The Great Healthcare Payment Switch: Fee-for-Service to Pay-for-Performance

Tweet this Kareo storyEveryone agrees the United States’ healthcare system needs improvement, but nobody can agree on how that should happen. Transitioning from a fee-for-service payment model to a pay-for-performance model is one of the more popular solutions being touted as a potential fix. It could improve service for everyone—or make it worse.


As far as the United States’ government is concerned, the debate is over.
Fee-for-service in healthcare is out, and performance-based compensation is in. Tweet this Kareo story
Incentive programs have been designed and deployed, and new acronyms such as HVBP and PQRS have entered the medical lexicon. Billions of dollars in incentives have been paid to providers and care organizations to encourage them to adopt electronic health records (EHR) software. This technology gives regulators and payers access to an ocean of patient data, which will presumably help improve population health modeling, and ultimately treatment outcomes—if the hype is to be believed.

‘Big Data’ has already transformed sales, marketing, logistics, and innumerable other industries—why not healthcare? Normalizing healthcare data is not without challenges though. Many providers refer to the same procedures with different names. For example, a ‘heart attack’ could be referred to as a myocardial infarction, MI, or just simply as the layperson’s aforementioned ‘heart attack’. A person reading notes with those terms would identify them all as the same condition, but a machine reading those notes would identify three separate conditions. This is one of the biggest problems with data-driven medicine: subjectivity. Disparate terminology isn’t technically subjectivity, but the root problem is that freethinking, and most importantly—unique—humans will have varied habits, speech patterns, subjective opinions, and other nuances that objective machines struggle to decipher, let alone match.

Reconciling disparate terminology for the machines’ benefit is one of the reasons behind adopting new codes sets, such as ICD-10, that turn physicians’ narratives or superbills into machine-readable diagnoses and progress notes. If the idea of health 2.0/3.0 is going to be realized, this is something that must happen. Natural language processing software has made great strides, but it would still be a struggle finding regulators, lawyers, or patients willing to have a machine fully interpret a doctor’s note. It will always be necessary to have a trained human to interpret and perform medical procedures—in some capacity—barring science-fiction levels of advancement in artificial intelligence.

While population health modeling and data-backed treatment protocols are all seen as positive outcomes of such standardized systems, physicians can sometimes end up feeling like glorified data entry specialists at best, or as one physician said, off the record, “barcode machines” at worst. The same physician went so far as to say that having clinicians collect the data being provided to payers and CMS is “akin to having a condemned man dig his own grave.”

Hyperbole aside, the most cited problem with American healthcare is the high cost of treatment. The only data important to the bottom line in a fee-for-service, non data-driven payment model is the number of procedures performed. In a performance-based system, instead of doing more procedures to earn more money, physicians must instead improve treatment outcomes.

Starting this transition now gives providers the ability to not only get used to the coming changes, but also influence the metrics by which their performance will be measured. If the story is in the data, it benefits organizations to capture as much data as possible, and analyze that data to discover trends in their own patient outcomes.

The elephant in the room, of course, is whether or not outcome-determined compensation will negatively affect the amount, variety, and quality of care available. Exceptions will likely be made to encourage clinical trials, but where will the line be drawn elsewhere, if at all? If any sort of approval, arbitration, allocation, or negotiation process is necessary, data will likely be the determining factor. Being able to demonstrate outcome ‘improvement’ in any treatment-attributable, measurable way will be necessary for proactive providers and organizations.

In short, anyone holding out on electronic health records, data analysis, or other technologies because of supposed complexity or expense is rapidly running out of time to get in front of new mandates. If expense is an issue, Meaningful Use Incentive Program eligible providers have only until Oct. 3, 2014 to take advantage of over $23,000 in incentive payments, and only until next year to avoid a one percent penalty in Medicare reimbursements. Even non-eligible providers have reasons to learn more about data analysis in healthcare—even if they’re only monetary.


About the Author

Charles Settles is a product analyst at TechnologyAdvice. He covers topics related to healthcare IT, business intelligence, and other emerging technology.

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