Predictive Healthcare Analytics Platform

Predictive Healthcare Analytics Platform

Predictive Healthcare Analytics Platform and solutions is a part of advanced analytics that is used to predict future events. Predictive analytics practices many ways from data mining, statistics, modeling, machine learning, and artificial intelligence to examine current data to make forecasts about the future.

Good healthcare boosts the economy of the nation. Precision medicine along with Big Data is leveraging in building better patient profiles as well as predictive models to diagnose and treat diseases.
TeleMedicine and AI in healthcare is indeed a miracle remotely performing treatment of patients using Pattern Recognition, optimizing duty allocation, monitoring live data.
Real-Time Big Data for Infection Control to predict and prevent infections through networks creating safer environments.
Patient Data Analytics for a patient dealing and preventing readmissions and better pharmaceutical supply chain management and delivery.

Challenges for Building Predictive Analytics Platform

Interface for the patient to search nearby doctors by particular Healthcare categories.
Enable patient visibility to see doctor’s availability online and communicate via text chat, audio or video call.
Visible allotment number to the patient in the waiting queue.
Communicate with the doctor as well as test or medicine suggestions to the patient.
Interface for the patient to contact nearby labs to collect a sample and upload test reports on the server followed by the push notification when the report is ready.
Share report with a doctor followed by a prescription to the patient.
Search for nearby medical stores and place an order for the prescription got from the doctor.

Solution Offerings for Real-Time Monitoring

Develop a Healthcare platform to fully automate using the latest technologies and distributed Agile development methods.

Real-Time Monitoring of User’s Events

Apache Kafka & Spark Streaming to achieve high concurrency, set up low latency messaging platform Apache Kafka to receive Real-Time user requests from REST APIs (acting as Kafka producer).

Apache Spark Streaming (processing and Computing engine) Spark-Cassandra connector, stored 1 million events per second in Cassandra. Built Analytics Data Pipeline using Kafka and Spark Streaming to capture user’s clicks, cookies, and other data to know users better.

Microservices using Spring Cloud, NetFlix OSS, Consul, Docker, and Kubernetes

Develop REST API’s using Microservices architecture with Spring Cloud and Spring Boot Framework using Java language. Moreover, use Async support of the Spring framework to create Async controllers that make REST API easily scalable.

Spring to deploy REST and use Kubernetes for secure containers and their management. For API gateway, use NetFlix Eureka Server which acts as a proxy for REST API and the lot of Microservices, Consul as DNS enables auto-discovery of Microservices.

Source: XenonStack/Use-Cases

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Thursday, 9 March 2017

healthcare technology 3/09/2017 11:23:00 am

Healthcare is Drowning in Data, Thirst For Knowledge

The Amount of Data in Healthcare is increasing at an astonishing rate. However, in general, the industry has not deployed the level of data management and analysis necessary to make use of those data.

As a result, healthcare executives face the risk of being overwhelmed by a flood of unusable data.

Consider the many sources of data. Current medical technology makes it possible to scan a single organ in 1 second and complete a full-body scan in roughly 60 seconds. The result is nearly 10 GB of raw image data delivered to a hospital’s Picture Archive and Communications System (PACS).

Clinical areas in their digital infancy, such as pathology, proteomics, and genomics, which are the key to personalized medicine, can generate over 2TB of data per patient.

Add to that the research and development of advanced medical compounds and devices, which generate terabytes over their lengthy development, testing and approval processes.

Doctors Are Drowning In Data

Technology isn't enough to improve healthcare. Doctors must be able to distinguish between valuable data and information overload.

One of the hopes of Electronic Health Records (EHRs) is that they will revolutionize medicine by collecting information that can be used to improve how we provide care. Getting good data from EHRs can occur if good data is input.

This doesn't always happen. To see patients, document encounters, enter smoking status, create coded problems lists, update medication lists, e-prescribe medications, order tests, find, open, and review multiple prior notes, schedule follow-up appointments, search for SNOWMED codes, search for ICD-9 codes, and find CPT codes to bill encounters(tasks previously delegated to a number of people) and compassionately interact with patients, providers have to take shortcuts.

But We have to Say HealthCare Drowning in Data Elements not yet interoperable onto one Platform.

First, the Data Exchange and Interoperability between EMRs, HIEs, Hospitals, Nursing Homes, Home care, ERs, portals, etc., must be addressed and industry standards need to emerge on the technology, but also the costs need to be defined. Who is going to pay for what and when?

It seems like the deepest pockets in the industry – pharmaceuticals and insurance – have put a dime into technology solutions or Big Data. Yet they have the most to gain. This is a huge disconnect because physicians and hospitals cannot afford to capitalize this start up by ourselves.

I believe that they will need to be influenced to contribute to this effort, in kind or with cash, for this system to be made whole and meaningful.

HIT industry leaders need to sit down with busy clinicians to create a workflow of automated Big Data in a way that provides all the stakeholders with the data to improve all levels of efficiencies and outcomes.

Decisions Through Data-Small data, Predictive modeling expansion, and real-time analytics are three forms of data analytics.

Healthcare data will continue to accumulate rapidly. If practices, hospitals, and healthcare systems do not actively respond to the flood of unstructured data, they risk forgoing the opportunity to use these data in managing their operations.

Small data and Real-Time Analytics are two methods of data analytics that allow practices, hospitals, and healthcare organizations to extract meaningful information.

Predictive Modeling is best suited for organizations managing large patient populations. With all three methods, the applicable information mined from raw data supports improvements in the quality of care and cost efficiency.

The use of Small Data, Real-Time Analytics, and Predictive Modeling will revolutionize the healthcare field by increasing those opportunities beyond reacting to emerging problems.

About RayCare:

RayCare is an Integrated HealthCare Platform Starting From Connecting Doctors, Labs, Medicine, Dieticians and Get Healthy Life Tips to Creation of Health Profile, Medical Reports, Daily Health Tracking to Predictive Diagnostic Analytics and Second Option Consultation & Recommendations. Know More!

For More, Visit - XenonStack.com/Blog

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