Electronic health records and health care quality over time in a federally qualified health center

Transcription

1 Electronic health records and health care quality over time in a federally qualified health center RECEIVED 21 November 2014 ACCEPTED 15 December 2014 PUBLISHED ONLINE FIRST 9 March 2015 Lisa M. Kern 1,2,3, Alison M. Edwards 1,2, Michelle Pichardo 4, Rainu Kaushal 1,2,3,5,6 ABSTRACT... The longitudinal effects of electronic health records (EHRs) on ambulatory quality are not clear. It is not known whether adoption and meaningful use of EHRs result in a brief period of quality improvement that then plateaus, or whether with ongoing use quality improvement continues. We studied health care quality at six sites of a Federally Qualified Health Center in New York State over 3 years ( ) for unique patients. Patients were twice as likely to receive recommended care on a set of 12 quality measures (11 of which are included in Stage 1 Meaningful Use) 3 years post- EHR implementation, compared to 1-year post-implementation (odds ratio 1.97; 95% confidence interval, ). The magnitude of absolute improvement ranged from 5% to 20% per measure. EHRs were associated with continuing improvement in health care quality for at least 3 years post-implementation in the safety-net setting of a Federally Qualified Health Center.... Key words: quality of health care; electronic health records; safety-net providers INTRODUCTION Physician adoption of electronic health records (EHRs) has increased steadily over the past decade, with a marked increase in adoption in the past few years due to the federal EHR Incentive Program. 1 Although there is some evidence from cross-sectional studies that EHRs are associated with higher quality of care in the ambulatory setting, 2 the longitudinal effects are still not clear. 3 It is not clear, for example, whether adoption and meaningful use of EHRs results in a brief period of quality improvement that then plateaus, or whether with ongoing use quality improvement continues. 4 As EHRs are being adopted across the country, there is also some evidence that underserved communities are adopting EHRs at lower rates than other communities. 5,6 The federal government is facilitating the adoption of EHRs in underserved communities through technical assistance efforts to Federally Qualified Health Centers (FQHCs), which provide communitybased care for underserved populations. 7 Few studies have assessed the effects of EHRs on quality in FQHCs, 8 and this is important, because it is not clear that quality effects observed in other settings can be generalized to FQHCs. The same challenges that may have slowed EHR adoption in these centers resource constraints, organizational complexity, and lack of expertise for integrating EHRs into practice workflow 6 may also influence the effects of EHRs on quality. We sought to determine the association between EHRs and quality of care in a large FQHC, during the 3 years following EHR implementation. We assessed quality with 12 measures, 11 of which are included in Stage 1 of the federal EHR Incentive Program. METHODS We conducted a longitudinal study of practice-level quality over 3 years ( ). The Institutional Review Board of Weill Cornell Medical College and the practice s own Institutional Review Board approved the protocol. This study took place at the Institute for Family Health (IFH), a FQHC in New York. 9 We collected data from the six IFH sites north of New York City, which serve patients, making visits each year. These sites implemented a commercially available EHR in The same EHR had already been in place for 4 years at IFH s other practice sites in New York City. At the time the data were collected, providers were not receiving financial bonuses for achieving meaningful use or other internal benchmarks. This study builds on our previous work, which identified and tested quality measures that could capture the effects of interoperable EHRs and were suitable for electronic reporting. 10,11 We considered for this study the same 12 quality measures we previously tested: (1) appropriate asthma medication; (2) breast cancer screening; (3) cervical cancer screening; (4) colorectal cancer screening; (5 8) for patients with diabetes: hemoglobin A1c test done, hemoglobin A1c test <7%, hemoglobin A1c test >9% or no test, low-density lipoprotein cholesterol Correspondence to Lisa M. Kern, Department of Healthcare Policy and Research, Weill Cornell Medical College, 402 East 67th Street, New York, NY, USA; lmk2003@med.cornell.edu VC The Author Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please journals.permissions@oup.com For numbered affiliations see end of article. 453

