The Worst-Case COVID-19 Predictions Turned Out To Be Wrong. So Did the Best-Case Predictions.

http://www.reason.com/2021/06/22/

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An argument for humility in the face of pandemic forecasting unknown unknowns.

“Are we battling an unprecedented pandemic or panicking at a computer generated mirage?” I asked at the beginning of the COVID-19 pandemic on March 18, 2020. Back then the Imperial College London epidemiological model’s baseline scenario projected that with no changes in individual behaviors and no public health interventions, more than 80 percent of Americans would eventually be infected with novel coronavirus and about 2.2 million would die of the disease. This implies that 0.8 percent of those infected would die of the disease. This is about 8-times worse than the mortality rate from seasonal flu outbreaks.

Spooked by these dire projections, President Donald Trump issued on March 16 his Coronavirus Guidelines for America that urged Americans to “listen to and follow the directions of STATE AND LOCAL AUTHORITIES.” Among other things, Trump’s guidelines pressed people to “work or engage in schooling FROM HOME whenever possible” and “AVOID SOCIAL GATHERINGS in groups of more than 10 people.” The guidelines exhorted Americans to “AVOID DISCRETIONARY TRAVEL, shopping trips and social visits,” and that “in states with evidence of community transmission, bars, restaurants, food courts, gyms, and other indoor and outdoor venues where people congregate should be closed.”

Let’s take a moment to recognize just how blindly through the early stages of the pandemic we—definitely including our public health officials—were all flying at the time. The guidelines advised people to frequently wash their hands, disinfect surfaces, and avoid touching their faces. Basically, these were the sort of precautions typically recommended for influenza outbreaks. On July 9, 2020, an open letter from 239 researchers begged the World Health Organization and other public health authorities to recognize that COVID-19 was chiefly spread by airborne transmission rather than via droplets deposited on surfaces. The U.S. Centers for Disease Control and Prevention (CDC) didn’t update its guidance on COVID-19 airborne transmission until May 2021. And it turns out that touching surfaces is not a major mode of transmission for COVID-19.

The president’s guidelines also advised, “IF YOU FEEL SICK, stay home. Do not go to work.” This sensible advice, however, missed the fact that a huge proportion of COVID-19 viral transmission occurred from people without symptoms. That is, people who feel fine can still be infected and, unsuspectingly, pass along their virus to others. For example, one January 2021 study estimated that “59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms.”

The Imperial College London’s alarming projections did not go uncontested. A group of researchers led by Stanford University medical professor Jay Bhattacharya believed that COVID-19 infections were much more widespread than the reported cases indicated. If the Imperial College London’s hypothesis were true, Bhattacharya and his fellow researchers argued, that would mean that the mortality rate and projected deaths from the coronavirus would be much lower, making the pandemic much less menacing.

The researchers’ strategy was to blood test people in Santa Clara and Los Angeles Counties in California to see how many had already developed antibodies in response to coronavirus infections. Using those data, they then extrapolated what proportion of county residents had already been exposed to and recovered from the virus.

Bhattacharya and his colleagues preliminarily estimated that between 48,000 and 81,000 people had already been infected in Santa Clara County by early April, which would mean that COVID-19 infections were “50-85-fold more than the number of confirmed cases.” Based on these data the researchers calculated that toward the end of April “a hundred deaths out of 48,000-81,000 infections corresponds to an infection fatality rate of 0.12-0.2%.” As I optimistically reported at the time, that would imply that COVID-19’s lethality was not much different than for seasonal influenza.

Bhattacharya and his colleagues conducted a similar antibody survey in Los Angeles County. That study similarly asserted that COVID-19 infections were much more widespread than reported cases. The study estimated 2.8 to 5.6 percent of the residents of Los Angeles County had been infected by early April. That translates to approximately 221,000 to 442,000 adults in the county who have had the infection. “That estimate is 28 to 55 times higher than the 7,994 confirmed cases of COVID-19 reported to the county by the time of the study in early April,” noted the accompanying press release. “The number of COVID-related deaths in the county has now surpassed 600.” These estimates would imply a relatively low infection fatality rate of between 0.14 and 0.27 percent. 

