Many health insurance Marketplace shoppers have a lot of choices — more than those of us with employer-sponsored coverage. For states participating in HealthCare.gov, the average number of plans offered for 2016 coverage is 48. After learning, from a recent study, how little help is available for most Marketplace consumers, I’m sure I’d be highly challenged and frustrated by the task of choosing among this many plans.

After all, choosing coverage is hard. As I’ve written in several, prior posts, many consumers make mistakes in doing so. But they can do a better job if they have the right tools. For example,

Eric Johnson [...] conducted a series of experiments on people similar to those who would shop for marketplace coverage. Each study participant was asked to presume he’d use a certain amount of health care and, based on that, to choose the lowest-cost plan from among eight choices, which varied by premium, doctor co-pay and deductible. Only 21 percent could accomplish this task, a figure not statistically different from chance. [...]

But when study subjects were provided with a tutorial or with a calculator that revealed the full cost of each plan, or if they were placed in the lowest-cost plan by default (from which they could voluntarily switch), their chance of selecting the cheapest plan was much higher, upward of 75 percent in some experiments.

But, not many Marketplaces provided tools and features like this for the 2016 coverage year (or earlier years). I know from experience how useful they could be. Because I am eligible to participate in the Federal Employees Health Benefits Program (FEHBP) I have more choices than most workers, though less than half as many as Marketplace shoppers. The several times I looked over my 20 or so choices I yearned for two things: (1) to filter out plans that did not include the doctors and hospitals my family prefers and drugs we take; (2) to rank order remaining plans by expected total cost to me (premium plus out of pocket costs) if my family’s health care use remained about the same or if it grew substantially (e.g., if I or a family member got very sick).

Lacking those tools, I have stuck with my current plan for years. Switching, even though it might save me money, always seems like too much work and risk. So, I really feel for Marketplace shoppers.

For several years, Charlene Wong and other researchers at the University of Pennsylvania’s Leonard Davis Institute have examined the choice architecture of state Marketplaces. Last year, they found only a few that provided some of the tools consumers need. For example, only three states offered cost estimators. Recently, Wong and colleagues looked at what tools were available for selecting plans for 2016. Among their findings:

  • Estimates of total out of pocket costs were available only from two (of 13) state Marketplaces — California and Kentucky — during the “real” shopping phase. However, during “window shopping,” such a tool was not available in California but was available for HealthCare.gov, Connecticut, District of Columbia, and Minnesota. (The “real” shopping phase is when consumers actually establish an account to select an enroll in plans; “window shopping” can be done before account creation.)
  • Eight state Marketplaces (out of 13) and HealthCare.gov offered an integrated provider lookup (to readily find in-network providers without going to plan-specific websites), but only one could sort on provider inclusion and two could filter on it.
  • Neither HealthCare.gov nor any state Marketplace offered an integrated drug lookup during the real shopping phase, but Colorado did so during window shopping.
  • Two Marketplaces — Massachusetts and Rhode Island — provided some indication of network size.

Overall, these (and other results) represent progress form prior years. However, it may be confusing and inconvenient for consumers that some of platforms that provide certain tools only do so in the real or window shopping phases, but not both.

The authors wrote,

In the window-shopping experience, for example, the number of Marketplaces that offered total cost estimators increased from zero in the first enrollment period to five in the third, including HealthCare.gov for the first time. More Marketplaces had integrated provider lookups (there were three in the first enrollment period and eight in the third) and pop-up definitions (five and nine, respectively).

Most sites have continued to list plans by the premium amount. However, compared to previous enrollment periods, in the third period more sites were experimenting with alternative orders, including by estimated total out-of-pocket spending or with best-fitting or silver-tier plans first—especially for consumers who qualified for tax credits and cost-sharing reductions.

This is good, but more could be done. To be fair, it is not trivial to provide these tools. It requires planning, design, and implementation. That all costs money that some states may not have. Things will likely improve in future years. HealthCare.gov already announced that for the 2017 plan year, consumers will be able to select from “Simple Choice plans” that have a uniform structure, aiding comparisons. For example, deductibles and cost sharing won’t vary across the plans (within metal tier).

Another way to make progress on helping consumers is to learn more about how they select plans and to what extent different tools help them do so. That could be done with data from Marketplaces or HealthCare.gov about their choices and how they make them. To date, such data are not available.

Austin B. Frakt, PhD, is a health economist with the Department of Veterans Affairs, an Associate Professor at Boston University’s School of Medicine and School of Public Health, and a Visiting Associate Professor with the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health. He blogs about health economics and policy at The Incidental Economist and tweets at @afrakt. The views expressed in this post are that of the author and do not necessarily reflect the position of the Department of Veterans Affairs, Boston University, or Harvard University.

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I’ve written about telemedicine on this blog and at The Upshot, but neither post covered some recent work that nicely characterizes its state and hints at its future.

