How to Make Better Decisions: Combining Analytics, Machine Learning, and Decision Support to Improve the Laboratory's Value
Speaker: Dr. Anand S. Dighe, MD, PhD
When: Wednesday 17th January 2024 8.00 am to 9.00 am
Where: Online.
Supported by APFCB Working Group on Clinical Decision Support
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Anand
S. Dighe, MD, PhD
Deputy Chief for Clinical Operations, Mass General Brigham Pathology
Clinical Lead, Pathology, Mass General Brigham Digital
Associate Professor of Pathology, Harvard Medical School
Boston, MA USA
Biography
Dr. Dighe is an Associate Professor at Harvard Medical School. Dr. Dighe received degrees in Biology and Chemistry from the Massachusetts Institute of Technology (MIT), and M.D. and Ph.D. degrees from Washington University in St. Louis. He completed a residency in Laboratory Medicine and training in Clinical Informatics. He is currently Deputy Chief of Clinical Operations for the Mass General Brigham healthcare system, a group of 16 hospitals anchored by two large academic medical centers, Mass General Hospital and Brigham and Women’s hospital. Dr. Dighe oversees an operation that performs over 70 million laboratory tests per year and has over 2,800 staff. He additionally serves as the Digital Health Clinical Lead for Pathology at Mass General Brigham.
Lecture summary
How to Make Better Decisions: Combining Analytics, Machine Learning, and Decision Support to Improve the Laboratory's Value
Data analytics, clinical data science and Clinical Decision Support (CDS) are rapidly becoming essential parts of the laboratory toolbox. This presentation will demonstrate how labs are harnessing the power of these tools to transform their operations, improve laboratory utilization, reduce costs, and enhance patient care. First, the presentation will explore the key foundational concepts that underlie the application of decision support throughout the laboratory testing process. Then it will be demonstrated how labs are combining the power of analytics and decision support to drive change and improve outcomes. Further, it will be shown how advanced techniques such as machine learning are a natural fit for making sense of the complexity of patient laboratory testing and developing algorithms that improve patient care. Practical approaches for the integration of machine learning and other advanced tools into clinical laboratory workflows will be detailed.