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.
In today’s world, we have access to more data than ever before. Analysis of big data is allowing us to solve ever more complicated problems at scale in a way that was not previously possible.
It’s a revolution.
Now it’s time to use the revolution of artificial intelligence in medicine. But what does this mean for the way we diagnose and develop new treatments?
Giving researchers access to pools of big data and equipping them with the tool of machine learning allows then a great opportunity to speed up processes that would have previously taken years. The typical timeline for new drug development from concept to market-ready product can range between 12 and 30 years. A significant portion of this time is devoted to research and development.
Patient diagnosis and treatment also experience, in particular for more acute diseases and ailments, a substantial amount of study to perfect. How can we leverage artificial intelligence in medicine, and its application in machine learning to help speed up this process?
We are starting to see the power of this technology to support medical diagnosis and treatment in several recent papers published by medical research institutes. These papers show the breadth at which the technology can be applied to increase the efficiency of the processes of diagnosis and treatment significantly. Thus allowing doctors to spend more time working directly with patients and saving institutions money.
Here we look at how artificial intelligence in medicine is driving forward our understanding of a variety of conditions. From drug development, through to improving the testing process and finally onto diagnosis — it is clear that there are multiple benefits of partnering with the technology.
Step 1: Drug Development
A recent article published in Nature highlighted several companies using machine learning to improve the drug development process. One company highlighted is Berg.
Berg is using machine learning to better understand and map human biology in far greater details. In their own words, “instead of hypothesizing the mechanism of a disease and focusing on only a few related compounds, we profile the entire disease by analyzing various patient biofluids (OMICS) and cell models (bio systems) as well as clinical information (EHRs).” This knowledge is then fed into Berg’s data analysis systems to be applied to drug development. The use of this artificial intelligence in medicine helps us to develop drugs more quickly and efficiently.
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.
Global IT consulting firms, especially those with an India heritage, have had a long run at high growth rates, fueled by one idea: Outsourcing information technology (IT) operations to countries with low labor costs and a skilled engineering talent pool with a strong English language proficiency offered a huge arbitrage opportunity. The Indian IT services industry monetized this idea with a single-minded focus over the past 25 years and is now estimated to be more than $150 billion. Over the past decade or so, many western multi-national firms, including end-user clients for these companies, have jumped on the labor arbitrage model.
My firm has been focusing on how global technology consulting firms have been doing in the healthcare markets, and reviewing their financial and operating performance since 2015. Our latest mid-year review of 11 publicly held global technology consulting firms indicates that organic growth from the healthcare vertical is trailing overall company growth for most consulting firms, and is slowing down relative to previous quarters. Three of the 11 firms we cover in our report have seen leadership exits for the healthcare business, and the Board of Directors of one high-profile firm is looking to replace its CEO. No major contract signing in healthcare has been reported by any of the firms.
Where is the whole sector headed next? No one knows for sure, but the labor arbitrage model is now surely past its sell-by date. The question therefore is: what’s the next killer app for global IT consulting firms ?
If one were to go by the term most-often used by these firms in earnings calls, annual reports, and marketing messages, it would seem that the new killer app goes by the all-encompassing name of “digital.”
However, unlike a clear concept like low cost labor, digital is much harder to understand. In much the same way as that other overused term – artificial intelligence or AI — the technology vendor market has whipped itself up into a frenzy of “digital” offerings, with each firm defining “digital” in its own way.
However, the global IT consulting firms, and in particular the India heritage firms, seem to discuss a common theme when referring to digital. They are mostly referring to automation when they say digital. These firms are betting on intelligent automation (IA) as the killer app for the future of their business model.
Consulting firm KPMG has released a report declaring that IA is fueling the next generation of outsourcing. As IT operations move to the cloud, automation targets the same IT operations that substituted high-cost labor pools a generation ago, and is eliminating the low-cost pools with even lower cost robots a.k.a robotic process automation or RPA. The implications are like a double-edged sword: Automation is a new revenue opportunity, but it is one that necessarily cannibalizes a current revenue opportunity. The implications reach far beyond the board rooms of multi-billion dollar tech firms, and raise questions about a low-cost labor-pool based model of IT services.
