ECG Archives - InnoHEALTH magazine

Apple Watch Can Soon Prevent Skin Diseases

InnoHEALTH Magazine — Wed, 26 Jun 2019 08:55:29 +0000

Apple watch can soon prevent skin cancer, premature skin ageing and sunburns

The US Patent and Trademark office has recently granted a patent to the Cupertino, California-based technology company that would help the Apple watch to monitor the ultraviolet light exposure or in other words use a UV light sensor to detect if a user is outdoors and how much he/she has been exposed to the harmful ultraviolet radiation.

The system provides the information regarding the total exposure to UV light either via a text message or an alert including guidance on preventive measures if the levels are excessively high. The data from the sensors is first fed into an analyser. The analyser compiles the total exposure time and amount of UV light the user has been exposed to and then alert the user.

Furthermore, the patent suggests that the UV index value and the total amount of exposure to the UV rays would be combined to determine if the user stands at a risk of skin damage and guiding the users for appropriate preventive measures to minimize the damage.

Additionally, the smart watch would also feature an ambient light sensor and an infrared light sensor that would be used to determine the amount and state of light the user is getting exposed to.The new feature would add various health benefits associated with the new Apple watch series 4 which can help prevent sunburns, premature skin ageing and even skin cancer by keeping a track on the UV rays exposure time.

The new Apple watch also has a feature named ‘Fall Detection’ that will alert emergency contact numbers in one’s mobile phone in case the user meets with a hard fall.

The new watch also has an electric heart sensor to send across low heart rate notifications and faster heart rate reading. The watch will also immediately notify the user about the user’s abrupt heart rate for when one’s temper is going haywire requiring the user to cool down the mind. The electric heart sensor is capable of taking an electrocardiogram using the ECG app which by now should be available in the US as it was already approved by the FDA in 2018.

SOURCE: www.sciencenews.org

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The Morphing Face of Healthcare in the Artificial Intelligence World

InnoHEALTH Magazine — Wed, 27 Mar 2019 09:23:50 +0000

Artificial Intelligence (AI) is a hot topic, simply put – it’s a way of making a computer think intelligently, in a way human think and over a decade now it has managed to be fairly successful. It has found application in several domains, from consumer electronics like smartphones and smart home devices like Amazon’s Alexa to very niche applications in academic research. What began as a nascent academic pursuit to enable computers to think and solve problems using human-like cognitive capabilities has now invaded most aspects of human life, medicine and healthcare is no exception.

Modern medicine has discovered around 60,000 ways things can go wrong with the human body and over thousands of years have probed these illnesses and disorders to better understand and treat them, one drug, one technique at a time. In recent years, however, there has been a dramatic shift in the pace of innovation in healthcare, especially with the advent of artificial intelligence. Artificial Intelligence is an umbrella term used to cover a wide array of algorithms which mimic human cognitive functions and are self-correcting, and can ‘learn’ from a dataset.

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

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Software as Medical Device

InnoHEALTH Magazine — Wed, 09 May 2018 05:23:22 +0000

The tectonic shifts in technology are transforming human life in ways unfathomable just a few years ago. Health-tech and med-tech are touching our lives continuously through a number of ways – from simple wearable devices to complex invasive devices; simple AI software which can predict and sense to complex AI software which can diagnose; sensors and other hardware devices including the mobile phone with ever-increasing computing power.

Some of these have made human lives so dependable on these devices, gadgets, software. In some cases, these are dumping human intelligence. We witnessed software wherein by looking at a camera on the mobile phone. The software can predict the heart rate and many other vitals. What if human intelligence gave way in believing the reading as true? The software or the camera is not a medical device. Hence outside the purview of regulations usually applicable to medical devices. Can we ignore the risks? If so, should software be treated as a medical device?

Software as Medical Device (SaMD)

A broadly accepted definition of a SaMD is the one issued by the International Medical Devices Regulation Forum (“IMDRF”); Currently, Australia, Brazil, Canada, China, Europe, Japan, Russia, Singapore and the USA are member countries to this Forum. The FDA has adopted this definition in the US. The Medical Device Directive adopted by the EU in 2010, and in major countries such as Australia, Canada and Japan.

The term “Software as a Medical Device” is defined as software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device. It includes an in vitro diagnostic medical device. SaMD must be capable of running on a computer platform that is not of a medical purpose. And should not need a hardware medical device to achieve its purpose. It can be interfaced or used in a combination with other devices. But cannot be used to drive a hardware device.

