“The healthcare industry is in the midst of a technological transformation with the goal of improving patient care, and IT needs to be ready to support this progression.” By Susan Biddle, Sr. Director of Healthcare, Fortinet.

During the past decades, many healthcare organizations have incorporated digital technologies into their operational sectors. However, they frequently adopted a fragmented strategy for many initiatives, such as installing electronic health record (EHR) systems, developing apps, and experimenting with disruptive technologies like artificial intelligence (AI), while holding a conventional approach in their treatment procedures and consumer engagement. Only recently have we begun to see technology being leveraged to deliver accurate, user-friendly solutions that are supported by reliable data.

Thus, emerging technology is ushering in a new wave of developments in healthcare fundamentally changing the ways of patient consultations, diagnosis, and treatment procedures. The digital era opens the scope of rendering remote health check-ups and treatments. Over the years, the healthcare sector has undergone some transformational changes in the areas of technology usage. However, the transition in the healthcare domain was happening considerably at a slower pace as compared to the other industries until the pandemic struck the overnight service response to telemedicine, monitoring the health remotely. This indeed turned out as a convenient and safer way to manage health issues not only during the COVID 19 times but also times in general. According to the Centers for Disease Control and Prevention (CDC) estimate there are 1.7 million hospital-acquired infections annually, which lead to 99,000 deaths. 

Key technology drivers to bring change in the healthcare practices.

Remote Patient Monitoring

RPM is essentially the use of interconnected electronic tools to record patient health and medical information in one place, which is then examined by a clinician remotely. RPM enables practitioners to monitor patients in-between clinic visits or when in-person care is not feasible. It can be used to treat both acute and chronic diseases. RPM involves the use of many different kinds of equipment, including blood glucose meters, blood pressure monitors, weight measuring scales, oximeters, heart monitors, and much more. Moreover, other types of RPM include wearables like Fitbits and smartwatches that are designed to collect the data of users’ personal health and exercise. These are useful to track user’s overall health conditions and treat long-term diseases. This practice is becoming so beneficial that 88% of healthcare providers had invested in or were evaluating adding RPM to their practice. In addition to removing time and geographical restrictions, this technology has also increased patient awareness of and engagement in the treatment process.

Artificial Intelligence

The application of AI in medical health has had it substantiate share of debate considering the fact that the use of AI in medical practices can literally be a matter of life and death. Numerous instances have shown how AI has the ability to help physicians, nurses, and other healthcare professionals in their day-to-day job. To briefly state some of the benefits, AI in healthcare can improve the overall treatment outcome, improve people’s wellbeing, provide accurate data for diagnosis, and so on. By analyzing huge data collected from public sectors and other standard sources, AI can help predict the spread of many contagious diseases. In fact, AI has the potential to be a key component of the global public health effort in the fight against pandemics and epidemics.

Furthermore, AI can help to resolve mental illness by detecting behavioral signs of anxiety and depression with over 90% accuracy.  Brain-computer interfaces (BCIs) backed by artificial intelligence can help people suffering from neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. In conditions when the patient is unable to communicate, BCIs acquire brain signals, analyze them, and translate them into commands that are relayed to output devices. Such technology terms that once seemed like jargon are becoming today’s imperative for solving health issues.

Digital Therapeutics (DTx)

Nowadays, we are capable of monitoring our health easily with the help of various apps, mobile devices, sensors, virtual reality, IoT, and other tools. Digital therapeutics, one of the most popular trends in the mobile health (mHealth) domain, implies the confluence of software and healthcare to deliver better clinical outcomes. It’s a user-empowering technology that provides customized health services enabling users to monitor and prevent health problems. There is a fair possibility that DTx helps in curing some of the complex ailments like Alzheimer’s, insomnia, type II diabetes, congestive heart failure, depression, etc.

