572439
research-article2015
AJMXXX10.1177/1062860615572439American Journal of Medical QualityKern et al
Article
The Meaningful Use of Electronic Health Records and Health Care Utilization
American Journal of Medical Quality 1–7 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1062860615572439 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 This study sought to determine the effects on health care utilization of meaningful use (MU) of electronic health records (EHRs) compared to typical use of EHRs without MU. This was a cohort study of primary care physicians in New York State (2010-2011). A total of 7 outcomes (primary care visits, specialist visits, laboratory tests, radiology tests, emergency department visits, admissions and readmissions) and 11 potential confounders were considered. The study sample included 213 physicians (50% of whom had achieved MU) and 127 353 patients. There were 17 fewer primary care visits and 61 fewer laboratory tests for every 100 patients whose physicians achieved MU, compared with patients whose physicians did not achieve MU (P < .05 for each). There were no differences for other outcomes. Achieving stage 1 MU was associated with fewer primary care visits and laboratory tests, suggesting that effects of MU are distinct from effects of typical EHR use. Keywords electronic health records, health policy, health services utilization
Since 2011, the federal government has been investing $20 billion in incentives for adoption and meaningful use (MU) of interoperable electronic health records (EHRs).1 The federal government has a specific operational definition of the MU of EHRs, which includes measures of the process of implementing EHRs, the process of using them in clinical practice, and the process of measuring and improving clinical quality.2 Since 2005, New York State has invested $800 million in EHRs and other forms of health information technology.3,4 Both federal and state investments are based in part on the premise that making health care more electronic will yield more efficient use of health care services over time. One mechanism by which interoperable EHRs might make health care more efficient is the use of those EHRs to share clinical information among providers.5 When providers do not have access to clinical information collected previously by other providers, health care services may be repeated. If interoperable EHRs facilitate sharing of information, unnecessary use of health care services may be averted.5 There have been few empirical studies to date exploring the associations between EHRs and health care utilization.6,7 Similarly, there have been few empirical studies to determine whether MU of EHRs, as defined by the federal government, yields a different effect on health care utilization compared with typical adoption and use of EHRs (without meeting the federal MU standards).
The study objective was to determine any associations between EHRs with and without MU and health care utilization.
Methods Overview This was a longitudinal cohort study of primary care physicians in the Hudson Valley region of New York, conducted over 2 years (2010-2011). The institutional review boards of Weill Cornell Medical College and Kingston Hospital approved the protocol.
Setting and Context The Hudson Valley consists of the 7 counties immediately north of New York City. This study builds on the researchers’ previous evaluations of the Hudson Valley Initiative, a 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 M. Kern, MD, MPH, Department of Healthcare Policy and Research, Weill Cornell Medical College, 402 East 67th Street, New York, NY 10065, USA. Email: [email protected]
Downloaded from ajm.sagepub.com at University of Otago Library on November 14, 2015
2
American Journal of Medical Quality
community-based initiative that seeks to transform health care delivery through health information technology, practice transformation, and value-based purchasing.8 This initiative is the combined work of THINC,9 a not-forprofit, coalition-building organization; the Taconic Independent Practice Association (IPA),10 a not-for-profit physician organization; and MedAllies,11 a for-profit health information services provider. This initiative included community-based efforts to facilitate implementation of EHRs and assistance with implementing the processes needed to qualify for MU incentives. This study builds on the researchers’ previous work in this community, most of which involved data collection prior to MU.12-16
Data Data for this study were received from 4 sources. First, data on physician characteristics were received from the Taconic IPA. The Taconic IPA conducted a comprehensive survey of physicians in the community in 2010 and in 2011 to determine which physicians were using EHRs. This survey confirmed the presence of an EHR by collecting data on the EHR vendor and software version number. In addition to EHR status, the Taconic IPA collected and provided data on IPA membership status (yes/ no), physician sex, age, degree, specialty, county, practice size, and Patient-Centered Medical Home (PCMH) status (ie, recognition as a PCMH by the National Committee for Quality Assurance). Second, the researchers accessed publicly available data on which providers received incentives under the stage 1 federal MU program in 2011 through Medicare.17 Third, data on which providers received incentives under the stage 1 federal MU program in 2011 through Medicaid were received from the New York State Department of Health, including payment for the Adopt Implement Upgrade level of the program. Finally, the researchers received aggregated claims data for measuring health care utilization. A total of 5 health plans contributed data: 2 national commercial plans (Aetna and UnitedHealthcare), 2 regional commercial plans (MVP Health Care and Capital District Physicians’ Health Plan), and 1 regional Medicaid health maintenance organization (Hudson Health Plan), which together cover approximately 60% of the community’s commercially insured population. Building on previous work, the plans contributed claims for calendar years 2010 and 2011 to a third-party data aggregator, which ensured completeness and adherence to standardized specifications. The data aggregator attributed each claim to a specific patient and then attributed each patient to a primary care physician.15 All the patient’s health care utilization was assigned to the primary care physician to whom the
patient was attributed, regardless of who ordered the health care services. Data were provided that were aggregated at the level of the attributed physician. There were 7 different categories of health care utilization: (1) primary care visits, (2) specialist visits, (3) radiology and other diagnostic tests, (4) laboratory tests, (5) emergency department visits, (6) hospital admissions, and (7) 30-day all-cause readmissions. These 7 were chosen because together they represent the large majority of all health care utilization and because measuring them separately allows the capture of any shifts in health care utilization from one category to another. The data aggregator generated 3 additional physician characteristics for each year: the total number of patients attributed to that physician (panel size), case mix, and plan mix. Case mix was derived using DxCG software.18-20 Plan mix was a series of 5 physician-level variables, one for each health plan, that expressed the proportion of the physician’s attributed patients covered by that plan.
