Examination Analytics that Optimizes Imaging Facility Operations

Pramod Sridharamurthy
Thursday, February 13, 2020

Deep machine data analytics is getting more sophisticated each year and is now becoming more pervasive in radiology facilities used as an optimization tool instead of relying on plain vanilla billing spreadsheets to get precise information on the usage of the machines, for instance, are our machines underutilized or over-utilized? If they are, then what action can I take to get the most of the MRIs or CT Scanners?

This blog explores the new world of a central analytics ‘hub’ that is aiding how efficiently imaging machines are used on a day-to-day basis. As a result, budgets are keeping pace with adoption and a more strategic redesign of an imaging facility’s workplace is taking place.

"Imaging analytics-powered operational optimization can result in significant improvement in operational efficiency of an MRI department while maintaining high diagnostic standards," said Szatmari, MRI program manager at imaging center operator Affidea. From the article ‘Imaging analytics platform optimizes MRI operations’ by Erik L. Ridley, AuntMinnie staff writer.

Clinsights: Bridging the Gap between Humans and Imaging Machine Data

As radiology centers/departments rebuild their foundations to harness the power of data and advanced analytics, machine learning tools offer the best way to train radiologists to exploit the potential of analytics at scale. Clinsights, our healthcare industry-focused app helps radiologists to tap into machine data by helping them embrace data exploration and inter-department data analytics effectively.

Here’s how radiologists are spotting opportunities that are otherwise inaccessible. Let’s look at the untapped opportunities at a radiology center from a point of view of conducting patient examinations.

Perfecting the Patient Flow through the Examination Room, Efficiently

Regrettably, knowing if an MRI or CT Scanners are being used optimally is driven by intuition the world over. Often, billing data and other such ad hoc sources are used as quick-fix tactics to understand and optimize an imaging center’s operation – both the machines and operators.

For instance, data from billing data only tells you if an MRI was performed on a given day and what that MRI was for with no insights into information like, was there a delay in the schedule, how long did the exam take and was that duration normal, which operator did that exam, how much time was spent in prepping the patient and if it was a CT, what was the dosage within acceptable limits and more. These methods, techniques, and tools do not prepare business leaders, radiologists, and imaging center managers to work and think in new ways and are an impediment to keeping up with the blazing pace of change in both technology and volume of patients flowing through their centers.

The worse-case-scenario is basing capital expenditures, for example, ‘buying a new MRI machine due to patient overload’ on such intuition or fractured data. While buying a new MRI/CT machine can address a seemingly immediate resource need, but, it sidesteps a critical look at the operations: capability building across all levels based on ‘real’ usage data.

This is best accomplished by looking at avenues such as:

  1. Is my imaging center under pressure due to unbalanced patient schedules or just a feeling it is busy throughout the day?
  2. Have I maxed out the machine use per day by tightening the processes?
  3. Are the operators evenly distributed throughout the day?
  4. Is the load consistent and can I address the areas where it has been inconsistent?
  5. Are they levers that I can employ to push more exams through the doors without affecting quality?

“Radiology is reinventing itself to meet the challenges of cost-reduction imperatives and the opportunities presented by imaging’s emergence at the epicenter of a fully integrated approach to diagnosis and treatment. I believe this reinvention is largely dependent on the connectivity and data analytics required to make imaging operationally efficient.” Rob Cascella, CEO Imaging Business Groups in his blog post: ‘Connectivity and data analytics will reinvent radiology’.

What Are the Future Radiologists Conducting Patient Exams Looking For?

To scale up examination operations, business leaders must make three shifts. First, they must bring a diversity of perspectives to decision making, for instance, can I stretch the existing machine capacity further by knowing if the change over time between exams can be reduced.

Secondly, employ data-driven decision making to arrive at better answers to underutilization or overutilization of machines. For instance, should I relocate machines from the other centers where the demand is less to a center where the traffic is high or in other instances can I understand the median examination time per operator and find ways to retrain underperforming operators so more patients can be accommodated?

Finally, embrace the need to benchmark performance based on industry performance as well as internal staff performance and understand the throughput for examining patients by the time taken and the exam type.

Figure 1 Examinations per facility compared to the previous period

As a cloud-based application, Glassbeam Clinsights helps determine the relationship among a variety of data sources including billing, machine logs, CRM, and so on, allowing users to pull up data sets in a form for a variety of charts with levers to adjust and work with leading to insights faster.

Here’s a brief look at some of the levers radiologists can use:

What if a trend analysis provides the ability to forecast the demand in exams conducted at a facility. A trend forecast chart, for instance, can help us build an exam throughput scorecard or discover if there is a need for incentives to encourage patients to reschedule exams at an underutilized time of the day or week.

Figure 2  Exam Throughput Trends for an Imaging Facility

To healthcare leaders, being data-driven means changing how they make decisions. It’s much less judgment based but much more about empowering radiology technicians and managers to supplement their experience with AI-driven algorithms to help improve the way they make decisions.

To learn about Clinsights and how it can help uncover opportunities at your radiology facility, go over here.

This is the first of a series of blog posts where I will slice and dice an imaging facility’s operations to help uncover the collaborative practices of using data-driven decision making, tackle adoption of Analytics at your imaging facility, and reinforce the idea of change to help business leaders as you succeed at your healthcare organization.

If you have questions or comments on this blog post or want to share your point of view on this topic, do send me an email at pramod@glassbeam.com. I would love to hear your thoughts on this topic.

Here are a few resources that complement this reading: