The American Journal of Accountable Care. 2019;7(1):31-33

The US healthcare system is experiencing a sea change, with new, complex therapies entering the market and innovative approaches to healthcare delivery being implemented. Concurrently, health policy is trying to keep pace, with new legislation being introduced to address major issues like rising drug prices, challenges facing vulnerable populations, and persistent epidemics.

Health policy also represents the infrastructure that allows for these innovations in healthcare to be effective and sustained, and during the AcademyHealth National Health Policy Conference, held February 4-5, 2019, in Washington, DC, healthcare experts from around the country sought to make sense of the abundance of changes occurring in the healthcare system. In addition to policy changes meant to address prescription drug prices, these experts discussed the implications of recent midterm elections,

 innovative ways of delivering care, and challenges facing certain patient populations.

HHS Secretary Alex Azar opened the conference by discussing the steps that the Trump administration is taking to lower healthcare costs, beginning with tackling the current rebate system. Taking aim at pharmacy benefit managers, which he called the middlemen of the drug supply chain, Azar explained that more than $150 billion in rebates float around the drug pricing system each year, but patients reap no benefit. In response, the administration has proposed ending the current rebate system and replacing it with a new one in which rebates will be passed directly to patients at the pharmacy counter.

 Additionally, President Donald J. Trump has proposed significant change to the Anti-Kickback Statute safe harbor that exempts rebates from antikickback laws, which Azar called the “single biggest change to the way Americans’ drugs are priced at the pharmacy counter ever.”

Azar also highlighted other measures taken by the administration, including prohibiting the so-called gag clauses

 that bar pharmacists from telling consumers when it would cost less for them to pay cash for a prescription than to pay the co-payment through their insurance, as well as requiring TV ads to disclose the list prices of drugs if they cost more than $35 for a month’s supply.

Health Policy in Washington, DC, and the States

The first panel of the conference dove into the November 2018 midterm elections, in which healthcare played a role, but not in the way that most expected. Rather than hoping for major change, voters largely wanted healthcare to stay the same, with concerns that patients would lose Medicare benefits or their health insurance, explained Melinda J. Beeuwkes Buntin, PhD, a professor in the Department of Health Policy at Vanderbilt University Medical Center.

Voters also demonstrated increased awareness of surprise or unexpectedly high medical bills, with 2 in 3 respondents from a Kaiser Family Foundation health poll reporting that they were actively concerned about getting such a bill.

 Policy makers have turned their attention to these bills, with some pitching the idea of tying them into the Medicare fee schedule in some way.

States have also emerged as incubators for innovation, often taking the reins in addressing the challenges facing their populations. Some have touted the idea of importing certain drugs from other countries as a way to lower drug prices. For example, Vermont last year introduced legislation that would allow the state to import drugs from Canada.

In January 2018, the Trump administration released guidance

 for states interested in imposing a requirement for able-bodied Medicaid recipients to work or take part in volunteer or job-training activities. Since then, multiple states have introduced work requirements; some have been approved.

Grace-Marie Turner, president of the Galen Institute, explained that the administration also gave more flexibility to states through section 1332 waivers. These allow states to pursue innovative strategies for providing high-quality, affordable care to their residents, as well as through a finalized rule for short-term, limited-duration health plans that expands such plans’ duration to 12 months, with renewal for up to 3 years.

The panel agreed that the role of states in determining health policy will continue to evolve in the year ahead.

Innovations Changing the Delivery of Healthcare

During one conference session that looked toward the future of healthcare, experts took a comprehensive view of innovations shaping the new wave of healthcare delivery. Bechara Choucair, MD, senior vice president and chief community health officer of Kaiser Permanente, began by underscoring the importance of health systems’ leaders thinking about their patients’ social needs, how to predict and screen for those needs, and how to then address them.

“It’s not fair to expect patients to live healthy lives if they don’t have a stable, safe roof over their head,” he said, as he explained that people who are homeless live, on average, 27 years fewer than people who are housed; they also have an increased rate of, and length of stay during, hospital readmissions. They are additionally at increased risk of infectious disease, chronic disease, addiction, and mental health issues, he said.

Stakeholders ranging from health systems to providers to employers have paid increased attention to these social needs, experimenting with different ways of addressing them. Kaiser Permanente, for example, last May launched a $200 million impact investment to address housing instability and homelessness. Since then, Kaiser has started to implement 3 initiatives through partnerships with both national and local organizations:

Investing in a multiunit building in Oakland, California, to preserve and expand affordable housing.

Anchoring a $100 million loan fund to protect affordable housing across its healthcare system.

Identifying 515 homeless people in Oakland older than 50 years with at least 1 chronic condition, and working with community partners to obtain housing for them.

Choucair emphasized the importance of partnerships like these, explaining that to effectively implement strategies that address social determinants of health, federal, state, city, county, and local governments all need to be involved.

However, addressing these social needs doesn’t necessarily solve the problem facing many Americans, which is access to care, said Naomi Fried, PhD, founder and chief executive officer of Health Innovative Strategies. Digital tools like virtual health and telemedicine have worked to erase this barrier, allowing providers to reach patients who are in rural areas or in other situations in which they cannot easily access a provider in person.

“Digital health leverages technology to deliver care and information to patients and providers that’s more convenient, cost-effective, and often more personalized,” she said. “It has potential to decrease costs, improve quality, improve efficiency, and deliver care and information in ways that could not be done before.”

In recent years, digital technologies have expanded beyond virtual health and now include technologies like clinical-grade digital information and digiceuticals, explained Fried.

Starting with clinical-grade digital information, Fried explained that these technologies collect information from the patient that can be acted upon by their provider, who can then make a therapy decision and evaluate that patient’s condition. For example, researchers from Massachusetts Institute of Technology have created an algorithm that analyzes a person’s typing patterns to determine if they have Parkinson disease or to track Parkinson progression.

