Rainu Kaushal, MD, MPH

It is increasingly clear that high-need, high-cost patients are not a homogenous group, but rather a diverse set of patients with varied circumstances and needs. Acting on this insight requires comprehensive data networks we have not traditionally had, and most analyses to date have focused primarily on claims data. We argue that making clinical and financial gains will require data-sharing networks that integrate clinical factors, genomic information, and social determinants from multiple health systems. Investing in these networks may allow us to better anticipate the unique needs of patients, conceptualize care models to meet those needs, and put targeted interventions into action.

It is increasingly recognized that high-need, high-cost patients make up a heterogeneous group of patients with varied medical, functional, and socioeconomic circumstances.

Designing and implementing targeted care delivery models will require comprehensive data networks with information on medical claims, clinical factors (including genomics), and social determinants of health.

Creating and maintaining such data networks may require public investment and greater coordination among health systems. A number of challenges remain, including privacy and cost concerns.

What would it take for health systems to better care for high-need, high-cost (HNHC) patients? Countries across the world are contending with growth in healthcare expenditures, and a disproportionate share of spending is concentrated on small groups of patients with high healthcare utilization.

 In the United States, 1% of the population accounts for 21% of healthcare expenditures and the costliest 5% account for more than half of all health spending.

HNHC patients have traditionally been identified simply as those with high healthcare utilization. But recent work, including a report from the National Academy of Medicine, aims to stratify patients into subgroups, recognizing their varied circumstances and needs.

 These needs often extend beyond those caused by their medical conditions and include behavioral, functional, and social challenges.

“Precision medicine,” customizing diagnostics and treatments based on a patient’s genetic profile, is generally thought of as essential for the future of personalized care. But for HNHC patients today, “precision health” may be just as important: developing care models based on patients’ unique medical and sociocultural needs and matching those models with the patients most likely to benefit. Creating meaningful taxonomies and translating them into clinical and financial gains will require comprehensive databases with information on medical claims, clinical factors (including genomics), and social determinants of health.

Some health systems have begun collecting and analyzing data on HNHC patients, but current efforts are limited in important ways. First, they do not always include data from multiple hospitals and health systems. Healthcare utilization is often distributed across several systems, particularly in urban settings, and many HNHC patients are not identified by individual hospital analyses in the absence of robust data-sharing arrangements. For example, a study of HNHC patients in Maryland found that only 30% visited 1 hospital, whereas 38% visited 3 or more hospitals.

 Yet hospitals in more competitive markets are less likely to engage in health information exchange.

Second, many analyses are limited by payer type, often with a focus on Medicare claims. But differently insured patients have different drivers of healthcare utilization.

 Healthcare spending on Medicare patients is driven largely by end-organ sequelae of multiple chronic conditions, whereas mental health disorders predominate in Medicaid patients. Catastrophic injuries and specialty drugs are important drivers of high costs in commercially insured populations.

Third, most analyses rely primarily on claims data. Using claims data alone may underestimate the prevalence and severity of chronic disease and mental illness. It also fails to capture important clinical factors, such as functional status, or genomic information, which is increasingly recognized as contributing to variability in drug response, dosing, and adverse effects and may allow for safer and more targeted medication choices. Clopidogrel (Plavix), for example, requires biotransformation in the liver, and CYP2C19 homozygosity is associated with reduced response. Finally, claims-based analyses do not incorporate social determinants of health, which are widely understood to be important, sometimes modifiable, contributors to healthcare costs.

Addressing these concerns will require large structured databases that include inputs from multiple health systems and incorporate claims, clinical, and social information. Such databases would allow better prediction of the low- or medium-utilizer patients who will progress to become high utilizers, which high utilizers will remain high utilizers, which care delivery models will be effective for particular subgroups, and how to allocate limited health system resources in targeted ways to improve outcomes and curb costs.

One such effort is under way at the New York City Clinical Data Research Network (NYC-CDRN). Funded by the Patient-Centered Outcomes Research Institute (PCORI), the NYC-CDRN brings together 22 institutions—health systems, medical colleges, research groups, and government agencies&mdash;to share data, develop common tools, and collaborate on research. The network includes comprehensive longitudinal data on millions of patients and uses open-source code to develop high-utilizer “phenotypes” and focus interventions. Similar data-sharing initiatives are being developed in 12 other regions around the country, although they do not currently focus on HNHC patients.

These data-sharing networks allow for both a more complete identification of HNHC patients and a deeper understanding of how clinical and social factors interact at the population level to produce unique challenges for particular subgroups. For example, how does lack of transportation or unstable housing differentially influence care, costs, and outcomes for older, frailer patients compared with disabled patients or mentally ill patients? Transportation may be critical for HNHC patients from particular zip codes or with functional limitations but less relevant for a young patient with sickle cell disease. Integrated behavioral health services may be the most important intervention for patients with coexisting mental illness, but they are unlikely to affect utilization for patients with advanced illness and robust social support.

