Ransomware today accounts for 85 per cent of the malware in the healthcare industry. As the IoT ecosystem expands, so does the attack surface for cyber criminals.

In other words, the more hospitals rely on connected technology in our day-to-day lives, the more vulnerable these become to the cyber threats that are increasingly tailored to exploit vulnerabilities and security design flaws in IoT devices at healthcare facilities.

In a recent blog post, Salwa Rafee, IBM Security Industry Leader (Public Sector) says that new cybersecurity threats are emerging in healthcare almost daily.

“Today, hackers target medical and IoT devices that provide, transmit, and access confidential data because they can exploit the fact that most manufacturers did not consider cybersecurity when designing those devices,” she writes.

All of this increases vulnerability to ransomware. Many healthcare providers are forced into paying to get their data back.

Attackers are getting more sophisticated, organized and less obvious as they attempt to snare staff and administrators with rapidly changing tools.

Therefore, security training and guidance is critical to minimize staff exposure to phishing attacks and malware intrusion, as is reasserting policy and penalties for staff with bad intentions.

Tech giant Microsoft which aims to transform the healthcare sector through effective use of analytics- driven insights decoding the complex data available, is very seriously looking at securing the data.

For David Houlding, Principal Healthcare Programme Manager, Industry Experiences at Microsoft, healthcare is drowning in data. “Every patient brings a record that could span decades, with x-rays, MRIs, and other data that can affect every decision. Providers and payers bring their own collateral to the table. Skills, policies, and certifications are just the start,” he said in a blog post recently.

Global cyber security leader Sophos has also found that the healthcare industry in India is the most vulnerable and weak while implementing cyber security solutions for protecting IT infrastructure.

“The ‘State of Endpoint Security’ in Indian hospital needs more attention and preparedness for handling cyber threats like ransomware and malware attacks,” emphasizes Sunil Sharma, Managing Director Sales at Sophos India and SAARC, adding that more and more advanced cyber threat actors are turning their attention to attacks against the healthcare sector.

The attacks are growing exponentially across the world. In July last year, the personal health data of 1.5 million people, almost a quarter of the Singapore’s population, including the details of the city-state’s Prime Minister Lee Hsien Loong, was hacked. The hackers broke into the government health database in a deliberate, targeted and well-planned attack. The hack compromised patients’ names, identity card (NRIC) number, address, gender, race and dates of birth.

Following the hack, SingHealth temporarily banned staff from accessing the Internet on all 28,000 of its work computers. According to IBM’s Rafee, finding the right security staff also poses many challenges to a healthcare organization. “Security is a competitive field and attracting the right talent with limited resources has proven to be difficult. Expanding your applicant pool to people with more diverse backgrounds is effective,” she advises.

Not just Singapore, Hong Kong’s Department of Health became the next big victim of a cyber attack after its computers were hit by ransomware which left data inaccessible. In the meantime, the fifth annual Healthcare Breach Report published by Bitglass has found that total number of records exposed in the healthcare sector globally rose to 11.5 million in 2018. On average, nearly 40,000 people were affected per breach, which is more than double the average number affected in 2017.

Is there any full-proof remedy against such malicious activities?

Sophos says that healthcare business needs to move from traditional security software like antivirus and deploy sophisticated security solutions. “Given the speed at which IT threats are evolving and becoming more persistent and coordinated, it is a deep concern to see the adoption of the next- generation predictive technologies. While we all do our best to assure the integrity and confidentiality of sensitive data, the methods we use leave us with a staggering number of false positives and logs to manage.

“It is important for organizations to keep up in this dynamic world of IT threats. Organizations need effective anti-ransomware, anti-exploit, and deep learning technology to stay secure,” Sharma emphasizes. Transparency is key. Reporting details of a breach to the public quickly and efficiently is now a requirement. “No organization wants the perception that they don’t disclose information that should be reported timely. I believe the public, and your patients, understand the risk that any organization is likely to be hacked or attacked at some level,” says Rafee.

