546547
research-article2014
AJMXXX10.1177/1062860614546547American Journal of Medical QualityKern et al
Article
The Meaningful Use of Electronic Health Records and Health Care Quality
American Journal of Medical Quality 2015, Vol. 30(6) 512–519 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1062860614546547 ajmq.sagepub.com
Lisa M. Kern, MD, MPH1,2, Alison Edwards, MStat1,2, and Rainu Kaushal, MD, MPH1,2,3; with the HITEC Investigators
Abstract The federal government is investing approximately $30 billion in incentives for adoption and meaningful use (MU) of electronic health records (EHRs). Whether achieving MU improves quality of care is unclear. The researchers conducted a longitudinal study of 514 primary care physicians in New York State from 2010 to 2011. Quality of care provided by those who achieved stage 1 MU was compared with the quality provided by those who used EHRs but did not achieve stage 1 MU. Generalized estimating equations were used to determine whether receipt of MU incentives was independently associated with performance on 9 MU quality measures. In 2011, 44% of physicians achieved MU and 56% did not. No difference in quality was found between those who achieved stage 1 MU and those who were using EHRs but had not achieved MU. Longer follow-up is needed to observe the full effects of this multistage national policy. Keywords electronic health records, ambulatory care, quality of care The adoption and use of electronic health records (EHRs) has been associated with higher quality of care in the ambulatory setting, compared with paper health records.1,2 However, the federal government’s EHR Incentive Program provides financial incentives not for adoption and use of EHRs but for meaningful use of EHRs.3 The federal government seeks to ensure that providers do more with their EHRs than merely document electronically the tasks that they previously documented on paper: the federal government intends for EHRs to be used by providers to improve health care in real time and in ways not previously feasible with paper records.4 The federal government has operationalized the concept of MU, so that it can be determined objectively which providers have achieved this goal. For eligible providers, achieving MU requires fulfilling a set of specified “core” objectives and choosing from and fulfilling a subset of “menu” measures, which reflect processes of care for implementing and using EHRs in clinical practice.3 Adoption of EHRs has climbed steadily since the start of the MU program,5 and as of November 2013, more than 334 000 providers across the country had received payment through Medicare or Medicaid for achieving MU in stage 1 of the program.6 This represents more than 50% of eligible providers. The effect of MU on ambulatory quality, compared with typical use of EHRs, has not yet been established. Given the scope of this health policy initiative, it is
important to determine whether the MU program is actually increasing health care quality as intended. Previous studies have started to measure the impact of the MU program by considering physicians’ self-reports of health care quality, but few studies have measured actual quality.7 This study sought to determine the association between MU and quality, comparing meaningful users with typical EHR users (ie, those who have adopted EHRs but have not achieved MU).
Methods Overview The research team conducted a cohort study of primary care physicians over 2 years (2010-2011). The quality of care provided by the physicians who achieved stage 1 MU in 2011 was compared with that of physicians who used EHRs but did not achieve stage 1 MU. The protocol 1
Weill Cornell Medical College, New York, NY Health Information Technology Evaluation Collaborative, New York, NY 3 New York-Presbyterian Hospital, New York, NY 2
Corresponding Author: Lisa Kern, MD, MPH, Weill Cornell Medical College, 402 East 67th Street, New York, NY 10065. Email: [email protected]
Downloaded from ajm.sagepub.com by guest on November 15, 2015
513
Kern et al was reviewed and approved by the institutional review boards of Weill Cornell Medical College and Kingston Hospital.
Setting and Context This study took place in the Hudson Valley of New York, a 7-county region north of New York City. This is a multipayer, multiprovider region with predominantly fee-for-service reimbursement. Three community organizations in this region (THINC, the Taconic Independent Practice Association, and MedAllies) have collaborated to transform health care through EHRs, workflow redesign, and changes to physician reimbursement.8-12 This work builds on some of the research team’s previous evaluations in this community.2,13,14
Data Data from the Taconic Independent Practice Association were used to determine EHR status as well as physician sex, age, degree, specialty, county, practice size, and patient-centered medical home (PCMH) status (ie, recognized as a PCMH by the National Committee for Quality Assurance [yes/no]). Publicly available data from the Centers for Medicare & Medicaid Services were used to determine which physicians received financial incentives through Medicare for stage 1 MU.15 Data from the New York State Department of Health were used to determine which physicians received financial incentives through Medicaid for stage 1 MU. The research team also used claims data that had been aggregated across 5 health plans: 2 national commercial plans (Aetna and United), 2 regional commercial plans (MVP Healthcare and Capital District Physicians’ Health Plan), and 1 regional Medicaid health maintenance organization (Hudson Health Plan). These plans together cover approximately 60% of the community’s commercially insured patients. The plans contributed claims to a third-party data aggregator, who applied attribution logic for each year of data, as described elsewhere.14 The data aggregator also provided variables for patient health plan and provider panel size (the number of patients attributed to each physician within the data set). International Classification of Diseases, Ninth Revision codes from the claims were used to generate a count of major aggregated diagnostic groups for each patient to express the burden of comorbidity.16 The research team considered 9 quality measures that they had tracked in this community previously and that were present in the same or similar form in the stage 1 MU clinical quality measures set: (1-4) for adult patients with diabetes: eye exams, hemoglobin A1c testing, lowdensity lipoprotein cholesterol testing, and nephropathy
testing; (5) breast cancer screening; (6) Chlamydia screening; (7) colorectal cancer screening; (8) appropriate medications for people with asthma; and (9) testing for children with pharyngitis. All quality measures were derived from the aggregated claims data.
