By Sean Otto, vice president of business development, Cyient.
The approval of electrocardiogram’s (EKG) through the FDA that enables atrial fibrillation detection right from a patient’s watch band is just one example of how the digitization of medical devices, a part of the Internet of Things movement, is leading product development and innovation in medicine. However, while medical devices built on a connected services platform include components for data storage, security, accessibility, and mobile applications, along with advanced analytics, successfully implementing artificial intelligence to drive actionable intelligence remains a challenge from an execution perspective.
According to Gartner, 85 percent of data science projects fail. Successful integration of data science into medical device development requires a rethinking around the role of data science in product design and life-cycle management.
Viewing data science as a product
While data science is rightly defined as the process of using mathematical algorithms to automate, predict, control or describe an interaction in the physical world, it must be viewed as a product. This distinction is necessary because, like any medical product, data science begins with a need and ends with something that provides clear medical utility for healthcare providers and patients.
It is erroneous to restrict the realm of data science to just the designing of algorithms. While data scientists are good at fitting models, their true value comes from solving real-world problems with fitted data models. A successful algorithm development process in data science includes business leaders, product engineers, medical practitioners, and data scientists collaborating to discover, design and deliver. For instance, a typical data science integration with a medical device product would include many of the following activities:
Identifying the medical need
Identifying proper data variables
Developing the right analytic models
Designing analytic algorithm integrations
Performing testing and verification
Deploying beta versions
Monitoring real-time results
Maintaining and updating algorithms
Considering data science as a product or feature of a product provides organizations with a different paradigm for execution focused on a tangible outcome. Data scientists are trained to develop accurate models that solve a problem, but the challenge many companies face is operationalizing those models and monetizing their outputs. Furthermore, conceptualizing data science as a product will ensure companies focus on its implementation, rather than just its development.
Advanced analytics: Part of the process, not an afterthought
Designing intelligence (even AI) into a connected medical device first depends on whether the data is being used to make a real-time decision or report on the outcome of a series of events. Most companies don’t realize the layers of advanced analytics that create actionable intelligence. By understanding these layers, which range from simple rule- and complex rule-based analytics to asynchronous event rules, complex event processing, and unsupervised learning models, companies can move quickly into developing mature analytics that have an impact from day one. As a company matures its analytics system from descriptive and diagnostic to predictive and prescriptive, it should also evolve to include strategic opportunities to provide business value, including automating decisions that can be delegated to a smart decision-support system.
Successful integration involves viewing advanced analytics as an architecture and not as a single solution to be implemented. The best way to make sure that you are successful in analytic development is to follow a continual process of discovery, design and delivery. For instance, data science architecture may begin with a business question, requiring you to determine if you have the right data and can actually leverage that data in the existing IT system. If you don’t answer this basic question, you will have challenges fully vetting the analytic opportunities available to you.
Recognizing common challenges in data science execution
Data science execution is often impaired by common missteps, like incongruence between customer and business needs and solving technical problems when it’s too late to have a positive impact. Another significant mistake from the business side is treating data science like a one-time accomplishment and not realizing it is a continuous process, or like a software development process with an unwarranted fixation on tools rather than skills and capabilities.
By Pauline Farris, who speaks Portuguese, English, Spanish and Italian. She is a voting member of the American Translators Association and an active participant of the Leadership Council of its Portuguese Language Division.
Technological advancements always happen so our lives can become a lot easier. We have incorporated technology into every aspect of our lives, from housework to teaching and now even in the health sector. Artificial intelligence has had a great effect on how healthcare works and has helped us take great steps towards making it more efficient and approachable.
Not only does it offer a way to save a lot of time when it comes to treating patients but in general it can help make everyday tasks a lot easier for medical practitioners. There are so many different ways to incorporate AI technology in health and here are some of the reasons why it is so important and useful.
Eliminate waiting time by virtual nursing assistants
One of the biggest complaints in most hospitals and clinics is the fact that patients have to wait so long in order to get checked by a doctor. In the UK, college hospitals in London are starting to introduce AI technologies which will help with taking notes of the patients’ symptoms and prioritizing them based on the severity of their conditions.
