Authors: Audrey Greening, John Walpole
In this whitepaper, we will provide insight on the following developments:
Even before the pandemic, digital platforms and artificial intelligence (AI) were becoming common tools in the understanding and delivery of care. Huge investments had already been made that would reshape the industry. Big data and algorithms were being used to process copious amounts of information to help their critical thinking in predicting, explaining and managing different scenarios in the industry. Similarly, providers had developed reliable telehealth and doctor-on-demand platforms to deliver care more efficiently and closer to patients. COVID-19 not only upset the regular delivery of care, but has accelerated the use of digital platforms and AI in healthcare, requiring risk managers to understand new systems, exposures and solutions. It is vital that risk managers engage with the technology, and respond to the new exposures and opportunities.
The pandemic has affected U.S. healthcare in many ways. It has forced providers and researchers to address (and think anew about) physical property, and its use and location for delivery of care; creative ways to sterilize personal protective equipment; and new protocols to allow multiple patients to share ventilators.
As we emerge from the shock of the crisis, we can consider the critical question of what the healthcare system in the U.S. will look like in the future. The pandemic has created a storm of problems for the healthcare industry, but also the opportunity to leap forward.
Digital Strategies Can Positively Impact Healthcare Revenue
Clearly, the pandemic has had a significant impact on hospital finances, with some organizations reporting volume declines up to 99% for certain revenue-generating procedures,1 compounded by the expectation that it may take some time for revenues to return to pre-COVID-19 levels from procedures like elective surgeries.
While federal funding from the CARES Act and state funding will provide financial help, healthcare systems and providers acknowledge that they are facing long-term challenges, like a decrease in commercial payments and a reduction in demand for services. Given the financial demands healthcare organizations will face, there is both the opportunity and the imperative to use new technology and digital platforms that can help reduce costs in the delivery of care.
Digital strategies, such as telehealth/telemedicine, are at the forefront of initiatives used by healthcare systems to grow revenues. Historically, arcane licensing regulations that require physicians to hold a separate license for each state in which they practice have constrained the use and adoption of telemedicine in the U.S.2 However, the pandemic and the need to mobilize all possible avenues for the delivery of care led to the federal government enacting the Stafford Act. This waived certain federal licensing requirements so doctors could provide services across state lines without those licensing constraints, allowing an increase in telehealth services to provide much-needed remote doctor visits.
Similarly, the Centers for Medicare & Medicaid Services (CMS), which historically limited the amount that providers were reimbursed for telemedicine services, recently changed course and increased its payments for those services. Many private insurers are following CMS’ lead.
Accessibility Powered a Changing Mindset
Pure-play telemedicine companies reported significant increases in volume visits at the height of the pandemic. This increase was driven not only by regulatory changes but also the ongoing concern of some patients to maintain social distancing. Another key driver is the changing mentality that telemedicine is not an inferior interaction with a provider, but merely a different platform to access care. The pandemic has certainly accelerated the use and acceptance of telemedicine, as healthcare organizations and providers embrace this technology as a means to serve their broader patient communities going forward.