Analysis Only those primary care physicians (general internists, family medicine physicians, and pediatricians) who were using EHRs by 2011 and who had any patients with quality data in the claims data set for both 2010 and 2011 were included. The cohort was stratified by whether they received financial incentives for MU in 2011 (yes/no) through either the Medicare or Medicaid program. Descriptive statistics were used to characterize the primary care physicians. Study groups were compared using χ2 tests for dichotomous or categorical variables and t tests for continuous variables. The Wilcoxon rank sum test was used for nonnormally distributed continuous variables. The research team measured the proportion of eligible patients who received recommended care by measure, overall, and stratified by study group within each year. These unadjusted measures of performance were compared across study groups within each year using χ2 tests. Generalized linear models and maximum likelihood estimation were used to adjust for potential confounders. Patient-level data were used to generate multilevel hierarchical models with generalized estimating equations.17 With each patient-measure combination treated as a separate trial, the models expressed the overall likelihood of receiving recommended care. Patients contributed quality data to at least one study year, and they changed study groups if the provider to whom they were attributed changed. The research team used an independent working correlation structure with robust standard errors to adjust for clustering related to multiple quality measures per patient, multiple patients per physician, multiple physicians per practice, and repeated measures over time. The research team modeled the relationship between study group and the change in quality over time. The team adjusted for patient characteristics and provider characteristics. Backward stepwise elimination was used to derive the most parsimonious model while including clinically important variables regardless of their P values. The research team adjusted for clustering related to multiple quality measures per patient, multiple patients per physician, multiple physicians per practice, and repeated measures over time. P values
research-article2014
AJMXXX10.1177/1062860614546547American Journal of Medical QualityKern et al
Article
The Meaningful Use of Electronic Health Records and Health Care Quality
American Journal of Medical Quality 2015, Vol. 30(6) 512–519 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1062860614546547 ajmq.sagepub.com
Lisa M. Kern, MD, MPH1,2, Alison Edwards, MStat1,2, and Rainu Kaushal, MD, MPH1,2,3; with the HITEC Investigators
Abstract The federal government is investing approximately $30 billion in incentives for adoption and meaningful use (MU) of electronic health records (EHRs). Whether achieving MU improves quality of care is unclear. The researchers conducted a longitudinal study of 514 primary care physicians in New York State from 2010 to 2011. Quality of care provided by those who achieved stage 1 MU was compared with the quality provided by those who used EHRs but did not achieve stage 1 MU. Generalized estimating equations were used to determine whether receipt of MU incentives was independently associated with performance on 9 MU quality measures. In 2011, 44% of physicians achieved MU and 56% did not. No difference in quality was found between those who achieved stage 1 MU and those who were using EHRs but had not achieved MU. Longer follow-up is needed to observe the full effects of this multistage national policy. Keywords electronic health records, ambulatory care, quality of care The adoption and use of electronic health records (EHRs) has been associated with higher quality of care in the ambulatory setting, compared with paper health records.1,2 However, the federal government’s EHR Incentive Program provides financial incentives not for adoption and use of EHRs but for meaningful use of EHRs.3 The federal government seeks to ensure that providers do more with their EHRs than merely document electronically the tasks that they previously documented on paper: the federal government intends for EHRs to be used by providers to improve health care in real time and in ways not previously feasible with paper records.4 The federal government has operationalized the concept of MU, so that it can be determined objectively which providers have achieved this goal. For eligible providers, achieving MU requires fulfilling a set of specified “core” objectives and choosing from and fulfilling a subset of “menu” measures, which reflect processes of care for implementing and using EHRs in clinical practice.3 Adoption of EHRs has climbed steadily since the start of the MU program,5 and as of November 2013, more than 334 000 providers across the country had received payment through Medicare or Medicaid for achieving MU in stage 1 of the program.6 This represents more than 50% of eligible providers. The effect of MU on ambulatory quality, compared with typical use of EHRs, has not yet been established. Given the scope of this health policy initiative, it is
important to determine whether the MU program is actually increasing health care quality as intended. Previous studies have started to measure the impact of the MU program by considering physicians’ self-reports of health care quality, but few studies have measured actual quality.7 This study sought to determine the association between MU and quality, comparing meaningful users with typical EHR users (ie, those who have adopted EHRs but have not achieved MU).
