Ask consumers what matters when choosing a healthcare provider, and you'll probably hear a long list: quality of care, convenient locations, short wait times, easy scheduling, friendly staff, insurance coverage, strong communication, digital tools, and more.
If you ask people to rate these elements by importance, you may discover that almost everything is "very important." That's not necessarily wrong. But it isn't always helpful.
When organizations need to understand what consumers truly prioritize when they have to make tradeoffs, MaxDiff can provide a much clearer answer.
MaxDiff, also known as Best-Worst Scaling, is a quantitative research technique used to measure the relative importance or preference of a set of items.
Instead of asking respondents to rate every item individually, MaxDiff presents smaller groups of options and asks respondents to make a tradeoff: Which is the most important or appealing, and which is the least important or appealing? This exercise is repeated across different combinations of items, allowing researchers to understand which options consistently rise to the top and which fall to the bottom.
For example, imagine a healthcare provider wants to understand which messages are most compelling to potential patients.
Respondents might first see the following introduction:
Next, you will see several statements that a healthcare provider might use to describe its care and services. As you review them, please think about which would be more or less likely to appeal to you when choosing a healthcare provider.
The messages tested could include:
Care from doctors who take the time to listen and understand your needs
Access to leading specialists across a wide range of medical specialties
Convenient appointments that fit your schedule
Advanced treatments and technology for even the most complex conditions
High-quality care at locations close to home
A care team that coordinates your treatment every step of the way
Trusted expertise backed by the latest medical research
Compassionate care that treats you as a person, not just a patient
Rather than evaluating all eight messages at once, respondents would see a smaller subset in each task.
For example:
Which ONE of the following messages would be MOST appealing to you when choosing a healthcare provider, and which ONE would be LEAST appealing to you?
Respondents repeat this exercise across different combinations of messages. Behind the scenes, the experimental design ensures that each item is compared systematically to the others.
The result is a clear hierarchy of preferences, showing which messages have the greatest relative appeal and which are less compelling when respondents are forced to make tradeoffs.
That distinction is exactly what makes MaxDiff useful.
Traditional rating scales have an obvious limitation: people can say everything is important. An organization may learn that nearly every option matters, but still be left wondering where to focus.
MaxDiff forces respondents to make tradeoffs, revealing what matters most relative to the other options being considered. Rather than evaluating each item independently, respondents repeatedly identify the most and least important options from a series of smaller choice sets.
Research has found that MaxDiff often provides greater differentiation among items than traditional rating approaches while reducing some of the scale-use biases associated with ratings.
This makes it especially useful when organizations are trying to prioritize things like:
Brand messages
Product or service features
Patient experience improvements
Benefits or value propositions
Reasons for choosing a provider
Strategic investments
Essentially, MaxDiff helps answer the question:
If we can't do everything, what matters most?
MaxDiff results can be translated into preference or importance scores that make it easy to see how items rank relative to one another.
Rather than seeing every message receive a rating of "very important," researchers can identify clear leaders, a middle tier of moderately important options, and lower-priority items.
This helps organizations move beyond knowing that multiple things matter and instead understand which options rise to the top when consumers are forced to make tradeoffs.
One of the things we like about MaxDiff is that it more closely reflects the tradeoffs people face when making decisions.
While a MaxDiff exercise is still a structured survey task, it mirrors the reality that consumers are often balancing competing priorities rather than evaluating each option independently.
In real life, consumers rarely consider one factor in isolation.
MaxDiff introduces those kinds of tradeoffs into the research process.
It also takes advantage of something people tend to do well: identify extremes. Choosing the best and worst options from a small set can be easier than making fine distinctions across a long list of items that all seem somewhat important.
For that reason, MaxDiff fits naturally with behavioral science approaches that focus less on what consumers say matters in isolation and more on how priorities shift when options compete for attention.
Imagine a health system is planning its patient experience strategy for the next three years.
Leadership has identified 15 potential areas for investment, including shorter wait times, easier scheduling, improved facility navigation, expanded telehealth options, more convenient locations, clearer billing communication, more personalized follow-up, and enhanced digital tools.
Every initiative has internal advocates, and patients may tell you that almost all of them are important.
A MaxDiff exercise changes the conversation.
Patients repeatedly choose which improvements would make the biggest difference to their experience and which would make the least. The health system can then see a clear hierarchy of priorities and explore how those priorities vary across patient populations.
Maybe younger patients place greater value on digital scheduling, while patients managing chronic conditions prioritize communication and continuity of care. Maybe reducing appointment wait times emerges as a much stronger priority than nearly every other improvement on the list.
Those findings provide leadership with something far more actionable than a collection of high ratings and help guide investments toward the areas most likely to improve the patient experience.
MaxDiff isn't the right methodology for every research question. But when an organization has a long list of ideas and needs to understand what deserves attention first, it can be incredibly powerful.
For healthcare organizations balancing patient expectations against very real budget, staffing, and operational constraints, that distinction can make research far more useful.
Because sometimes the most valuable insight isn't learning whether something matters.
It's learning how much it matters, relative to everything else.
Sawtooth Software. What Is MaxDiff?
Sawtooth Software. Creating a MaxDiff.
Schuster, A.L.R., et al. (2023). The rise of best-worst scaling for prioritization: A transdisciplinary literature review. Journal of Choice Modelling.
Hollin, I.L., et al. (2022). Best–Worst Scaling and the Prioritization of Objects in Health: A Systematic Review. PharmacoEconomics.