A great technology that can truly cut down the wait time for all the patients and make the doctor’s job a lot easier is a new tool called scribes. This great tool works similarly to other tools we already have inside our homes, for example Alexa. If you have ever paid a visit to the doctor, you probably have notices how much time it takes for the doctor to write down all your information and create a record.
This tool simply listens in to your conversation and records everything that is being discussed. At the end of the appointment, the doctor can make it automatically create a record of what has been mentioned in the visit. This can help save a lot of time for both the patient and the doctor.
Another problem is that people who want to book an appointment have to wait a big amount of time in order to actually meet with the doctor. Artificial intelligence can provide the patients with a way to get a better understanding of their condition as well as write down and understand their symptoms and help them cope with their health issue until they can visit the right practitioner.
Making patents’ diagnosis a lot easier
The good thing about AI technologies is that they can store and recall a very large amount of data without any effort. Most doctors work for an extreme amount of hours every week and while they are always prepared to handle any case, some external help could really help them with diagnosing a patient.
The main focus of AI technology in these situations is to provide hospitals with tools which will help make consultations a lot quicker and diagnoses more accurate. These tools will remind the doctors to ask additional questions depending on the patient’s symptoms, order additional tests and even suggest fitted treatment plans which can decrease the healing time and make treatment a lot easier for both the patient and the doctor.
Most likely, in one of the few lucid moments you have in your hectic, even chaotic schedule you contemplate healthcare’s greatest problems, its most pressing questions in need of solving, obstacles and the most important hurdles that must be overcome. And how solving these problems might alleviate many of your woes. That’s likely an overstatement. The problems are many, some of the obstacles overwhelming.
There are opportunities, of course. But opportunities often come from problems that must be solved. And, as the saying goes: For everyone you ask, you’re likely to receive a different answer. What must first be addressed? In this series (see part 2 and part 3), we ask. We also examine some of healthcare’s most pressing challenges, according to some of the sector’s most knowledgeable voices.
So, without further delay, the following are some of the problems in need of solutions. Or, in other words, some of healthcare’s greatest opportunities — healthcare’s most pressing questions, problems, hurdles, obstacles, things to overcome? How can they be best addressed?
Nick Knowlton, VP of strategic initiatives, Brightree
Throughout the healthcare ecosystem, patient-centric interoperability has historically been a huge challenge, specifically throughout post-acute care. This problem results in poor outcomes, unnecessary hospital re-admits, patients not getting the treatment they deserve, excessive cost burden and poor clinician satisfaction. This challenge can be solved through creating better standards, adapting existing interoperability approaches to meet the needs of post-acute care, implementing more scalable interoperable technologies, and involvement with national organizations, such as CommonWell Health Alliance and DirectTrust, amongst others.
Cybersecurity is one of the most pressing hurdles in the healthcare industry. The life and death nature of healthcare and the shift to electronic health records (EHR) creates an environment where hackers that successfully deploy ransomware and other cyberattacks can extort large sums of money from healthcare entities and steal highly sensitive data. To address this challenge, healthcare entities need to continue to increase their investment in cybersecurity and focus on improving their overall security posture by implementing tools and processes that will monitor all devices and assess their compliance with security policies; stop phishing attacks; keep all servers patched and current; ensure third party vendors comply with policies; and train employees on proper security hygiene.
Cyberattacks continue to expose the security vulnerabilities of healthcare institutions, keeping many industry stakeholders awake at night. This is why every organization handling protected health information (PHI) needs to build security frameworks and risk sharing into their infrastructure by implementing risk-mitigation strategies, preparedness planning, as well as meet industry standards for adhering to HIPAA requirements. Hospitals and healthcare systems must keep their focus on strategies and tactics that ensure business continuity in the event of an attack as it’s clearly not a matter of if a breach can happen but when.
The core problem for healthcare isn’t science, technology or caregiving intervention. It’s making sure that the systems of delivery and communications are thought through and actually respond to the way patients need and expect healthcare to be delivered. This means it doesn’t matter how advanced and perfected your health system may be — unless it conforms to culture — the way people think and behave — it will do nothing but confuse and frustrate patient needs, which are psychological and social, as well as physical and mental.
