May 12 2022 07:05 AM
May 12 2022 07:05 AM
Written by Trent Norris, HLS Cloud and Data Partner Alliances
The healthcare industry has come a long way from putting pen to paper on a pharmacy script or clinical SOAP note to now, being able to deliver primary care in the emerging hospital at home. My career in the healthcare and life sciences (HLS) industry has spanned different roles including: a military clinician, life science entrepreneur, clinical research application scientist, and business leader. Currently, I head the Partner Alliances team for Microsoft’s global health and Life sciences Cloud and Data engineering and product group. Today, I consider myself an HLS generalist bridging the gap between engineering and the application of it in the wild. I look forward to continuing to listen to the needs, implement solutions, and partner with others to bring forward meaningful change in healthcare.
Last month, we launched Azure Health Data Services, a platform as a service (PaaS) offering designed exclusively to support Protected Health Information (PHI) in the cloud, built on the global open standards Fast Healthcare Interoperability Resources (FHIR)® and Digital Imaging Communications in Medicine (DICOM). Watching the team work to develop this product, I feel compelled to share how intentional our product team is at building healthcare technologies for an industry that is currently experiencing historically unprecedented transformation. We are deploying technology that can ingest, transform, and persist data, allowing our customers to use their data to span workflows from discovery research to clinical end points.1 The underlying technology enables our customers to engage in activities ranging from novel biomarker identification to virtual clinical decision support. For example, today our customers can combine cellular assay data, pathology data, molecular imaging, genomics, handwritten, voice, and text derived notes. With so much data, the goal is to enable our customers to derive insights from a single system of record, so that they can optimize the user experience for patient, research and clinical workflows so that adherence to treatment increases, scientists gain faster contextual evidence to support their early discoveries and clinicians can spend more time focused on delivering healthcare without experiencing burnout and information overload. The bottom line is, when you can bring these data sets together in a meaningful way, you inherently increase your signal to noise ratio since you are no longer looking for a needle in a haystack; you are looking for a book in a library.