Healthcare firms have made progress in bolstering their security and reducing the number of breaches over the last few years. “However, the growth in hacking and IT incidents does deserve special attention. As such, healthcare organizations must employ the appropriate technologies and cybersecurity best practices if they want to secure the patient data within their IT systems,” said Rich Campagna, CMO of Bitglass. As you read this, an unauthorized user accessed a “limited number” of employee email accounts at UConn Health (branch of the University of Connecticut), compromising personal data of more than 326,000 patients.

The US Department of Health and Human Services (HHS) reported that organisations paid out more than $28 million in settlement fees in 2018 – an all-time high in healthcare breach enforcement activity. “In October, the US health insurance provider agreed to pay a whopping $16 million and introduce “substantial corrective action” following a series of cyber-attacks that led to the largest US health data breach in history,” said HHS. The Cloud environment has changed the way companies manage, store and share their data, applications and workloads. Along with a wide range of benefits, though, the Cloud infrastructure also introduces a new, fertile and attractive environment for attackers who crave the enormous amount of available computing resources and sensitive data it holds.“

While we consider the cloud to be an organization’s weakest link, threats posed to them via their employee’s mobile and IoT devices are also to be taken seriously as one of many attack vectors from which sensitive data can be stolen or leveraged to launch an attack,” informs Check Point. As cyber attacks grow and hackers aim patients’ data to make quick money, what patients are carefully watching is how well the healthcare providers are prepared to respond, mitigate future threats and move forward.

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.

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.

The meeting saw some very eminent and important speakers from around the healthcare industry. The first keynote was given by Dr. Vibha Jain, AGM IT, Max Healthcare. The keynote was about how AI improves the patient doctor communication?

The rise of the Internet of Things and Artificial Intelligence is critical to the digital transformation of modern healthcare as far as healthcare monitoring and delivery are concerned. The aim of patient and doctor communication is to create a good and caring relationship, facilitate exchange of information for accurate diagnosis and treatment, and including patients in decision making and counseling. But whether  the doctors are able to create a caring relationship or whether the patients are satisfied with the care, is the point of concern With the evolving technology, can Artificial Intelligence empower healthcare and improve patient doctor communication? There can be endless possibilities in medical imaging and diagnostics, patient care, RnD and management.

One of the patient doctor communications medium is voice. Companies like Google and amazon are out with devices which are voice based and are betting heavily on voice based skills. As these companies are market movers, the question arises: Are we investing in making voice  one of the skills needed to help the patients?

People with chronic conditions are often advised to follow detailed treatment plans; end-of-life care often requires many complex (and emotionally draining) decisions; options for treating certain diseases are increasing drastically. How much could health outcomes be improved with more effective communication between patients and healthcare providers?

One of the panelists brought light on how the voice is substantial to the patient doctor communication. Voice as an input is a useful tool as it gets us more data to refine our systems and algorithms. Voice as an output have some issues in the current scenario, as the synthesis is not real, it is hard to deploy the voice based system. Also there is a language barrier, as the systems are mostly deployed in English, there lacks trust and empathy. There is a vast opportunity to make these systems demographically viable. The question now arises is, if we use a chat bot or a bot service, can we have an authentication that it is a bot speaking not a human being.

Countries like Sweden have a white label application which helps the patient to communicate to the hospital or doctor by the help of a virtual appointment. This makes the environment conducive. In India, big hospitals like Max healthcare can also come up with the same system to improve the patient interaction with the hospital. Government can also come up with a plan to incentify the hospitals and doctors to use this kind of application.

As we are heading towards connected health, efficient communication between doctor and patient is just a small step towards faster healthcare delivery. The pace and evolving changes in the segment of healthcare extend right across the spectrum – from the development of new treatments and medicines to the front line delivery of innovative and paramount outpatient and emergency care. Surprisingly, it is at the front line where the most amazing advancements are happening and where the advantages are registering in the biggest volumes.