Unfortunately, from the vantage of 14 months, those hopeful results have not been borne out. Santa Clara County public health officials report that there have been 119,712 diagnosed cases of COVID-19 so far. If infections were really being underreported by 50-fold, that would suggest that roughly 6 million Santa Clara residents would by now have been infected by the coronavirus. The population of the county is just under 2 million. Alternatively, extrapolating a 50-fold undercount would imply that when 40,000 diagnosed cases were reported on July 11, 2020, all 2 million people living in Santa Clara County had been infected by that date.

Los Angeles County reports 1,247,742 diagnosed COVID-19 cases cumulatively. Again, if infections were really being underreported 28-fold, that would imply that roughly 35 million Angelenos out of a population of just over 10 million would have been infected with the virus by now. Again turning the 28-fold estimate on its head, that would imply that all 10 million Angelenos would have been infected when 360,000 cases had been diagnosed on November 21, 2020.

COVID-19 cases are, of course, being undercounted. Data scientist Youyang Gu has been consistently more accurate than many of the other researchers parsing COVID-19 pandemic trends. Gu estimates that over the course of the pandemic, U.S. COVID-19 infections have roughly been 4-fold greater than diagnosed cases. Applying that factor to the number of reported COVID-19 cases would yield an estimate of 480,000 and 5,000,000 total infections in Santa Clara and Los Angeles respectively. If those are ballpark accurate, that would mean that the COVID-19 infection fatality rate in Santa Clara is 0.46 percent and is 0.49 percent in Los Angeles. Again, applying a 4-fold multiplier to take account of undercounted infections, those are both just about where the U.S. infection fatality rate of 0.45 percent is now.

The upshot is that, so far, we have ended up about half-way between the best case and worst case scenarios sketched out at the beginning of the pandemic.

Healthcare’s Leading Financial Challenges and Opportunities in 2019

https://www.jpmorgan.com/commercial-banking/insights/healthcare-financial-challenges-2019

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Faced with slim margins and rising costs, the healthcare industry is looking to blockchain, data analytics and innovation to help drive savings and unlock new revenue.

The healthcare industry is facing an urgent need to reduce costs and increase revenue. Research from the Healthcare Advisory Council reveals the not-for-profit health system will need between $40 million and $44 million annually in cost avoidance over the next eight years to maintain a sustainable margin. The challenge is significant, but emerging technologies and innovative strategies are creating opportunities for greater efficiency, better patient care and decreased costs, according to executives and other leaders in healthcare.

Making a Margin on Medicare

Health systems with the best margin sustainability pursue effective cost-avoidance practices, including:

  • Embedding cost discipline throughout the organization
  • Escalating spending decisions
  • Reducing unnecessary hires
  • Matching patient acuity to the level of care
  • Reducing drug formulary costs

But even with these practices, cost avoidance is challenging—particularly when it comes to Medicare-reliant seniors, who often require frequent medical treatments and hospital admissions. Turning to advanced electronic medical records (EMRs) that are designed around a health system’s risk and workflow can improve treatment decisions and continuity of care, leading to decreased admissions, better cost effectiveness and a greater profit margin.

Simultaneously, some health systems are looking to a pre-paid, value-based medicine model, as opposed to the more common fee-for-service model. Value-based medicine moves the payment upstream, incentivizing providers to focus on maintaining patient health rather than on providing medical interventions. Decreasing the amount of care needed to keep patients healthy has a direct impact on the size of an organization’s margins.

Blockchain: The Potential to Change Healthcare

One of the most common inefficiencies in healthcare is how physicians are credentialed. The months-long process for clinician credentialing commands significant time and costs. Emerging blockchain technology may be one solution to this persistent point of inefficiency.

With blockchain, rather than sending a clinician credentialing application to several organizations for verification, the physician and all credentialing locations—as members of a dedicated blockchain network—can have access to the physician’s highly encrypted log. Any changes to the physician’s log can be transmitted to the network and validated by private keys known only to each party and with algorithms agreed upon by the network. In this, trust transfers from a third-party clearinghouse to the network as a whole.

