By Dan Schulte, MBA, CHFP, senior vice president, provider operations, HGS.
As outbreaks of COVID-19 continue to crop up around the country, the ongoing public health crisis is just one facet of the situation; economic disruption is another grim reality, including for the healthcare industry itself. The American Hospital Association estimates COVID-19 will result in losses of $202.6 billion for the country’s hospitals and health systems due to factors such as the cancellation of nonemergency procedures; the high cost of treating a patient with COVID-19; and the millions of Americans who could become suddenly uninsured due to the economic implications of the virus.
Providers must improve cash flow to remain stable, which will require new revenue cycle management strategies supported by technology. Artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) together can provide an effective automation strategy that will help healthcare systems recover and retain more of their revenue — while boosting patient satisfaction — as they navigate this costly crisis.
Nine revenue cycle functions ripe for automation include:
Prior authorizations: With manual prior authorizations requiring an average of 21 minutes and as much as 45 minutes per transaction, the opportunity to drive cost savings through automation is significant. Because of well-defined business rules in this area and structured data that systems exchange in conducting prior authorizations, RPA can significantly improve this process: Implementing a “bot” that can perform the same tasks repetitively and without variation can help reduce error rates, so patients can get the authorization they need quickly, and lower the likelihood of claim denials.
Eligibility and benefit verification: While fully electronic transactions account for more than 84% of all eligibility and benefit verification transactions — a positive development — more can be done to reduce wasteful spending in this part of the revenue cycle. As the starting point for care delivery, this function represents a significant potential for improvement via intelligent automation. The focused manager will ensure that the EDI tools bring the right data across to the patient accounting system (timely, accurate and complete data), and will have the necessary add-ons to find the last 15% of data from screen scraping and outsourcing to a reliable service provider.
At Electronic Health Reporter, we take innovations from healthcare companies very seriously. For nearly a decade, we’ve featured their work, products, news and thought leaders in an effort to bring our readers the best, most in-depth insight about the organizations powering healthcare. That mission lies at the heart of all we do, for the benefit of our audience.
For the first time, we are officially naming some of the most progressive companies in healthcare technology, in our inaugural class of the best, most innovative brands serving health systems and medical groups. Our call for nominations for this “award” series received hundreds of submissions. From these, we selected the best companies from that class. The work these organizations are doing is forward-thinking; award-worthy, we think. We think you’ll agree with all of our choices.
In each of the profiles to come in this series, we’re share their stories — from their own perspective, through their own responses to our questions about what makes them remarkable. Some of the names featured here you’ll recognize, some you won’t. But we believe you’ll agree – all those profiled are doing innovative, groundbreaking work! That said, here’s a member of our inaugural class:
What is the single-most innovative technology you are currently delivering to health systems or medical groups?
We use AI-backed systems to help hospitals resolve avoidable variation, harm, and mortality with typical monitoring and reporting systems that currently are only able to detect 10% of what our systems can detect. Meaning, through our systems, we can see substantially more information then what current hospital systems are providing executives. Using the world’s largest patient dataset (140 million records from 46 countries) and built around the work of the developer of the world’s most commonly used patient safety system, POSSUM, we have built predictive applications that save lives, prevent harm and help hospital systems improve margins.
There’s never been a more exciting era in the healthcare IT space than now. The intersection of disruptive technological innovation and a more tech-savvy generation of customers provides endless opportunities across a wide range of medical applications.
The healthcare industry has traditionally been reluctant to embrace tech. Given the strict regulations, the sensitivity of healthcare, and the potentially deadly consequences if something does go wrong, this reluctance is understandable. Slowly but surely though, healthcare is embracing IT, thereby unleashing new levels of efficiency and customer satisfaction.
Here’s a look at some of the key tech trends that are fast establishing a foothold in healthcare.
Technology has changed a dramatic amount over the last ten years alone, and digital health is now ever-present. From telemedicine and health-related wearables to online medical providers and health resources, digital health is growing faster than ever.
Consumers are using digital resources to better manage their health levels, and medical facilities are using digital technology to track, manage, and improve the health of their patients. Now, patients do not even have to meet in person to get the treatments or advice they need.
Putting power back into the hands of the patient while giving doctors and medical professionals access to the tools and data they need; the rise of digital healthcare is something that cannot be ignored.
Digital Healthcare for the Individual
Consumers have access to more technology than ever before, and that’s good news for those in the healthcare sector. Now that consumers can easily buy a wide range of wearable technologies, they can monitor their health levels from anywhere, and provide their doctors with detailed information. Going further than external wearables like fitness trackers, we have also seen pacemakers with their own dedicated monitoring apps.
This unprecedented level of data gathering is proving vital for catching early signs of health issues. The health industry is being forced to keep up the pace of tech innovation simply because of the wide range of benefits that those new technologies bring.
More than 50 organizations – from major tech giants to startups and healthcare industry leaders – convened by the Consumer Technology Association (CTA) have developed the first-ever ANSI-accredited standard for the use of artificial intelligence in healthcare. This standard, part of CTA’s new initiative on AI, is the first in a series that will set a foundation for implementing medical and healthcare solutions built on AI.
