Healthcare organizations are in a precarious financial position. With operating margins still hovering near zero, revenues are at heightened risk because of a surge in third-party audits following the expiration of the public health emergency as well as increased scrutiny by federal and commercial payers alike to identify – and recover – billions in improper payments and penalties.
This sharp uptick in audit activity has many healthcare organizations – even those that have already adopted revenue cycle management (RCM) technologies to streamline workflows – struggling to comply with both the volume of incoming documentation requests (ADRs) and the timeframes within which they must reply.
The appearance of artificial intelligence (AI), specifically conversational AI, is promising to change that, making it possible to convert the highly unstructured data populating the audit process into information that can be both analyzed and automated.
The Audit Environment
Ferreting out fraud and abuse remains high on the federal government’s priority list. In fiscal year 2022, the U.S. Department of Justice (DOJ) collected more than $1.7 billion in improper payments, while the Office of the Inspector General (OIG) reported identifying more than $200 million in expected audit recoveries and over $277 million in questioned costs in its 2023 Semi-Annual Report to Congress.
Meanwhile, the Centers for Medicare & Medicaid Services (CMS) is expected to claw back $4.7 billion from Medicare Advantage plans over the next decade thanks to recent adjustments to its risk adjustment data validation (RADV) program. Add to all that the influx of demand letters in the wake of the expiration of the federal PHE – along with many of the waivers that kept external audits in check – as well as claim changes and heightened regulatory and billing practice scrutiny by federal contractors and commercial payers.
All this comes at a time when hospital margins remain “well below historical norms,” per Kaufman Hall, and revenue cycle leaders are facing severe labor shortages, with more than 41% reporting that up between 51% and 75% of RCM and billing department roles are currently vacant.
By Tara Mahoney, head of healthcare practice, Avaya.
COVID-19 has forever changed the U.S. healthcare system with the acceleration of digital transformation and remote collaboration. As 2020 past us now, we’re getting a clearer picture of what post-pandemic healthcare in the U.S. will look like (or rather, require). Based on my industry background at Avaya, here are four predictions as we continue into 2021:
Prediction #1: Telehealth is here to stay and it’s forcing us to reimagine current care models. It must and will evolve.
The pandemic thrusted organizations into the inevitable telehealth revolution, but it’s not likely COVID-19 will push the timetable forward as much as some claim. Telehealth is about much more than “just” video-based physician visits. It will evolve to cover many workflows where patients and care teams cannot be together, including virtual rounding, remote patient monitoring, bedside consultation. It’s about being able to seamlessly coordinate across the entire health organization in a way that positively impacts key measures of clinical quality – all while addressing information security concerns and abiding by HIPAA regulations.
It’s about the use of the Internet of Medical Things (IoMT) for collecting important healthcare data in real-time to enable proactive, remote care delivery. It’s about Artificial Intelligence (AI) and data analytics to make critical predictions about patient diagnoses, treatment side effects, staffing, and expenses. It’s a complex journey, only made more complex by historically slow-to-change industry policies.
Health systems pulled together in 2020, but that’s not enough for sustainable digital transformation. Organizations will take their time navigating the complexities of digitization and remote collaboration as they embrace a new future of operations and patient care. We will see current care models change, albeit incrementally.
A recent Wall Street Journalarticle pointed to a biased algorithm widely used in hospitals that unfairly prioritized white patients over black ones when determining who needed extra medical help.
While AI has been cited as a data-driven technology that doesn’t make decisions based on emotions, but on actual facts – the reality is that the facts can be misleading.
In the above example, race wasn’t a deliberate factor in how the AI algorithm reached its decision. It actually appears to have used predictive analytics based on patients’ medical spending to forecast how sick patients are.
