Tennessee officials said they approved the proposal after the two systems demonstrated that they would create a public benefit to Northeast Tennessee that would outweigh any downsides of a monopoly of services.
The proposed merger between Mountain States Health Alliance and Wellmont Health System took a big step forward on Tuesday when Tennessee officials approved the health systems’ Certificate of Public Advantage application.
“We believe this merger will result in sustaining high-quality health care for our region, will reduce the growth in costs and will create one of the nation’s leading health systems,” said Mountain States President/CEO Alan Levine, who would be executive chairman and president of the combined systems if the merger is finalized.
The new system, to be called Ballad Health, still requires approval from Virginia, but executives at Johnson City, TN-based Wellmont and Kingsport, TN-based Mountain States said they expect the merger to be approved by the end of the month.
For Tennessee to approve the merger, the systems agreed through the legislative process and in a series of public meetings to demonstrate that their merger would create a public benefitto Northeast Tennessee that would outweigh any downsides of a monopoly of services, Tennessee officials said.
“We appreciated how Wellmont and Mountain States assisted our office and the department during this process and certainly want to acknowledge the commitment of the community leaders to reach this point,” said Tennessee Attorney General Herbert H. Slatery III. “Everyone’s objective is to employ a new idea, a new structure to fundamentally improve the health of the region. We wish them great success.”
According to the terms of the COPA, the systems have 90 days to complete the merger, when they will complete the legal work to form Ballad health. Virginia officials are expected to issue their decision by the end of the month. With approval from Virginia, the merger is expected to be finalized in early 2018.
Ballad Health said it will “make significant investments to improve the health of our region, to advance academics and research, to improve children’s healthcare, and to strengthen and better align rural healthcare offerings.”
Tennessee Department of Health Commission John Dreyzehner, MD, said state officials worked with the healthcare systems to create an index of benchmarks to improving key health outcomes in the region. The index includes recommendations from the COPA Index Advisory Group, which was comprised of 16 people from the region. The group held five listening sessions and subsequent working meetings in the spring of 2016.
FTC Opposition
Tennessee approved the deal despite the longstanding opposition by the Federal Trade Commission, which sees the merger as anti-competitive. In several comments submitted to Virginia and Tennessee, FTC staff have repeatedly stated that “the lost competition from the proposed merger of Mountain States and Wellmont would significantly harm residents of northeast Tennessee and southwest Virginia.
“The staff emphasizes that the two hospital systems have failed to show that the consumer harm from the proposed merger would be outweighed by its purported benefits, or offset by the applicants’ proposed commitments,” the FTC said in a media release.
“FTC staff conclude that the applicants’ consultants’ reports “fail to provide sufficient additional information or analysis to demonstrate by clear and convincing evidence that the purported benefits of this merger would outweigh the serious competitive harm that would likely result from creating a near-monopoly.”
Jay Levine, an anti-trust attorney with Porter Wright Morris & Arthur LLP, said there might not be much the FTC can do to block the deal at this point.
“Depending on how the COPA law and approval process is structured, the parties may be entitled to state action immunity,” Levine said. “In that case, the FTC can’t do anything, even if they think the merger is anticompetitive.”
“Absent an immunity, merely because state officials cleared the merger under one set of regulations, which focus on things that are not necessarily antitrust-related, the FTC can still argue that the merger substantially reduces competition,” he said. “Given state approval, though, the FTC may need a bit more evidence than usual to decide to challenge the merger.”
Wellmont and Mountain States provided this statement when asked Tuesday about the ongoing FTC opposition:
"We have pursued a robust state approval process in Tennessee and Virginia for two and a half years. Should both states approve our applications, both will play an active role in supervising the new health system. Under longstanding U.S. Supreme Court legal doctrine, state approval of the kind obtained in Tennessee and under consideration by Virginia protects the merger from such an FTC challenge."
"The FTC staff submitted written comments to the Tennessee Department of Health on various occasions, urging the Department to deny the parties’ application to merge. We cannot speculate on what the FTC might do. We respect the role they play, but we believe our merger is lawful and we would vigorously defend it if any action is taken to challenge it."
Seventy-six percent of primary care residents received 50 or more job solicitations during their medical training while 55% received 100 or more, survey data shows.
When it comes to physicians, it's definitely a sellers’ market.
Demand for medical doctors is so high that half of new doctors report receiving 100 or more job offers during training, according to a survey by Merritt Hawkins.
The Dallas-based physician search firm surveyed 926 MDs in their final year of residency and asked them about their career plans and expectations. Seventy percent of the new doctors said they received 50 or more job solicitations, while 50% said they received 100 or more solicitations.
“The search for newly trained physicians is on the verge of becoming a feeding frenzy,” said Mark Smith, president of Merritt Hawkins. “There are simply not enough physicians coming out of training to go around.”
Job solicitations for MDs came in as phone calls, emails, and direct mail from recruiters at hospitals, medical groups and physician recruiting firms.
Primary care residents, including those in family medicine, internal medicine and pediatrics, are particularly sought after, the survey shows. Seventy-six percent of primary care residents received 50 or more job solicitations during their training while 55% received 100 or more. Psychiatrists are also in heavy demand, with 78% of psychiatry residents received 50 or more job solicitations while 48% received 100 or more.
Other types of physicians, including surgical and diagnostic specialists, also received numerous job solicitations, though somewhat fewer than primary care and psychiatry residents. Sixty-four percent of surgical and diagnostic specialists received 50 or more job solicitations during their training, while 46% received 100 or more.
Bad News for Rural Areas: Physicians Still Hard to Hire
Unfortunately for rural America, which already faces a severe dearth of physicians, only 1% of residents said they would prefer to practice in communities of 10,000 people or fewer and only 3% would prefer to practice in communities of 25,000 people or fewer.
The survey also shows that a majority of newly trained physicians would prefer to be employed and that few seek an independent, private practice setting. Of those seeking employment, 41% prefer employment with a hospital, while 34% prefer employment with a medical group. Only 1% prefer a solo practice.
The availability of free time is the number one consideration of most residents, explaining in part their preference for employment, which offers more regular schedules than does private practice, the survey found.
“The days of new doctors hanging out a shingle in an independent solo practice are over,” Smith said. “Most new doctors prefer to be employed rather than deal with the financial uncertainty and time demands of private practice.”
Despite a welcoming job market, some new doctors are unhappy about their new profession, and 22% said that, given the option, they would have selected another field.