The definition, Mobile Application Meeting, also consider as SaMDs. The medical purpose‟ that it must intend to serve can be diagnostic, preventive, investigative, life-sustaining, for treatment of disease or injury, disinfection, control of conception or purely informative. In some jurisdictions, aids for persons with disabilities, devices for assisted reproduction and devices incorporating animal and/or human tissues are also recognized.

A SaMD can also be a means to suggest mitigation of a disease or provide aid to diagnosis. There are further guidelines on the definition of changes to SaMDs- they can be adaptive, corrective or preventive in nature. The manufacturer of SaMD would be a natural or legal person who has the intention for the software to be used under his/its name. It would not include a distributor or the manufacturer of an accessory. The final legal responsibility lies with the manufacturer unless it specifically imposes on another party by the country’s regulatory authority.

Putting them to use

SaMDs are now available in abundance in the Indian market. Both foreign manufacturers, as well as Indian manufacturers, are introducing so many forms of SaMDs. This includes the use of artificial intelligence, IoT, general software etc. Interesting, many SaMDs are enjoying high adoption rates not only for early users but continued users. The glaring point is that there is no legislative framework or guidance policy which works as a guiding principle for the SaMD manufacturers or at least as a self-regulating piece of legislation, in India.

The Medical Devices Rules, 2017 which has come into effect from January 1, 2018. Now defined medical devices and has made a clear distinction between drugs and medical devices. But still, this definition does not include SaMDs or software as a medical device. Interestingly, the draft Medical Devices Rules, 2016 on basis of which the Medical Devices Rules, 2017 have been formalized included software in the definition of medical device. AI, IoT, general software, wearable, and wellness and customized medical devices are flooding with the market. Software as a medical device as a whole should consider with equal importance in the sector. Curiously, the definition of medical devices under Foreign Direct Investment policy includes software.

Regulations

The IMDRF has worked extensively in setting guiding principles for governing SaMD and has put in place a regulatory structure for how the SaMDs shall be governed, regulated, clinically evaluated and how the data shall be evaluated and then used by the SaMD. While India has not yet included any software or apps in its regulatory purview. Countries like U.S.A, Singapore, Australia, EU and Japan has issued guidance documents to make the app developers aware of what might subject to regulation.

The common theme that determines the classification is the level of risk that these apps pose to the consumers. For example, let’s take an app which allows a user to take ECG test by putting their fingers on an external device which is connected wirelessly to the smartphone. It checks the electrical activity of the heart. Such apps may be considered as risky and be subject to regulation since the belief is that any incorrect analysis may hamper a user’s treatment. However, the Government authorities need to strike a balance while assessing these risks so that not all apps need to be certified under the law so that innovation is not hampered.

It is indeed a very fine balance. General wellness apps or products such as apps tracking and assisting in maintaining healthy body weight. Products are generally kept out of the purview of law versus apps which tracks and assists in say monitoring blood sugar or other vitals or treats specific health issues or provides guidance for treatment of specific illnesses. The whole purpose to bring these apps under regulation is that there is a certain amount of rigour before the apps are released to the market. And there is onus and responsibility on these makers. It should enable the app developers to be mindful of how is the product advertises, claims as well.

To protect consumers, certain jurisdictions, like Singapore, have mandated the manufacturers/ app developers to put a clarification statement on their product or on their apps. This statement should clearly state that this app or product is not intended to be used in a diagnosis, monitoring, management or treatment of any disease. Keeping all the innovation in health-tech space, India should provide guidance on SaMDs. The regulatory framework in India for medical devices is by the Central Drugs Standards Organization widely known as CDSCO.

The new Medical Devices Rules, 2017 are comprehensive and now extensively covers almost 351 medical devices and about 247 in-vitro medical devices. It still does not cover SaMDs. Given the increased use of mobile technology and awareness, guidelines on SaMDs could contribute to improving the affordability and availability of healthcare, including rural India, which has a huge user base. Gradual rigour in legislation will allow India to meet increased need, according to when resources for monitoring and enforcement become more available. India already follows the IMDRF regulations with respect to clinical trials. And the clinical evaluation of medical devices, with respect to documents, licensing and safety standards.