In an issue brief for the Health Care Cost Institute (HCCI), Fernando Wilson and colleagues report the number of state laws pertaining to telemedicine. The growth in state legislative activity pertaining to telemedicine is conveyed in their chart reproduced just below. Half of all such laws were enacted in the last five years.

telehealth laws Number of state telemedicine laws by year of implementation

Naturally, not every state regulates telemedicine the same way. One way to categorize state regulations is according to the three main types of telemedicine:

  1. Live video — This is what it sounds like, the use of video transmissions to allow patients and providers to interact, remotely and in real time
  2. Store and forward — The capture of images (e.g., radiologic or of skin conditions) or signals (e.g., electrocardiogram) for future examination by a remote specialist
  3. Remote patient monitoring (RPM) — Real time conveyance of patient vitals and conditions (e.g., those for a patient in an ICU) to distant providers

The chart below, from the same HCCI issue brief, indicates which states have enacted policies pertaining to each type of telemedicine. Twenty-four states have laws pertaining to live video. Among them, 21 require insurers to reimburse for service so delivered. Fourteen states have enacted store and forward policies, four of which require coverage for it. Six states have RPM policies, four requiring coverage.

state telehealth law type Number of states with enacted telemedicine policies, by transmission mode (RPM = remote patient monitoring)

A natural question is, to what extent are telemedicine services being delivered and how have they grown? Using data from five large insurers covering over 50 million people and spanning care delivered between 2009 and 2013, Wilson et al. examined telemedicine use by primary care physicians. It’d be an overstatement to say it’s modest. Of nearly 100 million primary care claims over that period, only about 6,500 were for telemedicine services (about 0.0065%).

Number of primary care telemedicine claims (2009-2013). * denotes fewer than 10. Number of primary care telemedicine claims (2009-2013). * denotes fewer than 10.

And yet, telemedicine use has nearly doubled from 2009 to 2013. It quadrupled since 2010. (For some reason it halved from 2009 to 2010. I don’t know why, and neither do the authors, but it’s one reason to be a little suspicious of the data, at least for 2009.)

Telemedicine is not reimbursed to the same extent as in-person visits. In 2013, the average reimbursement for a regular visit was $68. For telemedicine it is $38, or 45% lower. Perhaps it’s the lower reimbursement rate that explains low use of telemedicine services, though one might expect the cost of telemedicine to also be somewhat lower.

However, things could change, at least in some states. As of last year, seven states (Arkansas, Delaware, Hawaii, Minnesota, Mississippi, Tennessee, and Virginia) require commercial market reimbursements for telemedicine be on par with in-person visits. Wilson et al. also offer that it’s possible there’s some delay in physician practice response to the changing telemedicine environment.

In interpreting rate of change in telemedicine use, we also cannot rule out data issues. “One [14-year old] study of teleconferencing use in a university hospital found nearly one-third of teleconferences were not logged and thus not billed,” the investigators wrote. And, it needs to be emphasized that the Wilson study is only of primary care claims and only from five insurers. Other physicians reimbursed by other insurers, as well as public programs, may use telemedicine to a larger extent.

I think it’s likely telemedicine will continue to grow, even accelerate. One quarter of patients in the US do not have or lack access to a primary care provider and telemedicine is one way to improve access. In various ways, the Medicare program and the Affordable Care Act increasingly support telemedicine. According to a study by Jennifer Polinski and colleagues, the vast majority of patients are satisfied with telemedicine.*

We are several decades into the information age. Consumers are accustomed to information services — as much, though not all, of medicine is — to be available electronically, on demand (or at least with far less waiting), and far more conveniently than doctor visits have historically been. Though regulations and low payment rates may slow telemedicine, I don’t think they will stop it. I think, in time, they will change to meet demand.

* It should be noted that this finding is based on a survey of patients that self-selected into telemedicine and without contrast to comparable patients experiencing in-person visits.

Austin B. Frakt, PhD, is a health economist with the Department of Veterans Affairs, an Associate Professor at Boston University’s School of Medicine and School of Public Health, and a Visiting Associate Professor with the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health. He blogs about health economics and policy at The Incidental Economist and tweets at @afrakt. The views expressed in this post are that of the author and do not necessarily reflect the position of the Department of Veterans Affairs, Boston University, or Harvard University.

 

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For over 30 years, AcademyHealth’s Annual Research Meeting (ARM) has been the premier forum for health services research, where attendees gather to discuss the health policy and health system implications of research findings, sharpen research methods, and network with colleagues from around the world. This year’s meeting in Boston was the largest one yet with more than 2,900 attendees and included 150 sessions with more than 700 speakers and nearly 1,500 posters.

In this post-ARM series, blog posts will summarize key takeaways from sessions on four hot topics:

  1. Data and methods: Dealing with increased volume, variety and velocity of data
  2. The Affordable Care Act: Evaluating the latest in health care reform
  3. Translation and dissemination: Moving evidence into action
  4. Race, ethnicity and health

This is the fourth and final post:

Race, racism and diversity are at the forefront of conversation in America today. Across the board, industry leaders and communities are working to combat racial bias, reduce disparities and make strides towards inclusion— the health care and health services research fields are no exception. As stated in a recent report, “AcademyHealth believes the moment is right for a different kind of conversation to find new solutions about race, privilege, and equity in HSR.” This sentiment was an important, and prominent, theme at this year’s Annual Research Meeting (ARM) with sessions covering the role of both research and practice in finding and implementing new solutions to improve health equity.

Race, Ethnicity, and Health: The Role of Research

AcademyHealth Board Member Eduardo Sanchez began the first plenary of ARM16 by declaring the environment and atmosphere of the address as a “safe space” for “open conversation and honesty.” Moderated by Sanchez, this plenary set the tone for ongoing conversation throughout the meeting and was referenced in many subsequent sessions. During their presentations, panelists sought to examine the many ways that health services and policy research (HSR) can and should address questions of race and ethnicity to inform policy and practice and ultimately improve health and the performance of the health system.

Early on in the plenary, several attendees took to social media to live tweet and offer positive feedback on the opening address.