“This standard creates a firm base for the growing use of AI in our healthcare—technology that will better diagnose diseases, monitor patients’ recoveries and help us all live healthier lives,” said Gary Shapiro, president and CEO, CTA. “This is a major first step – convening some of the biggest players in the digital health world – to help create a more efficient healthcare system and offer value-based healthcare to Americans.”
AI-related terms are used in different ways, leading to confusion – especially in the healthcare industry, including telehealth and remote patient monitoring. To address this problem, CTA announced the working group with 30 members less than a year ago, which now includes a wide range of decision makers from 52 organizations and member companies to develop a standard built on consensus.
The standard – 11 definitions and characteristics – provides a framework for better understanding AI technologies and common terminology so consumers, tech companies and care providers can better communicate, develop and use AI-based healthcare technologies.
A broader AI committee at CTA also published an ANSI-accredited standard that addresses the pervasiveness of AI-enabled technology across the entire consumer technology industry. The standard defines over 30 terms including machine learning, model bias, artificial neural network and trustworthiness.
“So far, common terminology has defined the intent of use — and that’s one of the most significant challenges in developing standard application of AI,” said Rene Quashie, vice president of policy and regulatory affairs, digital health, CTA. “As health systems and providers use AI tools such as machine learning to diagnose, treat and manage disease, there’s an urgent need to understand and agree on AI concepts for consistent use. This standard does exactly that.”
As the healthcare system deals with clinician shortages, an aging population and the persistence of chronic diseases in the US, technologically driven solutions, such as AI, will increasingly be used to meet clinician and patient needs, the group notes.
By Valerie Barckhoff, principal and healthcare advisory practice lead, Windham Brannon.
Hospitals and health systems throughout the country are constantly looking for ways to streamline finances and fine tune operating margins. Many are now looking outside the box for solutions to help increase their operating revenue and combat the continued pressure to stretch budgets to include data security, attracting top talent and facility upgrades. Artificial Intelligence (AI), as an example, is showing promising results in healthcare to more effectively address revenue cycle inefficiencies.
AI has penetrated nearly every touchpoint in medicine, from the way emergency medical technicians (EMTs) are dispatched to assisting physicians during surgery. AI is enabling smart devices to detect cancer or a stroke, and consumers can even get help to quit smoking or address opioid addictions with the help of AI. So, it was only a matter of time to apply AI to tackle health revenue cycle inefficiencies. But how?
RCM Represents Prime Opportunities for AI
Even as revenue cycle management (RCM) becomes increasingly more complicated, there are a number of repetitive and predictable processes involved that make it an area perfect for the efficiencies that AI and intelligent automation offer?for instance, prior authorizations.
Prior authorizations, the process by which insurance companies and payers determine if they will cover a prescribed procedure or medication, are meant to help patients avoid surprise bills and unexpected out-of-network costs. However, this largely manual process is time-consuming and error-prone, resulting in $30 billion in annual costs for wrongful denials, inefficiencies and clerical errors. AI can reduce the need to assign resources to repetitive, “simple” pre-authorization requests, allowing healthcare leaders an opportunity to deploy staff to more complex, acute requests that require additional clinical information, peer-to-peer review, and/or other payer required information
Studies show that 84% of physicians surveyed said the burdens associated with prior authorization were high or extremely high, and 86% said the burdens associated with prior authorization have increased significantly (51%) or increased somewhat (35%) during the past five years.
The ability to apply AI to the revenue cycle provides yet another tool to identify inefficiencies, then allow hospitals to redesign their processes and re-allocate internal resources to maximize their net revenues going forward? to focus on more patient care instead of administrative burdens. There is a huge opportunity to gain 25- to 50-percent efficiencies for hospitals and health systems.
Artificial intelligence is poised to make a major impact on healthcare and healthcare technology. Investment in the healthcare AI sector alone is predicted to reach $6.6 billion by 2021. By 2026, that number will balloon $150 billion. And there’s no doubt about the transformative power of artificial intelligence, however, in terms of healthcare, its restorative effects are truly life changing.
Today, there’s a term in healthcare called the “iron triangle.” The iron triangle refers to three combined factors that can have negatives trade offs: affordability, access, and effectiveness. Though closely interlocked, improving one area without neglecting another is very difficult—even in modern times. With AI, the healthcare is much better equipped to tackle these conundrums. Here’s how artificial intelligence will impact the future of healthcare tech:
One of the biggest benefits of AI in healthcare is the ability to predict potential issues and eradicate them before they become too serious. Machine learning is a major part of prevention intervention. With machine learning, computer systems are handed data and use statistical techniques to identify patterns over time and “learn” more about the information it processes. Doctors can use these targeted analytics to make more accurate diagnosis, spot potential issues before they arise, assess risks, and offer better treatment plans.
By Adarsh Jain, editor, Transparency Market Research.