“With declining reimbursement, increasing paperwork, and the uncertainty of health reform, many physicians are under duress today,” Smith said. “It is not surprising that many newly trained doctors are concerned about what awaits them.”
Student Loan Debt
According to the Association of American Medical Colleges, nearly 74% of new medical school graduates had education debt in 2016. The median education debt levels for graduates rose to $190,000 in 2016 from $125,372 in 2000, adjusting for inflation.
The average starting salary for family physicians is $231,000, according to the 2017 report, up from $198,000 in 2015, an increase of 17%, while the average starting salary for general internists is $257,000, up from $207,000 two years ago.
Lawmakers are traipsing along three wildly divergent paths toward fixing or replacing Obamacare. They have yet another bill to repeal the Affordable Care Act, a bipartisan action to stabilize state health insurance markets created by the ACA, and a bill to enact a single-payer system.
When it comes to healthcare reform, there are no safe spaces in Congress.
A new Affordable Care Act repeal effort sponsored by Republican Sens. Lindsey Graham of South Carolina, Bill Cassidy of Louisiana, Dean Heller of Nevada, and Ron Johnson of Wisconsin, reportedly has the support of 50 of the Senate’s 52 Republicans,according to published reports.
According to a media release issued by Sen. Graham’s office, the proposal repeals the structure and architecture of Obamacare and replaces it with a block grant given annually to states to help individuals pay for healthcare.
Specifically, the bill:
Repeals the individual and employer mandates.
Repeals the Medical Device Tax.
Strengthens the ability for states to waive Obamacare regulations.
Returns power to the states and patients by equalizing the treatment between Medicaid Expansion and non-expansion states through an equitable block grant distribution.
Protects patients with pre-existing medical conditions.
The bill also eliminates the inequity of three states receiving 37% of Obamacare funds and brings all states to funding parity by 2026. As an example, Pennsylvania has nearly double the population of Massachusetts, but receives 58% less Obamacare money than Massachusetts, Graham said in his media release.
Critics of the legislation accused the Senate backers of making false claims to mitigate the harmful effects.
"In reality, the Cassidy-Graham bill would have the same harmful consequences as those prior bills,” according to an issues brief by the left-leaning Center on Budget and Policy Priorities.
“It would cause many millions of people to lose coverage, radically restructure and deeply cut Medicaid, eliminate or weaken protections for people with pre-existing conditions, and increase out-of-pocket costs for individual market consumers.”
Meanwhile, Majority Leader Mitch McConnell (R-KY) is said to be pushing for a floor vote as early as this week.
Bipartisan Compromise Sought
This latest – and perhaps last – repeal effort comes as other Senate Republicans are working Democrats to stabilize the wobbly state health insurance exchanges.
Senate Health Committee Chairman Lamar Alexander (R-TN) said he is optimistic about the prospects for a bipartisan deal, and that a bill could be finalized this week.
“For seven years, hardly a civil word was spoken between Republicans and Democrats on the Affordable Care Act,” Alexander said in a press release.
“But for the last 10 days, senators from both sides of the aisle have engaged in serious discussions about what Congress can do between now and the end of the month to help limit premium increases for the 18 million Americans in the individual health insurance market next year and begin to lower premiums after that, and to prevent insurers from leaving the markets where those 18 million Americans buy insurance,” Alexander said.
The Tennessee Senator said that a series of hearings in the past month fleshed out “three themes” that represent a working consensus for stabilizing premiums in 2018.
“First, is Congressional approval of continued funding of the cost-sharing payments, for a specific period of time, that reduce co-pays and deductibles for many low-income Americans on the exchanges,” Alexander said.
“Second, senators from both sides of the aisle suggested expanding the so-called ‘copper plan’ already in the law so anyone—not just those 29 or under—could purchase a lower premium, higher deductible plan,” he said.
“The third – advocated by state insurance commissioners, governors, and senators from both sides of the aisle – is to give states more flexibility in the approval of coverage, choices, and prices for health insurance.”
Single-payer Bill Introduced
And out in Left Field, Sen. Bernie Sanders(I-VT) has introduced “Medicare for All” legislation. Single payer has no chance of passing a Republican-controlled Congress, but the bill is red meat for Sanders’ supporters, and a ploy to energize the Democratic and Progressive base for the 2018 and 2020 general elections.
In an op-ed piece last week in the New York Times, shortly after he introduced the bill, Sanders described “a pivotal moment in American history.”
“Do we, as a nation, join the rest of the industrialized world and guarantee comprehensive health care to every person as a human right?” Sanders asked, “Or do we maintain a system that is enormously expensive, wasteful and bureaucratic, and is designed to maximize profits for big insurance companies, the pharmaceutical industry, Wall Street and medical equipment suppliers?”
The introduction of the single-payer bill last week prompted aTwitter spat between Sanders and President Donald Trump, who tweeted: "Bernie Sanders is pushing hard for a single payer healthcare plan - a curse on the U.S. & its people."
To which Sanders immediately responded in a tweet: “No Mr. President, providing healthcare to every man, woman and child as a right is not a curse, it's exactly what we should be doing.”
The heat-related deaths of eight people at a Florida nursing home has prompted a criminal investigation. The probe comes amid reports that the owner of the facility has a history of healthcare fraud.
Police in Hollywood, FL, have obtained a search warrant in their criminal investigation of the deaths of eight elderly patients exposed to sweltering heat inside a Miami-area nursing home that continued to operate with little or no air conditioning after Hurricane Irma struck.
On Thursday Florida Gov. Rick Scott directed the state's Agency for Health Care Administration to end the provision of Medicaid at the Rehabilitation Center at Hollywood Hills facility. The Rehabilitation Center is pushing back against immediate efforts by state authorities to close its doors. Coral Gables attorney Gary Matzner, who is representing the Rehabilitation Center at Hollywood Hills, told POLITICO on Thursday night that he will challenge the Florida Agency for Health Care Administration’s emergency moratorium on new nursing home admissions.
The eight deaths at The Rehabilitation Center at Hollywood Hills, days after Irma struck, stirred outrage at what many saw as a preventable tragedy, and heightened concerns about the welfare of the state's large elderly population.
"It was unnecessary," Bendetta Craig, whose 87-year-old mother was among dozens of patients safely removed from the center, told reporters on Thursday. "I don't know what happened inside. I wasn't there. I hope the truth comes out. It is just senseless."
