Healthcare Analytics

The Big Data and Business Analytics are fore frontier of technologies that are generating huge competitive advantage to the companies. Big Data Analytics are playing an important role in developing new innovative products that are enabling companies to engage their customers in a new and disruptive manner.

According to McKinsey Global Institute Report "Big data: The next frontier for innovation, competition, and productivity" - Big Data Capturing its value: $300 billion potential annual value to US health care and $600 billion potential annual consumer surplus from using personal location data globally. Healthcare reaps huge benefits from Big Data Analytics.

We specialize in developing Big Data Healthcare Analytics products. Our first Big Data Healthcare Product, Murai Data Analytics helped save healthcare device makers IT deployment costs in the range of 25% to 40% annually. We have applied Big Data Analytics to Electronic Health Records (EHR) and able to predict disease management (working in collaboration with Indian Council for Medical Research (ICMR) and Health Account Scheme).

Gold Winner
Winner of 2018 IEEE Big Data Cup - FEMH Voice Challenge
Developed Neural Networks that has predicted with 90% accuracy pathological issues in voice detection. The Voice samples were obtained from a voice clinic in a tertiary teaching hospital (Far Eastern Memorial Hospital, FEMH), which included 50 normal voice samples and 150 samples of common voice disorders, including vocal nodules, polyps, and cysts (collectively referred to Phonotrauma); glottis neoplasm; unilateral vocal paralysis. Voice samples of a 3-second sustained vowel sound /a:/ were recorded at a comfortable level of loudness, with a microphone-to-mouth distance of approximately 15–20 cm, using a high-quality microphone (Model: SM58, SHURE, IL), with a digital amplifier (Model: X2u, SHURE) under a background noise level between 40 and 45 dBA. The sampling rate was 44,100 Hz with a 16-bit resolution, and data were saved in an uncompressed .wav format.

Sensor EHR Analytics

Mobile is increasingly ubiquitous. With 6.8 billion mobile subscriptions world wide, access anytime, anywhere through smart gadgets is now putting cheap and connected, mobile computing power in the hands of millions of consumers and healthcare practitioners. Mobile Sensors – accelerometers, location detection, wireless connectivity and cameras– offer another big step towards closing the feedback loop in personalized medicine. There is no more personal data than on-the-body or in-the-body sensors. With so many connected devices generating various vital health related data, integrating these data points to Electronic Health Records not only provide more accurate picture of the patient but also helps to connect with the doctor. In this paper, we propose Sensor integration framework with Electronic Health Records (EHR).We have successfully integrated Mobile Sensor ecosystem with Electronic Health Records to provide location and patient context information to the healthcare professionals. The integration, importantly, not only helped doctors to deliver better care to patients but also enabled patients to manage thier health and disease monitoring.

Healthcare Analytics Platform

Analytics platform consists of two core engines: Healthcare Machine Learning and Patient Recommendation Engine


Preventive Healthcare

As part of Sanjeevani Electronic Health Records Cloud (EHR), we collect health records data from individual users. The data include: individual doctor visits data, medications data, immunization data, allergies, doctor notes and medical tests data. We correlate User health records data to fitness activities and update EHR real-time. Our EHR, importantly, correlates individual heath records to family history, geo location data, user demographics data, World heath organization (WHO) identified key parameters data, user behavior data (fitness & food in-take) to recommend exclusive & actionable insights. For instance, a user with sedentary life style (due to professional nature or personal life-style choices), the impact of low physical activities on user’s health.

Fig3. - Healthcare Analytics Platform

We provide the user , importantly, the collective intelligence of the users with similar demographics and their respective health trends through the application of collaborative recommendation. The collaborative recommendation provides comparative snapshot view between users EHR data to similar demographics in following areas: health trends, doctor notes, medication usage, allergies and food-nutrition. Sanjeevani provides preventive healthcare actionable insights to users.

We apply Sanjeevani built-in preventive machine learning models on user demographics and user location data to deliver preventive health recommendations to users. For instance, a user with Asthma will get preventive healthcare notification when Sanjeevani Cloud detects change in pollution levels that would adversely affect the asthma user.

We also mine, importantly, social media to alert user when asthma related epidemiological conditions occur near users location. Sanjeevani Healthcare Cloud glean through demographics healthcare data to recommend, importantly, preventive health recommendation of the users with similar demographics that learn good health learning process over the time.

Fig4. - Healthcare Analytics & Recommendation Platform
request a demo