Ohio Health Policy News

More states are testing Medicaid programs that’ll provide more people with healthy foods and, potentially, lower health care costs (Source: “Can food cure high medical bills? Pilot 'food as medicine' programs aim to prove just that.” USA Today, Feb. 15). 
 
Medicaid typically only covers medical expenses, but Arkansas, Oregon and Massachusetts received approval from the Centers for Medicare & Medicaid Services last year to use a portion of their Medicaid funds to pay for food programs, including medically tailored meals, groceries and produce prescriptions (fruit and vegetable prescriptions or vouchers provided by medical professionals for people with diet-related diseases or food insecurity). The aim is to see whether providing people with nutritious foods can effectively prevent, manage, and treat diet-related diseases.  
 
A study published last fall estimated that if all patients in the U.S. with mobility challenges and diet-related diseases received medically tailored meals, 1.6 million hospitalizations would be avoided, with a net savings of $13.6 billion annually. Another study in 2019 found that over the course of about a year, the meals resulted in 49% fewer inpatient admissions and a 16% cut in health care costs compared with a control group of patients who did not receive the meals. 
 
This spring, the American Heart Association and the Rockefeller Foundation plan to launch a $250 million “Food is Medicine” Research Initiative to determine if such programs can be developed cost-efficiently enough to merit benefit coverage and reimbursement for patients.

As new generative AI models like ChatGPT gain popularity, some experts are saying that to ensure such tools work in healthcare, implicit racial biases baked into health data must be accounted for (Source: “Google, Microsoft execs share how racial bias can hinder expansion of health AI,” Fierce Healthcare, Feb. 23). 
 
The goal is for AI to one day “support clinical decision-making [and] enhance patient literacy with educational tools that reduce jargon,” said Jacqueline Shreibati, M.D., senior clinical lead at Google. 
 
However, there are gaps around the use of these models in healthcare. Chief among them is that clinical evidence is always evolving and changing. Another key problem is the data themselves may have racial bias that needs to be mitigated. 
 
“A lot of our data has structural racism baked into the code,” Shrebati said.

Twenty years after a landmark report tied systemic racism to health disparities, experts say little has changed (Source: “20 years ago, a landmark report spotlighted systemic racism in medicine. Why has so little changed?,” Stat News, Feb. 23).

“Unequal Treatment” was the first major report to point to longstanding systemic racism — not poverty, lack of access to health care, or other social factors — as a major reason for the nation’s deeply entrenched health disparities. The authors, a blue-ribbon panel of the National Academies’ Institute of Medicine, hoped their work would kickstart a national discussion and lead to much-needed change.

At the time, the report sent shock waves through medicine.  But today, the disparities — poorer outcomes and higher death rates for nearly every medical condition the panel examined — and the structural racism underlying them, remain. That grim truth has been made startlingly clear by both the pandemic and by statistics that show Black Americans continue to die up to five years earlier than those who are white.

“There hasn’t been a lot of progress in 20 years,” said Brian Smedley, a health equity and policy researcher with the Urban Institute who served as the report’s lead editor. “We are still largely seeing what some would call ‘medical apartheid.’”

A study of a healthcare artificial intelligence program that inputs patient responses rather than information from doctors found that the new approach could reduce racial disparities (Source: “New Algorithms Could Reduce Racial Disparities in Health Care,” Wired, Jan. 25, 2021).

Health diagnostic software typically learns from doctors by digesting thousands or millions of x-rays or other data labeled by expert humans until it can accurately flag health problems by itself. A study published last month in the journal Nature Medicine took a different approach—training algorithms to read knee x-rays for arthritis by using patients as the AI arbiters of truth instead of doctors. The results revealed that radiologists may be missing important details when it comes to reading Black patients’ x-rays.

The algorithms trained on patients’ reports did a better job than doctors at accounting for the pain experienced by Black patients by discovering patterns of disease in the images that humans usually overlook.

“This sends a signal to radiologists and other doctors that we may need to reevaluate our current strategies,” says Said Ibrahim, a professor at Weill Cornell Medicine, in New York City, who researches health inequalities, and who was not involved in the study.

Algorithms designed to reveal what doctors don’t see, instead of mimicking their knowledge, could make health care more equitable. In a commentary on the new study, Ibrahim suggested it could help reduce disparities in who gets surgery for arthritis. African American patients are about 40 percent less likely than others to receive a knee replacement, he says, even though they are at least as likely to suffer osteoarthritis. Differences in income and insurance likely play a part, but so could differences in diagnosis.

An investigation by health news website STAT News found that a common method of using analytics software to target medical services to patients is infusing racial bias into decision-making about who should receive stepped-up care (Source: “From a small town in North Carolina to big-city hospitals, how software infuses racism into U.S. health care,” STAT News, Oct. 13).

While a study published last year documented bias in the use of an algorithm in one health system, STAT found the problems arise from multiple algorithms used in hospitals across the country. The bias is not intentional, but it reinforces deeply rooted inequities in the American health care system, effectively walling off low-income Black and Hispanic patients from services that less sick white patients routinely receive.

These algorithms are running in the background of most Americans’ interaction with the health care system. They sift data on patients’ medical problems, prior health costs, medication use, lab results and other information to predict how much their care will cost in the future and inform decisions such as whether they should get extra doctor visits or other support to manage their illnesses at home. The trouble is, these data reflect long-standing racial disparities in access to care, insurance coverage, and use of services, leading the algorithms to systematically overlook the needs of people of color in ways that insurers and providers may fail to recognize.

