Improving Population Health Through Housing

For a while now I’ve been meaning to write a post about the connection between housing instability and health. Of late, this topic has drawn a good deal of coverage in high-profile news outlets, and organizations across the healthcare spectrum are taking notice. The idea is buoyed by the notion that addressing negative social determinants of health (SDOH) is a worthy aim not just because it’s the right thing to do, but because there’s a solid business case to be made for adopting such an approach. 

I’ve written multiple posts about the interplay between insurance and population health, but in this post I’d like to discuss how health insurance companies are investing in housing to improve the health outcomes of some beneficiaries. At first it might sound counterintuitive that payers, who are in the business of making money, would shell out significant amounts of cash to either fix up substandard housing or subsidize their beneficiaries’ accommodations outright. But a deeper look shows how this strategy is paying dividends, both in terms of better health outcomes and enhanced bottom lines.

Photo courtesy of Free Stock photos by Vecteezy

Housing Instability

The connection between housing instability and poor health outcomes has been known for some time. Indeed, as a 2017 Health Affairs article put it, “Access to health care has been shown to improve health, and housing instability is correlated with poor access to health care.” The authors of that same study found that those receiving US Department of Housing and Urban Development (HUD) housing assistance had “a lower uninsurance rate and a lower rate of unmet need due to cost” when compared with those who hadn’t received housing assistance.

Put another way, when people are given financial help, they don’t have to worry as much about making tradeoffs between healthcare visits and paying rent each month. Ensuring access to healthcare is a central tenet of population health, and alleviating the difficult choice between healthcare and housing seems to have finally caught on with insurers across the country.  

But before we launch into a discussion about payers and subsidized housing, I’d like to clarify what I mean by the term “housing instability” and its synonym, “housing insecurity.” While it may be tempting to think of housing instability as equivalent to homelessness, it is actually a separate, if related, term. The Office of Disease Prevention and Health Promotion defines it this way:

“Housing instability encompasses a number of challenges, such as having trouble paying rent, overcrowding, moving frequently, or spending the bulk of household income on housing.”

So being housing unstable means that the costs associated with housing are burdensome to a given person or group of people. If left unmitigated, the factors leading to this turmoil can precipitate homelessness. While the problem of housing instability is multifaceted, high housing costs relative to income is a primary culprit. The U.S. Department of Health and Human Services frames the issue of expensive housing this way:

“Households are considered to be cost burdened if they spend more than 30 percent of their income on housing and severely cost burdened if they spend more than 50 percent of their income on housing. Cost-burdened households have little left over each month to spend on other necessities such as food, clothing, utilities, and health care.”

Substandard Housing

Not to belabor the topic of housing instability, but I want to zoom in a little further on its component parts to give us a deeper appreciation for what insurers are up against in combating it. For starters, substandard housing is one cause of housing instability that can lead to sub-optimal health. I’ve written about this topic in the past, and how some community organizations have banded together to overcome it.

The National Center for Healthy Housing calculates that nearly six million U.S. homes fall into the substandard category. To get a better handle on how this problem is quantified, it’s instructive to consult the American Housing Survey (AHS), which is sponsored by HUD and conducted by the U.S. Census Bureau. The AHS rates housing as “severely inadequate” based on eight criteria:

  • Unit does not have hot and cold running water.
  • Unit does not have a bathtub or shower.
  • Unit does not have a flush toilet.
  • Unit shares plumbing facilities.
  • Unit was cold for 24 hours or more and more than two breakdowns of the heating equipment have occurred that lasted longer than 6 hours.
  • Electricity is not used.
  • Unit has exposed wiring, not every room has working electrical plugs, and the fuses have blown more than twice.

For the eighth criterion, the habitation in question must show evidence of five or six of these structural conditions:

  • Unit has had outside water leaks in the past 12 months.
  • Unit has had inside water leaks in the past 12 months.
  • Unit has holes in the floor.
  • Unit has open cracks wider than a dime.
  • Unit has an area of peeling paint larger than 8 by 11 inches.
  • Rats have been seen recently in the unit.

An issue brief by the Robert Wood Johnson Foundation states that “Substandard housing such as water leaks, poor ventilation, dirty carpets and pest infestation can lead to an increase in mold, mites and other allergens associated with poor health.” In addition, note the brief’s authors, “Concentration of substandard housing in less advantaged neighborhoods further compounds racial and ethnic as well as socioeconomic disparities in health,” a phenomenon often referred to as “housing inequity.”

To this latter point, it should come as no surprise that substandard housing affects some demographic groups more than others. A 2016 article from the Brookings Institution titled “Time for justice: Tackling race inequalities in health and housing” notes the following:

“Substandard housing conditions such as pest infestation, lead paint, faulty plumbing, and overcrowding disproportionately affect black families and lead to health problems such as asthma, lead poisoning, heart disease, and neurological disorders. Blacks are 1.7 times more likely than the rest of the population to occupy homes with severe physical problems. Concentrated housing inequity also disproportionately exposes black communities to environmental pollutants and isolates black populations from essential health resources…” 

Overcrowded Housing

In addition to substandard housing, overcrowding is another factor leading to housing instability. Sometimes referred to as a form of “hidden housing instability,” 3.7 million people lived in overcrowded housing in 2019. Robust recent data on the number of children living in overcrowded homes in the U.S. is sparse, but it has been established that “living in a crowded home can negatively affect academic performance, educational outcomes, behavioral health, and physical health.”

Although there doesn’t seem to be a consensus on what constitutes overcrowding, a 2016 study by HUD’s Office of Policy Development and Research that examined various interventions to combat homelessness used an outcome measure called “persons per room” to track crowding. Looking at “information collected from the adult respondent about the number of rooms in the housing unit (not counting kitchens, hallways, and bathrooms) and the number of people living in the housing unit,” in this formulation, housing situations where more than one person occupied a single room were considered crowded.

Because the parameters of overcrowding aren’t well-defined, measuring overcrowding or, as it’s sometimes called, “doubled-up” homelessness, is challenging. In addition to HUD’s definition above, a range of organizations quantify the concept differently, taking different variables into account. One measure that seems to fill in a few gaps found in other approaches was put forward by the authors of a 2021 study called “Quantifying Doubled-Up Homelessness: Presenting a New Measure Using U.S. Census Microdata.” The authors’ rationale for arriving at an overall number is as follows:

“(W)e defined doubled-up homeless persons as poor or near-poor individuals in a poor or near-poor household (at or below 125% of a geographically adjusted poverty threshold) who met the following conditions: a relative that the household head does not customarily take responsibility for (based on age and relationship); or a nonrelative who is not a partner and not formally sharing in household costs (not roomers/roommates). Single adult children and relatives over 65 may be seen as a householder’s responsibility, so such cases are included only if the household is overcrowded — an arrangement that we believe, based on the literature and feedback from experts working in the homelessness response system, provides evidence of economic hardship and involuntary doubling up.”  

Homelessness and the U.S. Population

Now let’s focus on the separate but related concept of homelessness. According to the HUD Exchange, “individuals who lack resources and support networks to obtain permanent housing meet HUD’s definition of homeless.” As with the housing unstable population in the U.S., thanks to a raft of aid programs, homelessness has not worsened significantly in recent years. It bears saying, however, that this trend will likely be influenced by aid programs ending with the impending end of the Public Health Emergency (PHE) and the eviction moratorium. But because data on the end of the PHE won’t be available for some time, it’s worth noting trends in this population over the past few years.

According to a December 2022 report produced by HUD, there was “a .3% increase in the number of people experiencing homelessness from 2020 to 2022.” This number includes a drop in the number of veterans experiencing homelessness between 2020 and 2022 of 11%, and, during the same period, a decrease in the number of families with children experiencing homelessness by 6%.

As mentioned above, while these numbers have been heading in the right direction, some experts worry that with the Supreme Court’s ruling that overturned the eviction moratorium in 2021, housing instability and homelessness may once again be on the rise. Similar to those experiencing housing instability, this burden falls unevenly, with certain underserved populations experiencing homelessness at rates much higher than the rest of the country.

Indeed, the Centers for Disease Control and Prevention (CDC) reports that “People who are Black or African American and those who are American Indian or Alaska Native have higher rates of homelessness.” Similarly, America’s elderly are predicted to see a significant downturn in their housing status. In a recent article, the Los Angeles Times quoted a 2019 study by the University of Pennsylvania that predicted that “the U.S. population of people 65 and older experiencing homelessness will nearly triple from 40,000 to 106,000 by 2030, resulting in a public health crisis as their age-related medical problems multiply.”

Home ownership, which can serve as a rough proxy for housing stability, does not break evenly along demographic lines. The Pew Research Center notes that “Nationwide, about 58% of households headed by Black or African American adults rent their homes, as do nearly 52% of Hispanic- or Latino-led households…By contrast, roughly a quarter of households led by non-Hispanic White adults (27.9%) are rentals, as are just under 40% of Asian-led households.” In other words, African-American and Hispanic households are more likely than White households to be burdened by costs associated with housing.

A Nationwide Problem

Issues like substandard housing, overcrowding, homelessness, and other challenges related to ensuring that everyone in the U.S. has adequate housing are set against a housing crisis that’s persisted more or less since the Great Recession of 2007-2010. While the number of owner-occupied homes is not insignificant at 64.6%, that leaves about a third of U.S. adults who rent their homes. Estimates of the housing shortfall in the States range from 1.5 to 5 million homes. For myriad reasons the US homeowner vacancy rate, which tracks the percent of units available for occupancy, is at near historically low levels, which equates to rising prices.

While there is an argument to be made that, financially speaking, it makes more sense to rent than to buy in a hot housing market like the one much of the country is still experiencing, in the long run, as the Brookings Institution puts it, “Owning one’s home…provides greater stability and predictability of housing expenditures than renting.”

Given the shockwaves that the COVID-19 pandemic sent through most sectors of the economy, many have faced housing instability over the past three years. As bad as things got, however, if not for government interventions like the eviction moratorium instituted by the CDC in September of 2020, the situation could have been worse. The moratorium helped many renters by giving them a reprieve from losing their accommodations, and as we’ll soon see, maintaining stable housing has a direct impact on positive health outcomes.

A Role for Insurers

When taken as a whole, the above issues negatively affect access to quality healthcare. And when people can no longer keep themselves healthy, everyone loses — including insurers. Key to the success of any payer is maintaining balanced risk pools, which have been defined as “groups of individuals across the medical complexity spectrum, which allow both private and public payers to potentially offset the cost of sicker individuals with higher medical expenses with premiums from healthier individuals with lower utilization rates.”

Large and balanced risk pools often lead to more predictable — and thus more stable — premiums. But if the balance between sick and healthy beneficiaries is thrown off and the insured population grows more and more sick, a phenomenon called “adverse selection” can occur where an outsized number of unhealthy beneficiaries crowd out healthier members. When this happens, it becomes hard for payers to manage financial risk and keep premiums at manageable levels without sacrificing profit.

After all, healthier people are not as likely to need expensive treatments, which equates to fewer claims for the insurance company to pay out. By extension, this helps keep costs down, resulting in lower overall premiums. So with the aim of reducing healthcare costs by addressing SDOH related to unstable housing, some health insurance carriers have begun to proactively offer housing support to their lower-income beneficiaries.

