Friday, January 17, 2020

Data revolution in Healthcare CES 2020 Highlights

CES 2020 Highlights
From #Samsung NEON to Impossible pork! Check out the coolest and weirdest new fitness gadgets at CES 2020 #digitalhealthCES
Big shout out to the Influencers.
#ces2020 #digitalhealth #publichealth #healthtechnology

International Finance Corporation, World Bank & TechEmerge innovations in Health

If you have been following my posts, you know that in 2016 IFC launched a unique health innovations acceleration program called "TechEmerge". It is a World Bank initiative to Accelerate the Adoption of Health Technology Innovations in Emerging Markets. The first program took place in India, Brazil and will be launched later this year in East Africa. 

I worked as a consultant and Advisor to the program to shortlist innovations, review pilot proposals, bring hospitals onboard and launch pilot projects. I also held webinars and mentoring sessions with startups to help them understand business models, market opportunities and challenges. You can see some info sessions on this link:
More on

An Open Call for #TechEmergeHealth was announced today at #CES2020 as part of @The World Bank and @CES #GlobalTechChallenge.
Take your company to an emerging market and improve healthcare services in East Africa region! 🌍
Apply now here 👇🏾!!! #technology #innovation #digitaltransformation #eastafrica

IOT in Healthcare #digitalhealth

The healthcare industry is in a unique position to take advantage of the digital revolution, since healthcare’s goals align naturally with the promises of digital transformation to better leverage data, improve customer-centric services and reduce costs. However, pursuing these promises requires balancing efforts across complex and competing demands, and contending with seismic industry and technology shifts. The Internet of Things is redefining healthcare as we know it. We’re moving on to a whole new level when it comes to the way that apps, devices, and people interact when delivering healthcare solutions. IoT has given us a fresh outlook as new tools that accommodate an integrated healthcare network, subsequently, the care that is provided is of a higher standard. The use of IoT in healthcare allows for the automation of processes that have previously taken time; these processes previously allowed human error. For example, nowadays many hospitals use connected devices to control the airflow and temperature in operating theatres. hashtaghealthtechnology hashtagdigitalhealthces hashtagdigitalhealth What is your IOT Story?

HealthCursor Consulting Group at Arab Health 2020

I am traveling to Dubai for Arab Health, Dubai, UAE between 27th Jan to 30th Jan 2020. In case you would like to meet, please drop me a line with a brief agenda. Looking forward to meeting companies interested in #digitaltransformation #digitalhealth #healthtechnology #publichealth #iotsolutions #healthcareinnovations

Thursday, June 27, 2019

Deep Learning for Cancer

Image Credit: MIT
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks.MIT’s Computer Science and Artificial Intelligence Lab has developed a new deep learning-based AI prediction model that can anticipate the development of breast cancer up to five years in advance. Researchers working on the product also recognized that other similar projects have often had inherent bias because they were based overwhelmingly on white patient populations, and specifically designed their own model so that it is informed by “more equitable” data that ensures it’s “equally accurate for white and black women.”

That’s key, MIT notes in a blog post, because black women are more than 42 percent more likely than white women to die from breast cancer, and one contributing factor could be that they aren’t as well-served by current early detection techniques. MIT says that its work in developing this technique was aimed specifically at making the assessment of health risks of this nature more accurate for minorities, who are often not well represented in development of deep learning models. The issue of algorithmic bias is a focus of a lot of industry research and even newer products forthcoming from technology companies working on deploying AI in the field.

This MIT tool, which is trained on mammograms and patient outcomes (eventual development of cancer being the key one) from over 60,000 patients (with over 90,000 mammograms total) from the Massachusetts General Hospital, starts from the data and uses deep learning to identify patters that would not be apparent or even observable by human clinicians. Because it’s not based on existing assumptions or received knowledge about risk factors, which are at best a suggestive framework, the results have so far shown to be far more accurate, especially at predictive, pre-diagnosis discovery.

“Neural network” models of AI process signals by sending them through a network of nodes analogous to neurons. Signals pass from node to node along links, analogs of the synaptic junctions between neurons. “Learning” improves the outcome by adjusting the weights that amplify or damp the signals each link carries. Nodes are typically arranged in a series of layers that are roughly analogous to different processing centers in the cortex. Today's computers can handle “deep-learning” networks with dozens of layers. Image credit: Lucy Reading-Ikkanda (artist).
AI Algorithms from some of the most innovative companies across the world are now available for $1/scan and are targetted towards developing countries to improve diagnosis and save time.
Worldwide, the cancer of the cervix (lower portion of the uterus) is the fourth most common cancer. It is also one of the most common causes of deaths due to cancer in women.