The facility was is just one of nearly 700 nursing homes across the state, about 50 of which still lacked power as of Friday morning, according to theFlorida Health Care Association.
The owner of The Rehabilitation Center has a history of health care fraud charges. Dr. Jack Michel in 2006 settled claims after he and five others were accused of agreeing to send patients to his Miami hospital, Larkin Community, for unnecessary treatment, according to the Department of Justice. Federal prosecutors said that Michel received kickbacks as part of the deal and that some of the patients came from assisted living facilities that he owned.
Florida Sen. Bill Nelson has asked the U.S. Department of Health and Human Services to investigate the tragedy. “What has happened here is inexcusable," Nelson said. "These kind of facilities should be regulated with a strong, tight rein ... and it hasn't happened... we will get to the bottom of it.
Memorial Regional Hospital Chief Nursing Officer Judy Frum said that the ER arrival of three patients with “extraordinarily high” body temperature “set off a red flag. We walked over to see if we could offer assistance.”
What Frum and others from Memorial found at The Rehabilitation Center sent them into crisis mode. The scene at the nursing home was chaotic: Sweltering heat filled the building, where the air conditioning had been knocked out since Sunday.
After an estimated 215 people died in hospitals and nursing homes in Louisiana following Hurricane Katrina in 2005, policy makers realized that the nation’s healthcare institutions were ill-prepared for disasters.
One of the new federal rules that takes effect in November will require that nursing homes have “alternate sources of energy to maintain temperatures to protect resident health and safety.”
But the rule does not specifically require backup generators for air-conditioning systems — the nursing home in Florida, Rehabilitation Center at Hollywood Hills, did not have such a generator — and now some are questioning whether the rule should.
When the rooms at The Rehabilitation Center became too hot to bear on Tuesday night, some of the elderly patients were rolled out in their beds and wheelchairs into the hallway on the second floor. They were left there — some of them naked — a video shot by a resident’s daughter and viewed by the Miami Herald shows.
“With multiple deaths, it calls into question everybody who works there and what did they know and when did they know it,” said Fort Lauderdale attorney Ken Padowitz, a former state prosecutor in Broward who has tried dozens of homicide cases.
The acquisition is the latest in a string of deals that UnitedHealth and Optum have been actively pursuing in recent years, targeting companies that offer geographic growth or strengthen the product line.
UnitedHealth Group, Inc.’s acquisition of The Advisory Board Company healthcare business for $1.3 billion is viewed as a credit positive by Moody’s Investors Service.
The deal, which is expected to close by early 2018, should increase growth and earnings at UnitedHealth’s Optum division, which will have access to ABCO’s research and IT services, Moody’s said.
“UNH has not indicated how it will finance the $1.3 billion purchase price of ABCO,” Moody’s said in a credit brief. “However, the transaction size is modest relative to our estimate of the parent company’s annual net cash flow of approximately $4 billion (net of shareholder dividends, capex and interest expense). We expect the transaction will not interfere with UNH’s stated goal of lowering financial leverage (debt to capital) to below 40% by the end of third-quarter 2017.”
ABCO provides research, technology, and consulting to healthcare organizations and educational institutions and had revenues of $803 million in 2016. Before selling the healthcare business to Optum, affiliates of Vista Equity Partners will acquire ABCO’s education business for $1.55 billion, Moody’s said.
“Joining Optum will enable us to better serve our members, thanks to Optum’s unmatched data analytics resources, investment capacities and operational experience in delivering large-scale solutions and services to all health care stakeholders,” Robert Musslewhite, CEO of The Advisory Board Company, who will continue to lead its healthcare advisory business, said in a media release announcing the deal.
Optum is a health services information business and it’s UNH fastest-growing division. It generated about 18% of the company’s revenue after intercompany eliminations and 34% of pre-tax earnings during the first half of 2017, Moody’s said.
UnitedHealth and Optum have been actively acquiring businesses in recent years, and Moody’s said the targets include companies that offer geographic growth or strengthen the product line. Those acquisitions include Surgical Care Associates, MedExpress and ProHealth, pharmacy benefits managers Catamaran in 2015, about $13 billion, largely debt-financed, and workers' comp claims specialiststs Helios in January 2016, the terms of which were not disclosed.
“Because of the Catamaran acquisition, UnitedHealth Group’s financial leverage remains slightly higher than our expectation for the rating,” Moody’s said. “However, credit support for UNH’s holding company obligations reflects significant non-regulated cash flows, primarily from Optum, in addition to statutory dividends from the group’s regulated insurance operations. The group’s adjusted financial leverage (where debt includes operating leases) was 42.2% at 30 June 2017. We expect that the company will reduce adjusted leverage to about 40% by year-end 2017.”
The combined system would focus on access and affordability, improving clinical care, growing its academic model and contributing to the region’s economy.
Two of the largest not-for-profit health systems in North Carolina are negotiating a merger that could be finalized by December.
“Carolinas HealthCare System and UNC Health Care have signed a Letter of Intent to join their clinical, medical education and research resources,” the two systems said Thursday in a joint media release.
“Under the LOI, the two organizations have agreed to start a period of exclusive negotiations, with the goal of entering into final agreements by the end of the year.”
The two health systems said that combining would allow them to focus on “healthcare’s most pressing challenges” in four strategic areas: increasing access and affordability, improving clinical care expertise, growing their academic model and contributing to the economy.
There are no plans to open new hospitals or close existing hospitals under the combined system, nor are layoffs anticipated, although both systems said “there will likely be some restructuring required to integrate operations, but we anticipate growth and development opportunities.”
"Together with UNC Health Care, we believe that the opportunities to be a national model and to elevate health in North Carolina are nearly limitless," said Gene Woods, president/ CEO of Carolinas HealthCare and future CEO of the new entity.
“For example, since our organizations already serve almost 50% of all patients who visit rural hospitals in our state, we are perfectly positioned to participate in the reinvention of rural healthcare in partnership with others,” Woods said. “Ensuring there is great healthcare in rural counties is not only important to our patients’ physical wellbeing, but is also vital to the economic well-being of those communities as well.”
William Roper, MD, dean of the UNC School of Medicine and CEO of UNC Health Care, will serve as executive chairman of the combined health system.
“By integrating our organizations, we are combining the strengths of two great health systems, providing greater access to a full range of services and leading-edge treatments for patients, enabling better coordination of care and advancing research,” Roper said.