2 <100 mg/dl; (9 and 10) for patients with ischemic vascular disease: appropriate antithrombotic medication, low-density lipoprotein cholesterol <100 mg/dl; (11) influenza vaccine; and (12) pneumococcal pneumonia vaccine. 11 Of those 12 quality measures, 11 are among the 44 Stage 1 Meaningful Use Clinical Quality Measures. 12 The 12th included measure, not represented in Meaningful Use per se but used widely as a quality measure, is number 5 above: whether patients with diabetes had their glucose control measured or not. Of the 11 measures that overlap with Stage 1 Meaningful Use, 10 are included in Stage 2 Meaningful Use; one of the two measures of glucose control (proportion of patients with glucose controlled) was not retained. 13 Decision support was in place in IFH s EHR at the time of implementation and throughout the study period to remind providers when patients had not received recommended care for many of the measures above: breast cancer screening, cervical cancer screening, colorectal cancer screening, hemoglobin A1c test >9% or no test, lipid screening, influenza vaccine, and pneumococcal pneumonia vaccine. All participating providers were exposed to ongoing training efforts by IFH in the optimal use of EHRs and quality improvement. We included all patients who had 1 office visit during the study period and who were eligible for at least one quality measure. We allowed patients to contribute to more than one Table 1: Characteristics of patients quality measure. We used automated electronic reporting, using previously described specifications, to determine which patients were eligible for each measure (denominator) and, of those, which patients had received recommended care (numerator). 11 We considered patients to have received recommended care if the numerator criteria were met, regardless of which provider had ordered that care. We considered the patient the unit of analysis. We used descriptive statistics to characterize patients, using chi-square tests (for categorical variables) and analysis of variance (for continuous variables) to assess changes over time. We described the practice s yearly performance on each quality measure as a proportion: the number of eligible patients receiving recommended care divided by the total number of eligible patients for that measure. We used two-sample tests of proportions to compare performance for each measure over time, using pairwise comparisons of different years. To assess change in overall quality, we used generalized estimating equations. We considered time as the independent variable and receipt of recommended care as the dependent variable. Each quality measure was entered into the model separately. We adjusted for multiple measures per patient and repeated measures over time. Our main analysis considered all 12 measures. For the generalized estimating equation models, we excluded one glucose Patient characteristic Year p Value* n ¼ n ¼ n ¼ Gender: female, n (%) (72) (72) (72) 0.78 Age (as of December ): mean (SD) 49 (19) 48 (19) 49 (19) <0.001 Race, n (%) a White (75) (75) (73) Black or African American 1086 (7) 1270 (8) 1478 (8) Other 681 (5) 854 (5) 1139 (7) More than one race 176 (1) 266 (2) 284 (2) Unknown 1845 (12) 1494 (9) 1806 (10) <0.001 Insurance type, n (%) a Private 5532 (37) 5577 (35) 6054 (35) Medicare 4304 (29) 4173 (26) 4773 (27) Medicaid 3629 (24) 4331 (27) 4875 (28) Uninsured 1233 (8) 1291 (8) 1454 (8) Other/unknown 377 (3) 389 (2) 317 (2) < a Sums of percentages may not add to 100 due to rounding. *p Values were generated from chi-squared tests for categorical variables and analysis of variance for continuous variables. 454

3 control measure (hemoglobin A1c >9% or test not done) to avoid co-linearity. We conducted a sensitivity analysis with only eight measures, excluding the three measures that our previous work suggested might not be reliably reported electronically: appropriate asthma medication, pneumococcal vaccination, and cholesterol control in patients with diabetes. 11 We considered p < 0.05 to be significant. All analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA). RESULTS We found more than patients per year to be eligible for at least one quality measure in any given year ( patients in 2008; in 2009; and in 2010). We excluded <1% of patients each year (41 patients in 2008, 50 in 2009, and 88 in 2010), because they did not have a primary care provider designated in the EHR. The final dataset included unique patients, who were cared for by about 80 primary care physicians. In the first study year, approximately three-fourths of patients were female (table 1). The average patient was 49 years old. Approximately 75% of patients were white, 7% were black, and 18% were from another racial group or had race unknown. Slightly more than one-third of patients had private insurance, nearly one-third had Medicare, and nearly one-third had Medicaid. The patients had, on average, five visits to the FQHC in any given year, providing multiple opportunities to receive recommended care. The proportions of patients with white Table 2: Health care quality for 12 measures, over the 3 years following implementation of an electronic health record Quality measure Proportion of patients receiving p Values* recommended care 2008, n (%) 2009, n (%) 2010, n (%) 2009 vs vs vs Asthma Appropriate asthma 457/1561 (29) 612/1822 (34) 685/1836 (37) 0.01 < medication Cancer screening Breast cancer screening 790/2651 (30) 1100/2706 (41) 1015/3300 (31) < < Cervical cancer screening 2183/8659 (25) 4023/8985 (45) 5219/9889 (53) < < < Colorectal cancer screening 1413/6668 (21) 2782/6781 (41) 3427/7921 (43) < < Diabetes Hemoglobin A1C test done 1347/1858 (72) 1710/2052 (83) 1850/2247 (82) < < Hemoglobin A1C test <7% 678/1858 (36) 867/2052 (42) 991/2247 (44) < Hemoglobin A1C test >9% 733/1858 (39) 603/2052 (29) 669/2247 (30) < < or no test a LDL cholesterol <100 mg/dl 955/1858 (51) 1030/2052 (50) 1185/2247 (53) Ischemic vascular disease Appropriate antithrombotic 189/248 (76) 399/512 (78) 485/620 (78) medication LDL cholesterol < 100 mg/dl 133/248 (54) 266/512 (52) 315/620 (51) Vaccines Influenza vaccination, 1434/4487 (32) 1538/4593 (33) 1903/5288 (36) 0.12 < age 50 years Pneumococcal vaccination 851/3049 (28) 1423/2987 (48) 1822/3570 (51) < < a Note that for this measure, a lower proportion is more desirable. *p Values were generated from two-sample tests of proportions. LDL, low-density lipoprotein. 455

