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

Health leaders and executives globally are focused on addressing a key strategic question: “How do we activate, cultivate, manage and accelerate digital innovation within our organizations?”

There’s a right and a wrong way to address this question and successfully ignite digital transformation. Let’s start by taking a look at the wrong way.

Why the traditional technology-centric approach towards digital transformation is flawed ?
Customarily, health organizations have taken a very technology-centric approach to fostering digital transformation. Here is a simplified overview of how this strategy is usually applied:
• Step I: Learn about a new technology trend or tool
• Step II: Decide to implement pilot projects to test it
• Step III: Exemplify it for how the organization is digitizing its operations and approach
While simply testing various technologies is clearly an important first step for organizations seeking to further their digital transformation efforts, this approach has the following flaws.
• It promotes silo building: In many cases, the digital health innovation work is disconnected from the larger organizations, promoting the creation of innovation silos that are distant from other parts of the organization, and thereby receiving less support in due course of time.
• It is difficult to scale: Pilots are by their very definition, short-term experiments. Without careful consideration of how they fit into larger strategic and tactical objectives, they can be difficult for the organization to scale over time.
• Lessons and best/worst practices are often not shared broadly: Because these pilots are often being led by small teams or innovation leaders, sometimes — because they are disconnected from the larger organization — lessons from these pilots are not being shared widely or used to tackle related businesses and organizational issues.
• Leadership and budgetary support is limited: Leadership may be aware of these technology-focused projects, but view them as short-term “side projects”, disconnected from the larger strategic and tactical imperatives. This lack of leadership support translates into more limited budgets for the initial and ongoing digital innovation.
• It is hard to prove return on investment (ROI): Without carefully thinking about how these innovations will either reduce expenses or drive revenue (via improvements in workflows, better health outcomes, etc.) it is often difficult to determine a positive ROI for these efforts.
It is clear that the technology-centric approach may deliver short-term benefits in terms of improving understanding and the ability to implement certain digital innovations. However, it has significant limitations when viewed from the context of helping health organizations develop a more viable and sustainable digital transformation strategy.
Fortunately, there is a better way!

A superior approach: Understand and manage the four fundamental forces shaping digital innovation at health organizations
For more than 10 years, I have worked with leading health organizations (including government, pharmaceutical firms and payers) on various digital transformation-related initiatives. And, late last year, I developed a unique research initiative called the State of Digital Health Innovation. This study is designed to identify the forces that are accelerating or slowing digital initiatives at health organizations. This experience and research clearly demonstrates that while technology plays an important role in fostering digital transformation, it is far from the only factor. In fact, as illustrated in the image below, there are four.

Health organizations that fail to consider, account for and manage the four fundamental forces shaping digital innovation will ultimately be less successful in their transformation efforts. These forces are:

• Human: This involves:
• Ensuring that innovation initiatives — whether large or small — are supported by leadership. This does not just mean garnering support from the C-suite, but also ensuring that the team and functional leaders understand how these innovations can benefit them in due course of time.
• Creating opportunities for people throughout the organization to be educated about innovation — not only the ones of immediate relevance, but also other, potentially high-impact trends and technologies that are being developed almost daily. We found that a major drag on innovation is personnel who are incurious and uneducated about the evolving digital technology landscape. Going to events, and holding innovation days are not enough. Organizations must find ways to inject innovation into executives’ daily routines and workflows in easy and unobtrusive ways.
• Economic: This is about answering a fundamental question on innovations — even at the earliest stage. Can technology increase revenues or reduce health spending (via improved workflows, better health outcomes, etc.)? Failing to address this question early and often is another major barrier to successful digital transformation. If internal innovators can’t demonstrate the potential positive impact of these digital activities on the organization, they will be less likely to be funded or receive support from team, functional and organizational leadership.
• Technological: This is not just about ensuring that people within the organization have the ability to execute around one or more innovations. It is also about asking tough questions regarding whether the key stakeholders who will utilize these technologies, have the capacity to use them. There are many examples of organizations that have deployed digital solutions to patients, providers and other stakeholders; solutions that have failed because innovations were too difficult to use, people were unfamiliar with the technology and other factors. Digital transformation will be stalled if internal and external technology-related issues are not considered and addressed.
• Policy/regulatory: This area is one of the vital, but frequently overlooked, forces impacting digital transformation efforts. There are two sides to this issue:

• Internal policies and procedures: For many organizations, well-crafted policies and procedures can be the lubricant of innovation progress. If effective policies exist, executives and leaders can move forward with a clear understanding of the guardrails that shape their decisions and processes. All-to-often policies are too restrictive, but if crafted well, they can also spur innovation because people can move forward with confidence and certainty.

