Health information technology can enhance physicians' ability to provide high-quality care, suggesting that physicians should use it more extensively in their practices.
Objectives: To investigate the relationship between the use of health information technology (HIT) and physician perceptions of providing high-quality care and to determine whether this relationship has changed over time.
Study Design: We used 2 waves of longitudinal data from the Community Tracking Study Physician Surveys, 2000-2001 and 2004-2005.
Methods: Three measures of HIT were examined: a binary variable measuring the use of at least 1 type of HIT, a continuous variable measuring the total number of HIT types, and a binary variable measuring use of all 5 HIT types related to “meaningful use” of HIT as defined by Centers for Medicare & Medicaid Services. Three multivariate models were estimated to study the effect of each HIT measure on physicians’ perception of providing high-quality care. Individual fixed-effects estimation also was used to control for individual time-invariant factors.
Results: Using at least 1 type of HIT significantly enhanced physicians’ perception of providing high-quality care in 2000-2001, but not in 2004- 2005. The marginal effect of adding 1 extra HIT type was positive and statistically significant in both periods. The association between using all 5 HIT types related to meaningful use and perceived quality was statistically significant in 2000-2001, but not in 2004-2005.
Conclusions: Health information technology has become a multifunctional system and appears to have enhanced physicians’ perception of providing high-quality care. Physicians’ perceptions of medical care quality improved as the number of HIT types used increased. This study supports more extensive use of HIT in physician practices.
(Am J Manag Care. 2011;17(3):e66-e70)
Data from the Community Tracking Study Physician Surveys were used to examine the relationship between use of health information technology (HIT) and physician perceptions of providing high-quality care.
The Institute of Medicine has identified health information technology (HIT) as one of the most significant tools to improve healthcare quality in the United States.1 Health information technology is defined as the use of information and communication functions to support the delivery of patient care and patient self-management.2 Health information technology has become an integrated and multifunctional system3 that includes personal electronic health records (EHRs), e-mail communication between providers and patients, clinical alerts and reminders, computerized decision support systems, handheld devices, and other technologies that store, protect, retrieve, and transfer clinical and administrative information electronically within the healthcare setting.2 Since physicians play a critical role in the delivery of healthcare services, HIT has been touted as having the potential to enhance their ability to provide high-quality care to patients.4
The determinants of physicians’ adoption of HIT have been studied extensively.5-16 Previous studies have shown that HIT adoption rates vary considerably by physician specialty,5 practice types,8,9 practice revenues,7 patients’ characteristics,11 regional location of the practice,6 and other factors.8,10,12-16 A number of programs have been implemented to help bridge gaps in HIT adoption rates.17-29
Although the benefits of HIT are significant in theory, empirical evidence of its effects on healthcare outcomes is rather limited.30-32 This study seeks to understand the relationship between the adoption of HIT in medical practices and physicians’ perceptions of their ability to provide quality care to their patients.
METHODSData and Sample
We used data from the Community Tracking Study (CTS) Physician Surveys maintained at the Center for Studying Health System Change.33 The survey sample includes 11,963 physicians in 2000-2001 and 6306 physicians in 2004-2005. Due to cost considerations, the 2004-2005 CTS survey sampled 50% fewer physicians than the 2000-2001 survey. The response rate of CTS Physician Surveys ranged from approximately 50% to 60%.34
The CTS survey comprises a nationally representative sample of physicians in the United States.35 Physicians were selected from 51 metropolitan areas and 9 nonmetropolitan areas, using a probabilistic stratified sampling strategy and including physicians who engaged in direct patient care for at least 20 hours per week. The survey questions asked about use of HIT in physicians’ practices, perception of the physician’s ability to provide quality care to patients, and a variety of physician and practice characteristics.