Most of my patients that participated in my public health project had either dementia, Alzheimer's or were frail and sometimes immobile. They would forget their surroundings, spouse name and even getting a regular medical checkup was a challenge. These women, when asked to go for cervical cancer diagnosis, opted out and never showed up. Most of these tests are widely available but are uncomfortable and invasive. Patients are also not keen to go for them unless indicated.

1 in 5 cancer patients across the world experience delay in diagnosis and, it holds true for cervical cancer as well. Cervical cancer is diagnosed more frequently at more advanced stages.

The human papillomavirus (HPV) infection is responsible for 90 percent cases. However, all women infected with this virus will not develop cervical cancer. Of the 150-300 known strains of HPV, 15 are classified as high risk for causing cervical cancer. Other risk factors include a weak immune system as due to HIV infection, malnutrition, having sex from an early age, multiple sex partners, multiple pregnancies, and smoking. Oral contraceptive pill use for a long time has been associated with increased risk of cervical cancer.

Training models (Deep Learning) is just the first of many steps in translating exciting research into a real product. A pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer, in particular, a pathologist’s diagnosis has a profound impact on a patient’s therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well. Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses.

Studying and reviewing all the biological tissues visible on a slide is done by a Pathologist. There can be multiple slides per patient, and at 40X magnification each is more than 10+ gigapixels when digitized. There is a lot of data to cover, and often time is limited.

To address these issues of limited time and diagnostic variability, we are evaluating ways deep learning methods like automated detection algorithm can be applied to digital pathology, helping improve diagnosis and also complement pathologists’ workflow.

In various instances and research on the incidence of cervical cancer and ineffective diagnosis, the following results were found. Patients were either:

1) Adequately screened with normal results

2) Inadequately tested with normal results

3) Unscreened

4) Low-grade abnormality

5) High-grade abnormality

The microstructure of normal tissue is uniform but as the disease progresses the tissue microstructure becomes complex and different. Based on this correlation, Scientists have created a novel light scattering-based method to identify these unique microstructures for detecting cancer progression.

The morphology of healthy and precancerous cervical tissue sites are entirely different, and light that gets scattered from these tissues varies accordingly. It is difficult to evaluate with naked eyes the subtle differences in the scattered light characteristics of the normal and precancerous tissue. The AI (Artificial intelligence) identifies precancerous tissue, and also the stage of progression in minutes.

Cervical cancer diagnoses and deaths are predicted to rise among women over the age of 50. However, deaths from the disease among the young who have been vaccinated are likely to be almost eradicated thanks to school vaccination programs for HPV.

One such company to mention that we worked with at TechEmerge is MobileODT. MobileODT turns mobile technologies into intelligent visual diagnostic tools that enable any health provider, anywhere in the world, to conduct visual inspections at the level of an expert practitioner.

AI and deep learning methods give a promising start to discover the unknown, but there is a lot that still needs to be done. AI Algorithms from some of the most innovative companies across the world are now available for $1/scan and are targetted towards developing countries to improve diagnosis and save time.

Recommended reads:

Healthcare Transformation with #AI power

Artificial intelligence’s #AI transformative power is reverberating across many industries, but in one healthcare its impact promises to be truly life-changing.

The total public and private sector investment in healthcare AI are stunning: All told, it is expected to reach $6.6 billion by 2021, according to some estimates. Even more staggering, Accenture predicts that the top AI applications may result in annual savings of $150 billion by 2026.

In theory, artificial intelligence and machine learning (AI/ML) can be applied to nearly every process in healthcare. In practice, however, entrepreneurs, enterprise leaders, and investors need to discriminate between incremental improvements and the 10X improvements that will transform the industry.

In developing markets as well #AI driven companies are gaining attention and VC. Companies like Mfine has raised more than $24 million and has around 200 staff in Bengaluru and Hyderabad. But #AI faces several hurdles as well. When patient files are faxed, emailed as unreadable PDFs or set as images of handwritten notes, it will be quite difficult for AI to extract useful information. However, Apple and other big tech companies have the advantage here as they are familiar with onboarding a large network of partners including healthcare providers and #EHR vendors.