Big data has arrived, and in healthcare, it has landed on our desks with a resounding thud. The challenge ahead lies in discerning how to analyze information and use it to effectively improve patient outcomes, costs and efficiencies.
Many of us are already influenced by machine learning and artificial intelligence (AI). For example, if buying hiking boots online, items of a similar nature also appear as suggested purchases, like bug spray or sunscreen. The data analytics behind those recommendations includes a wealth of information about the user, including demographics, such as age, gender, education and income level, as well as location and other factors that influence buying decisions. It will only be a matter of time until we are able to apply the same principles to healthcare data.
Imagine a doctor who can review operational and clinical data in real time for a patient who had knee replacement surgery. After the patient goes home, she is given a Fitbit to monitor her step count. If her steps trend downward, it is probably time for someone to intervene because she is potentially in pain or not ambulating correctly. That same physician could also see where she has received care, the cost of the care, and who performed the surgery. Then, the physician could compare her progress against others with similar demographic and health backgrounds by using machine learning and streaming analytics that not only gather relevant data across the entire care continuum—from hospital to rehab facility to home—but draw inferences from that information in real time to truly influence cost and care outcomes. In addition, if the patient had three MRIs that cost $2,000 each and someone with similar demographics and health conditions had one MRI that cost $500—caregivers can explore why that happened and work toward more uniformity.
This idea is inspiring, but a more practical look can be taken for how AI can support the business operations of healthcare as an achievable first step, along with connecting that operational data with remote care, device data and patient EHRs. Here are next steps for creating efficiencies with the power of AI and interoperability:
Step 1: Unlock Human Potential
As a recent Advisory Board report states, “AI works best when paired with humans.” The goal is to use this technology to create efficiencies across the care continuum that not only help staff in their roles, but that free clinicians, caregivers and office staff to focus on more valued activities. AI can help augment and automate human tasks and functions where appropriate, and sooner rather than later it may be able to offer advice, ultimately allowing caregivers to focus entirely on patient care.
Step 2: Optimize the Supply Chain
AI can quickly answer employee queries, buy supply, such as bandages from a certain supplier, and can also track unused supplies to minimize excess inventory. In addition, AI can help alleviate the amount of time—and frustration—nursing and clinical staff spend searching for supplies by not only providing location, but automating future order and delivery.
Step 3: Enhance and Expand Employee Self-Service
For those healthcare employees without regular access to a computer, such as lab technicians, AI can quickly and accurately empower cross-functional self-service. All employees need to do is ask for answers about anything, from paid time off (PTO) balances to company holidays.
Step 3: Automate Financial Processes
AI can augment the payment process, detecting payment, vendor and invoice patterns, and suggesting automating payments for a specific invoice that is approved 99 percent of the time.
The healthcare sector is hopeful for the future, as innovations in the IT sector will continue to provide opportunities to improve the deliverability of crucial services. One thing’s for sure, more and more companies will continue to realize just how big of an impact HITs can bring, and the situation alone is urging companies to invest more on new software and technologies — even as these innovations are still in the works.
Several key innovations in this area, such as 3D printing and artificial organs, are still being tested and developed. It would take time before these breakthroughs can penetrate the market. What’s important, meanwhile, is the fact that AI will continue to drive technological adoption in the healthcare industry.
As technological tools have become increasingly sophisticated, the demands for these tools are also becoming more complex. Still, organizations are in the right when they invest a huge bulk of their resources in AI-based solutions. Apparently, they know all too well that these products are capable of improving the delivery of care and other services.
They help healthcare providers to thrive
End users will certainly reap the benefits that AI entails. If anything, effective software and IT products are being sought by businesses that want to get the most out of their investments. Analytics plays an important role in maintaining the efficiency of an organization, whether it involves using organizational psychology to retain productive employees or managing the workflow of hospital staff.
One thing that makes HITs relevant is the fact that they lighten the workload and that they simplify complex processes. With the use of AI, healthcare organizations can accelerate their services without compromising quality. This would allow healthcare managers to focus on exploring ideas for expanding their bottom lines.
They help in patient outreach
A key trend in HIT is the rise of AI virtual assistants. Doctors normally have their hands full engaging patients with unique histories — considering this, there will always be room for error. This technology can help by automating the way they handle individual cases.