2016-06-26_11-15-44

Panelist Paula Braveman from the University of California San Francisco, School of Medicine, followed Sanchez with a challenge to attendees saying, “When a race variable predicts an outcome, I challenge you to ask, ‘Is it race or racism?’” Braveman also encouraged attendees to be aware that the race variable represents the totality of the experience that a person has had over his or her life—including experiences with health and the health care system. Eliseo Perez-Stable, Director, National Institute on Minority Health and Health Disparities, emphasized how important it is that researchers take a broad look at health disparities research by including an examination of both race and social class. Perez-Stable reminded attendees of the link between disparities and other social disadvantages due to discrimination, sharing a personal story of a patient’s expressed gratitude for his notes to patients being written in Spanish.

The final panelist, Joan Reede, Dean for Diversity and Community Partnership at Harvard University, noted that diversity has an important role in dealing with complex issues, posing the question: “What are we asking diversity to do?”. As our country becomes more diverse, Reede explained, diversity is important to realize our values, resolve complex issues and contribute to viability. Reede concluded her remarks by confronting and addressing current inadequacies in HSR, noting, “In a time of evidence and data, when it comes to diversity we do not track data/evidence”.

ARM2016_blogquote

Advancing Health Disparities Research Methods

Panelists at this session included, Aswita Tan-McGrory, Rosalind Raine, Amol Navathe with session chair, Alyce Adams.Presenters shared methods for identifying at-risk populations and for testing interventions to reduce disparities with research focused on critical barriers in the ability to conduct health disparities research. As the first panelist to present, Raine from the University College of London revealed that while England’s publicly funded healthcare system, the National Health Service, is free for everyone regardless of their ability to pay, widespread socio-economic disparities exist both in use and outcomes. Her research evaluated different interventions to address disparities in screening uptake and found that a reminder letter reduced the socioeconomic gradient in screening uptake. Tan-McGrory presented findings on best practices for collecting patient race, ethnicity, language, and disability data, which included asking the patient how they choose to identify as well as what language they prefer. Tan-McGrory concluded by sharing a personal story about her biracial children noting that one of her daughters self-identifies as Irish (like her dad) while her other daughter may choose to identify with her mother’s ethnic background.

Exploring the Health Care Provider Contribution to Health Inequities

During this session panelist explored the intersections of race, gender and socioeconomic status, health care delivery and the patient-provider relationship. Presenter Amelia Haviland noted that disparities, including differential treatment, within a plan or provider, may be based on patient characteristics. Haviland’s findings concluded that care may be improved and disparities may be reduced if physicians and advocates encourage patients to voice concerns. Other panelists, Anushree Vichare, Diana Burgess, Ruth Ludwick,presented findings highlighting the importance of healthcare provider communication, focusing on how patient income levels might play a role in the perception of a care giver’s communication skills.

Race Matters! An Update From the CMS Office of Minority Health (OMH)

Sponsored by the Centers for Medicare & Medicaid Services, Office of Minority Health (CMS OMH), participants in this session learned about the recent activities of CMS OMH and discussed cutting-edge research findings related to racial and ethnic disparities. Panelists, Judy Ng, Joseph Betancourt, Sai Loganathan and Amelia Haviland, emphasized the value of quality data in enabling organizations to monitor performance. When asked if organizations are both collecting and using data adequately, Betancourt shared that many organizations know they have a long way to go, but that it should certainly be a priority in achieving quality research on disparities. “Data collection is the foundation on which local and national interventions are built,” Betancourt said.

Diversity, Equity, and Culture Change in Research and Practice

This special session, held on the final day of ARM 2016, was moderated by AcademyHealth Vice President and co-author of the recent diversity report, Margo Edmunds. In her opening remarks, Edmunds explained the goals of AcademyHealth’s diversity efforts were to build community around health equity and inclusion and encourage new partnerships, all while creating a safe space for all. Edmunds also detailed the session’s connection to the opening plenary as a companion session. When asked who was in attendance for the plenary, most session attendees raised their hand. Panelist at this session included, Marshall Chin, University of Chicago, Soma Stout, Institute for Healthcare Improvement, Reginald Tucker-Seeley, Dana-Farber, Cancer Institute and Harvard School of Public Health and Rachel Hardeman, University of Minnesota School of Public Health. Throughout the session panelists emphasized ideas shared during the opening plenary, particularly the value of leadership and the challenge to attendees to “do better.” Presenters agreed that staff training is critical to assist employees in working with diverse populations. During the question and answer period, session attendees reflected on sentiments shared during the opening plenary and asked panelists to share their thoughts and reactions to the many challenges discussed. Panelists noted that a culture change of this magnitude will take time and dedicated effort, but the benefits of creating a “scholarship of belonging” would be many.

While race, ethnicity and health was certainly one of the most talked about themes at ARM this year, the conversation did not end with the conclusion of the meeting. AcademyHealth is committed to continuing the conversation on equity, diversity and inclusion in HSR by implementing a plan for promoting it throughout the field, communicating clearly about goals for increasing diversity and inclusion, publicly reporting on progress, promoting best practices and expanding training opportunities and recruitment strategies with communities of color. For more on AcademyHealth’s diversity efforts, read the report on workforce diversity 2025 roundtable here.

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It’s one thing to talk about the massive amount of money, trillions of dollars, that get spent every year in the US health care system. In 1997, that number was $1.5 trillion; in 2012, it had risen to $2.8 trillion, and health care spending as a share of GDP had increased by an absolute 4%. Last year, about one in every six dollars contributed to GDP was spent on health care.

But merely reciting the numbers makes it sound like it’s easy to cut. As I’ve said many times on our blog, though, one person’s waste is another person’s income. One in seven American workers is employed in the health care sector.

