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Analytics & Research

The Future of Disease Surveillance with EHR Data

Iyue Sung, Director of Core AnalyticsEvery year around this time, we anticipate news coverage on the flu season, the time when influenza is at its most prevalent in the U.S., and something we’ve tracked in past years. But this year has been quite different: First, a broad and severe outbreak of enterovirus has changed the conversation about infectious disease, the virus leading to high rates of hospitalizations since it first surfaced in the Midwest in August; second, the first confirmed case of Ebola in the U.S. was reported last week in Dallas.

With these developments, coordinated and accessible data is now more vital than ever for understanding the patterns of these diseases; where they’re rising, ages that are most at risk. While officials at the Centers for Disease Control and Prevention (CDC), working with local public health departments, are vigilant in monitoring and providing guidance for these and other outbreaks, data from electronic health records (EHRs) can and should play a more integral role. You see this starting to happen with Meaningful Use, the government incentive program designed to increase adoption of EHRs. The program requires that providers be able to “submit syndromic surveillance data to public health agencies.”

However, the ability to implement and develop the capabilities to feed this data into one streamlined network for public health agencies may be several (or more) years away. This is simply because the adoption of EHRs and the “connection” of data is a massive undertaking. There are existing infrastructures to monitor and manage disease outbreaks — the National Notifiable Diseases Surveillance System being the most notable one. As data from EHRs become more widely centralized and accessible, the hope is these officials will be able to spend more time analyzing the data versus collecting it.

What could the future of disease surveillance look like with real-time EHR data from ambulatory care settings being fed to public health agencies? The athenaResearch team examined the latest disease trends for enterovirus and influenza, based on diagnoses across our network of more than 55,000 health care providers. These providers all use either athenahealth’s cloud-based billing, or its EHR applications, both of which document diagnoses and procedures from patient encounters. And since these applications are “single instance” (everyone uses the same software version), the data is centralized in a common database. In other words, our providers and their data are already connected on a common network.

Figure 1 shows the rate of upper respiratory infection (URI) during the first 39 weeks of the year (week 39 ended Saturday, September 27) in the Midwest states of Illinois, Indiana, Michigan, Missouri and Ohio. These five states were chosen because the recent enterovirus outbreak roughly started in this band of states; URI was selected since it is a common diagnosis related to the virus.

Comparing 2014 diagnosis rates to those in 2012 and 2013, we can see a sharp peak in week 37 (week ending September 13) that coincides with the Midwest outbreak. Now, URI is a much broader diagnosis and doesn’t necessarily indicate enterovirus. However, the point is that this data is available from our cloud-based network practically upon demand — and calculating alternative measures that may indicate enterovirus (short of confirmed lab tests) is a straightforward process.

Just as important is that this type of data provides geographic- and demographic-specific information. For example, Figure 2 shows pediatric URI rates for each state, for the last four weeks.

Given that flu season is near, we’d also like to share our tracking of influenza-like illness (ILI). athenahealth has been participating in flu tracking over the past year, primarily to determine whether our data reflects national health care trends, and to fill in as a supplemental data source during the government shutdown last fall.

Figure 3 shows rates of ILI from two sources: the CDC’s outpatient illness surveillance network (ILINet), and estimates from athenahealth’s cloud-based network (athenaNet). Although our estimates do not exactly match those from ILINet, the patterns are fairly striking, with “peaks” and “valleys” occurring in parallel.

Our goal over the next year is to improve the accuracy of our estimates and continue to learn how this type of data can be valuable for disease surveillance. We are currently conducting a series of pilot partnerships with public health agencies to provide data that is more detailed than is available through other sources. For example, we are working with four agencies in Ohio — Ohio Department of Health, Columbus Public Health, Cuyahoga County Board of Health and Delaware General Health District — to provide them an additional data source for ILI, URI and flu vaccination rates, at the county and age group levels.

We compile simple summary statistics on a weekly basis. But these statistics come from a network of data from millions of patient visits; data that we are quickly accessible and available at scale — providing data for one county health department is as simple as providing data to multiple counties.

More important, it is data that health care officials and researchers can access for quick and actionable insight into disease outbreaks. The key to effective disease surveillance is the connected, open flow of data between sites of care and public health authorities.

For more information, see the athenaResearch team’s post on disease surveillance.