Statistical Analysis This study considered the physician to be the unit of analysis and included primary care physicians (general internists and family medicine physicians) practicing in the Hudson Valley who had any patients in the aggregated claims for 2011. To maximize reliability, primary care physicians were then required have at least 200 patients in the aggregated claims in 2011. A further requirement was that they have at least 200 patients in 2010 as well. Only physicians who were using EHRs by 2011 were included, excluding those who were using paper medical records. The researchers excluded any physicians with conflicting data on EHR status from the various data sources. Physicians were classified into 2 study groups based on their status in 2011: those who achieved MU and those who did not. The groups’ characteristics were compared using t tests for continuous variables and χ2 tests for categorical variables, except for the comparison for practice size, for which a Kruskal-Wallis test was used because of the non-normal distribution. Panel size was logtransformed for statistical tests. In all, 7 regression models were used, one for each health care utilization outcome. The researchers used negative binomial regression because each utilization outcome is nonnegative, positively skewed, and overdispersed (ie, the mean is smaller than the variance).21,22 This technique also allowed for adjustment for repeated measures over time. For hospital readmissions, the researchers used zero-inflated negative binomial regression because this outcome also has a larger-than-expected number of zero counts.21 Rates of utilization were calculated for each provider, incorporating panel size in the denominator to yield
Downloaded from ajm.sagepub.com at University of Otago Library on November 14, 2015
3
Kern et al observed events per 100 patients. Rates of utilization were averaged across providers within study group and within each year. Coefficients from the negative binomial models were used to calculate the mean differences in rates over time and the difference-in-differences between study groups over time. The multivariate models all adjusted for the same set of potential confounders—those physician characteristics that were associated with study group in bivariate models at baseline (P < .20). All models allowed county, practice size, panel size, case mix, and plan mix to vary over time (if those variables were selected for the multivariable model). All providers had complete data, and all models adjusted for the clustering that occurs with repeated measures over time. P values
research-article2015
AJMXXX10.1177/1062860615572439American Journal of Medical QualityKern et al
Article
The Meaningful Use of Electronic Health Records and Health Care Utilization
American Journal of Medical Quality 1–7 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1062860615572439 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 This study sought to determine the effects on health care utilization of meaningful use (MU) of electronic health records (EHRs) compared to typical use of EHRs without MU. This was a cohort study of primary care physicians in New York State (2010-2011). A total of 7 outcomes (primary care visits, specialist visits, laboratory tests, radiology tests, emergency department visits, admissions and readmissions) and 11 potential confounders were considered. The study sample included 213 physicians (50% of whom had achieved MU) and 127 353 patients. There were 17 fewer primary care visits and 61 fewer laboratory tests for every 100 patients whose physicians achieved MU, compared with patients whose physicians did not achieve MU (P < .05 for each). There were no differences for other outcomes. Achieving stage 1 MU was associated with fewer primary care visits and laboratory tests, suggesting that effects of MU are distinct from effects of typical EHR use. Keywords electronic health records, health policy, health services utilization
Since 2011, the federal government has been investing $20 billion in incentives for adoption and meaningful use (MU) of interoperable electronic health records (EHRs).1 The federal government has a specific operational definition of the MU of EHRs, which includes measures of the process of implementing EHRs, the process of using them in clinical practice, and the process of measuring and improving clinical quality.2 Since 2005, New York State has invested $800 million in EHRs and other forms of health information technology.3,4 Both federal and state investments are based in part on the premise that making health care more electronic will yield more efficient use of health care services over time. One mechanism by which interoperable EHRs might make health care more efficient is the use of those EHRs to share clinical information among providers.5 When providers do not have access to clinical information collected previously by other providers, health care services may be repeated. If interoperable EHRs facilitate sharing of information, unnecessary use of health care services may be averted.5 There have been few empirical studies to date exploring the associations between EHRs and health care utilization.6,7 Similarly, there have been few empirical studies to determine whether MU of EHRs, as defined by the federal government, yields a different effect on health care utilization compared with typical adoption and use of EHRs (without meeting the federal MU standards).
The study objective was to determine any associations between EHRs with and without MU and health care utilization.
Methods Overview This was a longitudinal cohort study of primary care physicians in the Hudson Valley region of New York, conducted over 2 years (2010-2011). The institutional review boards of Weill Cornell Medical College and Kingston Hospital approved the protocol.