With this much advancement in connected health technology we have silos in the process. Healthcare providers apparatus needs to dismantle silos to usher in jointness communication between the doctor and patient. Can we leverage digital tech to manage patient life-cycle for convenience and safety? Max healthcare is also working on many measures to help the patient such as, billing transparency and estimation of the cost, bed allocation, risk assurance etc. Implication of all these is the journey/life-cycle of the patient improves. It gives a holistic care to the patients.

The core is to maximise value for patients: that is, achieving the best outcomes at the lowest cost. In this scenario the AI is helping the healthcare providers to move away from a supply-driven health care system organized around what physicians do and toward a patient-centered system organized around what patients need.

Growing demand for out-patient and at-home care along with connectivity advancements are also driving the use of devices powered by the IoT and AI. Today, privately-based medical insurers are making use of two-way smartphone applications for smartly connecting general practitioners with customers when it comes to initial consultation and diagnosis.

This serves as a cost-efficient option for avoiding a probably unnecessary in-person consultation for a minor ailment. The next steps ahead for this is to accurately and completely share vitals with medical practitioners on a real-time basis. This will be beneficial to enable multi-faceted tele-health services by making use of affordable devices for interfacing users together. It also proves useful for data transmission through connected devices like monitors, pacemakers, and other devices.

It is evident that the core of the connected health is made of technology, but we are not talking about just tools and devices here. This topic is relevant also from a philosophical, ethical and business point of view.

Government and private payer policies are shifting toward the use of connected health; We will likely see increasing adoption of connected health services by hospitals and providers as health care systems move towards holistic care to patients. It is the innovation and execution of information-based technologies that are likely to be transformative in improving health in a world of increasingly restrained resources. And in that effort, the newly empowered consumer is likely to lead the way.

This is a growing area of health care, and despite current challenges, presents significant opportunities for health care improvement. If you are not considering connected heath and Artificial intelligence in healthcare, now is the time!

The meeting witnessed some very eminent and influential speakers from around the healthcare sector. The former Enforcement Director (ED) Karnal Singh who has investigated many high- profile cases graced the meeting as the Chief Guest.

Innovation is exciting and there are more innovations happening in the private sector, and with increasing technology come increased risks. With the rapidly growing technology, ranging from wireless devices such as mobile phones, tablets, and laptops to smart refrigerators, cars, and even medical devices like pacemakers, etc., it seems that the growing connections around us are difficult to detect. Therefore, the risk that comes together in operating within this brave, new and a connected world is high.

Shri Karnal Singh shared his expert viewpoint on the impact and importance of artificial intelligence, connected healthcare and need for cybersecurity in healthcare. How the digitization of healthcare is important and the rural and urban divide of same. He also insisted on the improvement of infrastructure and facilities available in the rural healthcare industry. In addition, he discussed the gap between the medical industry and government institutions where the research is needed and that we adopt mostly from our partnering and supporting foreign countries and how a change in mindset and adoption of new and improved technology can enhance the Indian healthcare. The future of healthcare is on the Internet and the Internet is vulnerable. Technology is the solution and it will come whether we want it or not. Therefore, the need of the hour is OUR PREPAREDNESS. We should welcome and be prepared for the connected network of healthcare.

An interesting session on the basic concepts of AI was the icing on the cake. It acquainted the participants with AI emphasizing on the fact that one should not get bogged down by the technology. Instead, one should focus on the use cases of it and there lies the innovation.

Often, the terms “digital health” evoke images of smartwatches or apps that can process the health data and can give a readout of parameters like heart rate, blood pressure, etc. That is a part of digital health, to be sure, but today we also focus on a newer aspect called “Precision Health” and the session “Prerequisite and journey of India to offer (precision) healthcare to all: Powered by Digital, Genetics, and AI” offered a far more expansive vision for digital health technologies, genetics and AI.

