Picture two relatively healthy female patients, both in their 50s. Neither “Mrs. Smith” nor “Mrs. Johnson” has a family history of breast cancer or any other significant risk factor for this disease. Each woman has been a patient at the same family practice and, over time, each has established a positive, trusting relationship with their doctor, whom we’ll call “Dr. Williams.”
During separate health visits, Williams takes the opportunity to discuss the benefits and downsides of mammography for breast cancer screening. But in the end, Smith schedules a mammogram, while Johnson does not.
Did Williams provide better quality care to Smith?
Most would agree that understanding primary care performance is an important undertaking. Asking these kinds of questions helps providers know if they’re doing a good job, where deficiencies exist and where best to focus their improvement efforts. They help researchers advance our understanding of the best way to deliver services. They help those paying for care, including governments, know whether they’re facilitating beneficial services while reaping efficiencies and saving costs.
And for patients, the focus of all these efforts, they help determine whether the care they receive is consistent with the highest quality evidence available.
But it’s important that these performance indicators reflect the nuances of what it means to provide quality primary care, which is heavily influenced by the patient-doctor relationship. Quality primary care depends on mutual trust, an understanding of individual values and preferences, and a shared decision-making approach.
In this context, what is right for one patient is not always necessarily right for another. Williams provided no better care to Smith than to Johnson, even though the outcome of each discussion was different.
These qualitative concepts are difficult to disaggregate and measure. However, we gravitate instead toward more easily definable, readily accessible metrics, such as mammography rates.
But are these “measurables” actually meaningful?
Many of the metrics we use to evaluate primary care are based on dated evidence, no longer align with current guidelines, or have been invalidated altogether.
Examples from Health Quality Ontario’s individualized primary care performance reports include the 30-day hospital readmission rate, physician visit within seven days after hospital discharge, emergency department visits for conditions best managed elsewhere, renal protection for all patients with diabetes, statins for all patients with diabetes and mammography for breast cancer screening.
Even the blood sugar target (A1C) we use to monitor diabetes, widely accepted as the cornerstone of favourable health outcomes, has come under recent, paradigm-shifting criticism.
Aggressively chasing metrics we know are meaningless (or at best, not as meaningful as we once thought) not only risks overburdening our patients and our health system, it also threatens to undervalue dimensions of primary care that, while difficult to measure, are consistently associated with quality care and positive patient outcomes: patient-centredness, the patient-doctor relationship, appropriate access, patient empowerment, social determinants, and continuity of care.
Just because these attributes are not easily extractable from an existing database doesn’t mean we can’t study them.
Some clinics are already integrating tablet-based software with their existing electronic health record (EHR) systems to collect experiential data from patients attending their appointments. EHR appointment schedulers could be used to trigger electronic post-visit surveying as well.
Practitioners can participate in efforts to survey their practices’ organizational attributes, which is important for understanding how care is best delivered. The recent QUALICO PC (Quality & Costs of Primary Care) study is an example.
Social determinants of health – critical to understanding many disparities in technical care and health outcomes – are currently not encoded uniformly (or at all) into EHRs. Perhaps they should be.
Many validated instruments already exist to assist in studying these more complex dimensions of primary care, such as the PDRQ-9 questionnaire, the World Health Organization’s health-related quality of life questionnaire, and the Short-Form 36 Health Survey. Ongoing mixed-methods research should help provide further direction.
It’s imperative we find inexpensive and unobtrusive ways to accurately and comprehensively understand primary care performance. Otherwise, we’ll continue to miss opportunities to convince policymakers to invest where disparities truly exist.
Decisions based on only a partial understanding will result in funds flowing into the wrong areas of primary care, or preferentially into acute care settings where governments can more readily see the results of their investments.
We all want to improve primary care. We know that a strong primary care system makes for a healthier, more productive society. But it seems the current performance indicators are not serving us as well as we’d hoped, and collectively, it’s time to undertake the difficult task of measuring the things that really matter.