Yet, the problem is that black patients have historically incurred lower healthcare costs than white patients with the same conditions, so the algorithm put white patients in the same category (or higher) than black patients whose health conditions required much more care
Bias is inherent in a lot of things we do and often, we just don’t realize it. In this case, the data assumed that people who paid more for services were the sickest. As illustrated, we have to be considerate of the data we use to train algorithms, Cost of services or amount paid shouldn’t be information we use to determine who is sicker than another.
In another example, if skin-cancer-detection algorithms are typically trained on images of light-skinned patients, they would be less accurate when used on dark-skinned patients, and could miss important signs of skin cancer. The data must be inclusive to provide the best results.
While AI can accelerate disease diagnoses, bring care to critical patient populations, predict hospital readmissions, and accurately detect cancer in medical images, the example illustrates the caveat: AI bias –whether because of a lack of diverse data, or the wrong type of data – exists in healthcare and it can lead to social injustice, as well as harm to patients.
In addition to racial bias, unchecked algorithms can cause other types of bias as well, based on gender, language or genealogy. In fact, according to IBM research, there are more than 180 human biases in today’s AI systems, which can affect how business leaders make their decisions.
As an example of gender bias in healthcare, for many years cardio-vascular disease was considered a man’s disease, so information was available based on data collected from men only.
This could be fed into a chatbot and lead a woman to believe that pain in her left arm was less urgent – possibly a sign of depression – with no need to see a doctor right away. The consequences of this oversight could be devastating.
By Ken Perez, vice president of healthcare policy, Omnicell, Inc.
Discussions about the application of artificial intelligence (AI) in healthcare often span multiple areas, most commonly about making more accurate diagnoses, identifying at-risk populations, and better understanding how individual patients will respond to medicines and treatment protocols.
To date, there has been relatively little discussion about practical applications of AI to improve medication management across the care continuum, an area this article will address.
The Significance of Medications
What’s the first thing that comes to mind when someone mentions prescription drugs in the United States? In poll after poll, the high and rising costs of medications are American voters’ top healthcare-related issue.
This concern is well founded. The U.S. spends almost $400 billion a year on medications–$325 billion on a retail basis and about $75 billion for inpatient and outpatient use.
To put the $400 billion in perspective, it is equal to about 11% of total U.S. healthcare expenditures, and it’s one of the top reasons why the U.S. spends much more on healthcare than other industrialized countries.
Medication Management Shortcomings
Unfortunately, there are a lot of issues with the medication management system, broadly defined.
It’s estimated that 20-30% of prescriptions are not even filled, not even picked up at the retail pharmacy. According to the Centers for Disease Control and Prevention (CDC), each year, adverse drug events result in 1.3 million visits to the emergency department, and of those ED visits, over a fourth, 350,000, result in hospitalizations, which result in significant costs.
Over the past 50 years, much legislation has been passed to regulate and reform the U.S. healthcare system, and this has significantly increased the administrative burden on healthcare provider organizations. As a result, according to data from the Bureau of Labor Statistics, the National Center for Health Statistics, and the United States Census Bureau’s Current Population Survey, the number of administrators has grown by 3,200% since 1970, while over the same period, the number of physicians has been relatively flat, in line with population growth. Correspondingly, per research funded by the Physicians Foundation, it is estimated that the average physician and/or his or her staff spends 785 hours per year on quality reporting.
The administrative burden also falls heavily on pharmacists. According to a national survey by the American Society of Health-System Pharmacists (ASHP), pharmacists spend over three-fourths of their time on non-clinical activities—mostly manual, administrative processes.
In spite of the massive amount of spending on medications, the medication management system is fraught with errors at multiple steps in the medication-use process, prescriptions are often not filled, and over one-fourth of all hospital readmissions are potentially preventable and medication related.
Current Health announced that it has launched a collaboration with Mayo Clinic to develop remote monitoring solutions that accelerate the identification of COVID-19-positive patients and predict symptom and disease severity in patients, healthcare workers and other at-risk individuals in critical service sectors.
