The U.S. has never had one healthcare system, but rather, hundreds of regional systems spread across the country. Each system having its own, unique approach to how patient care is provided and an associated medical spending strategy.
Depending on where a patient lives, he/she can experience a tremendous variability with regards to the number of physicians seen, days in the hospital each year, and what medications are prescribed. Supporting and at times encouraging this variability are the potentially subversive themes of collaboration, referrals, and the number of competitors all having equal weight to scientifically validated medicine. As recently as this past decade, the vast majority of Americans are being provided with average care at best while far, far, fewer received poor or excellent care (Mayes, 2006).
Healthcare in the U.S. had evolved into a bloated system that encouraged mediocrity. This variability contributed in a dramatic way to the increased interest in an alternative approach to medical reimbursement that included economic incentives aligned with the quality of care delivered and the associated outcomes. Quite simply, reward the best, encourage improvement, and pay less to low performers.
But
what is quality?
With so much emphasis being placed on quality, how should it be defined?
Logically one might assume the best medical practices lead to better outcomes.
If a person did, he/she would be wrong!
In point of fact, there is only a minor correlation at best. This difficulty arises out the complexity of healthcare, in general, and the overwhelming number of variables. It is difficult to isolate and determine how any one variable is correlated to a patient’s outcome.
However, more recent advances have begun to surface with metrics around the timeliness of therapies related to critical events such as a myocardial infarction, discharge instructions, and even patient satisfaction. While improved, establishing a firm link between how care is delivered and outcomes is still elusive. There is simply a long way to go toward improving the metrics (Evans, 2013).
Should this mean we abandon our approach? No, it’s the best we have at the moment until advancements are identified.
The idea of measuring performance and variable compensation is here to stay. Ultimately, improvements in both care and outcomes will be derived from the development of care models built with deep insights related to specific patient populations.
Sample
use case: Diabetes
Until recently, few reports described the return on investment (ROI) that
providers could expect, if care were delivered in a population specific manner.
The early evidence began to emerge in 2006, which paved the way for where we are
today. At the time, payment incentives related to volume and adherence to
performance measures were becoming well established though an ROI was still not
well understood (Curtin, 2006).
While population health was still not yet a buzzword, the approach of today was embodied in the concept of Health Maintenance Organizations (HMOs) at the time. Essentially, adherence to specifically designed care pathways, expert systems of care by disease, resulted in maximizing, end-of-year payments related to factors such as quality, efficiency, and patient satisfaction.
More specifically, in the case of diabetics, these “bonuses” were linked to a physician’s ability to manage the current disease state through both acute and preventative measures, while minimizing the potential for commonly associated secondary pathologies, such as coronary artery disease (Curtin, 2006).
The findings were impressive. Measured against a cost of US$1.1million to establish a Pay for Performance (P4P) approach in a mid-sized community, savings of US$2.4million, per year, were realized year over year (Curtin, 2006).
Indeed, a well-organized P4P system can be cost effective. So what are the next steps? At this time, there are many population health initiatives spreading throughout the country. However, the next advances won’t come from the delivery of care itself, but rather a deeper understanding of our own form. Precision medicine is the next frontier.
Precision Medicine accelerates population health
Historically, medicines of all kinds have been developed, tested, and marketed
for the average patient. This approach has turned a blind eye to diversity and patient mix of real-world settings as a one-size-fits-all approach
ignores the efficacy and safety profiles for a significant portion of the
population (Dankwa-Mullen, 2015).
Precision Medicine (PM) addresses this issue. PM treatment protocols are specifically tailored to the individual differences associated with a patient's genes, lifestyle, environment, and many other factors. Greater insights are gained which are, in turn, leveraged for better predictability with regard to efficacy and cost effectiveness. In fact, at this very moment, the National Institute of Health (NIH) is enrolling one million Americans who are committed to research to advance our understanding of disease at the level of the individual.
No place is this approach advancing more rapidly than in cancer care.As a result, the past few years have witnessed a variety of new treatments for patients based on a patient’s, or a tumor’s, genetic makeup. This approach has all but become mainstream, as cancer patients of all types now undergo routine molecular analysis. The results of these analyses are more targeted therapies, with a much higher success rate, and lower overall costs, not to mention increased patient satisfaction.
The potential of PM is only just being realized. Managed correctly, the future growth of PM must include sub-populations of patients who typically have been ignored. If so, PM holds the promise of harnessing innovation while, at the same time, reducing health disparities (Dankwa-Mullen, 2015).
Conclusion
Led by initiatives emerging from the federal government, employers, and payers,
the opportunity to increase care quality is significant. Even with the
additional burdens placed on hospitals, as a result of these flexible
reimbursement plans, P4P is an idea that is here to stay, even though the
science still needs to advance in areas of the tracking of appropriate measures and
the definition of quality.
In this rapidly evolving environment, providers must maintain their vigilance about measuring performance in comprehensive and meaningful ways. Only through the adoption of advanced care models that are both population and disease specific can we hope to achieve the kind of outcomes “all” patients deserve at a cost that is acceptable.
References
Curtin, K., Beckman, H., Pankow, G., Milillo, Y., & Greene, R. A. (2006).
Return on Investment in Pay for Performance: A Diabetes Case Study. Journal Of
Healthcare Management, 51(6), 365-376.
Dankwa-Mullan, I. (2015). Precision medicine and health disparties: Advancing the science of individualizing patient care. American Journal Of Public Health, 105S368. doi:10.2105/AJPH.2015.302755.
Evans, M., & McKinney, M. (2013). Quality paradox. Modern Healthcare, 43(1),
Mayes, R. (2006). The origins of and economic momentum behind “Pay for Performance” reimbursement, 15(2)