It is important for the legislation to allow the industry to grow and achieve its potential. Especially in a country like India where there is a need for the better point of care medical solutions. But at the same time provide unambiguous guidance. A good starting point would be a self-regulating mechanism with a set of standards, methods and procedures, clinical evaluation process. Such guidance would help improve innovation as well and guide the nascent Indian SaMD industry.

(Author is a qualified lawyer who has a keen interest in how tectonic shifts in technology is impacting healthcare delivery. The intersection of law, innovation, interaction with man and machine excites him. Manas works with NovoJuris Legal deeply in AI, IOT, health-tech, med-tech, devices and more in the healthcare segment)

A new paradigm for use of machine intelligence in healthcare

Want to write for InnoHEALTH? send us your article at magazine@innovatiocuris.com

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Rural healthcare: Solutions lie in innovations

InnoHEALTH Magazine — Mon, 23 Apr 2018 11:10:53 +0000

It has become imperative to make rural and remote areas healthy and free of diseases and infections to make ‘National Health Mission’ a success. There are still many parts of the country where medical services are still not accessible. With the iKure’shealthcare delivery system, the dream of providing minimum health service to the rural parts of the country has become true. Armed with Wireless Health Incident Monitoring Systems, Community Health Workers have been at forefront to provide doorstep solutions at minimal cost. iKure’shealthcare has become a new frontier to drive innovations in rural healthcare delivery

There are over 330 CHWs who are trained and strategically deployed across six states in rural India and have been rendering services under iKure’shealthcare delivery system.

Jayashree is one the CHW who works relentlessly in her village Hubli(Karnataka). One day, she visited a woman named Pushpa who was in her first trimester and looked quite unwell.On enquiring, Pushpa was found to suffer from Epilepsy. Her family also revealed that she has been prescribed with medicines by a local doctor, but she didn’t take them regularly.

Epilepsy is a disorder of the nervous system and such patients during pregnancy have major concerns. Jayashree felt the need and promptly captured her vitals on her smartphone loaded with Wireless Health Incident Monitoring Systems, (WHIMS), and pushed the patient’s vitals on to the cloud for a quick doctor reference.

For the next few days, Jayashree monitored and screened her closely and counselled her and family about different preventive measures pertaining to her health conditions. Soon after, Pushpa recovered and gave birth to a healthy child.

In the context of rural settings, it is never possible to create doctors or health clinics as required. To bridge the gap, the grassroots community members are empowered to provide clinical careenabled through WHIMS.The technology application is designed with an intuitive graphics user interface that can be efficiently used by a health worker even with a basic education.

For instance, WHIMS screen has a complete photograph of a human body.When a health worker clicks on the head, WHIMS gives options of potential symptoms of the head, such as ache, dizziness, seeing dark spots, and so on.

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Usually with the standard diagnostic tool, low accessibility and portability remain a huge constrain. WHIMS is accompanied with low-cost portable instruments to measure basic statistics and loaded on a tablet is carried by the CHWs in the remotest villages to diagnose patients. The patients pay an affordable rate for basic diagnostics and consultations, through which they earn additional income.

Unlike the Accredited Social Health Activists (ASHA workers) who work as community mobilizer and not authorized to deliver clinical services, these CHWs meet both the ends.

Equipped with a medical kit that consists of devices including BP machine, stethoscope, pulse oximeter, thermometer, first aid kit, weight machine, Height measuring scale and gloves,they carry medicines for basic common ailments, non-invasive hemoglobin meter, haemoglobin kit, mobile phone based sphygmomanometer, urine analyzer, calorimeter, ECG, flipcharts/ Hoardings & registers.

Through these CHWs, every patient is also provisioned with Digital Health Card (DTH) encrypted with a QR (Quick Response) code and during home visits. DTH gets updated with latest clinical indicators. The health data obtained from the health cards is synced to a cloud for effective utilization in the upward heath chain at both secondary and tertiary levels.

CHW’s contact and familiarity within the community drive effective community mobilization measures. As they become an integral part of the healthcare delivery model, CHWs act as a catalyst to drive behavioral change. While they interacts and communicates the necessity on the ground and identifies appropriate health needs, it is through street plays, hoardings and message dissemination on mobile phones, theymobilize patients at different levels. They are also attuned to provide nutritional counselling, and spectacle services based on the community needs.

Further, WHIMS is upgraded with robust supply chain management framework, innovative algorithm, and Data Warehousing solutions. Driven by its CHWs, iKure aims to bring efficiency in public system & programe and improve lives.