“Carolinas HealthCare System is one of the most innovative healthcare organizations in the nation, particularly in combining world-class clinical care with a community care model,” he said. “By combining our two extremely mission-focused organizations, we will offer an unparalleled array of services, expertise and experiences for our patients and communities – beyond what either of us could do independently.”
UNC Health operates six hospitals in North Carolina, five of which are located in Chapel Hill. The health system also has 1,700 faculty physicians.
Charlotte, NC-based Carolinas HealthCare has more than 900 care locations across North and South Carolina, including nearly 40 acute-care hospitals, and 60,000 employees.
Jay L. Levine, an antitrust attorney with PorterWright, said the merger could draw the attention of state and federal anti-trust regulators.
“The issue will be whether there are other hospitals in the geographic markets served by these two that will compete with them for patients and managed care dollars,” Levine said. “If sufficient competition remains, then the merger should not raise prices.”
“If there are some other hospitals that compete with them, the next question will be are these two uniquely positioned as each other’s next best substitute – either because of location, quality, prestige, etc. – that MCOs need one of them in their network?” Levine said. “If that is the case, then there is a very steep hill to climb. It sounds like they are expecting a lot of positive efficiencies and synergies from the merger, but if there are clear anticompetitive effects arising from the merger, these efficiencies/synergies almost never overcome the concerns.”
If that deal clears regulatory review, it would create the largest health system in South Carolina, and one of the 50 largest health systems in the nation, with 13 hospitals and hundreds of physician practices and ambulatory centers.
Physicians occupy LinkedIn’s top six highest-paying jobs, and eight of the top 15 spots. The findings are the latest in a long line of studies showing that physicians are the nation’s highest-paid professionals.
Physicians accounted for eight of the top 15 highest-paying jobs, with most earning more than $300,000 a year, according to a new report from LinkedIn.
The report, based on data gleaned from more than two million LinkedIn members in the United States, also found that healthcare is the only top-five paying industry with a greater proportion of women. The average salary for a medical doctor is $161,200, which is more than $80,000 higher than the average salaries of people with a four-year degree.
Physicians occupy the top six highest-paying jobs on the list, and eight of the top 15 spots. The total median compensation in the LinkedIn report includes median cash bonuses, which can vary from $25,000 to $90,000 based upon specialty.
The physicians, their rank, and their median total compensation in the Top 15 highest-paying jobs include:
Orthopedic surgeon, $450,000
Cardiologist, $382,000
Radiologist, $374,000
Plastic surgeon, $350,000
Anesthesiologist, $350,000
Emergency surgeon, $314,000
Ophthalmologists and medical directors were 14th and 15th on the list, respectively, with each reporting median compensation of $250,000.
The findings are the latest in a long line of studies showing that physicians are the highest-paid occupation in the nation.
In April, the 2017 Medscape Physician Compensation Report, which compiled responses from more than 19,200 physicians in 27 specialties, found that orthopedic surgeons' annual compensation averaged $489,000, nearly $50,000 more than plastic surgeons, the second-highest average annual earners.
However, the survey also found that 48% of orthopedic surgeons felt they weren't "fairly compensated" for their labors, even as their income increased by an average of 10% in the past year, one of the highest rates of growth among specialists.
About half of physicians told MedScape they weren't satisfied with their compensation. Of those malcontents, 46% of primary care physicians and 41% of specialists said an increase of between 11% to 25% would make them happy.
A report in June from physician recruiters Merritt Hawkins also found that most physicians are seeing double-digit increases in their annual compensation.
The top 11 best-paying jobs, as reported by U.S. News & World Report, were all in the healthcare sector.
With Medicare spending projected to grow to $1.4 trillion by 2027, the federal government is looking for alternative payment models to slow spending growth, and a new report suggests those efforts are delivering.
Accountable care organizations in Medicare’s Shared Savings Program reduced net spending by nearly $1 billion and improved quality metrics over the first three years of the program, a federal audit shows.
“While policy changes may be warranted, ACOs show promise in reducing spending and improving quality,” the Office of the Inspector General for the Department of Health and Human Services said in its review issued this week. “However, additional information about high-performing ACOs would inform the future direction of the Shared Savings Program as well as other alternative payment models.”
Medicare spending is projected to hit $1.4 trillion in 2027, which is more than double the $689 billion spent on the program in 2016. The Shared Savings Program is one of the largest alternative payment models, accounting for $168 billion in Medicare expenditures over its first three years, from 2013-2015.
OIG analyzed beneficiary and provider quality, spending and utilization data over the first three years of the program, which involved 428 ACOs and 9.7 million beneficiaries.
“During that time, most of these ACOs reduced Medicare spending compared to their benchmarks, achieving a net spending reduction of nearly $1 billion,” OIG said. “At the same time, ACOs generally improved the quality of care they provided, based on CMS data on quality measures.”
ACOs improved their performance on 82% of the individual quality measures, and outperformed fee-for-service providers on 81% of the quality measures. In addition, a subset of high-performing ACOs reduced spending by an average of $673 per beneficiary for key Medicare services during the review period.
That contrasts other Shared Savings Program ACOs and the national average for fee-for-service providers showed an increase in per beneficiary spending for key Medicare services.
The OIG report also found that:
The number of ACOs grew over time, with 220 ACOs participating in 2013, increasing to 333 in 2014, and 392 in 2015. A total of 36 ACOs dropped out of the program in the first 3 years.
In 2015, ACOs served 7.3 million beneficiaries, up from 3.7 million in 2013, 19% of all Medicare beneficiaries in 2015, compared to 10% in 2013 ACOs served 9.7 million unique beneficiaries over the first 3 years.
Each ACO served an average 18,500 beneficiaries in 2015, compared to 16,700 in 2013.
ACOs served less than 1% of Medicare beneficiaries in Hawaii and 49% of beneficiaries in Vermont in 2015. ACOs were more likely to serve beneficiaries in States along the East Coast and in parts of the Midwest.
The composition of ACOs also changed over the 3 years. The percentage of ACOs that were made up solely of physicians decreased, as more ACOs began including other entities such as hospitals and nursing homes.
In 2013, 42% of ACOs were made up solely of physicians; this decreased to 34% of ACOs in 2015. Of the ACOs that were made of both physicians and other entities, 75% included hospitals in 2015. These ACOs also commonly included home health agencies (39%), nursing homes (33%), and hospices (32%).