A rosy picture is always a tricky perception. Artificial Intelligence or AI as it is known, is also very similar. Research publications are inundated with the findings that integrate AI with industry for better outcomes. And in most cases, these findings prove to be worthy in one way or other. But, the picture for healthcare is not as rosy as it seems to be. While tech companies across the world have invested in developing products that will assist the practitioner in making better decisions, doctors have their own doubts in implementing them. Despite these doubts, there is enough hope for players in an upcoming market like AI in healthcare, finds a Transparency Market Research study.
Will AI take away jobs?
In most industries, this has been the million dollar question. And, as serious as it may sound, experts have, time and again, clarified that AI is not going to take away jobs. It, of course, will augment decision-making and, thus, help produce better outcomes. Healthcare should be no different.
Before medical practitioners make a hasty call, it is important for them to realize that AI is a machine, and it clearly works based on algorithms. A machine does not have a human brain to be able to take conscious decisions. It, however, is only a critical aid in taking better decisions. There is no doubt that a large pool of data on admissions, medical history, procedures, conditions, etc. remains untouched across the world, states the TMR study. Processing and inferring from this large a pool of data is humanly impossible. This is where AI can aid physicians. What has happened is that the use of AI has affected radiologists and pathologists the most. For long, these two healthcare professionals have been the backbone of discerning abnormalities in human body functions or detecting conditions.
While healthcare organizations and experts, including government representatives, have proposed the use of AI in diagnosis, the risk of misjudgment bothers medical professionals. A wrong diagnosis or treatment could result in loss of life, and invite severe action against the healthcare professional. This argument from the healthcare professionals hold weight and, perhaps is a strong emotional reason for doctors from being reluctant towards introducing AI in their practice.
Lack of regulations is the caveat
A process bound by regulations is always more effective. In most countries, there is no law that defines the use of AI in medical practice. There is hardly any information that speaks about the limit to which the use of AI should be restricted. Also, the lack of information on the right usage, and ensuring all inferences or decisions based on AI are error-free is a huge caveat.
At a time when tech giants, especially in the United States, are vigorously working towards rolling out AI in healthcare, it is prudent to have regulations that define usage. It is just the matter of one country making the move, and when that happens, the rest are likely to follow suit, states the TMR report.
Artificial intelligence is a topic that should interest us all – as it revolutionizes the world with every second and in unimaginable ways. 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.
To read the full article I wrote recently, visit MedSource Consultants’ website.
The world of healthcare is changing and those changes impact how we deliver care, our approach to engaging patients and the relationships between stakeholders across the healthcare value chain. Each day, we witness advances in genomics, imaging and pharmacology, and learn about the use of artificial intelligence (AI) to drive these advances. Indeed, healthcare is in the midst of a major revolution and AI seems to be at the very core of this transformation. How much of the AI story is hype and how much is real?
Innovaccer Inc., a San Francisco-based healthcare data activation company, is hosting a breakthrough AI webinar on June 20 with guest speakers Dr. Peter Lee, corporate vice president, Microsoft Healthcare, and Stephen K. Klasko MD, MBA, president and CEO, Thomas Jefferson University and Jefferson Health, who will be discussing the new healthcare domains of AI, and it’s “never imagined” impact. They will be joined by webinar moderator, David Nace MD, chief medical officer at Innovaccer.
The use of AI in healthcare has lagged behind other industries, in large part because of the lack of comprehensive, pristine data. The webinar, titled “Beyond Interoperability: Data Activation and Artificial Intelligence for Healthcare,” will focus on the recent AI hype, tease fact from fiction, and explain how advances in data activation can solve the accuracy and interoperability problems in the space.
Dr. Lee has extensive experience in managing the process of going from basic research to commercial impact. Past illustrative examples include the deep neural networks for simultaneous language translation in Skype, next-generation IoT technologies, and innovative silicon and post-silicon computer architectures for Microsoft’s cloud. He also has a history of advancing more “out of the box” technical efforts, such as experimental under-sea data centers, augmented-reality experiences for HoloLens and VR devices, digital storage in DNA, and social chatbots such as XiaoIce and Tay.
Lee is a member of the board of directors for the Allen Institute for Artificial Intelligence and the Kaiser Permanente School of Medicine. He served on President’s Commission on Enhancing National Cybersecurity. And, previously, as an office director at DARPA, he led efforts that created operational capabilities in advanced machine learning, crowdsourcing, and big-data analytics, such as the DARPA Network Challenge and Nexus 7.
Under Dr. Klasko’s leadership, Jefferson Health has grown from three hospitals in 2015 to 14 hospitals today. His 2017 merger of Thomas Jefferson University with Philadelphia University created a pre-eminent professional university that includes top-20 programs in fashion, design and health professions, coupled with the first design-thinking curriculum in a medical school, conducting the nation’s leading research on empathy, an essential component of medicinal practice that is often overlooked in the academic setting. As a disruptive leader in the academic ecosystem, Dr. Klasko brings a valuable point of view to the Innovaccer Strategic Advisory Council.