4 Figure 1: Rates of receipt of recommended care over the 3 years following implementation of an electronic health record. a Note that this measure was inverted to be consistent with the directionality of the other measures; a higher proportion is more desirable. race and with private insurance decreased slightly over time (table 1). In 2008, the first year after EHR implementation, fewer than half of eligible patients received recommended care for seven of the 12 quality measures (table 2): appropriate asthma medication (29%), breast cancer screening (30%), cervical cancer screening (25%), colorectal cancer screening (21%), hemoglobin A1c <7% (36%), influenza vaccination (32%), and pneumococcal vaccination (28%). From the first year (2008) to the second year (2009), the proportion of eligible patients receiving recommended care improved significantly for eight of the 12 quality measures (table 2): appropriate asthma medication, breast cancer screening, cervical cancer screening, colorectal cancer screening, hemoglobin A1c test done, hemoglobin A1c <7%, hemoglobin A1c >9% or no test, and pneumococcal vaccination. The magnitude of the absolute improvement ranged from 5 to 20 percentage points per measure (table 2). Quality of care continued to improve significantly from the second year (2009) to the third year (2010) for five of the 12 measures, with absolute improvements ranging from 2 to 8 percentage points for that year alone. Performance on one measure (breast cancer screening) worsened significantly in that year, reverting to baseline levels. Over the entire study period, absolute improvements ranged from 4 to 28 percentage points per measure over 2 years for eight of 12 measures (table 2 and figure 1). Performance for the other four measures did not change significantly. Overall, patients were twice as likely to receive recommended care 3 years post-ehr implementation (in 2010), compared to 1 year post-implementation (in 2008, odds ratio (OR) 1.97; 95% confidence interval (CI), ; p < 0.001), adjusting for clustering by patient. This finding persisted after excluding the three measures with questionable reliability (OR 2.04; 95% CI, ; p < 0.001). DISCUSSION We found that quality improved over the 3 years following EHR implementation at a large FQHC. The proportion of patients receiving recommended care increased significantly for eight of 12 measures from the first to the second year postimplementation and for five of 12 measures from the second to the third year post-implementation. The absolute magnitude of improvement was 4 28 percentage points per measure. This effect size is consistent with those previously observed with EHRs in private practices (3 13 percentage points) 2 and in hospitals and some hospital-based ambulatory settings (12 20 percentage points), 14 with our work expanding the generalizability of those findings. There are several potential explanations for this observation. EHRs could improve performance through decision support, such as alerts, reminders, evidence-based recommendations, and smart tools for ordering and documentation for preventive services and management of chronic disease. 15 Similarly, the combination of EHRs plus an organizational culture that emphasized quality improvement using EHRs, could have contributed to this finding. 16 Alternatively, our results could represent an improvement in documentation of the care provided, without necessarily involving changes in the underlying rates of receipt of recommended care. While it is difficult to rule out this possibility, quality is measured only through what is documented; if care provided is not documented, it is assumed not to have 456