 Consider a HNHC patient with multiple admissions for heart failure exacerbations (

). How might merging claims, clinical, and social data in a multisystem registry help detect the patient's needs and customize care? Integrated claims data would allow us to correctly identify the patient, especially if they are seeking care at multiple hospitals, and provide an estimate of disease severity based on 

International Classification of Diseases, Tenth Revision

 codes; number of comorbidities; and frequency of admissions. Clinical data would enhance our ability to risk stratify the patient using, for example, brain natriuretic peptide levels, recent weight fluctuations, and frequency of diuretic dose changes. Genomic information may ultimately inform warfarin or β-blocker dosing. Finally, social data would provide additional avenues for intervention: Does the patient have an air conditioner? Does the patient live in a neighborhood with limited access to healthy food?

Widespread application of integrated data networks does not yet exist, but recent efforts offer a sense of how multidimensional assessments of patient needs could focus care delivery. Parkland Health and Hospital System in Texas, for example, has developed a risk model to predict the likelihood of readmission for patients with heart failure based on 29 clinical, social, behavioral, and utilization factors. The safety-net health system concentrates an intensive set of interventions—counseling, monitoring, specialty follow-up care, pharmacy, and care management services&mdash;on patients identified as highest risk, allowing more efficient use of limited resources. The program has resulted in a 26% relative reduction in the odds of readmission.

Using a grant from the Center for Medicare & Medicaid Innovation, Denver Health, a vertically integrated health system in Colorado, developed a predictive algorithm to divide patients into 4 risk tiers and personalize care based on their medical and social needs.

 The Department of Veterans Affairs has developed hot-spotter algorithms using population-level data to identify homeless veterans at risk for high healthcare utilization and then target services such as housing, food assistance, care management, and substance use support.

 Comprehensive data networks would enrich these efforts and allow health systems to tailor and scale programs for patients with similar profiles.

Moving toward precision health for HNHC patients will require greater public investment in data-sharing infrastructure. The NYC&shy;-CDRN, for example, is supported by PCORI, and HHS could allocate more funding to promote interoperability of electronic health records and facilitate research across data-sharing networks. Payment models that create incentives for institutions to share data may help allay health systems’ financial and competitive concerns. Dedicated training of a multidisciplinary informatics workforce, including health services researchers, computer scientists, and systems engineers, will also be necessary to compile, organize, and clean data.

Serious challenges remain. Data integration and transparency raise privacy concerns, and health systems must work to relieve patient fears about data security. Managing expansive data networks is expensive and will likely require additional or reallocated funding. It is also not clear who should “control” such data networks and how competitors should share potential costs or savings. Finally, the extent to which care models based on integrated data networks can improve health outcomes will require rigorous and prolonged study.

Nonetheless, better health, especially for HNHC patients, will likely require more precise population health. It is increasingly clear that HNHC patients are not a homogenous group, but acting on this insight requires integrated multifaceted data networks that we have not traditionally had. Investing in these networks would allow us to better anticipate the needs of patients, conceptualize care models to meet those needs, and put targeted interventions into action.

Department of Healthcare Policy & Research (DK, RK), and Department of Medicine (DK), Weill Cornell Medical College, New York, NY.

 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 (DK, RK); acquisition of data (RK); analysis and interpretation of data (RK); drafting of the manuscript (DK, RK); critical revision of the manuscript for important intellectual content (DK, RK); statistical analysis (RK); provision of patients or study materials (RK); obtaining funding (RK); administrative, technical, or logistic support (RK); and supervision (RK).

 Dhruv Khullar, MD, MPP, Department of Healthcare Policy & Research, Department of Medicine, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065. Email: khd9010@med.cornell.edu.

Fiscal Sustainability of Health Systems: Bridging Health and Finance Perspectives

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3. Long P, Abrams M, Milstein A, et al, eds. 

Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health

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. 2016;176(6):856-858. doi: 10.1001/jamainternmed.2016.1248.

5. Powers BW, Chaguturu SK. ACOs and high-cost patients. 

. 2016;374(3):203-205. doi: 10.1056/NEJMp1511131.

6. Amarasingham R, Patel PC, Toto K, et al. Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study. 

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7. Johnson TL, Brewer D, Estacio R, et al. Augmenting predictive modeling tools with clinical insights for care coordination program design and implementation. 

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8. O’Toole TP, Pape L. Innovative efforts to address homelessness among veterans. 

Articles

Better care for high-need, high-cost patients will require targeted care delivery models based on integrated data networks that include clinical, genomic, and social information.
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“Precision Health” for High-Need, High-Cost Patients

 To measure the impact of hospital participation in Meaningful Use (MU) on disparities in 30-day readmissions associated with race.