Just over 10% of all digital health VC dollars have gone to AI/machine learning companies since 2011—(last week’s deals were no exception). But not all use cases are getting equal attention from AI entrepreneurs and investors. Recently, Rock Health dug some numbers and found that drug discovery and population health management top the charts. It is hard to determine however which areas of healthcare will be first to see sizable shifts from AI integration.

Seventy-seven percent of healthcare executives reported that their organizations are accelerating investments in big data analytics and artificial intelligence (AI), citing disruptive forces and industry competitors as major motivators for increased spending, a cross-industry report from NewVantage Partners revealed.

However, nearly 80 percent of data analytics leaders said their organizations still struggle with big data analytics and AI adoption, with 92.5 percent naming cultural and organizational resistance as major barriers.

Adding to the investments in AI, The Russian Direct Investment Fund (RDIF) agreed to invest 100 million RUB in Oncobox, a company developing solutions for cancer diagnosis based on artificial intelligence (AI). Led by CEO Andrew Garazha, Oncobox is currently developing a digital platform for performing molecular diagnostics of cancers.

Talking of the big wigs, Facebook, Apple, Microsoft, Google, Amazon (FAMGA) are the leading acquirers of artificial intelligence startups (CB Insights Report). Google is leading the #healthcare space. In the last seven years, Google contributed to 16 cybersecurity startups, including CrowdStrike, and 14 healthcare startups. Security and data management is where Google is investing most heavily with a primary focus on the healthcare and financial industries.

Health-care investment can be unpredictable, requiring big bets on new technologies, a handful of which can yield marketable products, while many others go down in flames. Pharma companies are increasingly turning to artificial intelligence and machine learning to speed the costly process of discovering and developing new drugs. Benevolent AI Ltd., based in London, is using the approach to discover treatments for brain cancers, Parkinson’s disease, and other disorders, and just entered a collaboration with AstraZeneca Plc to research treatments for lung and kidney illnesses. The company was valued at just over $2 billion last year when it completed a financing round with investors including Woodford, who first invested in 2014, according to his website.

Read more on how #Google plans to use #AI to reinvest $3 Trillion #healthcare industry. #digitalhealth #artificialintelligence

Monday, June 17, 2019

Glaucoma detection in a Paediatric Setting

Has your child suffered from an eye problem lately? It can be #glaucoma !

Many of the devices currently available for IOP measurement require cooperation from the subject so that accurate and repeatable reading can be obtained. Dr. Sirisha Senthil, MS, FRCS heading the VST Centre for Glaucoma care at LV Prasad Eye Institute mentioned that younger children who cannot cooperate for Goldmann applanation tonometer are tested with Icare tonometer in their Hospital.

#ophthalmology #digitalhealth #eyes #child #medical #medical #innovation #diagnostics #LVPEI

For a vast majority of the population glaucoma in children is unheard of. It is a gradual process, and since there is almost no indication of loss of vision, many people do not realize they have it. Timely diagnosis of Glaucoma is hence, a challenge. When it comes to children, the challenge is even more significant. Children often cannot do a reliable visual field test. They are less cooperative and hence doctors, rely more heavily on pressure measurements in identifying and managing Glaucoma in children than in adults, which isn't very comfortable for children.

Here is the story of Rachna. Rachna recently relocated to Bengaluru with her family and her 4-year-old son Vihaan. Within a few months, Vihaan complained pain in eyes with sensitivity to light. Rachna ignored it for a while but then started noticing that the child has become irritable, is not eating well and is complaining to discomfort in eyes often. She went in to see an ophthalmologist. It was a stressful day mentions Rachna. An eye examination isn't comfortable with children. In spite of encouragement by the mother and requesting cooperation, Vihaan wasn't cooperative.

"The doctor was kind to us and wanted to measure the intraocular pressure, but the moment he tried to measure IOP with applanation tonometer, Vihaan started crying. It looks like the doctor was trying to touch his pupil and Vihaan got so scared that he almost ran away. It was a nightmare, and we had to reschedule the appointment that day,", added Rachna.