Meanwhile, digiceuticals are digital therapeutic products that allow providers to change patient behaviors and improve outcomes, said Fried. A computer program from GAIA Therapeutics has been clinically demonstrated in randomized controlled trials to be as effective as drugs for treating depression linked to multiple sclerosis, for instance.

Fried also outlined the necessary next steps with these technologies, including addressing privacy and security challenges, increasing patient engagement, and bridging the digital divide that exists between older and younger generations. Panelists agreed that these technologies must close inequalities rather than exacerbate them.

Despite advances like these, certain patient populations in the United States continue to struggle with access to adequate healthcare. With higher numbers of Medicare and Medicaid beneficiaries and significantly fewer providers, rural communities face unique challenges and disparities compared with metropolitan and urban settings, agreed panelists during a discussion focused on health in rural communities.

From 1999 to 2010, there was a 50% quicker decrease in mortality rates compared with prior years for both metropolitan and nonmetropolitan areas, the panelists explained. However, from 2010 to 2016, mortality rates remained flat in nonmetropolitan areas but continued to decline, albeit at a slower rate, in metropolitan areas. As a result, the 6% mortality gap in 1999 grew to 18% in 2016, explained Mark Holmes, PhD, professor of health policy and management at the University of North Carolina Gillings School of Public Health.

Individuals in rural communities continue to experience higher rates of motor vehicle and other types of accidents, suicide by firearm, as well as heart attacks, cancer mortality, and chronic lower respiratory disease. These populations are also sicker, older, lower-income, and more often unemployed, thus presenting challenges to providers in the area.

At the same time, these communities continue to see a shift away from inpatient care and toward outpatient care, struggle with recruitment and retention of providers, and have negative profit margins. Holmes went on to explain that rural hospitals around the country, especially in the South, have been closing in response; this has resulted in not just closures of whole units, but also service lines. Between 2004 and 2014, for instance, 9% of rural communities lost their obstetrician services.

Rural health clinics (RHCs) have also seen management shifts, said Bill Finerfrock, executive director for the National Association of Rural Health Clinics. Between 2012 and 2016, a significant number of independent or physician-owned RHCs converted to provider-based or hospital-owned RHCs. According to Finerfrock, provider-based or hospital-owned RHCs accounted for 51% of visits in 2012 and this proportion grew to 63% in 2016.

Often, when these independent or physician-owned RHCs are converted, they are converted to small hospitals. Finerfrock explained that this is largely because of payment challenges that independent and physician-owned RHCs face. The per-visit reimbursement rate for Medicare is capped at $84.70 for independent and physician-owned RHCs in 2019, whereas the rate is uncapped for small hospitals, with the per-visit reimbursement rate averaging $206 in 2018.

Looking at rural health policy, there has historically been a focus on taking measures to combat the financial and workforce challenges facing these communities, explained Thomas Morris, MPA, of the Health Resources and Services Administration of HHS.

“While the focus on finance and workforce is important, I’m not sure all of the tweaks in the world to how we reimburse providers or train them [are] necessarily going to get at addressing these disparities,” he said.

To fully and effectively address these challenges, he said, it’s crucial to support access and capacity building through enhanced payments via Medicare and Medicaid; workforce training and clinician placement programs; commitment to improving public, community, and mental health; investments in technology, like telehealth; and key federal resources beyond HHS.

1. Joszt L. 5 healthcare-related ballot measures from the 2018 midterm elections. 

 website. ajmc.com/newsroom/5-healthcarerelated-ballot-measures-from-the-2018-midterm-elections. Published November 9, 2018. Accessed February 21, 2019.

2. Inserro A. HHS proposes to end drug rebates in Medicare, Medicaid managed care. 

 website. ajmc.com/newsroom/hhs-proposes-to-end-drug-rebates-in-medicare-medicaid-managed-care. Published February 1, 2019. Accessed February 21, 2019.

3. Caffrey M, Inserro A. Senate votes 98-2 to ban pharmacist gag clauses. 

 website. ajmc.com/newsroom/senate-votes-982-to-ban-pharmacist-gag-clauses. Published September 18, 2018. Accessed February 21, 2019.

4. Caffrey M. Trump officials outline plan to put drug prices in TV ads. 

 website. ajmc.com/newsroom/trump-officials-outline-plan-to-put-drug-prices-in-tv-ads. Published October 16, 2018. Accessed February 21, 2019.

5. Rau J. Surprise medical bills are what Americans fear most in paying for health care. Kaiser Health News website. khn.org/news/surprise-medical-bills-are-what-americans-fear-most-in-paying-for-health-care. Published September 5, 2018. Accessed February 21, 2019.

6. Luthra S. Vermont legislators pass a drug importation law. So what? Kaiser Health News website. khn.org/news/vermont-legislators-pass-a-drug-importation-law-so-what. Published May 18, 2018. Accessed February 21, 2019.

7. Caffrey M. CMS tells states how to require work for Medicaid. 

 website. ajmc.com/newsroom/cms-tells-states-how-to-require-work-for-medicaid. Published January 11, 2018. Accessed February 21, 2019.

8. Caffrey M. Kentucky first state approved for Medicaid work requirements. 

 website. ajmc.com/newsroom/kentucky-first-state-approved-for-medicaid-work-requirements. Published January 12, 2018. Accessed February 21, 2019.

Articles

During the AcademyHealth National Health Policy Conference, held February 4-5, 2019, in Washington, DC, experts from around the country sought to make sense of the abundance of changes occurring in the healthcare system.

