How can IoT deliver the much- needed booster shot to people living in the far-flung areas and hinterlands of the country?

Imagine rural folks sitting in front of an Internet-connected screen, listening to a doctor sitting in a Metro hospital who, basis on their heart test results, gives them sane advice to either go for specialty tests or resort to prescribed medicines easily found in the vicinity; or envision a city-based doctor looking at a patient’s chest X-ray and decides on the next course of action – all via connected network of IoT devices.

The benefits of IoT are multifold – in providing telemedicine, personalized healthcare, video-based consultations and future wellness strategies – without the patient leaving the village sans emergency.

The IoT infrastructure needs is reliable internet, low-cost IoT devices, security and data analytics and India is slowly picking up on these fronts.

Thanks to the 4G revolution, better Internet connectivity has reached most of the parts in the country, helping the stakeholders in the healthcare industry aim for the last-mile connectivity.

As far as low-cost devices are concerned, several IoT health start-ups have now come to the fore, and are set to change the course of the healthcare industry.

Since IoT is a concept of devices talking to one another, there is a lot of data transfer happening.

To make sense of the data, Artificial Intelligence (AI) and Machine Learning (ML)-capable solutions are already here which – with the agility, scalability and security of cloud – can give predictive analysis of a patient’s data in a jiffy to the doctor.

India’s IoT market size is expected to expand to $9 billion by 2020, according to market research firm Deloitte, and healthcare sector is going to be the main beneficiary.

In a country like India, building state- of-the-art hospitals at every nook and corner is a daunting task. Telemedicine, where patients sitting in remote areas are brought face-to-face to a city doctor via a laptop is the best option.

Therefore, a heart patient can begin talking to a specialist working in a super- specialty hospital in New Delhi, discuss reports, and together work towards whether the patient needs to visit a nearby District Hospital for further check-ups and come to the Capital for advance check-ups or medical procedures.

Once back after a medical procedure or a surgery, the rural patient need not go back again for follow-ups.

Now bring low-cost IoT devices into the picture and the data that is being generated, goes in the Cloud and reaches the doctor faster. This helps the specialist better understand the case and build an online repository of research for future references. The process of well-documented research has today made telemedicine very efficient.

In rural India, nearly 37 per cent of people have access to In-Patient Department (IPD) facilities within a 5-km distance and only 68 per cent have access to an Out-Patient Department (OPD).

Most patients in rural areas do not have access to specialists. Thus, several big hospital chains are offering remote consulting services in rural villages using IoT network capabilities. The doctors can see and interact with patients in remote telemedicine centers, with the case history and medical data automatically transmitted to the doctor for analysis. With the advent of new technologies, various state governments are now leaning towards IoT.

Take the example of the Telangana Government which is using Microsoft’s Cloud-based advanced analytics IoT solution to screen children from birth to 18 years of age for major conditions affecting their health. The data is useful to safeguard rural population. It provides the capability of applying ML and AI to cardio-vascular health records to develop an Indian-specific heart risk score. This data can provide rich insights to doctors on treatment plans and early diagnosis. Such solutions for diagnostics in rural India, with the help from Machine Learning algorithms, can accurately diagnose and predict conditions.

In a paper “IOT in Healthcare: A Study,” M Surya Prabha and B Sarojini from Avinashilingam Institute for Home Science and Higher Education for Women University in Coimbatore mention that IoT devices are being used in many hospitals for patient care and get their health details in India.

“IoT healthcare system connects all the available resources as a set of connections to perform healthcare activities such as diagnosing, monitoring, and remote surgeries over the Internet,” they note. The monitoring of the patients has become possible through the wireless solutions connected through the IoT. These solutions help to securely capture a patient’s health data from a variety of sensors, apply complex algorithms to analyze the data and then share it through wireless connectivity with medical professionals who can make appropriate health recommendations. IoT devices also boost patient’s confidence and satisfaction by allowing patients to get connected with their doctors virtually.

The concept of IoT, say the researchers, has been comprehensive to cover more type of devices such as Global Positioning System and mobile devices. Smartphones (India has over 450 million smartphone and over 700 million feature phone users) can be used to build an inclusive IoT network.The mobile devices can aid healthcare workers to diagnose, track and control infectious diseases.

