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My previous post discussed Challenges to doing ML in healthcare. This one suggests ways to apply AI in that overcome these challenges to realize the potential of AI in the real “healthcare” world. Strategically Choose use cases well, and design to get quick wins to prove ROI early. You will likely encounter a lot of resistance to … Continue reading Applying AI in real “healthcare” world
A number of trends have paved the way for increasing adoption of machine learning (ML) in healthcare. We’re capturing more volume and types of health data than ever. Software for ML are evolving fast. Hardware advances have made the computing power cheaper, more agile, modular and scaleable than ever. However, despite these significant advances, adoption … Continue reading Challenges to applying ML in healthcare
Some of you may remember the 1998 movie The Horse Whisperer. In it, Robert Redford’s character, who had a remarkable talent to understand horses, was able to help a horse and its owner overcome fears and recover from a tragic accident. It’s a lovely slow paced movie, well worth enjoying a popcorn over 🙂 In … Continue reading A health data whisperer’s tricks
Health insurance is primarily in the business of receiving premium from policyholders and paying for their medical expenditure. To do that, the health insurance companies need to ensure that the premium charged is sufficient to pay for these medical and administrative costs. Analysts new to health insurance might find these concepts difficult to grasp. Some … Continue reading Health Insurance Analytics Metrics
EHRs… ahhh, love them, hate them. If you do any work in the medical space, you will need to interact with them. In this post, I talk about what they are, what they do, their strengths and weaknesses. What are they? EHRs (electronic health records) are digital records of patient’s clinical records. EHR platforms are software platforms … Continue reading EHRs, what’s all the fuss about?
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