ACOs made available more primary care physicians and specialists to their beneficiaries over time. In 2015, ACOs had one primary care physician for every 166 beneficiaries, compared to one primary care physician for every 178 beneficiaries in 2013. Similarly, ACOs had one specialist for every 463 beneficiaries in 2015, compared to one specialist for every 611 beneficiaries in 2013.
If you haven't given much thought about how to harness the potential of AI at your healthcare organization, you'd better start. Ignoring this budding technology could be at your own peril.
This article first appeared in the September 2017 issue of HealthLeaders magazine.
Promises and prognostications about the potential of artificial intelligence are being made in Silicon Valley, the Boston area, and other high-tech hot spots.
Most hospitals and other clinical sites, however, do not exist in these cutting-edge environs. For many of the clinicians and hospital executives in this country who are busy enough grappling with a complex mix of challenges in a changing industry, AI in healthcare represents the latest technological buzzword, the hyped-up, futuristic stuff of drawing boards and tech magazines.
The skepticism toward another technological innovation is understandable in an industry that is still struggling to identify an exact return on investment for the massive spending on the electronic health records mandate over the past decade.
While experts in this budding era of AI and machine learning in healthcare warn against falling victim to the hype, they also caution against ignoring the inevitable and profound changes that the new technology will bring to every corner of healthcare.
From diagnosing individual patients to monitoring population health, from staff scheduling to financial projections to patient throughput, AI is coming, it's going to disrupt the way you do business, and it's going to happen faster than you think. If you haven't given much thought about how your healthcare business will enter this new world, you'd better start.
Mercy's journey into AI
Todd Stewart, MD, an internist and vice president of clinical integrated solutions at St. Louis–based Mercy, a Catholic health system with 43 acute care and specialty hospitals and more than 700 physician practices and outpatient facilities, says Mercy began its AI journey three years ago, with a focus on nettlesome procedural challenges around standardizing care pathways.
"We started with some of the highest-volume, high-dollar procedures, like total hip/total knee. Across Mercy we do thousands of those," Stewart says. "The idea was that if we standardize the process, limit care variation, there is strong evidence that we could extract financial value and also patient value in terms of lower mortality, lower length of stay, better outcomes."
Like most health systems since the advent of electronic health records, Mercy had at its disposal mountains of data around outcomes, use, and supply chain. It was time to put that data to work.
"Can we use machine learning algorithms to lower length of stay? What cohort of patients had really good length of stay compared with others? Can we use our existing data to help guide some of these best practices?" Stewart says. "That type of approach with machine learning is precisely what you can get. It can show you relationships that are almost impossible to find except by luck with just humans. What we found was that it absolutely added to the value process."
On a second front, Mercy tapped AI to improve patient throughput, analyzing patient logistics and flow data compiled throughout the health system. "Where do we have bottlenecks of patient flow and how can we improve those?" Stewart says. "We started in the emergency departments, and it's moved into the other care locations in the hospital, from the ED to the ICU and inpatient and outpatient beds and the OR. We're applying supervised algorithms to predict when we are going to have problems and then being able to know how best to respond to those to prevent and manage them."
The data on patient flow that was sifted from AI is now applied systemwide at Mercy under a three-year project called CaRevolution.
"The idea there is to apply the supervised machine learning algorithms so we can identify where patients are unnecessarily having to wait between care locations and trying to eliminate that so patients don't have to wait six hours in the ER," Stewart says. "Standardizing the process of caring for people, that plays into the flow, that goes back to the care pathways work."
AI algorithms have begun tracking invoices at Mercy, and forecasting monthly inpatient and outpatient claims and collections. "What proportion of those invoices will we collect and what do we need to book into what category? We've gone from 70% forecasting accuracy to greater than 95% accuracy in a few weeks," Stewart says.
He says benefits of the improved insights through AI are savings between $14 million to $17 million tied to Mercy's clinical pathways in fiscal year 2016 (or roughly $800 savings per case for patients treated on a pathway), and in a pilot, Mercy saw a 24-minute reduction in emergency room wait time and reduced length of stay by 20% for patients treated in the ED.
"Mercy covers four states, so we have a lot of variety in the payer landscape. How do we think about all of those payers and groups of patients as a portfolio? Which ones do we need to take other action on from a financial standpoint? Are there more complex patients in this particular insurance group? Do we need to do more case management? We have a couple of large ACOs with some financial risk. How do we quantitate that risk across the portfolio of people we are supposed to take care of? It's a number of variables that you have to analyze to try to find trends and meaning, and it is just exponentially difficult to do."
Mercy is also using AI to target sepsis. "This is one of the most difficult problems, and any clinician will tell you there are definitely some unsolved areas in sepsis that we just don't fundamentally understand," Stewart says. "This is another area where machine learning can be highly beneficial in finding patterns that have eluded the best and the brightest of human analysts."
Going forward, Mercy will use AI to anticipate the financial fallout from the churn in the insurance markets, owing to proposed changes to Medicaid, the health insurance exchanges created under the Patient Protection and Affordable Care Act, and the potential for upheaval if reforms proposed in Congress become law.
While there is always a risk when you're an early adopter, Stewart says Mercy is careful of overreach.
"There is a way to do this without excessive risk," he says. "Dedicate a small group of people and a limited amount of capital and a time frame to explore. Even if it fails miserably you've contained it and then you're dedicated to learning why it failed. Use very straightforward, well-contained use cases. Don't overgeneralize and think we're going to be experts in machine learning. Take very small use cases that have a high probability of machine learning getting you to areas that human intelligence can't do. Understand that it may be an R&D thing. It may be a 100% loss financially, but you will start to learn what this technology is and the promise it holds and do better your second iteration."
Healthy skeptics
Those on the leading edge of the AI movement see its vast potential for medicine within the decade and sooner, but they take their dose of wonder and hope with a sprinkling of skepticism. AI may someday play a role in curing cancer, but that's not going to happen next week.
"It's appropriate to be cautious. I certainly am," says Isaac Kohane, MD, PhD, Marion V. Nelson professor of biomedical informatics and chairman of the department of biomedical informatics at Harvard Medical School. "Almost 30 years ago, my PhD in computer science focused on the topic of medical applications for artificial intelligence. Back in the day we used to call it expert systems. Those were very clearly overhyped, and they're not being widely used now."