There are now apps to help rural population. India’s largest online health camp called “myUpchar” recently launched a “Saathi” app. “myUpchar” partners in operating villages along with volunteers and support staff who collect basic details of the patients such as weight, height, blood pressure, glucose, pulse and temperature, and shares those on the app with the doctor.

A recent study published in the journal Nature found that people can make use of apps that employ the phone’s camera to interpret test results, and send them to local clinics or healthcare workers before being uploaded to a central online database.

Globally, the healthcare IoT market is expected to reach $72.02 billion by 2021. According to Nutanix, a global leader in cloud software and hyper converged infrastructure solutions, the healthcare organizations need to address a variety of critical IT needs, which include a need for increased security, protection of sensitive patient data and meeting regulatory compliance. From a patient’s and clinician’s perspective, adopting a cloud model, hybrid or otherwise, also allows for providers to undergo a digital transformation of healthcare delivery.

Infrastructure innovations allow hospitals to manage different appli- cations and data types, take advantage of automation and create new service lines such as telehealth or remote monitoring, thus leading to improved patient engagement. Healthcare companies outpace the averages for certain applications, such as data analytics and Internet of Things (IoT). The fact is that India has always looked to provide a premier healthcare system for its citizens. Thus, investing in the next generation IT and IoT infrastructure is the first step in preserving the current quality of care and bringing healthcare to all.

About the author

Meenakshi Iyer is a New Delhi-based freelance journalist covering health, technology and latest innovations. With more than 15 years of experience, she has worked with top media publications in the country.

This is because of the singular and unique ability of the TB bacterium to hide inside the human body in a dormant state without causing the disease or revealing any outward symptoms. Such cases are classified as latent TB infections and can be revealed by diagnostic tests in healthy seemingly uninfected individuals. Studies indicate that close to 40% of the Indian population is positive for latent TB and without treatment 5%-10% of these will develop active TB at some point in their lives. Unfortunately, due to the high burden of TB in India, the detection and treatment of latent TB are not prioritized, resulting in a huge reservoir of dormant and potentially infectious bacteria primed to emerge under favorable conditions.

In 2017, 10 million people were infected with TB and 1.6 million died worldwide, surpassing AIDS as the world’s leading cause of death due to an infectious disease. WHO has designated March 24t has the World TB Day to raise public awareness about TB and its deleterious effects on health, society and the economy. In 2015, it began to implement the ‘End TB Strategy’ which envisions a world free of TB with zero TB related deaths and zero incidences of the disease. In order to do this, WHO aims to support and promote a patient-centric approach to care and prevention, encourage proactive policies and support systems by involving the government and private sector healthcare and finally intensify research and innovation in the field of TB treatment, prevention, and diagnosis. The Government of India has instituted a similar strategy, the Revised National Tuberculosis Control Program (RNTCP). RNTCP involves adopting WHO guidelines of Direct Observation Therapy (DOT) to the Indian scenario and includes direct monitoring and administration of anti-TB drugs to the patient, rapid TB diagnosis and treatment, and increased partnership between public and private healthcare systems. RNTCP’s objective is to eliminate TB by 2025. The plan outlines four directives – Detect, Treat, Prevent and Build and includes a system for free and sensitive diagnostic tests for TB, screening of the high-risk population, free TB drugs for all patients, implementation of a uniform treatment regimen, increased social support and monitoring and treatment of latent TB. Finally, the plan will work towards translation of political commitment to action through strengthening support structures for surveillance, research, and innovation.

Several technological innovations have facilitated easier and improved patient monitoring, such as 99DOTS which is a low-cost solution for improving patient compliance during TB treatment. Patients using 99DOTS receive medication in specially packaged blister packs. Dispensation of a dose reveals a hidden and unique phone number that the patients can call for free and confirm that the medication has been taken. In this way, remote monitoring of patient compliance has been made possible. Several digital adherence technologies have also been developed as an electronic way to monitor medication and provide reminders. These include digital pill boxes (evriMED, Wisepill, etc.) that are given to patients with their medication inside. The digital boxes provide visual cues such as colored LED lights which turn on and remind the patient of the dose and the medicine that is due. These boxes can also record medication events in real time by logging the opening of the box for each dose, allowing for real-time monitoring of patient compliance. Pilot studies are also underway in the US for ingestible sensors that are embedded inside each pill, which transmits information that the pill was consumed upon activation inside the patient’s stomach. However, the cost of such technology is yet to be estimated and given WHO and Govt. of India goals to reduce the economic burden of TB, cost-effective ingestible sensors may remain only a distant possibility.