An example of this can be seen with insurance giants Humana and UnitedHealth. In recent years, these companies have begun to invest heavily in housing. According to a recent article in Forbes, in reaction to their own Medicaid data showing a link between housing instability and sub-optimal health outcomes, “In 2022, UnitedHealth invested $100 million in building affordable housing in parts of the country where they operate. That’s in addition to the $700 million they’ve already invested in the past decade, creating a total of nearly 20,000 homes — so far — for low-income residents.” Interestingly, the housing projects UnitedHealth is developing include on-site health services for nearby residents and access to public transportation, among other services. 

For its part, Humana has invested $90 million in affordable housing since 2021. Billed as “Humana’s Bold Goal,” the company has dedicated itself to improving the health of the communities they serve by 20% “by addressing the health of the whole person.” For the time being, their approach seems more limited in scope than UnitedHealth’s in that they aim to stabilize housing insecure beneficiaries for 90 days, providing services like job training and behavioral health. In addition, Humana has explored promoting permanent supportive housing programs through various funding models in the “pay for success” mold. These models resemble healthcare value-based payment models in that they are outcomes-based approaches that “align payment for support services to priority objectives” and can potentially establish sustainable funding for these programs.  

With housing issues mounting, these are just a few ways insurers are looking to both improve health outcomes and control healthcare costs. Medicare and Medicaid have also entered the subsidized housing market, using rebates and Section 1115 waivers to offer stable living conditions to their beneficiaries. I may focus a future blog post on how public payers are changing the housing landscape of the United States. Let me know in the comments if you know of any other similar approaches taken by insurers either in this country or around the world.

PHM and Rural Healthcare — Part 2

In a post last month, I explored the current state of rural healthcare in America. Building on that theme, I now want to look at how the concept of population health management (PHM) can play a key role in improving health outcomes in rural areas. While it might not be the first setting one thinks of when considering a population health approach, when done with an eye toward leveraging existing resources and forging strong bonds between community and clinical partners, PHM can be a dynamic way to deliver quality care.

Why Population Health in Rural Settings?

I always like to start with definitions when possible, and I think that since the term “population health” is so flexible, we need to be specific when applying it to various situations. Since we’ll be discussing rural America in this post, I think this definition by the CDC works well:

“CDC views population health as an interdisciplinary, customizable approach that allows health departments to connect practice to policy for change to happen locally. This approach utilizes non-traditional partnerships among different sectors of the community – public health, industry, academia, health care, local government entities, etc. – to achieve positive health outcomes. Population health ‘brings significant health concerns into focus and addresses ways that resources can be allocated to overcome the problems that drive poor health conditions in the population.’”

The emphasis here is mine. To me, the aspects highlighted in this definition contribute to effective population health management in rural settings: a focus on care change happening locally; healthcare entities actively partnering with community-based organizations, sometimes with the assistance of bridge organizations that navigate patients between clinical and behavioral health centers, the latter of which usually provide mental health and substance abuse disorder treatment services; and allocating scarce healthcare resources to those patient populations that will most benefit from them.

So why should we consider applying a population health approach in rural settings? On the face of it, there are a raft of challenges that might make such an enterprise appear daunting: for starters, rural hospitals are on average far smaller than their urban counterparts, and they also have much less money at their disposal. A report by the Center for Healthcare Quality and Payment Reform provides a few details that point in this direction:

  • Most urban hospitals have over 200 inpatient beds, whereas most rural hospitals have 25 or fewer beds.
  • One-half of urban hospitals have expenses of more than $250 million, whereas only 2% of rural hospitals are that large.
  • One-half of rural hospitals have total expenses of less than $35 million, compared to only 4% of urban hospitals.

In addition to these numbers, the American Hospital Association points out that “59% of the decline in the number of U.S. community hospitals between 2015 and 2019 were rural hospitals,” and an article out of the Leonard Davis Institute of Health Economics at Penn notes that “rural hospitals typically have less than half the median profit margins of urban hospitals.” As a consequence, the same Penn article highlights the fact that in excess of “130 facilities have closed since 2010” leaving “about 2,250 remaining rural hospitals out of about 5,000 facilities nationwide.”

All this leaves aside the stark reality of a physician shortage in the U.S. While this shortfall is hitting the nation as a whole, it is most pronounced in rural areas, a fact that makes coordination of care – a hallmark of population health management – much more challenging. Indeed, according to data provided by the National Rural Health Association, the patient-to-primary care physician ratio in rural areas falls far short of that in urban areas: 39.8 physicians versus 53.3 physicians per 100,000 people, respectively. Further, as an article by the Association of American Medical Colleges mentions, “while 20% of the U.S. population lives in rural communities, only 11% of physicians practice in such areas.” 

This being the case, I’ll ask again: why should we consider applying a population health approach in less densely inhabited settings? Answers vary, but many believe that, despite the drawbacks mentioned above, rural areas hold some relative advantages to their urban counterparts. In a report titled “Advancing Population Health in Rural Places: Key Lessons and Policy Opportunities” by the Rural Policy Research Institute (RUPRI), the authors highlight eight resources (framed as areas of “capital”) that make less populated areas a favorable training ground for PHM solutions.

While some of these “areas of capital” may strain credulity – pointing to rural areas as home to more walkable areas than urban areas, for instance, might be a bit far-fetched – by-and-large the authors have a point. Most importantly for our purposes, two themes run throughout many of these areas of emphasis that redound to population health’s advantage: 1) healthcare entities’ familiarity with community partners and 2) an emphasis on local solutions. If I were to boil these ideas down still further, the common denominator here seems to be a strong sense of trust.

Building Clinical-Social Bonds

This idea of relatively deeper bonds of trust existing between residents and institutions in rural settings is an interesting one, and one that’s borne out by the numbers. For instance, a 2018 Pew Research survey found that, while inhabitants of urban, suburban, and rural areas report a nearly equal familiarity with at least some of their neighbors (53% vs. 49% vs. 47%, respectively), and despite the fact that adults in all three geographic categories attest to similar levels of attachment to their local community, “about six-in-ten of those in the suburbs (62%) and in rural communities (61%) say they have a neighbor they would trust with a set of keys to their home, compared with about half (48%) in urban areas.”

In a similar vein, a 2021 report by the Survey Center of American Life found that “Americans in more densely inhabited places are…much less willing to leave their doors unlocked,” with just 35 percent of those residing in big cities being willing to leave the doors of their homes unlocked versus 69 percent in rural settings.

While these examples may seem superficial at first glance, I think they reinforce a larger point that once a personal bond is formed, this relationship often rests on a more solid foundation in rural areas than it does in others (at least insofar as people self-report their personal inclinations). To this point, the authors of a study called “Growing Up in Rural America,” which was produced by the Johns Hopkins University Project Muse program, noted that, when it comes to social bonds between rural residents, “the importance of local social relationships and working collectively on common issues and the limited number of neighbors makes developing these relationships easier.”

For instance, the authors point out that rural counties often boast infrastructure that facilitates greater communication between healthcare entities and community-based organizations, a key partnership when trying to open up care access to all segments of society. “For example,” the RUPRI report tells us, “a grocery store could collaborate with the hospital on promoting healthy diet changes. Similarly, a school could be a common point of contact for informing community residents about how the hospital and other community organizations can address needs such as hunger and housing.”

Building on this idea, the same report argues that deep reserves of political capital exist in many rural communities such that “the influence that individuals and organizations hold…can be used to achieve population health goals. Unlike in larger communities,” the report goes on to say, “rural leaders of population health activities are likely to be individuals that community members know personally. This familiarity can facilitate trust and community buy-in throughout the process.”

The bottom line is this: we can expect stronger social and institutional bonds to exist in less populated places, and these relationships can persuade folks otherwise disinclined to seeking medical or behavioral care to do so.

Who Pays for Population Health?

With all this talk of building reserves of trust and strategically realigning healthcare resources in rural settings, it’s important to identify how population health approaches are funded. I should start by saying that none of the research I’ve done suggests that implementing this type of care delivery model is easy or straightforward, no matter what environment you’re talking about. And arguably the biggest barrier a site or system will face is in regards to cost.

Plainly stated, it costs money to implement electronic health record platforms and closed-loop communications systems. It costs money to allocate staff time to working closely with community health partners. It costs money (not to mention time) to change workflows that more tightly coordinate care between providers.

To my mind, if we proceed with our eyes open to this reality, we’ll be better equipped to identify strategies that work in the real world. With this in mind, let’s focus on a few trends that have begun to emerge that allow population health management to flourish in rural communities where non-clinical social determinants of health (SDOH) play a substantial role in people’s health outcomes.

Private and Public Insurance

More and more these days, insurers are better able to reimburse healthcare providers for addressing SDOH. For example, in a report called “Advancing Population Health in Rural Places: Key Lessons and Policy Opportunities,” which was produced by RUPRI, the authors note the following:

“Medicare Advantage (MA) plans can now pay for services addressing some social determinants of health, such as transportation (including to grocery stores), meal kits, and telehealth; and Medicaid can use managed care organizations, State plan amendments, and waivers to do the same.”

While a range of insurers can support such an effort, rural settings often feature a unique payer mix that could present roadblocks for the faint of heart. In their 2019 Rural Report, the American Hospital Association noted that “Rural hospitals are more likely to serve a population that relies on Medicare and Medicaid. However, these programs reimburse less than the cost of providing care, making rural hospitals especially vulnerable to policy changes in payment of services.” 

The difference in reimbursement levels between public and private insurers is pretty staggering. In one Kaiser Family Foundation issue brief, the authors reviewed the findings of 19 studies that compared Medicare and private health insurance payment rates for both physician services and hospital care. I was bowled over by the results: “Private insurers,” note the authors, “paid nearly double Medicare rates for all hospital services (199% of Medicare rates, on average), ranging from 141% to 259% of Medicare rates across the reviewed studies.”

Of the eight studies considered in the issue brief that compared private insurance to Medicare payment rates for inpatient hospital services, “Private insurance payment rates for inpatient hospital services averaged 189% of Medicare rates across studies.” This gulf in reimbursement levels helps illustrate the challenges facing CMS when it tries to entice rural healthcare practitioners into entering risk-based contracts, some of which are built around population health management: in an environment where healthcare providers are getting lower payments for their services, it’s not immediately clear why most would want to risk being further penalized for not meeting quality benchmarks. 

Left out of this discussion so far have been private health insurance companies. While accounting for relatively fewer beneficiaries in rural America, it’s noteworthy that these companies often take their cues from CMS. This is no more true than when it comes to employing risk-based models that foreground accountable care. Leaving aside the fact that some private insurers collaborate with CMS to administer Medicare, i.e. through Medicare Advantage plans, private insurers have followed CMS’ lead in structuring value-based reimbursement models.

Of note, in 2021 the Health Care Payment Learning & Action Network (LAN) reported that “Private payers covered 62 percent of the lives represented in the LAN’s data…Additionally, more payments made to providers by private payers (11.1 percent) were tied to two-sided risk models in 2019. The report shows that 53.5 percent of payments were from fee-for-service, too.”    

In addition to the preponderance of government-funded healthcare in rural counties, the composition of patient populations in such places also presents challenges. This patient mix often involves older, less affluent, and sicker patients than other settings. Added to this is the fact that there are simply fewer patients to pay for services, which makes providing quality care, and ensuring access to that care equally across demographic segments, not as straightforward as in other, more well-funded settings.