In other words, the killer app of tomorrow is killing the jobs of yesterday, a fact that the KPMG report delicately refers to as a “negative though not significant (at least in the near term) impact on the use of traditional outsourcing and shared services.” However, the Schumpeterian principle of creative destruction may be at play here, with an entirely new class of jobs and entirely new revenue opportunities on the horizon arising from the increasing savings and efficiencies brought about by automation.
We can all agree the healthcare system in the United States is in dire need of an overhaul. Technological advances propel us into the twenty-first century, but the way data is created, maintained, and shared is archaic and confusing (and deliberately-so.) Instead of collaboration and transparency, healthcare is (in the eyes of many) a confusing minefield littered with smoke and mirrors.
Many people (even those with insurance) forgo treatment through choice or necessity–hoping things will get better–and instead end up in the emergency room. This adds bloat to an already-overtaxed, inefficient ecosystem, and patients end up footing the bill.
What does this have to do with blockchain? Well, blockchain thrives in an environment where lots of parties (who don’t trust each other) need to have consensus on the “correct” version of the truth. Broadly-speaking, blockchain helps integrate layers of trust and efficiency that have otherwise been missing in healthcare. It is essentially a distributed database that can’t be “gamed”, as any attempts by bad actors to change records to “bend the truth” are impractical and improbable.
Put another way: blockchain rewards desirable behavior (truth, accuracy, information flow), while helping to mitigate undesirable behavior (ex: lying, cheating, stealing.) Truth and accuracy breed possibility.
Imagine a world where every single ingredient inside every single pharmaceutical you take is traced from start to finish, so you know you’re not being poisoned, misled or ripped-off. Imagine having verifiable reviews from actual doctors based on verifiable outcomes to help you compare healthcare procedures and practitioners just like you would compare insurance or blenders.
Imagine being compensated for taking better care of your own health, or for securely-sharing your medical data to help advance research. All of this is possible with blockchain technology (and more.) Long-term, blockchain technology has tremendous potential when paired with technologies like artificial intelligence (AI), where meaningful healthcare research can be advanced, shedding new light on trends and unlocking a holistic, real-time picture of the global healthcare landscape.
With all its positives, blockchain isn’t itself a magic, one-size-fits-all solution to the many problems plaguing healthcare. However, what blockchain technology can do is provide a framework that opens up the secure flow of information between healthcare consumers, practitioners, and everyone else in between. Access to more and better information helps streamline inefficiencies, thus reducing costs and elevating the level of care that can be provided.
Beyond Limits is a pioneering AI company with a unique legacy from the US space program. The company is transforming proven technologies from Caltech and NASA’s Jet Propulsion Lab into advanced AI solutions for forward-looking companies on earth. Beyond Limits delivers AI software capable of tackling complex industrial and enterprise challenges for leading global customers to transform their businesses and industrial operations in areas such as healthcare, oil and gas, finance, transportation and logistics.
Breakthrough cognitive technology goes beyond conventional AI, blending deep learning and machine learning tools together with symbolic AI that emulates human intuition to produce our cognitive intelligence.
AJ Abdallat is CEO of Beyond Limits. AJ Abdallat worked with HP on key NASA/JPL projects. One of the key projects was a collaboration with NASA/JPL Center for Space Microelectronics Technology (CSMT) and Caltech Center for Advanced Computing Research (CACR). The collaboration which Abdallat managed led to the installation of a 256-processor Exemplar supercomputer with a peak performance of one teraflop (one million computations per second). The Exemplar supercomputer at the time was the fastest supercomputer in the world.