Making Sense of Changes in Healthcare: Lessons From the AcademyHealth National Health Policy Conference

Medicare’s Merit-based Incentive Payment System and other value-based payment systems hold providers accountable for population-level measures that are driven by many factors, including those beyond the providers’ direct control (eg, psychosocial stressors, environmental hazards). In this commentary, we identify and illustrate this misalignment of accountability and capability at the individual clinician level and at the provider organization level, which can hinder population health improvement. We also envision the roles of administrators and frontline clinicians amid a new culture of shared accountability and rigorous evaluation, with the potential to accelerate advances in population health care delivery.

The American Journal of Accountable Care. 2019;7(1):28-30

The healthcare system’s ongoing transition from fragmented care and volume-based payment toward coordinated population healthcare and value-based payment has been slow. This transition has been hampered by the misalignment of measurement and accountability systems with population health care delivery goals. The Institute of Medicine report 

For the Public’s Health: The Role of Measurement in Action and Accountability

 articulated that a well-functioning accountability system for improving health must identify bodies “with a clear charge to accomplish particular steps toward health goals.”

 Current efforts to improve population health have been stymied significantly because it is not clear which entities—within the healthcare system or across the broader public health system&mdash;have such a charge and in what contexts.

When identifying where accountability for achieving population health goals should lie, a natural first choice is the individual clinician, given the importance of physicians, nurse practitioners, and other frontline clinicians in determining patients’ care pathways. Consequently, current value-based payment systems often integrate performance measures that hold the individual clinician accountable for patient outcomes. This is problematic, however, as these outcomes are often affected by numerous factors, many of which are beyond the clinician’s direct control.

To illustrate, Medicare’s Merit-based Incentive Payment System (MIPS) holds clinicians accountable for care practices such as major depressive disorder prevention and antidepressant medication management. Clinicians may reasonably be expected to screen patients for depression and administer Patient Health Questionnaire-9 (PHQ-9) evaluations to help reduce the percentage of patients remaining on antidepressant medications after a designated time period. However, the population-level impact of these efforts is likely to be small, whereas the potential impact could be much greater with better-coordinated, collective efforts—those integrating both medical and nonmedical actors&mdash;to manage the underlying psychosocial stressors and complex, multifactorial processes that drive depressive symptoms.

(PHM) embodies the vision of achieving population health improvement by these means. Coordinated, multifaceted PHM interventions may include established models of chronic disease prevention and care management, as well as new collaboration with social services, improving the physical environment and safety, engaging in community education and outreach, and addressing food insecurity and other sequelae of poverty. Significant economies of scale are needed to efficiently perform most PHM interventions, such as geographic analysis of community-level health hazards and other sophisticated measurement and reporting functions,

 which require substantial investments in information technology and big data analytics. Moreover, it is beyond the training of most clinicians to prioritize, coordinate, and execute such multidimensional initiatives effectively. For both reasons, such outcomes-based MIPS measures place too much accountability on the individual clinician and thus may be ineffective in improving population health.

This misalignment is increasingly recognized among thought leaders, payers, and policy makers, as reflected in proposed redesigns of MIPS

 and the growing interest in new value-based payment models such as Accountable Health Communities, which emphasize managing attributed populations collectively.

 Provider organizations have begun to demonstrate how they may leverage their greater economies of scale to advance PHM goals, including through hospital leadership and shared services agreements among groups of smaller organizations.

Still, skeptics worry that provider organizations are just as unprepared as individual clinicians for the challenges of implementing effective PHM interventions. Doing so may require undertaking tasks far removed from traditional modes of medical care, such as forging and maintaining relationships with social services agencies and other public health institutions across communities. Indeed, few healthcare provider organizations—let alone individual providers&mdash;have demonstrated the ability to undertake them effectively.

Provider organizations have also experienced challenges effectively coordinating PHM interventions internally. The system-level measures used to determine payments within value-based contracts (eg, use of certified electronic health record technology, diabetes screening rate) hinge on the actions of the system’s individual clinicians. For example, if an organization leverages its big data capabilities to microtarget screening interventions to a high-risk patient subgroup, the individual clinician must still identify the patient in his or her exam room as high risk and perform the screening. Yet, rarely have provider organizations’ leadership effectively articulated how their clinician employees benefit when the organization’s PHM initiatives are executed faithfully or made substantial efforts to cultivate buy-in and engender a culture of collaboration among clinicians and staff in support of PHM. Perhaps more importantly, provider organizations’ leaders typically have not passed along to or shared with clinician employees the financial incentives the organization faces under value-based contracts, nor have they provided organizational or financial guardrails to ease clinician employees’ transitions into population health—oriented delivery and payment models.

 Although provider organizations are indeed starting to build in PHM-oriented performance bonuses for their clinicians, the predominant compensation structures remain salary based or productivity driven, unlinked to population health improvement. Thus, although payment systems mislay accountability when focused on the individual clinician, it is unclear whether population health will improve when accountability for PHM rests with the provider organization instead.

Individual clinicians and provider organizations each have important roles to play in improving population health care delivery: principally, in executing the organization’s PHM initiatives and in formulating and resourcing these initiatives, respectively. It is incumbent on administrators and frontline clinicians alike to understand these roles and coordinate the organization’s care delivery priorities together.

 Internally, such coordination must include, for employed clinicians, negotiated internal compensation models that align with the terms of the organization’s various payer contracts. Externally, provider organizations must coordinate with payers to negotiate payment contract incentives that can be easily passed along to or shared with their employed clinicians. Respecifying MIPS quality measures so that they focus on individual clinicians’ activities (eg, administering PHQ-9 evaluations) rather than downstream outcomes (eg, antidepressant medication management) represents one federal opportunity to foster providers’ internal coordination in this way. Provider organizations may also work with local payers to facilitate partnership agreements between network providers and entities (eg, social services) that can enhance their PHM initiatives’ capabilities and scale. Importantly, these arrangements must evolve over time with the community’s PHM efforts and as all involved parties identify opportunities to improve their effectiveness.