Conclusion

Currently, the healthcare industry is at the pinnacle of success in terms of technological expertise. The pathbreaking developments brought about by advanced technologies are going to positively impact millions of lives across the globe. Going onwards there is an ample hope of finding cures for many untreated ailments. Most importantly, the healthcare sector is also experiencing a transformation in its approach of giving care. Physicians are showing more openness and faith in relying upon information about patients gathered from DIY devices. As a result, there has been a tremendous improvement in patient engagement with the care provider and the treatment process.

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Throughout history, technology has surpassed human capabilities time and again. Since the Industrial Revolution people had been outsourcing the labor work to machines- leaving the “thinking” work to humans. Consider the influence of inventions such as steam engines or the telephone on the lives and livelihoods of people. But this time it’s different; today’s technologies like AI/ML, Blockchain, or IoT in fact have the potential to virtually replace people’s jobs across domains.

The notion of Artificial Intelligence and the thought “can machine think?” crossed minds back in the 20^th century. Fictional characters like the humanoid robot, Gort, or the ‘heartless’ Tin Man gave us glimpses of the capabilities of machines to think and act or even outsmart humans. Alan Turing, the British logician and computer pioneer marked the beginning of AI. He explored the mathematical possibilities and gave the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence. Since then, AI has progressed from simply defeating humans in games like chess and board games using essentially memory and computing power, to becoming a part of our daily life, using more human-like skills such as speech recognition, image recognition, spatial judgment, conversational ability and so on.  Many of these capabilities are already built into, the now commercially available and ubiquitous, digital assistants like Apple’s Siri, Google Now, Amazon’s Alexa, and Microsoft’s Cortana. From problem-solving logic to understanding and influencing human emotions and decisions, AI has come a long way. This journey has been accelerated by forming a synergetic relationship with big data, in order to gain better clarity and a deeper understanding of the factors that influence our decisions.

According to Microsoft co-founder Bill Gates, the rise in artificial intelligence will help society to be able to do more with less. “AI is just the latest in technologies that allow us to produce a lot more goods and services with less labor. And overwhelmingly, over the last several hundred years, that has been great for society,” says Gates.

Redefining work to restore humanity

Despite recent claims that AI would soon surpass human intelligence, this idea can be disproved by analyzing what AI cannot do in comparison to natural intelligence. Our existing work process, as we know is systematic and standardized with a data-driven approach, which artificial intelligence can clearly perform more efficiently than humans. Having the technology to take up the rigorous and mundane tasks, people have a significant opportunity to rethink the fundamentals of “value creation” “meaningful work” and “work-life culture” across industries. Therefore, the potential of AI to perform automated tasks allows us to explore unique human characteristics like imagination, creativity, curiosity, morality, ethics, and emotional, cultural, and social intelligence. However, the fact that AI is growing at an electric speed also creates a lot of challenges for us to acclimatize and be ready for future transformation.

Google’s CEO Sundar Pichai made a bold statement on AI, saying “It’s the most important thing humanity is working on. It is more profound than, I dunno, electricity or fire”.
Perhaps that statement isn’t as far-fetched as one might believe, given how AI is already assisting humans in combating climate change, exploring space, and developing cancer treatments, among other things.

Collaborative Intelligence of Humans and AI

The key question “Can AI get dangerously out of control, and can it turn against humans?” is a difficult one to answer- yet.

We first have to discover whether AI can develop emotions, judgment, imagination, and all of those intangible abilities that make us human; only then can we decide whether we have created the best thing for the human race or our own worst enemy. Might it be too late by then- who knows?
But having a single global governing body/regulatory authority that oversees how AI is developing, and how that might impact humans using a common framework is not a bad idea. In addition, some other questions of crucial importance are how transparent is the AI algorithm? What can we do about the AI biases? Because AI algorithms are completely data-driven, it may simply replicate the existing biases in data representation. For instance, when Amazon was developing an AI-based recruiting system, they discovered a flaw: the AI was favoring male candidates over female candidates based on data from their previous hiring procedures. While humans are striving to eliminate biases from society, AI appears to automate the process without even realizing it.