Perception of Providing High-Quality Care. This measure is based on physicians’ response to questions about their perceptions of providing quality care. Each physician was asked his or her opinion regarding the following statement: “It is possible to provide high-quality care to all of my patients.” Physician responses were based on a 5-point scale of (1) agree strongly, (2) agree somewhat, (3) neither agree nor disagree, (4) disagree somewhat, and (5) disagree strongly. We constructed a binary measure of being able to provide high-quality care equal to 1 if the physician responded either 1 or 2 on the scale and equal to 0 otherwise.
Health Information Technology Variables. Physicians from both waves were queried about the use of 7 HIT types. Specifically, physicians were asked if they used HIT to (1) obtain information about treatment alternatives or recommended guidelines; (2) exchange clinical data and images with other providers; (3) access patient progress notes, medication lists, or problem lists; (4) obtain information on formularies; (5) write prescriptions; (6) generate reminders about preventive services; and (7) communicate about clinical issues with patients by e-mail.
In order to promote the adoption of HIT in physician practices, the Centers for Medicare & Medicaid Services recently offered Medicare and Medicaid EHR Incentive Programs, which specify 3 main components of Meaningful Use Requirements: the use of a certified EHR in a meaningful manner (eg, e-prescribing); the use of certified EHR technology for electronic exchange of health information to improve quality of healthcare; and the use of certified EHR technology to submit clinical quality and other measures.21 Among the 7 HIT types we studied, the first 5 types described above appear to align most closely with these Meaningful Use Requirements. This study examines the effects of HIT implementation by testing the following hypotheses: (1) whether using at least 1 type of HIT enhances physicians’ perceived ability to provide high-quality care; (2) whether the addition of 1 extra HIT type improves physicians’ perception of quality care; and (3) whether using all 5 HIT types related to the Meaningful Use Requirements enhances physicians’ perceived ability to provide high-quality care. In addition, we investigated whether these relationships changed over time by comparing the estimated marginal effects of HIT between the 2 time periods considered.
Other Explanatory Variables. We also controlled for physician and practice characteristics that may affect the physician’s perception of healthcare quality. These characteristics included sex; race (white or nonwhite); the physician’s specialty (internal medicine, family/general practice, pediatrics, medical specialties, surgical specialties, psychiatry, and obstetrics and gynecology); board certification status; foreign medical graduate status; years of practice experience; yearly practice income; practice ownership (not an owner, part owner, or full owner); practice type (solo or 2 physicians, group with 3 or more physicians, health maintenance organizations (HMOs), medical school, hospital, or other practice type); percentage of practice revenues from Medicare, Medicaid, and managed care; and the competitive status of the physicians’ market area (not competitive, somewhat competitive, or very competitive). In addition, we included geographic indicators (60 CTS survey sites) to account for potentially relevant but unobserved geographic characteristics that could influence the adoption of HIT and/or physician perceptions about quality of care.7
Bivariate analysis began with a student t test for continuous variables and a X2 test for categorical variables to examine the use of HIT and physicians’ perceived ability to provide high-quality care. Multivariate probit regression was used to examine the effects of HIT types, using Stata version 11 (StatCorp LP, College Station, TX) for statistical analyses.
The adoption of HIT is potentially endogenous, as some unobserved factors may lead physicians to choose some specific HIT types. Just using HIT may lead physicians to think that they provide high-quality care when in fact they do not. Reverse causality is another potential reason for endogeneity. Endogeneity may bias the coefficient estimates for the HIT types upward. But given the number of different information technology measures and the lack of available instruments, instrumental variables estimation was not feasible. Instead, we estimated additional models that attempt to control for confounders adequately so that endogeneity concerns might be mitigated. First, we pooled 2 waves together as a panel, because we could identify the same physicians in 2 waves (6194 physicians in both 2000-2001 and 2004-2005 as the panel sample). Then we estimated all models described above by fixed-effects estimation. Probit estimation is not available for fixed effects, so we use the fixed-effects linear probability estimation as an alternative. The fixed-effects estimation helped us control for unobserved time-invariant physician characteristics and estimate the within-physician impact of HIT adoption on perceived quality with a linear probability model.36
Table 1 compares the 2 waves in the CTS Physician Surveys with respect to sample characteristics. The percentage of physicians who were able to provide high-quality care to all patients did not differ significantly between the 2 time periods (78% in 2000-2001 and 79% in 2004-2005). The proportion of physicians using at least 1 type of HIT in their practice increased significantly, from 75% in 2000-2001 to 85% in 2004-2005 (P <.01). The average number of HIT types used in 2000-2001 was 2.11. This number increased to 2.85 in 2004-2005 (P <.01). In 2000-2001, only about 4% of physicians used all 5 HIT types related to the Meaningful Use Requirements, but this percentage increased to 11% in 2004-2005.
