One of the trickiest parts of telehealth is diagnosing any illness or issue. When so many ailments share an incredible amount of symptoms, doctors and medical professionals often need patient interaction to fully diagnose an issue. Artificial intelligence has begun to find ways around this and help to deliver accurate prognoses to patients.
Massive companies like Google have set their sights on the telehealth and e-health markets in recent years. Wearables allow doctors to diagnose issues from a distance by taking vitals such as body temperature and blood pressure. The inclusion of wearable smart gadgets keeps tabs on patients to help ensure that proper treatment is being adhered to as well, logging the data to help medical professionals gather all the data they need for treatment.
Connecting Health Cards
In a world so increasingly connected to the internet, there’s been a powerful increase in the use of electronic medical cards. Artificial intelligence is being trained to help advise patients in the best card and plan for them to keep a healthy balance between care and cost. Google’s efforts to use artificial intelligence as helpful assistants to medical professionals include being updated on card plans and working with patients to keep all parties as informed as possible.
Telehealth and electronic medical cards commonly go together, mostly due to their similar nature of an online-focused existence. This means that any advance in one field is likely to be tied to the other in some way. The training of artificial intelligence for telehealth provides a massive boon for those that utilize electronic medical cards for their health plans.
When a patient is put on a new medication, keeping an accurate schedule can prove challenging. Studies show that as many as half of all patients fail to take their medicine on time, or at all. This is partially because so many patients fail to remember the new part of their daily regimen, allowing the important medicine to go forgotten.
Artificial intelligence is often utilized in speakers and home-based assistants to help with reminders. A common usage is to set a time each day where the AI will inform their patient to take their medicine. Some doctors have even begun having telehealth patients log their doses with artificial intelligence programs to keep real-time tabs on whether a patient is following the instructions they were given.
Telehealth has grown considerably over the last years and shows no signs of stopping. Through the utilization of electronic medical cards, telehealth provides an excellent alternative for those who may have difficulty travelling or otherwise reaching medical help. The ever-growing internet of things draws telehealth and artificial intelligence closer into an excellent combination for patients in need.
Artificial intelligence is transforming the healthcare industry – it is creating opportunities that have been never thought possible while opening up the realm of new possibilities beyond human capabilities.
Powered by increasing availability of healthcare data and advances in machine learning, artificial intelligence aims to mimic human cognitive functions, assisting physicians to make better clinical decisions or even replace human judgement in certain functional areas of healthcare. A major part of AI involves the use sophisticated algorithms to ‘learn’ features from a large volume of healthcare data, and then use the obtained insights to assist clinical practice. It can also be equipped with learning and self-correcting abilities to improve its accuracy based on feedback. It can assist physicians by providing up-to-date medical information from journals, textbooks and clinical practices which can help to reduce diagnostic and therapeutic errors that are inevitable in the human clinical practice.
Here are some recent advances of artificial intelligence in practical use.
Cardiac Intervention “Mapping” the heart and, in many cases, mapping the signal of the heart allows physicians to understand specific problems before deciding on a solution. Taking arrhythmia as an example, by using AI, you can use the mapping to get much clearer understanding of what is the exact problem that causes the irregular heartbeat. Another example involves planning interventions with a catheter. Mapping provides the exact anatomical structure of the arteries so you make decisions on the exact kind of catheter to be used and the exact behavior of the arteries at the specific point where you have to do the intervention. Mapping usually occurs prior to an operation but sometimes it can be used during the operation itself, when you have images from the fluoroscopy; then you can do analysis of the images and get precise information about the location and the structure of the arteries.
Spinal Surgery This is a very interesting and challenging application where you have to be very precise, especially when you are putting in screws into the vertebrae. Precision cannot be gained very easily just by what the surgeon sees because, in many cases, it’s a percutaneous procedure. It’s difficult to see exactly where the vertebra line up. Artificial intelligence assists in the navigation by utilizing pre-op scanning, along with information provided by the x-ray in the operating room. Algorithms can combine those two sources of information which allows the surgeon to accurately navigate to the exact point of insertion.
This also holds true for hip or knee replacement. Using AI algorithms, during the planning phase, you can decide on a specific implant that will be for a particular patient. Mapping also provides very good segmentation of the bones prior to the operation. This helps avoid doing generic work with an implant that might not fully fit the knee or hip and the patient will suffer from future problems.
The megalithic healthcare conference, HIMSS19, has come and has gone from the vast former swampland of central Florida. While I’m a relative newcomer to the show’s trajectory – I’ve been to four of the annual tradeshows since 2011 – this year’s version was, for me, the most rewarding and complete of them all. This could be for one of several reasons. Perhaps because I no longer represent a vendor so sitting in the exhibit hall in a 30×30 booth with a fake smile wondering when the day’s tedium would end and the night’s socials would begin may impact my rosy outlook.