Indeed, in a document titled “Report of the Council on Medical Service: Addressing Payment and Delivery in Rural Hospitals,” the American Medical Association notes the following:

“Low patient volume represents a persistent challenge to the financial viability of rural hospitals. There is a minimum level of cost needed to maintain the staff and equipment required to provide a particular type of service, whether it be an ED, a laboratory, or a primary care clinic. As a result, the average cost per service will be higher at a hospital that has fewer patients.”

Bridge Organizations

On the topic of insurer-funded population health, in recent years a consensus has been forming around the idea that gaps exist between clinical care and community services, the latter of which is commonly classified as a mix of mental health and substance abuse disorder treatment, otherwise known as behavioral health.

In response to this, CMS has begun making a significant push to facilitate connections between clinical and community-based health organizations. Funding so-called “bridge organizations” to connect these two entities, CMS is signaling its willingness to support this type of care coordination both financially and logistically.

One high-profile example of this effort is a care delivery model called the Accountable Health Communities Model (AHCM). Initiated by CMS in 2017, the AHCM tests “whether systematically identifying and addressing the health-related social needs of Medicare and Medicaid beneficiaries’ (sic) through screening, referral, and community navigation services will impact health care costs and reduce health care utilization.” In other words, by addressing non-clinical SDOH, this CMS Innovation Model seeks to lower overall costs and improve health outcomes for Medicare and Medicaid populations served by participating organizations. 

Of the thirty-two original participants in the program, twenty-eight organizations remained as of 2021. Throughout its existence, the model has sought to address non-clinical, health-related social needs such as “housing instability, food insecurity, utility needs, interpersonal violence, and transportation needs” along two different tracks:

Assistance Track – Provide community service navigation services to assist high-risk beneficiaries with accessing services to address health-related social needs

Alignment Track – Encourage partner alignment to ensure that community services are available and responsive to the needs of the beneficiaries

The results of the program have been mixed, but offer some cause for optimism. A 2020 report noted that most data collected on program participants up to that point had been for Medicare fee-for-service beneficiaries, whereas the authors speculated that the program would yield the biggest value to Medicaid patients. Since not much data was available on Medicaid patients at the time of the report, however, evaluation of the program’s true reach remains elusive:

“The early impact analysis, which focused on Medicare FFS beneficiaries in the Assistance Track, shows reductions in the number of ED visits, although impacts on other outcomes were not statistically significant. The lack of statistical significance is attributable partially to the relatively few Medicare beneficiaries exposed to the Assistance Track intervention in the first year…Future analyses will incorporate data for Medicaid beneficiaries, who comprise more than 70% of the navigation-eligible sample.”

State-Level Patient Navigation

Although not affiliated with the AHCM, a state-level initiative in the AHCM mold that’s yielding encouraging results is Colorado’s Regional Accountable Entities (RAE) program. Designed to provide bridging services between clinical and community-based health organizations to keep patients out of the hospital system, the RAE program is an outgrowth of a reform effort within the state’s Medicaid program, which is called Health First Colorado.

From its inception in 2018, this restructuring effort promised to have a significant impact on Colorado residents living in rural areas, since three quarters of a million people reside in rural-designated areas of the state. About 300,000 of these Coloradans are enrolled in Medicare, Medicaid, or both. Indeed, states an article on the Colorado Health Institute’s website, “Rural Colorado counties have higher rates of public insurance enrollment than their urban counterparts (38.7 percent compared to 33.5 percent).”

Like the AHCM, the RAE program – which includes seven distinct entities and is part of a larger, two-part restructuring of Colorado’s Medicaid program called the “Accountable Care Collaborative” – is turbocharging integration of primary care and behavioral health services on behalf of its Medicaid population. The program’s core goals align with two major aims of population health management:

“The RAEs’ responsibilities include ensuring Health First Colorado members have access to primary care and behavioral health services, coordinating members’ care and monitoring data to ensure members are receiving quality care.”

The emphasis here is mine. In addition to prioritizing access to care and data monitoring, bonus payments are used to incentivize primary care doctors affiliated with the RAEs to improve the care they provide. This risk-sharing aspect works to hold the private organizations that coordinate the efforts of the state’s RAEs (and administer Medicaid services) accountable for providing quality care. Accountable care is another hallmark of value-based payment models, which in turn are closely associated with population health management.     

Nonprofit Hospitals

Besides payers, tax-exempt hospitals can also be drivers of population health adoption. In order to keep their tax-exempt status under Section 501(c)(3) and Revenue Ruling 69-545PDF, hospital organizations are required to meet certain requirements that address the health needs of the communities in which they operate.

Among other requirements, the IRS mandates that every three years these tax-exempt organizations conduct a Community Health Needs Assessments, or CHNAs. Further, after completing a CHNA, these organizations must develop a plan to remediate identified problems.

I wrote about CHNAs in a prior post so I won’t go into much depth here, but suffice it to say that CHNAs “Take into account input from persons who represent the broad interests of the community served by the hospital facility, including those with special knowledge of or expertise in public health.” In addition to the views of public health authorities, insights from representatives of “medically underserved, low-income, and minority populations” are also taken into account. 

Although CHNAs – along with an associated community benefit mechanism called Community Health Implementation Plans (CHIP) – should be essential tools in any nonprofit hospital’s PHM arsenal, some feel they’re not living up to the hype. As the authors of a 2020 scoping study pointed out, while CHNAs and CHIP hold great promise for incentivizing population health adoption, some articles “continue to suggest that non-profit hospitals should take a larger role in population health improvement and to use community benefit as cornerstone of such work.”

In 2021, of the 85% of American hospitals designated as “community hospitals” (defined as “All nonfederal, short-term general, and specialty hospitals whose facilities and services are available to the public”), there were 2,978 nonprofit hospitals in the U.S., as compared to 1,235 for-profit hospitals and 944 in the “state/local government hospital” category. With the majority of hospitals having to fulfill a mandate to segment out and prioritize care delivery to the distinct patient populations they serve, nonprofit hospitals should act as an incubator for effective population health management.

There is much more ground to cover when it comes to PHM in rural America, and it’s a subject to which I’ll likely return in the future. I’ll leave it there for now, but will continue to learn all I can about this fascinating topic.

PHM and Rural Healthcare — Part 1

With the recent passage of the bipartisan fiscal year 2023 omnibus appropriations legislation, and considering its positive impact on rural healthcare, I figured this was a good time to focus on rural population health. In this first of a two-part series, I’ll focus on the state of healthcare in rural America, and in the second part I’ll look at why a population health approach in rural settings is being touted by many as a viable solution to improving overall health.

Among many other provisions in the $1.7 trillion appropriations bill, there are two in particular that bolster rural health: an extension of flexibilities that ensure continued access to telehealth put in place during the Covid-19 public health emergency (PHE), as well as new rules that, while soon ending restrictions against disenrolling Medicaid beneficiaries who no longer qualify during the coverage redetermination process, bolsters support for other underserved groups.

I’ll come to why these two aspects of the new law are of particular note when it comes to rural population health a little further on. But first, let’s establish a baseline understanding of what healthcare looks like in more rural areas. 

Rural Health in the U.S. 

For starters, let’s define what we mean by “rural.” The authority for this in the United States is the U.S. Census Bureau, which considers rural areas to be “any population, housing, or territory NOT in an urban area.” This is obviously tied to their definition of an urban area, which the Bureau has broken down into two parts:

  • “Urbanized Areas” have a population of 50,000 or more.
  • “Urban Clusters” have a population of at least 2,500 and less than 50,000.

Now that we understand the difference between what constitutes an urban versus a rural environment, let’s look at health in these areas by the numbers. Depending on the source, between 15% and 19% of the U.S. population lives in rural counties. Despite these differing population estimates, there is a broad consensus that rural residents tend to be older, sicker, and less affluent than their urban counterparts. As of 2015, the median age in rural settings was 51, whereas it was 45 in more urban places.

Educational attainment also maps well onto underserved rural populations, with districts populated by those with lower academic achievement often experiencing worse health outcomes than higher-achieving districts. According to the County Health Rankings & Roadmaps, which is put together by the University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation (and which I discussed in a previous blog post), rural counties are “disproportionately represented among counties with school funding deficits, particularly those with large deficits. On an annual basis, 70% of counties with deficits of more than -$4,500 per student are rural.”

A key marker of health, the uninsured rate, can also help explain the health disparities that exist between urban and rural counties. Between 2010 and 2019, the uninsured rate for rural residents was about 2-3 percentage points higher than those in urban areas. Provisions of the American Rescue Plan (which I wrote about in a previous post) and Medicaid expansion have improved things somewhat, but uninsured rates remain disproportionately higher in states that haven’t yet expanded Medicaid. And perhaps most significantly of all, between 1999 and 2019 the age-adjusted death rate in rural areas worsened from 7% above that in urban areas to 20% higher.

As with other underserved groups, the COVID-19 pandemic shone a spotlight on rural health disparities and, in many cases, made them even worse. Rural health researchers with the National COVID Cohort Collaborative (N3C) examined data collected during much of the pandemic, and found that health outcomes in rural areas were far inferior to those in urban centers. The authors found the following:

“In rural communities that are near urban areas, people with COVID-19 were 18% more likely to be hospitalized, and those who lived far from urban areas were 29% more likely to be hospitalized. Mortality rates showed an even sharper disparity. After adjustments, rural residents — no matter how near they lived to urban areas — were about 36% more likely than urban residents to die within 90 days after COVID-19 hospitalization.”

These numbers are unbelievably high, and they bear some exploration. But before we go any further, let’s define what we mean by “health disparities.” There are a number of definitions out there, but the one I prefer comes from the U.S. Department of Health and Human Services’ (HHS) Healthy People 2030 initiative: 

“(A) particular type of health difference that is closely linked with social, economic, and/or environmental disadvantage. Health disparities adversely affect groups of people who have systematically experienced greater obstacles to health based on their racial or ethnic group; religion; socioeconomic status; gender; age; mental health; cognitive, sensory, or physical disability; sexual orientation or gender identity; geographic location; or other characteristics historically linked to discrimination or exclusion.”

Populations in Rural America

Although some folks may have a preconceived notion that rural America is mono-cultural and its landscape reflects a bygone era, that is a bit of a misconception. While it’s true that, as mentioned above, rural areas have higher poverty rates and that they’re populated by more elderly residents — and that on average the populations there are less diverse than their urban counterparts — it’s also true that, for instance, these areas are becoming more racially and ethnically diverse by the year, with people of color making up 24% of rural America in 2020. This represents an increase of 3.5 percentage points between 2010 and 2020.

In other words, a number of subpopulations reside outside the borders of most cities, many of which can be seen to one degree or another in urban settings as well. This segmentation is an important consideration when applying a population health approach to improving health outcomes, which we’ll further explore in a subsequent blog post. For now, let’s be content to say that individual subpopulations like military veterans or people in certain age groups can be targeted for healthcare interventions. 

So with limited resources and, on average, a higher disease burden to contend with, how do rural healthcare providers keep these populations healthy? For some insight into this, I’ll spotlight two provisions in the new omnibus appropriations bill, telehealth and an altered Medicaid coverage redetermination process, to see how the federal government is helping rural residents continue to fight the pandemic and improve health outcomes. 

