Doctor Archives - InnoHEALTH magazine

MASC Medical

InnoHEALTH magazine digital team — Mon, 30 Jan 2023 08:04:37 +0000

Patients are clients who seek medical attention from expert medical professionals. They expect a certain degree of customer service every time they visit their primary care doctor. It’s essential that, as a medical practitioner, you and your staff will be able to provide excellent service to your clients to satisfy their needs.

Regardless if you’re in your primary practice or on another site, you must ensure that you will provide top-notch service for your patients. If you wish to expand your practice and secure another post that can provide decent exposure, look for reputable locum tenens companies that can help you locate great opportunities. These enterprises can find you suitable posts that will also help you develop bedside manners and customer service.

Here are some ways to ensure that you provide excellent customer service.

Make a good first impression

One of the best ways to ensure you will have an exceptional reputation is to have a good first impression with your patients. This means you must hire courteous and personable front office personnel who can multitask efficiently. You must also be presentable for your patients so you can exude professionalism.

Keep your promises

Among the things that clients appreciate is you or your staff keeping your promises. If you promise you’ll get back to them, ensure that you will get back to them within the specified timeframe. You must make it a habit to keep your commitments to your patients, regardless of how small the act may seem. It will make them loyal to you and help your reputation.

Listen to your patients

Most patients will complain to their doctors about medical conditions that bother them. However, some patients will also take time to tell you about their day, so lend an ear to them. You can act as a sounding board for your clients when they need to get something off their chest. They’ll appreciate it if you can listen to their gripes and advise them as needed.

Make it easy for your patients

Patients appreciate not waiting too long before receiving appropriate care and attention. However, if you want exceptional service, ensure that you schedule their appointments properly and conduct the necessary follow-ups to ensure they arrive on time. Ensure that you also have a good room layout to make the area easy to navigate.

Go above and beyond what your patients expect

Try to deliver more than what your clients expect. They expect that you will provide them with adequate medical attention and expertise, so it will be essential for you to be as detailed as possible in providing service.

Be open to making apologies

You and your staff aren’t expected to be perfect, but if you commit mistakes during the day, don’t be afraid to admit to your patients that you made a blunder. This will make you more relatable and will help cement your relationship, as it can cultivate loyalty to your practice.

Endnotes

Being a doctor can be challenging, with various demands on your time and practice. However, you can provide exceptional customer service by being detail-oriented and attentive to your patients’ needs.

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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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New Age Solutions for Diabetic Care

InnoHEALTH Magazine — Wed, 13 Feb 2019 10:47:37 +0000

Diabetes may not have a medically approved cure yet, but it sure can be turned a few knobs down with a well-balanced diet, regular exercise, and medication. Of course, one should follow a doctor’s advice for a lifestyle change. In the real world though, you look longingly at those French fries and convince yourself a few wouldn’t harm. Or you hit the snooze button and tell yourself, I will go for that long run tomorrow. And on and on you go, till your next visit to the Principal’s office, aka your doctor, where you hope miraculously for an improved report card. You are not alone my friend. Sticking to a regime requires discipline or an occasional reminder from your wellwishers (if you were me). A sight towards your goal and how far you have come in conquering it help matters further. Thankfully, plenty of new-age start-ups are here to make diabetes management and lifestyle changes easier.

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?

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Study : Long Term Impact of Stroke

InnoHEALTH Magazine — Mon, 26 Nov 2018 09:36:42 +0000

A new study looks at what problems affect people most after a stroke and it provides a broader picture than what some may usually expect to see.

Stroke affects more than just physical functioning, according to a study recently published in Neurology, the medical journal of the American Academy of Neurology.

“People who have an only mild disability can often have ‘hidden’ problems that can really affect their quality of life,” said study author Irene L. Katzan, MD, MS, of the Cleveland Clinic in Ohio and a member of the American Academy of Neurology.

“And for people with more disability, what bothers them the most? Problems with sleep? Depression? Fatigue? Not many studies have asked people how they feel about these problems, and we doctors have often focused just on physical disability or whether they have another stroke.”

The study involved 1,195 people who had an ischemic stroke or a stroke where blood flow to part of the brain is blocked. They were asked questions about their physical functioning, fatigue, anxiety, sleep problems, thinking skills such as planning and organizing, how much their pain affects other aspects of their life and their satisfaction with their current social roles and activities. Participants took the questionnaires an average of 100 days after their stroke, and about a quarter of the participants needed help from a family member to fill out the questionnaires. Researchers also measured their level of disability.