Or, maybe I was simply content to engage in the totality of the experience, attend some quality sessions, meet with many high-class people and discuss so-called news of the day/week/year. Doing so felt, well, almost like coming home. Or, perhaps my experience at the conference this year was so good because of running into former colleagues and acquaintances that drove me to such a place of contentment while there. No matter the reason, I enjoyed every minute of my time at the event.
Something else felt right. An energy – a vibe – something good, even great, seems/ed about to happen. Something important taking place in Orlando, and I was blessed to be a part of it. Kicking off the week, CMS created news – like it does every year at about this time – with its announcement that it will no longer allow health systems and providers to block patients from their data. This was a shot across the bow of interoperability and the industry’s lack of effort despite its constant gibberish and lip service to the topic.
Another fascinating thing that finally occurred to me: no matter the current buzzword, every vendor has a solution that’s perfect for said buzzword. Be it “patient engagement,” “interoperability,” “artificial intelligence,” “blockchain”; whatever the main talking point, every organization on the exhibit floor has an answer.
But, no one seems to have any real answers.
For example, after nearly a decade, we still don’t have an industry standard for interoperability. Patient engagement was once about getting people to use patient portals for, well, whatever. Then it was apps and device-driven technologies. We’re now somewhere in between all of these things.
AI? Well, hell. It’s either about mankind engineering the damnedest algorithms to automate the hell out of everything in the care setting (an over exaggeration) or that AI/machine learning will lead to the rise of machines, which will help care for and cure people – before ultimately turning on us and killing or enslaving us all (again, I’m overly exaggerating).
Artificial intelligence has the potential to revolutionize all fields, and healthcare isn’t exempted.
This technology, which involves machine and deep learning, enables computers to gain the capacity to better understand and process complex forms of data. Essentially, they would have the ability to learn through examples.
When implemented correctly, it’s a development that comes with many possibilities, especially in a data-driven field like healthcare. Machine learning has the potential to improve patient care, provide faster service and diagnoses, and generally provide a better experience for both healthcare providers and patients.
Anyone involved in healthcare (which basically means everyone) can stand to gain from learning more about how AI might affect the industry.
We are quickly moving to a patient-centric world in healthcare where treatment is coming to the patient, the patient is treated more like a customer, and medical facilities of all types must use technology from the business sector. Business sector software designed to improve the customer experience can now be used to improve the patient experience. No technology is driving this shift faster than artificial intelligence (AI). AI is propelling us into an increasingly digital medical experience where patients expect personalized experiences that take into account their individual needs and values, and empower them to get information fast and accurately.
Prescription drugs are ground zero for AI innovation
Although AI has been touted for everything from diagnosis to automating medical imaging to drug discovery, we believe that ground zero for AI innovation in patient-centric healthcare is prescription medicine. Prescribers and patients are suffering in countless ways from the complexity and associated errors in prescriptions.
A single drug has hundreds of factors that must be considered by a doctor or a pharmacist when prescribing or dispensing a drug to a patient. We examined 50 of the most popular drugs and found that the average number of considerations for a single drug is enormous:
It’s an understatement to say that AI and machine learning were among the forefront of conversation drivers recently at HIMSS19 in Orlando. One session actually diving into the ethics of AI while leaders from Microsoft, the American Medical Association and the Cleveland Clinic spoke of the need for organizations to develop the right framework for innovative clinical delivery.
Hemnant Pathak, associate general counsel at Microsoft, led the round table discussion, which included Peter Lee, CVP AI and research at Microsoft; Dr. Susanna Rose, Phd., chief experience officer at the Cleveland Clinic; and Sylvia Trujilo, MPP, JD, senior Washington counsel for the American Medical Association.
Virtually every industry is being sharpened by the emergence of these new technologies, Pathak said during his opening remarks, while pointing out the prevalence of the technology throughout the HIMSS exhibition hall. “In many cases, existing frameworks can be adapted, but as they continue to improve we’re going to need sources from every sector and they need to be given equal weight for their considerations” to the healthcare landscape.
From faster diagnosis to enhancing operations across care management, compliance and accounting, artificial intelligence (AI) is revolutionizing the healthcare landscape. While AI is not a new concept, its adoption in the healthcare industry has lagged behind because of significant changes in technology and the exponential growth of data. AI has tremendous application for healthcare, but leaders will need to balance data management and governance to properly enhance the patience experience and outcomes through AI implementation.
2018 was another year of rapid changes in the healthcare industry. Over the past twelve months, I’ve had fascinating conversations with executives in the provider and payer community and they have described the challenges they face and their visions for the future. One thing has become clear among everyone in the healthcare ecosystem: expectations are changing.