Where does all that money go, though? Last month, Sherry Glied, Stephanie Ma, and Claudia Solis-Roman published a paper in Health Affairs that gets at exactly that question. They used data from the Bureau of Labor Statistics and the Economic Census, from 1997, 2002, 2007, and 2012, and combined that with data from the Economic Census’s annual industry accounts. This allowed them to get some pretty detailed answers on health care revenue, costs, and expenses.

In 2012, revenues exceeded expenses by 10.2%. Half of this revenue went to workers. Almost half of all labor compensation went to physicians and nurses (23%); other health care practitioners and support staff (12%); and management, administration, and information technology staff (14.9%). More than a third of the total revenue went toward purchasing intermediate goods and services.

Labor costs have declined over time, though. From 53.2% of revenue in 1997 to 49.8% in 2012. That decline was sharpest in hospitals (54.1% to 48.8%).

One thing that fascinated me was the change in the workforce. In 1997, one in every 17.8 people working in the three major subsectors was a doctor. But by 2012, that increased to one in every 16.6 people. The number of employed doctors grew by nearly a third, which was faster than the growth of the entire healthcare sector (24.5%). For all of the talk of the doctor shortage (which I will explore in detail at a later date), we seem to have been adding physicians to the system at an improved clip.

Additionally, for all the complaints of doctors about reimbursement, inflation-adjusted earnings for doctors outpaced both the health care sector overall and the economy at large, rising by more than 35% over the studied period. Nurses also increased in number (33.2% over the study period), which was three times as much as the 11.3% increase in total US employment over the same period. Their inflation-adjusted earnings increased by more than 30% as well. If you combine employment growth with the increase in earnings, the share of total revenue paid to doctors and nurses grew by more than 80% over the study period.

Still, an even more rapid increase in employment happened in health care support positions, like aides and assistants, which grew by more than 53%. Growth in physicians’ offices more than doubled. IT positions also increased rapidly, by more than two thirds. But since IT comprised so little in overall numbers in 1997, the total numbers in 2012 are still relatively small.

There are much more data in the actual paper, and I encourage you to go read it in full. But the overall picture leads the authors to suggest that health care spending had been influenced by a number of factors, including changes in regulations and the market, trends in the economy, and the changing and expanding role of technology. While payments from insurers have decreased, providers have adjusted expenses to compensate, pretty well in fact. Labor as a share of revenue has declined, though, as goods and services, especially technology, have increased as a share of expenses. Their conclusion:

Changes in the health care sector—including the development of new delivery systems and the introduction of new technologies—are likely to alter where the money in the sector goes and who receives how much of it in the future. Monitoring these aggregates therefore serves as a useful corollary to studies of specific reforms and a necessary element in sensible policy design.

Aaron

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For over 30 years, AcademyHealth’s Annual Research Meeting (ARM) has been the premier forum for health services research, where attendees gather to discuss the health policy and health system implications of research findings, sharpen research methods, and network with colleagues from around the world. This year’s meeting in Boston was the largest one yet with more than 2,900 attendees and included 150 sessions with more than 700 speakers and nearly 1,500 posters.

In this post-ARM series, blog posts will summarize key takeaways from sessions on four hot topics:

  1. Data and methods: Dealing with increased volume, variety and velocity of data
  2. The Affordable Care Act: Evaluating the latest in health care reform
  3. Translation and dissemination: Moving evidence into action
  4. Race, ethnicity and health

This is the third post:

Health services research produces evidence that can have a transformative effect on healthcare quality and value, but the real promise of this information is in what happens after the research project ends. Moving knowledge into policy and practice is a key component of AcademyHealth’s mission, and can be seen in the regular presence of dissemination and implementation (D&I) science as a track at the Annual Research Meeting, in the conduct of AcademyHealth programs and projects, and in the formation and programing around our Translation and Dissemination Institute.

In fact, D&I research was woven throughout the 2016 Annual Research Meeting (ARM) in Boston, with sessions touching upon new methods in moving evidence to action as well as how individuals in health care and beyond are working to tackle obstacles to effective dissemination. Below is a sampling of the D&I-related sessions hosted at ARM.

Entrepreneurship in Bridging Evidence, Policy and Practice: A Conversation

As moderator Michael Gluck began, entrepreneurs tend to share characteristics: they have an idea for something new; embrace the idea of “disruption;” are willing to take risks, acting even when the outcome isn’t assured; and are flexible, able to operate in an environment of ambiguity.

This discussion-formatted session featured four individuals who have been successful at bridging the gap between evidence and policy. Each of the panelists was motivated by a frustration with the status quo and a desire to disrupt it:

  • Robin Strongin (Disruptive Women) wanted to innovate across the decision chain and demonstrate how social media, namely a blog of diverse women working across law, bench science, and health care, could be used to leverage information and exchange ideas about the health care information being released.
  • Karen Wolk Feinstein (Pittsburgh Regional Health Initiative) was frustrated working in a health care environment with no grounding in safety science and engineering and used the Turner “Knowledge Network” model as inspiration for new organizational alignment, staff education, and unity.
  • Gregg Gonsalves (Treatment Action Group) triggered a social movement of “normal people” who took on federal agencies after becoming fed up with the incredibly slow drug approval process, but then identified a gap of information in the drug market and made it the group’s mission to expand and accelerate research and community engagement; and
  • Ran Balicer (Clalit Research Institute) overcame an organizational mindset against research to create an evidence-based and evidence-oriented practice to enhance efficiency, provide clinicians with a better understanding of their workloads, and ensure alignment with the organization’s priorities.

The conversation was robust and captivating, with panelists urging attendees to break down walls, let people in from the outside, be interdisciplinary, first tackle the “critical points,” and, let passion be their guide.