The people with stroke had scores that were considerably worse than those in the general population in every area except sleep and depression. Not surprisingly, the area where the people with stroke were most affected was physical functioning, where 63 percent had scores considered meaningfully worse than those of the general population, with an average score of 59, where a score of 50 is considered the population average.

On the question about whether they were satisfied with their social roles and activities, 58 percent of people with stroke had scores meaningfully worse than those of the general population.

“People may benefit from social support programs and previous studies have shown a benefit from efforts to improve the social participation of people with stroke, especially exercise programs,” said Katzan.

The thinking skills of people with stroke in executive functions, or planning and organizing, were also affected, with 46 percent having scores that were meaningfully worse than the population average.

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“The social participation and executive functioning skills are areas that have not received a lot of attention in stroke rehabilitation,” Katzan said. “We need to better understand how these areas affect people’s well-being and determine strategies to help optimize their functioning.”

Limitations of the study include that the questionnaires did not ask about other problems that can occur after stroke, such as communication issues.

Also, the study participants had milder strokes on average than people with stroke overall and the average age of participants was 62, which is lower than the average age of 69 for people with stroke overall.

Informatively, American Academy of Neurology is the world’s largest association of neurologists and neuroscience professionals, with 34,000 members. AAN is dedicated to promoting the highest quality patient-centered neurologic care. A neurologist is a doctor with specialized training in diagnosing, treating and managing disorders of the brain and nervous system such as Alzheimer’s disease, stroke, migraine, multiple sclerosis, concussion, Parkinson’s disease, and epilepsy.

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Artificial Intelligence Coming Big Way in Healthcare Sector

InnoHEALTH Magazine — Mon, 19 Nov 2018 10:13:51 +0000

Brig Arvind Lal, CMD of Dr Lal Path Labs, is a pioneer in bringing laboratory services in India at par with the western world. In 1977, he took charge of the medical diagnostics laboratory founded in 1949 by his late father. Under his expert guidance and leadership, the initiative has become one of the most reputed laboratories in Asia, having to its credit quality accreditations from various national and international bodies.

The critical care environment has undergone significant alterations in the past several years. This has happened because our lifestyles in the fast-paced lives of modern India are ensuring that most people, in the age group of 30-50 years are falling prey to life-threatening cardiac diseases and strokes, in addition to diabetes, hypertension, cancers, liver, kidney and lung diseases – these diseases being called Non-Communicable Diseases or NCDs. They are now responsible for killing more than 65% of our population says Dr Arvind Lal, known for his diagnostic labs across the country.

Flagging concerns on such trends, these patients need high-cost intensive care, be it for complications of heart attacks, strokes, diabetes, hypertension, cancer or lung diseases. This is where the importance of Point-of-care testing (POCT) comes in. It helps in almost diagnosing the patient instantly and improves the physician’s ability to take immediate corrective action and decreases hospital stay. One such test is Troponin – that has revolutionized cardiac care by diagnosing heart attacks or myocardial infarction.

This article is based on the keynote address on the occasion of 2nd Annual International InnoHEALTH Conference 2017 – ‘Transforming Healthcare Through Innovation’ in New Delhi, said there are numerous promising diagnostic technologies. The key message is that in a country where 70% of the population lives in rural surroundings, ‘it is our duty to rapidly adopt disruptive innovative affordable technologies including telemedicine. Thus, our underserved population would be able to avail of the best treatment possible and bring in massive visible change’. He said the importance of bringing quality healthcare needs no reminder and the time has come for India to change the direction of healthcare for the masses.

Healthcare is a right – and access to good healthcare should not depend on where one lives and how much he or she earns. But sadly, that is exactly what plagues India’s healthcare today, he lamented. India faces a severe shortage of both hard infrastructure and talent. With about one doctor and one functional bed per 1000 population, healthcare is truly underserved in India. Add to this the regional imbalances and variations in healthcare delivery. The healthcare infrastructure is skewed towards urban over rural India.