 A retrospective cohort study that compared the likelihood of 30-day readmission for Medicare beneficiaries discharged from hospitals participating in Stage 1 of MU with the likelihood of readmission for beneficiaries concurrently discharged from hospitals that were not participating in the initiative.

Inpatient claims for 2,414,205 Medicare beneficiaries from Florida, New York, and Washington State were used as the primary data source. The study period (2009-2013) included at least 2 years of baseline data prior to each hospital initiating participation in MU. Estimates were derived with linear regression models that included hospital and time fixed effects. By including both hospital and time fixed effects, estimates were based on discharges from the same hospital in the same time period.

 MU participation among hospitals was not associated with a statistically significant change in readmissions for the broader Medicare population (percentage points [PP], 0.6; 95% CI, —0.2 to 1.4), but hospitals’ participation in the initiative was associated with a lower likelihood of readmission for African American beneficiaries (PP, –0.9; 95% CI, –1.5 to –0.4).

 Hospital participation in MU reduced disparities in 30-day readmissions for African American Medicare beneficiaries.

Hospital participation in Meaningful Use (MU) was not associated with declines in 30-day readmissions for the broader Medicare population.

Hospital participation in MU was associated with a decline in readmissions of 0.9 percentage points for African American Medicare beneficiaries.

Readmissions among Medicare beneficiaries are common and often reflect care of poor quality.

 Racial minorities are more likely to be readmitted than other Medicare beneficiaries.

 Policy makers have been optimistic that hospitals’ adoption of health information technology (IT) will lead to lower readmission rates,

 and reducing health disparities is a priority of the federal Meaningful Use (MU) initiative.

 A recent study evaluated the impact of hospitals’ participation in MU on readmission rates of Medicare fee-for-service (FFS) beneficiaries,

 but the relationship between hospitals’ participation in the initiative and racial disparities in readmissions has not been examined.

The MU initiative was created through the Health Information Technology for Economic and Clinical Health (HITECH) Act. Approximately $30 billion was made available through HITECH to promote the adoption and use of electronic health records (EHRs) certified by the Office of the National Coordinator for Health Information Technology.

 Hospitals have received MU incentive payments through the Medicare and Medicaid EHR Incentive Programs. For Stage 1 of the incentive programs, attestation requirements focused on capturing patients’ clinical data electronically and making health information available to patients electronically

 by requiring hospitals to meet 11 core objectives and 5 additional objectives selected from a menu of 10.

 Hospitals were required to meet performance thresholds for the objectives and report their performance on them for 90 continuous days in order to receive incentive payments. Ongoing financial penalties imposed on hospitals that do not attest to MU requirements have further incentivized participation. 

Hospitals have been responsive to the MU incentive payments and financial penalties, with 98% of eligible hospitals participating in the initiative as of 2016.

 MU participation among hospitals has not been associated with reductions in readmissions occurring within 30 days of discharge.

 However, given that racial minorities have higher readmission rates

 and that reducing disparities is a priority of the MU initiative,

 it is possible that hospitals’ participation in the initiative has had a greater impact on readmissions of racial minorities. The objective of this study was to measure the impact of hospital participation in MU on disparities in 30-day readmissions associated with race.

The likelihood of 30-day readmission for Medicare beneficiaries discharged from hospitals participating in MU was compared with the likelihood of readmission for concurrent beneficiaries discharged from hospitals that were not participating in the initiative. The study period covered 2009 through 2013. Hospitals in our sample initiated MU participation at different times following the implementation of the initiative in 2011. The differential timing of hospitals’ participation in MU was used to estimate its impact on readmissions.

It is possible that hospitals that were early participants in MU differed from later participants and nonparticipants in ways that affected the likelihood of readmission. Two steps were taken to address this issue. First, regression models included hospital fixed effects to control for permanent differences among hospitals. Second, only hospitals that began MU participation by the end of the study period were included in the sample.

The primary data sources for the study were State Inpatient Databases for Florida, New York, and Washington State from the Healthcare Cost and Utilization Program (HCUP) within the Agency for Healthcare Research and Quality. HCUP State Inpatient Databases include all discharges, regardless of payer, from community hospitals in a given state. The American Hospital Association Annual Survey provided information on hospitals’ use of EHRs, and Stage 1 MU payment records from the Medicare and Medicaid EHR Incentive Programs were used to identify participants. The unit of analysis was the hospitalization. Therefore, some patients were observed multiple times over the study period.

The study sample included 2,414,205 unique Medicare beneficiaries 65 years and older, in both FFS and Medicare Advantage, who were discharged to home or postacute care from 291 nonfederal hospitals. The MU incentive programs were implemented in 2011. Therefore, the study period (2009-2013) included at least 2 years of baseline data before each hospital began MU participation.

The outcome was an indicator of all-cause 30-day readmission derived from the HCUP algorithm for hospital revisits.