Robust scientific evaluations and evidence-building efforts are critical for overcoming barriers to improving PHM efforts, especially considering that it remains unknown what the most effective models of delivering population health are and how to apportion accountability for them. Given the multifaceted nature of the psychosocial determinants of population health, in most communities it may be critical that payers and other organizations traditionally ancillary to the healthcare industry (eg, employers, municipal health departments) participate in community-level deliberations to define the community’s population health goals, formulate plans and economic incentives for achieving them, and evaluate efforts. MIPS could foster this by modifying its Improvement Activities list to include, among other potential options, substantial (eg, 25%) representation from these community entities on the provider organization’s board or documentation of referrals and coordinated handoffs of underserved patients to local social services programs for nutrition or housing support. Moreover, each community must conduct its own evaluations, as coordinated PHM is a local endeavor. For instance, the set of organizations needed at the table to support achieving a particular population health goal may vary with the community’s sociodemographic and environmental characteristics and with the evolution of community organizations as they take on new roles and build up the “capacity to undertake the required activities”

Throughout the course of a community’s innovation, intervention, evaluation, and evolution, effective PHM will accelerate when the coordinated networks of the community’s partner organizations expand and each partner is held accountable more precisely for achieving what it is capable of achieving in the interest of improving the population’s health. MIPS and other value-based payment systems will continue to significantly inform how shared accountability is defined, incentivized, and implemented in many communities. It is important that they do so deliberately, for until shared accountability is realized, our efforts to realize meaningful gains in population health will continue to lag.

 Rollins School of Public Health, Emory University (ASW), Atlanta, GA; The Health Management Academy (SJ), Washington, DC.

 Dr Wilk is employed by Emory University, which engages in population health management efforts through Emory Healthcare, and owns stock as a minority shareholder in United Health Group. Dr Jain is employed by The Health Management Academy, which is a network of delivery systems.

 Concept and design (ASW, SJ); analysis and interpretation of data (SJ); drafting of the manuscript (ASW, SJ); critical revision of the manuscript for important intellectual content (ASW, SJ); and administrative, technical, or logistic support (ASW, SJ).

 Adam S. Wilk, PhD, Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA 30322. Email: adam.s.wilk@emory.edu.

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Report to the Congress: Medicare and the Health Care Delivery System

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Current population health efforts have been hampered by the misalignment of accountability. Thus, population health management will improve when shared accountability among stakeholders is achieved.

Effective Population Health Care Delivery Under Medicare’s Merit-based Incentive Payment System: Realigning Accountability With Capability

As the shift from volume to value in healthcare expands, efforts to develop alternatives to hospitalization are gaining momentum. This study explores home health care initiated directly from the emergency department (ED) using the Medicare-reimbursed home health benefit as a potential alternative to hospitalization. We address barriers to home-based care by comparing costs and utilization of care for older adults dispositioned to home health care versus hospital admission.

We conducted a retrospective institutional and carrier claims analysis of 5% of total Medicare fee-for-service beneficiaries from January 2012 through December 2013 using 2 cohorts: patients treated in the hospital following an ED visit (inpatient) and patients treated at home following an ED visit (home health). Patients had 1 of the following: congestive heart failure, chronic obstructive pulmonary disease, urinary tract infection, pneumonia, or cellulitis.

 Propensity score—weighted regression was used to measure the total cost of care for 90 days post index visit, hospital admissions/readmissions, and ED revisits.

Total 90-day costs were lower for the home health cohort than for the inpatient cohort ($13,012 vs $20,325; 

 <.0001). The home health cohort also had lower hospital admissions/readmissions (23.7% vs 33.0%; odds ratio, 1.535; 

 <.0001) compared with the inpatient cohort. Although the home health cohort had fewer ED revisits, the difference was not statistically significant.

The findings suggest that risk-bearing healthcare organizations could use home-based alternatives to hospital admission as a means of providing high-quality care at a lower cost.

The American Journal of Accountable Care. 2019;7(1):10-16

Healthcare spending in the United States exceeded $3.3 trillion in 2016, which translates into an average of $10,348 per capita annually.

 Despite some slowing in growth in 2016, experts still predict an unsustainable spending trajectory.

 Of total healthcare spending, hospital-based care accounts for the largest percentage at 32%, or $1.056 trillion. In 2016, hospital costs increased by 4.6%, which, although 1% slower than the growth in 2015,

 is still unsustainable. Because of this unsustainable growth in healthcare costs and poor value for the amount we spend, major efforts are underway nationally to shift Medicare from a volume-based system to a value-based system. Due to the high cost of hospitalization, alternatives to hospital admission have become a focal point of that shift.

Providing hospital-level care at home as an alternative to hospitalization is showing promising results in value-based care and may become a significant asset to a chronic care model in a senior service line portfolio.

 A growing body of evidence suggests that providing higher-intensity acute care in the home achieves the quadruple aim of improving population health, lowering costs, and improving patient and provider experience.

 This particular type of hospital-level care at home has demonstrated significantly better outcomes for selected patients compared with standard inpatient hospitalization, including comparable mortality,

 In late 2017, the Physician-Focused Payment Technical Advisory Committee recommended that the secretary of HHS implement the hospital-at-home model as an advanced payment model.

Despite evidence that home-based acute care models are cost-effective and safe, the scaling and sustainability of these models has been limited by the historical lack of payment reimbursements by Medicare parts A and B.