Telehealth and Rural Residents

Gaining a better understanding of how these two provisions from the omnibus bill work will provide a backdrop to how creative thinking can be employed to shore up healthcare gaps in rural America. First off, let’s look at telehealth. During the early part of the PHE, when social distancing was key and it was advisable to avoid elective procedures in medical settings, telehealth became an important mechanism for delivering care.

A report by the Assistant Secretary for Planning and Evaluation noted that “The use of telehealth services surged during the COVID-19 pandemic. A 2020 study found that telehealth use during the initial COVID-19 peak (March to April 2020) increased from less than 1 percent of visits to as much as 80 percent in places where the pandemic prevalence was high, and a recent ASPE report found that Medicare telehealth utilization increased 63-fold between 2019 and 2020.”

As the PHE stretched on, telehealth became a significant component of healthcare: telehealth utilization peaked at more than 32% of Medicare claims in April 2020, then came down to between 13% and 17% by July of 2021. This is still orders of magnitude higher than its usage before the pandemic started. According to the Harvard Business Review, “The investments that have already been made in technology and training were only possible due to the waiver of a mountain of federal regulations that have hampered telehealth adoption for decades.”

The bottom line is this: telehealth adoption has opened up care access to underserved groups in general, and to rural populations in particular — especially as it pertains to Medicare and Medicaid beneficiaries. For the Medicare population, the omnibus spending legislation extends by two years certain telehealth flexibilities. Among other provisions, during the PHE (which is still ongoing at the time of this writing), beneficiaries are permitted to remain in their homes for telehealth visits that are reimbursed by Medicare; previously, beneficiaries needed to travel to a healthcare facility in person for these services to be covered.

In addition, telehealth visits have been authorized to take place on smartphones for those enrolled in Medicare, as opposed to being restricted to only taking place using equipment with both audio and video capability. This is key for rural residents, who on average have lower levels of access to broadband internet than do their urban counterparts. Indeed, according to the Federal Communication Commission’s 2020 Broadband Deployment Report, “22.3% of Americans in rural areas and 27.7% of Americans in Tribal lands lack coverage from fixed terrestrial 25/3 Mbps broadband, as compared to only 1.5% of Americans in urban areas.”

For Medicaid enrollees during the PHE, telehealth extensions have been similarly effective, and they’ve been ubiquitous:

“All 50 states and DC expanded coverage and/or access to telehealth services in Medicaid. States have broad authority to cover telehealth in Medicaid and CHIP without federal approval, including flexibilities for allowable populations, services and payment rates, providers, technology, and managed care requirements.”

Although these allowances may be tied to either the federal or state PHEs, states are planning to enshrine some Medicaid telehealth flexibilities into law.

Medicaid Coverage Redetermination

Another change enacted during the PHE that impacted rural residents was a change in Medicaid coverage redetermination requirements. At the beginning of the pandemic in March of 2020, Congress enacted the Families First Coronavirus Response Act (FFCRA), a piece of legislation that I wrote about in a previous blog post. Many of the provisions of this law, including enhanced federal funding for Medicaid and also for maintenance of effort (MOE) protections, were put in place with the understanding they’d be phased out at the end of the PHE.

More specifically, for anyone enrolled in Medicaid as of March 18, 2020, or for anyone newly enrolled between that date and the end of the national PHE, the FFCRA made it so states could not disenroll any beneficiary. CMS permitted states to “suspend or continue income checks or redeterminations during the emergency,” and conduct regular renewals. But importantly, the MOE prevents states from dropping Medicaid coverage during the PHE. Put another way, it mandated that these enrollees be given continuous eligibility unless they decided to move out-of-state, or they requested voluntary termination from the program.

Since nearly a quarter of individuals under the age of 65 who reside in rural areas are covered by Medicaid, and since 22 percent of them are dually enrolled in Medicaid and Medicare, the FFCRA had an outsized impact on rural residents. That said, the Consolidated Appropriations Act, 2023 actually makes changes to “the continuous enrollment condition and availability of the temporary increase in the Federal Medical Assistance Percentage (FMAP) under section 6008 of the Families First Coronavirus Response Act” such that it separates the end of the continuous enrollment condition from the end of the PHE, ending that condition on March 31, 2023.
This will allow states to terminate Medicaid enrollment of those beneficiaries who no longer meet Medicaid eligibility requirements. While in many ways this move is suboptimal, the coverage redetermination process restart will likely result in savings that will in turn be put toward funding other Medicaid coverage priorities such as “making permanent the postpartum coverage state option and requiring all states to establish 12 months continuous eligibility for children.”

The kind of creativity and flexibility demonstrated by the administration in extending telework allowances and amending the Medicaid coverage redetermination process during the PHE should be used when thinking about how to apply a population health mindset to rural healthcare delivery. I’ll examine how this might work in my next blog post.

Top Posts of 2022

To round out the year, I thought I’d choose my favorite blog posts in a “Best of 2022” post. This year brought so many interesting aspects of U.S. healthcare to light, from the unclear future of multiple plans meant to help folks through the pandemic, to the U.S. Government Accountability Office’s research highlighting how maternal outcomes worsened during the pandemic. So here, without further ado, and in no particular order, are my picks for top blog posts of the year:

Benchmarking APMs

How do insurance plans structure arrangements that encourage healthcare providers to deliver increasingly better care year after year, while also not sacrificing quality? That’s where benchmarks come in. In this post I get into the nitty-gritty of how benchmarking works, and how healthcare benchmarks work in different payment models.

Maternal Mortality and PHM part 1

The U.S. leads all wealthy nations in terms of maternal mortality rates. This statistic is hard to swallow, especially since two in three such complications are preventable. In part one of a two-part series, I explore why maternal and infant health in the U.S. is substandard when compared to other peer nations.

Maternal Mortality and PHM part 2

In this second installment of a two-part series, I look at how population-level care strategies might optimize maternal and newborn health. 

What is a Health Insurance Exchange? 

So what is a health insurance exchange anyway? We’ve all heard about them, but admittedly I never looked into them in much depth. In this installment of my “What Is…?” series, I put a magnifying glass up to what these exchanges are, and how to maintain coverage if legislation like ARPA and FFCRA expire. 

Mental Health and PHM

Counter to the widely-accepted view that mental and physical wellbeing are inextricably linked, care for mental and physical conditions is often not coordinated between healthcare providers. Indeed, behavioral health services (which include both mental health and substance use treatment services) are often located in geographically separate locations from physicians’ offices. In this post, I look at the challenges of matching people up with appropriate mental healthcare in such a system.

Place-Based Care in the UK

In this deep-dive piece, I delve into an approach to PHM undertaken by National Health Service (NHS) England, namely its so‐called “Place‐Based Systems of Care” program. Somewhat related to the piece linked to above about mental health and PHM, the Brits have decided that co-locating care along geographic lines makes the most sense when rationing limited healthcare resources. 

Helping Children with Asthma

Being as I live in the Washington, DC area, this post is particularly close to my heart. In it, I examine an ongoing program called the Healthy Housing Virtual Home Visiting (VHV) Program, which is a collaboration of medical and housing experts. The group works to repair homes in low socioeconomic areas of Washington, DC to cut down on the level of allergens and improve the health of children with asthma.  

Involving Specialists in PHM

This post looks at a Harvard Business Review article that makes a compelling argument for health systems to recruit specialists to become entry points into the population health management ecosystem. In some instances, specialists may stand a better chance at enrolling patients into early disease management programs that help address negative social determinants of health.

Benchmarking APMs

With the release of the CY 2023 Medicare Physician Fee Schedule Final Rule on November 1st, I saw a flurry of online activity about how, after much consideration, CMS seemed not to have accepted many of the comments offered during the open comment period. In consequence, they appeared not to have changed much of anything from their Proposed Rule back in July.

One Twitter thread on this topic caught my attention in particular, and made me curious about how alternative payment models (APMs) – and specifically population-based payment (PBP) models that require plans and providers to manage total cost of care – will be financed going forward:

I’ve been studying APMs for a while now, but until I saw this tweet, I never drilled down into the particulars of how CMS decides how to incentivize plans and providers that rely on PBPs to perform at high levels, a process called benchmarking. Here is my take on how the process currently works, and how it might work going forward.

Benchmarking Basics

Before we delve too far into the complexity of scoring healthcare providers and reevaluating performance standards, it might be good to cover some basics. For starters, although the term “benchmarking” emerged in the 1800s during the industrialization of weapons manufacture, the concept can be applied to a range of disciplines where results of one endeavor are compared to those of another. For example, some common continuous improvement tools like client surveys and SWOT analyses make use of benchmarking.

Philip de Vroe, a.k.a. the Finance Storyteller, has a good primer video on benchmarking, which he describes as “Making meaningful comparisons to others, and identifying opportunities to improve.” Deciding on which peer groups against which to compare your performance, along with focusing on the drivers of that performance, are key elements of benchmarking. This process is one way, de Vroe says, to identify a current leader in a given field, zero in on any gaps between you and the market leader, and take action to eliminate the gap.

Although benchmarking has been around in healthcare for a while (for example, the federal government has been keeping track of total health care spending in the U.S. via the National Health Expenditure Accounts since 1960), the complexity with which insurers now deploy the concept when entering into shared-risk arrangements with providers is relatively new. Benchmarking has evolved into a solution for quantifying and addressing areas like cost, care quality, and other gaps on the individual physician, practice, hospital, or health system level.

Although much of this blog post is devoted to examining benchmarks assigned to large groups of physicians, hospitals, and health systems, on the individual practitioner level, benchmarks can act as guardrails that help clinicians monitor and improve key metrics in the clinical, operational, financial, and equity spaces, among others. Apart from keeping track of the day-to-day operations of clinicians, the term “benchmarking” is also flexible enough to describe efforts such as the Physician Practice Benchmark Survey, an ongoing initiative of the American Medical Association to track “the practice arrangements and payment methodologies of physicians who take care of patients for at least 20 hours per week and don’t work for the federal government.”

A Role for ACOs

Speaking of payment methodologies, benchmarking has become a key way insurers gauge the provision of value-based, cost-effective care with respect to payment arrangements like MIPS, advanced APMs, and a subset of APMs called accountable care organizations (ACOs). I’ve mentioned ACOs a few times in past blog posts (here, here, and here), but I’d like to elaborate on them a bit more here and examine how incentive structures and attendant bonuses and penalties are derived from contract period to the next.

The concept of the ACO in all its complexity deserves its own blog post, but for our purposes here, we can think of ACOs as “groups of doctors, hospitals, and other health care providers, who come together voluntarily to give coordinated high-quality care to their Medicare patients.” Best described as Medicare population-based payment models, or PBPs (I wrote about PBPs in a past blog post), ACOs incentivize providers to efficiently manage total cost of care, i.e. to provide the right care at the right time and avoid duplication of effort – a concept called “coordinated care.” Any cost savings realized while not sacrificing quality is passed along to the ACO.

It’s important to note that over the past decade or so, CMS has increasingly pushed affiliated physicians to enter into risk-sharing arrangements, with an ultimate goal of 100 percent of traditional Medicare (TM) beneficiaries being covered under an accountable care relationship by 2030. One of the main vehicles for delivering this result, if it is to be achieved, will be ACOs.