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Risk Factors for TB

Being primarily a respiratory disease, TB is disseminated by airborne infection as the bacteria is exhaled from an infected individual and inhaled by another. This often happens in poorly ventilated and over-crowded settings typically seen in cramped housing where the disease can spread like wildfire. Indeed, earlier TB was thought to be hereditary as it used to wipe out entire households. Weakening of the body’s immune system due to age, substance abuse, air pollution, diabetes, HIV infection, and malnutrition, among others, play a major role in increasing susceptibility to TB. Its spread is seen across the socio-economic strata. As Dr. Zarir Udwadia, one of the world’s leading pulmonologists puts it, “TB does not distinguish between the chauffeur driving the Mercedes and the CEO sitting at the back!”

Detection and Diagnosis of TB

One of the main challenges in the fight against TB is the very first step of diagnosis. Millions of TB cases go undetected and unrecorded all over the world due to the shortcomings of technology. Conventional TB diagnosis is performed by sputum smear microscopy, wherein the sputum of the patient is examined under a microscope for TB bacilli. However, this approach is only 36-43% sensitive which means that many cases of TB infection are missed by this method.

Sputum smear microscopy also suffers from false positives, as some non-TB bacilli may also pick up the TB specific stain. Therefore, a positive sputum smear needs to be confirmed by culturing the bacilli obtained from the sputum in the lab, which can then be tested to confirm TB. Unfortunately, growing patient strains and analyzing them requires resources and technical expertise that are not available in most of the high prevalence countries. In addition, growth-based confirmation of TB positivity takes a minimum of 21 days since the growth rate of the bacilli is extremely slow.

A third technique, which involves the detection of TB DNA using polymerase chain reaction (PCR) and named Xpert MTB/RIF promises results of sample positivity within 2 hours and is currently the gold standard in PCR based TB diagnosis. However, this system suffers from high costs per assay and the requirement for infrastructure and constant electricity, which are not available in many cases.

Treatment for TB

Active TB infections are treated with a standard 6-9 months treatment regimen involving four antibiotics: rifampin, isoniazid, ethambutol, and pyrazinamide for varying durations. This treatment duration is one of the longest for bacterial infectious disease, second only to the treatment of leprosy. Side effects like itching, rashes, fever, nausea, diarrhea and reddish/orange discoloration of body fluids including urine, tears, and saliva is another cause of worry which takes a physical, emotional and mental toll on the patient.

Since most infected individuals begin to feel better after a few weeks of treatment and the length and side-effects of the drug treatment are arduous, many patients stop the treatment prematurely or skip doses. Rampant patient non-compliance when it comes to the completion of anti-TB therapy is one of the main factors for the emergence of a more severe form of the disease, one which is resistant to common drugs! This is a big reason for TB reactivation and the continued prevalence of this disease even though it is completely curable in most cases.

Drug resistance in TB has been known since the first antibiotics to treat this disease were put into place. An increasingly larger repertoire of drug-resistant strains necessitated and increasingly larger arsenal of drugs to treat the disease. As of today, TB strains resistant to one of the first line of four antibiotics are called resistant strains. Strains resistant to two first-line antibiotics are called multi-drug resistant strains of TB or MDR-TB. These are treated with a second line of antibiotics, consisting of fluoroquinolones (levofloxacin, moxifloxacin, and gatifloxacin) or injectable drugs such as amikacin, kanamycin, and streptomycin. More recently, the third category of strains labeled Extensively drug-resistant- or XDR-TB has been described with resistance to a fluoroquinolone and at least one of three injectable drugs. A total of 12 drugs are approved today, each of which individually and in combination is used to treat these three categories of TB, MDR-TB, and XDR-TB. However, a little characterized fourth type of strain dubbed totally drug-resistant- or TDR-TB has been reported in India, Italy, Iran, and South Africa. These strains are completely resistant to all known anti-TB medication and given the airborne and infectious nature of the disease, are ticking time bombs of catastrophe.