Kohane warns that the promise of AI could be overwhelmed by the hype in the suddenly crowded vendor space. "The loudest talkers may not be the best performers," he says. "If the loudest talkers who are not the best performers get the limelight and they fail, it is going to put the hopes that a lot of us have for this technology at risk—not because the technologies are bad, but because people will lose interest and optimism and a willingness to invest."
Michael Blum, MD, professor of medicine and cardiology and associate vice chancellor for informatics at the University of California–San Francisco (UCSF), says he sees great promise in AI, but that more than 20 years of practicing medicine, and training as an engineer before that, have kept him grounded.
"I have seen many silver bullets that were going to revolutionize medicine, and there have been many well-known, well-hyped technologies that have come before this," he says. "These are all tools that go into the tool kit, and when they are used appropriately with available assets they can sometimes be very effective. But whenever something is getting to be incredibly popular and talked about in the lay press all the time, the likelihood of it truly transforming healthcare probably goes down."
Blum, who also serves as director for the Center for Diagnostic Health Innovation at UCSF, views machine learning at this stage in its development as another source to help clinicians improve outcomes.
"There was a lot of talk that big data was going to transform healthcare as it did other industries, but it turns out that big data is just another tool. Big data will power artificial intelligence development, but in and of itself it is not going to transform healthcare," he says. "Having said that, I am much more optimistic about the capabilities of these technologies than I have been in quite some time in terms of how they are going to transform the way we work. They have the ability to allow mundane and limited complexity tasks to be done by machines already, which allows providers to go to the human side of care, spend more time with patients, and deliver better care without having to worry about a lot of the minutia that the computer can take care of."
Farzad Mostashari, MD, the former national coordinator for health information technology at the Department of Health and Human Services, warns that the prodigious amounts of data churned from machine learning algorithms are only as good as the data that goes in. For AI to work, Mostashari says the algorithms must be given specific tasks that rely upon accurate data. "You have to be able to set up the problem really well and clean the data in such a way that it most neatly answers the question that you are trying to answer," he says.
"The more you turn over the wheel, as it were, to an autonomous driver, the more important it is to tell the driver clearly where you're going, and what the question is you want answered, and for that machine to have really clear maps and data about the world," says Mostashari, who is the founding CEO of Aledade, Inc., which operates ACOs in 16 states.
"One of the challenges for healthcare is to be careful not to just blindly throw these methods at problems without having done that prework," he says. "It's tempting—and I have seen this for less-trained people who don't really understand the healthcare context—to find a bunch of data somewhere and throw the machine at it. And then what? Then you get some answers, but you have no idea if there was some aberration in the data or how you defined your outcomes that led you to that false conclusion, and no way of really interrogating the black box to say ‘Why did you come up with this answer?' "
Clinicians don't want to be given a specific list of recommendations or a care regimen with no context, Mostashari says; they want to know why.
"Don't just tell me that this patient probably has this diagnosis. I also have a processor. It is called a brain, and I also want to process and learn why you think this patient is high risk," he says. "So there is a need, I believe, in healthcare where you are not asking the AI to just do it—just be an autonomous car and take me there. We want humans and machines to be greater than either machine or human alone. In those situations, the human and the machine have to be able to understand and trust one another. It requires more transparency than some of the traditional AI methods."
Dip your toe or dive in?
While there is potential for what AI will do for healthcare delivery, and how soon, most experts warn against a two-footed leap into the new technology.
"Anyone who tells you that's the way to go is trying to sell you something you don't need to buy," Harvard's Kohane says. "You want to test, experiment, but you don't say, ‘It's going to work on the first time around so I want to implement a whole system.' That is super high risk and often a failure. Starting small is definitely the best advice."
Anil Jain, MD, a part-time internist with Cleveland Clinic, and vice president/CMO of IBM Watson Health, says a Big Bang implementation of AI likely will not work for most healthcare providers. "With things like AI, you want to do it in phases, and with pilots where those who would benefit the most and those who are going to be able to give you an honest assessment of what is good and what is bad and what is working and what is not are able to do that," Jain says.
"I would start by looking at some of those products that are on the market today, where I can help my oncologist or my primary care doctors do a better job immediately by looking at the analytics and cognitive insights that can be brought in," he says. "Either you wait for the broader EHR market to deliver something meaningful, or you get into these pilots and procure these solutions that do these things, knowing that as the solution evolves, you will too. The nice thing about cloud-based solutions is that as these solutions get enhanced, you are not having to reinstall things and the training cycle is not going to be significant."
Kohane says a good place to begin the clinical AI journey would be around image interpretation, analysis, and diagnoses in oncology, pathology, and ophthalmology, which have undergone rapid improvement over the past five years, and which he believes will create disruptive change within the next two or three years.
"This really unexpectedly strong performance in image recognition is only going to improve the productivity of doctors," Kohane says. "I don't think ophthalmologists would mind if they had to spend less time screening retinas and spend more time productively engaged in the operating room or with a patient. If I am a surgeon and I want to have a fast, high-quality read on an x-ray of a patient who I am seeing in the operating room right now, maybe I don't need to have a radiologist read that anymore, or wait for the pathologist to read it. They can run it through a program."
The beauty of these highly complex, cloud-based algorithms, Kohane says, is that they can function at the clinical site on commodity-level hardware that costs a few thousand dollars. "In some sense, we are heading in this direction anyway, using humans instead of computers," he says. "A lot of hospitals use off-shore radiologists in another part of the world, like India or Australia, so that overnight the x-rays are read by doctors. But what if you didn't even have to wait for it to be done overnight? You could have it done for much less money, much faster."
Comparison to EHR
With respect to the potential impact on care delivery, the advent of machine learning in medicine can be somewhat compared to the rollout of electronic health records over the past decade. Harvard's Kohane says he believes the process will go smoother this time.
"Although we were thrilled that the HITECH Act invested in the process, it was pretty clear at the time that the available shovel-ready technology was state of the art for the 1980s and it was not going to be comparable to what our kids were using for video games. That was a predicable outcome," Kohane says. "This looks different. This will be adopted because it gives productivity and financial gain and accuracy right away when you implement it, as opposed to the promissory note around EHRs, which has not yet really shown itself to be robust."
IBM Watson Health's Jain was involved in the EHR rollout at Cleveland Clinic, and he says "absolute lessons" from that experience can apply to any kind of technology innovation.
"There's the hype cycle aspect that we all deal with, whether it's a new smartphone or a new car, or even a marriage," he says. "Initially there are high expectations, then you sort of ramp down to what it really means to be doing this, and you get into a groove where you begin to understand the reality of how people need to use this tool in their daily work as a hospital or provider. What we have to do as an industry is make sure we don't get stuck in the trough of disillusionment or on the peak of great expectations, but that we get our patients to the plateau of productivity."
Jain also sees the important distinctions.
"Whereas EHR was just an electronic form of the paper medical record, at least originally, to help documentation and billing, AI is a fundamental change in the way we do things. We don't want to lose sight of the fact that you can't just plop something like AI in and keep everything else the same. You have to transform the other parts," he says. "If all you do is put a new solution in place without understanding the impact on the other moving parts, we as an industry will lose. We have to think about transformation as more than technology. It is also people and process, perhaps even politics and governance. Where technology becomes an enabler, AI is the glue that connects all those things. It cannot be thought of in a vacuum.
"An example would be looking at the role that AI may play in assisting clinicians in interacting with patients and documenting their care. Today, studies show that for every hour of direct patient care, two hours are spent in desk and EMR work. With AI, clinicians should be able to spend more time interacting with patients, with insights being presented to clinicians in a contextually aware manner rather than having clinicians hunting and gathering data from complex EHRs to find patterns. That will change the way that medical assistants, clinicians, and care managers interact in the workflow—this newfound direct patient-facing time could be an opportunity for building relationships and reinforcing needed behavior change in some patients."
Mercy's Stewart says the EHR rollout called for a big, up-front investment and a flip-of-the-switch implementation that won't be necessary for AI.
"There was no way to piecemeal an entry into the EHR space. That type of approach to technology can be very painful. The lesson learned is that if you can avoid that type of faith-based investment, try to avoid it," he says. "Approaching machine learning as finding the biggest, baddest single vendor out there and betting the farm—that would make me very uncomfortable. The alternative is more cautiously step by step, with a smaller financial outlay, the focus being on early results and learning."
While acknowledging the snares and pitfalls of the HITECH Act, Mostashari says it's important to remember that today's promise of machine learning would not be possible without data created by EHRs. "Ten years ago the stuff of healthcare was not electronic. It was trapped in dead trees. What we were trying to do was really jump-start the transition that might have taken decades and compressed it into a four- to five-year period," he says. "To this day, I believe that you couldn't have had that without a strong role for government. In the case of AI, I don't see that same parallel. The private sector is fully capable of using these tools on this data platform that has already been built to solve market problems."
Blum says the EHR rollout demonstrated a need to anticipate what new workflows would look like, and that's an important lesson for AI. "You can't build the EHR as a stand-alone that doesn't talk to anyone else. There needs to be data moving in and out of the EHR to other applications, and the AI algorithms are a class of those applications," he says. "You can't think that the whole process is done once you've implemented the EHR because there are many pieces that aren't touched. Advanced analytics like AI are not going to be intrinsically delivered by an EHR vendor. They're going to be powered by the EHRs' data, and they need to interact with the providers who are using the EHR. So you have to think carefully about how that is going to work out."
Metrics & ROI
For all the hope and promise of AI, at some point there has to be a return on investment, and so far that's been difficult to ascertain. How do you project costs and potential savings around an unproven technology? For that matter, how do you measure results to ensure you're on the right track?
"It's a tough question," Stewart concedes. "I think about ROI in two ways: qualitative and quantitative. Most people want to focus on quantitative ROI."
As noted earlier, Mercy uses machine learning algorithms as part of a systemwide initiative to identify the total value of standardizing the care process, but AI was only one component of a large project with many moving parts. "We set a goal for three years to save $50 million. Fiscal '18 will be the third year of the project, and we are on track to hit that goal," Stewart says. "Now, out of that $50 million in savings, what proportion of that comes from our machine learning work versus what comes from standardizing the processes and operational things? That is the extreme difficulty in knowing how much the machine learning contributed to that overall savings. That's where it gets almost impossible to really know. Honestly, we just don't really attempt to do that."
Stewart says Mercy does not look at machine learning in isolation. "It's a tool. It is qualitative. You look at the process, you talk to the people who are using it, it definitely has a value," he says. "We aren't going to try to go down to the penny or dollar for what proportion of that was from machine learning. When they reduced the level of ‘not seens' in the ED by X percent, how many dollars did that translate to relative to the spend on the machine learning side? I don't know.
"If you add up all the spend on the machine learning side for that one use case it may be a negative in terms of the return," he says. "But knowing that we can apply that same protocol going forward to many other use cases that are highly beneficial, it's more of qualitative. We know there is value there, and this is stuff we have to commit to organizationally if we're going to have that ability."
As for metrics, Stewart says he believes that the overall success of initiatives in which AI played a role—such as standardizing care regimens systemwide—can provide a good sense that the new technology is on the right track.
"It's a lot like the concept of what is the ROI for an EHR. It's difficult to measure. You can measure dollars spent, but it is much harder to quantitate dollars on the back side, where it puts you in a competitive position; we have to have that EHR data," he says. "More or less we are viewing machine learning as similar."
Blum says the uncertainty around ROI and appropriate metrics in AI provides a good reason for a slow, incremental approach to implementation.
"For instance, with a collapsed lung, you can do fairly straightforward measurements before and after the use of the algorithm to see both how accurate the algorithm is in its recognition, how many times it is finding things more quickly, and how many times it is alerting providers to true findings versus false positives or false negatives," he says. "Then, you can look at how much sooner on average those findings are getting communicated to the providers and treatments are getting put in place than what the historical data looks like. There is a fair amount of historical data from emergency departments and ICUs on how long it takes x-rays to be interpreted after they're shot. Those are more straightforward examples."
Obviously, improving the speed and accuracy of diagnostics is critical in the care of every individual patient. When AI can extrapolate those findings to a patient population, the potential for improving care outcomes and costs savings becomes enormous. Blum says that day is not yet here, but within three years or sooner.
"When you look at improving diagnostic accuracy, complex decision-making for populations is going to require more sophisticated and larger looks at outcomes and process measures along the way in order to determine how accurate they are," he says.
Because providers will want a better understanding of ROI before they invest in these algorithms, Blum says vendors will have to do a better job providing evidence supporting their claims.