 Patient identifiers in the State Inpatient Databases only allowed a readmission to be identified if it took place in the same state as the index admission. For Washington, patient identifiers were not consistent from year to year and, therefore, index admissions occurring in December were excluded for this state. Control variables included indicators for age (65-69, 70-74, 75-79, 80-84, 85 years and older), gender, race, quartiles of median household income for the patient’s zip code, Deyo-Charlson Comorbidity Index scores (1, 2, 3, 4, 5 or higher),

 weekend admission, and a continuous measure of length of stay.

 Race categories for white and African American beneficiaries have shown reasonable reliability in administrative claims, but other race and ethnicity categories are less reliable.

 Therefore, race was categorized as white, African American, and other nonwhite race. Additionally, a binary indicator for hospital participation in MU was included in the model. Once a hospital received a Stage 1 payment from either the Medicare or Medicaid EHR incentive program, the measure identified the hospital as a MU participant from that year forward through the rest of the study period. The initial Stage 1 incentive payment was made to a hospital up to 8 weeks after it completed the 90-day attestation period.

 Therefore, each hospital had been meeting MU objectives for up to 5 months when the initial Stage 1 payment was received. The determinants of interest were interaction terms for a hospital’s participation in MU and patient race categories. Estimates associated with the interaction terms indicated whether hospital MU participation differentially impacted the likelihood of readmission for racial minorities.

Linear probability models with hospital and year fixed effects were used for estimation. This approach controls for permanent differences among hospitals by including a dummy variable for each. By including both hospital and time fixed effects, estimates were based on discharges from the same hospital in the same time period. However, the inclusion of fixed effects in the regression model results in wider confidence intervals for the estimated coefficients. 

In a secondary analysis, an indicator for EHR use in the year prior to MU participation was added to the specification. This was done to examine whether any changes in the likelihood of readmission were due to hospitals’ MU participation or simply use of an EHR. The standard errors were adjusted for clustering at the level of the hospital in both models.

 SAS version 9.2 (SAS Institute; Cary, North Carolina) and Stata MP version 14 (StataCorp; College Station, Texas) were used for analyses.

The study was approved by the Human Protections Office of Weill Cornell Medical College.

The mean age of Medicare beneficiaries in our sample was 78.2 years, and 56.6% were female (

). The racial composition of the sample was 77.2% white, 8.4% African American, and 12.9% other nonwhite race; 1.4% of the sample was of unknown race. Slightly more beneficiaries resided in middle-income zip codes than low- or high-income zip codes, with 22% in the lowest quartile, 26% each in the second and third quartiles, and 25% in the highest quartile. For hospital discharges, the mean score on the Deyo-Charlson index was 2.3, mean length of stay was 5.4 days, and 19.9% were weekend admissions. Among the hospitals in our sample, the mean number of licensed beds was 341.2, the majority (71.9%) were not-for-profit, 23.8% were government hospitals, and 39.5% were affiliated with a medical school. In 2011, the first year of the Medicare and Medicaid EHR Incentive Programs, 39.6% of the hospitals in our sample had an EHR but did not participate in MU and 33.5% initiated participation in the initiative. In 2012, the proportion of hospitals with EHRs and no MU participation fell to 7.1% and the proportion of hospitals participating in the initiative grew to 92.4%.

Unadjusted 30-day readmission rates for all Medicare beneficiaries rose in the first 2 years of the study period (

), climbing from 20.6% in 2009 to 21.1% in 2011, when the MU initiative was first implemented. Readmission rates then declined to 19.6% in 2013, the last year of the study period. A similar trend in unadjusted 30-day readmissions was observed for white beneficiaries, although the annual rates were lower in magnitude than rates for all Medicare beneficiaries in each year of the study period. For African American beneficiaries, readmission rates were higher throughout the study period compared with the rates for all beneficiaries. However, after remaining at 24.3% in 2009 and 2010, the readmission rate for African American beneficiaries fell to 23.8% in 2011 and 22.1% in 2013. Unadjusted readmission rates for beneficiaries who were not white or African American followed the same trend as those for all Medicare beneficiaries, but were higher in each year.

) did not indicate reductions in readmissions associated with hospital participation in MU for the overall Medicare population in our sample (percentage points [PP], 0.6; 95% CI, —0.2 to 1.4). The estimate for the interaction between MU participation and African American race (PP, –0.9; 95% CI, –1.5 to –0.4) indicated statistically significant declines in readmissions compared with white beneficiaries, but the interaction of MU participation and other nonwhite race did not indicate a reduction in these events compared with white beneficiaries (PP, –0.4; 95% CI, –0.8 to 0.0). The decline of 0.9 PP represents a 3.7% relative decrease in readmissions for African American beneficiaries using the unadjusted rate prior to MU (24.3%) as the baseline. Similar to our primary analysis, results from our secondary analysis that controlled for prior EHR use did not suggest a relationship between hospitals’ participation in the initiative and readmissions among the broader Medicare population (PP, 1.5; 95% CI, –0.1 to 3.1), but indicated a decline in the likelihood of readmission among African American beneficiaries (PP, –0.9; 95% CI, –1.5 to –0.4). Additionally, a statistically significant decline in readmissions for beneficiaries of other nonwhite race was associated with hospitals’ participation in MU in the secondary analysis (PP, –0.7; 95% CI, –1.1 to –0.3).