Just as the 2008 financial crisis forced an industry-altering boom in the financial sector and more consumer-driven interfaces with the end user, COVID-19 may lead to a reexamination of the status quo in healthcare and invite more innovation. Prior to the financial crisis, most interactions with a person’s financial institution happened inside a physical branch, and people accessed financial services with one bank and one wealth manager. When the banks were impacted and American’s 401(k)s evaporated, it led to distrust and resentment in many existing institutions. This resulted in the unbundling of banks. Many consumers then went to competitors who promised new front-end experiences, which led to innovative, user experience-driven start-ups gaining popularity and attracting large quantities of venture capital dollars.3
Will we see similar industry-shaping innovative changes in healthcare following this crisis, particularly driven by technology? Already, major healthcare organizations are exploring the use of AI to detect COVID-19 in patients based on CT scans. Traditionally, COVID-19 lab tests can take up to two days to complete, but AI has the potential to analyze large amounts of data more quickly.4
According to a report from Accenture, key clinical health AI applications could create up to $150 billion in annual savings in the U.S. healthcare economy by 2026. Growth in the AI health market is expected to reach $6.6 billion by 2021. In just the next five years, the health AI market could grow more than 10 times. Growth is already accelerating as the number of healthcare-focused AI deals increased from less than 20 in 2012 to nearly 70 by mid-2016.5
Advancing the Speed and Accuracy of COVID-19 Diagnosis
When COVID-19 arrived in the U.S., the Centers for Disease Control and Prevention (CDC) announced that radiologists would be the medical providers on the front line of diagnosis, and needed to be prepared to handle the looming influx of patients. Because most patients present with respiratory distress, CT scans are used as the primary screening tool, since they identify potential virus cases faster than any other method. Chest CT scans have been useful not only in detecting, quantifying the severity and assessing the progression of the disease, but also in evaluating the potential response to therapy alternatives.
Then, one major hospital showed how effective AI may be in diagnoses. Using the scans of over 900 patients, including those from hospitals in China, researchers integrated the data from the CT scans and used this clinical information to develop an AI algorithm: “The results showed that the algorithm had statistically significantly higher sensitivity, at 84%, compared to radiologists’ sensitivity of 75% when evaluating the images and clinical data, researchers said.”4 Additionally, the algorithm identified 68% of COVID-19 positive cases that radiologists identified as negative.
These technologies are also being used in the hunt for a COVID-19 vaccine and other therapies. A Hong Kong–based AI company specializing in drug discovery was among the first companies to respond to COVID-19. The company used its generative chemistry AI platform to design new molecules to target the main viral protein responsible for replication. AI and machine learning are ushering in an era of faster and cheaper cures for mankind. Drug discovery and the pharmaceutical industry as a whole will be revolutionized.6
Technology is a tool, and it can be designed to improve processes and methodologies as well. Healthcare faces so many pressure points under current circumstances. Physicians are stretched, especially in underserved and rural areas, to respond to the expanding needs of the population. This compels healthcare organizations to embrace new technology and increase the incorporation of predictive analysis to serve their communities.
The Association of American Medical Colleges (AAMC) publishes an annual report on physician shortage. Even before the pandemic, the AAMC forecasted a shortfall of 40,000 to 122,000 physicians over the next decade, with a shortage of 29,000 to 42,900 doctors in 2020. The expansion of technology into the healthcare industry may be key when hospitals get overwhelmed with a surge of patients, while also dealing with the growing physician shortage.
Data Efficiency and Enhancing Clinical Knowledge
The potential for AI goes beyond diagnosis and treatment. Scheduling appointments, paying insurance bills and other processes may be more consumer-friendly. AI, combined with robotic process automation, can analyze workflows and optimize processes to deliver significantly more efficient medical systems, improve hospital procedures, and streamline insurance fulfillment.
Most physicians have to deal with a vast amount of medical images and data every day. Technology and AI can help relieve the pressure, while boosting efficiency and accuracy. By combining AI methods like natural learning and machine learning with clinical knowledge, it is possible to collate all clinically relevant information in one dashboard. That allows physicians to spend less time capturing information from reports to get a complete picture of the patient.
