Data Challenges and Opportunities in the Evolving Medical Imaging Practice

by Bruce I. Reiner, MD

This article was originally published in the Journal of the American College of Radiology - Volume 12, Issue 9, Pages 940–941, September 2015 

In God we trust, all others must bring data. (W. Edwards Deming)

Introduction

Although quality and safety in medicine are well established as principal determinants of clinical outcomes, medical imaging economics have not consistently rewarded the highest performing quality and safety practice providers. This is due in large part to the fee-for-service reimbursement model, in which providers receive fixed unbundled payments directly tied to utilization. To maximize revenue, imaging providers strive to maximize examination volume, often prioritizing “high-technology” examinations with the highest reimbursement rates. At the same time, the fact that quality and safety are not directly “revenue generating” has resulted in technology innovation focused largely on productivity and work flow enhancement, which can be directly quantified to operational costs and savings.

The recent passage of the Patient Protection and Affordable Care Act (PPACA) [1] will lead to fundamental changes in health care quality, safety, and economics. In large part, the provisions and mandates established by PPACA and CMS [2] are in direct response to a number of publications by the Institute of Medicine that have stressed the need for data-driven quality and safety improvements in medical care delivery directly tied to payment [3, 4, 5, 6].

A number of central themes can be derived from PPACA and recent CMS initiatives, including quality and safety analytics, consumer engagement, public data accessibility and transparency, payment reform, the creation of clinical data registries, and provider collaboration. The common denominator is standardized data, which provide the ability to perform comparative analysis, develop objective standards, and create of evidence-based best practice guidelines.

Changing Medical Imaging Economics

When asked why he robbed banks, Willie Sutton infamously responded, “Because that’s where the money is.” All businesses, including medical imaging, follow a similar cost/benefit analysis premise. Efforts and resources are focused primarily on those endeavors that bring the highest return on investment. I personally experienced this concept in my first year of private practice, when I was chided by the chief radiologist in a small hospital practice for recommending a CT-guided drainage procedure to a urologist colleague. In the opinion of the chief radiologist, the time required to perform this procedure (and the associated revenue) would pale in comparison to the amount of revenue that could be generated interpreting imaging studies. Several years later, this concept was reinforced to me as I watched PET sales explode, largely in response to the excessively high PET technical reimbursement rates, encouraging many radiologists to become active owners in PET technology. Economic analysis demonstrated that the greatest return on investment was no longer in rendering professional interpretation services alone but instead in assuming ownership to garnish the disproportionately high technical fees.

As we fast forward to 2014 and beyond, we will begin to see fundamental changes in payment reform, calling for a transition from fixed fee-for-service reimbursement to bundled “pay-for-performance” reimbursement, directly tied to quality and safety measures. To successfully navigate through these economic reforms, medical imaging providers must redefine their role, from that of “individual readers” to that of “clinical collaborators” and quality and safety experts. The focus of attention will be on quality, safety, and “patient experiential” data, which will directly affect reimbursement. In the end, payment will be merit based, and data will prevail.

Medical Imaging Data and Analytics

In medical imaging practice, metrics can be assigned to individual and collective steps in the imaging chain, beginning with examination ordering and ending with communication of report findings (Table 1). The creation of these individual and collective quality and safety metrics could eventually lead to the creation of medical imaging quality and safety scorecards [7, 8, 9], analogous to the diabetes report card currently being created by the US Department of Health and Human Services and the Centers for Disease Control and Prevention [10]. This would in effect create a data infrastructure in which service and technology providers would become active participants in quality and safety data collection, while being economically compensated in accordance with defined performance standards.