Analytics & Research

EHR Data Shows 1 in 10 Visits to the Pediatrician Result in the Diagnosis of a Mental Health Condition

Michelle Mangino,  Social Media ManagerOver the past year, the athenaResearch team has published three data-focused reports on the evolving state of mental health among children in the U.S. Drawing from patient visits maintained by our EHR on our cloud-based network, athenaNet, the research team identified the prevalence of increased mental health diagnoses from July 2009 through June 2013 based on visits by patients ages 6-17, seeing 431 pediatricians. (Mental health conditions are more common among school age children than among pre-school children).

athenaResearch found that one in 10 visits to the pediatrician yields the diagnosis of a mental health condition. And the team has observed that the increase was primarily driven by five diagnostic categories: Attention Deficit Hypertension Disorder (ADHD), anxiety, autism, depression and eating disorders.

I won’t go into too much detail here as the athenaResearch has done a very thorough job of that in their two posts, “Data Points to Behavioral Health as a Growing Challenge for Pediatricians” and “No Lack of Attention to ADHD.” Some of you may be familiar with one or both of these posts, but I would like to bring this insight back to the forefront, as it is an important one for health care providers, patients and families. This may be an especially relevant time for pediatricians to keep these insights in mind as back-to-school visits can often bring these patients back into their practice at a greater volume.

Finally, here’s a recently created infographic that highlights the top-level findings from the athenaResearch team’s mental health reports. (Note: The data in the infographic is based on mental health diagnoses from January 2010 through the end of 2013.)

pediatric mental health data sets

If you have questions or suggestions for further analysis, please direct them to athenaResearch Vice President Josh Gray at or @JoshGray_hit.

This infographic was originally published as part of the Where Does It Hurt? series in The Atlantic, which examines how data is transforming health care.

Analytics & Research | Healthcare Policy & Reform

ACAView: Medicaid Gap Widening Between Expansion and Non-Expansion States

Iyue Sung, Director of Core AnalyticsIn measuring the effects of health insurance coverage expansion as part of our ACAView initiative with Robert Wood Johnson Foundation (RWJF), an important factor to consider is state policy towards Medicaid expansion.

The intention of the Affordable Care Act (ACA) was to expand coverage through two mechanisms: 1) People with moderate incomes could gain coverage through the exchanges, often encouraged by subsidies; and 2) those with lower incomes could gain coverage through an expansion of Medicaid eligibility to include groups that had not traditionally qualified for Medicaid.

For many years, states had widely varying Medicaid eligibility rules, with some states covering only women and their children in need of public aid and low-income people with disabilities. Other states had expanded eligibility to include people at income levels higher than the federal poverty level.

Given the differing Medicaid expansion decisions among states, we examined our data on visits to primary care physicians (PCPs) separately for states with and without Medicaid expansion.

Figure 1 shows proportions of visits between January 2012 and May 2014 for four groups of adults (18-64): uninsured individuals in Medicaid-expansion states; uninsured individuals in non-Medicaid expansion states; Medicaid beneficiaries in expansion states; and Medicaid beneficiaries in non-expansion states.

Two observations are worth noting:

  1. ACA coverage expansion appears to be widening a pre-existing gap between states that have elected to pursue Medicaid expansion and those that have not. Providers in the Medicaid-expansion states were already seeing higher proportions of Medicaid beneficiaries in 2013. For example, in December of 2013, 12.3% of 18-64 year- old visits to PCPs in expansion states were from Medicaid beneficiaries, compared with 5.9% in non-expansions states, a 6.4 percentage point differential. By May 2013, that difference had expanded to a 9.3 percentage point differential, as the percent of Medicaid visits increased in Medicaid expansion states but held constant in non-expansion states.
  2. The proportion of uninsured fell in both categories, from 4.5% to 3.3% in expansion states and 7.0% to 5.8% in non-expansion states (figures for January through May for both years, respectively).

Figure 2 expands the Medicaid payer mix analysis to other specialties.

In Medicaid expansion states, all four specialty types showed a substantial increase in the proportion of visits by Medicaid beneficiaries. In contrast, in non-Medicaid expansion states, the proportion of visits by Medicaid beneficiaries decreased for all four specialty groups.

As a result of these changes, by early 2014 PCPs, surgeons, and other specialists in expansion states saw two to three times more adult Medicaid patients (in proportional terms) than in non-expansion states (for example, 15.6% versus 6.3% for PCPs; 11.6% versus 3.1% for surgeons).