Although rural India accounts for about 70% of the population, it has less than one-third of the nation’s hospitals, doctors and beds, resulting in large disparities in health outcomes across urban and rural India. British Medical Journal (BMJ) has observed that there is a remarkable saving of lives in India if good healthcare facilities consisting of operation theatres, surgeons, anaesthetists, blood banks are available within 50 kilometres of the patient providing quality medical services within the ‘golden hour’.

Though there has been a sea change in the last five decades, India now needs to reinvent the field of diagnostics as laboratory tests are responsible for 70% of all clinical or medical decisions.

In today’s life where internet rules the roost, the patients have become very knowledgeable, thanks to the globalisation of healthcare, and are demanding very high-quality healthcare for themselves. They are insisting on a very wholesome and satisfying experience rather than being told that the ‘treatment is over’.

Soon, a time will come when the tests shall be ordered by the patients based on clinical history and clinical findings that shall be answered by an Artificial Intelligence (AI) application. Artificial intelligence has already found several areas in healthcare from the design of treatment plans to assist in repetitive jobs to medication management and drug designing. The most obvious application of artificial intelligence in healthcare is data management. Collecting it, storing it, normalizing it, tracing its lineage – it may well be the first step in revolutionizing the existing healthcare systems.

Recently, the AI research branch of the search giant, Google, launched its Google Deepmind Health project, which is used to mine the data of medical records in order to provide better and faster health services. The project is in its initial phase, and at present, they are working with Moorfields Eye Hospital of NHS Foundation Trust, UK to improve eye treatment.

Just a few years ago the patient after giving the sample used to come back in the evening to the lab to collect a physical copy of the test report. This was replaced by making the report available on the internet that could be downloaded by the patient in the comfort of his home. These days this has been further replaced by making available an App on his mobile phone wherein he can book an appointment for the sample to be collected at home and the report being later available on the same mobile App.

‘IBM Watson, whose headquarters I had the privilege of visiting a few months back in the Silicon Valley, is an AI-based engine that has launched its special program for oncologists to provide clinicians evidence-based treatment options. The program has an advanced ability to analyze the meaning and context of structured and unstructured data in clinical notes and reports in its encyclopedic memory that may be critical to selecting a treatment pathway’. IBM launched another algorithm called Medical Sieve. It is an ambitious long-term exploratory project to build a next-generation ‘cognitive health assistant’ that is able to analyze radiology images to spot and detect abnormalities faster and more reliably. This shall help radiologists in the future to look at the most complicated cases where human supervision is essential.

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‘Wearable Tech is another area which I am personally very excited about. It has the potential to change the world as it helps people understand their own bodies by using mass data collected on a daily basis. From fitness bands to smartwatches to eye based wearables, they are being adopted widely. Take the case of Zephyr’s Anywhere Bio Patch which is an FDA-approved, small device that is attached to a patient’s chest and monitors their vitals minute-by-minute and collects medical-grade data for doctors’ use. These devices will connect our organs digitally, enabling disease detection at very early stages. It has the potential to bring down cardiac and other deaths drastically. This offers immense potential to do remote testing, monitoring and thus assisting the doctor in timely treatment’.

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Point of Care Testing: Technological advancements in laboratory automation, including POCT, and initiatives to increase patient satisfaction are transforming the clinical laboratory market. POCT has come a long way from a handful of simple tests to a multibillion-dollar global market that holds great promise for the future. Not so long ago, laboratory data would often arrive at the bedside too late to be of significant use in the active, continuing care of critically ill patients. Now, most clinicians acknowledge that POCT is a prerequisite for early recognition of life-threatening conditions as they require that laboratory results are made available in real-time and, if possible, at the critically ill patient’s point of care. The College of American Pathologists defines POCT as tests designed to be used at or near the site where the patient is located, that do not require permanent dedicated space, and that are performed outside the physical facilities of the clinical laboratories.

Examples include kits and instruments that are hand-carried or transported to the vicinity of the patient for immediate testing at that site (e.g. capillary blood glucose) or analytical instruments that are temporarily brought to a patient care location (like operating room, intensive care unit). In many cases, the simplicity was not achievable until technologies developed that was simple and affordable. For example, various kinds of urine test strips have been available for decades, but portable ultrasonography did not reach the stage of being advanced, affordable and widespread until recently. Similarly, pulse oximetry can test arterial oxygen saturation in a quick, simple, non-invasive, affordable way today, but in earlier eras, this required an intra-arterial needle puncture and a laboratory test. Thus, over decades, testing continues to move toward the point of care.

