My Clinician Interview Guide

The clinician interview guide that I used across 18 interviews for my MPH capstone (link here) for UC Berkeley’s School of Public Health.

Visit my Building Trust and Adoption in Machine Learning in Healthcare site (link here) for the abridge and full version of my MPH capstone as well as upcoming summaries of interviews and additional research.

Context

I am a dual degree MPH/MBA grad student at UC Berkeley, focusing on machine learning in healthcare. I am working on my Master’s Capstone / Thesis on how machine learning, or ML, might be used in assisting clinicians in screening, population health triage, diagnostics, and/or monitoring. I am interviewing a diverse set of clinicians — physicians, NPs, RNs, and others — from diverse backgrounds — across the country, different specialties, ranges of tenure, and different levels of interest or adoption of ML tools.

Your name associated with the insights from this interview will be kept private amongst myself and my readers. I may ultimately publish select summaries of my interviews, but those will be de-identified and approved by the interviewee prior to distribution. Once my research is complete, I would love to share the final product with you. Do you have any questions on process or expectations?

Familiarity with ML in healthcare

• To start off, what have you heard about artificial intelligence and/or machine learning in healthcare?

Past and future use

• Have you used any ML tools? Would you?

Excitement and concerns

• What about ML in healthcare is concerning or exciting for you? What else is exciting or concerning for you?

Ethics and privacy

• Where do ethics play into this? What could go wrong, or what could be done well?
• How does privacy fit into all of this?
• How should the data be used? Who should or should not have access to it?
• Who else should help inform you or decide for you if an ML tool is ethical and sufficiently private?
• Do you trust these ML tool developers to have access to these data? Why or why not?

ML knowledge and model explainability

• At what level do you need to understand how the model makes its prediction?

External validation needs

• For you to be willing to use an ML tool, what external validation would you need to see? What types of government and/or non-government institutions would play a role?

Clinical education

• How would clinical education be impacted?

Desired use cases

• Where are there opportunities to assist clinicians with ML? Imagine this: A world-class technology company, developed an ML tool that suggests possible diagnoses or triages a patient population. What is the best thing for them to build now and why?

Implementation

• When an ML tool gets implemented, how should that be done? Who should have access first; who should not?

Buying process

• Where you practice medicine, who all would be involved in making the decision to purchase and use an ML tool?
• What data, references, and promises would they need to learn about to ultimately say yes or no?