From the outset, however, ACOs have experienced mixed results, with “fierce debates over the ability of these organizations to meet their performance goals, as well as any unintended consequences that could adversely impact members of the health supply network.” Overall momentum in the adoption of APMs has slowed in recent years, and savings generated by ACO models haven’t kept pace with expectations. 

Still, ACOs and other APMs likely represent the best chance U.S. healthcare has of decoupling from the fee-for-service (FFS) payment model and becoming more efficient at delivering high-quality care. Physician-led ACOs are leading the way when it comes to delivering cost savings coupled with improved outcomes, often producing better results than hospital-led ACOs. A key factor in their success appears to be longevity of program participation mixed with both a nimbleness to learn how care partners achieve efficiency levels, and a willingness to implement these approaches.

So how do insurance plans structure arrangements such that they encourage providers to find new ways of providing increasingly better care while also not sacrificing quality? That’s where benchmarks come in.

Benchmarking ACOs

As population-based payment models like ACOs have matured, so too have their methods of motivating healthcare providers to provide top-notch services while also being mindful of costs. This concept is best encapsulated in the Institute for Healthcare Improvement’s Triple Aim, where a balance is struck between the patient experience of care, healthcare outcomes, and reducing per capita costs. One useful definition of benchmarks as they relate to ACOs has been provided by the National Association of ACOs (NAACO): 

“ACO performance is measured using a multi-step process that evaluates an ACO’s effectiveness in lowering expenditures for a group of assigned beneficiaries against a financial benchmark reflective to historical costs. Benchmarks are initially established for new ACOs, updated during agreement periods and reset or rebased when ACOs enter subsequent agreement periods.”

As things stand now, one issue that challenges broad uptake of the ACO model is this “rebasing” process. We’ll get more into rebasing a little further on, but for now, suffice it to say that rebasing occurs when benchmarks are adjusted as a result of an organization’s past success or failure at controlling spending. I’m using the term “success” loosely here, because as we’ll soon see, physicians who manage to lower costs while maintaining quality care are often “penalized” by having the bar for success raised ever higher, a phenomenon called “ratcheting”.

Although often a net positive in the short run because it helps lower program spending, in time this upward adjustment of standards can dampen plan or provider incentives to participate in an ACO as it becomes increasingly harder to identify new efficiencies. In consequence, new opportunities to save money dry up, leading to lower levels of shared savings. This vicious spiral often leads to plans or providers being less inclined to participate in ACOs over the long haul.

Despite this suboptimal playing field, CMS has persisted with the rebasing process. Reasons for this are varied, but the authors of a 2021 paper discussing the merits of the Medicare Advantage (MA) program versus PBPs explain it this way:

“Medicare can save money if the benchmark is set below what would otherwise have been spent, if Medicare keeps a large enough share of any savings, or if any efficiencies in care delivery spill over to populations outside the PBP model. Higher benchmarks induce plan or provider participation but increase program expenditures. Lower benchmarks may reduce available benefits in MA or reduce plan participation in MA or provider participation in voluntary ACO models.”

We’ll talk about spillover effects in a little bit, but suffice it to say that relying on the efficiencies you’ve realized in caring for ACO beneficiaries to spill over to non-ACO patients may not be the most optimal strategy in building an enduring framework for value-based care delivery.

This brings up an interesting point: hospitals and health systems administer care to patients belonging to a patchwork of different insurance carriers (and often to patients who have no coverage at all). So does that mean they cater to ACO beneficiaries differently than everyone else? And if so, how do they pay their contracted providers within the ACO versus non-ACO practitioners? The paper quoted above provides a useful explanation of how this works: 

“ACOs typically operate on budget-based versions of PBP, where FFS payment is used to pay all claims, but bonuses (or penalties) are paid to ACOs at the end of the year based on accrued FFS spending relative to a benchmark.” 

So if I’m understanding this correctly, providers who work with ACO beneficiaries are paid using a FFS model, and the rewards and deductions are handled separately. It’s an interesting idea, but isn’t one of the core purposes of ACOs to transition medicine away from the FFS model?

The short answer, as far as I can tell, is yes; however, until FFS no longer dominates the reimbursement landscape, hospital and health system executives and their insurer partners have to work within the system to effect change. To do this, many ACOs predicated on budget-based payment systems like bundled payment, capitation, and shared savings arrangements base bonus payments on projected FFS spending. This configuration abrogates the need for ACOs to contract with non-ACO providers while maintaining the overall value-based incentive structure.

External Empirical Benchmarking

As the above example illustrates, there are any number of approaches one can take in tracking an ACO’s performance over time. But even so, there are three forms of benchmarking that are commonly used: empirical benchmarks, bidding-based benchmarks, and administratively set benchmarks. Since empirical benchmarking currently dominates the APM landscape, let’s focus on it for the remainder of this post.

For PBP in Medicare, empirical benchmarking has proven attractive to many ACOs up to this point. It’s important to note that benchmarking for MA programs is slightly different than benchmarking for ACOs, with empirical benchmarks for MA programs called “external” benchmarks while those for ACOs are known as “circular” benchmarks. Despite these differences, however, there are a number of similarities in the ways MAs and ACOs chart performance and, as a result, they are often treated almost synonymously.

In both approaches, CMS seeks to save money by sharing in the savings when MAs or ACOs spend below their benchmarks, or by charging them when they overspend. As mentioned earlier, CMS also can benefit financially from changes in practice patterns brought about by the MAs or ACOs that spill over to other non-attributed patients. Although there have been some successes in using benchmarking, it has been argued that, particularly in the case of the Medicare Shared Savings Program (MSSP), which is a type of ACO, results have been skewed for a variety of reasons

Starting with MA benchmarking, an external sector must first be chosen that provides a status quo that risk-based contracts aim to beat. These benchmarks are set using “observed spending.” The “external sector” against which MA programs are measured has often been TM populations. In other words, they factor in a given entity’s county-specific benchmark for non-MA attributed Medicare patients. The benchmark is a multiple of the average spending in the TM sector for each county in a given plan’s service area, often with a slight discount built in. This approach is different from that taken by ACOs because in the MA configuration, a given MA’s historical spending patterns are not factored into the benchmark.

In the early days of benchmarking the MA program, the vast majority of Medicare patients didn’t fall under the auspices of an alternative payment model. For this reason, comparing their performance to TM beneficiaries provided ample opportunities to out-perform the benchmark. At that time, benchmarks were set at 95% of spending in the TM system. Despite the passage of time, and even though legislation has been instituted to bolster program participation, the tenant of basing benchmarks on TM has endured for both MA programs and ACOs.

This may prove to be a problem going forward, however, because when the TM population against which an MA is benchmarked shrinks too much, the benchmarks can fluctuate unpredictably, thereby invalidating a core purpose of these payment models which is to stabilize revenue. As a result, plans may find it too hard to endure such vicissitudes and choose to drop out of the program at the end of their contract period.

If, on the other hand, plans persist and remain a part of the payment model, this can become a problem not just for participating physicians but for patients, particularly if patients in TM prove less costly over a given time period. In this instance, lower benchmarks mean lower levels of benefits and higher premiums charged to plan beneficiaries. If costs pass a threshold beyond which patients cannot afford their care, this becomes an access to care issue. So as more MA programs enter the market, it becomes less useful to rely on external empirical benchmarks to chart performance.

Circular Empirical Benchmarking

Now let’s turn to empirical benchmarking with regards to ACOs, a practice that has been termed “circular” benchmarking. As mentioned above, like those of MA programs, ACO benchmarks are based partly on TM spending in the ACO’s service area. But unlike “external” MA benchmarks, circular ones also take into account an ACO’s historical spending, blending it with TM spending in a given market to arrive at a hybrid number. Adjustments in this kind of benchmark are informed by either projected or actual TM spending growth.

The component of the benchmark derived from the ACO’s historical spending patterns comes into focus when it’s time for a new contract period to begin. As the authors of the 2021 paper put it, “When an ACO transitions to a new contract period, the ACO-specific component of the benchmark is rebased such that the spending in the performance period of the first contract period contributes to the baseline of the next contract period. The regional component of the benchmark rises with regional TM spending and receives increasing weight over time, up to 50%.”

This creates a circular pattern in the sense that historical spending feeds directly into the calculation of subsequent years’ new benchmarks. So one major issue with circular benchmarking is the “ratchet” effect I touched on earlier. Another issue arises from the regional component in this scenario. Specifically, if one ACO (or a small group of ACOs) dominates a certain market, they can basically dictate where the benchmark is set, discouraging wider participation.

Aside from what’s been mentioned above, there are additional advantages to using empirical benchmarks, which include the following:

  • When ACOs stay in a given market instead of dropping out due to incurring too many penalties, the regional component of empirical benchmarks exposes ACOs to competitive pressures that force them to emulate the success of other ACOs in their market, which could improve overall savings (although there’s a fine balance, as mentioned above, and dominance by one ACO or a small cadre of ACOs can have undue influence on a given region).

  • Empirical benchmarks are flexible in the sense that they can be adjusted for forces that affect spending outside an insurer’s or provider’s control such as shifts in the economy, novel technologies coming online, and changes to care standards.

Despite these positive aspects, I have to wonder if empirical benchmarks will be around for the long haul. As I mentioned earlier, CMS has set a goal of 100 percent of TM beneficiaries being covered under an accountable care relationship by 2030. And with around 68% of Medicare beneficiaries who are enrolled in both Medicare Part A and B being currently enrolled in MA or attributed to an ACO or direct contracting entity, empirical benchmarks may cease to be useful in the near future as more Medicare patients are siphoned away from TM.

There’s a lot left to be said about benchmarking, including an examination of types of APMs and their varying levels of success with respect to using benchmarks. A particularly interesting example of this is the aforementioned MSSP, which has been called an “off ramp” to relying on FFS. But I’ll reserve evaluation of various benchmarking configurations for another time, and will continue learning all I can about this fascinating topic. 

Maternal Mortality and PHM — Part 2

Since the U.S. Government Accountability Office (GAO) recently released a report titled “Maternal Health: Outcomes Worsened and Disparities Persisted During the Pandemic,” I thought this would be a good opportunity to write the promised follow-up to my first blog post about maternal mortality and population health. After doing further research since I first published that post, in retrospect a more accurate title for the series would’ve been “Maternal, Infant, and Population Health” since maternal health and newborn health are often closely linked, and maternal mortality only tells part of a larger story.

But what’s done is done, so let’s proceed.

In my first post I largely focused on strategies for improving maternal health before, during, and after giving birth, mostly zeroing in on the utilization of midwives. Contrary to the post’s title, however, I didn’t introduce much of a population health perspective. So let’s take this a step further and look at how population-level care strategies might optimize maternal and newborn health.

A Broken System

Since I delved pretty far into how the U.S. stacks up against other peer countries in terms of our healthcare system (spoiler alert: we rate significantly worse in almost every category, including maternal health), I won’t cover the same ground here. To help orient us, though, I do want to offer a snapshot of where we are now that the COVID-19 pandemic is (hopefully) receding, and the GAO report helps us do that.