5 been given. 17 Thus, improvements in documentation carry clinical relevance. To our knowledge, this is one of the first studies to examine quality over time after EHR implementation. One previous study, with a cross-sectional design, found no association between self-reported duration of EHR use and quality of care. 3 Another study found modest improvements 2 4 years after EHR implementation in the three quality measures it considered (advice on smoking cessation, cervical cancer screening, and retinal examination in diabetes). 18 Most of the previous literature has used cross-sectional or serial cross-sectional designs to determine associations between EHR use and quality, without measuring duration of EHR use or following EHR users over time. 2,19 24 This is also one of the first studies to measure the effects of EHRs in FQHCs. One previous survey of FQHCs considered the association between health information technology capacity (measured by specific EHR functionalities) and process measures (such as timely appointment for specialty care). 8 Another study, conducted in a large health care system, included a subgroup analysis of the sites that served safety-net populations and found a positive association between EHRs and quality over time for patients with diabetes. 25 This study had several limitations. First, we were not able to measure quality prior to EHR implementation, so we cannot compare absolute levels of quality pre- and post-implementation. Second, this study only considered a subset of all quality measures, and it should be recognized that quality overall is much broader than what we were able to capture. Finally, it took place in six sites of one health care network; more research is needed in other locations to confirm generalizability. In summary, we found that quality of care improved over the 3 years following EHR implementation at a large FQHC. This study took place prior to the federal Meaningful Use program but includes 11 of its Stage 1 Clinical Quality Measures. The results are consistent with the theories behind the Meaningful Use program, which expect that EHRs will increase quality of care. 26 The FQHC in this study also placed great organizational emphasis on quality improvement, which may also be encouraged by the Meaningful Use program. FUNDING This work was supported by the Agency for Healthcare Research and Quality (AHRQ grant #R18 HS ). COMPETING INTERESTS None. ACKNOWLEDGEMENTS The authors thank Jonah Piascik and Rina Dhopeshwarkar, MPH for their assistance with data collection. REFERENCES 1. Hsiao CJ, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, NCHS Data Brief 2012;(111): Kern LM, Barron Y, Dhopeshwarkar RV, et al. Electronic health records and ambulatory quality of care. J Gen Intern Med. 2013;28(4): Zhou L, Soran CS, Jenter CA, et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc. 2009;16(4): Abramson EL, Malhotra S, Osorio SN, et al. A long-term follow-up evaluation of electronic health record prescribing safety. J Am Med Inform Assoc. 2013;20(e1):e52 e Hing E, Burt CW. Are there patient disparities when electronic health records are adopted? J Health Care Poor Underserved 2009;20(2): King J, Furukawa MF, Buntin MB. Geographic variation in ambulatory electronic health record adoption: implications for underserved communities. Health Serv Res. 2013; 48(6 Pt 1): Heisey-Grove D, Hawkins K, Jones E, et al. Supporting health information technology adoption in Federally Qualified Health Centers: ONC Data Brief, No. 8. Washington, DC; February Frimpong JA, Jackson BE, Stewart LM, et al. Health information technology capacity at federally qualified health centers: a mechanism for improving quality of care. BMC Health Serv Res. 2013;13: The Institute for Family Health. Accessed November 18, Kern LM, Dhopeshwarkar R, Barron Y, et al. Measuring the effects of health information technology on quality of care: a novel set of proposed metrics for electronic quality reporting. Jt Comm J Qual Patient Saf. 2009;35(7): Kern LM, Malhotra S, Barron Y, et al. Accuracy of electronically reported meaningful use clinical quality measures: a cross-sectional study. Ann Intern Med. 2013;158(2): Centers for Medicare and Medicaid Services, U.S. Department of Health and Human Services. Medicare and Medicaid Programs; Electronic Health Record Incentive Program; Final Rule. Fed Regist. 2010;75: Centers for Medicare and Medicaid Services, U.S. Department of Health and Human Services. Medicare and Medicaid Programs; Electronic Health Record Incentive Program - Stage 2. Final rule. Fed Regist. 2012;77: Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10): Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10): Ryan AM, Bishop TF, Shih S, et al. Small physician practices in new york needed sustained help to realize gains in quality from use of electronic health records. Health Aff. 2013; 32(1):

6 17. Soto CM, Kleinman KP, Simon SR. Quality and correlates of medical record documentation in the ambulatory care setting. BMC Health Serv Res. 2002;2(1): Garrido T, Jamieson L, Zhou Y, et al. Effect of electronic health records in ambulatory care: retrospective, serial, cross sectional study. BMJ 2005;330(7491): Friedberg MW, Coltin KL, Safran DG, et al. Associations between structural capabilities of primary care practices and performance on selected quality measures. Ann Intern Med. 2009;151(7): Keyhani S, Hebert PL, Ross JS, et al. Electronic health record components and the quality of care. Med Care. 2008; 46(12): Linder JA, Ma J, Bates DW, et al. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med. 2007;167(13): Poon EG, Wright A, Simon SR, et al. Relationship between use of electronic health record features and health care quality: results of a statewide survey. Med Care. 2010; 48(3): Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med. 2011;171: Walsh MN, Yancy CW, Albert NM, et al. Electronic health records and quality of care for heart failure. Am Heart J. 2010;159(4): e Cebul RD, Love TE, Jain AK, et al. Electronic health records and quality of diabetes care. N Engl J Med. 2011;365(9): Blumenthal D, Tavenner M. The meaningful use regulation for electronic health records. N Engl J Med. 2010;363: AUTHOR AFFILIATIONS... 1 Center for Healthcare Informatics and Policy, Weill Cornell Medical College, New York, USA 2 Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, USA 3 Department of Medicine, Weill Cornell Medical College, New York, USA 4 Institute for Family Health, New York, NY, USA 5 Department of Pediatrics, Weill Cornell Medical College, New York, USA 6 New York-Presbyterian Hospital, New York, USA 458