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Newer Strategies in TB Healthcare

The emergence of drug resistance, the persistently high rate of TB incidence and TB mortality and the challenges associated with patient non-compliance demand improved methods to detect TB with high accuracy, newer drugs to treat resistant forms of TB and increased awareness of the disease.

Bedaquiline and delamanid are two new drugs for TB that have shown promising results in clinical trials after nearly 40 years of failed attempts. These two drugs have received approval for use in the treatment of MDR- and XDR-TB. Additionally, a new drug combination involving bedaquiline, pretomanid and linezolid is reported to have successfully treated XDR-TB in six months with a lower mortality rate than current treatments. However, these new treatment regimens are yet to enter mainstream clinical practice, with further and larger clinical trials needed to make an accurate cost-benefit analysis of these strategies.

Looking to the Future

Although the current situation of TB in India and worldwide looks bleak, the global TB incidence is falling at a rate of 2% per year. According to WHO, this rate needs to increase to 5% to achieve the WHO Global Plan to End TB milestones of reaching 90% of all people with TB through national TB programmes, reaching at least 90% of vulnerable high-risk population through affordable treatment and achieve at least 90% success in the treatment of all people diagnosed with TB. Achieving both WHO and RNTCP milestones will require a concerted effort by policymakers, healthcare providers, TB researchers, patients, and the general public. What we need today is increased public spending on healthcare to make TB therapy affordable and accessible, improvement of research funding and infrastructure and stronger public awareness campaigns to disseminate the reality and requirements of ending TB.

Over just the past two centuries, TB is estimated to have killed 1 billion people, which is more than the number of people killed due to AIDS, cholera, influenza, plague, and smallpox combined! It is time we take the initiative and responsibility to help the millions that are suffering from this deadly disease.

About the Author

Vignesh Narayan is a scientist and science writer at the Indian Institute of Science who currently studies the molecular biology of the TB pathogen Mycobacterium tuberculosis in an attempt to understand how the bacterium senses and responds to its environment and develop new drugs and drug targets to combat the disease.

A mountain of unstructured data

One area where AI would do heaps of help to physicians and medical practitioners is to deal with the insurmountable amount of clinical unstructured data. Nearly 80% of the clinical information is “unstructured” and in a format incomprehensible to health information systems. Thus, getting useful information from these so-called unstructured databases becomes a labor-intensive task. To top that, clinical data is doubling every three years; which leaves the healthcare system with a massive volume of unsorted heterogeneous patient information which may hold answers to several health challenges, but strictly speaking, is of little use in its current form. This challenge in healthcare of too much data, too little insight can be alleviated by employing Natural Language Processing (NLP), a form of AI which identifies key information from spoken or written human input, such as physical examination records, handwritten lab notes, discharge summaries etc. The promise of NLP lies in its ability to turn this big data into smart data. It can be applied to mine big blocks of clinical data and convert that into organized curated easy-for-retrieval information, which can make documentation of clinical information more manageable. In 2014, IBM’s Watson collaborated with Epic Systems and Carilion Clinic to analyze massive 21 million records in just six weeks and pulled important information about risk factors and other features from examination notes written by physicians and clinical laboratory results into organized EHR templates, and further used predictive modeling to identify patients at risk to congestive heart failure with an assuring 85% accuracy rate. Similar efforts of using NLP to tackle cancer and genomics datasets are in process. NLP algorithms thus can be employed with much effectiveness to unlock healthcare’s big data crisis to extract clinically relevant information and make it available for doctors to make smart decisions about their patients.

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Can Artificial Intelligence replace a doctor?

Another facet of healthcare where artificial intelligence can find use is analyzing structured data namely genetic data, imaging data from X-ray scans, CT scans, MRIs, etc. and electrophysiological data obtained from electrography of the heart, brain, and other body parts. Machine learning plays a major role owing to its ability to ‘learn’ and make predictions from data without explicit programming. Of the many machine learning algorithms, two such algorithms have been used extensively in both research and healthcare, namely Support Vector Machine (SVM) and Neural Networks (NN), both use supervised learning models. SVM, in particular, has been useful in tasks involving classification and for novelty detection. For example, a 2012 study used SVM to identify imaging biomarkers of neurological and psychiatric disease. SVM has been used as prediction models for diabetic and prediabetic patients. In 2010, a research group from Korea applied SVM to make predictions about heart failure patients and their adherence rate to their medication. Two researchers from Australia used SVM for the diagnosis of cerebral palsy gait with an accuracy rate of 96.8%.