Elon Musk stated that “If AI has a goal and humanity just happens to be in the way, it will destroy humanity as a matter of course without even thinking about it…It’s just like, if we’re building a road and an anthill just happens to be in the way, we don’t hate ants, we’re just building a road.”

The future might probably see the collaboration of AI and humans, closely working and improving each other’s capabilities. Human talents and the technical prowess of AI together can bring greater developments than the world has seen before. Instead of eliminating our jobs, AI can create the opportunity to make our work more humane. Many existing work processes will be disrupted, updated, and transformed. Organizations can be more concerned about their people, resulting in a more compassionate work environment. While AI can take care of desk-bound manual jobs, there are several fields such as social work, psychotherapy, or critical customer service that require natural human traits of empathy and emotions are likely to find profound importance for the betterment of society and the world.

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Transition to Big Data Processing

The advent of smartphones and SaaS systems has led to the generation of information at an enormous rate that cannot be handled by traditional data processing tools and methods. Nearly 90% of all data generated has been in the past two years (Kommandotech). The use of digital tools to analyze huge sets of data and retrieve essential information and interpretations of the data, forms the foundation of Big Data Processing. Smartphones and other devices generate vast amounts of data containing highly sensitive information like bank details, transaction details, and personal details too that could be retrieved from data storage using big data analytics, bringing about the need to create fail-safes that will prevent abuse of these tools.

Data Security using Big Data Analytics

Big Data Analytics has a wide number of applications in Data Security as it helps facilitate information retrieval from various security sources like firewalls, security devices, web traffic etc. Its ability to integrate unstructured data from multiple sources under a single analytical network enables superior data analysis and interpretation for companies and enterprises. A few of the applications of data security using big data analytics are:

• Network Flow Monitoring to Track Botnets – Analytical tools like MapReduce can identify and track infected hosts participating in a botnet by evaluating enormous amounts of NetFlow data within a short span of time, largely simplifying data processing as compared to traditional processing systems. It is the process of discovering patterns in large data sets using methods from artificial intelligence, machine learning, statistics, and database systems. Data mining is used to extract information from a data set and convert it to an analytical structure.

• Enterprise Event Analytics – Multinational Companies and enterprises generate overwhelming amounts of data every day, creating a need for highly efficient analytical tools to generate valuable information by analyzing data. An effective enterprise analytics strategy can provide a comprehensive vision and end-to-end roadmap for data management and analysis. It can help with risk management, mapping out a company’s data management architecture, identifying and removing redundant data, establishing responsibility and accountability, and improving data quality, among other things.
   
• Advanced Persistent Threats Detection – Advanced Persistent Threats are one of the most serious threats faced by organizations today. It is the strategized attack of specific, high-value assets in the digital architecture that operates in different modes like “Low profile” and “Slow” to avoid detection and prolonged execution respectively. Detection and tracking of such threats are cumbersome as huge loads of data must be evaluated to identify them, making big data analytics the ideal solution for tracking them. It is suitable for compliance needs and forensic investigations while also offering insights on user behavior that help track future threats efficiently.
• Data Sharing and Provenance – The use of big data analytical systems allow companies and enterprises to research and review the results of cybersecurity experiments conducted across the world. The Worldwide Intelligence Network Environment (WINE) (Cloud Security Alliance) provides a platform for data sharing and analysis to research on the field data aggregated online by Symantec. These platforms allow companies to test out and validate novel ideas on real-world data and compare different algorithms and systems against reference data sets to evaluate efficiency. Data Provenance is information about the origin and process of data creation.  Such information helps in debugging data and transformations, auditing, evaluating data quality and trust, modelling authenticity, and implementing access control for derived data.

Conclusion

Big Data Analytics holds the potential to unlock high levels of efficiency and performance from companies and enterprises as it simplifies data analysis of massive amounts of data and provides access to actionable information easily. The element of versatility it holds in serving various applications in data analytics makes it a critical requirement for data processing companies. Big data analytics helps in making better-informed decisions, improving the supply chain, operations, and other strategic decision-making areas.

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