The CHW model of empowering the women community and gender mainstreaming represents transformational change in rural healthcare delivery. As they champion tech-savvy cultural values in otherwise austere facilities, and limited supply of doctors, such effort has established inspirational leaders driving catalyst change in sustainable rural development.

“For a basic diagnostic long distance travel time, high cost of services and prolonged waiting hours at the local PHCs was a huge burden for these rural residents. Use of P-O-C devices is extremely portable, affordable with high accuracy rate and can be used by any health worker with a short training. This has been a critical step in rural healthcare delivery with a positive outcome on prevention, curative care and treatment procedures”.

Dr. Ajay Agrawal, Medical
Director-iKure

“Initially when I used to visit pregnant women, they were apprehensive and reserved. But repeated visits have given them confidence to open up. They ask many questions, show doctor’s report and ask for medicines. I have a joint family and few among them are pregnant, even they are educated, but they would consult me and ask for my opinion. I feel valued and found a voice through iKure”.

Gayathri Acharya
iKure CHW, Hubli, Karnataka

(Author: Dr. Tirumala Santra Mandal is a research and communication analyst at iKure. With a doctorate degree in corporate communication from IIT Kharagpur, she has worked in diverse fields of communications such as lie detection, multimedia communication, communicative English. Prior to working with iKure, she has worked as an editor and authored several publications in referred and business journals both at national and international levels.)

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Re-Shaping of Healthcare Through Innovations

InnoHEALTH Magazine — Fri, 01 Dec 2017 11:04:09 +0000

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The World economy is changing drastically from agriculture to manufacturing to services. Among all services, healthcare services are one of the significant contributors to the economic growth. Further, it has become easy to implement due to advances in information and communication technologies (ICT ), Internet of Things (IoT), efficient network of organisations. IoT is a network of organisations based on their complementarity strengths / resources available. IoT is connecting homes, cars cities as never before to make life simple and more comfortable [1].

Innovations has become critical capability of many business organisations including healthcare organisations. New technologies such as Internet of Things (IoT), nano technology, semiconductor products, stem cells, genetic engineering are driving the healthcare across the world. Among all, medical sciences have advanced significantly in terms of adopting latest technologies throughout the process of care delivery. Innovations in healthcare industry primarily aimed at improving quality of life, life expectancy, treatment options and cost efficiency.

Healthcare Innovation can be defined as the introduction of a new concept, idea, service, process, or product aimed at improving treatment, diagnosis, education, outreach, prevention and research and with the long term goals of improving quality of life, safety, outcomes, efficiency and costs [2].

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There are several innovations happened in healthcare delivery. Some of these are given below (figure 1):

• Product innovation
• Process Innovation
• Service design Innovation
• Service delivery Innovation
• Business model Innovation
Innovations in healthcare improves adaptation and affordability.

Product Innovation is concerned with introduction of new types of goods and services for the external markets. This includes significant improvements in technical specifications, components and materials, software, user friendliness or other functional characteristics. Examples include wearable devices, portable ECG of GE, Intra Ocular lens of Aravind Eye Hospital, Jaipur foot etc. These innovations help in generating incremental revenues [2]. Some of the main products include patient monitoring devices, home care and health and fitness. Wearable devices market is growing at the rate of 4.8% on year-on-year growth [3].

Process Innovation is mainly implementation of a new or significantly improved production or delivery method. This includes significant changes in techniques, equipment and/or software. These innovations help in improving internal capabilities and quality improvement [2]. Some of the examples include use of computerisation of medical records, improvement in the use of medical equipment like in Aravind Eye Hospital where surgical equipment is being used for two tables.

Service Design Innovation mainly focus on design of man-machine system compatibility including customisation of devices and its functioning. For example, customising the pulse rate of pacemaker implanted to a patient. Examples include telemedicine, mobile healthcare units, small clinics in the vicinity of patients, collaboration with other stakeholders etc. Figure 3 shows

Service Delivery Innovation include the use of new methods in delivering the service and helps in cost reduction as well as turnaround time of care delivery. This include innovation in medication, surgical interventions, procedure etc.

For example, use of iphone for taking picture of eye and checking for preliminary diagnosis in place of a computer or other equipment. MRI, CT Scan, Cataract surgery etc. Figure 4 shows arrangement of eye check-up equipment on a two wheeler which can reach to the location where four wheeler mobile unit cannot reach.