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Many of the devices currently available for IOP measurement require cooperation from the subject so that accurate and repeatable reading can be obtained. Dr. Sirisha Senthil, MS, FRCS heading the VST Centre for Glaucoma care at LV Prasad Eye Institute mentioned that younger children who cannot cooperate for Goldmann applanation tonometer are tested with Icare tonometer in their Hospital. "We use this as a routine tonometer in pediatric clinics including pediatric glaucoma, cornea, retina and general pediatric clinics" added Dr. Senthil.

Glaucoma, says the WHO, is the second most common cause of blindness in the world, and the fourth most common in India — mainly because there's not enough awareness about the condition. Increased Intraocular pressure (IOP) can damage the optic nerve situated at the back of the eye. Initially, patients will suffer from defects in the field of vision and then progress to complete blindness.

An IOP measurement is an essential component in the assessment of a child suspected of having Glaucoma. Something as simple as automated visual field testing to measure peripheral vision isn't useful until a child reaches age 7 or 8. The decision to treat a child with drugs or proceed to surgery is dependent on whether the IOP is acceptable in a particular situation. Although examination under anesthesia enables a careful analysis of most clinical parameters, it is associated with potential adverse events from an allergy to death. There is also a significant financial implication involved in anesthetizing a child. The use of advanced Icare tonometers lowers the frequency of exams under general anesthesia. Anesthesia is costly, exposes the child to well-known risks and can sometimes falsely alter the awake pressure readings.

The standard Goldmann applanation tonometry is commonly used for measuring IOP among the adult population, but it has limitations. It is not portable, requires the patient to sit upright, and takes more time due to the anesthetic drops & Fluorescein. This means that it is often not the most practical choice for both pediatric and adult patients. Icare tonometer is easier and faster to use, especially for children making Icare the ideal tonometer for all IOP measuring applications.

Figure 3: Dr. Sirisha Senthil, MS, FRCS Head VST Centre for Glaucoma care at LV Prasad Eye Institute
On asking why Icare tonometry is superior, Dr. Senthil mentioned, "In several cases like scarred corneas, IOP would have been recorded subjectively by finger tension rather than qualifying with Icare. Finger tension method is not very comfortable with children. Icare is a superior technology as it helps to check scarred corneas and other corneal pathologies, over the worn contact lens in eyes as well."

Icare has been working with leading eye hospitals in India and is present in more than 85 countries. At LV Prasad Eye Institute, we collected feedback on the use of Icare tonometer and its quality, efficiency, running costs, service support and they scored Icare tonometer "High" on quality, value for money and efficiency. Icare is recommended for excellent service support and low running costs, which is a common perception for the best quality product available in India.

Icare has been innovating its product line continually. The latest model is the ic200 tonometer which was introduced in the European and Indian markets in late 2018. Icare ic200 is fully portable, requires no anesthesia, and allows measuring the patient not only in sitting, standing, and lateral recumbent positions but also in half-sitting and supine positions. As young children can be less cooperative, unfinished readings are also helpful, and ic200 can save incomplete measurements and store IOP results with time and date. The readings can be transferred to the computer or printed using a wireless Bluetooth printer.

Icare is working with the L.V. Prasad Eye Institute (LVPEI), India. LVPEI is an institution of repute for eye care and has become the first in the world to perform over 2,000 corneal transplants in a year. It performed 2,043 corneal transplants in 2016-17, setting the record for the highest ever corneal transplant surgeries in the world by a single center. LVPEI believes in its vision of providing equitable and efficient eye care to all sections of society.

For more information on the Icare tonometers please visit or contact

Wednesday, March 6, 2019

Data analytics for cell and gene therapy

Cell and gene therapies are becoming more and more popular because of encouraging clinical results worldwide. Major pharma manufacturing companies have invested in the concept's commercialization worldwide. Recently, we read about Takeda’s license for commercialization
of Aloficel (developed by TiGenix), Celgene’s acquisition of Juno Therapeutics or Gilead’s acquisition of Kite Pharma.

As this sector grows further, there is hope that more and more complex therapies will enter the market leading to a consequent increase in the number of treated patient population. This will put further pressure on manufacturing and R&D leading to larger adoption of QbD (Quality by design) principles. The data volume will increase significantly and that is where the concept of big data analytics will kick in.

Data is important- it guides the manufacturing operations; allows proper monitoring and control to ensure quality and assures efficiency, production quality, and regulatory compliance. Below are listed the possible use cases for use of predictive models of data analytics in the pharma and life sciences industry.

Predictive Models for manufacturing outcomes- Predictive Analytics modules can be used for process Improvement Analysis to Improve Scheduling and Throughput. Take an example of this biotech company which is the only final stage bio-manufacturing facility in the world. The company is struggling to meet rapidly increasing customer demand. Repeated unanticipated production delays and starvation at critical parts of the operation were causing not only late and missed deliveries, but the expiration of batches of product at a cost of approximately $1 million per batch. Predictive analytics can be deployed in such a facility to uncover the root cause(s) of the unanticipated delays creating the late and missed deliveries; project such incompetence in advance; Run “what if” scenarios to see if additional capacity from the new facility would be required; the facility could produce two more lots per month; better way to do long-term expansion planning and predict when and where more line capacity would be required in future.

Predictive Models for RISK Monitoring- Traditional monitoring typically allocates resources equally among study sites, regardless of clinical data or the risk to patients. Routine visits to all clinical sites with 100% SDV (comparing all data points on every case report form to all subjects’ medical records) are common — and are the largest cost driver in clinical trial budgets. Predictive analytics allows sponsors to assess investigator risk and allocate monitoring resources where they are needed most. Real-time predictive modeling in the form of risk-based monitoring enables a study sponsor to adjust the level of monitoring as risk changes at individual sites.

Predictive Models for Financial Modelling and Cost-effectiveness

If you have a rich data asset then using Predictive Analytics you can benchmark cost and visualize data in that context to help sponsors forecast, budget and negotiate the cost for outsourcing clinical trials. Predictive Models for Performance and Operational Analytics- For a mature CRO, determining the productivity, utilization, and profitability of clinical research initiatives can help the CRO team allocates the most effective team members to certain projects. Based on the client which contracts the study, trends such as:

Invoice-to-Cash cycle times
The propensity of the client to request amendments to the research deliverables
Ensure the scheduling of clinical researchers and clinical laboratory space is realistic, based on the nature and volume of projects. One can also get a sneak peek to trial site performance capabilities for selecting the best investigators, eliminating non-performing sites and reducing enrolment timelines. With such insights, planning and forecasting clinical enrolment performance, rescuing off-track trials and optimizing contingency plans for those trials becomes easy.

Predictive Models for Patient/Subject discovery- Today, a researcher, for example, might use data mining to find clusters of disease subtypes in hope of finding subtypes to focus on that specific target or hopefully enable a more precise treatment course. Attribute-importance algorithms now help researchers, for instance, select the subset of genes most likely used in discriminating types of cancer. Researchers can use predictive analytics to find factors associated with a disease or predict which patient might respond best to an experimental treatment.

Carefully conducted clinical trials are performed in human volunteers to provide answers to questions such as:

  1. Does this treatment work?
  2. Does it work better than other treatments?
  3. Does it have side effects?

Clinical trials also provide important information on the cost-effectiveness of treatment, the clinical value of a diagnostic test and how a treatment improves quality of life. Ever wondered how patients get selected for clinical trials without these advanced algorithms? Traditionally, physicians have selected trials by manual analysis of patients’ data. The review of resulting selections has shown that they usually do not check all clinical trials and occasionally miss an appropriate trial.

Until now we only have some web systems to address the problem to an extent. To address this problem, Industry has developed near expert systems that help to select trials for each patient. It prompts a clinician to enter the results of medical tests and uses them to identify appropriate trials. If the available records do not provide enough data, the system suggests additional tests. This is a cumbersome process to find eligible patients and doesn't help reduce any related costs.

With Predictive analytics models, Patients are identified to enroll in clinical trials based on more sources—for example, social media—than doctors’ visits. Furthermore, the criteria for including patients in a trial could take significantly more factors (for instance, genetic information) into account to target specific populations, thereby enabling trials that are smaller, shorter, less expensive, and more powerful.


Csaszar E, Kirouac DC, Yu M et al. Rapid expansion of human hematopoietic stem cells by automated control
of inhibitory feedback signaling. Cell Stem Cell 2012; 10(2), 218–229.

Food and Drug Administration. FDA Data Integrity and Compliance With CGMP – Guidance for Industry. 2016.