Setting and Context The Hudson Valley consists of the 7 counties immediately north of New York City. This study builds on the researchers’ previous evaluations of the Hudson Valley Initiative, a 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 M. Kern, MD, MPH, Department of Healthcare Policy and Research, Weill Cornell Medical College, 402 East 67th Street, New York, NY 10065, USA. Email: [email protected]
Downloaded from ajm.sagepub.com at University of Otago Library on November 14, 2015
2
American Journal of Medical Quality
community-based initiative that seeks to transform health care delivery through health information technology, practice transformation, and value-based purchasing.8 This initiative is the combined work of THINC,9 a not-forprofit, coalition-building organization; the Taconic Independent Practice Association (IPA),10 a not-for-profit physician organization; and MedAllies,11 a for-profit health information services provider. This initiative included community-based efforts to facilitate implementation of EHRs and assistance with implementing the processes needed to qualify for MU incentives. This study builds on the researchers’ previous work in this community, most of which involved data collection prior to MU.12-16
Data Data for this study were received from 4 sources. First, data on physician characteristics were received from the Taconic IPA. The Taconic IPA conducted a comprehensive survey of physicians in the community in 2010 and in 2011 to determine which physicians were using EHRs. This survey confirmed the presence of an EHR by collecting data on the EHR vendor and software version number. In addition to EHR status, the Taconic IPA collected and provided data on IPA membership status (yes/ no), physician sex, age, degree, specialty, county, practice size, and Patient-Centered Medical Home (PCMH) status (ie, recognition as a PCMH by the National Committee for Quality Assurance). Second, the researchers accessed publicly available data on which providers received incentives under the stage 1 federal MU program in 2011 through Medicare.17 Third, data on which providers received incentives under the stage 1 federal MU program in 2011 through Medicaid were received from the New York State Department of Health, including payment for the Adopt Implement Upgrade level of the program. Finally, the researchers received aggregated claims data for measuring health care utilization. A total of 5 health plans contributed data: 2 national commercial plans (Aetna and UnitedHealthcare), 2 regional commercial plans (MVP Health Care and Capital District Physicians’ Health Plan), and 1 regional Medicaid health maintenance organization (Hudson Health Plan), which together cover approximately 60% of the community’s commercially insured population. Building on previous work, the plans contributed claims for calendar years 2010 and 2011 to a third-party data aggregator, which ensured completeness and adherence to standardized specifications. The data aggregator attributed each claim to a specific patient and then attributed each patient to a primary care physician.15 All the patient’s health care utilization was assigned to the primary care physician to whom the
patient was attributed, regardless of who ordered the health care services. Data were provided that were aggregated at the level of the attributed physician. There were 7 different categories of health care utilization: (1) primary care visits, (2) specialist visits, (3) radiology and other diagnostic tests, (4) laboratory tests, (5) emergency department visits, (6) hospital admissions, and (7) 30-day all-cause readmissions. These 7 were chosen because together they represent the large majority of all health care utilization and because measuring them separately allows the capture of any shifts in health care utilization from one category to another. The data aggregator generated 3 additional physician characteristics for each year: the total number of patients attributed to that physician (panel size), case mix, and plan mix. Case mix was derived using DxCG software.18-20 Plan mix was a series of 5 physician-level variables, one for each health plan, that expressed the proportion of the physician’s attributed patients covered by that plan.
Statistical Analysis This study considered the physician to be the unit of analysis and included primary care physicians (general internists and family medicine physicians) practicing in the Hudson Valley who had any patients in the aggregated claims for 2011. To maximize reliability, primary care physicians were then required have at least 200 patients in the aggregated claims in 2011. A further requirement was that they have at least 200 patients in 2010 as well. Only physicians who were using EHRs by 2011 were included, excluding those who were using paper medical records. The researchers excluded any physicians with conflicting data on EHR status from the various data sources. Physicians were classified into 2 study groups based on their status in 2011: those who achieved MU and those who did not. The groups’ characteristics were compared using t tests for continuous variables and χ2 tests for categorical variables, except for the comparison for practice size, for which a Kruskal-Wallis test was used because of the non-normal distribution. Panel size was logtransformed for statistical tests. In all, 7 regression models were used, one for each health care utilization outcome. The researchers used negative binomial regression because each utilization outcome is nonnegative, positively skewed, and overdispersed (ie, the mean is smaller than the variance).21,22 This technique also allowed for adjustment for repeated measures over time. For hospital readmissions, the researchers used zero-inflated negative binomial regression because this outcome also has a larger-than-expected number of zero counts.21 Rates of utilization were calculated for each provider, incorporating panel size in the denominator to yield
Downloaded from ajm.sagepub.com at University of Otago Library on November 14, 2015
3
Kern et al observed events per 100 patients. Rates of utilization were averaged across providers within study group and within each year. Coefficients from the negative binomial models were used to calculate the mean differences in rates over time and the difference-in-differences between study groups over time. The multivariate models all adjusted for the same set of potential confounders—those physician characteristics that were associated with study group in bivariate models at baseline (P < .20). All models allowed county, practice size, panel size, case mix, and plan mix to vary over time (if those variables were selected for the multivariable model). All providers had complete data, and all models adjusted for the clustering that occurs with repeated measures over time. P values
