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Intelligent Scanning, Lack of Interoperability as Barriers to Quality Measures

February 12, 2020


Dr Wong responds to a blog by Tom Valuck, MD, JD, and colleagues, which argues that if CMS' Oncology Care First model is adopted as proposed, CMS will be missing a critical opportunity to advance quality measurement for oncology care by not addressing important gaps in the OCM quality measure list.  Dr Wong contends that CMMI needs to reexamine their current surrogate and direct measures of quality with the goal in mind that in the future, interoperability and expanded datasets will allow true measurement of quality of care.


Establishing and measuring the quality of oncology care has perhaps been a most desired and sought-after goal. Since the initial discussions and promotion of clinical pathways, the question of the impact to quality, however you wish to define it, has been at the forefront. As such, you can define quality on the basis of clinical outcomes, the incidence of clinical complications, quality of life, or indirect costs like absenteeism and productivity. In a perfect world, all of these parameters would, and should, be taken into account. 

While many organizations have attempted to help develop and define measurements of quality, the main barrier has always been and continues to be the ability to efficiently measure the quality metric. Even when measured, there is little consensus as to what the standard should be. As pointed out by our colleagues at Discern Health, CMMI dropped the measurement of cancer-specific measures due to the burden and inability to accurately evaluate and report quality impacts. In some respects, the inability to measure quality may well be due to the complexity and diversity of the cancer patient, as well as the lack of a true accepted standard of a measurement of quality that can be easily measured through claims data. Treatment variability is also another barrier where the treatment options will differ based upon the patient’s physical condition, comorbid conditions, or patient desires. As we promote the engagement of the patient into the treatment decision-making process, patient desires represent an unpredictable variable that can skew any performance measurement. Other conditions where the impact to quality of care is more easily measured (ie, diabetes and hypercholesterolemia) do not have the complexity and diversity of the oncology patient.

In a perfect world, I am in agreement with Tom Valuck, MD, JD, and colleagues that we need to strive for better measures of quality, including PROs and cancer-specific clinical endpoints.  However, lack of interconnectivity, as well as “intelligent scanning,” is a barrier. Intelligent scanning capability is a reality in cases where electronic medical records are totally integrated across the entire spectrum of care, and reports are scanned for “key” words. These “key” words are indicators of the care being given. Until we are in a world of total interoperability, we are relegated to the use of surrogate metrics as our measure of quality. While not a perfect and exact science, it is efficient and measurable. Yes, one can argue that a decrease in emergency room visits is a sign of improved quality of care. Or is it?

In conclusion, CMMI needs to reexamine their current surrogate and direct measures of quality with the goal in mind that in the future, interoperability and expanded datasets will allow us to truly measure quality of care. However, even then, I would ask, is this just an exercise in futility or are we truly making an impact? Especially in a diverse and complex disease as cancer and the possible variability of cancer care, if we determine that we are making an impact, from whose perspective is that impact being made? Is the impact improvement from the perspective of the oncologist, who is looking for a clinical response with the least amount of adverse effects? Or is it from the perspective of the payer, who is looking at the cost of care? Or is it from the patient, who is looking at from the perspective of quality of life, financial responsibility, and other indirect cost related to caregivers and employment?

In the end, even if we develop a process to measure “quality,” will we be confident that we are truly measuring quality?

Read the original blog by Tom Valuck, MD, JD, and colleagues.


 

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