In 1998 Caltech hired Dr. David Baltimore as president. Dr. Baltimore vision was to make technology commercialization at Caltech/JPL the hallmark of his administration. In 1998 AJ Abdallat and Dr. Carl Kukkonen (CSMT Executive Director), left to launch a Caltech/JPL startup to commercialize JPL technologies with the support of Dr. Baltimore. In 1999 Dr. Mark James and other JPL scientists joined Abdallat in the Caltech/JPL startup and efforts to commercialize JPL smart sensors and AI technologies. Between 1998 and 2012, Abdallat founded and launched several spin-off companies from Caltech/JPL in the fields of AI, smart sensors, gas sensing, finance and homeland security.
Since 2012, Abdallat has been focused on AI and cognitive reasoning systems from NASA/JPL. In 2014 Abdallat launched Beyond Limits, a NASA/JPL AI and Cognitive Computing startup. He secured Series A investment in 2014 and in early in 2017 closed Series B funding from British Petroleum (BP). Abdallat is currently working on securing Series C investment to accelerate delivery of Industrial-grade AI.
Beyond Limits delivers AI software capable of tackling complex industrial and enterprise challenges for leading global customers to transform their businesses and industrial operations.
The advanced intelligence solutions developed by Beyond Limits magnify human talent, enabling people to apply their attention, experience, and their passions to solving problems that truly matter. Many pioneering JPL scientists now work at Beyond Limits, building solutions for companies in down-to-earth industries, such as oil and gas, healthcare, finance, transportation and logistics.
With more than 40 technologies developed for NASA’s famed Jet Propulsion Laboratory (JPL) Beyond Limits offers cognitive AI and reasoning systems available for the first time for commercial use. Beyond Limits delivers cognitive solutions with the resilience, reasoning, and autonomy required by the harsh environment -of space to improve the performance of industrial and enterprise systems on Earth.
Powered by Beyond Limits innovations, the company’s technology is an evolutionary leap beyond conventional AI to a human-like ability to perceive, understand, correlate, learn, teach, reason and solve problems faster than existing AI solutions.
Who are your competitors?
Beyond Limits has no direct competitors developing AI solutions for healthcare. Competitors may include Troops, SparkCognition, Vicarious and Butter.ai; however, Beyond Limits provides advanced intelligence solutions that go far beyond conventional AI. Our cognitive computing technology mimics human thought processes and provides autonomous reasoning to aid human-like decision-making.
How your company differentiates itself from the competition and what differentiates Beyond Limits?
Our breakthrough cognitive technology goes beyond conventional AI, blending deep learning and machine learning tools together with symbolic AI that emulates human intuition to produce our cognitive intelligence. Unlike “black box” machine learning solutions that cannot explain their results, a Beyond Limits system provides clear explanations of its cognitive reasoning in transparent, evidence-based audit trails. Our systems are both educated and trained, which greatly reduces the amount of data that is needed to make them intelligent. This means we can solve problems that deep learning approaches alone cannot do.
Our goal is to create automated solutions with human-like reasoning powers that magnify the capabilities of people. We pride ourselves as the only AI company that provides solutions for problems that cannot be solved using conventional AI approaches.
Beyond Limits goes beyond conventional AI by delivering advanced intelligence solutions that have been tested and proven in the harshest, most extreme conditions in space and the most demanding conditions here on Earth. We deliver cognitive solutions with the resilience, reasoning, and autonomy required by the massive scale and unimaginable distances of interplanetary space to improve the performance of industrial and enterprise systems on Earth.
Dental health has always been an important aspect of your overall well-being. While most people may perceive dentistry as a means to improve one’s aesthetic, this is but an extra perk of visiting your dentist regularly. There are a wide variety of diseases and they all function the same way —through infection.
When a pathogen is able to gain ingress into your body that is called an infection. And one of the means of ingress are the teeth. A tooth cavity or an abscess are both dangerous in the sense that they are infections waiting to happen.
In the digital age, daily life is enhanced by the technology that we have. For one instance, traditional X-ray images had to be printed on a metal sheet and processed the way you would a camera film. Today, thanks to digital photography, the image is instantly projected onto monitors and saved to a database. There’s no longer the waiting phase. It goes straight to the diagnosis phase.