Table 2 shows selected results of adopting at least 1 HIT type, using a binary measure. In 2000-2001, the use of at least 1 HIT type significantly increased the probability of physicians’ perceptions of providing high-quality care by 1.85% (P = .04). However, in 2004-2005, this marginal effect was reduced to 0.21% (P = .88). The effect of having at least 1 type of HIT was not statistically significant in the fixedeffects model (P = .36). Adding an extra HIT type increased this probability by 0.71% (P <.01) in 2000-2001 and by 0.62% (P = .03) in 2004-2005. The marginal effect of HIT on perceived quality of care only diminished slightly from 2000-2001 to 2004- 2005, even though physicians substantially increased the use of HIT in their practices. The fixed-effects estimation produced a larger effect: 0.86% (P <.01).
Table 2 also shows selected results from using all 5 HIT types related to the Meaningful Use Requirements. In 2000-2001, the marginal effect was 4.41% (P = .02) if physicians used all of these 5 HIT types, but it was not statistically significant in 2004-2005. The results from fixed-effects estimation were also not statistically significant.
Due to multicollinearity issues, we were unable to include 7 dummy variables for each HIT type in 1 equation. However, in an attempt to ascertain which HIT types might be included within the same regression model, we also performed stepwise regression (forward selection) by adding each HIT type into the estimation and requiring a 0.2 significance level for retaining an independent variable. In 2000-2001, only 2 HIT types were chosen by the forward selection: exchange clinical data and image (P <.01) and communicate about clinical issues with patients by e-mail (P = .11). In 2004- 2005, only the HIT types of communicating about clinical issues with patients by e-mail (P <.01) and accessing patient notes, medication lists, or problem lists (P = .11) were selected. Due to space considerations, the stepwise regression results are not reported here, but are available from the authors upon request.
Health information technology is used much less in privatepractice settings than it is in hospitals, HMOs, and medical schools; hence, the effects of HIT may vary across settings. To investigate this issue, we reestimated our empirical models individually for 3 types of physician practice settings: solo or 2-physician private group practice (4126 physicians in 2000-2001 and 2116 physicians in 2004-2005); private group practice with 3 or more physicians (3503 physicians in 2000-2001 and 1834 physicians in 2004-2005); and practice in HMOs, medical schools, hospitals, or other settings (4334 physicians in 2000-2001 and 2356 physicians in 2004-2005). We found that HIT did not enhance physicians’ perceptions of healthcare quality in the first 2 types of privatepractices under any of the estimation strategies reported in Table 2, but significantly improved quality perceptions in HMOs, medical schools, hospitals, or other settings (P <.05 in every case). Adoption of HIT in private practices is very limited in the United States, but considerably higher in other settings, which are more heavily subsidized for adopting these technologies. These differential adoption rates may help to explain the above results.
Health information technology is considered a potentially important way to improve the efficiency and quality of healthcare in the United States.5-16 However, there remain wide variations in HIT adoption rates among physicians.26-29 Few empirical studies have directly examined the relationship between the adoption of HIT and healthcare quality.32 Using a nationally representative longitudinal data set, we found that HIT appears to promote quality of care.
Our results must be interpreted with caution, however. First, this study was limited by having only physicians’ perceptions of providing high-quality care. We cannot measure the actual quality provided to patients. Second, this study did not completely address the endogeneity of HIT adoption. Third, unobserved heterogeneity across physician practice settings may have affected the relationship between HIT and perceived quality of care. Finally, the significance levels in our results may have reflected the large sample size; thus, the practical significance should be evaluated carefully.