Using digital biomarkers collected by Current Health’s FDA-cleared remote monitoring sensors and platform, experts from Mayo Clinic and Current Health will also be able to expedite identification and assessment of treatment efficacy and improve care for patients with or at risk of COVID-19 infection. Through this collaboration, Current Health and Mayo Clinic aim to improve patient outcomes while preserving and optimizing health system capacity worldwide.
Today, more than 40 hospital systems around the globe use Current Health’s remote patient monitoring platform to monitor and manage patient health. These systems are now increasingly using Current Health to monitor and manage patients infected with COVID-19 at home and in the hospital. The next stage is to use digital biomarkers collected by the Current Health solutions, such as temperature, heart rate, oxygen saturation, activity and posture, to develop AI-based algorithms that can detect and predict symptom and disease severity to enable proactive treatment.
This collaboration will leverage Current Health’s existing patient database – which already includes anonymized vital sign data and raw physiological sensor data from hundreds of patients infected with COVID-19 and thousands of uninfected patients – as well as algorithms developed by Mayo Clinic, which will be used to provide individualized care to patients with complex and critical medical conditions. By working together, Current Health and Mayo Clinic hope to scale data analytics, add to Mayo Clinic’s major advancements in accelerating COVID-19 detection and diagnosis, and further efforts to understand and treat this disease.
“Our collective ability to save lives hinges on our ability to understand this virus quickly. COVID-19 has presented in many ways across different people, which has made it very challenging to understand the virus and how it develops,” said Chris McCann, CEO and Co-Founder, Current Health. “By combining our platform with the deep medical and scientific expertise that exists at Mayo Clinic, we seek to explore both known and novel biomarkers, as well as how they manifest in entirely diverse populations. This will be critical to determining how we define, and enable effective treatment of, this disease.”
“Combatting the COVID-19 pandemic is our number one priority,” said Jordan D. Miller, Ph.D., who directs the Center for Surgical Excellence and leads the investigative team at Mayo Clinic.
“Real-world, continuous data – from patients infected and not infected with the disease – is essential to understanding and predicting how the disease presents and evolves,” says Abinash Virk, M.D., an infectious disease expert at Mayo Clinic. “If we are successful in accomplishing our goals, we believe we will improve how patients with COVID-19 are identified, monitored, managed, and ultimately help with their recovery.”
Mayo Clinic will also become an investor in Current Health as part of this collaboration.
The Mount Sinai Health System has partnered with Sana Labs to launch Project Florence, a personalized learning platform to enhance the skills of nurses treating COVID-19 patients in New York City. The group, facilitated by the New York Academy of Sciences, is also making the platform available for free to hospitals around the world to improve medical response and care during the pandemic.
The virtual training platform, available through Sana Labs, provides a curriculum developed by Mount Sinai that includes the latest on industry resources and policies from organizations including the American Association of Critical Care Nurses.
After users complete an AI-powered adaptive assessment that measures their knowledge, the platform recommends personalized content in real time to address individual skills gaps. It can be accessed from any internet-connected device including phones, tablets, laptops, and desktop computers. The project was officially launched at the Mount Sinai Health System on Monday, April 13.
“The profound shortage of intensive care nurses and respiratory therapists will be one of the most significant hurdles facing U.S. hospitals treating critically ill COVID-19 patients,” said Jane Maksoud, RN, MPA, senior vice president and chief human resources officer, Mount Sinai Health System. “Project Florence will be a great benefit to staff preparing to care for critically ill patients. We are grateful for the partnership we have developed with Sana Labs and the work we have done together to assist our nurses on the front line.”
A projected 4.8 million Americans will be hospitalized for COVID-19, according to the American Hospital Association. Of those hospitalized, an estimated 40 percent or nearly 2 million patients will require admittance to the ICU. While there are currently about 550,000 critical care nurses in the United States, tens of thousands of nurses will be in demand in the coming months.