The lab-on-a-chip (LOC) is another device that integrates one or several laboratory functions on a single integrated circuit (commonly called a “chip”) of only a few square centimetres to achieve automation and high throughput screening. Imagine that a patient comes to one of our 2,100 collection centres in the remote tier three or tier four towns in India with the high fever. We take a drop of blood from his finger and inform the clinician almost immediately that the patient is suffering from Chikungunya and not from Malaria or Typhoid, or Dengue fever or Japanese Encephalitis – all in a matter of minutes! The driving notion behind POCT is to bring the test conveniently and immediately to the patient. Needless to add, the patient’s data by POCT shall be made available to update the patient’s electronic health records (EHR).

Talking about POCT Instruments: Currently, two broad type of POCT instruments are available: Small benchtop analyzers (for example, blood gas and electrolyte systems) and handheld, single-use devices (such as urine albumin, blood glucose, and coagulation tests). Now let us talk about if POCT is Boon or Bane? The strong point of POCT is speed and the rapidity with which it shall save lives in emergencies. As India marches towards quality healthcare delivery, in course of time regulatory compliances shall have to be adhered to in the interest of the patient’s health.

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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.)

The post Rural healthcare: Solutions lie in innovations appeared first on InnoHEALTH magazine.

TIME TO TAKE SOME INTELLIGENT DECISIONS

InnoHEALTH Magazine — Tue, 28 Nov 2017 10:34:57 +0000

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The author Mikko Kotila has 12 years of continuous research and development in machine intelligence, and is the core developer of Autonomio, the first rapid machine intelligence prototyping platform for non-programmers. Mikko is the principal of Botlab, a nonprofit foundation focused on long-term thinking on machine intelligence, and decentralization, and a co-founder of Autom8, one of the world’s first deep learning focused startup foundries.

A new paradigm for use of machine intelligence in healthcare

Many national economies are on the brink of collapse under the burden of state-supported health insurance programs. For example in the US, a country that does not provide universal healthcare, the national debt is expected to double as a result of healthcare related liabilities over the next three decades. There is an even greater price to pay for the inefficiencies found in healthcare systems. When already overburdened systems are pushed to their limits, healthcare professionals are forced to make bad decisions.

In a startling example, during Hurricane Katrina, doctors and nurses at New Orleans’ Memorial Hospital found themselves incapable of making even simple decisions. In her Pulitzer Prize-winning coverage, Sheri Fink reported how patients deemed the least likely to survive, were injected with a lethal combination of drugs — even as the evacuation was already on its way.

This chilling anecdote sheds light on a greater problem underpinning many of the biggest threats hurting human society and the eco-system of our planet.

We humans are not equipped for making good decisions under stress. Positive outcomes across a multitude of fields, including healthcare, largely depend on the ability to make good decisions under unpredictable and stress inducing conditions. We, humans, are not only bad at making decisions under pressure but are poorly equipped for making any rational decisions at all.

Daniel Kahneman, in his Nobel Prize winning work on decision making, explains how after years of deliberation leading to a single decision, the person making the decision might still completely ignore the entire process of deliberation and instead make a decision driven by emotions in a whim of the moment.

An Age of Automated Decision Making

As the information age is about the focus on automating processes related to access to information, the next age, the age of “decisioning”, will focus on automation of processes related to decision making. Whereas humans are excellent in pattern detection and pattern making, an essential requirement for creating intelligent computer systems, computers are strong in making decisions where processing of facts is of vital importance. This almost magical ability of computing systems to process information includes the capability to identify and utilize extremely subtle connections between otherwise seemingly disconnected pieces of information, a feat us humans could never do on our own with the kind of precision and scale even a simple computer system can. In no other field, rational decisions are of such vital importance as in healthcare.

Not only healthcare is the most significant economic liability for many nations, but it is also the only field of practice affecting people’s everyday lives that can be considered truly as “life and death” matter. It is, therefore, the area where the human society most desperately needs help.

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Computer-aided decision systems can be categorized into three evolutionary stages, each with a corresponding quality of results, and the requirement for a level of human involvement.

