With more than 4 million nurses, the largest segment of the U.S. healthcare sector, nurses have indisputably demonstrated an ability to improve healthcare outcomes. We are just beginning to utilize Healthcare IT data and AI to improve patient outcomes. One of the key benefits of AI will be the ability to leverage the data from nursing care plans and nursing diagnoses to perform work load balancing for nursing staff. This is a key solution to future management of the problem of the shortage of nurses.
Another problem that needs attention is the possible disconnects which can result from nurse to nurse hand offs with the use of virtual nurses who remotely monitor patients. They enter data into their own EHR system – not the same one in use by the hospital where the patient is located. We will discuss here the nature of the data, technologies and frameworks, the nursing information model and the structure of the data elements needed to provide care needed to implement solutions for staffing, interoperability and workflow improvements.
The National Academy of Medicine’s committee background report on the Future of Nursing 2020-2030, Activating Nursing to Address Unmet Needs in the 21st Century, found the worsening health profile in the United States requires “more than a traditional medical response.” As professionals in the care team, nursing documentation requires a standardized framework to achieve consistent data quality in healthcare communications about the work of nurses. This standardized framework recognized for professional nursing documentation is the American Nurses Association (ANA) Nursing Process. This ANA framework is essential to nurses for managing and improving healthcare outcomes, safety and reimbursement as proposed by the Institute for Healthcare Improvement (IHI).
In most electronic health information record systems, the standard nursing data implemented (sometimes called the system terminology, data dictionary, or nomenclature) is proprietary with a pre-existing data structure/framework. The proprietary framework acts as a barrier to nursing documentation by constraining the available concepts for nursing documentation and the nursing care plan fields.
Without interoperable electronic data concepts available for documentation, nursing care notes become unstructured free-text and are not included in coded health information exchanges. Due to the highly structured design of EHR systems, nursing practice is determined by the system’s terminology and ontology framework configuration. If nurses do not select the ANA framework; nursing care data takes on the sedentary shape of the local proprietary data structures, rather than nesting in a flexible, portable and universal tool to enable nurses and other episodic care providers to improve future nursing interventions, practice and care outcomes.
The American Nurses Association (ANA) describes the common nursing framework of the documentation of professional nursing practice as the Nursing Process. The Nursing Process is the foundation for the documentation of nursing care. Yet, in the EHR, nursing documentation is reused during the patient’s stay, over and over, with the documentation being done from the nursing assessment as if the documentation was a template. The Nursing Process is the framework and essential core of practice for the registered nurse to deliver holistic, patient-focused care.”
Producing effective EHR systems for nursing requires a deep understanding of how nurses create and conduct cognitive documentation as well as task-oriented documentation. Most EHR systems dictate rather than adapt to nursing workflows and nursing information is not organized to fit the ANA model of care. The EHRs often assume a nursing care delivery model that is represented as algorithmic sequences of choices, yet nursing care is iterative with reformation of patient goals, revising interventions and actions and updating care sequences with individual patients based on encountered condition changes and constraints. In the dictated workflow of EHRs, nursing data is collected as care assessments with nursing diagnoses, interventions and actions in formats used to create single patient encounters.
As the country’s baby boomer population continues to age, the healthcare industry is gearing up for a whole new level of demand that it has never before gone through. With greater numbers of people requiring doctor visits and hospital care, the industry is looking for ways to be even more productive and efficient to ensure that the quality of healthcare that people are receiving doesn’t suffer.
One of the most exciting advances to hit the health sector is artificial intelligence or AI. This technology is looking to have a huge impact, not just on healthcare in the immediate future but moving forward. Here’s a closer a look at just how it’s changing the course of the industry.
Medical records and data are benefiting from the technology
When it comes to the areas that AI is having the largest impact, medical records and data keeping is a big area to focus on. When you think about the vast amount of information that needs to be collected, stored, and analyzed for each and every patient it can seem rather overwhelming. This is exactly why data management has become such a priority for AI.
Robot technology is now being used to actually collect the information, store it, find specific data when required, and allow for quick and seamless access across the board.
Wearable medical devices
Wearable medical devices are another area where AI is having an impact and bringing about some really exciting and promising products. It’s not just about devices that provide potentially life-saving alerts and information, it’s also devices that can help the wearer better their own personal health by tracking various details. Devices such as the Apple Watch and Fitbit are great examples of this kind of technology that can be useful to everyday people.
Now as for the devices that can actually offer life-saving capabilities and tools, look to options such as the Bay Alarm Medical which is a great medical alert system. While this device isn’t going to track any information or take readings, it can be worn 24/7 and with the push of the button, it connects you to a live operator that can get you the help you need.
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).
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.
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.
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.