 The objective of this paper is to address this issue by investigating the potential for Medicare cost savings and reductions in utilization when providing home-based acute care. This study is unique in that it explores the implications of using the Medicare home health benefit to pay for the delivery of needed care after an emergency department (ED) visit, as opposed to the typical stand-alone hospital substitute model described in the literature. We compare the costs and utilization for seniors transitioned from the ED to home health care versus admission to the hospital from the ED. Data were analyzed to explore (1) whether care delivered in the home following an ED visit has lower costs than that delivered in a hospital and (2) whether ED and hospital utilization are reduced in the 90 days following an episode of care for similar patients. Answers to these 2 questions have important implications for policy makers and the medical community, as adoption of acute home-based care innovations is currently limited in the United States.

We conducted a retrospective analysis of 5% of Medicare claims data using carrier and institutional claims from January 2012 through December 2013. Patients included in the analysis were 65 years or older and had visits originating in the ED with a principal diagnosis, identified by Clinical Classifications Software (CCS) groupings, of congestive heart failure (CHF), pneumonia, urinary tract infection (UTI), chronic obstructive pulmonary disease (COPD), or cellulitis. Patients with end-stage renal disease (ESRD) and metastatic cancer were excluded. Patients needed to be enrolled in Medicare fee-for-service (FFS) continuously as age-eligible beneficiaries for 6 months before the index ED visit and 3 months after ED or hospital discharge.

To answer our research questions, we constructed 2 cohorts: (1) patients admitted to hospitals from the ED (inpatient cohort) and (2) patients dispositioned from the ED to home health care (home health cohort). 

 shows how we created these cohorts and indexed the visits. For the inpatient cohort, the index episode of care was defined as the hospital stay. For the home health cohort, the index episode of care was defined as the home health stay.

To construct an inpatient cohort comparable with the home health cohort in terms of the potential to be dispositioned to care in the home, we selected patients with diagnosis-related groups (DRGs) with and without comorbidities or complications (CCs) but without major comorbidities or complications (MCCs). The following DRG codes were included: 292, heart failure and shock with CCs; 293, heart failure and shock without CCs/MCCs; 194, simple pneumonia and pleurisy with CCs; 195, simple pneumonia and pleurisy without CCs/MCCs; 690, kidney and urinary tract infections without MCCs; 191, COPD with CCs; 192, COPD without CCs/MCCs; and 603, cellulitis without MCCs.

Patients in the inpatient cohort also needed to have a LOS of fewer than 4 days. Additionally, inpatient cohort designees could not have a procedure code that may have required an overnight stay.

To generate a list of codes representing events that normally would require an overnight hospital stay, we first tabulated all 

International Classification of Diseases, Ninth Revision

) procedure codes, revenue center codes, and Healthcare Common Procedure Coding System (HCPCS) codes associated with hospitalizations for CHF, pneumonia, UTI, COPD, and cellulitis. Then, 2 healthcare practitioners, an emergency physician and a critical care nurse (J.H. and A.R.S.), each with clinical and research experience, independently examined these codes to identify procedures that conservatively would require a hospital overnight stay. Their resulting list of exclusion codes were then presented to a second emergency physician, not connected to the research team, for review. A final list was developed by these 3 clinicians based on discussion and consensus. A complete list of exclusion codes is listed in the 

Patients assigned to the home health cohort were those transitioned directly to home health care following an ED visit. To be included in this cohort, patients needed to have a visit by a home health provider within 2 days of an ED visit with CHF, pneumonia, UTI, COPD, or cellulitis as the principal diagnosis by CCS grouping in both the ED and the home health claims. The 2-day limit was imposed to ensure that only home health visits immediately after ED visits were included and no other claims from other providers were processed. To ensure that the 2 cohorts were comparable, we applied the same exclusion criteria to the home health cohort as we applied to the inpatient cohort, including procedure codes, revenue center codes, and HCPCS codes, and exclusion of any patient with metastatic cancer or Medicare eligibility due to ESRD.

This research compares the costs and utilization for the 2 cohorts. Cost variables include the amount of out-of-pocket (OOP) dollars the patient paid and the amount of Medicare reimbursement, including total reimbursements for the episode of care and costs in the 90 days following the index visit. The cost for the index episode of care included all facility fees, physician fees, and durable medical equipment. The 90-day follow-up costs included all costs from inpatient, outpatient, skilled nursing facility, home health, and hospice claims from both institutional and carrier claims. We used 2 common hospital measures to track utilization within 90 days of the index visit: (1) percent of inpatient hospital readmissions and (2) percent of ED revisits.

Regression models were used to compare the costs and utilization of the ED/hospital for the 2 cohorts. Ideally, the characteristics of these cohorts should be comparable. To account for differences among member profiles of the 2 cohorts, we used propensity scoring to balance the characteristics of the cohorts. The propensity score indicates the probability of a patient being in a group after all observable characteristics are controlled for. Through this technique, researchers can conveniently control for the probability of belonging in the case group (home health) or the control group (inpatient) and create a quasi-experimental design.

Typically, a logistic model is used to generate a propensity score. In this research, a simple logistic model was problematic in generating propensity scores because the sample sizes of the patient cohorts were imbalanced. The number of observations in the inpatient cohort was about 50 times the number of observations in the home health cohort. Ideally, the probability of being in a group should be higher than 0.1 because the logistic model may underestimate rare events.

 To address this issue, we first drew a 10% random sample from the inpatient cohort, then we combined this 10% random sample with the home health cohort and used a logistic model to calculate the probability of being in the home health cohort.

 Covariates for the propensity score model included age, gender, Medicaid eligibility, geographic locations, types of disease, a history of hospitalization, and Charlson Comorbidity Index (CCI) score.

We used a propensity score—weighted regression to evaluate costs and utilization of services.

