CHBase is Get Real Health’s powerful data aggregation and application development platform that combines clinical data and personal health data for a 360° view of a patient’s health. A development platform to connect the world of patient data and it has the following features: Allows patients to contribute data from their favorite apps and home health devices while retrieving clinical data from providers. Data can be pulled into patient-oriented health applications or population health management and customer analytics systems.
Model Library Access to a library of pre-trained cost prediction models for healthcare use cases. Quickly get access to high-performing predictions to support your organizational needs. Custom Models Highly customizable models designed to help customers achieve their machine learning tasks. Predictions are easily accessible via APIs or Lumiata’s user-facing application. ML Model Toolkit Self-service tools to quickly build machine learning models without deep Data Science expertise. Automatically develop model features, generate high-performing models, and fine tune your models using relevant parameters.
Doc.Ai puts the power of your health data where it belongs - in your hands to unlock personalized health insights and accelerate medical research for all. Your health in one place From the daily step activity in your Apple Health Kit, to the lab-work that exists in hospitals, you can conveniently view siloed health data in one transparent and secure location. Predict what can be prevented Join crowd-backed research to unlock personalized health predictions, with an assurance that your unique circumstances have been taken into account.
Healthcare data analytics ranges from basic statistical summaries of data and inferences to advanced predictive models. These models help to gain insight into disease progression and they support of the process of selecting the right treatment for individual patients. Statistical methods used include: Prediction Quantitative analysis Pattern matching Structured data analysis Identification of abnormal patterns Processing large time-series data Regression analysis Unsupervised / supervised learning Descriptive / inferential statistics Risk stratification Statistical analysis of large historical One factor that these methods have in common is that they reveal patterns in data, and from these patterns, inferences can be made to support decisions.
MedicaSoft’s reporting and analytics capabilities help organizations deliver performance and accountability reporting while creating new data driven insights and business opportunities. Our platform will help save you valuable time and resources, optimize your entitled reimbursements, and support your future business decisions with trusted, actionable, quality data. REAL-TIME DATA ANALYSIS Deliver information when and where you need it to optimize your workflows and maximize your revenue streams. CLOUD-BASED & SECURE Cloud-based solutions that securely store and manage all of your data so you don’t have to worry about data loss or breaches.
The Most Comprehensive Solution to Mitigate Social Determinants Understanding the factors that affect your population health can be tricky. With Innovaccer, help your patients find community services, track social needs, and coordinate care across the continuum to radically improve the health of the people you serve. Features: CURATE Collect a number of insights into your patients’ education, economic situation, living conditions, and more. Additionally, the multiple language support ensures that there are fewer barriers between care teams and their patients.
Defining Predictive Analytics in Healthcare Predictive analytics and machine learning in healthcare are rapidly becoming some of the most-discussed, perhaps most-hyped topics in healthcare analytics. Machine learning is a well-studied discipline with a long history of success in many industries. Healthcare can learn valuable lessons from this previous success to jumpstart the utility of predictive analytics for improving patient care, chronic disease management, hospital administration, and supply chain efficiencies. The opportunity that currently exists for healthcare systems is to define what “predictive analytics” means to them and how can it be used most effectively to make improvements.
Features: Sends HL7 result and query messages. Receives HL7 acknowledgements and HL7 query response. Can be configured to send message length and size encoding prior to sending actual HL7 message. Automatically re-send message if no acknowledgement is received or remove message out of the queue. Can be configured to send non-HL7 message via TCP/IP user-defined protocols such as XML documents, Words documents, PDF documents, etc.
Features: Sends and receives HL7 messages via HL7’s Minimal Lower Layer Protocol (MLLP) over TCP/IP. Supports non-standard, custom and user-defined headers and trailers. HL7 Messages can be filtered by sending facilities, message types or by any of HL7 segment’s fields. Listens or sends on any port number, LAN, VPN. Error log and detail log file for interface trouble shooting. Accepts all HL7 message types and automatically validates and sends standard HL7 message acknowledgements back to client(s).
Plug & Play HL7 TCP/IP Communication Drivers Benefits: Latest generation software with proven track records of sucess, virtually trouble free. Plug & Play communication drivers. No development work, save time and resource of your organization by eliminate the need to do programming. The LINKMED® IE HL7 TCP/IP drivers are approximately 500 KB each. This makes them easy to download and install from a remote location. These HL7 TCP/IP drivers are efficient and less resource intensive.