Reimagining Radiology Departments: Shaping Digital Connections in the Diagnostic Examination Practice with Big Data Analytics
Not only does analytics from every diagnostic examination offer cost containment insights, but it can also uncover referral inconsistencies, plug scheduling delays, and describe a successful approach to evaluate a technician’s productivity.
The aim of this blog is to highlight the dynamics that are opening up innovative opportunities for radiology leaders and, to provide insights into the underlying digital connections that are creating new sources of value and how to use them.
Here’s the framework that works.
Growing Use of Quantitative Metric-based Decision Support System
Just under a decade, ITN Online had reported the efficacy of PACS and DICOM and how it revolutionized the collection and archiving of imaging data. Specifically, I wanted to highlight this from the article’s call for decision support tools as a mechanism to reduce duplicate exams and the need to revamp the physician order systems:
“The application of radiology order-entry software and decision support tools is accelerating the availability of transparent, clinically based appropriateness criteria where it is needed most – at the point of care for referring physicians. The adoption of this technology closely mirrors the evolution of electronic prescribing, which began with medication reconciliation and computerized physician order entry in the inpatient setting and migrated to the outpatient setting.” Hugh Zettel
Flash forward; while PACS, DICOM have found their place in the hospital networks, with increasing digital connections to disparate data sources, we are seeing a growing use of decision support tools aided by machine learning algorithms to circumvent a wide array of challenges. For Example, not just to link a physician order system but to also understand the underlying factors such as the radiologist’s efficiency to conduct examinations, the schedule delays in allocating time slots to patients due to unavailability of machines, technicians, or downtime, and a host of other impediments.
What’s more, such undiscovered factors could be the reason behind the revenue losses; a potential source for the loss could be physicians restricting their patient referrals to the radiology unit in a hospital. Such factors contribute to decreased quality of care, negatively impacting the hospital’s competitiveness.
“You must ask yourself if you have too much or too little equipment to meet the demands of your department,” said Hirschorn, radiology informatics researcher at Massachusetts General Hospital “Is one machine being used a lot? Are patients waiting a long time? Either way, you could be losing business, so you have to find a way to quantify how your equipment is utilized to know if you’re making effective use of time.” Article by Whitney L. Jackson, Diagnostics Imaging
An Innovative Approach to Serving Patients
Many healthcare imaging facilities across the world are starting to apply prescriptive analytics to derive instantaneous insights into radiology operations, to optimize the radiologist’s workload through digital tools that offer insights into errors, discrepancies, and inconsistencies in the radiology practice.
Big data analytics has a happy knack of making things happen. Healthcare networks are already achieving considerable success from the efforts to integrate physician referral, radiology workflow, and machine maintenance data.
What is the starting point?
The diagnosis of the challenges starts with an understanding of ‘what are the digital levers I possess to analyze the discrepancies in the radiology facility?’
Let’s take the scenario of reduced exam throughput that results in reduced revenues. A multitude of factors are responsible for the drop in patients visiting the facility, some of which are related to the performance of the facility and some that are outside the control of the facility. With granular data collection across the entire radiology workflow, hospitals can uncover chinks in the radiology workflow affecting facility performance by asking the right questions and use the levers available for optimization.
Where is the drop?
A drop in exam throughput is because of reduced referrals or drop in direct patients. Reduction in patients coming in directly to the facility could be because of a reduction in quality of exams, longer wait times, delays on committed schedules or even appointments being frequently canceled because of machine downtimes. If the reduction is referral patients, the next question to ask is which of your top doctors have stopped referring? Is it because of the doctor moving out of his/her practice to a different city or do the doctors have other concerns that need to be addressed? It is imperative to also verify if the drop is sudden, gradual or seasonal.
What percentage of the exams miss the scheduled time and by how much?
One of the key reasons why patients prefer particular radiology facility over others is the time that they must wait for their exams. If the number of times exams that have been scheduled are getting delayed, then it adds to the negative patient experience.
Figure 1 Referral Analysis
Why is there a delay in scheduled exams?
Beyond patients walking in late, schedules change because of a variety of reasons. There could due to machine downtime, exams taking longer than planned, longer change over time between patients, and more patients scheduled during a specific time period of the day.
Figure 2 Delayed Exam Analysis
What is the uptime of my radiology equipment? Do they go down frequently? Why? Can I be proactive about impending issues and increase my uptime?
Machines stop working and that cannot be helped, but the impact of a part failing or the entire machine going down can be reduced. Predicting when your major components fail, proactively notifying on degraded working conditions that in the future can lead to downtimes, analyzing conditions which includes the environment that leads to frequent downtimes or even if there was a downtime, getting to the root of the problem in the shortest time are all levers that one needs to employ to run a smooth facility.
Do I have to make changes to my operators’ shifts?
While evenly distributing the patient schedules is great, it might always not be practical. Knowing the specific time periods patients prefer and adjusting the working hours and operator shifts accordingly can go a long way in improving patient experience.
Figure 3 Looking at Hotspots in Scheduling
Which exams are taking longer? What is my median change over time? Are these restricted to specific facilities or operators? What are the busier times and less busy times per facility? What is the typical turnaround time (TAT) of my radiologist after the exam is completed?
Figure 4 Longer Duration Exam Analysis
There can be multiple reasons for an under-optimized operation from specific exams taking longer than planned, longer change over time during specific periods of a day, untrained operators taking longer than expected or too many patients scheduled back-to-back during some hours while the facility is idle at other hours.
There are several such levers available for a radiology facility that is collecting granular data across the entire workflow and analyzing that data.
Our experience of working with dozens of hospital networks on data-driven insights for radiology facilities shows that new value can be created from all parts of the diagnostic examination process: managing patient schedules demand, optimizing the technician effort, managing machine uptime, and delivering a superior patient experience.
The Glassbeam Advantage
In a way, the ideas discussed above are obvious. But, at Glassbeam, we saw that no one has really done it in such a collaborative, connected space where radiologists, physician networks, machine maintenance personnel, and scheduling administrators throughout the hospital network spend their effort to collectively improve patient care but unfortunately are disconnected by the current state of data workflows.
This leads directly to Glassbeam’s mission – deriving insights from connected, disparate data sources – the arena on which the future of hospital networks is being built.
Clinsights: Making Digital Connections Where None Seem to Exist
Clinsights as that digital lever helps hospital networks asses the radiology facilities’ current capabilities and performance, identifying the digital levers that will best close the gaps between current performance and the future vision.
Clinsights offers a range of phenomenal benefits to radiologists. Here’s the impact. Within weeks of deploying a purpose-built system to plug referral losses, Clinsights, referrals started to improve. Other aspects of a radiology practice are making strides in integrating Clinsights into the operations, too. Hear for yourself what our customers have to say and see the difference Clinsights can make at your facility.
I suggest a related reading to this blog post, ‘Examination Analytics that Optimizes Imaging Facility Operations’. In addition, download the blueprint based on which hospital networks check the viability of Clinsights at their facility.
As always, I am happy to hear what you have to say about our approach and if you have any questions, do send me an email [email protected]. Look out for this short feature list that describes the nuts and bolts of Clinsights in greater detail.
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