Neural Networks, on the other hand, form another major chunk of AI algorithm in healthcare. NN algorithms are vaguely based on biological neural networks, in which a collection of interconnected nodes processes the data like how neurons communicate in a human brain. The potential of NN has been multiplied manifold, thanks to the advent of Deep Learning which is an evolved form of NN, it uses multiple hidden layers that can be used to process complex multidimensional data like a human brain. A huge portion of NN algorithms is used for diagnostic imaging. Early last year, a study published in Nature used CNN, a type of deep learning NN algorithm to identify skin cancer from clinical images. The algorithm which was trained on 29,450 clinical images, was highly specific and sensitive to detection and was on par with the performance of an expert dermatologist with over 90% accuracy. A 2016 study used a variant of deep learning NN to identify interstitial lung disease using CT scan images with 85.5% accuracy. Google’s artificial intelligence team employed deep learning algorithms to study pictures of the back of the eye, for the detection of diabetic retinopathy, a blinding disorder in diabetic patients. Their results showed above 90% accuracy in both sensitivity and specificity of detection, which is at par with a skilled ophthalmologist.

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Some areas where artificial intelligence surpasses humans is in looking for patterns in data and in making predictions about that data. Processing thousands of images and looking for a subtle discernible pattern within huge volumes of data is a tough task for humans, but that’s what Shinjini Kundu, a physician at the University of Pittsburgh Medical Center has been doing. Her AI algorithms examine images like MRI scans for subtle differences which may not be perceptible to the human eye, and she has employed this to study osteoarthritis and to predict its development way before it’s diagnosis with a whopping 86.2% accuracy. Similar algorithms can be used to see nuanced differences in electrocardiograms, CT scan images and even in oncology to look for invisible patterns of disease onset and progression. As artificial intelligence algorithms get better after each iteration, routine lab tests like X-rays, CT scans, MRI scans, ECG etc. would fall into the domain of artificial intelligence for more quick and reliable results.

Investment in AI-centered healthcare

Beyond research laboratories and hospitals, the emergence of AI has caused exponential growth in policies regarding AI and investment in AI around the world. AI-based startups have seen rampant growth. Startup Health, an incubator in the US recently reported that there were 7,600 healthcare start-ups around the world working on digital health innovation, a major portion of which involves AI based innovation. An Accenture report published in late 2017 states, “Growth in the AI health market is expected to reach $6.6 billion by 2021 – that’s a compound annual growth rate of 40%”. Another report by CIS India published this year states that AI could add a whopping $957 billion to the Indian economy by 2035. Even state governments are pushing for growth in AI-based sectors. The government of India aims to increase healthcare spending to 2.5% of the Gross Domestic Product (GDP) by the end of its 12th five-year plan, and to 3% by 2022. Such high rates of adoption are due to several AI start-ups and involvement of major players like Microsoft and IBM.

Given the skewed ratio of doctors to patients in India, AI-based healthcare techniques would provide much-needed help in providing healthcare amenities to the masses. Globally, US government have made heavy investments in two of its AI-centered healthcare initiatives, with $1 billion proposed budget to its Cancer Moonshot Program and another $215 million in its Precision Medicine Initiative.

Ethics and issues with AI in healthcare

As rapidly as AI has been embraced by the medical and healthcare community, its benefits cannot be actualized without understanding its ethical pitfalls. But there are several concerns when applying these algorithms at a large scale to make real clinical decisions. Algorithms, albeit self-learning is products designed by human and may reflect their biases in the results they produce. These algorithms may reflect the biases of its designer or biases caused by the dataset on which the algorithm was trained. For example, algorithms developed by private sector entities can be biased to ensure outcomes of their interest or healthcare institutes may use AI systems selectively based on say, insurance plan or economic status of that patient or any other parameter.

Even though Deep Learning algorithms can perform sophisticated predictions on imaging data, they are essentially not fed by an explicit code of information but are self-taught systems and even though the prediction score it gives, for example, whether the lesion is malignant or benign are surprisingly accurate when corroborated with the diagnostic report by a doctor, there’s no way to determine how exactly it came to that conclusion, thus rendering AI systems as a black box; with little clarity on how it works. Recently though there have been several predictions to understand how deep learning works, the information bottleneck theory being a prominent one, but the debate is far from settled.