AI has an amazing potential to revolutionize our lives, including healthcare. AI and data analytics featured largely in the healthcare industry’s line of defense against COVID-19. Researchers leveraged tools to do everything from tracking hospital capacity to identifying high-risk patients, and many believe that these technologies are critical to preparing for similar situations in the future.7
With sufficient data as a foundation, AI can establish health data benchmarks for individuals and for populations. According to Anil Jain, MD, fellow of the American College of Physicians and chief health informatics officer for IBM Watson Health, “Healthcare in the U.S. is in the midst of an unprecedented push for quality, safety and performance-based payment, coupled with volcanic upsurge in comparison shopping by patients. Today, there’s an urgent need for healthcare systems to achieve granular understanding of their patients’ data.”8
There are huge opportunities to use AI models and algorithms for new drug discovery and medical breakthroughs in genomic sequencing, stem cells, CRISPR strategies and more cutting-edge techniques. In today’s pharmaceutical world, the cost to develop a treatment is touted as one of the drivers of healthcare costs, and we know that a large part of this cost is generated from the money and time spent on unsuccessful trials. With AI, scientists can use machine learning to model thousands of variables and how their compounded effect may influence the responses of human cells.
AI Risk Management Tips
As we expect AI to improve and change the delivery and accuracy of care, we should not lose sight of the risk inherent to the adoption of these technologies. The challenge of governing AI is not necessarily managing completely new types of risk, which may manifest in unfamiliar ways. Risk managers will need to enhance existing procedures to account for emerging issues that may arise with AI and the changing digital platforms in the clinical setting.
Risk management professionals should be involved in decisions regarding the adoption of these new platforms and AI implementation, not least of which to ensure they can help manage the potential liability exposure. Due diligence is a key strategy when reviewing the options.
Establishing guidelines will help mitigate the risks that may accompany the expected benefits of applying AI and different digital platforms in the healthcare field. These guidelines include the following.
- Ensure that medical professionals are knowledgeable about how AI and the different digital platforms work to support their critical thinking as they treat patients. It’s important that they understand the technology does not supplant their own expertise and experience. Consider whether they need additional education to gain understanding of using these platforms.
- Designate a lead or co-chair of a committee or task force within the organization to vet the new technologies, and include stakeholders in legal, operations, clinical leadership, and patient safety discussions to help identify and address potential liability exposures.
- Structure a risk management process that combines creativity and research. Many of these technologies are in their infancy, making guidelines, protocols and educational programs difficult to find. In developing a risk process for telemedicine, considerations should include the following:
- Define informed consent that addresses the risk of the clinical advice and telemedicine consult.
- Incorporate the consult into the peer review process.
- Enhance credentialing protocols.
- Reference state and other appropriate accreditation standards.
- Ensure documentation includes direction for immediate and follow-up care.
- Education is key. Competency in the telehealth consult requires:
- Education that focuses on communication skills in conveying limitations of consult.
- Communication skills for maintaining concise discussion.
- Conveying information the patient considers negative, or saying no.
- Repairing a difficult or damaged conversation.
- Additional questions to consider:
- Do our current insurance policies provide coverage for these new platforms? If not, what options do we have to ensure coverage is available? If available, should we use our wholly owned captive insurance companies? Do we need to discuss underwriting data with our carriers, as they may require us to consider commercial coverage?
- Does the use of AI or the digital platform impact how we credential providers?
- Do we need to change our informed consent to disclose the use of AI or the digital platform?
- Have we explored the necessary protective measures to ensure patient privacy?
- Are we in compliance with state regulations for providers (MD, PA, NP, RN, etc.)?
- Are we reviewing and understanding the manufacturer’s specifications, and is risk management involved in the oversight to ensure where the liability exposure resides?
- How are we managing the quality assurance of cases? Peer review of providers?
- Is there an arbiter of discrepancies between AI and clinician interpretations?
Conclusion: AI Shaping Healthcare
While the pandemic compelled organizations to take a deeper look at their systems and identify where there were shortfalls, the promise of AI and demand for different digital platforms will force changes in the healthcare industry that will improve care and ultimately reduce expenses.
Healthcare systems and providers that embrace these changes will be better positioned for success as the industry evolves, including improving the patient experience, access and outcome. It may trigger a whole new nomenclature and environment for providers and risk management professionals, force organizations to rethink risk profiles and improve patient safety, and challenge healthcare systems to rethink definitions of risk and how to cover those risks.