This study is a first attempt at investigating the relationship between the adoption of HIT and physicians’ perceptions of healthcare quality. Further research is needed to determine whether this relationship is truly a causal one. Future work should also focus on which HIT type has the greatest effect on quality of care and how quality of care itself can be measured more accurately.
Author Affiliations: From the Department of Health Systems, Management, and Policy (HF), Colorado School of Public Health, University of Colorado Denver, Aurora, CO; College of Nursing (KLP), University of Colorado Denver, Aurora, CO; Department of Political Science, Economics, and Philosophy (JC), College of Staten Island, City University of New York, Staten Island, NY; and Department of Economics, Department of Preventive Medicine (JAR), Stony Brook University, Stony Brook, NY.
Funding Source: None.
Author Disclosures: The authors (HF, KLP, JC, JAR) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (HF, KLP, JC); acquisition of data (HF); analysis and interpretation of data (HF, JC, JAR); drafting of the manuscript (HF, KLP, JC, JAR); critical revision of the manuscript for important intellectual content (HF, KLP, JC, JAR); statistical analysis (HF); provision of study materials or patients (HF); administrative, technical, or logistic support (HF, JAR); and supervision (HF).
Address correspondence to: Hai Fang, PhD, MPH, Assistant Professor, Department of Health Systems, Management, and Policy, Colorado School of Public Health, University of Colorado Denver, 13001 E 17th Pl, Room E3312, Campus Box B119, Aurora, CO 80045. E-mail: email@example.com.
1. Committee on Quality of Health Care in America, Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001.
2. The Health Resources and Services Administration, Department of Health and Human Services. Health information technology. Tools for improving health care quality. http://www.hrsa.gov/healthit/. Accessed November 8, 2009.
3. 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):742-752.
4. Kilo CM. Transforming care: medical practice design and information technology. Health Aff (Millwood). 2005;24(5):1296-1301.
5. Corey C, Grossman JM. Clinical information technology adoption varies across physician specialties. Data Bull (Cent Stud Health Syst Change). 2007;(34):1-2.
6. Mattocks K, Lalime K, Tate JP, et al. The state of physician officebased health information technology in Connecticut: current use, barriers and future plans. Conn Med. 2007;71(1):27-31.
7. Furukawa MF, Ketcham JD, Rimsza ME. Physician practice revenues and use of information technology in patient care. Med Care. 2007;45(2):168-176.
8. Grant R, Campbell E, Gruen R, Ferris TG, Blumenthal D. Prevalence of basic information technology use by U.S. physicians. J Gen Intern Med. 2006;21(11):1150-1155.
9. Grossman JM, Reed MC. Clinical information technology gaps per sist among physicians. Issue Brief Cent Stud Health Syst Change. 2006;(106):1-4.
10. Reed MC, Grossman JM. Limited information technology for patient care in physician offices. Issue Brief Cent Stud Health Syst Change. 2004;(89):1-6.
11. Eger MS, Godkin RL, Valentine SR. Physicians’ adoption of information technology: a consumer behavior approach. Health Mark Q. 2001;19(2):3-21.
12. Yusof MM, Stergioulas L, Zugic J. Health information systems adoption: findings from a systematic review. Stud Health Technol Inform. 2007;129(pt 1):262-266.
13. Tierney CD, Yusuf H, McMahon SR, et al. Adoption of reminder and recall messages for immunizations by pediatricians and public health clinics. Pediatrics. 2003;112(5):1076-1082.
14. Kazley A, Ozcan Y. Organizational and environmental determinants of hospital EMR adoption: a national study. J Med Syst. 2007;31(5): 375-384.
15. Pizzi LT, Suh DC, Barone J, Nash DB. Factors related to physicians’ adoption of electronic prescribing: results from a national survey. Am J Med Qual. 2005;20(1):22-32.