Direct Observation Methods for Dissemination and Implementation Research

This engaging, didactic workshop, hosted by the Innovation Station, featured three researchers who provided an introduction into direct observation, a quantitative and qualitative method used in health services research and implementation science that can provide researchers with direct insight into the environment being studied. Direct observation involves continuously or instantaneously watching a given environment for better understanding of processes. It can capture routinized, unconscious behaviors, provide (work/life) context of the staff or clinic, document a process, behavior or interaction, illustrate what’s happening in a program or complement quantitative data.

Using a clip of documentary “The Waiting Room” as a simulation of real life, Megan McCullough, Bo Kim, and Gemmae Fix allowed participants the opportunity to experience direct observation for themselves. Teams then huddled together to examine the footage from different lenses and highlight what was important as well as to identify the research questions that could be answered from the footage.

To continue the conversation about D&I Science, join us at the 9th Annual Conference on Dissemination and Implementation in Health, December 14-15, 2016.

 

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By: Danielle Robbio, AcademyHealth

In a time of significant health care transformation, many health insurers and health care providers are moving toward payment models based on the quality of care rather than the quantity in an effort to attain the Triple Aim of better care, smarter spending, and healthier people. The U.S. Department of Health and Human Services is working toward having 90 percent of Medicare payments tied to quality by 2018, and, at the state level, many are exploring a variety of new payment approaches through State Innovation Models funded by the Center for Medicare and Medicaid Innovation (CMMI).

While these payment reform efforts have a clear tie to the smarter spending aspect of the Triple Aim, what about their impact on people’s health? Right now, most of these value-based payment models, as they’re known, focus on clinical services and specifically focus on the needs and outcomes of a particular health care provider’s patients, a health plan’s enrollees, or the purchaser’s employee subscribers. Other payment models focus on a targeted sub-population of individuals with a defined clinical condition, such as patients with diabetes or depression. As such, payment and financing models are not yet adequately supporting community-wide, that is, geographically-based, population health. The incentives in these models do not yet reward health care providers for creating healthy communities, nor do they incentivize other sectors—transportation, housing, education—for population health improvements.

In light of this disconnect between payment reform and community-wide population health, AcademyHealth, with support from the Robert Wood Johnson Foundation, is leading a new effort called Payment Reform for Population Health to identify where momentum and opportunities exist to close the gap. As a neutral broker of information, AcademyHealth supports the generation of new knowledge and the transfer of knowledge into action. Supported by a broad and diverse network of researchers, policymakers, and practitioners, AcademyHealth is uniquely positioned to collaborate and facilitate connections, create shareable resources, and co-create strategies for using payment reform to support population health improvement.

This cross-team, multidisciplinary effort brings together AcademyHealth’s experts in payment reform, population and public health, health care data management, and research and analytic methods to identify where payment reform and population health intersect (to learn more about the project team, click here).

Past RWJF-supported initiatives as well as state and federal-led demonstrations have shown that payment reform efforts supporting population health improvement face persistent challenges. Yet, the team has observed encouraging innovations in the Population Health Community of Practice during the past year and by those expected to come from the recently-launched Office of the National Coordinator funded Community Health Peer Learning Program. Beyond AcademyHealth, the team is also looking to the innovative work of others, such as the Health Care Payment Learning and Action Network (LAN). These programs and networks illustrate the on-going efforts of health systems and other stakeholders to innovate and implement successful population-focused interventions.

One of the hallmarks of this Payment Reform for Population Health initiative is its emergent approach. Because working emergently with complex issues requires ongoing attention to multiple levels of change, AcademyHealth has set up processes that support adaptation to the changing context. We have also committed to supporting discovery and knowledge sharing through strategic collaboration with those already improving population heath as well as with those developing and implementing alternative payment models. Specifically, an in collaboration with our Guiding Committee, practitioners, and other key stakeholders, we will work to:

  • leverage and learn from efforts underway and work to support and enhance their activities;
  • assist in the spread of ideas, knowledge and evidence; and
  • support those initiatives to overcome persistent, yet surmountable barriers to achieve success.

After conducting structured interviews with 18 experts and convening our first in-person Guiding Committee meeting, we’ve already identified several barriers to payment reform for population health, including:

  • multiple definitions of population health;
  • the complexity of how various payment models can impact social determinants of health;
  • misalignment of financial incentives;
  • lack of appropriate data;
  • lack of adequate outcomes measures;
  • insufficient evidence for which population health initiatives might have the greatest impact;
  • inability to replicate and scale innovations across various communities; and
  • the lack of a business case or return-on-investment for such activities.

Next steps include digging deeper into these challenges and working to identify and disseminate promising solutions to overcome shared challenges.

Stay tuned here, at the AcademyHealth blog, for updates on our initiative’s progress and emerging findings. If you are doing related work, please contact Enrique Martinez-Vidal; we’d like to include you in our efforts!

Danielle Robbio, is a research associate on the Public and Population health team where she works to bridge the gap between public health and health care.

 

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For over 30 years, AcademyHealth’s Annual Research Meeting (ARM) has been the premier forum for health services research, where attendees gather to discuss the health policy and health system implications of research findings, sharpen research methods, and network with colleagues from around the world. This year’s meeting in Boston was the largest one yet with more than 2,900 attendees and included 150 sessions with more than 700 speakers and nearly 1,500 posters.

