Greg Freiherr, Industry Consultant
Greg Freiherr, Industry Consultant

Greg Freiherr has reported on developments in radiology since 1983. He runs the consulting service, The Freiherr Group.

Blog | Greg Freiherr, Industry Consultant | Information Technology | May 03, 2018

Technology Can’t Do It Alone

We are a nation in search of simple solutions. We want silver bullets to slay whatever ails us. Value-based medicine is one of those bullets.

By putting the patient first, it promises to remedy the shortcomings that have caused healthcare in the U.S. to fall short of reliably providing services to all who could benefit. But, if it can do so at all (and that remains the subject of debate), value-based medicine will not do so simply.

It is comprised of multiple elements that only together can make medicine more efficient, more cost-effective and patient-centric.

Ironically, the current fee-for-service system was founded on the same noble idea as value-based medicine. Its intent was to take the financial burden from patients. But compromised by years of uncontrolled expansion, the system has led to spiraling costs that have made the achievement of patient-centric medicine all but impossible. It has, however, created arguably the most comprehensive and distributed base of imaging services anywhere in the world.

How to throw out the bathwater and keep the baby? Government is trying to do so by administering financial alternatives to fee-for-service, ones that reward frugality, yet maintain quality of care. The problem? While everyone agrees that value-based medicine is the way to go, the term itself is difficult to define. That is especially so in radiology.

 

Building Blocks of Value

Breaking it down into its three core elements would seem to help. But it helps only so much, especially when imaging is considered. From the patient perspective, achieving a clinical outcome as good or better than currently done would seem essential. But radiology is fundamentally a diagnostic discipline. Just finding the problem does not ensure a better outcome.

Uncovering an incurable form of cancer, no matter how early, has no effect. Similarly, incidental findings — if treated aggressively — may do more harm than if they were not found, particularly if those findings are of slow-growing pathologies that may, or may not, develop clinically. Then there is the use of ionizing radiation and its much debated role in causing cancer. And what about gadolinium-based contrast media and its association with the development of NSF (nephrogenic systemic fibrosis)? What level of risk is acceptable for the occurrence of NSF and cancer due to exposure to ionizing radiation? For that matter, how much patient discomfort is acceptable to obtain a diagnostic image? Consider the case of a claustrophobic patient. Is the risk posed by anesthesia acceptable? And at what level?

Value-based medicine is on much firmer ground when it comes to operational and financial consideration, particularly as they relate to radiology. Improved efficiency promises to allow radiologists to expand their practices — to become more involved in patient management as part of the care team; to do more with the same — or even fewer — resources, so as to expand beyond the role of finding and localizing disease. Operational improvements naturally lead to improved cost-effectiveness in terms, for example, of increased throughput and greater revenue. But this only applies if a care team is amenable to bringing the radiologist onboard. And, as regards increased productivity, a backlog of patients is needed to achieve increased throughput.

 

Teamwork

But benefits go beyond these. Faster MRI exams, as might be achieved through Compressed Sensing (whereby less information is acquired, but done wisely so as to get only the necessary data points) mean increased patient comfort. Workflows might be improved to allow increased contact between technologist and patient, as in the case of CT controls at the gantry rather than in a separate control room. Or “on-demand” dual-energy CT that captures data at different energies for future use if and when they are needed to make a definitive diagnosis.

This is truly disruptive innovation. Its selective implementation might nudge radiology closer to value-based medicine. But it will not be the technology so much as the combination of people and technology.

To achieve this critical pairing, we have to acknowledge that, while technology has profoundly improved medicine over the years, what it can do by itself is inherently limited. This is apparent in oncology.

Cancers often extend beyond their radiologically defined boundaries, as exemplified in neuroblastomas. And pathologies associated with some cancers, those of the breast, for example, are not equally threatening or well understood. Yet, there is a tendency to gloss over these limitations, especially when dealing with patients or the public in general, particularly in the case of screening programs. (Except in breast cancer screening, nowhere in healthcare are apparently healthy people purposely — and regularly — exposed to a cancer-causing agent.)

As we enter this new age of pay-for-performance, it is important to recognize that just improving performance does not necessarily increase patient benefits. We need to be exceedingly careful in how we apply technology and define the goals underlying their use.

 

Yes … But Wisely

It’s laudable to increase efficiency in radiology — but it is pragmatic to do so only for purposes that are beyond reproach. With increased efficiency may come increased cost-effectiveness. Patients may benefit from this, if accompanied by consumer-driven aspects — transparency in the prices of scans so as to create a competitive marketplace supportive of patients struggling to handle rising deductibles; timely and convenient care achieved through extended hours, online scheduling and strategically placed sites that reflect the needs of patients.

Can the sharing of data seen in enterprise imaging provide radiologists the details that will help them form diagnoses? For example, when the referring physician writes “liver mass” as the reason for a scan, could a smart algorithm cull data from physician notes so as to show the patient previously had cancer and that the mass might be a metastasis?

Can appropriateness criteria be devised that match the scan to the needs of the patient — scheduling the claustrophobic on an open MRI, for example, to maximize the chance of scan completion without costly anesthesia.

In itself, technology is not a solution. It is a tool that needs to be judiciously applied by smart people who seek tightly defined goals.

If that is not done — if we put too much stock in technology as the way to achieve value-based medicine — it won’t be long until we are asking why technology failed us.

Related Content

Sponsored Content | Videos | PACS | June 29, 2020
Kevin Borden, Vice President of Product, Healthcare IT for Konica Minolta, talks about Improving Access and Aiding Wo
Universal digital operating system for surgery enables health tech companies and start-ups to accelerate, scale and grow

Stefan Vilsmeier, President and CEO of Brainlab Photo courtesy of Brainlab

News | Artificial Intelligence | June 26, 2020
June 26, 2020 — ...
n support of Mayo Clinic’s digital health and practice transformation initiatives, the Mayo Clinic Department of Laboratory Medicine and Pathology has initiated an enterprise-wide digital pathology implementation of the Sectra digital slide review and image storage and management system to enable digital pathology. 
News | Enterprise Imaging | June 26, 2020
June 26, 2020 —  In support of Mayo Clinic’s digital health
Visage announces cloud implementation, Visage 7 Workflow and semantic annotations
News | Enterprise Imaging | June 26, 2020
June 26, 2020 — Visage Imaging, Inc.
The American College of Radiology (ACR) Center for Research and Innovation (CRI) is pleased to announce the development of the COVID-19 Imaging Research Registry (CIRR), an effort by the ACR CRI and the ACR Data Science Institute in collaboration with the ACR and the Society of Thoracic Radiology (STR). Sharyn Katz, M.D., director of research for thoracic radiology at the University of Pennsylvania, chairs the effort’s multiple-disciplinary steering committee, which includes representation from across the i

Getty Images

News | Coronavirus (COVID-19) | June 25, 2020
June 25, 2020 — The American College of Radiology (ACR) Center for R
QuEST Global, a global product engineering and lifecycle services company, announced that it has developed a robust artificial intelligence (AI)-powered solution that will enable healthcare professionals to accelerate the screening of COVID-19 patients with pneumonia symptoms.

Getty Images

News | Coronavirus (COVID-19) | June 18, 2020
June 18, 2020 — QuEST Global, a global product engineering and lifecycle services company, announced that it has deve
RapidAI acquires comprehensive aneurysm management innovator and expands scope to include aneurysm, extending its leadership in cerebrovascular imaging
News | Artificial Intelligence | June 12, 2020
June 12, 2020 — RapidAI announced the acquisition of