Hospital participation in MU was not associated with a reduced likelihood of readmission for the overall Medicare population in our sample, but it was associated with reductions in these events for African American beneficiaries. The results of a secondary analysis that controlled for use of an EHR prior to a hospital’s MU participation were consistent with these findings.

Our study represents an important addition to the existing literature because, despite the nation’s $30-billion investment in health IT through the MU initiative,

 evaluations of provider participation have shown little evidence of improvements in the quality of care.

 It is important to note that previous studies of provider participation in MU, including this one, have only examined Stage 1 of the initiative. Use of more advanced functions, such as interoperability of EHRs, in addition to improving patient outcomes were not emphasized until later stages of the initiative. However, better documentation of patients’ clinical information and improved access to that information through Stage 1 attestation requirements may have affected patients at higher risk of readmission,

 including racial minorities, more than other patients. Additionally, MU participation may have led to reductions in readmissions among broader groups of patients after the study period as hospitals gained more experience with EHRs, including increased use of health information exchange, and learned to use these systems to improve patient outcomes.

Our study has limitations to consider. Due to our reliance on administrative claims, it is possible that unobserved differences among hospitals influenced our results. Our inclusion of hospital fixed effects and limiting the sample to hospitals that initiated participation in MU prior to the end of the study period mitigated the potential for bias, but we cannot rule out this possibility. For example, these approaches would not have controlled for unobserved differences among hospitals or patients that changed over time. Additionally, records for incentive payments were used to identify the timing of MU participation, which likely introduced some measurement error. This would introduce bias toward the null leading to estimates that understated the impact of hospitals’ MU participation on readmissions. Differential 30-day mortality rates may also have influenced estimates of readmission. With the HCUP State Inpatient Databases, it was not possible to identify beneficiaries who died after hospital discharge. Although 30-day mortality is a competing risk, previous studies of Medicare readmissions have not shown mortality rates to be a considerable influence on estimates of hospitalization rates, including readmissions.

 We also included only 3 race categories: white, African American, and other nonwhite race. Combining multiple groups into “other nonwhite race” could have masked differences among these groups and introduced measurement error, biasing the estimate toward no effect. It was also not possible to determine whether a patient’s primary care physician was notified of a hospitalization or if the physician had an interoperable EHR that was capable of receiving the patient’s information electronically from the hospital. Timely follow-up care after hospital discharge and better coordinated care enabled through the electronic exchange of a patient’s health information could reduce the likelihood of readmission. However, findings of a previous study indicated that physician participation in MU did not lower the likelihood of 30-day readmissions for Medicare beneficiaries.

 Lastly, our sample only included hospitalized Medicare beneficiaries from Florida, New York, and Washington. However, these states are located in different regions of the country and have diverse populations.

In addition to the limitations listed above, other policy initiatives that may have been implemented during the study period with the goal of reducing readmissions may have affected our estimates. These include Medicare’s Hospital Readmissions Reduction Program (HRRP) and the Medicare Shared Savings Program (MSSP) for accountable care organizations (ACOs). Hospitals could also potentially have used EHRs adopted through MU participation to improve their performance in these initiatives. The HRRP was implemented in October 2012 and the first MSSP ACOs began in April 2012. If early participants in MU were more likely to participate in these programs, which led to more resources being focused on patients with a higher likelihood of readmission, such as racial minorities, our estimates may overstate the impact of hospitals’ participation in MU.

To our knowledge, this is the first study of the relationship between hospital participation in MU and racial disparities in readmissions. We found a statistically significant relationship between hospitals’ participation in the initiative and reductions in racial disparities associated with 30-day readmissions among Medicare beneficiaries. Additional study is needed to determine if disparities were further reduced as hospitals gained more experience with EHRs and outcomes were emphasized in later stages of MU.&ensp;

The authors are grateful for programming and data management support from Phyllis Johnson, MBA.

 Department of Healthcare Policy and Research, Weill Cornell Medical College (MAU, H-YJ, RK), New York, NY; Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University (JRV), Indianapolis, IN; Regenstrief Institute, Inc (JRV), Indianapolis, IN.

 National Institute on Aging (R03AG046671, PI: Mark A. Unruh).

Concept and design (MAU, H-YJ, RK, JRV); acquisition of data (MAU); analysis and interpretation of data (MAU, H-YJ, RK, JRV); drafting of the manuscript (MAU, H-YJ, RK, JRV); critical revision of the manuscript for important intellectual content (MAU, H-YJ, RK, JRV); statistical analysis (MAU, H-YJ, JRV); obtaining funding (MAU); and supervision (MAU).