 The propensity score weighting method was used for 2 reasons. First, there were relatively few observations in the home health cohort. Using propensity score matching further risked reducing the number of observations in the home health cohort. In addition, research has shown that propensity score weighting is more robust to misspecifications of regression models. It is more likely to generate an unbiased estimate of treatment effect, even when the model is not specified correctly.

A summary of the descriptive statistics by cohort, weighted and unweighted (available in eAppendix), shows that before propensity score weighting, observations in the 2 cohorts were different in several ways, including demographics, comorbidities, Medicaid eligibility, and geographical locations. Upon weighting, cohorts were more evenly balanced. The differences in age groups, average age, gender, Medicaid status, principal diagnosis, CCI score, and history of hospitalization became statistically nonsignificant. The only remaining statistical differences were geographical location and having a comorbid cancer diagnosis. Most important, once these groups were weighted, they had similar propensity scores: 0.0198 for inpatient versus 0.0200 for home health (

 = .812). This means that after weighting, patients in the inpatient cohort had near identical opportunities to be in the home health cohort.

The average actual OOP costs for the patient, average Medicare payment, and total average reimbursement for the index episode and the 90-day follow-up period are shown in 

. The average reimbursements for the home health cohort were much lower than those for the inpatient cohort. The average Medicare reimbursements for the home health cohort were $986 for patient OOP costs, $12,025 for the episode, and $13,012 for the 90-day total. For the inpatient cohort, reimbursements were $1965, $18,248, and $20,325, respectively (

 summarizes the 90-day inpatient hospital readmissions and ED revisits. Patients in the home health cohort had lower inpatient hospital readmissions at 23.7% (n = 84) compared with 32.8% (n = 5790) readmissions for patients in the inpatient cohort (odds ratio [OR], 3.796; 

 <.0001). There were no statistically significant differences in 90-day ED revisits between the home health cohort at 39.0% (n = 138) and the inpatient cohort at 42.7% (n = 7496) (

provides least squares mean linear regression modeled average cost differences for the index episode and the 90-day follow-up period: OOP, Medicare reimbursements, and total reimbursements comparing the inpatient and home health cohorts.

Propensity score—weighted regressions were used to model the impact of discharges to home health as opposed to inpatient admissions from the ED. The average OOP costs to the patient, Medicare payment, and total reimbursement difference for the index episode and 90-day follow-up were $975, $6338, and $7425, respectively, which means that the inpatient cohort costs were greater than those of the home health cohort with statistical significance (

The ORs for ED revisits and inpatient hospitalization within the 90-day period are reported in 

. Overall, compared with the home health cohort, patients in the inpatient cohort were more likely to be readmitted to the hospital (OR, 1.535;

<.0001). The OR for ED visits in the 90-day follow-up period was not statistically significant, at 1.806 (

In a comparison of patients transitioned from the ED with 1 of 5 conditions, those patients transitioned to home health care had overall cost and utilization advantages compared with those admitted to the inpatient setting. When costs were compared for the index episode plus the 90-day follow-up period, the lower costs of the home health cohort were highly statistically significant. When episode of care and follow-up period are considered, the cost delta reached $7495 per patient. This number could represent a significant cost reduction opportunity for Medicare and for patients. In this study, the inpatient cohort had 21,608 patients per 9-month period. Annually, this accounts for 576,213 patients across the nation who might have similar admission profiles. If acute home-based care options were available to all emergency physicians across the nation for just these 5 conditions, this could account for an annual savings of $3.7 billion in total costs, $3 billion in Medicare savings, and $520 million in patient OOP expenses. This suggests that programs that support more acute care at home could have a major impact on lowering healthcare costs.

Finally, our research shows that moving patients to home health care directly from the ED could result in a reduction in utilization and associated costs. Although we have considered the patient’s and the payer’s financial perspective in this analysis, one must also look at the hospital’s point of view. In general, inpatient admissions for the DRGs in this study may not be of major value to hospitals, as the DRG weights from the Medicare fiscal year 2015 final rule are all less than 1, with the highest geometric mean LOS at 3.8 days and arithmetic mean LOS at 4.5 days.

 Hospitals offering clinicians and patients a home-based care alternative could create an increased hospital capacity for planned procedure-based admissions that could enhance revenue and reduce overcrowding in the hospital as well as in the ED. Thus, the transition to home health care from the ED, when appropriate, would support a viable financial model from the perspectives of the patient, payer, hospital, and home health agency, assuming that the home health agencies are being appropriately reimbursed for these conditions, in the right setting.

This research sought to corroborate the improvement in outcomes related to cost and utilization that were previously demonstrated in the substitutive hospital model for Medicare Advantage and Medicaid patients.

 We used purely operational information from the Medicare FFS 5% claims so we could get a firm measurement of the cost, as carrier claims are available only in the 5% data set. Our model looks nationally at beneficiaries who were transitioned to home health care directly from the ED. We used an artificial construct, because currently, on a national scale, transitions of patients to home health care from the ED are a rare occurrence relative to inpatient admissions.

All factors within the claims data that we could abstract were used to control for acuity, comorbidity, geolocation, and other demographic factors. Our acuity and comorbidity control factors included using DRGs that did not contain MCCs, a LOS of less than 4 days in the inpatient group, CCI scores, revenue center codes, 

 procedure codes, and HCPCS codes that would not require hospitalization. In addition, we included patient age, gender, payer status, hospitalization history, principal 

 diagnosis by CCS category, age eligibility, and exclusion based on metastatic cancer and stays in the intensive care unit. Although we used propensity scores to balance these characteristics across the 2 cohorts, we recognize that physicians face more variables when they make the decision to disposition a patient to home health care or to admit a patient to the hospital. We did not attempt to control such differences in the study, as they are not present within the claims data set.

