“How do I find primary care physicians and endocrinologists who are willing to put a total of 1,000 type 2 diabetics, who are not well-controlled on metformin, for a pragmatic study of a recently FDA-approved extended-release metformin patch, and report their data so that we can find out how the patch is used and its outcomes in clinical practice?”
Let’s use this question, posed in Episode 5, to illustrate recruiting for a particular type of widely-distributed pragmatic study (WDPS), so that we can understand (and act on) how it is used, and how well it performs, with the wide variety of practice environments and patients in the real world.
Cut to the punchline: In a real-world pragmatic study--especially the widely-distributed kind--we want to study the use and outcomes of treatments over a wide spectrum of physicians, practices and patients. We engage physicians who in turn select patients who meet the study criteria and recruit them to be randomized or to offer them a clinically-appropriate treatment. Today we explore this process--from specifying qualifying-patient criteria through using data for screening, engaging physicians, and enrolling patients. We note potential sticking points and foreshadow considerations of process and technology platform… topic of our next post.
Recruiting for a WDPS differs from prospective randomized controlled trials (PRCTs) in certain respects. Most prominently, WDPS are not conducted solely in well-circumscribed centers; rather they’re conducted by physicians (and their patients) in the gamut of community settings. Still, like PRCTs, pragmatic studies need at least one intervention (study) group, and a comparison group (who received an alternate treatment or “usual care”).
With a WDPS, are patients actually “recruited?” Of course they are if the protocol involves randomization. If instead the physician determines whether to do a test or give an approved treatment to their appropriate patients, is the patient really being ‘recruited?’ From both the physician and patient perspective, the physician is offering them an approved clinically appropriate treatment, discussing its advantages and potential drawbacks, and helping them decide whether to take it. This is what good medical care looks like. So in this scenario, it’s the physician who is ‘recruited’ to a study in which they will offer their patients who meet criteria an already-approved treatment (or test), and likely do some extra work relating to background education and documentation.
This is currently a fuzzy area; we strongly recommend consulting your IRB or CRO.
Now let’s consider the WDPS ‘recruitment architecture’ or scenarios The doctor is recruited because he or she is the type of doctor who treats patients who meet the study criteria (and the available data say he or she has lots of patients who could be candidates); is asked to identify qualifying patients in their practice; and to offer them test or treatment.
Step 1: Specify patient and doctor criteria. We start off with a set of inclusion and exclusion criteria for patients qualified to be studied, and for physician specialties, if the protocol calls for that. The criteria are converted to algorithms (rules) and translated into queries against the available data (claims and/or electronic health records).
Step 2: Apply the algorithms or rules. The rules are about both doctors (right specialties?) and patients (meet inclusion and exclusion criteria?). The patient rules should be considered screens, especially with healthplan data, which will be used to determine which physicians are outreached-to.
Step 3: Outreach to physicians. We compile a list of physicians to invite to the study, based on who treats the most patients that met the screening criteria (Pareto analysis). We reach out to these physicians. After fully informing them about all aspects of the study (including what we’ll ask them and their patients to do), those physicians who enroll will identify qualified patients from their practice (note that membership in one or more specific healthplans may be a qualifying criterion; and that additional data may need to be collected to support qualification (2)).
Step 4: Physicians recruit patients. Physicians enroll qualified patients into the study. If patients are assigned to treatment by randomization, the physician discusses how the study works and enrolls consenting patients to be randomized. If the design specifies that all clinically-appropriate patients are offered the treatment (3), an informed decision is made by physician and patient (just like in the rest of healthcare, though the study IRB will determine whether informed consent is needed).
Step 5: Follow patients through the protocol-specified period. The study protocol will specify what data to collect and when; a follow-up visit (or telemedicine) schedule. For randomized studies, these data will be collected on both study and comparison group patients. For nonrandomized studies, protocol determines what data to collect. Note, however, that for nonrandomized studies, untreated patients may come from those in the enrolled physicians’ practices who were clinically-appropriate for the study but declined the treatment, and/or from matched patients in non-enrolled practices.
Step 6: Analysis and reporting. Most protocols specify interim reporting to determine whether the study should be stopped prematurely because of safety issues or because primary endpoints were met, or to surface unanticipated design issues. All protocols specify what analyses must be done at study completion. Proper analysis of the various types of clinical trials and studies is beyond the scope of this series, but we’ve hinted at several considerations (and will, no doubt, continue to do so).
Information platform is an important factor to the success of your WDPS! You want to make it easy for physicians to qualify (apply study criteria, including collecting any additional data needed), inform, enroll (with consent if required) and track patients in your study. We’ll cover platform later in this series.
Data considerations for recruiting: Inclusion and exclusion criteria must be converted from plain English to algorithms in the ‘language’ of the data you’re using to recruit (e.g. healthplan, clinical or patient-reported), bearing in mind where you want to be on the sensitivity-specificity spectrum. Let’s illustrate this using healthplan data for our opening question; we’ll need:
For now, we’ll ignore qualifiers such as:
The protocol will specify all of the above in detail with code tables, algorithms and any timeframe requirements. Here’s a partial illustration using healthplan data (medical and pharmacy claims plus lab results data (4):
I understand that studies can fail or be delayed because of poor accrual (enrollment). How can we make it easier? This is a matter of process-and-technology platform, and we look forward to your joining us to dive into it next time. In the meantime, we invite your comments (below), or to reach out to us directly, HERE.
Closing thoughts | Summary