 Mark Aaron Unruh, PhD, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065. Email: mau2006@med.cornell.edu.

1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. 

. 2009;360(14):1418-1428. doi: 10.1056/NEJMsa0803563.

2. Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. 

. 2011;305(7):675-681. doi: 10.1001/jama.2011.123.

3. A record of progress on health information technology [news release]. Baltimore, MD: CMS; April 23, 2013. cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2013-Fact-sheets-items/2013-04-23.html. Accessed March 20, 2017.

4. Buntin MB, Jain SH, Blumenthal D. Health information technology: laying the infrastructure for national health reform. 

. 2010;29(6):1214-1219. doi: 10.1377/hlthaff.2010.0503.

5. Brice YN, Joynt KE, Tompkins CP, Ritter GA. Meaningful Use and hospital performance on post-acute utilization indicators [published online March 2, 2017]. 

6. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. 

. 2010;363(6):501-504. doi: 10.1056/NEJMp1006114.

Hospital participation in Meaningful Use was associated with reduced disparities in 30-day readmissions for African American Medicare beneficiaries.

Hospital Participation in Meaningful Use and Racial Disparities in Readmissions

 Since 2009, federal policies have incentivized medical organizations to provide medical record access to patients. We sought to track personal health record (PHR) adoption and differences by sociodemographic group over time.

Random-digit-dial survey conducted for 4 consecutive years in New York state.

 The Empire State Poll is a random digit-dial survey conducted every year in New York state, with an annual sample size of 800 individuals, weighted to create a representative state sample. We analyzed 4 consecutive years of poll data to examine trends.

 The proportion of New Yorkers using PHRs rose sharply, from 11% in 2012 to 27% in 2015. By 2015, there were no significant differences in PHR use between blacks and other races, but Hispanics and low-income respondents were less likely to use PHRs.

 During a 4-year period in which federal policies incentivized medical organizations to give medical record access to patients through PHRs and electronic portals, rates of PHR use increased rapidly in all sociodemographic groups. However, a digital divide remains evident, linked to Hispanic ethnicity and lower income.

		More patients are using personal health records (PHRs) than ever before.

		Current data suggest blacks and other races are equally likely to use PHRs; however, Hispanics and patients with Medicaid are still less likely to use PHRs.

		Additional outreach may still be necessary to ensure disadvantaged groups are using available technologies.

Giving patients access to their medical data through personal health records (PHRs) and electronic patient portals has been promoted as a way to engage them in healthcare decisions and health promotion.

 An electronic patient portal is a Web-based account provided by a healthcare organization that allows patients to view their medical records, correspond securely with healthcare providers, and perform other health-related tasks, such as requesting medication refills. Portals are sometimes called “tethered PHRs” because of their close link to a single healthcare organization. Portals have become more common in the United States

 as a result of the Federal Electronic Health Record (EHR) Incentive Program (informally known as the “Meaningful Use” program).

Since 2010, this program has been incentivizing healthcare providers and hospitals to adopt EHRs, and when they do, they are required to make the electronic data available to patients.

 However, early studies of these consumer types of information technology (IT) showed that patients in disadvantaged socioeconomic groups were less likely to use them, leading to concern that the “digital divide” might prevent some of the neediest patients from benefiting.

 Additionally, patients with less education may also have less confidence in the benefits of health IT.

 To investigate the rate of public adoption of health IT over time, we asked about portal/PHR use in random-digit-dial telephone polls for 4 consecutive years in the state of New York.

The Empire State Poll is a survey of adult New York state residents (n = 800) conducted by the Survey Research Institute of Cornell University.

 Each year, samples are drawn from random-digit-dial lists covering cell and land lines. Response rates (the number of completed surveys divided by total eligible sample) have been 22% to 33% each year, with cooperation rates (the number of completed surveys divided by eligible individuals contacted) between 66% and 70%.

Since 2011, we have included questions to assess public attitudes and perceptions of health IT.

 From 2012 through 2015, we included the following question: “Some hospitals, doctor’s offices, health plans, and different organizations are offering websites where you can get, keep, and update your health information online. This information could be lab test results, medicines, doctor visits, or other information. You would get a user name and password so that only you could see your information on this website. These websites are sometimes called personal health records (PHRs) or patient portals. Have you ever used one of these websites where you can get, keep, or update your health information?”

Chi-squared tests and Cochrane-Armitage tests for trend were performed using survey weights to account for the sampling design and produce statewide estimates. The study was approved by the Cornell University Institutional Review Board.