For OB-GYN, the ratio between the proportion of visits by Medicaid beneficiaries in the expansion and non-expansion states is much smaller, 19.4% versus 13.4%. This may reflect more generous Medicaid eligibility in non-expansion states for pregnant women compared to other adults.

As we monitor these metrics, a few questions will be of particular interest:

  • Where will the increase in Medicaid volumes in expansion states level off?
  • To what extent is the increase in Medicaid visits driven by established patients who were previously uninsured?
  • What are the effects of increased Medicaid volumes on medical practices?

We will attempt to address these (and other) complex issues throughout the year.

For a better understanding of our goals, methodology, data sample size, and full findings since the inception of the ACAView series, please read our first report, “First Observations Around the Affordable Care Act.” And if you have questions or suggestions for further analysis, please direct them to athenaResearch Vice President Josh Gray at or @JoshGray_hit .

Analytics & Research

Data Drives Insight into Value-based Care Decisions

Anne Meneghetti, MD, Executive Director of Medical Information, Epocrates, an athenahealth companyHow many times have you experienced the angst of finding out that a patient never filled a prescription because of personal finances? How often have you seen a patient go without treatment while prior authorization hurdles were being worked out? Practices are spending more time than ever before on affordability issues like these.

We recently surveyed 70 clinicians about their biggest affordability challenges. Topping their list of responses was the lack of available information on actual drug or procedure costs for patients, cited by 43% of respondents, while patient inability to afford care was listed second, by 28% of those surveyed.

In Jonathan Bush’s provocative new book, “Where Does It Hurt? An Entrepreneur’s Guide to Fixing Health Care,” the athenahealth CEO advocates for greater freedom to choose one’s own care options based on cost-effectiveness and personally meaningful differentiators. Yet without reliable insights into the specific costs and multi-dimensional outcomes for various options, neither clinicians nor patients are positioned to make clear choices.

Thankfully, guidance is emerging from multiple sectors.

The Patient-Centered Outcomes Research Institute (PCORI) was authorized by Congress to fund comparative effectiveness research to drive informed care decisions. A viewpoint piece in the June issue of the Journal of the American Medical Association (JAMA) calls upon specialty societies, such as the American Academy of Family Physicians (AAFP) and American College of Physicians, to create and disseminate specialty-specific tools and guidelines that help clinicians make complex value-based decisions. Specialty society guidance has also emerged from the American Board of Internal Medicine’s Choosing Wisely® Campaign, which encourages professional organizations to spark conversation between providers and patients about the value of various tests and treatments. For example, AAFP has suggested guidelines on when to question antibiotics for sinusitis or otitis media, and when to reconsider routine screening with PSA, DEXA, carotid artery stenosis testing and Pap smears. The American Academy of Pediatrics recommends criteria for routine CTs for minor head injury, simple febrile seizures, and abdominal pain. In addition, the organization offers advice on use of antibiotics for asymptomatic bacteriuria, cough and cold medicines for children under four years old, and GERD medications for “happy-spitter” infants.

Epocrates is doing its part to help bring clinical intelligence to the exam room to support value-based prescribing decisions. Within our core app, you can find hundreds of health plan and retail pharmacy formularies. Select “Alternatives” to compare affordability of other options in the same therapeutic subclass. Search the “Manufacturer/Pricing” section for a comparison between retail costs and formulary coverage; in some cases, retail pricing may be the most affordable choice.

The increasing focus on comparative effectiveness research, guidance from specialty societies, and cost data integrated into medical apps like Epocrates, will offer clinicians relevant insights for making complex value-based decisions of care.

What are your biggest pain points in identifying and obtaining affordable options for patients?

Analytics & Research

ACAView: Measuring the Impact of Patient Acuity

Since launching ACAView, our joint initiative between the Robert Wood Johnson Foundation (RWJF) and athenahealth, in early April, open enrollment under the Affordable Care Act (ACA) has closed for 2014 and The White House has issued final numbers: eight million people enrolled through the marketplace and five million outside the marketplace. Add another three million enrolled in Medicaid or the Children’s Health Insurance Program (CHIP) and the total number of people enrolled under the ACA’s individual mandate is close to 16 million.

Since some of these enrollees had previous forms of insurance coverage, it is important to estimate overall reductions in the number of uninsured. RAND estimates that 9.3 million more Americans have insurance in Q1 of 2014, compared to Q3 of 2013, but these figures exclude the surge of enrollments in the last half of March. The Congressional Budget Office (CBO) estimates 12 million net newly insured people through either the marketplace or Medicaid (including 1 million who lost insurance), but these estimates exclude enrollments outside the marketplace.