The authors of the report analyzed CDC data through much of the pandemic and compared it to the same data sets collected in pre-pandemic years. They found some interesting things, all of which point to the fact that, as the report’s title suggests, maternal health declined during the pandemic. Here are their top-line findings:

  • Maternal deaths increased during the pandemic compared to 2018 and 2019
  • COVID-19 contributed to 25% of maternal deaths in 2020 and 2021
  • The maternal death rate for Black or African-American women was disproportionally higher compared to White and Hispanic or Latina women

Access to care was flagged as a major contributing factor to these outcomes. And key to any discussion of population health, negative social determinants of health (SDOH) played a central role:

“Stakeholders and Department of Health and Human Services (HHS) officials told GAO that the pandemic exacerbated the effects of social determinants of health—factors such as access to care, transportation, or technology; living environment; and employment—on maternal health disparities.”

Given that the U.S. healthcare system was not well-positioned to intervene in social drivers of maternal health problems before the pandemic, lockdowns and other measures meant to protect people’s health ironically led to a worsening of the situation. So that we don’t drop the ball again and allow pregnant women to go untreated until it’s too late, how can we learn from our failures over the past few years? I believe that a population health approach holds many of the answers.

Incentivizing Maternal and Infant Care

One doesn’t have to look farther than the annual measurement efforts done by the Health Care Payment Learning & Action Network (HCPLAN) to see that among other insurers, Medicare and Medicaid are pushing medicine toward two-sided risk alternative payment models (APMs), where both insurers and providers bear a portion of the financial risk for treating patients, with special emphasis on so-called Population-Based Payment Models, or PBPMs (which I discussed in a previous blog post).

Between 2019 and 2020, U.S. healthcare payments flowed through two-sided risk APMs at rates that increased from 16.5% to 17.9% of payments. Yes, PBPMs only made up a small fraction of these payments, but it’s clear CMS is pushing in this direction. As promising as two-sided risk APMs are to improving care of all kinds, including maternal care, in their current form they only go so far, often focusing on process measures instead of more holistic care that targets access-to-care issues.

One possible solution to this issue would entail combining something called “bundled payments” with a PBPM such as an accountable care organization (ACO). This idea, which is being championed by the folks at the Duke Margolis Center for Health Policy, involves some outside-the-box thinking as it entails aligning disparate stakeholders to ensure a continuity of care of both mother and child over a much longer time horizon that often currently exists.

The Importance of Bundling Care defines bundled payments like this: “A payment structure in which different health care providers who are treating you for the same or related conditions are paid an overall sum for taking care of your condition rather than being paid for each individual treatment, test, or procedure. In doing so, providers are rewarded for coordinating care, preventing complications and errors, and reducing unnecessary or duplicative tests and treatments.”

This coordination of care is key to improving maternal and infant health – in the form of physical care and, for the mother, behavioral health services – before, during, and after pregnancy. including coverage for whole-person care where midwives and doulas play a critical role (I discussed the central importance of midwives to maternal and infant health in my previous post on the topic).

On the payer side, continuity of care is very important to shoring up healthcare disparities, considering that so-called “Medicaid enrollment churn” can lead to disruptions in care; for example, one study found that 55 percent of women enrolled in Medicaid or the Children’s Health Insurance Program (CHIP) between 2005 and 2013 experienced a coverage gap in the six months prior to giving birth. And since Medicaid covers 42 percent of all births, CMS can exert significant influence in improving longitudinal health outcomes and reducing healthcare disparities, since many of those covered by Medicaid for pregnancy-related services are in minority or rural populations.

As things stand, relatively few perinatal bundles of care focus on intervening in social drivers of health. Indeed, as the Duke report makes clear, “most existing evaluations of perinatal bundles focus on performance measures such as spending, care processes, and limited pregnancy outcome measures (such as rates of early elective delivery, vaginal delivery and cesarean delivery), but not on patient experience or broader maternal health outcomes like morbidity and mortality.”

Returning to the idea of combining bundled payments with PBPMs, the authors of a 2021 JAMA study found that “compared with inclusion in bundled payments alone, simultaneous inclusion in both ACOs and bundled payment programs was associated with lower institutional postacute care spending and readmissions for medical episodes and lower readmissions but not spending for surgical episodes.” In other words, maternal and infant patients who receive care simultaneously under ACOs like a PBPM and bundled payments can experience optimal outcomes. This approach seems to hold great potential to shore up gulfs in coverage and longitudinal care, and it’ll be interesting to watch how it progresses.

Vox Populi

Can a population health approach to healthcare exist beyond the bounds of organized medicine and public health? Technically, at least right now, the answer appears to be no. Although there is no single agreed-upon definition of population health, the one many point to was posited by Kindig and Stoddart in their seminal 2003 paper titled “What Is Population Health?”:

“We propose that the definition be ‘the health outcomes of a group of individuals, including the distribution of such outcomes within the group,’ and we argue that the field of population health includes health outcomes, patterns of health determinants, and policies and interventions that link these two.”

Without a way to quantify and track health outcomes not just of groups, but of the distribution of health outcomes within those groups, a healthcare solution cannot properly be called population health. Still, this strikes me as rather limiting; is preventative, segmented care only the domain of public health professionals, physicians, and academics…or can anyone with the right mix of ambition and good intentions take it upon themselves to intervene and improve upstream negative social determinants of health (SDOH) irrespective of metrics?

And if such interventions prove successful, can they not in turn be thought of as adjuncts to a population health model of care delivery? To me, any endeavor that helps people live their best life is worth trying, whether or not it proceeds within a deploy-measure-iterate model such as that outlined in the Institute for Healthcare Improvement’s “10-Step Path to Progress” in population health (details on page 7 of the document).

That being said, let me be clear that I’m not a physician or a mental health professional, and the thoughts in this post are strictly the result of my own research. None of this should be taken at face value, and in some cases – for instance, if one has profound mental health issues – seeking help from a trained professional is the right way to go. It’s clear that depending on one’s circumstances, agency in one’s own healthcare will vary.

But for those who feel a persistent, non-clinical level of melancholy, I’m encouraged that the possibility exists to take the upper hand and improve levels of contentment. This mindset aligns with the “self-care” movement, which has been defined as “the ability to care for oneself through awareness, self-control, and self-reliance in order to achieve, maintain, or promote optimal health and well-being.” This outlook reflects a growing awareness that people don’t have to necessarily fill a prescription to optimize their experience of life.

Part and parcel of this DIY approach (again, within reason) is, I believe, a loosening of the definition of population health – or at least an expansion of the definition that includes grassroots initiatives that may not tick every box of an improvement regimen. One aspect of this more inclusive description involves not always assigning metrics to every aspect of one’s life. While some people are motivated by numbers (as we’ll see in a moment), for those who don’t look at health maintenance as a competition, moving the needle on improving population health will likely be more qualitative. 

Health By the People, For the People

To set the stage for our discussion, let’s talk for a minute about how metrics in self-care can induce health improvements. For those who like to quantify improvement in a way that keeps them in the driver’s seat, the healthcare landscape of today truly differs from that of even a few years ago. With respondents to a recent survey from the Deloitte Center for Health Solutions saying they’d “be ‘likely to’ or ‘maybe would’ use retail clinics for preventive care (55%) or mental health care (47%),” it’s becoming clearer every day that people are more willing to try new approaches to healthcare that are more tailored to their own lifestyles. 

Put another way, a clear majority of people are willing to at least entertain the idea of seeking healthcare at places like CVS and Walmart, and nearly half of respondents said the same thing about mental healthcare. This makes a sort of sense, given that Walmart stores are often closer to many people’s homes than their doctor’s office.

This is a big change, and it doesn’t stop with retail medicine. According to Insider Intelligence, “use of wearable technology has more than tripled in the last four years and “more than 80% of consumers are willing to wear fitness technology,” signaling broad interest in people monitoring their own health. This includes tech we’re all familiar with like FitBits and smart watches, but extends to wearable electrocardiogram and blood pressure monitors, and even self-adhesive patches that collect data on a person’s biological signals.   

Opinions may vary on the effectiveness of self-monitoring tech, and I need to do more research before coming to a conclusion on it. But the more I read about it, the more optimistic I’m becoming that many of us can (or will soon be able to) exercise a degree of control over our health status with the help of personalized health technology. 

Another way in which people are practicing so-called DIY population health is by banding together to overcome certain shared barriers. And these barriers, while related to medical conditions, often reside outside of clinical settings. In addition, given their improvised nature, the practitioners of this approach often don’t have the means to collect and analyze data to track health improvement. Although stories of success don’t always guarantee a one-size-fits-all approach, a couple of real-life examples illustrate how this organic, crowd-sourced approach to addressing negative SDOH can be done well. 

Men’s Sheds

One interesting illustration of this can be found in England. In that country, as in the United States, an epidemic of loneliness has swept through in recent years. Made worse by the COVID-19 pandemic, many vulnerable groups have been hit particularly hard. One of these groups, the elderly, are in a class by themselves: the NHS states that “more than 2 million people in England over the age of 75 live alone, and more than a million older people say they go over a month without speaking to a friend, neighbour or family member.”

One of these people was Philip Jackson, a man born in England who moved to Australia in adulthood before returning to his homeland in old age. Although Jackson expected a level of familiarity in returning to his native town, he was met by the unwelcome fact that everyone he’d grown up with had either left town due to a decline in industry or passed away. This left Jackson unexpectedly rootless and lonely. 

But then something interesting happened: realizing he wasn’t the only lonely person of a certain age, Jackson recalled hearing about The Australian Men’s Shed Association. The organization, which grew out of a desire to help men form social bonds with other men through “communal woodworking” and thus improve their mental and physical health, defines their modus operandi this way: “A good Men’s Shed has a Management Committee that has developed a safe and happy environment where men are welcome to work on community projects, specific Men’s Shed projects or a project of their choice in their own time and where the only ‘must’ is to observe safe working practices….all in a spirit of mateship.”

It is this “spirit of mateship” that Jackson was sorely missing. Through the help of a grant and generous donations, he was able to set up a site and stock it with woodworking equipment in hopes that it would attract like-minded men in his town (he also set up a “She-Shed,” a communal space for local women who also shared his interest in woodworking). In time, community involvement in the shed grew to the point where it now boasts a membership ranging in ages from 22 to 87 and from all walks of life.

They “make everything from ornaments, dog kennels, bird and plant boxes through to wheelbarrows,” but more than that, says Jackson, it’s an excuse for people to break out of their usual routine and establish friendships in a comfortable environment. During COVID, Jackson took the lead on checking in on everyone to ensure they were OK, going so far as to set up live chats online to keep up people’s morale.

Looked at one way, the idea of establishing a Men’s Shed (and a She-Shed) in a community is nothing more than an expression of civic pride, a way to cultivate a sense of fellow spirit in a world that sometimes feels out of balance. But I contend that it goes deeper than that: if you look past the fact that no one in the Men’s Shed movement seems especially concerned with measuring the happiness and health levels of its members, to me this is an organic example of population health in action.

Caregiver Support

In some cases, the more specific and non-traditional a person’s circumstances are, the more the need may exist for them to improvise when seeking both mental and physical healthcare (within reason, of course). People facing unique challenges may not fit into traditional population health care delivery models, particularly if they themselves are caregivers.   