In this case, using automated VAs to organize patient data and notify patients about their appointment schedules and regular medication can ultimately lessen the amount of work doctors will have to handle. As VAs are being developed to become more intelligent and predictive, these innovations will certainly provide ample opportunities to forge stronger patient links.
They make accuracy central
Human error is natural. We are basically prone to make mistakes. But in the healthcare industry, errors can sometimes cost you money or, even worse, a patient’s life.
Indeed, new technologies in the field of diagnostics are helping organizations to identify and analyze diseases more accurately. This, in turn, can help doctors to make the proper prescriptions and suggest the right treatment plans.
In recent years, the public sector has become increasingly aware of the multifaceted potential of big data. These days, government agencies around the world collect vast amounts of data from people’s activities, behaviors, and interactions—a virtual treasure trove of information that the public sector can utilize and turn into actionable insights, and later, into actual solutions.
The big challenge, however, is that government agencies are falling under more and more pressure to find relevant insights from complex data while relying on limited resources and technologies with inadequate capabilities. And with the sheer size of data being generated nowadays, it’s becoming more difficult to extract meaning from what the data hides beyond their colossal façade.
Thankfully, there is one critical technology that is redefining the way public sector agencies study and utilize data, and this is artificial intelligence. Today, artificial intelligence solutions that make use of technologies like machine learning and topological data analysis can automatically process big data and discover patterns and anomalies no matter how complex and seemingly disjointed these different points of data are.
Precisely because of these advantages, artificial intelligence solutions are now being used by numerous public sector agencies and institutions around the world. In this article, we’ll fill you in on the basics of three important areas of the public sector in which AI is currently making waves.
The financial industry is one of the biggest producers and exchangers of data, and as such, it stands to benefit immensely from artificial intelligence technologies. Every day, government-owned banks and other financial institutions buy, borrow, and trade currencies and financial products, generating massive amounts of data from customers, partners, and other stakeholders.
In many jurisdictions over the world, value-based care is being adopted as an alternative healthcare model that focuses more on accountability, and on the type and quality of service provided to patients instead of the volume of care provided. With this increased emphasis on value, government-aligned health providers and insurance systems have begun using artificial intelligence software in order to become more agile and proficient at managing the risks of patient pools.
This way, even with the immensity and complexity of patient data, public sector providers and health payers are able to better understand clinical variations, as well as automatically predict individual and subpopulation risk and health condition trajectories. Through insights gained from these data, clinicians and other personnel across the healthcare spectrum can then better determine the best and most affordable courses of care, in addition to being able to confidently recommend the most appropriate health programs for their governments to implement.
Artificial intelligence is a topic that should interest us all, as it changes the world with every second. And the healthcare system is one of the areas that AI has already started to revolutionize. These are the main ways in which that is happening.
Due to the introduction of personalized diagnosis and precision medicine, now doctors can treat a patient’s condition, by taking into account his/her background, as opposed to merely treating the disease. This is accomplished by using proteomics, which is a type of DNA mapping, as well as advanced AI machine learning.
Killing Occam’s Razor
Occam’s Razor is also known as the Law of Parsimony, and it refers to providing a range of solutions to a given problem. Also, according to this principle, the simplest solution is, most of the time, the correct one. Considering that both machine learning and AI doesn’t have the human assumption element, their capacity of reading and analyzing amounts of data can significantly increase the accuracy of the diagnosis.
Accordingly, this can be really helpful in diagnosing elderly patients, in particular, as they are more likely to suffer from various diseases at the same time.
Google Can Spot Eye Disease
DeepMind is a Google-owned AI company that has come up with a way of diagnosing eye disease. After assessing and attentively analyzing the medical records of a significant number of patients, it has created machine learning technology that should help doctors diagnose eye illness earlier. This merely outlines that, even though AI is innovating almost every field, it still relies on human help.
Automated Cancer Treatment
It appears that AI can also play an important role in treating cancer, which affects more and more people. Accordingly, the CareEdit tool can be utilized by oncologists for crating practice guidelines. To be more specific, the tool analyzes considerable amounts of data such as past treatment regimens, aiming at comprising a clinical decision support system that should help physicians treat each patient. This can significantly enhance the rate of survival, while cutting down the costs associated with the treatments.