Precision medicine is an emerging approach of medicine which focuses on the cause of an illness, and not addressing the symptoms alone. Precision medicine may not be very well- known as Personalized Medicine. But they are the same. It takes into consideration individual variability in genes, environment, and lifestyle for each person.

Precision healthcare improves efficiency and accuracy to healthcare treatments. With precision healthcare, doctors can potentially develop targeted and precise treatment and therapies for masses as well as for an individual. This can improve patient treatment to large populations in countries like ours.

It’s based on evidence and data which comes from a series of medical tests on a patient. Thus, the data is its most important aspect. Very soon, along with genome sequencing and health sensors data coupled with the growing amount of Big Data, patient data will create large data sets of valuable information which can be used for more tailored treatments. But as an individual, a physician is not equipped to manually analyze these newly available data sets, which is where the advanced technologies such as AI come into picture.

The challenge that the clinicians see in the adoption of AI is the gap between the technical person and a clinician in regard to understanding the implementation of “normal reference values”. For example: If the normal value of hemoglobin for a person staying at planes (say Delhi NCR) is 13.5, the value 17.5 is perfectly normal at Ladakh. Is that incorporated in the AI system? Something that works in the USA might not work in India. The doctor or a clinician will have a great role to play in case of developing the AI as he/she can only give the correct inputs for the training sets.

With the new wave of digitization, medical records have seen a paradigm shift in the healthcare industry. As a result, the healthcare industry is witnessing an increase in the absolute volume of data in terms of complexity, diversity, and timeliness. Since the healthcare experts look for every possible way to lower the costs while improving the care process, delivery and management, and so is the aim of InnovatioCuris (IC), big data emerges as a persuasive solution with a promise to transform the healthcare industry. This paradigm shift from reactive to proactive healthcare may result in an overall reduction in healthcare costs and gradually may lead to economic growth. The above-stated and many more discussion points were there that became the part of the very interactive and interesting second session on “Mitigating data security and privacy challenges for digital healthcare in India”.

Data transmission is also an important aspect of implementing technology and safeguarding it. The Digital Information Security in Healthcare Act (DISHA) is one such step taken by the Indian Government in the long journey to securing the healthcare data of patients in India.

One of the biggest challenges is the lack of availability of data in a democratic manner and it’s all in silos in short data interoperability is a huge challenge. The major concerns that the clinicians have at the moment regarding the implementation of DISHA act are that it says that no court shall entertain any complaint unless made by the Central Government or central data authority, state data authority or the person affected and there would be times when PIL (public interest litigation) is there on the data security as well. The challenges will always pertain but what we need to do is to have trust in the law and protection by it. Major challenges regarding data security or cybersecurity, in hospitals or the clinical establishments, is that we do not have implemented laws that may protect our data. With the hope that they are definitely going to come, we can only stay prepared with our changed mindsets.

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.

Also Read: Medical Devices In India Witness Churning

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.

Also Read: Advantages & Disadvantages: Real Time Health Monitoring Devices

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

Take for instance Life In Control and Wellthy Therapeutics. These digital platforms connect patients with doctors and coaches and provide personalized care in terms of diet and exercise plan. Users can create long-term health goals, follow-up daily tasks and keep tabs on their health profile including HbA1C and glucose levels, track medicines intake and understand how their daily routine affect these measures. These start-ups also give doctors the flexibility of reaching out to patients outside their clinical hours as well as to create and coach them on their personalized care plans. In the language of numbers, Life In Control has over 100 doctors on the platform and appears to have helped over a lakh patient with their app. Wellthy Therapeutics took the approach of a pilot program with diabetic patients and claims to have helped reduce both HbA1C levels by a little over 1% and an average weight reduction of 3.4 kgs over 16 weeks. It also got an endorsement by Asia’s largest diabetes association, Research Society for the Study of Diabetes in India. Both the apps are available for download on Android and iOS platforms.