Just such an example was featured on NPR recently: the story involves a woman named Jacquelyn Revere who has attracted a large following on TikTok by authentically talking about her life providing care for her mother, who had Alzheimer’s.  In her late 20s Revere was forced to move back home when her mother, who herself was caring for Revere’s ailing grandmother, was diagnosed with Alzheimer’s. Like the 16 million Americans who annually provide more than 17 billion hours of unpaid care for loved ones suffering with Alzheimer’s disease and other dementias, Revere was asked to put her life on hold.

Understandably, dealing with multi-generational health issues became hard for Revere to navigate. And it wasn’t just her loved ones’ health that worried her. As with so many people facing mounting doctor’s bills, she worried about bills going unpaid and ultimately losing their home – in other words, upstream social determinants outside the clinical sphere. 

When she tried explaining her predicament to friends her age, they just couldn’t relate. And support groups were no help either, as they were often composed of much older, more financially secure people. Because of her relative young age and financial burden, it is perfectly possible that in these early days of understanding upstream SDOH – not to mention the lack of a social safety net for younger people in our society in general and for caregivers in particular – there may not be a segmented population into which Revere neatly fits.

Finding these traditional means of support lacking, Revere turned to social media. TikTok became her platform of choice, allowing her to search for and ultimately find a community more in line with her own experiences. Taking the initiative to connect, she began posting under the handle @MomOfMyMom about her daily struggles, helping to build the community she could never find offline. In time she attracted over 650,000 followers who discuss everything from early signs of their loved ones’ cognitive decline to how to provide basic daily care.

Unfortunately, Revere’s mother passed away recently, and she has used TikTok as a means to express her grief. The platform has acted as a way for people to pay their respects, and because of this, Revere doesn’t have to hurt in isolation. In addition, the platform allows her to continue her work providing emotional support to other dementia caregivers online who may not have cultivated as big a following as hers. Social media has allowed Revere to connect and grow with a similar “population” of caregivers that just wouldn’t have been otherwise possible.

Revere’s example reminds us that when the system fails us, we can still rise above our circumstances to help others. From veterans’ groups organizing volunteer transportation for fellow veterans to attend doctor’s appointments, to a green building consultant on a mission to help land developers create sustainable buildings with people’s mental and physical wellbeing in mind, population-level health initiatives are showing up everywhere, and many times outside of their traditional confines. It will be interesting to see if and how such efforts are integrated into our understanding of population health going forward.

Social Prescribing

I recently learned about an approach to addressing negative social determinants of health (SDOH) called “social prescribing,” and it has the potential to be a complete game-changer. The movement, if I can call it that, seems to have caught fire in places like the United Kingdom, Canada, Australia, Singapore, and, to a lesser extent, the U.S. The concept, which combines the best parts of co-located care (which I discussed in a former blog post) and community-based healthcare, will very likely prove to be an important tool in the transition to value-based healthcare.

Social Prescribing and Community Health

Social prescribing, sometimes referred to as “community referral,” is a relatively new concept that, while in limited use in the United States, has taken on a key role in some countries’ approaches to population health. In the UK, where the concept has perhaps seen its greatest uptake, the National Health System (NHS) is aiming “to have nearly one million patients referred for social-prescription interventions by 2024.”

Social prescribing is seen as one part of a larger concept called “community health.” Community health is a fairly malleable term, but as defined by NHS England, “Community health services cover a wide range of services and provide care for people from birth to the end of their life. Community health teams play a vital role in supporting people with complex health and care needs to live independently in their own home for as long as possible.”

OK, so what does the “community” in “community health” mean exactly? The UK’s National Institute for Health and Care Excellence provides a useful definition of the term that I think works well for our purposes: “A community is a group of people who have common characteristics or interests. Communities can be defined by: geographical location, race, ethnicity, age, occupation, a shared interest or affinity (such as religion and faith) or other common bonds, such as health need or disadvantage. People who are socially isolated are also considered to be a community group.”

That last part about socially isolated or underserved people representing a community is key to social prescribing, which facilitates the improvement of people’s circumstances by employing non-medical interventions. For example, some of these interventions include things like “art classes for wellbeing, knitting, singing, or walking groups.” In other words, social prescribing allows general practitioners (GPs) in the UK to refer patients to designated community health professionals who can, in turn, involve these patients in non-clinical activities that can help improve their overall wellbeing.

Social Prescribing in the UK

Now that we’ve got a few definitions out of the way, let’s examine how social prescribing has come to occupy such an important place in the UK’s National Health System. Central to the success of this approach is something called a “link worker.” NHS England describes link workers like this:

“Link workers give people time, focusing on ‘what matters to me’ and taking a holistic approach to people’s health and wellbeing. They connect people to community groups and statutory services for practical and emotional support.”

In the service of improving the day-to-day lives of patients, link workers leverage an in-depth familiarity with local, community-based non-medical support resources. Social prescribing is ideally suited to people fitting one or more of the following descriptions:

  • Those with one or more long-term conditions
  • Those in need of support with their mental health
  • Those who are lonely or isolated
  • Those who have complex social needs which affect their wellbeing.

So how is any of this different from a typical social worker, one might ask? Well, for one thing, the sheer number of stakeholder agencies who can refer a patient to a link worker is quite broad: “general practice, pharmacies, multi-disciplinary teams, hospital discharge teams, allied health professionals, fire service, police, job centres, social care services, housing associations and voluntary, community and social enterprise (VCSE) organisations” represent this potential pool of referrers.

Secondly, the requirements for becoming a social worker (sometimes called a “social care worker”) in the UK differ significantly from those involved in becoming a link worker. Life experience, good communication skills, and a willingness to be trained — and, in some instances, other skills as described here — are all that are required to join the ranks of link workers. This sounds great, because given the pressing need for this kind of work, I can only assume that removing high barriers to entry would help to more quickly address the issue.

By contrast, there are relatively more rigorous requirements involved with becoming a social care worker. A list of these requirements, including having either a BA or a master’s degree, can be found on the British Association of Social Workers website. While there are fast-track options for going into this line of work, the profession seems more highly regulated. 

Why Social Prescribing?

When looked at through the lens of population health or population health management, it becomes clear why social prescribing can be so effective. If you skip to the 3:47 mark of this video produced by the Healthy London Partnership, you’ll see a physician attest to the fact that, although initially skeptical that social prescribing interventions could help his patients, he was bowled over by how much the link workers were able to lighten his workload by mitigating negative SDOH. And at the 4:12 mark, another physician explains how in his practice, they’ve seen a reduction in the number of GP appointments for patients where link workers have been involved.

Although its advocates focus on its many merits, real-world evidence for the effectiveness of social prescribing is mixed at best. For instance, a social prescribing pilot project was conducted with much success between April 2012 to March 2014 (and was subsequently re-contracted for another three years starting in 2015) in Rotherham, a metropolitan borough of South Yorkshire in the UK which has a population of over 100,000 people. Here is a summary of the project that appears on the Social Care Institute for Excellence website:

“The service is especially aimed at users with complex long-term conditions (LTCs) who are the most intensive users of primary care resources. The service receives referrals from GPs of eligible patients and carers, and assesses their support needs before referring on to appropriate voluntary and community sector services. The service also administers a grant funding pot, through which a ‘menu’ of voluntary and community sector activities is commissioned to meet the needs of people who use services.”

Top-line results include the following:

  • An estimated social return on investment (which the WHO defines as “value produced for multiple stakeholders in all three dimensions of development: economic, social and environmental”) of £570,000–£620,000 
  • A 7% reduction in non-elective inpatient episodes
  • A 17% reduction in accident and emergency department (A&E) attendances
  • An initial return on investment of 43 pence for each pound invested in terms of avoided costs to the NHS

All of these points speak to the potential positive impact of social prescribing, but I find this last point most compelling: the upfront investment in preventative care paid dividends. Devoting time and resources to improving people’s lives outside of the clinical space could save health systems money while at the same time leading to better patient outcomes — and this study proves it.

Data provided in the Open Data Institute’s (ODI) November 2021 report on social prescribing seem to back this up the practice’s effectiveness. “If GP appointments fall by 2-5% as a result of social prescribing operating at scale,” say the report’s authors, “it could lead to a diversion of between 3.2-8 million GP appointments per year.” This extra capacity could be used to see more patients, which is key in a system like the NHS which has seen major backups due to the pandemic.

As much success as the Rotherham social prescribing project enjoyed, however, the advent of the COVID pandemic has proved a major headwind for link workers. Given that so much of this kind of work is done face-to-face, strict social distancing orders and lockdowns over the past couple of years have taken their toll. 

Although a recent article in The Lancet highlights how primary care doctors have adapted social prescribing during the pandemic to leverage phone and internet engagement when connecting nurses and health-care assistants with patients, resources haven’t been fully aligned to maximize the practice. The ODI’s report exposed several barriers that have conspired to limit social prescribing’s potential in recent years. A few of these issues highlighted in the report include the following:

  • Local Authorities, health, and voluntary sector organizations maintain separate directories of locally available services which can lead to duplication of effort and issues with interoperability
  • Data sharing is limited when multiple community service directories exist in a local area due to a lack of trust between organizations
  • A lack of information on attendance/user satisfaction and challenges means that link workers may have to invest time exploring the suitability of a new service with which they may be unfamiliar

Despite these shortcomings, I think social prescribing holds a lot of promise. It hasn’t made many inroads in the U.S. yet, but it seems to be catching on across the globe. I may highlight examples of social prescribing in other countries in future blog posts.

Payment Models and Metrics

Healthcare quality measures are a study in contrasts. Their overall purpose is straightforward enough: quality measures incentivize physicians and other healthcare workers to provide cost-effective care that ideally leads to better patient outcomes. But the sheer number of them can seem overwhelming. What’s more, figuring out the interplay between these measures and healthcare payment models adds a layer of complexity that can leave the most dogged researcher scratching their head.

This being the case, I thought it would be interesting to explore what healthcare quality measures are, who should be using them, and how they’re connected to healthcare payment models.

An Ongoing Healthcare Crisis

Let’s start with a quick history lesson to put things into context. For many years, a range of experts have been warning that America’s healthcare system was on the brink of collapse. Since at least 1969, and to some extent before that, American leaders have been sounding the alarm about a looming “healthcare crisis.” The Health Affairs article linked to above notes that “national health expenditures have grown from 6.9 percent of GDP in 1970 to 17.9 percent in 2016, according to the Centers for Medicare and Medicaid Services (CMS),” with that number having grown to 19.7% by 2020.

Over time, several attempts have been made to reign in spending and improve patient outcomes. The latest of these major efforts was the 2015 Medicare Access CHIP (Children’s Health Insurance Program) Reauthorization Act, otherwise known as MACRA. Among other things, MACRA aims to tie healthcare provider performance to reimbursement, incentivizing providers to participate in alternative payment models (APMs). This effort is ongoing and, as we’ll soon see, has begun bearing fruit. 

While there is a lot of inertia holding the current fee-for-service (FFS) physician reimbursement system in place, according to the website for the American Society of Health-System Pharmacists, or ASHP, there’s ample reason to try another approach: “In a FFS payment model, the provider or facility get reimbursed for each service provided. This can create an incentive for providers to increase the volume and cost of services provided versus focusing on value of care provided. This model can also lead to uncoordinated and fragmented care through inefficiencies within the healthcare system, which can also drive up overall healthcare costs.” 

