By Sachin Kalra, vice president of customer success, Infostretch.
Rapid advances in technology mean the chatbot market
is now one of the fastest-growing segments in healthcare, with the market
expected to be worth more than $314 million by 2023.
In some ways, this growth is not surprising. Combined with the commercial benefits for healthcare providers, there is a genuine appetite for more advanced technologies to form part of patients’ healthcare. A recent study in the US revealed that more than half of consumers would use an app for remote general consultation if given the option, while research in the UK found that apps would be used by 47 percent of patients to book appointments, and 42 percent to manage prescriptions.
Before long, it is likely to be very commonplace for prescriptions to be re-ordered through your smart speaker, for medical appointments to be made by Alexa, and for medical disclaimers and drug side effects viewed in augmented reality (AR) via Google Home. In fact, chatbot applications such as these already exist as proof of concept projects and even, in some cases, as deployed systems in the US. The providers that successfully deliver systems like these which make the lives of patients fundamentally easier will inevitably gain mind share and market share, as the good news of the improved service spreads.
Beyond these immediate applications, the potential of
chatbot systems in healthcare is virtually endless, limited only by the
imagination and needs of physicians and their patients.
The three types of chatbot
Whether they’re employed in healthcare, customer
service or simply for general consumer use, there are three main types of chatbot.
The first of these is the task-oriented chatbot,
designed to deal with specific scenarios such as placing orders or scheduling
The second type are information-oriented chatbots,
which are more focused on the generative aspect of a conversation. Relying on
AI and expert systems, they’ll offer answers as creatively as possible,
avoiding repetition and attempting to keep the conversation interesting for the
person they’re chatting with. In the context of telemedicine, for example,
these chatbots could be used to explain side effects or to discuss concerns
over drug interactions.
The third type of bots are either open-domain,
designed to retrieve information for questions such as what the weather will be
like in a week’s time, or closed-domain. Also known as domain-specific, these
chatbots operate with regard to a particular area of interest, aiming to give
answers to narrow scenarios such as offering guidance through a museum by
providing visitors with very specific types of information.
Most healthcare chatbot apps would typically fall into this latter, closed-domain category. Whatever the category of chatbot, so long as they provide users with an improved quality of experience, healthcare providers will be able to deliver a better service to more people at a lower cost.
Applying artificial intelligence
The performance of these chatbot apps – especially
their ability to adapt as required – can be largely impacted by AI and machine
learning technology, the application of which can enhance a number of areas.
By eliminating human bias from interactions, natural
language processing can widen the topic of conversation, and increase the
number of valid responses available to a chatbot. Of course, being able to
answer a wider range of specific questions and provide more information will
only make these apps more useful.
AI can also improve business performance for
internal-facing bots which, in turn, will improve the customer experience for
both practitioners and patients. Automating patient/admin interaction will
enable more flexible scheduling options, for example, while the ability to more
thoroughly convey information on side effects and conflicts from drug
interactions will only improve patient outcomes.
What’s more, chatbots known as cognitive bots can use deep/machine learning to continually learn from their ongoing interactions, in order to provide more tailored responses to a patient’s needs. Accessing massive data sets and rapidly extracting insights from them is a task much better suited to AI versus humans who are limited by time. Longer term, cognitive bots will deliver improved healthcare outcomes for more patients at a lower cost to the provider.
Adoption and appetite
The growth in the adoption of chatbot technology is likely to be organic. With each success that is achieved, a wider set of needs will be recognized and the technology developed further to address them. Its adopters will range from the largest healthcare innovators, where we would expect to see such innovation, to the smaller rural healthcare facilities who are set to benefit most from the resource and cost efficiencies it offers.
Within the last few years many of us have become accustomed to using Alexa, Siri and Google Home in our daily routines. As the healthcare industry continues to embrace chatbots, it won’t be long before we think nothing of asking them for medical advice, to carry out administrative tasks, or even to speak directly with our doctors. And given the rate at which this technology is evolving, who knows what the next few years might hold?
By Manish Mathuria, chief technology officer and co-founder, Infostretch.
The truism that “prevention is better than cure” is especially true in software, where a defect can have serious, sometimes life-threatening, consequences. Digital health presents a unique set of challenges and opportunities for those operating in this competitive and demanding market. The pressure to innovate and advance is immense, but so are concerns about safety, functionality, cost and privacy, to name a few.
When clinical insights combine with IT brilliance, the results can lead to fascinating health innovations. Radical new approaches, such as wearables and mobile devices which monitor, analyze and diagnose conditions, bring special meaning to the importance of error prevention versus recovery.
Lightning-fast technological innovation, fierce competition and stringent regulation combine to bring special challenges to a tester. The implications of software failure are severe. Another adage, “evolve or die,” springs to mind. The traditional testing function is what needs to evolve in this sector perhaps more than any other.
The quality assurance approach to testing must now make way for quality engineering, a new way of tackling quality control which focuses on improving the inherent design of the product throughout the software development life cycle. Why? Because traditional testing, performed at the end of the SDLC is out of its depth in the new era of digital transformation.
