We primary care physicians hate data. Taken on their own, numbers are benign, but when we hear the word “data,” physicians are reminded of a litany of related issues that make our lives far more difficult: checkboxes. Regulatory compliance. Prior authorizations. Unnecessarily complicated payment schemes.

By virtue of our training and the principles that guided the decade we spent obtaining our degrees, we are empiricists by nature. It’s sad to think that, to many of us, data equals garbage rather than better care.

How in the world did we get here?

It is likely that our frenemy-ship with data started a couple of decades ago when computing entered the realm of health care. Players in the health care system — insurers, regulators, hospitals, and even physicians — became enamored with the possibilities that computing presented, so we started collecting data haphazardly and, at some point, everyone lost sight of the why behind data collection. And, at some point, It started being weaponized against physicians.

Our paychecks became tied to data entry in the form of a unique currency cryptically called “RVUs,” and our compensation from public payers started to revolve around exhausted metrics that stand in as proxies for quality. EHRs demand our incessant clicking and suck the data we create past the event horizon and straight into the EHR black hole, never to be meaningfully used (by us) again.

So. Data has become a four-letter word.

With all this data collection, we’re also pummeled with messaging that implores us to provide “high-quality” and “value-based” care.

But — get ready for this — we don’t know how to measure quality in primary care meaningfully.

No joke. We really don’t.

As a group and an industry, those of us working in primary care have not come up with a standardized, effective way to measure or prove quality.

I didn’t think this whole subject critically until well after I started my direct primary care practice in 2016. I was desperately trying to ensure I was playing by the rules and providing high-quality, high-value care — and I wanted the data to show it. But when I went digging, I realized that I couldn’t find any empirical guidance as to what I should be measuring.

The problem with measuring the quality of any given primary care physician or clinic lies in the vastness of the services provided and the nonlinear pathways undertaken in primary care. Primary care is, in essence, a nonlinear process. Compare this with, for example, a knee replacement.

The March/April 2017 issue of Annals of Family Medicine contained a fantastic article detailing this problem.

The authors outlined the following example: “… in primary care, even though many patients say they are willing to undergo colon cancer screening when asked by their physicians, uptake of recommended screening is low, often measured at less than 50 percent of eligible patients. Even in primary care centers of excellence, chronic disease targets are met and sustained less than 50 percent of the time, despite extra resources, such as health coaches. The impossibility of achieving 100 percent uptake makes it much more difficult to draw a summative conclusion about which primary care practices are providing high-quality care when contrasted against elective surgeries where nearly 100 percent compliance with preoperative antibiotic guidelines could reasonably be achieved.”

Herein is the problem: Traditional quality paradigms — the ones that have guided the past several decades of checkboxes and clicking and reporting— assume that “there is a definite and measurable right answer in a given situation.” In contrast, the Annals authors wrote, “primary care physicians often deliver high-value care by doing the best they can with the patient care card they are dealt, knowing that perfection will never be achieved.”

This is known as the paradox of primary care, as outlined a decade ago by the editors of Annals of Family Medicine: “Compared with specialty care or with systems dominated by specialty care, primary care is associated with the following: (1) apparently poorer quality care for individual diseases; yet (2) similar functional health status at lower cost for people with chronic disease; and (3) better quality, better health, greater equity and lower cost for whole people and populations.”

In plain English: Although primary care may miss targets on specific disease metrics and data points, we accomplish the triple aim of improving population health at a reduced cost through a better patient experience — but only when you take a look with a wider lens and back off from the minute details.

Before you scoff and write me off as passé because I said “triple” and not “quadruple” aim — that was intentional. Traditional, fee-for-service primary care is still struggling to take care of its physicians. And I don’t care how many burnout prevention sessions a hospital hosts, I can’t deep-breathe, yoga or meditate the burnout out of me. To me, that’s rubbish and blames the victim of an abusive system. The system that turned me into nothing more than a cog in the wheel, churning through visits, is what burned me out and sparked my interest in DPC. The health care system has created robots out of the caring professionals who need agility, autonomy, and agency to make the right decisions for the complex humans sitting in front of them. It turns us into data processors. I’m over it.

We need a better way to think about data in primary care if we’re going to keep physicians healthy — which keeps our patients healthy.

In direct primary care, there are no arbitrary data-input rules. This means that we have the opportunity to write the new rules on data.

So, where should we start?

I propose three new rules for metrics and data reporting in primary care — and maybe all of medicine.

1. We measure data for ourselves and our patients. And nobody else.

2. Metrics and data collection should never interrupt flow.

3. Metrics can — and should — be retired over time.

I should note here that there is interesting data about pay-for-performance that I’m not going to get into in detail, but it shows that when you incentivize doctors to achieve certain targets or metrics or to report certain data or file in a certain way, doctors and health systems do it. But they perform the processes to prove the metric more than they actually move the needle on the metric/outcome that’s supposed to be measured. In other words: Data reporting becomes a game, and we’re quite good at playing games. Pay-for-performance turns into payment for those who know how to report better. There’s a great article about the Quality and Outcomes Framework out of the United Kingdom that demonstrated this in a harrowing, expensive way.

With these three rules as guideposts, what should we measure? How should we measure it? And how can our technology partners facilitate this process?

I don’t have the answers — yet. More than anything, I want to start the conversation and invite others to join in. You can keep the conversation going on Twitter @Dr_A_Edwards, or share this article to bring others into the discussion.

We’re all highly intelligent empiricists. Let’s start acting that way.

Allison Edwards is a family physician and founder, Kansas City Direct Primary Care. She can be reached on Twitter @KansasCityDPC. This article originally first appeared in the American Academy of Family Physician’s Fresh Perspectives blog, Monday, June 24, 2019.

Image credit: Shutterstock.com