Geris L, Lambrechts T, Carlier A, Papantoniou I. The future is digital: In silico tissue engineering. Curr. Opin.
Biomed. Eng. 2018; 6, 92–98.

Streamlining data management & process analytics for the manufacturing of cell & gene therapies. 2018
Sébastien de Bournonville, Toon Lambrechts, Thomas Pinna, Ioannis Papantoniou & Jean-Marie Aerts, bioinsights.

Stanton D. Lonza: CAR-T Manufacturing Glitch an Industry Problem, not Just Novartis’s BioProcess
International 2018; [Online] therapeutic-class/lonza-car-t-manufacturing-
glitch-an-industry-problem- not-just-novartiss/.

Viazzi S, Lambrechts T, Schrooten J, Papantoniou I, Aerts JM. Real-time characterization of the harvesting process
for adherent mesenchymal stem cell cultures based on on-line imaging and model-based monitoring. Biosyst. Eng. 2015; 138, 104–113.

Tuesday, October 16, 2018

"Big Data for Health" Initiative by Bloomberg, Dhaka Bangladesh

Join me in #Dhaka, Bangladesh on 5th and 6th November at the #International Conference on #Big #Data for #Public #Health, a two-day event that will advance and identify promising Big Data applications that could significantly advance health outcomes for the people of Bangladesh. Partner @Bloomberg Philanthropies @D4H - Data for Health Access to Information #Ministry of Health and Family Welfare (MOHFW), #Bangladesh #dhaka #digitalhealth #innovation #bigdata

Effective #decision-making about #public #health policies and programs require robust data and rigorous analysis. However, the nature of both “data” and “analysis” are being fundamentally transformed. The explosive growth of smart devices, remote sensing/imaging technologies, and social media are generating streams of electronic data that are voluminous, varied, and high velocity. Deriving #insights from these #emerging sources (generally referred to as “Big Data”) often require #data #mining techniques that are significantly different from the #epidemiological #analysis that has been the traditional focus of public health training. Therefore, to stay at the forefront of #evidencebased decision-making, #publichealth authorities will need to master these new data sources and new analytic techniques.

Big Data to improve Patient outcomes

Join us HealthCursor Consulting Group on 1st November 2018 in Mumbai, India as we talk about the use of #Big #Data to Improve #Patient #Outcomes.

Event Partner: @Abbott

#Data plays a vital role in a society like India that has a huge population outside healthcare and where resources are limited. The opportunity for big data in India is fairly diverse and consists of providers, health insurance, researchers, pharma and medical device R&D. HealthXL is bringing thought leaders to discuss how we are currently leveraging data and analytics in India, how it can reduce health care costs and the opportunity at hand for the future. #innovation #bigdata #healthcare #analytics


This event will allow attendees to understand first-hand the cost-efficient, innovation paradigm at work in India – innovation solutions that can be exported to other markets throughout the world.
Abbott in India (primarily EPD) has consistently invested in creating digital tools with the objective of shifting the paradigm in doctor detailing and patient experience. Abbott uses tools such as Augmented Reality, Virtual Reality using Google Cardboard and Image Recognition to enrich medical detailing across therapy areas.

Non-communicable Diseases: Chronic Disease Management in India’s Digital Era
India has been quite successful in containing communicable, maternal, neonatal, and nutritional diseases. The contribution of NCDs to death and disability in India continues to grow and affect not just health, but also the economy. We want to highlight what initiatives are being taken up in the prevention and management of NCDs from primary care to tertiary care, and also highlight the role of technology in the digitally strengthened India.

Patient centricity: How India is making the patient it's most important customer
How are various stakeholders utilizing patient data more effectively for better R&D, diagnosis, treatment, and monitoring? How is India enhancing patient engagement and experience to make them active participants in all care related decisions?

Big Data to Improve Patient Outcomes
Data plays a vital role in a society like India that has a huge population outside healthcare and where resources are limited. The opportunity for big data in India is fairly diverse and consists of providers, health insurance, researchers, pharma and medical device R&D. We bring thought leaders to discuss how we are currently leveraging data and analytics in India, how it can reduce health care costs and the opportunity at hand for the future.

Data revolution in Healthcare CES 2020 Highlights

CES 2020 Highlights From #Samsung NEON to Impossible pork! Check out the coolest and weirdest new fitness gadgets at CES 2020 https://lnkd...