In previous iterations of the technology, the way that orthodontic diagnostics were performed was that dentists had to make a temporary mold of the patient’s crown (to be replaced) while the permanent mold of the crown would be made back at the lab.
Because of digital photography and 3D printing, dentists simply have to scan the crown that they intend to replace and add it to the database. The computer then simply prints out the replacement crown on the spot.
And while this technology seems impressive, there has been one piece of tech that has been on everyone’s lips for the past few months — artificial intelligence.
It first became publicly known when Google introduced it with its new line of Pixel phones. The artificial intelligence found in these phones was able to significantly improve the photo quality taken by the phone camera. A plethora of phone manufacturers, such as Asus, Huawei, and Oppo, followed suit thereafter.
What most people don’t know is that in the medical field, AI is currently being used to make the process of diagnosis more efficient and more accurate. IBM brought its Watson platform into the picture and it is currently used to help doctors make the best diagnosis and recommend treatment based on the patient’s medical history.
The software is even being further developed for it to be able to schedule medical procedures based on its estimated procedure durations. What this does is that it helps improve the efficiency at which hospitals operate by ensuring that time is used in the best way possible. So, this translates to an overall higher number of patients treated.
The same application can be brought into dentistry. A program known as VisualDX allows dentists and doctors alike to input images onto a computer. The computer is then able to produce a full list of all possible diagnoses.
Dthera digitized reminiscence therapy to enable people with dementia to see and hear their family and friends share familiar stories with them. Our first product, ReminX, is an artificial intelligence-powered consumer health product designed to improve the quality of life in individuals suffering from neurodegenerative diseases, such as dementia and Alzheimer’s disease, as well as seniors experiencing social isolation.
Edward Cox, CEO, and David Keene, CTO, both had an upbringing similar to millions of families around the world – they grew up with a grandparent in the home. The special relationships with their grandparents included time hearing stories from the Greatest Generation – from growing up in humble beginnings to traveling across continents for war, peace, work, love and family. At the time, they didn’t realize they were engaging in reminiscence therapy, but did realize the impact social isolation can have on the elderly, especially those suffering from dementia or Alzheimer’s disease. With ReminX, their goal is to improve the quality-of-life for millions of elderly suffering from dementia by digitizing and proactively advocating reminiscence therapy and making it available to all.
Researchers concluded that ReminX holds great promise for bringing reminiscence therapy to people suffering from dementia. Dthera is exploring additional collaborations with non-profit organizations, medical centers and elder care facilities. ReminX is available for purchase by families, caregivers and administrators at senior assistant living centers through direct response marketing. Complete this form to find out where to purchase ReminiX.
Our target market is the 46.8 million people worldwide living with dementia from Alzheimer’s, as well as from other neurodegenerative conditions. In the US alone, the Alzheimer Association estimated 5.7 million Americans have the disease and the cost to care for Alzheimer’s and other dementias will reach more than $277 billion in 2018. Dthera is focused on creating and delivering digital therapeutics that bring medically- validated treatments, such as reminiscence therapy, to patients suffering from dementia and severe forms of social isolation, to ease symptoms and create a better quality-of-life for them and their caregivers.
Who are your competitors?
In the digital therapeutics space, we are one of the only companies developing products for the elder care market, including dementia patients, but also people suffering from extreme social isolation.
As far as products to reach this group of the elderly, other tablets, social media or photo sharing sites could seem to be competitors with ReminX, but these products are not actually suited to this patient market. Apps and most tablets are too complicated for patients suffering from dementia to use, and none of these vehicles have active involvement of family members in the story-creation process designed into them. ReminX proprietary software includes an AI-interface app that engages family members to upload content and then optimizes it, and proprietary facial recognition software in the tablet provides feedback on what’s most effective.
Dthera designed ReminX with the elderly, their caregivers and families in mind. It automatically creates elegant documentary-like videos and plays stories on demand. There is no interface to learn, simply picking up the tablet starts stories and setting it down stops them.
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.