“I’m very excited to bring this innovative approach to Mount Sinai hospitals to help advance the skill set of our nurses,” said Diane Adams, MS, Chief Learning Officer of Mount Sinai Health System. “Not only are we advancing the essential skills of our staff, but we are also meeting the needs of our community during a particularly critical time across New York City, the United States, and the rest of the world.”
As hospitals shift priorities from other departments to ICUs, the two-day curriculum is tailored to each individual and suitable for nurses, as well as other medical professionals who are called to assist and may require an update on their understanding of ICU equipment and procedures.
Future technology is changing the world of health. As a result, new ways on how health research is conducted and performed are beginning to emerge. Major Contract Research Organizations or CROs are starting to employ AI in pre-clinical tests, thus revolutionizing the role of technology in healthcare.
Artificial intelligence is a type of intelligence displayed by machines and computer systems. Nowadays, there are several ways how pre-clinical CROs use AI in their studies. But, first, what are pre-clinical CROs?
Pre-clinical CRO Defined
Pre-clinical CROs, otherwise known as Pre-clinical Contract Research Organizations, are companies that provide knowledge, skills, and experience needed to transform a medical or pharmaceutical idea concept into a final product. There are a lot of processes involved before the final product is revealed, which include the discovery and development stage, pre-clinical research stage, the clinical research stage, and, lastly, the FDA review.
The period between pre-clinical tryouts and the unveiling of the product is where the role of a pre-clinical CRO is most critical. Drug ideas and prospective products may fail within this period; hence modern pre-clinical CROs, like Ion Channel CRO, continue to dig deeper into the capacity of future technology to increase efficiency in health research.
Reasons Why Pre-clinical CROS Are Using Future Technology/AI To Conduct Health Research
Reduces uncertainty in pre-clinical experiments – AI is now being used to reduce the improbability that comes with pre-clinical trials. This will go a long way in reducing time spent on research, cutting down financial costs, and optimizing data gathering.
Gathers data and obtains actionable insights – Researchers now use AI to streamline data collection and selection of recipients of pre-clinical tests. Data collection and analysis are an integral part of health research, and keeping up with the zillions of data available is impossible for the human researcher. However, with the aid of AI tools, such as deep learning and machine learning, it is possible to analyze, select patterns, and connect relevant data that can lead to drug discovery.
Researchers also make use of reports generated by AI to gain actionable insights during their pre-clinical studies. AI tools can also improve recipients’ selection by choosing the most appropriate group capable of responding to pre-clinical research and tests.
Last month saw the rollout of the latest upgrades to Amazon’s Echo speaker line: earbuds, glasses, and a ring that connect to Amazon’s personal assistant Alexa. These new products are just three examples of a growing trend to incorporate technology seamlessly into our human experience, representing the ever-expanding frontiers for technology that have moved far past the smartphone.
These trends and others are going to make a big impact in the healthcare space, especially as providers, payers and consumers alike slowly but surely recognize the need to incorporate tech into their workflows to meet the growing consumer demand for digital health tools. At the same time, the data-hungry nature of these innovations is creating its own problems, driving a discussion around privacy and security that is louder and more urgent than ever.
Here are three trends to look out for in the coming year:
Artificial Intelligence (AI) and Machine Learning are growing into themselves
In 2020, we will continue to see AI and machine learning push boundaries, while at the same time mature and settle into more defined patterns.
With the adoption of technologies like FaceID, facial recognition technology will be an important player in privacy and security. It can be leveraged to simplify the security requirements that make multi-factor authentication a time-consuming process for healthcare professionals — on average, doctors spend fifty-two hours a year just logging in to EHR systems. On the patient end, this same technology has the ability to detect emotional states of patients and anticipate needs based upon them, and the success of startups like Affectiva, the brainchild of MIT graduates, shows its tremendous promise..
Meanwhile, FDA-approved innovations from Microsoft and others claim the ability of computer vision for assisting radiologists and pathologists in identifying tumors and abnormalities in the heart. While robotic primary care is a long way off, some view AI as a rival to more niche clinical positions.