In short, “newly insured” and “enrollment numbers” are counted in different ways and can be confusing. But let’s conservatively assume that the number of net new insured individuals is roughly nine million, or 2.8% of the population. Are these new beneficiaries having a measurable impact on medical practices?

In our previous report, we saw that, at least for the first quarter, a national sample of 12,700 physicians across the athenahealth network did not see an increase in new patients[1] due to the ACA. While not all new patients are newly insured, an increase in this population would suggest that coverage expansion is having an impact on medical practices. Instead, the percentage of total provider visits with new patients actually dropped slightly in the first three months of 2014 compared to 2013. Several factors may help explain why the ACA’s coverage expansion has not led to an immediate and measurable impact:

  1. The number of newly insured patients in the first quarter of 2014 may have been too small to have a measurable impact.
  2. Not all newly insured patients required care.
  3. It may require weeks or months for patients to schedule appointments and be seen.

Our data suggests the influence of new patients on provider activity may take considerable time to unfold. Figure 1 shows the percentage of visits by new patients to Primary Care Providers (PCPs) at practice locations active before 2011. New patients account for 15% to 20% of office visits in the beginning of the year, growing as a proportion throughout the year. Note that a patient defined as new at any point during 2014 remains classified as new throughout the entire calendar year. In other words, these new patients are tracked as a cohort as the year progresses. We chose this definition to measure the level of effort physicians place in treating patients that are new to the practice across the year.

The proportion of visits by new patients in the first quarter actually dropped slightly between 2013 and 2014. As the newly insured seek out care, we will monitor the proportion of total provider visits for new patients compared to last year.

In addition to tracking the percentage of new patient visits, it is also important to consider whether those new patients have a higher rate of chronic conditions compared to previous years, and therefore, increase the proportion of care they receive from providers. That is, will the ACA result in the release of pent-up demand, with previously uninsured patients seeking care for a host of chronic and/or complex conditions that were previously left untreated?

So far, this does not appear to be the case. Figure 2 shows the proportion of visits, for Q1 of 2013, in which a chronic condition (diabetes, hypertension, hyperlipidemia) was diagnosed. New patients are compared to established patients, by insurance type (commercial, Medicaid, Medicare). Results for commercial and Medicaid beneficiaries are shown only for adults under 65 and results for Medicare beneficiaries are shown only for adults 65+.

Not surprisingly, Medicare beneficiaries have higher rates of chronic disease than those with private insurance or Medicaid. It is also unsurprising that Medicaid beneficiaries have high rates of chronic conditions. But the most relevant comparison is that established patients have a higher rate of chronic diseases compared with new patients. In other words, new patients have a lower burden of chronic disease compared with established patients. This is true regardless of which age group (0-17, 18-49, 50-64, 65+) was examined.

This comparison shows numbers only for 2013. Have we seen any changes in the prevalence of chronic disease for new patients, so far in 2014? For the most part, no.

Figure 3 compares chronic condition diagnosis rates for first quarter of 2013 to first quarter of 2014, for commercially insured patients between 18 and 64 years of age. On a national basis, neither new nor established patients saw an increase in diagnosis rates of chronic conditions.

We also examined chronic disease rates for different census regions (West, Midwest, Northeast, South); insurance types (commercial, Medicaid, Medicare), and practice size (1-5 providers, 6-20 providers, 21+ providers). In most of these clusters, the rate of chronic conditions for new patients did not increase between 2013 and 2014, any more so than the rate for established patients.

A potential exception is in the South. Figure 4 shows that for commercially insured patients of small practices in the South, ages 18-64, the rate of diabetes and high blood pressure diagnosis increased between 2013 and 2014 for new patients but remained fairly flat for established patients. We should caution that further observation and analysis is needed to evaluate whether this pattern holds up throughout the year.

It appears then, that during the first quarter of 2014, the ACA did not result in a shift of the composition of new patients towards those with more chronic diseases. A possible exception is with small practices in the South.

As part of our ACAView tag initiative, we will continue to provide updates on the impact the ACA has on physician practices. In coming months, we will update this look at new patient visits and disease profiles, and explore such topics as the amount that new patients owe for their care and whether they honor their financial obligations. As always, we welcome your comments.

Check out Josh Gray’s Google+ Profile. Follow @JoshGray_HCIT on Twitter.

1We define new patients as one who has not visited a particular physician in two years or more.

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