Apart from these four innovations, one of the most important one is business model innovation. This is mainly focuses on number of alternatives and pricing of products and services. Examples include offering two or three options for surgical services along with other support services like room with air conditioner or without air conditioner etc. Providing enough number of winning alternatives refers to business model innovation.

Diffusion of innovations in healthcare requires acceptance of innovations by the different stakeholders mainly the care receivers (patients), care givers (doctors and others). This requires alignment of knowledge, technology and institutionalisation of practices. Innovations in healthcare helps government to provide conducive environment for innovation and collaboration opportunity between public and private organisations. Facilitate in implementing healthcare initiatives in large scale by optimising resources and their deployment.

References:

1. S rivastava S C and Shainesh G (2015), Bridging the service divide through digitally enabled service innovations: Evidence from Indian Healthcare service providers, MIS Quarterly, Vol 39(1), pp: 245-267.

2. O machonu V K and Einspruch N G (2010), Innovations in Healthcare delivery systems: A conceptual framework, The Innovation Journal: the public sector innovation journal, vol. 15(1), pp: 1-20.

3. http://www.todaysmedicaldevelopments.com/article/wearable-medical-devices-2016-market-72816/
https://www.innovation.cc/scholarly-style/omachonu_ healthcare _3innovate2.pdf

About the author

S Venkat is an Associate Professor in Operations Management at IIM Lucknow, India. His interests include healthcare operations management, affordable healthcare design for bottom of the pyramid consumers, Managing operations in religious organisations etc.

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INTERNET OF THINGS IN HEALTHCARE

InnoHEALTH Magazine — Fri, 10 Nov 2017 10:08:05 +0000

According to the Medical Council of India, the doctor-patient ratio in India is around 1:1,674 against the WHO norm of 1:1,000. In such a scenario, IoT can actually be a great leveler.

As of now healthcare delivery in India happens when patients visit doctors for treatment. It is felt that IoT can bring the treatment to patients’ door step. Experts describe how the technology will benefit the sector. “The data of a patient can be captured via various wearables. A doctor can interface through a combination of consumer IoT (wearables) and other exponentials like Augmented Reality, which could change the way in which healthcare is delivered. Little wonder then that businesses are looking to explore this space. Here’s a look at a few startups in the healthcare IoT space in India.

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1. SPEACTRAL INSIGHTS

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Pathologists in India don’t lead an easy professional life. And since senior-level pathologists in India are way short in numbers to serve the 120,000+ pathology labs across the country, the work pressure on them is unexceptionally high. There are also several occupational hazards due to the laborious manual process of operating and viewing through the lenses on a microscope for such long time.

Startup firm Spectral Insights aims to come in rescue of pathologists by providing high quality digital copies of slides. The company insists that the technology they are using isn’t new as it’s available in the developed world but unaffordable by labs in India.

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So in this light the company has built an affordable automated microscope that makes digital images which are available to pathologists on a computer screen, either locally or over the Cloud. The company also builds various tools to help pathologists make rapid and informed diagnosis, thus increasing their productivity while raising the quality of the outcomes for the patient.

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2. CARDIAC DESIGN LABS

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It is estimated that India has a ratio of 64 million cardiac patients to 4000 cardiologists. Cardiac Design Labs designs and deploys intelligent devices which help in bringing advanced diagnostics beyond large hospitals. The company has developed a wearable device called MIRC aM (Mobile Intelligent Remote Cardiac Monitor) which is capable of providing real time advanced cardiac diagnosis and monitoring in remote settings. The company believes MIRCaM will bridge the doctor patient gap. Resting ECG which is the prevalent method for diagnostics at small hospitals is only good at detecting patients whose heart problems are acute. However, often many cases with early symptoms go undetected. MIRCaM is designed to solve this problem.

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3. FORUS HEALTH

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Forus Health is a medical technology startup founded by a group of engineers with the goal to eradicate avoidable blindness. According to data provided by the company, 80% of blindness in the world could be saved if screened and treated early. For this, Forus Health has built an imaging platform called 3nethra, a portable device for screening common eye problems.

Here, IoT helps in conducting remote diagnosis. The images are screened in remote locations and doctors in towns and cities grade them and the patient gets the report back in their respective locations wherein all this happens over the Cloud.

So far 3nethra has helped in more than 300,000 remote reading and reporting and has been able to touch around 150 locations. They aim to cover 3000 locations in coming years.

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Read all the issues of InnoHEALTH magazine:
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