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The American Journal of Managed Care January 2018
Measuring Overuse With Electronic Health Records Data
Thomas Isaac, MD, MBA, MPH; Meredith B. Rosenthal, PhD; Carrie H. Colla, PhD; Nancy E. Morden, MD, MPH; Alexander J. Mainor, JD, MPH; Zhonghe Li, MS; Kevin H. Nguyen, MS; Elizabeth A. Kinsella, BA; and Thomas D. Sequist, MD, MPH
The Health Information Technology Special Issue: Has IT Become a Mandatory Part of Health and Healthcare?
Jacob Reider, MD
Bridging the Digital Divide: Mobile Access to Personal Health Records Among Patients With Diabetes
Ilana Graetz, PhD; Jie Huang, PhD; Richard J. Brand, PhD; John Hsu, MD, MBA, MSCE; Cyrus K. Yamin, MD; and Mary E. Reed, DrPH
Electronic Health Record "Super-Users" and "Under-Users" in Ambulatory Care Practices
Juliet Rumball-Smith, MBChB, PhD; Paul Shekelle, MD, PhD; and Cheryl L. Damberg, PhD
Electronic Sharing of Diagnostic Information and Patient Outcomes
Darwyyn Deyo, PhD; Amir Khaliq, PhD; David Mitchell, PhD; and Danny R. Hughes, PhD
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Hospital Participation in Meaningful Use and Racial Disparities in Readmissions
Mark Aaron Unruh, PhD; Hye-Young Jung, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
Electronic Health Record Problem Lists: Accurate Enough for Risk Adjustment?
Timothy J. Daskivich, MD, MSHPM; Garen Abedi, MD, MS; Sherrie H. Kaplan, PhD, MPH; Douglas Skarecky, BS; Thomas Ahlering, MD; Brennan Spiegel, MD, MSHS; Mark S. Litwin, MD, MPH; and Sheldon Greenfield, MD
Racial/Ethnic Variation in Devices Used to Access Patient Portals
Eva Chang, PhD, MPH; Katherine Blondon, MD, PhD; Courtney R. Lyles, PhD; Luesa Jordan, BA; and James D. Ralston, MD, MPH
Hospitalized Patients' and Family Members' Preferences for Real-Time, Transparent Access to Their Hospital Records
Michael J. Waxman, MD, MPH; Kurt Lozier, MBA; Lana Vasiljevic, MS; Kira Novakofski, PhD; James Desemone, MD; John O'Kane, RRT-NPS, MBA; Elizabeth M. Dufort, MD; David Wood, MBA; Ashar Ata, MBBS, PhD; Louis Filhour, PhD, RN; & Richard J. Blinkhorn Jr, MD

Hospital Participation in Meaningful Use and Racial Disparities in Readmissions

Mark Aaron Unruh, PhD; Hye-Young Jung, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
Hospital participation in Meaningful Use was associated with reduced disparities in 30-day readmissions for African American Medicare beneficiaries.
ABSTRACT

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

Study Design: 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.

Methods: 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.

Results: 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).

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

Am J Manag Care. 2018;24(1):38-42
Takeaway Points
  • 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.1 Racial minorities are more likely to be readmitted than other Medicare beneficiaries.2 Policy makers have been optimistic that hospitals’ adoption of health information technology (IT) will lead to lower readmission rates,3 and reducing health disparities is a priority of the federal Meaningful Use (MU) initiative.4 A recent study evaluated the impact of hospitals’ participation in MU on readmission rates of Medicare fee-for-service (FFS) beneficiaries,5 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.6-10 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 electronically11 by requiring hospitals to meet 11 core objectives and 5 additional objectives selected from a menu of 10.12 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 2016.13 MU participation among hospitals has not been associated with reductions in readmissions occurring within 30 days of discharge.5 However, given that racial minorities have higher readmission rates2 and that reducing disparities is a priority of the MU initiative,4 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.

METHODS

Overview of the Research Design

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.

Data Sources and Study Population

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.

Variables

The outcome was an indicator of all-cause 30-day readmission derived from the HCUP algorithm for hospital revisits.14 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),15 weekend admission, and a continuous measure of length of stay.16 Race categories for white and African American beneficiaries have shown reasonable reliability in administrative claims, but other race and ethnicity categories are less reliable.17-19 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.20 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.

Statistical Analysis

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.21 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.

RESULTS

Sample Characteristics

The mean age of Medicare beneficiaries in our sample was 78.2 years, and 56.6% were female (Table 1). 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 Readmission Rates

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



 
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