In this post-ARM series, blog posts will summarize key takeaways from sessions on four hot topics:

  1. Data and methods: Dealing with increased volume, variety and velocity of data
  2. The Affordable Care Act: Evaluating the latest in health care reform
  3. Translation and dissemination: Moving evidence into action
  4. Race, ethnicity and health

This is the second post:

While provisions of the Affordable Care Act (ACA) began taking place in 2010, some of the most significant aspects of the law have taken effect in the last two years. Recently several in the health services research community have shared thoughts evaluating the ACA. Even President Obama has weighed in. This year’s Annual Research Meeting (ARM) was no different, with several sessions including late-breaking research on topics including coverage, access, quality and cost of care in a post-ACA world.

Late-Breaking Session: Does the Health System Have Enough Capacity in the Era of Health Reform?

These late-breaking abstracts examined capacity across a variety of settings and patient populations. Daniel Polsky presented research showing increased access to primary care and Megan Cole presented on improved quality in community health centers. Other papers drilled down to capacity for specific areas of care, such as dental coverage. Amber Willink’s research found that less than half of all Medicare beneficiaries had any dental coverage and Cameron Kaplan’s paper found that ACA tobacco penalties lead to lying and under-enrollment amongst this population. Caitlin Crowley presented research on the health center workforce that found that 95 percent of health centers have at least one clinical vacancy. Questions from attendees focused on how access has changed over time and how differences in service type and location might affect usage and volume.

Late-Breaking Session: New Evidence on Medicaid and Private Insurance Coverage Expansions

This session highlighted key findings from late-breaking research on public and private health insurance expansions under the ACA. Research presented by John Graves revealed new dynamics of U.S. health insurance. Benjamin Sommers presented research comparing three states’ (Arkansas, Texas and Kentucky) changes in utilization and health. Overall, researchers found positive downstream effects after expansion, whether via Medicaid or a private option as was the case in Arkansas. Affordability, preventative care and self-reported health outcomes all increased while reliance on emergency department care decreased. Joseph Thompson presented findings on Arkansas’ private option showing a clear benefit between Arkansas and neighbor states without expansion. Stacey McMorrow presented a study looking at the ACA’s effect on employee-sponsored and private insurance found that coverage expansions may have reduced churn between public coverage and uninsured status among low-income adults. Chima Ndumele presented results from a study showing no Medicaid quality erosion based on capacity constraints caused by expansion. Comments and questions from the audience focused on opportunities for further research looking at affordability and cost-sharing subsidies effects as well as comparisons with non-managed care plans.

The Impact of the ACA on Coverage

Research presented in this session examined which particular parts of the ACA have had a positive benefit on coverage and where challenges remain. While there are several incentives to encourage people to sign up for health insurance, research presented by Benjamin Sommers found that 2014 coverage gains were more sensitive to the percent subsidy than the premium dollar amount, and the mandate penalty had little effect. Other research looked at some adverse impacts of other provisions of the ACA such as Erin Trish’s data on small businesses self-insuring to avoid community rated premiums and Lisa Dubay’s presentation on the “family glitch,” which prevents some families from receiving marketplace tax credits because one adult has access to affordable work-only employer coverage. Questions from attendees focused on Kristin Kan’s research findings about a lack of access to pediatric subspecialty care and Lucas Higuera’s projections that the proposed Cadillac tax for employers to pay on high-cost health insurance plans would result in employers substituting increases in health insurance benefits with increases in wages.

The Impact of Coverage Options on Access, Quality and Health

Research in this session looked at how competing coverage plans are impacting individuals’ access to care, quality of care, and health outcomes. One study, presented by Sandra Decker, looking at the impact of Medicaid expansion on access to care and health found significant gains in insurance coverage, primary care visits, and specialist care visits in expansion states compared to non-expansion states, but no significant changes in self-reported physical or mental health. Chima Ndumele reported that plans that stay in the marketplace perform better in terms of quality than those that leave, but that there is no effect of plans leaving on those that stay. A third study from Sara McMenamin analyzed policy options to limit patient cost-sharing for prescription drugs noting that as of the beginning of this year, there were 16 states that have enacted legislation on this topic. In a study looking at high deductible health plan (HDHP) impacts presented by James Wharam, researchers found that women with HDHP experienced delays from diagnoses to treatment. In a similar concerning finding, Jennifer Lewey presented research showing that among commercially insured patients with chronic medical conditions, switching to a HDHP was associated with a significant and immediate decrease in adherence to evidence-based medications.

 

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Integrated delivery systems (IDSs) are vertically integrated health service networks that include physicians, hospitals, post-acute services, and sometimes offer health insurance. In short, within a single organization, they provide a broad spectrum of coordinated inpatient and outpatient care. Kaiser Permanente is the quintessential example, but there are many other IDSs.

It has long been claimed that the greater care coordination provided by IDSs improve quality and outcomes at lower cost, in large part from elimination of duplication and unnecessary or avoidable care. A larger organization might, for example, streamline and consolidate common needs such as information technology and human resources, reducing costs. An integrated organization might more successfully implement quality improvement programs or might more frequently deliver the right kind of care, in the right setting, at the right time, improving outcomes.

Last year, for the National Academy of Social Insurance, Jeff Goldsmith, Lawton Burns, Aditi Sen, and Trevor Goldmith surveyed the evidence on IDS performance and conducted some additional analyses. Their conclusion:

[T]here is little evidence that integrating hospital and physician care has helped to promote quality or reduce costs. Indeed, there is growing evidence that hospital-physician integration has raised physician costs, hospital prices and per capita medical care spending. Similarly, hospital integration into health plan operations and capitated contracting was not associated either with clinical efficiency (e.g. shorter lengths of stay) or financial efficiency (e.g. lower charges per admission).