Whereas up until a few years ago, most systems were still ‘descriptive’ with some examples of ‘predictive’ decision- making support systems, in the last two years the developments in the field of machine intelligence have for the first time made the dream of prescriptive expert systems realistic. Recent advancements in both open source software and commercial hardware have paved the way for rapid prototyping of ideas that promise to revolutionize the way decisions are made across a multitude of fields, including healthcare.

Examples of these advancements include deep learning platforms such as Google’s TensorFlow and Keras, unstructured data processing innovations such as word vectorization, and Nvidia’s data processing focused GPU product-line.

Regardless of the tremendous promise and the recent hype surrounding machine intelligence, some significant concerns remain without any serious attention. While machine intelligence innovators would like to focus on showcasing “what’s possible,” before introducing new ideas and processes into the healthcare apparatus, it is far more important to ask “what could go wrong”.

Morality and Healthcare Algorithms

Every machine intelligence solution can be reduced into two aspects; the data inputs available for the solution, and the means by which the solution process those inputs. These two act as the causes for the result the system provides. Algorithms underpin every decision a computer system makes, regardless of the kind of system it is. The unique feature in this regard, of modern machine intelligence systems, such as those based on neural networks and the deep learning method, is that humans cannot audit them. It is for this reason that we can get surprising results us humans could not arrive at without working with machine intelligence, and it is important to understand that surprising results may also be negative results. In many cases, adverse results in the healthcare context arise from causes that were set years or even decades before, and as of today, it is virtually impossible to establish true causality in such cases. This means that it would be almost impossible to know if a given machine intelligence solution is contributing positively in the long term or just driving shortterm efficiency. This will hold true at least for the next 100 years, or as long as it takes to understand causality in results that take decades to mature.

When in the 1980’s game theory-based principles were widely introduced in the western healthcare context, nobody predicted the consequences. For example, while nurses and doctors were incentivized to meet certain productivity quotas such as the number of days spent in the ICU, mortality rates of patients skyrocketed as a result of ICU beds being more available. In effect, a quota scheme is an example of a simple algorithm and can be used to highlight the danger that comes with introducing machine intelligence into healthcare.

This applies in particular in countries with universal healthcare and poorly performing national economy. Under such conditions, humans that make the decisions about the use of machine intelligence in the national healthcare system, are under tremendous stress. Not only their decisions affect individual patients’ lives but also have the potential for changing the destiny of an entire nation. It is very hard to see how under such conditions, non-experts, being bombarded with endless hype by self-proclaimed machine intelligence experts, would be able to make the right decisions.

In fact, government officials, healthcare professionals, nor computer and data scientists, are formally trained in morality and often lack even the most basic understanding of ontology, epistemology, and formal logic. The three legs of the stool on which rational decisions sit. As a result, as we have seen through examples in financial markets, online advertising, and other early embracers of algorithmic decision making, we end up with so-called greedy algorithms that optimize towards a given end ruthlessly without caring about anything else.

Unlike healthcare professionals, these algorithms are not afraid of losing their livelihood and reputation as a result of making the wrong decision that ends up hurting people.

While a healthcare facility or a professional working in one, could be sued for damages, in the case of machine intelligence systems, liabilities in the healthcare context have so far not been defined.

Security in Healthcare Systems

In the light of the recent events regarding ransomware, and the rapid growth in its popularity as a cybercrime tool, it does not seem too far-fetched that in the near-future entire hospitals will be targeted and held for ransom. Indeed many hospitals had already become victims of ransomware as a consequence of passive global or national attacks. In the recent WannaCrypt ransomware attack individual medical devices were rendered temporarily useless after being infected.

Siemens released multiple warnings about its healthcare devices being possibly vulnerable to WannaCrypt. Beau Woods, deputy director of Cyber Statecraft Initiative at the Atlantic Council said that it was likely that many important medical devices such as MRIs and other crucial computer-aided systems were rendered temporarily useless by the attack.

These examples show how healthcare organizations and their technology partners are currently incapable of securing important systems. Whereas in the human operated healthcare apparatus, the devastation is so easy to create, in a highly automated machine intelligence based healthcare apparatus, the problem would be significantly amplified.

Here too, regarding security and healthcare systems, we have to seriously consider the implications of the machine intelligence ideas that are adopted today, regarding the threat landscape over the next few decades.