The Rise of Alternative Payment Models

APMs have been proposed as an antidote to the U.S.’ fixation on FFS. These payment models come in different forms depending on a range of factors including how many direct patient encounters a physician/group has/have, and how much financial risk physicians are willing to take on. Financial risk-sharing refers to the concept that the cost burden for medical treatment should be shared by payers, physicians, and patients (as opposed to just insurers and patients). 

Within this concept is the idea of upside and downside risk-sharing: in upside risk models, physicians are permitted to share in any savings their efficiencies help create; in downside risk models, not only can physicians share in the savings, but they can also see their reimbursement levels adjusted downward if they miss quality goals. As the authors of one article put it when talking about a form of shared-risk payment model called “bundled payments,” “if healthcare costs exceed the set amount, providers lose out on the revenue they would have received from a traditional payment structure.”

Like most things, the big idea behind risk-sharing is that the bigger risk physicians are willing to take on, the bigger the potential reward.

Some flavors of shared-risk payment models include “accountable care organizations (ACOs), the Medicare Shared Savings Program (MSSP), pay for coordination, pay-for-performance (P4P), bundled payments, upside- and downside-shared savings programs, partial- or full-capitation, and global payments.” A thorough examination of APMs would require multiple blog posts, but, since capitation shares similarities with things like bundled payments and global payment models, looking at how capitation works might shed some light on the overall concept of risk-sharing. 

A useful definition of capitated payments can be found on the website of the American College of Physicians: “Capitation is a fixed amount of money per patient per unit of time paid in advance to the physician for the delivery of health care services. The actual amount of money paid is determined by the ranges of services that are provided, the number of patients involved, and the period of time during which the services are provided.”

In other words, payers and physicians look at historical trends to arrive at a lump-some payment for blocks of patients who require a range of services. If the doctors are able to provide efficient, high-value care that saves money, they can share in the savings; if, however, they go over budget for a given patient population, they’ll owe money to the insurer.

Capitation seems to work pretty well: a scoping review of 76 studies showed that, when done right, capitation can both improve health care utilization and reduce spending — all while not affecting the quality of care provided. Central to the concept of population health, the study also showed that capitation prompted an increase in preventative health visits. 

Importantly, physicians working in a capitated environment can’t restrict necessary care in the service of hitting agreed-upon benchmarks, so, as the ACP website notes, “managed care organizations measure rates of resource utilization in physician practices. These reports are made available to the public as a measure of health care quality, and can be linked to financial rewards, such as bonuses.”

While we’re on the topic of payment models, I should also say that there’s a parallel track for ensuring value-based care called the Merit-based Incentive Payment System, or MIPS. This value-based care system is reserved for qualifying physicians who meet certain criteria, including something called a “low-volume threshold” in which they fall short of dealing directly with a set number of patients and are thus exempt from full APM participation (although, somewhat confusingly, there’s a whole subsection of MIPS called “MIPS APMs”).

For the purposes of this blog post, I’ll focus on full APMs and leave MIPS and its associated value measures for a possible future blog post.

Alternative Payment Models in Practice

Pivoting away from the FFS payment model in which physicians are reimbursed for the individual services they provide instead of being held accountable for patient outcomes — and in the process attempting to mitigate suboptimal circumstances for each individual patient (a concept called whole-person care) – is, in a word, ambitious. This transition, which has been in full swing since 2015, is particularly challenging because not every physician provides the same level or intensity of care, so metrics for those participating in full APMs must be tailored such that the greatest number of doctors possible can participate. 

As a consequence, uptake of value-based care hasn’t been as swift as many had hoped. In 2015, Health and Human Services (HHS) chief Sylvia Burwell set the following targets: “85% of Medicare FFS payments should be tied to quality or value by 2016, and 30% of Medicare payments should be tied to quality or value through alternative payment models by 2016 (50% by 2018).” (The bold is mine.) These challenging targets would be met, HHS theorized, by enticing physicians and healthcare systems into upside and downside risk-sharing arrangements (as discussed above).

According to the latest data compiled by the Health Care Payment Learning & Action Network (HCPLAN), while a little behind schedule, FFS is on the road to becoming an antiquated concept. In its “2020 APM Measurement Effort,” the HCPLAN surveyed representatives from not just Medicare, but also commercial health plans, Managed Care Organizations (MCOs), state Medicaid agencies, and Medicare Advantage (MA) plans to find out the mix of FFS versus value-based reimbursement flowing through each of these “lines of business.”

For starters, 39.3% of U.S. healthcare payments were FFS payments not linked to quality and value (which includes more than just Medicare, as described above). In other words, over a third of payments made for healthcare services are doled out on the basis of discreet services performed by healthcare providers without any tie to coordinated care.

That being said, there is some good news: 19.8% of all reimbursements made in the year 2020 (again, across all “lines of business” surveyed) were FFS payments linked in some way to quality. The HCPLAN defines this FFS designation as “Payments that are set or adjusted based on evidence that providers meet quality standards or improve care or clinical services, including for providers who report quality data, or providers who meet a threshold on cost and quality metrics.” Further, 34.2% of payments made in 2020 belonged to a category called “APMs Built on FFS Architecture.”

Taken together with “Population-Based Payment Models” (which I covered in a previous blog post here), the HCPLAN website reports that “In 2020, 40.9% of U.S. health care payments, representing approximately 238.8 million Americans and 80.2% of the covered population, flowed through Categories 3&4 models” (category 3 and 4 models include  “APMs Built on FFS Architecture” and Population-Based Payments).

Comparing Payer Lines of Business

That’s great, but how does it match up with HHS projections way back in 2015?

Well, let’s take a look at what they said one more time: “85% of Medicare FFS payments should be tied to quality or value by 2016, and 30% of Medicare payments should be tied to quality or value through alternative payment models by 2016 (50% by 2018).” (Again, the bold is mine.)

I couldn’t find a snapshot of where things stood in 2016 with respect to uptake of value-based payment models, but if you look only at the Medicare-related “lines of business” on the most recent HCPLAN survey — that is, Medicare Advantage and traditional Medicare — payments linked to quality and value (and, consequently, not strictly FFS) accounted for 62% and 85%, respectively.

While Medicare has undoubtedly made progress toward ensuring that payments flowing to physicians are adjusted based on quality metrics, it’s significantly ahead of both commercial payers and Medicaid. If you zoom in on Categories 3 and 4 payments (again, the categories that most resemble pure APMs with no FFS elements) these markets accounted for only 35.5% and 35.4% of payments linked to value, respectively. It’s also important to realize that as of 2021, in all its forms, total Medicare enrollment was only 63,964,675. In a country of 331,893,745 people (as of 2021), that equates to about 19% of the population.  

While I grant that it’s easy to become jaded, and you might be forgiven if you think of value-based care as just the latest fad, destined to be tossed into the waste bin of history when it inevitably fails, I think this whole approach holds a lot of promise. What keeps me optimistic about it is that, in a sense, we can’t afford the quality care movement to fail. Too many people are losing everything to pay for their healthcare these days, and that’s just plain wrong (I’ve covered how insurance works in previous blog posts here and here). For this reason, we have to figure out a way to make care more affordable while also ensuring optimal patient outcomes.

The Importance of Metrics

One quote you often hear repeated when researching quality measures is some variation on “What gets measured gets done.” This thought is often attributed to a Scottish mathematician and physicist named William Thomson, a.k.a. Lord Kelvin. Even though Lord Kelvin didn’t have healthcare quality metrics in mind when he uttered these words, they can just as easily be applied to physics as they can to improving healthcare provider performance.

Given a growing emphasis on patients becoming active participants in their own care, it has been understood for some time that while quality metrics linked mainly to process or adherence to clinical practice guidelines (for instance, the percentage of diabetes patients who had their blood sugar tested and controlled) can play a role in physician compensation, patient-reported outcome measures would likely take center stage at some point.

And that point is quickly approaching. Patient-reported outcomes measures, also called PROMs, have indeed begun factoring into the mix. CMS defines PROMs as “tools used to capture patients’ reports of their outcomes, which measure developers can use as the basis for patient-reported outcome-based performance measures (PRO-PMs).”

PROMs have been around for a while, but only recently have they bubbled up to the surface enough for me to notice them. In a publication called RTI Press, the authors of a 2015 paper noted a range of PRO measures, from functional status measures, to those quantifying patient outcomes in dealing with chronic illnesses, to measuring the intensity of fatigue, to getting a handle on health behaviors.

One common element among these measures seems to be that they’re best reported by patients, as opposed to solely being assessed by physicians. Most PROMs I’ve read about involve healthcare interventions in one way or another, but I imagine some could be dedicated to measuring social determinants of health outside of the clinical setting. This bears more research and I’ll aim to follow up with more blog posts in the future on this topic.

Quality Measure Development

I’ve been looking into how to develop Patient-Reported Outcomes-based Performance Measures (PRO-PMs) lately, and I figured I’d list some helpful resources here. Quality metrics are important in medicine, in that they incentivize physicians to accomplish the IHI Triple Aim:

  • Improving the patient experience of care (including quality and satisfaction);
  • Improving the health of populations; and
  • Reducing the per capita cost of health care.

Payers like the Centers for Medicare & Medicaid Services (CMS) are increasingly trying to integrate patient preferences into these measures to enhance patient experiences of care and, thereby, improve their buy-in to maintaining their own health – which theoretically should lead to better patient health outcomes and lower overall costs to the system.

MIPS Value Pathways, or MVPs (which I’ve discussed previously here and here) are one manifestation of this effort. CMS’ Quality Payment Program website describes the MVP framework as one that works “to align and connect measures and activities across the quality, cost, and improvement activities performance categories of MIPS for different specialties or conditions.” And there’s a population health angle as well: “the MVP framework incorporates a foundation that leverages Promoting Interoperability measures and a set of administrative claims-based quality measures that focus on population health in order to reduce reporting burden.” 

Ultimately, the MVP framework strives to keep the patient at the center of care by aggregating performance data to help guide patients toward make better informed decisions about their own care. 

According to CMS’ “Finalized MVPs and Policies” (available for download here), MVPs have to include at least one outcome measure, and one such outcome measure is the PRO-PM. At the present time, CMS is encouraging “interested stakeholders” to submit MVP candidate measures for consideration “that measure the patient journey and care experience over time.” Additionally, CMS is exploring how MVPs can be used within a multi-disciplinary, team-based care model. 

There’s a lot more to say about MVPs and PRO-PMs, and as this framework evolves I’ll check back in to elaborate on how things are progressing. For now, here is a helpful description of the difference between patient-reported outcomes measures (PROMs) and PRO-PMs, and how they’re related: 

  • PROMs are tools used to collect patient-reported outcomes
  • Measure developers can use PROMs as the basis for patient-reported outcome-based performance measures (PRO-PMs). More specifically, a PRO-PM is a way to aggregate the information from patients into a reliable, valid measure of performance at the measured entity level, e.g., clinician. The CMS consensus-based entity (CBE) only endorses use of PRO-PMs in performance improvement and accountability. The same measure evaluation criteria and justification principles that apply to other outcome measures also apply to PRO-PMs.

And without further ado, here some helpful resources for the PRO-PM development process: