A biopharma preparing to launch a new treatment for osteoarthritis wanted to dissect the OA treatment algorithm in order to properly position their product. They also needed to understand how patient presentation interacts with physician beliefs about treatment options to drive physician decision-making. To address these issues, they utilized the InTask software to support in-depth qualitative interviews with primary care physicians, rheumatologists and orthopedic surgeons
In preparation for this research, the InTask design team programmed an osteoarthritis patient simulator for iPad, incorporating a range of patient types to probe on potential opportunities for their product across the patient journey. Rich media such as radiographs were included in the simulation to add realism and stimulate deeper discussion.
Availability of the InTask platform allowed the interviewer to employ cognitive interviewing (also known as “think out loud”), a proven approach long used in education and user experience research, but difficult to implement with physicians because it requires the respondent to engage in the actual decision-making task while describing their thought processes. The InTask platform addressed this problem by presenting the physician respondent with realistic, simulated patient interactions. In contrast to chart reviews in which the outcome is known and there is a bias toward rationalizing past actions, the InTask simulation created a forward-looking respondent mindset. With outcomes uncertain, physicians were forced to consider multiple future scenarios. This led to richer and more realistic discussions around treatment decisions.
After 24 in-depth interviews utilizing the InTask platform, all research objectives were met, and the biopharma commercial team gained key insights into some of the complexity which lies beneath a deceptively simple osteoarthritis treatment algorithm. In the words of the marketing lead overseeing this project, "We were able to truly get ‘into the heads’ of physicians with the simulation model to reveal patterns of treatment and rationales we would not have otherwise uncovered.”