Virtual Health Assistant
Interestingly enough, at the time being, there are apps that carry the roles of personal health coaches. This functions the same way as a customer service representative at a call center. What is more, the digital assistant can do as much as take notes, ask questions, even provide specific advice while streaming the information to the healthcare provider. This has the role of simplifying the process.
By Ben Flock, chief healthcare strategist, TEKsystems.
As technology advances, so does the healthcare industry, with technological breakthroughs increasing the ability of healthcare professionals to serve their patients, record and transfer patient data and more efficiently complete other tasks necessary to keep the industry moving. IT services provider TEKsystems recently released the results of a survey that polled almost 200 healthcare IT leaders (e.g., IT directors, chief information officers, IT vice presidents and IT hiring managers) in late 2017/early 2018 on a range of key issues, including technology maturity, workforce planning, critical roles and the top trends shaping healthcare IT today.
The results revealed a shifting focus from IT leaders: healthcare is behind the curve on initiatives that have the potential to shape the industry going forward, including artificial intelligence (AI).
Business demand is driving both the interests of IT leaders and the prioritization of AI in healthcare. Value-based care, regulatory mandates and the consumer push for precision/personalized care are driving the business prioritization of AI. These results indicate that while IT leaders know AI in healthcare is the future, they are currently taking a cautious approach to utilizing the technology. This is very likely rooted in security concerns, as there are federal, state and even local mandates dictating the protection and privacy of patient data.
Although cautious, healthcare organizations are actually proceeding on the AI front. As evidence, survey data shows a high percentage of healthcare organizations are in the implementation, evaluation or refining stage with respect to specific technology applications that leverage AI – digital health systems (75 percent) and telemedicine (51 percent). This pragmatic approach to AI will continue, and healthcare organizations will address this emerging industry imperative by providing IT resources, as well as enabling platform technologies and repeatable solutions capabilities in secure applications and solutions that leverage artificial intelligence.
To ensure IT employees are aware of the need to be cautious when implementing AI initiatives, organizations must ensure adequate onboarding and ongoing risk and compliance (R&C) training is provided. An annual “check the box,” activity, R&C training isn’t enough to help employees and third parties manage risk appropriately. The best strategy is to implement a risk-based approach by focusing on higher risk functional areas with direct access to consumers and/or protected health information (PHI), and creating targeted training. Simple education and awareness tactics can dramatically improve compliance when employees and third parties understand how to apply teachings to their area.
The healthcare industry is in a period of great uncertainty, with major questions looming around how regulations, standards and reimbursements – particularly regarding care quality and interoperability– will be changing for hospitals in the coming year. One thing is clear though: In order to provide the efficient and high-quality care needed to meet patient expectations, hospitals need to focus on the intelligent application of new technologies. Here are four trends that will influence healthcare IT in 2018:
The opioid epidemic will trigger growth in investments around patient and staff safety
The growing opioid epidemic now causes nearly 100 deaths each day, and is projected to cause 500,000 deaths over the next decade, primarily due to overdoses. That is not only putting pressure on hospitals to reevaluate how they use opioid medications and monitor patients once back in the community, but it is also forcing them to address the physical safety of staff and patients. This is because the opioid epidemic has led to an increase in violent crimes in healthcare facilities. Emergency departments in particular are under heavy strain, with more patients presenting with addiction symptoms, compounding wait times and leading to more patient disputes. Hospitals will have to invest significantly more in technologies to protect staff and patients, such as patient monitoring solutions and staff duress systems to prevent potentially dangerous patients from harming themselves or others.
Big data advancements will pave the way for the rise of predictive and prescriptive analytics
Regardless of how the major causes of uncertainty affecting the healthcare industry – such as the future of the Affordable Care Act – resolve themselves, it is certain that there will be no return to the pre-ACA era. As healthcare industry writer and consultant Edgar Wilson has pointed out in the context of primary care, the expansion of insurance coverage did not magically create more capacity. It challenged hospitals to find new ways to serve more patients, more personally, without adding cost. Hospitals will continue to look for practical ways to improve their efficiency by leveraging data to better predict patient care requirements, and demand for medications and equipment needs. The benefits of these predictive analytics capabilities are enormous.