In the blockchain world, the physician could provide access codes to the hospital to review their verified credentials. This could save as much as 80 percent of the current cost and time invested in physician credentialing. Using the same technology and process, blockchain may also be a valuable tool for finding efficiencies when working with patient records.

Venture Capital: Strategic Investing 2.0

Healthcare system-based venture capital funds are growing rapidly. In 2017, more than 150 distinct corporate venture groups operated within the healthcare arena, according to Health Enterprise Partners, and these groups participated in 38 percent of all healthcare IT financing.

There are four common objectives for starting such a fund:

  • Generate new income sources not subject to healthcare reimbursement pressure
  • Identify promising companies that executives might not otherwise encounter
  • Create a vehicle to enhance brand integrity and expand market reach
  • Foster a culture of innovation

Once healthcare investors establish their fund objectives (or mix of objectives), they define their investment approach. This includes establishing a decision-making chain with operational leaders and board members that can allow decisions to be made quickly and in an established pattern. It also includes building infrastructure and could mean adopting a rigorous information environment system, like a healthcare customer relationship management (CRM) system, as well as developing stringent custody and accounting procedures for securities.

Funds should gather resources to support the interactions between the investment fund and the companies in which they invest. At the outset, they should decide the relationship they will have with their investment targets and whether return on investment is a primary or secondary goal. As a part of choosing investment targets, it is important that funds address an important problem of the parent organization and in a way that the organization supports.

Time Is Money: Accelerating the Pace of Care

For health systems, every patient hour costs $250 in direct operating costs, more than half of which owe to labor. By this, improving efficiency and decreasing the time needed for tasks can save money and support a healthy margin. A mix of advanced analytical data and targeted interpersonal relations can help reduce the time required for common hospital and health system tasks. Predictive analytic modeling software can help yield clearer insight into operations, revealing ways to break down barriers between departments and more effectively manage census levels. This optimizes census distribution inside a complex medical center.

Another rich source of potential healthcare savings lies in the staff hiring process. Successful staff hiring for all income levels is one of the great challenges for health systems, but data analytics can help make the hiring process more efficient. With models built on the characteristics of successful hires, predictive analytics can point to applicants with the best potential for success, improving confidence in hiring decisions. Importantly, while analytics and automation can play a big part in finding the best applicants, once a candidate becomes an employee, important decisions like promotions or relocations require direct personal contact.

Data and Dollars Innovation

As health systems explore avenues for increased efficiency, lower costs and better margins, J.P. Morgan has developed digital innovations to support healthcare investment, strategy and operation. Two of the most applicable include:

  • Enhanced Healthcare Lockbox: J.P. Morgan has supercharged its lockbox technology with machine learning. The auto-posting rate has increased by nearly one-fifth, allowing hospitals and health systems to redeploy assets to other revenue-generating sectors like denial management. The high-tech upgrade has also saved three to four days in clients’ working capital.
  • Corporate Quick Pay: The need for hospitals and health systems to collect an increasing amount of money directly from patients has resulted in an explosion in low-dollar patient refunds. This creates a problem for the accounts payable departments of healthcare institutions, which were not designed to issue thousands of small checks to patients. J.P. Morgan’s Corporate Quick Pay solution allows health systems to send payments directly to a patient’s bank account using email or text message.

These innovations in artificial intelligence and machine learning drive efficiency across a range of areas. Consider the benefits one client enjoyed by virtue of J.P. Morgan’s digital tools:

  • 70,000 paper-based claims converted to electronic
  • 99.3 percent lift rate for all paper received in lockbox
  • 18 percent increase in auto-posting after implementation
  • Three to four days’ improvement to working capital

Going forward, emerging technologies and strategies are indispensable for healthcare systems striving to grow margins in a time when health costs and needs are increasing. Ultimately, hospitals and health systems that find pathways to greater profitability will be best positioned to achieve their primary goal: delivering better care that leads to better patient outcomes.