"Each time someone comes out with a new algorithm, it will have to come with ‘Here's how it was validated,' so you're sure it works. That validation will show that not only does it work well, but here is the measure that shows how much it improved care from how things were done previously," he says. "The Food and Drug Administration is very interested in this. They will be playing a role in how these algorithms are developed, how they are validated, and when they pass a threshold that they need to be regulated or don't need to be regulated, and when they do pass that threshold how they are going to be validated and can demonstrate that they're effective in improving care."
Stewart says providers who wait for competitors to make the ROI airtight or the technology iron-clad proven before they wade into AI will find themselves at a disadvantage.
"It's something you have to realize organizationally," he says. "It is something absolutely critical that your competition and the rest of the world is going to do, and it does shift the curve so significantly that if you are not adept in this space you are not going to be competitive in the very near future."
When to take the AI plunge
Harvard's Kohane says we'll know that AI has gone mainstream in healthcare when AI companies from outside of traditional care delivery begin poaching patients. For example, Kohane says 23andMe, the personal genomics and biotechnology company, was at first justifiably derided by the genomics community, which challenged the accuracy of the data used by the company. Then they got better, fast.
"They've matured. Now they have large population data sets and they have improved their algorithms, and they have FDA approval to move ahead and provide more clinical advice," Kohane says. "That is clearly happening outside of healthcare and geneticists, and consumers are driving that forward. You're seeing AI/machine learning applied to genomic data sets more and more, and if you start seeing interesting combinations of genomics and wearable and clinical data being directly marketed to patients, that is a good sign that it's ripe to get into it before you're disrupted."
Mercy's Stewart says the decision on when to jump into AI will be "highly individualized" for every provider.
"You'll have to assess your internal capabilities to do this," he says. "Right now, it would not be my recommendation to buy the hype and the buzz of the hundred startups in the past 12 months who will come in and say, ‘We are going to come in and revolutionize your operations with machine learning and AI.' It's still early, so I would be very conservative against those claims."
Skepticism and caution are not a pass to sit on your hands and do nothing.
"It seems as far-fetched as quantum mechanics or astrophysics, but it's really not. If you've got some internal capabilities to start thinking about some of these problems in your facility, there are some machine learning algorithms that you can initiate with Amazon cloud. Just send it and they give you an answer back," Stewart says.
"The accessibility of the technology now is unbelievably different than it was 18 months ago, and that is going to change. The rate of change is not linear; it is exponential. This is going to get easier and easier and more accessible for these small-focus use cases."
While AI has yet to transform medicine, Blum says those who ignore the new technology do so at their own peril.
"It is easy to get skeptical and say AI has been around for 50 years, but it hasn't changed much," he says. "Realize that there are autonomous driving cars. There is Siri and Alexa. While it is easy to poke fun and say they're imperfect and we have to be perfect in healthcare, the reality is these technologies are rapidly improving."
"There have been several technological breakthroughs over the past five years that are powering that. Storage is very cheap and computing power continues to get cheaper very quickly, so we are very much at the cusp of seeing a transition here," Blum says. "The same way that iPhones were a novelty one year and the next year everyone had them, you are going to see the same thing with AI. These will be a novelty. They'll be at the academic medical centers, and then fairly quickly they are going to be deployed as general purpose applications via commercially available cloud services."
Leap of faith
At some point, clinical and hospital leaders will have to make the leap to AI. For Blum, the leap is a necessary step for every provider, but one that must be carefully balanced.
"In the situation we are in, where there is so much financial pressure on healthcare organizations and every advantage can make a difference, the ability to deliver care more
cost-effectively and quickly makes a huge difference," he says. "At the same time, you don't want to make large investments that don't pay back quickly. I wouldn't recommend building a multimillion dollar program to start developing AI. In a smaller hospital, I would be paying attention when you see these things being offered by the well-established vendors, and they look like they can be integrated into your workflows and technology environment—that is going to be the time to jump in quickly and not lose too much of the advantage."
Stewart says that to some extent a leap of faith is necessary, "but you can inform your faith."
"How do you walk on ice?" he says. "Well, you don't sprint out like a dog and hope everything works out. You start at the thickest edge, and you're careful with your next step and you're paying attention, and if something cracks you step back. There is a way to do this with caution rather than reckless abandon. We don't want to play poker and take $200 million and push all the chips in and say we are going to have that kind of faith and hope it works out."
Stewart says look for "small chunks of specific problems" and speak to vendors who can address "$100,000 problems for a few thousand dollars."
"I'd do it in a well-controlled fashion that gives me the early learning and a comfort level with it," he says. "I might be willing to tiptoe on that ice."
A review by The Joint Commission finds broad and inconsistent uses for the term and definitions that are all over the map, hindering effective measures for the concept.
Despite all the time, money, and energy spent on improving healthcare quality and value, there is no “overarching concept” or consistent definition of what constitutes a “high-performing health system,” a review by The Joint Commission has found.
“The absence of a consistent definition of what constitutes high performance and how to measure it hinders our ability to compare and reward health care delivery systems on performance, underscoring the need to develop a consistent definition of high performance,” the review found.
In their search for a consistent definition of the term, The Joint Commission researchers sifted through English-language articles defining high performance with respect to a healthcare system or organization in PubMed and WorldCat databases from 2005 to 2015 and the New York Academy of Medicine Grey Literature Report from 1999 to 2016. The entity/condition to which the definition was applied was extracted from included articles.
The number and type of dimensions used to define high performance within and across articles was tabulated and the number and type of metrics used by performance dimension and by article was calculated.
Instead of a consistent definition, the researchers found that high performance was variably defined across different dimensions, including quality (93% of articles), cost (67%), access (35%), equity (26%), patient experience (21%), and patient safety (18%).
Most articles used more than one dimension to define high performance (75%), but only five used five or more dimensions. The most commonly paired dimensions were quality and cost (63%).
The Joint Commission researchers said in their review that measuring performance in the nation’s healthcare delivery system “has gained significant traction” over the years with policy makers, even though they apparently do not have a consistent definition of the term.
To support delivery system improvement nationally, the Agency for Healthcare Research and Quality recently funded three Centers of Excellence to study high-performing systems, particularly their ability to quickly move new evidence-based care practices into practice, The Joint Commission review noted.
“Research to understand what enables healthcare delivery systems to perform highly, and policy efforts to measure and recognize high-performing health care delivery systems, is predicated on an agreed-on definition of what it means to be high-performing,” the researchers said.
“Achieving consensus on what it means to be high-performing is essential to facilitate comparisons across delivery systems and in applied measurement activities, such as programs that designate and publicly recognize high performers.”