Their literature review included a 2006 study by Cuellar and Gertler that examined the effect of hospital-physician integration on costs, quality, and prices, using data from three states in the mid- to late-1990s. Overall, they found that integration does not reduce costs, but it increases prices by about 6%, with small improvements in quality. Integrated, large, nonprofit teaching hospitals, however, did not have higher prices and provided higher quality of care than non-integrated hospitals and other kinds of integrated ones. Working with Medicare data and also examining the mid- to late-1990s, Madison found that hospitals that employ physicians are associated with more procedures and higher expenditures, with no appreciable effect on outcomes.

Several more recent studies — also cited by Goldsmith et al. — have found hospital-physician integration associated with greater spending and higher prices. Baker, Bundorf, and Kessler examined a privately insured population between 2001 and 2007. They found that vertical integration increased hospital market share, prices, and spending, despite a small effect of reducing hospital admissions. With data from California spanning 2009-2012, Robinson and Miller found that physician organizations owned by local hospitals spend 10% more per patient than physician owned groups. When they’re owned by multi-hospital systems, they spend nearly 20% more. Two studies led by Gans found lower physician productivity associated with hospital employment, relative to physician-owned multi-specialty groups.

Hwang et al. conducted a systematic review of literature published between 2000 and 2011 on the effects of various kinds of physician integration on cost and quality. Only two peer-reviewed studies reviewed pertain to IDSs as defined here and compared their performance to non-IDS care.* Rittenhouse et al. found that hospital- or plan-owned physician practices were more likely to use evidence-based care. Similarly, Shortell et al. found that hospital or health plan affiliated medical groups were more likely to better manage care.

Carlin, Dowd, and Feldman examined changes in quality associated with two hospital acquisitions of three multispecialty clinics in the Minneapolis–St. Paul area. They found small improvements in cancer screening and appropriateness of ED use but also increased probability of ambulatory care sensitive hospital admissions when the acquisition disrupted prior physician-hospital admitting relationships.

Goldsmith et al. also examined 15 nationally prominent IDNs:

IDNs

Among their analyses was a comparison of each of the IDN’s flagship hospital to its main in-market competitor. They found that flagship hospitals with no revenue at risk (meaning fully reimbursed for all costs) have 8% lower Medicare per case costs than their competitors. Those with revenue at risk (e.g., sponsor their own health plan, are capitated, or subject to a two-sided ACO model) are 20% more costly than competitors. They found no meaningful differences in clinical quality or safety scores for IDN flagship hospitals, relative to competitors, but the vast majority of IDN flagships had a higher case-mix adjusted cost per case and spent more on end-of-life care.

Why do IDNs fail to consistently reduce costs and spending and only intermittently or marginally improve quality? One explanation is that a larger and more diverse organization is more difficult to manage.

A major contributor to the performance problem is that diversification increases the firm’s size and complexity, which in turn increases the firm’s cost of coordination, information processing, and governance and monitoring.

Related to this explanation is that IDN formation is only a structural change. It doesn’t necessarily, by itself, make a positive impact on processes of care that would be necessary to consistently improve outcomes and efficiency. Another explanation is that IDNs may command greater market power and use it to increase prices in the commercial market. If it’s perceived to be a “must have” organization in plans’ networks, it may not need to work as hard on quality either. An NBER working paper by Baker, Bundorf, and Kessler found that patients who see a hospital-owned physician are more likely to visit high-cost, low-quality hospitals.

The trend is for greater integration between hospitals and physician practices. Prior work on IDNs does not provide confidence that this trend will produce better outcomes at lower costs.

*Hwang et al.’s definition of an IDS includes multi-specialty physician group practices that are not hospital owned whereas, this post defines an IDS as integration between physicians and hospitals.

Austin B. Frakt, PhD, is a health economist with the Department of Veterans Affairs, an Associate Professor at Boston University’s School of Medicine and School of Public Health, and a Visiting Associate Professor with the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health. He blogs about health economics and policy at The Incidental Economist and tweets at @afrakt. The views expressed in this post are that of the author and do not necessarily reflect the position of the Department of Veterans Affairs, Boston University, or Harvard University.

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For over 30 years, AcademyHealth’s Annual Research Meeting (ARM) has been the premier forum for health services research, where attendees gather to discuss the health policy and health system implications of research findings, sharpen research methods, and network with colleagues from around the world. This year’s meeting in Boston was the largest one yet with more than 2,900 attendees and included 150 sessions with more than 700 speakers and nearly 1,500 posters.

In this post-ARM series, blog posts will summarize key takeaways from sessions on four hot topics:

  1. Data and methods: Dealing with increased volume, variety and velocity of data
  2. The Affordable Care Act: Evaluating the latest in health care reform
  3. Translation and dissemination: Moving evidence into action
  4. Race, ethnicity and health

This is the first post:

ARM 2016: Dealing with increased volume, variety and velocity of data

It is no secret that the environment for health services research (HSR) is evolving rapidly, especially with significant changes in the volume, variety, and velocity of data available and an increasing number of opportunities for collaboration across sectors. In response, AcademyHealth is working to build an infrastructure of training, methods, and governance to support evolving and emerging data streams and to address relevant research questions in rigorous and novel ways.

Formulated from the best abstracts submitted to this year’s ARM, “Best of ARM: New Developments in Data and Methods That Will Transform Our Field Over the Next 5 Years” seeded a provocative discussion on transformational changes in data and methods. The 90-minute discussion highlighted impact, accomplishments, and challenges of using data and methods to improve the health care system and an overall culture of health among Medicare beneficiaries, patient-research, relationships and more.