The weighted proportion of New Yorkers who reported using a PHR rose from 11% in 2012 to 27.1% in 2015 (

). Black individuals were initially substantially less likely to use portals/PHRs than whites, but the proportion of black respondents using these technologies rose sharply (4.3% in 2012, 7.5% in 2013, 14.3% in 2014, and 23.9% in 2015; 

 <.001 for trend). By 2015, the proportion of blacks who used portals/PHRs was not significantly different from the proportion of other races (23.9% vs 28.1%; 

A different trend was seen among individuals of Hispanic ethnicity. In 2012, the PHR adoption rate among Hispanics was not significantly lower than the rate among non-Hispanics (9% vs 11.1%; 

 = .52). By 2015, despite an increase, the adoption rate lagged significantly behind the rate for non-Hispanics (15.8% vs 29.3%; 

 = .001). Also, the portal/PHR adoption rate among low-income respondents (household income <$50,000) also rose, but remained about half the rate reported by higher-income respondents (7.2% vs 13.5% in 2012, 

As previously reported, the use of portals/PHRs was also more common among patients who used the Internet more frequently, those with higher education, and those using prescription medications.

During a 4-year period in which federal policies incentivized medical organizations to give medical record access to patients through PHRs and electronic portals, the proportion of New Yorkers who used these technologies more than doubled. Racial disparities in technology adoption largely disappeared during this time. However, disparities on the basis of income and Hispanic ethnicity did not narrow. Despite large and rapid across-the-board increases in the use of consumer health IT, a digital divide remains evident, linked to lower income and Hispanic origin rather than race.

Electronic patient portals and PHRs are anticipated to be an important tool for patient engagement in healthcare, and it is therefore important to determine whether they are reaching all patient populations. Our analysis of statewide polling data shows that the use of portals and PHRs has increased sharply in recent years. Although both black and white patients are now equally likely to use the technology overall, low-income and Hispanic patients remain somewhat less likely to use it. Healthcare organizations seeking to use these tools to engage patients should examine their efforts to reach all patient groups. Measures that should be investigated include making these technologies accessible in multiple languages, designing for usability by individuals with low levels of computer expertise, and ensuring access via mobile phone and Web browsers.

 Department of Healthcare Policy and Research, Weill Cornell Medical College (JSA, BH, RK), New York, NY.

 The survey was supported by the New York eHealth Collaborative. JSA is supported by an AHRQ grant: K01 HS021531.

 Concept and design (JSA, RK); analysis and interpretation of data (JSA, BH); drafting of the manuscript (JSA, BH); critical revision of the manuscript for important intellectual content (JSA, BH, RK); statistical analysis (JSA); obtaining funding (JSA); and supervision (JSA, BH, RK).

 Jessica S. Ancker, PhD, MPH, Weill Cornell Medical College, 425 E 61 St, Ste 301, New York, NY 10065. E-mail: jsa7002@med.cornell.edu.

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Over 4 years, rates of personal health record use increased rapidly across the board, but a digital divide remained evident.

Socioeconomic Disparities in Adoption of Personal Health Records Over Time

The proportion of US doctors using electronic health records (EHRs) has risen sharply in response to the federal Meaningful Use (MU) program, which incentivizes EHR adoption. To track consumer perceptions of EHRs during this period, we conducted a national telephone survey annually for 3 consecutive years, from 2011 to 2013, corresponding with the early years of MU.

The survey used random digit-dial sampling on a dual frame of landline and cell phone numbers in the continental United States, but was not otherwise stratified by geographic region, race, or other variables. Because our primary goal was to identify relationships between variables and EHR attitudes, we constructed post hoc survey weights to align all 3 samples with each other. Relationships between sociodemographics and EHR questions were assessed with logistic regression models using the survey weights. Cross-year comparisons were conducted with χ2 tests and Cochran-Armitage tests for linear trend.

Between 2011 and 2013, the proportion of respondents with a doctor who used an EHR rose from 64% to 71%. In 2011, 64% endorsed the belief that EHRs would improve healthcare quality, dropping to 62% by 2013. Simultaneously, the proportion concerned about the effects of EHRs on privacy dropped from 48% to 41%. Consumers whose doctors used EHRs were generally more likely to believe EHRs would improve healthcare quality and less concerned about privacy risks than those whose doctors did not use EHRs. 

Overall, we conclude that during the early years of the MU program, exposure to EHRs increased while confidence in the benefits of EHRs and concerns about privacy risks became less marked. The subset of people exposed to EHRs via their physicians continued to have more positive attitudes toward them than those without that exposure. These attitudinal trends may be linked to increased familiarity with health information technology.

		Between 2011 and 2013—the early years of the Meaningful Use electronic health record (EHR) incentive program&mdash;surveys found a drop in the number of consumers who believed that EHRs would improve healthcare quality, as well as in the number who expressed concern about the effects of EHRs on privacy. 

		Consumers whose doctors used EHRs were generally more likely to believe EHRs would improve healthcare and less concerned about privacy risks than those whose doctors did not use EHRs.

Since the passage of the Health Information Technology for Economic and Clinical Health Act (HITECH) in 2009, the federal government has invested billions of dollars to promote the adoption of the electronic health record (EHR) through the Meaningful Use program (MU).