Ohio communities will be part of a $350 million federal study analyzing drug intervention techniques and policies. (Source: “Ohio Counties to Be Part of $350M Ohio counties to be part of $350M federal opioid study,” Associated Press, Nov. 9, 2019)

About $66 million will be channeled for the National Institutes of Health’s HEALing Communities Study through Ohio State University to Ashtabula, Athens, Cuyahoga, Darke, Greene, Guernsey, Hamilton, Lucas, Morrow and Scioto counties.

Research sites will test community engagement strategies and several proven opioid prevention and treatment practices.

Nine Ohio counties — Allen, Brown, Franklin, Huron, Jefferson, Ross, Stark, Williams and Wyandot — will be part of the study’s second wave. The study will also look at communities in Kentucky, New York and Massachusetts.

The Federal Communications Commission is moving forward with plans to reform how funding is distributed for the agency's rural telemedicine program (Source: “FCC to prioritize telemedicine funding by 'rurality',” Modern Healthcare, Aug. 1, 2019).

The FCC on Thursday voted to adopt a report and order for its Rural Health Care Program, which helps fund broadband and telecommunications services for some healthcare providers in rural areas. A major part of the program involves subsidizing the difference between urban and rural rates for telecommunications services.

To address increasing demand for the program, last summer the FCC increased funding for the Rural Health Care Program to $571 million per year, up from its initial funding cap of $400 million.

With the new report and order, the FCC said it will reform the way it distributes Rural Health Care Program funding and take steps to guard against possible waste and inefficiencies in program costs.

In the event program demand outpaces available funding, the FCC now plans to prioritize support based on "rurality tiers," as well as whether the Health Resources and Services Administration designates a provider's area as part of a medically underserved population.

The Health Policy Institute of Ohio’s next forum, titled “Addressing Ohio’s Greatest Health Challenges through Technological Innovation,” will take place from 9:30 a.m. to 2 p.m. on Feb. 19 at the Fawcett Event Center at Ohio State University.

The forum will explore the role of technology in fostering innovative approaches to improving health and reducing health disparities, with a focus on addressing Ohio's greatest health challenges: mental health and addiction, infant mortality and chronic disease. Topics to be addressed at the forum include:

• Telehealth services that increase access to mental health and addiction services
• Transportation initiatives that enable better access to jobs and active living environments, while improving air quality
• Data analytics tools that identify issues related to the social determinants of health

To register, or for more information, click here

A recent change in the way Medicare pays for joint replacements is saving millions of dollars annually — and could save billions — without impacting patient care, a new study has found (Source: “Bundled Payments Work, Study Finds, But HHS Nominee No Fan,” Kaiser Health News, Jan. 3, 2017).

Under the new program, Medicare effectively agrees to pay hospitals a set fee — a bundled payment — for all care related to hip or knee replacement surgery, from the time of the surgery until 90 days after.  Starting in April 2016, CMS required around 800 hospitals in 67 cities to use the bundled payment model for joint replacements and 90 days of care after the surgery as part of the Comprehensive Care for Joint Replacement program. The program had previously been road-tested on a smaller number of hospitals on a voluntary basis, which formed the focus of the research.

Tom Price, the president-elect’s HHS nominee, a congressman from Georgia and an orthopedic surgeon, has actively opposed the idea of mandating bundled payments for these orthopedic operations, calling it “experimenting with Americans’ health,” in a letter to the Medicare agency just last September. In addition, the agency which designed and implemented the experiment, the Center for Medicare and Medicaid Innovation, was created by the Affordable Care Act to devise new methods for encouraging cost-effective care. It will disappear if the act is repealed, as President-elect Trump has promised to do.

The study appeared Tuesday in the Journal of the American Medical Association. Though one of its authors is Dr. Ezekiel Emanuel, a professor at the University of Pennsylvania who helped design the ACA, the research relies on Medicare claims data from 2008 through mid-2015, long before the presidential election.

The study found that hospitals saved an average of 8 percent under the program, and some saved much more. Price has been skeptical that bundled payments did save money, but the researchers estimate that if every hospital used this model, it would save Medicare $2 billion annually.

The Governor’s Office of Health Transformation today released a reported titled “Improving population health planning in Ohio.”

The report, created by the Health Policy Institute of Ohio, provides recommendations for strengthening Ohio’s population health planning and implementation infrastructure and outlines ways to align population health priority areas, measures, objectives and evidence-based strategies with the design and implementation of the patient-centered medical home (PCMH) model.

HPIO was commissioned by the Governor’s Office of Health Transformation (OHT), the Ohio Department of Medicaid and the Ohio Department of Health in September 2015 to facilitate stakeholder engagement and provide guidance on improving population health planning in Ohio.

Ohio's performance on population health outcomes has declined relative to other states over the past two decades, and Ohio has significant disparities for many health outcomes by race, income and geography. Ohio also spends more on health care than most other states.   “Part of the challenge is the lack of coordination across ten state-level health improvement plans and  110 local health district and 170 hospital community health assessments/plans,” according to an OHT release. 

In December 2014, the federal Center for Medicare and Medicaid Innovation awarded Ohio a four-year $75 million State Innovation Model, or SIM, test grant for implementation of episode-based payments and rollout of a state-wide PCMH model over a four-year period. As part of that funding, Ohio must also develop a population health plan.

OHT will coordinate the implementation of the HPIO recommendations in 2016.