Augmented reality (AR) is one of the hottest trends in technology today. Its popularity is equally reflected in projections, as the market is expected to be worth more than $160 billion by 2020, up from just $4 billion in 2016. But its use is not limited to simply chasing Pokémon and other games. With a growing number of applications across a range of industries, the technology is increasingly being adopted within the healthcare sector, where analysts predict its value will reach around $5 billion by 2025.
A number of healthcare providers and medical device manufacturers have already begun to realize AR’s potential for improving their efficiency and effectiveness. A handheld AR device developed by US-based AccuVein, for example, enables clinicians to quickly and easily locate veins for injections – scanning and projecting a virtual image of a patient’s veins on their skin. And in the UK, surgeons at London’s Imperial College Healthcare Trust use Microsoft’s HoloLens AR headset to create an accurate, real-time, virtual 3-D map of a patient’s blood vessels, muscles and bones before making a single incision.
Its impact isn’t only being felt by healthcare providers. In pharmaceuticals, for example, there are AR apps available which can give patients access to information such as dosage instructions and possible side effects. Those patients simply scan a particular prescription and the application recognizes the medication. Furthermore, solutions such as Ghostman are aiding patients with physical rehabilitation therapy following serious injury, and scientists are even exploring how the technology can be used to treat psychiatric and neurological conditions.
Given the benefits it offers both healthcare providers and their patients, AR’s growing popularity within the sector is not surprising. As with any new technology, though, implementing AR is not without its challenges.
Obstacles to overcome
While there may be a great deal of hype around future applications, it’s worth remembering that AR is still a relatively nascent space. There is currently little in the way of an ecosystem around the technology, as well as a lack of interoperability — both obstacles of implementation.
As it stands, developers are required to either build AR applications for one single platform or find ways of creating content for different platforms. Since each of these options has its own specific requirements, most AR apps today tend to be stand-alone projects. This situation is likely to be resolved over time with the implementation of common standards which will enable the creation of common frameworks, speeding up the overall development and deployment process. Once these standards are in place, it’s likely that AR will become more widely adopted within the healthcare sector.
Perception is also crucial — the future of AR depends on how it is perceived by end users. If AR is to be widely adopted, it’s important that developers ensure they put user experience at the heart of every project. As a burgeoning technology, AR is still something of an unknown quantity, so it is vital that sufficient time be given to ensure success. To do this, factors like loading and rendering three-dimensional objects, taking into account the real-world environment and conditions, and to carrying out extensive load testing will be required prior to release. Lag, or a lack of response in an AR application, might be frustrating when you’re trying to catch a Pokémon – but when we talk about care delivery, the consequences will be considerably more dire.
Guest post by Manish Mathuria, CTO and co-founder, Infostretch.
Digital transformation means different things to different industries. On the consumer front, Amazon didn’t even have to transform itself, because it was born in the digital age. On the other hand, for pharmaceutical and medical device manufacturers, much of their innovation is heavily dependent on the move from a physical, analog world to a digital world.
This brave new digital world is fraught with perils, partly because of the necessary regulation, and partly because many digital advances represent new ground, so there may be no precedent for assuring product quality (which in this example translates to patient safety). Indeed, topping the complexities facing many healthcare companies is the fact that they are operating in a regulated environment, both in the U.S. and globally. The U.S. FDA and other regulatory agencies worldwide require them to maintain strict vigilance on the testing of products, while at the same time they want to be doing rapid development.
Take LifeScan, for example, an operation of Johnson and Johnson. With a long history in the medical devices field, its blood glucose monitoring (BGM) line is one of the most-prescribed brands in the industry. LifeScan is taking the conventional BGM device full-bore into the digital era, with a concentration on mobile. As you might expect. their market is growing at a healthy rate (much as diabetes is growing at an “unhealthy rate”), and they face competition both from established companies and innovative newcomers, notes Ed Hein, Manager – Digital Verification and Validation at LifeScan.
LifeScan is enabling patients to track their blood glucose readings on their mobile devices and online; their healthcare providers and health management companies can access their data via API interfaces. This provides faster access to the data and more accurate tracking and trending. Being able to present that data to the patients, their providers and loved ones more accurately lets them live a normal life.
Like other companies in the healthcare field, LifeScan’s competitive advantage and market position was strengthened by its ability to accelerate cycle time to get new software-based capabilities to market faster and more efficiently. This meant changing its software testing approach from traditional –often manual– Quality Assurance (QA) to a more proactive Quality Engineering (QE) process that integrates software testing and development and leverages automation.
This transition has been common in some industries but is rather new in healthcare. The good news is that it is driving innovation and, because of more efficient and effective testing processes, accelerating product approvals (READ: time to market).
By integrating QA more tightly with the development process, LifeScan has also been able to integrate its organizational structure as well. This has provided additional visibility to additional opportunities to accelerate the development lifecycle.