Despite its unique challenges, the healthcare industry is adopting data science at scale to drive core business decisions and solve problems in diagnostics, operations, clinical trials, patient care, and more.
Empathy is a vital expertise for data scientists in healthcare so they can accurately identify, assess, and mitigate biases in technology and algorithms before they affect patients.
Alignment on a shared vision and increasing collaboration and communication between departments is key to succeeding at in large matrixed organizations.
Data literacy goes both ways in an organization. Data scientists need business literacy to understand how a clinician is inputting data and how they're interacting with an EMR system, or how on the insurance side, a care manager is identifying and reaching out to insured patients to help them coordinate their care and manage a chronic disease. Data scientists have to understand how that data comes in. Conversely, if data scientists show the value of the data to those delivering care, that part of the healthcare ecosystem is going to see the value and be able to work with them.
I'm really excited about the capabilities that are evolving around fairness, both being able to detect bias in the algorithm, and fixing that on the fly and at scale. It will empower data science, AI, and machine learning in healthcare, and it brings value to patients because we can make sure they're getting quality care that is fair. We're considering things that maybe we haven't been great at in the past and maybe this can make medicine, or any field within it, better.
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