If policies don’t exist, it will be much harder to scale innovations because people will be uncertain about what will be and will not be acceptable. For example, in the United States, the lack of clear policies around how social media technologies could be used by internal stakeholders, prevented many pharmaceutical companies from utilizing social tools more effectively and widely. A major reason for the lack of policy development was limited guidance from government agencies about what was acceptable from a social media implementation perspective.

• Legal/regulatory clarity: Another major issue impacting digital transformation across organizations is the legal and regulatory landscape, specifically, whether government is developing innovation-friendly legislation and regulations. This means that even if organizations get everything right internally, external legal and regulatory forces can have a significant impact on their success.

One relevant example has been the slow uptake of telemedicine technologies in South Korea. Although innovators have developed unique solutions, government has been slow to test these tools and produce legislation enabling physicians to examine patients and prescribe medications via remote technologies such as mobile and video. Failing to become educated about the shape, scope and future of the legal and regulatory landscape can doom digital acceleration efforts.

What is the status of health organizations in their efforts towards digital transformation and what are the common barriers to success?

It can be helpful for health organizations seeking acceleration of digital transformation to have a sense of how their national and international peers are doing. In addition, it can be beneficial for them to have a clear communication during the overall digital innovation process and for quickly identifying (and addressing) areas of strengths and weaknesses.

To help, I developed and released an industry-first digital maturity framework called the Digital Health Innovation Acceleration Curve (displayed below) in mid-2015.

This framework organizes organizational innovation activity into three stages:
• Stage I – Awareness and education: Becoming aware of and informed about new digital innovations
• Stage II – Engagement and capacity boosting: Engaging in pilot projects and other implementations that help build knowledge and understanding
• Stage III – Proficiency and scaling: Having the knowledge, ability and focus to scale innovations across the enterprise, use case studies, patients’ groups, therapeutic categories and more

This innovation maturity model is at the center of our State of Digital Health Innovation research initiative. Study participants from health organizations (and their partners, e.g., startups, technology firms, consultants) rated organizational readiness and ability in each of the four areas (economics, human, policy, technological) discussed previously. Scores in each of these areas were calculated and aggregated to determine organizations’ overall position on the Digital Health Innovation Integration Curve and provide important information about their strengths and weaknesses.

Overall, (as illustrated above) Wave I of this study reveals that only 5% of organizations globally are at Stage III, i.e., operating at the highest level of proficiency and benefitting from a digital innovation perspective. (State of Digital Health Innovation study)1

What is holding back digital innovation and transformation efforts?
We found that:
• Among Stage I organizations: Limited budgets, lack of technological ability and low levels of innovation education and knowledge among executives and others are major reasons behind organizations not being at Stage II

• Among Stage II organizations: Digital transformation efforts are hampered by limited budgets, lack of internal policies guiding innovation deployment and, in some technology areas (particularly wearable), limited technical ability and knowledge

The road ahead: Towards a more holistic digital transformation approach

Leaders and executives at health organizations struggling to ignite innovation and guide digital transformation have long understood the problem, but have had limited tools they could use to treat it successfully.
What the State of Digital Health Innovation research initiative provides is a means they can use to quickly assess (by participating in the research), identify and remove roadblocks to digital transformation progress.
It may be difficult to change organizational culture to move towards strategies that favor holistic rather than technology-centric digital transformation approach. However doing so is vital if we are to explore new opportunities to realize the promise of digital revolution for physicians, patients, caregivers, institutions and others in the health sector.

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