Here’s an interesting video on using Databricks to increase the efficiency of healthcare claim reimbursements.
ColdFusion highlights a recent advancement in bio-engineering which created something entirely new.
- Research Paper: https://www.pnas.org/content/early/2020/01/07/1910837117
- GitHub Link: https://github.com/skriegman/reconfigurable_organisms
In this Data Point, Frank shares his fascination with a new health tech device that has changed the game for his health.
Press the play button below to listen here or visit the show page at DataDriven.tv.
BBC Click visits a children’s hospital using a new low-cost portable ultrasound scanner and see the latest gadgets from Japan’s CEATEC expo.
Michael Hansen joins Scott Hanselman to explain what FHIR is and how to get started with FHIR on Azure. Fast Healthcare Interoperability Resources (or, FHIR) is a new standard for representing and exchanging healthcare data. Developed by the HL7 community to address problems with interoperability, pieces of healthcare data are represented as resources in FHIR (i.e, a patient is a resource, observation is a resource, etc.). Resources are healthcare data objects with properties (e.g., a patient has a name) and relationships. The FHIR specification also describes how to exchange these objects using a REST API. A FHIR server is a REST API that enables you to search, retrieve, modify, and delete healthcare data objects. Microsoft developed a first-party FHIR server, which is available as an open source project on GitHub and as a managed service, Azure API for FHIR.
Here’s an interesting blog blost co-authored by Shweta Mishra and Vinil Menon, both of CitiusTech. CitiusTech is a specialist provider of healthcare technology services which helps its customers to accelerate innovation in healthcare. CitiusTech used Azure Cosmos DB to simplify the real-time collection and movement of healthcare data from variety of sources in a secured manner.
With the proliferation of patient information from established and current sources, accompanied with scrupulous regulations, healthcare systems today are gradually shifting towards near real-time data integration.
The rise of Internet of Things (IoT) has enabled ordinary medical devices, wearables, traditional hospital deployed medical equipment to collect and share data. Within a wide area network (WAN), there are well defined standards and protocols, but with the ever increasing number of devices getting connected to the internet, there is a general lack of standards compliance and consistency of implementation. Moreover, data collation and generation from IoT enabled medical/mobile devices need specialized applications to cope with increasing volumes of data.
Siraj Raval has built and open sourced an app called Dr Source, your personal medical question answering service! It uses a model called BioBERT trained on over 700K Q&As from PubMed, HealthTap, and other health related websites.
He used Flutter to build an app around it and presents it to the world as a more thought out idea. There are millions of people in this world without access to healthcare, and while this app isn’t perfect, an automated diagnosis is better than no diagnosis.
In this video, the great and powerful Siraj Raval shows you how to build a healthcare startup with AI.
Here’s an interesting article on how machine learning can make our healthcare system more efficient and improve patient outcomes.
Health system leaders, whom Mr. Garg has spoken with, estimate more than half of their patient visits are unscheduled, a problem that is not well-understood or analyzed by those outside of the health care industry. While there are tools available to help manage patient information, he’s seen firsthand how staff are too busy to sort through dashboards and extrapolate how to best adapt to the ever-changing environment in front of them. This often results in a frustrated patient experience, exhausted care teams and operational inefficiencies.
AI is set to disrupt every field and every industry. Healthcare, in particular, seems primed for disruption. Here’s an interesting project out of Stanford.
“One of the really exciting things about computer vision is that it’s this powerful measuring tool,” said Yeung, who will be joining the faculty of Stanford’s department of biomedical data science this summer. “It can watch what’s happening in the hospital setting continuously, 24/7, and it never gets tired.”
Current methods for documenting patient movement are burdensome and ripe for human error, so this team is devising a new way that relies on computer vision technology similar to that in self-driving cars. Sensors in a hospital room capture patient motions as silhouette-like moving images, and a trained algorithm identifies the activity — whether a patient is being moved into or out of bed, for example, or into or out of a chair.