For example, current cryptographic methods are all based on so-called key exchange cryptography. Any sudden improvement in computation power, for instance in the form of quantum computing becoming practical, will lead to an immediate collapse of the key exchange based security paradigm. In a machine intelligence dominated healthcare apparatus, collapse of the key exchange based security paradigm has the potential for leading to the greatest human travesty in the history of the world. In the absence of serious discussion about such longer-term threats, it will not be possible to make the right decisions regarding decision-making automation and wider adoption of machine intelligence based expert systems in healthcare.

The Importance of Human Touch

Sometime in the distant future, we may be able to completely automate certain key aspects of healthcare, such as triage management. Even then it is of significant importance to not lose sight of the essence of healthcare; taking care of people. Taking care of people is one part taking care of their physical body, and one part taking care of their feelings.

In a completely machine intelligence and robotics based triage management approach it would be hard for patients to feel that they are being taken care of in the way they feel when an actual doctor is treating them.

On the other hand, in a very short period of time, the doctors would lose their ability to deal with the basic day-to-day taking care of patients that still today keep them overworked.

Because algorithms don’t feel anything, for example, empathy, it is also likely that as specialist doctors increasingly get their inputs from those algorithms, they become further distanced from the human aspect that some argue is necessary for healing practice. In this light, perhaps it’s more reasonable for the allopathic medicine to seek intelligence from its eastern counterparts such as TCM, TTM, and Ayurveda, as opposed to seeking it from machines. Machines that ultimate base their decisions on the combination of the information they are receiving and the algorithms that process the information. Both the information and the algorithms being a product of, and therefore limited by, the people who produce them.

Combining the instruments and other marvels of the western symptomatic healthcare approach with the more holistic but in some cases inferior Eastern practices, have the potential for driving significant change within the healthcare system as we know it today.

In terms of specific machine intelligence implementations, as the point-of-view presented in this article clearly shows, the focus should be on long-term macro effects of such implementations, as opposed to focusing on short-term and micro context.

Perhaps in a future world where the western and eastern medicinal practices are better integrated and institutionalized into a new era of taking care of patients, we will be better equipped to handle the challenges that come with machine intelligence focused healthcare.

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Read all the issues of InnoHEALTH magazine:
InnoHEALTH Volume 1 Issue 1 (July to September 2016) – https://goo.gl/iWAwN2
InnoHEALTH Volume 1 Issue 2 (October to December 2016) – https://goo.gl/4GGMJz
InnoHEALTH Volume 2 Issue 1 (January to March 2017) – https://goo.gl/DEyKnw
InnoHEALTH Volume 2 Issue 2 (April to June 2017) – https://goo.gl/Nv3eev
InnoHEALTH Volume 2 Issue 3 (July to September 2017) – https://goo.gl/MCVjd6
InnoHEALTH Volume 2 Issue 4 (October to December 2017) – http://amzn.to/2B2UMLw

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EXPANSION AND INNOVATION IN HEALTHCARE

InnoHEALTH Magazine — Tue, 07 Nov 2017 08:19:58 +0000

Apollo Sugar Clinics on expansion mode

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Apollo Sugar set up by Apollo Hospital group in collaboration with pharmaceutical company Sanofi, plans to set up 100 clinics by the end of 2018 and 300 clinics over the next three years. It currently operates 52 clinics across India. It aims at providing quality comprehensive diabetes care under one roof at cheaper rates. Apollo Sugar is focusing on tier-2 cities and rural areas for expansion. Right now, it is operating in metro cities like Chennai, Bengaluru and Hyderabad.

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According to estimates of Union Health Ministry, the total spending on diabetic care is about Rs 1.5 lakh crore per annum in India and every patient spends about Rs 30,000 a year on diabetic care.

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ICESENCE3

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IceCure Medical’s IceSence3 has been used by US doctors since 2011 to remove benign breast lumps in a 10-minute ultrasound guided procedure that penetrates the tumor and engulfs it with ice. The system is being clinically tested over the next few years against small malignant breast tumors as well. The procedure is done in a doctor’s office, clinic or breast center, and the patient can get up and leave afterward with no recovery period or post-care.

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TOTAL LIFT BED

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VitalGo Systems’ Total Lift bed is the world’s only hospital grade bed that can elevate a patient from a lying to a fully standing position and all points in between for treatment and transfer with no lifting required of the caregiver. Total Lift beds are used in the acute-care and home-care markets in the United States.

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