The issues mentioned above are all pertaining to the AI system and its functioning, but there are vital concerns about AI’s effect on people involved in care. Several studies have shown that patients prefer AI chatbots and virtual nurses over humans when learning about their diagnosis as they can proceed to learn at their own pace without the embarrassment of not keeping up with the doctor’s speed. Patients are also more open to conversation with a computer than a human being, part of the reason being the diminished shame and fear associated with being vulnerable. But Allison Pugh, a Professor of Sociology at the University of Virginia and a writer for the New Yorker, thinks that virtual nurses and AI bots offer nothing more than the thinnest veil of care. She writes, “[…] automating or using AI to deliver care would be the same as relying on a “cloth monkey”—a reference to a cruel experiment, carried out in 1959, in which infant monkeys were given a choice between two surrogate mothers, one made from welded wire, the other from terry cloth. (The infants preferred the cloth mother, even when only the wire mother gave them milk.) AI-driven care was a sorry version of the real thing.”

As demonstrated by several research groups, deep learning algorithms have achieved human-level accuracy and then some more. It can look for patterns which are invisible to the human eye. Thus, sooner or later, displacing and relegating doctors from their positions, at least in certain areas of healthcare. This can lead to massive burnouts in doctors as their roles shift drastically and may even lead to a gradual attrition of their skills. But there’s more to care than just interpreting blood reports and imaging data of a patient, it has much more to do about understanding the needs of patients, their mental state, etc. The secret of healthcare is not in reading out objective reports, but in the assurance and the warmth, a doctor’s cadence can provide. “Caring is expressed in listening, in the time-honored ritual of the skilled bedside exam – reading the body – in touching and looking at where it hurts and ultimately in localizing the disease for patients not on a screen, not on an image, not on a biopsy report, but on their bodies.”, writes Abraham Verghese, an author and a physician at Stanford.

Employing AI to most healthcare activities might also have a negative effect on how knowledge is generated. Most medical knowledge generated in the past has been curiosity driven. AI systems can tell us whether the lesion is a benign mole or a tumor, but it can’t provide answers to why the tumor has a corrugated surface or white patches etc.

Artificial intelligence is going to be pervasive across the spectrum of healthcare. From routine lab tests to offering a clinical decision, AI algorithms will play a major role in the future of healthcare. As deep learning algorithms get stronger and as the workings of the black box are revealed, AI technology will make further strides in healthcare. But advancements in AI-based healthcare doesn’t mean the downfall of human doctors. Healthcare is a highly emotional and human-centric field and the “human touch” will always play a pivotal role in the delivery of healthcare. Humans, even highly skilled doctors are fallible beings with inherent limitations and artificial intelligence will not sideline these practitioners but augment their abilities, in making an objectively better yet humane decision.

About the author

Pratik Pawar is a science writer based in Mumbai. He has a Master’s degree in Biotechnology and currently works as a freelancer writing science-centric pieces with a focus on neuroscience.

Pictures credit: InnoHEALTH Magazine

Trivitron Healthcare’s radiology division Kiran Medical Systems has introduced its innovation driven Ultisys range of radiography products during the Arab Health Convention in Dubai 2017.

The ultisys platform has been indegenously designed and developed with a wide range of clinical applications involving general radiography. The system features a floor mounted x-ray system with a wide range of generator power options from 32 kW to 82 kW, choice of tethered or wireless flat panel detectors. It is seamlessly integrated through a powerful imaging software platform that provides a highly intuitive user interface and image processing that makes even the smallest bone and soft tissue details visible in superb resolution.

Kiran’s portfolio of radiology products includes the Infinity and Elite series surgical C-Arm systems available in 3.5kW and 5.0kW power output and the option of 1Kx1K digital imaging chain with advanced image processing capabilities; a comprehensive range of radiation protection products, anti-scatter x-ray grids and imaging accessories including computed radiography systems, dry films for DICOM printers, Screen Films, Analogue Cassettes and Screens.

Its manufacturing facilities are approved by US-FDA, SGS-UK and PMDA-Japan amongst others. With approximately 50-65 % of the entire components designed and manufactured from scratch in India, there is a significant cost reduction in the development of the equipment subsequently enabling to make the products available at affordable prices.

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