This analysis of Medicare claims data compared a small population of patients transitioned directly from the ED to home health care with patients transitioned from the ED to inpatient hospitalization, both with selected conditions. The home health cohort had statistically significant lower costs as well as reduced readmissions at a significant level. The favorable outcomes for this small number of patients transitioned from ED to home health care, derived from the Medicare FFS claims data set, suggest that home-based alternatives to hospitalization, using the home health benefit, are worthy of further exploration and testing in real-world scenarios. Developing processes to support an ED to home health care disposition option may benefit risk-bearing organizations such as accountable care organizations and Medicare Advantage participants. Although the scale and scope of this study is limited, the possibility of financial and utilization benefits supports continued research in this area.

Scripps Memorial Hospital (JH), Encinitas, CA; Gary and Mary West Health Institute (TK, ARS, CC), La Jolla, CA; La Jolla Pharmaceutical Company (FZ), San Diego, CA.

This work was solely funded by the Gary and Mary West Health Institute.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (JH, TK, ARS, CC, FZ); acquisition of data (JH, TK, FZ); analysis and interpretation of data (JH, TK, ARS, CC, FZ); drafting of the manuscript (JH, ARS, FZ); critical revision of the manuscript for important intellectual content (JH, TK, ARS, CC); statistical analysis (JH, TK, FZ); obtaining funding (FZ); and administrative, technical, or logistic support (TK, ARS).

Tyler Kent, BS, Gary and Mary West Health Institute, 10350 N Torrey Pines Rd, La Jolla, CA 92037. Email: tkent@westhealth.org.

1. Schneider EC, Sarnak DO, Squires D, Shah A, Doty MM. Mirror, Mirror 2017: international comparison reflects flaws and opportunities for better U.S. health care. The Commonwealth Fund website. commonwealthfund.org/interactives/2017/july/mirror-mirror. Published July 2017. Accessed March 28, 2018.

2. Medical cost trend: behind the numbers 2018. PwC website. pwc.com/us/en/health-industries/health-research-institute/behind-the-numbers/reports/hri-behind-the-numbers-2018.pdf. Published June 2017. Accessed March 28, 2018.

3. Office of the Actuary, National Health Statistics Group. National health care spending in 2016. CMS website. cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/NHE-Presentation-Slides.pdf. Accessed January 30, 2019.

4. Siu AL, Spragens LH, Inouye SK, Morrison RS, Leff B. The ironic business case for chronic care in the acute care setting. 

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5. Vianello A, Savoia F, Pipitone E, et al. “Hospital at home” for neuromuscular disease patients with respiratory tract infection: a pilot study. 

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6. Shepperd S, Doll H, Angus RM, et al. Avoiding hospital admission through provision of hospital care at home: a systematic review and meta-analysis of individual patient data. 

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7. Cryer L, Shannon SB, Van Amsterdam M, Leff B. Costs for ‘hospital at home’ patients were 19 percent lower, with equal or better outcomes compared to similar inpatients. 

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8. Aimonino Ricauda N, Tibaldi V, Leff B, et al. Substitutive “hospital at home” versus inpatient care for elderly patients with exacerbations of chronic obstructive pulmonary disease: a prospective randomized, controlled trial. 

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A retrospective analysis of Medicare claims was used to study emergency department (ED) dispositions, specifically evaluating inpatient admissions compared with home health referrals.

Improved Cost and Utilization Among Medicare Beneficiaries Dispositioned From the ED to Receive Home Health Care Compared With Inpatient Hospitalization

Healthcare systems are increasingly focused on improving outpatient quality metrics to achieve better clinical outcomes. In this study, we aim to explore a model in an outpatient setting to achieve high-quality care in the context of risk-based contracts with major payers.

We used data from a population health initiative conducted in a primary care setting.

 In a primary care practice, a member of a healthcare network based in Massachusetts and southern New Hampshire, a population health initiative was implemented to improve screening rates for breast cancer, cervical cancer, and colorectal cancer and to improve control of hypertension and diabetes. This intervention consisted of a team-based initiative involving population health managers, a licensed practical nurse, medical assistants, and primary care providers, who identified gaps in these quality measures from 2015 to 2017.

Screening rates for breast cancer showed significant improvement, from 88% in 2015 to 97% in 2017 (

 <.01). Cervical and colorectal cancer screening rates improved from below network compliance rates to surpass network performance. Control of hypertension also showed significant improvement (

 = .05). Control of diabetes was not associated with significant improvement (

The exhibited trends indicate that within the confines of limited resources in a local community setting, it is possible to improve delivery of quality care, leading to significant improvements compared with a larger network with more resources, without increasing systemic costs.

The American Journal of Accountable Care. 2019;7(1):4-9

Healthcare cost in the United States has been steadily increasing due to America’s aging population and the burden of chronic diseases.

 The staggering costs incurred to address our population’s healthcare needs cannot be sustained over the coming decades without an overhaul in healthcare fund disbursement. A possible solution implemented by many healthcare organizations across the United States has been the adoption of global spending contracts. One mechanism to help improve quality, while controlling costs, has been the adoption of accountable care organizations (ACOs). In an ACO, a global budget is established by insurance payers, encouraging provider groups to work together to keep spending below a set target in order to take advantage of shared savings. Provider groups also assume risk for excessive spending, further promoting careful allocation of funds.

The potential benefits of risk-based contracts have led healthcare providers to shift their focus away from the provision of supply-driven healthcare, centered around the profitability of services dispensed, and toward the delivery of value-based care. This system focuses on outcomes achieved per healthcare dollar spent, aiming to reduce costs while improving care. This system is beneficial for all stakeholders involved, including patients, who receive better care, as demonstrated by improved quality outcome metrics in healthcare systems that have already adopted this model.