The four presentation in this methods-focused panel included:

  1. The Impact of Suppressing Substance Use Data on Measures of Chronic Conditions, Hospitalization, and Spending Among Medicare Beneficiaries
    Hundreds of studies of the U.S. health care system each year rely on administrative claims data, like those in the Medicare program, to evaluate the efficiency of the health system, utilization patterns of patients, and quality of care. The presentation of this paper by Julie Bynum explained how the redaction of substance use data has constrained efforts to evaluate what works and what does not work for populations with mental illness or substance use disorders. As a result, Bynum noted, the health system has lost a vital source of information on some of the nation’s most vulnerable patients.
  2. Identifying Patients at High-Risk for Readmission Using Socio-Behavioral Patient Characteristics
    During his presentation, Amol Navathe presented and discussed automated methods for analyzing physician notes which could enable better identification of patients with high socio-behavioral needs who are at increased risk for readmission. This information, he indicated, could improve health system allocation of care management resources to the highest need patients.
  3. Initiative to Support Patient Involvement in Research (INSPIRE)
    This presentation focused on the INSPIRE initiative, which aims to identify training and support needs and synthesize existing tools and resources to meet those needs, while creating a network to help researchers and patients better connect and collaborate in patient-centered outcomes research (PCOR). Danielle Lavalle presented INSPIRE findings around how individual patients and researchers approach research partnerships and how organizations can develop infrastructure and resources that encourage and support patient-researcher relationships.
  4. Linking to Clinical Registrants with the All Payer Claims Database: A Powerful Source of Data to Reform Health Care
    This presentation focused on a New England patient-centered medical home (PCMH) program, and its use of two large databases to guide its efforts: the state’s all-payer claims database and a statewide clinical registry with primary care practice data. Amy Kinner explained that data from the two databases are being used to drive efforts with accountable care organizations and payment reform initiatives focused on the statewide population—an example that could potentially be applicable to the rest of the United States.

These four papers provided clear examples of the potential data and methods have to advance health care, health services research, and improve the overall culture of health. Panelists noted the importance of remembering that each community and patient is different, and studies should be designed to best fit the population in need.

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So often, when we implement new policy, I wish we had better ways to capture its effects so that we could expand our knowledge base as to how decisions change health and health care. The Oregon Health Insurance Experiment, and its older brother the RAND HIE, were RCTs designed to look at how insurance affected utilization and health. While these were impressive studies, they had their flaws.

RCTs are hard to do, though; they’re also expensive. Sometimes, other designs are necessary. Recently, in Annals of Internal Medicine, Laura Wherry and Sarah Miller looked at how the Medicaid expansion has changed things. “Early Coverage, Access, Utilization, and Health Effects Associated With the Affordable Care Act Medicaid Expansions: A Quasi-experimental Study“:

Background: In 2014, only 26 states and the District of Columbia chose to implement the Patient Protection and Affordable Care Act (ACA) Medicaid expansions for low-income adults.

Objective: To evaluate whether the state Medicaid expansions were associated with changes in insurance coverage, access to and utilization of health care, and self-reported health.

Design: Comparison of outcomes before and after the expansions in states that did and did not expand Medicaid.

Setting: United States.

Participants: Citizens aged 19 to 64 years with family incomes below 138% of the federal poverty level in the 2010 to 2014 National Health Interview Surveys.

Measurements: Health insurance coverage (private, Medicaid, or none); improvements in coverage over the previous year; visits to physicians in general practice and specialists; hospitalizations and emergency department visits; skipped or delayed medical care; usual source of care; diagnoses of diabetes, high cholesterol, and hypertension; self-reported health; and depression.

As we’ve discussed many times before, only about half of states initially decided to join the Medicaid expansion. This meant that – in those states – anyone making less than 138% of the federal poverty line could get Medicaid. In other states, many poor people were out of luck. The law provided no subsidies to people earning less than the poverty line, and without new Medicaid, many had no affordable options for insurance.

This study sought to compare how adults who would qualify for the expansion compared to those in states with and without it. The researchers used data from the 2010 and 2014 National Health Interview Surveys to compare health insurance coverage, utilization, diagnoses of some illnesses, self-reported health, and depression. They used a quasi-experimental difference-in-difference design to compensate for secular changes as well as time-invariant differences in characteristics across all states. They excluded five states that already pretty much provided expansion-like coverage before 2014.

They found that, by the second half of 2014, adults in the expansion states had seen their health insurance coverage increase 7.4%; Medicaid coverage increased 10.5%. This isn’t surprising, as increased coverage was the main intent of the Affordable Care Act. Coverage was found to have “improved” as well (7.1%).

They also found that, in Medicaid expansion states, there were increased in physician visits (6.6%), hospital stays (2.4%), rates of diagnoses of diabetes (5.2%) and high cholesterol (5.7%).

Of course, this is an observational study. It’s possible that other confounders exist that are the reasons for these changes. These are also very short-term data. They also couldn’t find real differences in terms of access.

But, as I’ve discussed before, insurance coverage is just the first step in improving access. What this study adds are some data showing that expanding Medicaid through the ACA resulted in increased coverage, improved coverage, more physician visits, and more disease diagnosed.

It will be important for us to continue these types of studies as we move forward, to understand better the full impact of the ACA. But if future analyses continue to show improvements in coverage, utilization, and health from the Medicaid Expansion, it may become more difficult for the 19 remaining states to refuse it without offering alternative paths to the same achievements.

Aaron

For more reading on the effects of the ACA, you might also enjoy both the HSR systemic review blog piece written earlier this week here on the AcademyHealth blog and the President’s recent JAMA article

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