Methods Overview The research team conducted a cohort study of primary care physicians over 2 years (2010-2011). The quality of care provided by the physicians who achieved stage 1 MU in 2011 was compared with that of physicians who used EHRs but did not achieve stage 1 MU. The protocol 1
Weill Cornell Medical College, New York, NY Health Information Technology Evaluation Collaborative, New York, NY 3 New York-Presbyterian Hospital, New York, NY 2
Corresponding Author: Lisa Kern, MD, MPH, Weill Cornell Medical College, 402 East 67th Street, New York, NY 10065. Email: [email protected]
Downloaded from ajm.sagepub.com by guest on November 15, 2015
513
Kern et al was reviewed and approved by the institutional review boards of Weill Cornell Medical College and Kingston Hospital.
Setting and Context This study took place in the Hudson Valley of New York, a 7-county region north of New York City. This is a multipayer, multiprovider region with predominantly fee-for-service reimbursement. Three community organizations in this region (THINC, the Taconic Independent Practice Association, and MedAllies) have collaborated to transform health care through EHRs, workflow redesign, and changes to physician reimbursement.8-12 This work builds on some of the research team’s previous evaluations in this community.2,13,14
Data Data from the Taconic Independent Practice Association were used to determine EHR status as well as physician sex, age, degree, specialty, county, practice size, and patient-centered medical home (PCMH) status (ie, recognized as a PCMH by the National Committee for Quality Assurance [yes/no]). Publicly available data from the Centers for Medicare & Medicaid Services were used to determine which physicians received financial incentives through Medicare for stage 1 MU.15 Data from the New York State Department of Health were used to determine which physicians received financial incentives through Medicaid for stage 1 MU. The research team also used claims data that had been aggregated across 5 health plans: 2 national commercial plans (Aetna and United), 2 regional commercial plans (MVP Healthcare and Capital District Physicians’ Health Plan), and 1 regional Medicaid health maintenance organization (Hudson Health Plan). These plans together cover approximately 60% of the community’s commercially insured patients. The plans contributed claims to a third-party data aggregator, who applied attribution logic for each year of data, as described elsewhere.14 The data aggregator also provided variables for patient health plan and provider panel size (the number of patients attributed to each physician within the data set). International Classification of Diseases, Ninth Revision codes from the claims were used to generate a count of major aggregated diagnostic groups for each patient to express the burden of comorbidity.16 The research team considered 9 quality measures that they had tracked in this community previously and that were present in the same or similar form in the stage 1 MU clinical quality measures set: (1-4) for adult patients with diabetes: eye exams, hemoglobin A1c testing, lowdensity lipoprotein cholesterol testing, and nephropathy
testing; (5) breast cancer screening; (6) Chlamydia screening; (7) colorectal cancer screening; (8) appropriate medications for people with asthma; and (9) testing for children with pharyngitis. All quality measures were derived from the aggregated claims data.
Analysis Only those primary care physicians (general internists, family medicine physicians, and pediatricians) who were using EHRs by 2011 and who had any patients with quality data in the claims data set for both 2010 and 2011 were included. The cohort was stratified by whether they received financial incentives for MU in 2011 (yes/no) through either the Medicare or Medicaid program. Descriptive statistics were used to characterize the primary care physicians. Study groups were compared using χ2 tests for dichotomous or categorical variables and t tests for continuous variables. The Wilcoxon rank sum test was used for nonnormally distributed continuous variables. The research team measured the proportion of eligible patients who received recommended care by measure, overall, and stratified by study group within each year. These unadjusted measures of performance were compared across study groups within each year using χ2 tests. Generalized linear models and maximum likelihood estimation were used to adjust for potential confounders. Patient-level data were used to generate multilevel hierarchical models with generalized estimating equations.17 With each patient-measure combination treated as a separate trial, the models expressed the overall likelihood of receiving recommended care. Patients contributed quality data to at least one study year, and they changed study groups if the provider to whom they were attributed changed. The research team used an independent working correlation structure with robust standard errors to adjust for clustering related to multiple quality measures per patient, multiple patients per physician, multiple physicians per practice, and repeated measures over time. The research team modeled the relationship between study group and the change in quality over time. The team adjusted for patient characteristics and provider characteristics. Backward stepwise elimination was used to derive the most parsimonious model while including clinically important variables regardless of their P values. The research team adjusted for clustering related to multiple quality measures per patient, multiple patients per physician, multiple physicians per practice, and repeated measures over time. P values