 The long-term goal of this program is to improve the quality and safety of healthcare.

 Since the launch of MU, the prevalence of EHRs has increased dramatically, with about 72% of ambulatory physicians and 44% of acute care hospitals using some form of EHR as of 2012.

National and regional surveys conducted before and in the early years of MU generally suggest that consumers believe EHRs are likely to improve healthcare.

 We recently found that most consumers think that EHRs will improve healthcare, and positive opinions were even more common among those who said their doctors use EHRs.

 However, a relatively large percentage of respondents (18%) did not know whether their physicians used EHRs.

Despite this overall positive perception of EHRs, consumers express strong concerns about the security of their electronic medical data.

 Privacy concerns are generally associated with lower confidence that EHRs will improve healthcare

 and with an increased level of concern about personal medical data breaches.

 Agaku and colleagues recently reported that about 12% of patients have withheld health information from doctors out of concern about the privacy of electronic data.

 Privacy concerns are also common with regard to health information technologies (ITs) that support the electronic exchange of medical records between healthcare providers (ie, health information exchange).

 Our objective was to track national consumer perceptions of quality effects and privacy risks associated with EHRs over a 3-year period (2011-2013), corresponding with the early years of MU.

The Cornell National Social Survey is an annual random digit-dial telephone survey conducted by the Cornell Survey Research Institute. Every year, the sample size of 1000 provides a margin of error of ± 3.1 percentage points. The Cornell University Institutional Review Board approved the study and respondents provided oral consent.

Each year’s sample is a random digit-dial sampling conducted on a dual frame of landline and cell phone numbers in the continental United States, but not otherwise stratified by geographic region, race, or other variables. The proportion of cell phone numbers is calculated from county-level data on prevalence of cell phone-only households. Listed and unlisted numbers are included in the sample list, but known business, disconnected, and nonhousehold numbers are excluded. When the telephone is answered, the adult with the most recent birthday is interviewed—a technique that ensures each adult has an equal chance of selection.

Researchers may submit potential questions for inclusion in the survey, which are competitively reviewed by the Cornell Survey Research Institute. Three questions about EHRs were included for 3 consecutive years: 2011, 2012, and 2013. The questions, adapted from previous surveys

1. An electronic medical record is a computer-based version of the patient’s medical record. Does your doctor use an electronic medical record for you? (Answer options: yes/no/not sure/have no doctor.)

2. If doctors used electronic medical records instead of paper records, how do you think that would affect the quality of medical care? (Answers were based on a 5-point Likert scale from “greatly improve it” to “greatly worsen it”; midpoint at “no effect.”)

3. If doctors used electronic medical records instead of paper records, how do you think that would affect the privacy and security of medical information? (Answers were based on a 5-point Likert scale from “greatly improve it” to “greatly worsen it”; midpoint at “no effect.”)

No additional explanation was provided for the questions.

For the definition of an EHR, we deliberately used the label “electronic medical record” and wording similarly used by the California Healthcare Foundation so that our findings would be comparable with their 2010 survey, which reported that about 26% of consumers had physicians using an EHR.

The survey was conducted in English only, and data collection closed in November or December of each year. Survey administration took approximately 20 to 25 minutes.

Exploratory comparisons of our samples with national census data suggested that respondents were representative in terms of gender distribution, age, and employment status, but were somewhat more educated, affluent, white, and non-Hispanic, most likely due to differential response rates in different populations. Because our primary goal was not to estimate strength of support for EHRs nationally, but rather to identify relationships between variables and EHR attitudes, we opted not to construct post hoc survey weights to bring the sample closer to national distributions on all demographics. Instead, we constructed post hoc survey weights to align all 3 samples with each other. This ensures that year-to-year differences cannot be attributed to random variation in demographics from sample to sample. The normalized survey weights corrected for slight differences in the distribution of education and income from year to year. Two individuals were excluded for missing demographics, leaving a total sample size of 2998 over the 3 years. We report the weighted frequencies and percents. Relationships between sociodemographics and EHR questions were assessed with logistic regression models using the survey weights. Cross-year comparisons were conducted with χ

 tests and Cochran-Armitage tests for linear trend. Analyses were conducted in SAS version 9.3 (SAS Institute, Cary, North Carolina).

Each year, 67.1% to 71.4% of eligible respondents reached by telephone consented to participate (the “cooperation rate” according to American Association of Public Opinion Research definitions

). The samples were representative of the US population in terms of age, sex, and employment, but whites, non-Hispanics, and well-educated respondents were somewhat overrepresented (

The proportion reporting a doctor using an EHR rose over time (63.9% in 2011 to 71.1% in 2013; 

During the early years of the "meaningful use" program, surveys found decreases in both optimism and concerns about electronic health records.

Rainu Kaushal, MD, MPH

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