Targeting ≠ Feasibility: Why Some Healthcare Surveys Miss the Mark

Healthcare Surveys can be challenging to field

Key Points:

• Targeting the right audience does not guarantee a feasible survey: feasibility depends on universe size, response rate, and qualification rate.

• Misjudging any of these variables can result in delayed timelines, inflated costs, or compromised data quality.

• Strong feasibility modeling helps teams scope accurately and deliver confident, decision-ready insights.

When Targeting Isn’t Enough

In consulting-led healthcare research, targeting usually begins with a simple request: “We need 100 hospital pharmacists” or “Let’s reach 75 Medicaid plan decision-makers.” These targeting goals are valid; but they do not always reflect what is feasible.

Targeting defines who you want. Feasibility defines whether you can actually reach and qualify them at scale.

Too often, feasibility is treated as an afterthought: something to sort out once the survey is already in motion. But ignoring feasibility at the planning stage creates friction later. Surveys stall; costs rise; timelines shift. And most importantly: insights lose value when respondent quality or sample size falls short of expectations.

Universe Size: How Big Is the Real Audience?

Universe size refers to the total number of professionals who match your base-level criteria. In healthcare, this number is rarely as large as it seems.

For example: there may be thousands of U.S. oncologists. But when your study requires them to be community-based, prescribe biosimilars, operate outside academic institutions, and manage a specific patient mix, your addressable population becomes much smaller.

The more specific your targeting, the more important it becomes to validate that the audience exists in meaningful numbers. Without that validation, N=100 may be aspirational rather than attainable.

Response Rate: Will They Engage?

Even if a well-defined audience exists, that does not guarantee participation. Healthcare professionals are busy; many do not regularly engage in survey research. For those who do, their willingness to respond depends on timing, topic, format, and trust.

Response rate is not a fixed number. It is shaped by multiple factors:

• The seniority and bandwidth of the target group

• The clarity and professionalism of the survey invitation

• The device experience: whether the survey is mobile-accessible and user-friendly

• The perceived value of the honoraria

Ignoring these dynamics leads to underperformance. Studies assume a 10 percent response rate, only to see 2 percent engagement - because the experience did not match the expectations of the audience.

Qualification Rate: Will They Pass the Screener?

Qualification rate (or incidence rate, or IR%) - is the percentage of respondents who meet all criteria after beginning the survey. This variable is frequently underestimated, especially in healthcare.

Each added screener filter reduces the likelihood of qualification. Criteria related to setting, patient volume, prescribing behavior, and institutional role may all be necessary - but they also compound.

For example: if you start with a universe of 1,000 and anticipate a 50 percent qualification rate, you may expect 500 completes. But if the actual incidence rate is 20 percent, only 200 are eligible - and only a fraction will complete the survey.

Overestimating qualification rate often results in scrambling mid-field: expanding quotas, adjusting budgets, or extending timelines. These are preventable issues with the right modeling upfront.

When These Variables Are Overlooked

Projects that overlook universe size, response rate, or qualification rate often encounter the same friction points:

• Surveys go live, but completes come in slowly

• Quotas are missed, even with strong targeting

• Incentive costs increase without improving data quality

• Stakeholders lose confidence in the research

These issues are not random: they are the result of feasibility being treated as an assumption rather than a calculation.

How We Help Our Clients Avoid This

At Medical Mile, we believe feasibility should be a data-informed conversation from day one. For each project, we provide:

• A modeled estimate of reachable universe based on verified data

• Expected response rate calibrated to audience, method, and experience

• Estimated qualification rate based on targeting logic and historical benchmarks

• Timeline expectations based on real-world engagement - not guesses

This process helps our clients set accurate goals, avoid mid-field surprises, and stay focused on insights that drive action.

If your team is building a study and needs a feasibility check, or just wants a second opinion on universe size, incidence rate, or timeline, we’re happy to help.

👉 Contact us here