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Implementing effective Population Health Management takes more than just Analytics

Effective implementation of a Pophealth Management Program (PHM) requires diverse systems that aid in seamless functioning. For example, an effective analytical platform for service providers helps in diagnosing high-risk patients and deploying preventive care. Adoption rate of  best healthcare practices should be high for effective Population Health Management. Dynamic healthcare organizations are able to implement this change with ease.

5 lessons learned while assisting Healthcare startups

In the first 4 years of operations, we had worked with a Population health product which was pivoted from a Wellness product, a Patient Management product, and a Health Management platform. In the last 18 months, we have worked/ working on two TeleHealth products, another Population health, a Behavioral Health, and two Health Management products.


healthcare startups


Using Machine Learning for Resource Allocation in Healthcare

Desperate diseases seek desperate remedies. It now emphasizes urgency to deal with this healthcare scenario considering the seemingly insurmountable- resource allocation challenges in healthcare - triggering extraordinary measures to surmount it. Whether it is about grappling with the unknowns or dealing the demand vs supply imbalance, healthcare organizations are using machine learning for resource allocation in healthcare.

How do healthcare organizations use machine learning for resource allocation in healthcare?

Health Care Industry top trends For the Year 2020

As we are entering 2020 which completes first 20 years of 2000 era, it is time to look at the crystal ball and predict what lies ahead in 2020 for healthcare. As of now since 2000, Healthcare Industry has gone through many changes in terms of regulations, technologies and automation. Few new concepts surfaced on the floor like, Remote care, Interoperability (FHIR), AI, Machine Learning, Augmented reality, IoT, Robots and list is just keep growing. Few standards evolved which made patient-data interchangeability possible and help physicians to a greater extent.

Helping organizations identify cancer types using deep learning techniques

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Client Overview

Leading Health Tech company

Business Challenge

  • Process massive quantities of medical images to classify cancer type
  • Achieve maximum accuracy without any human intervention
  • Enable ease of access via Mobile, Web
  • Integrate it into APP and use camera to detect what category it is

Special care for patients through secure applications and data!

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Client overview

The Client is a worldwide genetic testing and diagnostics company that’s changing how doctors and patients manage genetic disease. Our team includes scientists, biostatisticians, researchers, and talented laboratory professionals from around the globe.

Business Challenge

3 Things to Consider to Leverage Patient Data Collection in Healthcare

Today, patient data can be acquired from various sources, which in turn poses challenges for healthcare organizations in terms of consolidating patient data. It becomes even more challenging considering the different forms as well as types of data encompassing flat files, databases and images among other types.

Overcoming 3 Key EHR implementation Challenges

Instant access is a term viewed with reverence in the Healthcare world, for instant access to medical care can mean a lot to patients. And with the electronic health record (EHR), digital version pertaining to a patient’s paper chart, making patient information instantly available, patient outcomes are being transformed for the better.

With the EHR systems allowing easy access to patient data, streamlining provider workflow, more and more providers want to use EHR implementation towards transforming patient engagement and outcomes.  

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