16. Garritty C, El Emam K. Who’s using PDAs? Estimates of PDA use by health care providers: a systematic review of surveys. J Med Internet Res. 2006;8(2):e7.
17. Paré G, Sicotte C, Jacques H. The effects of creating psychological ownership on physicians’ acceptance of clinical information systems. J Am Med Inform Assoc. 2006;13(2):197-205.
18. Poon EG, Jha AK, Christino M, et al. Assessing the level of healthcare information technology adoption in the United States: a snapshot. BMC Med Inform Decis Mak. 2006;6:1.
19. Rosenfeld S, Bernasek C, Mendelson D. Medicare’s next voyage: encouraging physicians to adopt health information technology. Health Aff (Millwood). 2005;24(5):1138-1146.
20. Lee J, Cain C, Young S, Chockley N, Burstin H. The adoption gap: health information technology in small physician practices. Understanding office workflow can help realize the promise of technology. Health Aff (Millwood). 2005;24(5):1364-1366.
21. Centers for Medicare & Medicaid Services. Overview. EHR Incentive Programs. 2010. https://www.cms.gov/EHRIncentivePrograms/01_Overview.asp#TopOfPage. Accessed September 30, 2010.
22. Loomis GA, Ries JS, Saywell RM, Thakker NR. If electronic medical records are so great, why aren’t family physicians using them? J Fam Pract. 2002;51(7):636-641.
23. Gans D, Kralewski J, Hammons T, Dowd B. Medical groups’ adoption of electronic health records and information systems. Health Aff (Millwood). 2005;24(5):1323-1333.
24. Burt CW, Sisk JE. Which physicians and practices are using electronic medical records? Health Aff (Millwood). 2005;24(5):1334-1343.
25. Miller RH, Sim I. Physicians’ use of electronic medical records: barriers and solutions. Health Aff (Millwood). 2004;23(2):116-126.
26. Ash JS, Stavri PZ, Kuperman GJ. A consensus statement on considerations for a successful CPOE implementation. J Am Med Inform Assoc. 2003;10(3):229-234.
27. Ash JS, Gorman PN, Seshadri V, Hersh WR. Computerized physician order entry in U.S. hospitals: results of a 2002 survey. J Am Med Inform Assoc. 2004;11(2):95-99.
28. Valdes I, Kibbe DC, Tolleson G, Kunik ME, Petersen LA. Barriers to proliferation of electronic medical records. Inform Prim Care. 2004;12(1):3-9.
29. DesRoches CM, Campbell EG, Rao SR, et al. Electronic health records in ambulatory care—a national survey of physicians. N Engl J Med. 2008;359(1):50-60.
30. Hillestad R, Bigelow J, Bower A, et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood). 2005;24(5):1103-1117.
31. Casalino L, Gillies RR, Shortell SM, et al. External incentives, information technology, and organized processes to improve health care quality for patients with chronic diseases. JAMA. 2003;289(4):434-441.
32. DesRoches CM, Campbell EG, Vogeli C, et al. Electronic health records’ limited successes suggest more targeted uses. Health Aff (Millwood). 2010;29(4):639-646.
33. Center for Studying Health System Change. CTS Physician Surveys and the HSC 2008 Health Tracking Physician Survey. http://www.hschange.com/index.cgi?data=04. Accessed February 9, 2010.
34. The Gallup Organization, for the Center for Studying Health System Change. Appendix. Community Tracking Study. Physician Survey Methodology Report 2004-05 (Round Four). January 2004. http://www.hschange.com/CONTENT/888/888appendix.pdf. Accessed April 4, 2010.
35. Center for Studying Health System Change. Community Tracking Study. Physician Survey Methodology Report 2000-01 (Round Three). Appendices. May 2003. Technical Publication No. 38, pp C19-C20. http://www.hschange.org/CONTENT/570/570a.pdf. Accessed August 30, 2008.
36. Woodridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: The MIT Press; 2002.