Adding to the growing list of startups are those that detect blood glucose and connect directly to your smartphone. BioSense SYNC, for instance, is a compact Glucometer with Bluetooth connectivity to a user’s phone. It provides a detailed analysis of blood sugar data collected over a number of days. Similar to other diabetes management apps, a user can map their glucose data with their eating and exercising habits. Taking these interventions a step further, the start-up BeatO not only provides a smartphone Glucometer but also a range of diabetic friendly food products to munch upon. Patients can order medicines, sync their fitness devices and count calories on food items consumed. As of today, BeatO has served more than 25000 customers with a daily user add-on of 75-100. Most of these diabetes management apps can instantly connect a patient to a diabetic coach in case the sugar levels are off, and alert the patient’s family as well on concerning trends.

Complimenting these health management gurus, patients are also likely to benefit if there were devices providing at home diagnosis for diabetic complications, such as neuropathy, kidney malfunction or diabetic retinopathy. Scintiglo by Cutting Edge Medical Devices, Anupath by PathShodh and Sparsha by Yostra Labs have created Point of Care (PoC) tests to detect the first two complications. Scintiglo is a small handheld device that detects urinary proteins (should be trace levels in a healthy individual) at a low cost with remarkable accuracy and connects to a smartphone through a custom app. Anupath uses unique test strips for detection of specific biomarkers of diabetes, while also eliminating the need of usual sample preparation that one sees in clinics. Patients can get a comprehensive reading of their plasma and urine protein levels, HbA1c levels blood glucose and hemoglobin. While Sparsha, as the name suggests, focuses on touch-based tests that can diagnose diabetic peripheral neuropathy and send reports via users’ smartphone. Funded by the Department of Biotechnology grant (BIRAC), these start-ups focus on low-cost solutions, so an average user can save money on constant check-ups. Low costs also allow these start-ups to cater to rural segments of India, where a healthcare worker can refer patients to tertiary hospitals based on initial reports by these devices.

The complication of retinopathy, as mentioned above, relates to the slowly progressive disease of the eye and is one of the leading causes of blindness in diabetic patients. This segment is addressed by start-ups like ChironX and Artelus that use artificial intelligence to detect or monitor diabetic retinopathy risks from retinal image scans. Both the start-ups have achieved high accuracy of detection (over 95%) while the technology of AI confers an easy benefit of scanning retinas at a much faster speed to doctors and hospitals. Artelus also boasts to have saved over 3900 eyes so far from diabetic complications.

Given the burden of diabetes in every aspect of life (behavioral, physiological and financial), being handheld into recovery could be motivating for many patients. For maximum impact, we need to reach the furthest demographic corners of India, as diabetes has a growing prevalence in all sectors of the country. Infact, why wait for diabetes to knock at your door? One can also mitigate pre-diabetic conditions or reduce the chances of developing diabetes later in life. These start-ups aim to provide that ecosystem, where a patient can be led into an improved, healthy lifestyle at minimal costs. And just as it is heartening to see the growing awareness of Indians towards their health, it is equally imperative that these solutions are also as democratic as possible. Importantly in a booming start-up scenario, maintaining data integrity, complete transparency between doctors, patients and companies as well as strict adherence to ethical standards would go a long way in ensuring patient trust in this system.

About the author

Urvashi (Raheja) Bhattacharyya is a Biomedical Science graduate from Delhi University and a Ph.D. from National Centre for Biological Sciences. When not running behind her toddler, she spends time reading stuff about the brain, AI, ML, and latest innovations. Writing keeps her calm.

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InnoHEALTH Magazine | Volume 4 Issue 1 | Innovation in Diabetes – Unmet Needs
Highlights

• Exclusive interview of H.E Klas Molin, Ambassador of Sweden to India on “Sweden-India collaboration in the health sector”
• Digital healthcare
• DISHA: Need of the hour. How crucial is DISHA (Act) for the healthcare industry?