According to a February 2017 report by the Society of Actuaries, 93 percent of healthcare providers said predictive analytics is important to the future of their business, and 57 percent believe predictive analytics will save their organization 15 percent or more over the next five years. In addition to predictive analytics, prescriptive analytics will have a growing impact. Ongoing advancements in the collection, aggregation and analysis of data will provide hospitals with greater operational insights, enabling them to optimize staffing levels and other aspects of operations while enabling staff members to deliver more effective, targeted care.
Staffing shortages combined with rising care expectations will drive adoption of AI and automationContinue Reading
Sherlock Holmes famously captured the popular imagination with his uncanny ability to make wild, but accurate, leaps of logic to solve mysteries. By observing Dr. Watson’s suit jacket sleeve, upon their first encounter, he was able to deduce that Watson was in fact a surgeon, in the British Army, and had recently returned from Afghanistan, where he had sustained an injury.
When he slowed down to explain his reasoning, it was easy to follow; what made his deductions impressive was how quickly he would skip from observation to conclusion. I’m no Sherlock Holmes, but it seems to me that chatbots are poised to take over much of modern healthcare.
As more data is moved to portals through EHRs and digital documentation, there is increased patient interest in and demand for other digital and remote encounters and health resources. This, along with improving technology and competitive solutions, is helping increase adoption of telehealth. So, patient portals lead to increased telehealth adoption.
Finally, although part of the premise and value of telehealth is enabling face-to-face encounters between caregivers and patients without respect to geography, hospital waiting rooms, or other physical barriers, it changes certain expectations. Like all mobile and web-based services, telehealth feeds a consumer mindset that expects everything on-demand, all but instantaneously, and highly customized at that.
While portable patient records facilitated by EHRs and interoperability can help this, customization and on-demand healthcare doesn’t just put pressure on records and data. Patients want fast and personalized answers. As customer service centers, tech support, banks and virtually every other consumer-facing industry has learned, a lot of the on-demand load can be pushed onto increasingly sophisticated chatbots.
So, telehealth leads to growing expectations for on-demand clinical encounters and chat, which is provided by chatbots.
The Case for Chatbots
Retail has previewed much for healthcare: See how customer service upgrades have turned everyone into “The Most Important Person Here” wherever they go, in person or online. Consumers demand personalization, expedition, authenticity and they want it all exactly when and where they want it. And now, see how AI is not yet taking over the world, but is making FAQs and other routine customer service interactions painless for those answering, and interactive enough for those asking.
Retail is even making inroads to healthcare, as consumer-facing devices promise to measure and track all manner of health metrics. Statistics-loving sports fans witness the increasing digitization and quantification of athletes, games, injuries and training, and they want a similar level of insight and precision for their own care. Mobile technology is redefining and disrupting even the oldest and most stable of markets and industries, bit by literal bit.
So how long until the dry, repetitive questions doctors routinely must answer in check-ups and physicals are ethically and effectively offloaded onto chatbots programmed to triage and educate patients without wasting valuable human resources? How long until using telehealth to keep nonemergency patients out of the emergency room merges with using chat and AI — the basic recipe for chatbots — to keep healthy but curious or concerned patients from wasting time and money going through full encounters simply to get their general questions answered?
It doesn’t take a lot of sophistication to realize the benefits of AI at scale. Google has all but taken over the modern world by connecting searchers with answers to their questions; Wikipedia has all but bankrupted the encyclopedia industry with free, accessible, general knowledge. In a world where health literacy is so lacking in the majority of the population, some interactive resources could go a long way to chipping away at ER overuse and healthcare overconsumption, just by giving people an alternative to seeing the doctor.
Automation of Care, Automation of Crime
As quickly as potential benefits can scale, very real risks and both moral and financial hazards scale even quicker.
The growing popularity and implementation of chatbots has given hackers and cybercriminals a new way to scam, defraud, and generally abuse unwitting consumers. Sometimes that means hackers take over a company’s chat system with their own bot and solicit data. Sometimes fraudsters attract visitors with a spoof website, then use a bot to similarly extract volunteered data at scale from misled visitors. However it is done, it scales almost as well as a more conventional data breach, and can be harder to detect or track.