Table 1

Representative quality metrics in the medical imaging chain

Step Quality Measures Stakeholders
Examination ordering Examination appropriateness
Accuracy and completeness of clinical data
Referring clinician
Scheduling Timeliness of exam scheduling
Customer satisfaction
Radiologist and clinician
Clerical Staff
Patient
Data retrieval Accessibility and review of clinical and historical imaging data Technologist
Radiologist
Image acquisition and processing Image quality
Completeness of data set
Technologist
Archival and distribution Examination retrieval time
Imaging examination and report accessibility
IT and PACS administrators
Image display Monitor quality control Physicist
Interpretation Diagnostic accuracy
Comparison with historical data
Radiologist
Reporting and communication Diagnostic confidence
Follow up recommendations
Critical results communication
Patient consultation and understanding
Radiologist
Clinician
Patient

To facilitate this large-scale data collection and meta-analysis, quality and safety data registries will be required, which can be integrated with electronic health records. The derived analytics will in some manner be accessible to a wide array of stakeholders, including service providers, technology producers, and health care consumers. By creating accessible and transparent data, all interested parties will be provided with the ability to review quality and safety analytics specific to their personal and professional needs. Service providers will have the ability to review customizable performance analytics, providing the ability to pursue targeted education and training programs along with decision support tools and technologies addressing specific deficiencies. Technology providers will be provided with objective data for comparative technology performance, requirements for product improvement, and opportunities for new technology development. Health care consumers, including patients, referring clinicians, and third-party payers will be provided with objective performance data to improve service provider selection, education, and collaboration. In the end, a new era of quantitative accountability will be created, which could eliminate existing commoditization trends and transform radiology practice from “survival of the cheapest” to “survival of the fittest” [11].

The collaboration and education requirements called for in the combined initiatives of PPACA, CMS, the Department of Health and Human Services, and the Institute of Medicine could facilitate the creation of multidisciplinary teams with the mandate to continuously improve quality and safety deliverables within the medical imaging continuum. In addition to traditional service providers (eg, radiologists, technologists, administrators, referring clinicians), these teams can also include technical personnel (eg, physicists, technology providers, IT specialists), data specialists (eg, informaticists, health care economists, statisticians), and consumer advocates (eg, patients, third-party payers, regulators). These multidisciplinary teams could directly benefit accountable care organizations in the identification, implementation, and monitoring of newly created programs aimed at improving patient care coordination, quality and safety, and economic efficiencies.

Rather than viewing these impending changes as a threat, the radiology community should view this as an opportunity for innovation in technology and clinical care, with the added benefit of rewarding providers on the basis of merit. The derived data analytics can serve as a source of empowerment for all stakeholders, perhaps most important of whom are patients.

References

  1. US Department of Health and Human Services, Office of Population Affairs. Affordable Care Act news. Available at: http://www.hhs.gov/opa/affordable-care-act. Accessed August 8, 2014.
  2. Centers for Medicare and Medicaid Services. Affordable Care Act in action at CMS. Available at: http://www.cms.gov/about-cms/aca/affordable-care-act-in-action-at-cms.html. Accessed August 8, 2014.
  3. Institute of Medicine. Rewarding provider performance: aligning incentives in Medicare. The National Academies Press, Washington, District of Columbia; 2007
  4. Institute of Medicine. Performance measurement: accelerating improvement. The National Academies Press, Washington, District of Columbia; 2006
  5. Institute of Medicine. Patient safety: achieving a new standard of care. The National Academies Press, Washington, District of Columbia; 2003
  6. Institute of Medicine. Priority areas for national action: transforming health care quality. The National Academies Press, Washington, District of Columbia; 2003
  7. Reiner, B. Opportunities for radiation dose optimization through standardized analytics and decision support. J Am Coll Radiol. 2014; 11: 1048–1052
  8. Reiner, B.I. Quantifying radiation safety and quality in medical imaging, part 2: the radiation scorecard. J Am Coll Radiol. 2009; 6: 615–619
  9. Reiner, B.I. Quantifying radiation safety and quality in medical imaging, part 3: the quality assurance scorecard. J Am Coll Radiol. 2009; 6: 694–700
  10.  Centers for Disease Control and Prevention. Diabetes report card 2012. Available at: http://www.cdc.gov/diabetes/pubs/pdf/diabetesreportcard.pdf. Accessed August 8, 2014.
  11. Reiner, B.I. and Siegel, E.L. Decommoditizing radiology. J Am Coll Radiol. 2009; 6: 167–170

About the Author

This article was originally published in the Journal of the American College of Radiology - Volume 12, Issue 9, Pages 940–941, September 2015