Massachusetts pioneered the adoption of alternative payment models by entering into risk-based contracts with insurance payers, such as Blue Cross Blue Shield (BCBS) and Tufts Health Plan, as early as 2009 and participating in the Pioneer ACOs under Medicare. Years of data collection have shown that healthcare providers that participate in global contracts reduce spending growth and increase quality improvements compared with providers that do not participate in the contracts. According to a 2015 report, 90% of BCBS Massachusetts—covered specialists participated in the global contract.

 Effective March 2018, 17 healthcare organizations across Massachusetts entered into an ACO arrangement with the state’s Medicaid payer, MassHealth,

 demonstrating the state’s pioneering leadership in the arena of healthcare reform.

ACO performance is measured through several quality measures that capture a variety of metrics, such as patient coordination, safety, and preventive health services uptake.

 Quality outcome metric guidelines are provided by the insurers as part of the risk-based contract model. Quality bonuses can be awarded to organizations based on reported performance data that are related to processes, quality, and patient satisfaction.

Preventive care is an important aspect of alternative payment models and, partnered with population health initiatives, can be a powerful way to drive down costs while promoting better care.

 This focus is especially crucial in populations of racial/ethnic minorities, non-English speakers, and low-income individuals, who are less likely to be included in preventive care efforts but can benefit greatly from necessary screenings. The greatest benefit is seen when healthcare providers implement patient navigation efforts that are designed to promote inclusion.

Despite defined guidelines on how to measure quality outcomes, there is insufficient knowledge on how to build strategic initiatives within healthcare organizations in order to meet these goals, especially in a limited-resource setting.

 The collection of quality outcome data can require specialized infrastructure, including automated reporting systems and dedicated population health personnel.

 Relying on practice physicians alone to deliver adequate preventive care is not always feasible, mostly due to time constraints. Prior research findings estimate that it would take 7.4 hours per working day for a physician to provide all recommended preventive care screenings to patients.

 To ensure administration and reporting of preventive care in a primary care setting, many healthcare providers have adopted a team-based approach, with delegation of tasks to multiple members of the practice. Incorporating medical assistants (MAs) and licensed practical nurses (LPNs) into the quality outcome workflow has been shown to be beneficial and can be a less costly way to improve workflow without expensive organizational overhauls.

 We sought to determine whether a team-based approach would improve preventive quality metrics under an alternative payment model in a resource-constrained community practice model.

This study started as a population health initiative in a community health center, aiming to improve compliance with preventive health measures based on the best practice guidelines accepted by 3 major commercial payers. Compliance data were collected from 1 point in time midyear each year to assess which preventive outcomes should be the focus of the clinic’s efforts for the remaining half of the year. The patient population studied was made up of patients coming to Community Medical Associates (CMA) who were covered by 1 of the following 3 major commercial insurance plans: Harvard Pilgrim, BCBS, and Tufts Health Plan.

CMA is a patient- and family-centered medical practice serving the population of the Merrimack Valley region and southern New Hampshire. It is part of a broad healthcare organization network, which includes many hospitals and outpatient practices in Massachusetts. Starting in 2015, a population health initiative was implemented in the CMA primary care practice to improve screening rates for breast cancer, cervical cancer, and colorectal cancer (CRC) and to improve control of hypertension and diabetes. This intervention included a team-based approach with population health managers, an LPN, MAs, and primary care providers identifying gaps in quality outcomes and systematically following up on outpatient outcomes from 2015 to 2017.

The study period spanned from 2015 to 2017. The studied measures for compliance rate were screenings for breast cancer, cervical cancer, and CRC and control of hypertension and diabetes. Blood pressure guidelines were measurements of less than 140/90 mm Hg in the population aged 18 to 59 years, as well as in the population with diabetes and less than 150/90 mm Hg in the population aged 60 to 85 years. Overall diabetes control was defined as the percentage of patients with glycated hemoglobin (A1C) of 9.0% or less. Of note, the same standards of care were applied for the entire patient population at CMA, including the Medicare beneficiaries, but this study included only members of the previously mentioned 3 commercial insurance plans, given that the focus during the study period from the network perspective included only commercial payers.

Data collection for the outcome measures was done through claims-based search and complemented by data found within electronic health records (EHRs).

A periodic review of the medical records for the studied measures was performed throughout the study period by an LPN. The EHR system used was eClinicalWorks. The LPN would periodically obtain claims-based data from the athenahealth database. Using the available data, an individual chart review would be performed next, to supplement any missing or noncaptured information with information available in the patients’ charts. In addition, daily review of each provider’s schedule would be done, and providers and MAs would receive a message in the office note “chief complaint” section, reminding them to address any missing outcomes. The process was promoted and reviewed by the Division of Population Health under CMA’s affiliated hospital, Lawrence General Hospital.

Periodic and complete review for all studied quality measures was carried out on a monthly or bimonthly basis. For any gaps in quality measures, the provider or MA would receive a message to address it at the patient’s next office visit. The provider would in turn address the missing measure at the earliest convenience.

For this study, compliance data were collected from 1 point in time midyear each year. The local practice and the healthcare network compliance rates were compared, and 

This study presents an example of a population health initiative in a limited-resource primary care setting that led to significant improvements in preventive care quality metrics in the context of major insurance payers.

March 2019

Making Sense of Changes in Healthcare: Lessons From the AcademyHealth National Health Policy Conference

Effective Population Health Care Delivery Under Medicare’s Merit-based Incentive Payment System: Realigning Accountability With Capability

Improved Cost and Utilization Among Medicare Beneficiaries Dispositioned From the ED to Receive Home Health Care Compared With Inpatient Hospitalization

Improvement of Outpatient Quality Metrics in a Limited-Resource Setting

ACO Use of Case Mix Index to Comprehensively Evaluate Postacute Care Partners

Safety Net Representation in Federal Payment and Care Delivery Reform Initiatives