Patients are looking for a retail-like experience for their treatment, and sure enough, a number of new, innovative providers are gaining traction by leading with convenience and patient-centricity. Patients are embracing alternatives to the traditional doctor’s office or the local hospital. Unwilling to wait for an appointment, patients are eager for more responsive, high-quality treatment options.
Healthcare providers are becoming more innovative in an effort to improve the quality and efficiency of patient care while continuing to look for ways to reduce costs. Payers are exploring new approaches to improve their back-office systems and are willing to partner with third-party vendors to accelerate this improvement, rather than trying to develop everything in-house.
I am confident that this year will be full of important change in the industry and that these changes will touch the lives of millions. Of course, there is still a long way to go since the healthcare industry is historically slow to embrace change – it is often difficult to break through a culture that normally stays with the status quo. However, the desire for change is definitely there. If I were to list my top three predictions for trends in healthcare in 2019, they would include the following:
1) Health plans will get much more serious about improving member experience and predictability.
Health plans are eager to improve their member experience, partially because of the emergence of new consumer-centric plan models. As the market becomes more competitive with new entrants, traditional health plans will continue to partner with agile startups to deliver an improved member experience. These partnerships will enable health plans and startups to pilot projects and scale them quickly, leveraging the strengths of both organizations.
2) Employers are focusing on providing access to targeted healthcare solutions that meet specific therapeutic needs.
As of 2018, 83 percent of employees say healthcare is very or extremely important to/for staying in a job or changing a job. Given the importance of healthcare to their employees, and that the average company spends $10,000 on healthcare annually for each worker, employers will recognize the need to invest in specific solutions in areas where traditional networks fall short. These include programs for infertility, women’s health and behavioral health.
Healthcare organizations face unprecedented compliance challenges when it comes to managing business associate agreements (BAAs) amid frequent data breaches, heightened federal scrutiny and anticipated privacy legislation. Actions by the Office for Civil Rights (OCR) have clearly demonstrated stricter enforcement of HIPAA rules in recent years, and the industry has already witnessed a notable uptick in public shaming and fines associated with missing just a single BAA.
Simply put, BAAs have become a cornerstone of OCR compliance initiatives. And the outlook is not likely to change as trends point to continued advancement of privacy laws. As of close of 2018, 12 states had already updated their privacy laws regarding notification to patients, shortening the standard 60 days from the federal guidelines to 45 days, and in some states (CO, FL), the breach notification window is down to 30 days.
Breaches involving protected health information (PHI) are typically reported publicly at the Covered Entity (CE) level. When a breach involving a third party, or Business Associate (BA), occurs, one of the first things the federal government investigates is whether a BAA is in place with the CE. If a BAA does not exist, it typically sets off a chain reaction of investigations into other areas of HIPAA compliance.
While most headlines related to BAA compliance relate to CEs, HIPAA experts predict that 2019 will usher in greater focus on BAs and their management of these agreements as well. Many believe that unprepared BAs—especially small and mid-sized companies that lack resources to address HIPAA compliance—will become targets, increasing industry concern over proper BAA compliance.
Healthcare’s BAA management conundrum
Today’s healthcare organizations are feeling the heat, yet most are challenged to effectively manage BAAs due to limited resources for reviewing and managing massive and growing numbers of these agreements—reaching upwards of several thousand in larger organizations and health systems. Exacerbating this challenge is the current consolidation trend, which creates a fragmented landscape for BAA oversight that extends across multiple departments, facilities, affiliations and a multitude of different owners.
Consequently, manual, inconsistent workflows common to BAA management in today’s organizations open the door to significant risk. In truth, the most basic information often eludes the executive suite in most CEs and BAs, including the total number of existing agreements, where they are located and the terms of each.
BAAs are also the subject of intense negotiations between CEs, BAs and other subcontractors that often result in obligations that go beyond HIPAA and HITECH, causing contractual obligations to vary significantly between agreements. Subsequently, when organizations need to know the terms of these agreements, they must manually extract the information one agreement at a time. Within a framework of manual processes, the resources required to conduct this kind of data extraction across hundreds or thousands of BAAs is simply unfeasible for many organizations.
Yet, compliance professionals need quick and easy access to this information to ensure optimal response to breaches, which have become the norm for healthcare organizations as opposed to the exception. Consider the findings of a 2018 Black Book Market Research study: 90 percent of healthcare organizations have experienced a data breach since the third quarter of 2016, and nearly 50 percent have had more than five.
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.
To use a common metaphor, data science is not a single moon shot, but laps around a track. Ultimately your goal is to run progressively faster around the track. An equally major drawback hindering execution is artisan thinking where design is seen as the ultimate end to the data science process. In fact, the most desirable approach is a modular system with emphasis on consistently maintaining and improving what has already been designed. This is particularly true for medical devices where innovation and changes in technology are continuing to better support and enable patients and practitioners.