expected to take, which provider is using the room, unforeseen complications that occur during surgeries, last-minute additions or removals to the schedule, and much more. Scheduling ORs is not an exact science, but thanks to advances in data analytics, its getting pretty close.

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New machine learning (ML) solutions can take a variety of data points gleaned from every procedure that happens in every OR in a health system. They analyze this information and make predictions that can be used to effect improvements and adjustments for the future whether its a slight shift in booking times, hiring more staff, or establishing new policies for rooms.

These solutions identify portions of time that can be repurposed, collecting underutilized blocks of time without impacting existing case volume. They also erase countless hours of manual research, conducting queries in mere seconds. (Read: How AI in Healthcare is Identifying Risks and Saving Money.)

Additionally, the insights they yield can save health systems considerable expense, while opening up more treatment opportunities, and thus increase revenue all while improving patient access and the overall patient experience.

Getting the right insights to the right people in OR environments can be an enormous challenge, especially when different groups are looking for answers to different questions. One unified data analytics platform can help, enabling each constituency to find exactly what they need to improve decision-making, which ultimately leads to advancing standards of care, through machine learning.

New machine learning solutions can uncover the various drivers behind high-level trends in volume and utilization across an entire group. For example, from one interface, an executive at a large health group could look at whats happening at a specific hospital or region over a designated time range (such as over the last quarter). The executive might want to generate a holistic view of measures such as OR turnover ratio, add-on ratio, on-time starts, and more.

With this information, it becomes much easier to see how the business of the facility is actually performing. Is it growing? How is its utilization trending?

Based on these results, the executive might want to take a more in-depth look, viewing certain categories year-over-year, grouped by month, in order to compare, spot trends, and get an aggregate historical progression.

Additionally, he could check case volume to look for drivers behind volume increases and which service lines (orthopedics, general surgery, or cardiac care, for example) have experienced the most growth.

Robots are valuable assets, so much so that many groups have committees focused on robots to establish how well they are being used. Robotic program leaders can leverage new data analytics solutions to monitor robot usage and ensure that assets are being fully utilized. With the right insights, committees can figure out if they need to buy additional robots or adjust policies around usage.

Reports can also separate out only robot cases or look at which providers do the most with robots. This helps robotic program leaders know how often ORs equipped with robots are being used for robotics cases only versus other cases, advancing standards of care through machine learning. (Read: What Do Patients Want From Healthcare Technology?)

They also can view specific rooms where robots live by day of the week and hour of the day. Maybe on Mondays a robot is not being used to its maximum potential or a particular provider who isnt using the robot consistently blocks the room. Insights such as these help robotic program leaders figure out where rooms are blocked and why so that they can be leveraged to the fullest.

New solutions can be invaluable to nurse managers as well, giving them the mechanism to make data-driven decisions about staffing across teams and service lines. They can use technology to see historical data (such as room occupancy) to help make future assignments.

Nurse managers might want to view aggregate room data for an entire facility, every hour of each day of the week over the last three months. This way they can spot the busiest (or least busy) times and staff accordingly.

Additionally, a nurse manager may hear from someone on staff that she is always working late. The manager can then run a query to validate if a problem exists by checking times in blocks that are running over and whether certain providers tend to take more time than budgeted. In this instance, the nurse manager could adjust staff hours to keep expectations in line with the likely reality.

These are just some examples of how hospitals are advancing standards of care through machine learning and data analytics, particularly in terms of their ORs. But in order for data to make an impact, it needs to be credible, timely and actionable. If it can be harnessed in this way, insights can change hospitals trajectories.

Data analytics and machine learning unlock the capacity of scarce assets by transforming core processes. Health systems are able to reduce operating costs and potentially defer the need for a facility or staff expansion and most importantly, they can reduce wait time for patients and increase access with greater OR availability. By making operations smarter, the profit-to-expense ratio moves closer to ideal, and organizations can grow efficiently into the future.

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Advancing Standards of Care Through Machine Learning - Techopedia