There was a great to digest in this week’s release of a new report by the Center for Connected Medicine (CCM) at the Pittsburgh-based UPMC health system, conducted in partnership with KLAS Research.
As we reported on Wednesday, Oct. 21, the CCM’s fourth annual “Top of Mind for Top Health Systems” report, which surveyed 117 executives representing 112 health care provider organizations, focuses on how innovation priorities shifted in response to COVID-19 and the role of key technologies in managing the pandemic. The report, conducted in partnership with KLAS Research from May to August 2020, was published on October 20.
Survey results indicate these technologies were priorities for the future at many health systems before the pandemic, but COVID-19 caused them to fast-track implementation, looking to telehealth to continue seeing patients, AI to enable better decision making and revenue cycle management technologies to improve efficiencies.
Among key findings, the Top of Mind report found that:
> Nine out of 10 organizations successfully met increased telehealth demand during the pandemic; however, quick implementation of solutions magnified opportunities for improvement, including integration and patient and clinician experience, that many will seek to address in the coming year.
> Three-quarters of respondents said their organizations are measuring and analyzing data from telehealth use; however, a limited number are examining health outcomes related to telehealth.
> Half of respondents reported using AI in response to the pandemic for applications such as clinical decision support, management of beds, staffing and devices, and analytics—experience that is boosting interest in the technology and pointing to greater utilization in the year ahead.
> A majority of respondents said their organizations are using 20% or less of their health care data to inform AI applications, suggesting health systems need to do more to standardize and share their data.
> Identified as an area most in need of innovation, revenue cycle management has become a greater priority for health systems, with 57 percent of respondents saying they are optimistic or very optimistic that innovation can happen in the coming year.
> Predictive analytics, AI, bots and automation are cited as the most-needed technologies to improve revenue cycle management (cited by 26 percent of respondents) as health systems look for opportunities to be more efficient.
Fascinatingly, the development of artificial intelligence and machine learning capabilities harmonizes beautifully with the top objectives cited by survey respondents, namely: improving revenue cycle management operations (26 percent); expanding telehealth and virtual care (26 percent); improving operations and efficiency in general (21 percent); enhancing population health management capabilities (11 percent); and integration and interoperability for improved patient care (11 percent). In fact, the development of AI, business intelligence, and bots, was itself the fourth-highest-ranked item on that list.
What does all of this data mean? One clear interpretation is this: even as the leaders of patient care organizations have been contemplating the idea of really intensifying and accelerating their use of AI and machine learning tools, the COVID-19 pandemic has presented the perfect use case. The pandemic has been a disaster for patient care organizations along every dimension. Not only have hospitals, medical groups, and health systems been profoundly stressed in caring for hundreds of thousands of patients with the coronavirus; tragically, many dozens of clinicians and other front-line healthcare workers have died from the virus, while hundreds have been sickened by it. And, of course, hospitals, medical groups, and health systems have experienced tremendous financial stress as a result of the panic, first because of the Centers for Medicare and Medicaid Services’ understandable announcement in March, shutting down all elective surgeries and procedures.
That effective ban plunged many hospitals, medical groups, and health systems into financial turmoil and distress for between two and three months, depending on which communities and states they were operating in. Even since then, the leaders of patient care organizations have struggled to achieve real financial stability, partly because of patients’ understandable fears of in-person care delivery encounters inside patient care organizations.
Thus, enter AI and machine learning. What hospital, medical group, and health system leaders are learning (and, on their side of the aisle, health plan leaders as well) is that artificial intelligence-fueled algorithms and programs are helping them to better plan for short-term and medium-term fluctuations of all sorts—in patient volume, in the availability of crucial supplies such as PPE supplies (personal protective equipment) in the supply chain; in patient flow; and in developing projections for all future trends. It really is the emergent moment for AI and machine learning in healthcare operations, on all sides of the house—clinical, operational, and financial.
Indeed, what seems to be happening now is that one of the major impacts of the pandemic, from an operational and strategic information technology standpoint, is the full realization on the part of senior healthcare leaders that AI and machine learning must quickly and robustly be brought into planning of all types—clinical, operational, and financial. As a result, healthcare leaders are developing models for predicting such elements as bed capacity, inpatient volume, outpatient volume, supply chain issues, and other key indicators. For instance, they are asking their data analysts to marry publicly available data on the evolution of COVID-19 in their local area, with their plans for such key elements as clinician staffing, bed availability, and supply availability.
Very significantly, AI- and machine learning-based algorithms are finally becoming incorporated on a wide scale into revenue cycle management operations. For so many patient care organizations, making that proactive move could mean the difference between relatively robust revenues or distressed ones. For so many community hospitals and medical practices, the operating margins in the current environment really have become even slenderer than they were in the months prior to the emergence of the pandemic in the United States. Revenue cycle management had already become the subject of intense focus, at a time when federal healthcare officials had been pressing more organizations to become involved in two-sided risk-based programs, such as the Medicare Shared Savings Program (MSSP).
But now? One area of intense focus for so many patient care organizations has been the path of Medicaid, as millions of Americans have lost their jobs and been thrown off their employer-based health insurance plans, flooding the Medicaid rolls. The planning for such trends will have to be exquisite going forward. Most intensely of all will be the financial planning among the hospitals and physicians groups that were already heavily reliant on Medicaid reimbursement, including, famously, the nation’s children’s hospitals. With razor-thin margins even before the pandemic hit, the nation’s pediatric hospitals are more endangered than ever before.
So the synchronicity of all of this is quite striking. And it speaks to the ancient adage, “Necessity is the mother of invention.” Always true in healthcare operations, it was never truer than now. And AI- and machine learning-based tools will clearly be vital for patient care organization leaders to leverage, in order to survive, and hopefully thrive, in the coming months and years. The time absolutely is now for AI—and healthcare leaders know it.
