The answer to a question asked in a SIIM 2015 Friday morning session was clear–medical imaging needs to make further progress to be in a position to provide value-based care.
This was the focus of Rasu B. Shrestha, MD, MBA, University of Pittsburgh Medical Center, John W. Nance, MD, Johns Hopkins University, and Kevin W. McEnery, MD, University of Texas MD Anderson Cancer Center in the session, “Providing Value-Based Healthcare – Should Imaging Lead, Ride Along, or Get Left Behind?”
Radiology: Data Rich, Information Poor
Dr. Shrestha began the session by focusing on what we mean when we talk about value-based care, how radiology needs to get to where it needs to be, and the opportunities to get there by discussing the barriers that are currently preventing this from happening.
The state of radiology is that the specialty is data rich and information poor. It has commonly had its innovators, and has led the charge in healthcare innovation (film to digital, etc.), but its innovation over the years has also been its downfall, as numerous silos have popped up within healthcare providers. The next stage for radiology is breaking down those silos and extracting the data so we can comb it for information, which according to Dr. Shrestha’s slide, showed that from the information we obtain from data, we can then gain knowledge about the patient(s), and eventually turn that knowledge to wisdom, with increased value provided to the patient along the way.
The main problem with the imaging workflow today is that radiology is image-centric and not patient-centric. The specialty stopped talking to other specialties within the hospital as it drowned in too much data, and not enough intelligence. Moving forward, context will be king–both obtaining context to produce better reports, and providing additional context once the exam has been done and the report created.
Dr. Shrestha’s main point was the importance of data liquidity, which is freeing data from the silos, liquidating the assets because of the immense amount of value it hold. The problem is that radiology has not been able to do this.
The technologies currently being developed will radiology in this direction. The next generation will be patient-centric, predictive protocols, cloud-based VNA, adaptive learning, contextual reports, and value-based imaging.
Radiology can only improve what it can measure. In a volume-based imaging model, report turnaround time, and number of studies read were the metrics. Moving forward, it will be superior outcomes, patient-centric care, clinical quality metrics, increased transparency, total cost management, and shared savings.
Definition of Value
Following Dr. Shrestha, Dr. Nance of Johns Hopkins University looked at the definition of value, its history in radiology, its current status, and barriers.
He started out with the honest definition of value, which is:
Value = Outcomes/$
Value does not equal quality, efficiency, safety, outcomes, or cost, per se, and he highlighted that there is currently an alphabet soup of organizations (government, nonprofit, associations) currently focused on outcomes, because the current measures are certainly not.
Dr. Nance went through common measures, and how diagnostic imaging is not a big part of them. The Healthcare Effectiveness Data and Information Set (HEDIS), has 81 measures and only three have anything to do with diagnostic imaging. None related to outcomes. Physician Quality Reporting System (PQRS)–254 measures, and only 13 deal with diagnostic imaging. Again, none related to outcomes. National Quality Forum has 636 measures, with 15 having to do with diagnostic imaging, even though imaging account for 14% of healthcare costs.
The fact is that diagnostic imaging lacks outcome measures.
ACR’s Imaging 3.0 is heading in the right direction by seeking to improve the value of radiology. The types of quality measures are focused around structure, process, and outcomes. Structure focused on underlying infrastructure of a system, which has serious limitations. Process measure are most common, contain a lot of value, have some advantages that are actionable, but again, have serious limitations. This is because people gravitate toward measures that are easily extractable, even though they may not be the most relevant.
Why are outcomes so allusive? You need data validity. Stringent national benchmarks, which are often lacking. Large sample sizes and follow up to show differences. There are good examples out there with large, randomized controlled trials, but it is not commonplace yet.
The challenge moving forward for radiology in this area will be diagnostic accuracy, the quality of communication, change in management of the specialty, and the effect on outcomes.
Transitioning from Volume-Based to Value-Based Imaging
Finishing up the session, was Dr. McEnery of University of Texas MD Anderson Cancer Center.
The objectives for his section centered around examining the transition of imaging from volume-based to value-based, and discussing the role of informatics support in demonstrating the value of enterprise imaging in the transition to value-based healthcare.
In a value-based system, Dr. McEnery showed that we must be achieving outcomes at the lowest cost that are patient-centered, focus on the patients’ needs with their outcomes achieved, and focus in the right locations for high-value care.
For high-value, the value-enhancing IT platform accomplishes the following:
- It is centered on patients
- It uses common data definition
- It encompasses all types of patient data
- The medical record is accessible to all parties involved
- The system includes templates and expert systems for each medical condition
- The system architecture makes it easy to extract information
With the change from volume-based to value-based imaging, we will go from being:
- Transactional to consultative
- Radiologist-centered to patient-centered
- Interpretation focused to outcomes focused
- Commoditized to integral
- Invisible to accountable
The imaging value patient context that Dr. McEnery showed was:
- Orders: Appropriate for the patients’ complete presentation
- Protocols: Optimized to inform the clinical decision process
- Acquisition: Optimized to inform at safest level, greatest clinical data
- Interpretation: Focus on findings that are pertinent to patient
- Reports: Optimized to efficiently show the information, data, and results
Dr. McEnery went on to explain how these changes to value-based care are on the way as health reform continues to take shape. This included the April 1, 2016 deadline of CMS lists qualified decision support providers for ordering professionals, and beginning January 1, 2017, CMS will not reimburse certain claims.
With these changes inevitable, Dr. McEnery ended his session focusing on the clinical decision support (CDS) process and how it will move diagnostic imaging to a value-based process. Essentially, CDS and EMR needs to inform the entire patient process, and significant changes are in process for the delivery and reimbursement of healthcare.
IT systems will need to evolve to allow radiologists to become a part of this evolution. As Dr. McEnery said at the beginning of his session:
“I don’t want to be in the backseat. I want to ride shotgun. I want radiology to ride shotgun in the innovation process.”