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Research Methodology Podcasts

Pilot Studies

Pilot study is the most problematic term, because there’s so many people who will tell you that any small, badly designed study that leads to no viable conclusions, that’s a pilot study. Any that’s not what a pilot study is. The word pilot is to pilot study, is the same as pilot in pilot boat. A pilot boat is a very small boat that goes out and it leads a huge ship safely into harbor. So the huge ship could be an aircraft carrier, or an oiler, an oil tanker, or it could be a cruise ship. The pilot boat cannot do the function of the big ship, but it’s an absolutely essential leading instrument.

And even if we’ve designed the big ship before we start the pilot study, chances are, when you start working with real patients under real circumstances, you’re going to find that some of the things you planned to do, you can’t. Or some of the things that you planned to do are not ideal. And a pilot study gives you a chance to tweak the design of your big study so you aren’t going to have to deal with these problems after the study starts when it would really be dangerous.

To learn more from Dr. Helena Kraemer listen to the podcast episode below.

Pilot Studies Part 1

Pilot Studies Part 2

 

Click here for more Research Methodology Podcasts

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Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).