Predictors of Physician Use of Inpatient Electronic Health Records

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The American Journal of Managed Care, April 2012, Volume 18, Issue 4

Hospital and physician-hospital alignment, but not loyalty, are predictors of integrated electronic health record adoption by admitting physicians in an integrated system.

Objectives:

To predict adoption (80% rate of use) of an electronic health record (EHR) by admitting physicians using a heuristic model.

Methods:

Administrative data collected for 326 physicians who admitted at least 10 patients to 3 hospitals during the 6 months following EHR activation represented more than 80% of the total admissions. Functions evaluated included computerized physician order entry (CPOE), electronic history and physical (EH&P), and electronic discharge summary (EDS). Independent variables included hospital size, physician alignment, physician group size, use of an offi ce EHR, age, sex, specialty, volume, hospital based, inpatient to outpatient ratio, and loyalty.

Results:

CPOE adoption was more likely (P <.01) for physicians who were employed, male, and had a high inpatient ratio, a lower patient volume, and a community hospital setting. EH&P and EDS adoption were more likely for physicians with financial alignment and a large academic hospital setting.

Conclusions:

Personal factors (loyalty, age, sex) were generally not predictive. Organizational factors (hospital setting, financial alignment) were most predictive of adoption. Study results may help administrators improve EHR installations.

(Am J Manag Care. 2012;18(4):201-206)We used a heuristic model to predict adoption of a Healthcare Information and Management Systems Society stage 7 electronic health record (EHR) by 326 admitting physicians. Adoption of computerized physician order entry (CPOE), electronic history and physical, and electronic discharge summary was evaluated.

  •  Physician-hospital financial alignment was the strongest predictor for all 3 functions, although hospital environment may affect each function differently.

  •  Demographic factors, volume, inpatient ratio, group size, and loyalty were not consistently predictive of EHR adoption.

  •  The benchmark of 80% use rate of CPOE is achievable in this environment.

Hospital electronic health records (EHRs) are thought to significantly improve healthcare processes, yielding safer, more cost-effective care.1-8 Electronic health records are complex, integrated systems and cost millions of dollars to develop and implement. Physicians are key users of the systems and hold signifi cant power to reduce hospital costs and improve quality,9,10 though physician nonuse of EHRs is a serious problem.11-13 We developed a pragmatic, predictive model for the adoption of a Healthcare Information and Management Systems Society stage 7 EHR using predictors at the hospital, physician group, and physician levels.

Although the cost of technological innovation has been blamed for more than half of healthcare cost increases,14 some innovations reduce the cost and improve the quality and safety of healthcare. Electronic health record systems are considered essential to future improvements in care.15,16 In systematic reviews of the literature, health information technology was shown to improve quality by increasing guideline adherence, decreasing medication errors, and enhancing disease surveillance.17,18 Research also showed improved effi ciency through decreased utilization of healthcare resources such as redundant diagnostic tests. The integrated EHR—seamlessly connecting primary, secondary, and tertiary providers of care—is regarded as the ultimate goal, but has only been attempted by a few large health systems in the United States.17,19 Current research regarding the use of EHRs, however, “is scattered across many different clinical contexts and involves many different types of EHR.”20

There are numerous functions included in an integrated EHR intended to improve the quality, safety, or effi ciency of care. Three functions recognized as important are computerized physician order entry (CPOE), electronic history and physical (EH&P) documentation, and electronic discharge summary (EDS) documentation.21

CPOE, augmented by computerized clinical decision support, is one of the processes expected to immediately improve clinical outcomes.17 The percentage of orders entered directly by the physician via the computer is advocated by the Agency for Healthcare Research and Quality as an important measure of EHR use.21,22 The benchmark CPOE rate of 80% has been advocated by several experts.23 The percentage of orders entered directly, however, varies widely from hospital to hospital and physician to physician.24

There is a dearth of research about the adoption of the EHR documentation functions. Studies suggest structured data entry into the EH&P and EDS can prompt completeness, provide greater accuracy and readability, and improve communication. 25 Additionally, electronic capture of these data assists in coding and extraction of data for future health services research.

While it is generally assumed that EHR adoption will yield positive outcomes, extant research on factors infl uencing physician adoption of EHRs implemented by hospitals is biased by several factors, including low survey response rates and methods too cumbersome to be practical for use by healthcare administrators. Our research uses a pragmatic, “common sense” multilevel view of adoption of innovation, examining level of EHS adoption at a model healthcare system as a function of variables at 3 levels: hospital, physician group, and physician.

METHODSStudy Setting and Sample

This study used retrospective administrative data to evaluate the rate of adoption of a Healthcare Information and Management Systems Society stage 7 EHR by physicians at 3 hospitals in an integrated healthcare system in Virginia. The EHR was interoperable between hospital, ancillary, and physician office locations. The cost of the EHR was about $30 million per hospital. The hospitals selected for evaluation activated the EHR within 9 months of one another. The software, hardware, and implementation and training methods were consistent and stable. The hospitals include 2 community hospitals and 1 large academic hospital in order to provide a better cross-section of a typical hospital system. The sample excluded low-volume admitters (fewer than 10 patients per 6 months), physicians who admitted patients during fewer than 3 of the 6 months evaluated, and physicians with prior exposure to the EHR.

Research Question and Variables

What variables predict physician adoption of an EHR, with adoption defi ned as the initial achievement of 80% use within 6 months after EHR activation? Dependent variables included rates of CPOE, EH&P, and EDS. Independent variables were based on a heuristic combination of constructs from the diffusion of innovations theory and the resource dependence theory. Adoption was hypothesized to be positively associated with you ng age, higher volume, high physician-hospital alignment, high levels of loyalty, hospitalbased practice, medicine specialties, being from larger groups, having offi ce-based EHRs, admission of a higher proportion of inpatients versus outpatients, and admitting at larger teaching hospitals. Physician hospital alignment was divided into 4 categories: employed by the health system, aligned by a contract, independent, and competing (contracted or employed by a competing health system). No a priori hypotheses were specifi ed for differences between CPOE, EH&P, and EDS.

Statistical Analysis

Because of the clustering of physicians within hospitals, models were estimated with generalized estimating equations with SAS/STAT software version 9.2 using PROC GENMOD. 26 The strengths of the individual variables with respect to their individual odds ratios (ORs), their alpha signifi cance, and their QICu statistics27,28 were considered in the determination of the best models of rate of adoption. The common correlation for all of the generalized estimating equation models was r = -0.0057, suggesting minimal clustering effect. Power was calculated for the models using R2 software.29 The calculations suggest adequate power (>.80) for this sample size and number of variables.

The physicians included in the sample accounted for more than 80% (n = 84,326) of admissions to the 3 hospitals during the study period.

RESULTS

Table

Separate models for CPOE adoption, EH&P adoption, and EDS adoption using all of the independent variables were fitted and subsequently reduced. Results for the bestfitting models are presented in the . For the CPOE full model the QICu was 264.98, and for the reduced model the QICu was 262.35. While there was not much change in QICu, the reduced model was slightly better fi tting. The likelihood of CPOE adoption was higher for physicians at the small community hospital (OR = 8.27) and the midsized community hospital (OR = 2.9) compared with the large academic hospital; higher for employed physicians (OR = 24.9) and contracted physicians (OR = 1.2) compared with competing physicians; higher for men compared with women (OR = 3.3); higher for surgeons compared with medical physicians (OR = 1.83); lower for physicians in the top quartile of discharges (more than 132) compared with the fi rst quartile of discharges (fewer than 36) (OR = 0.34); and lower for physicians in the second quartile of inpatient ratio (less than 46%) compared with the fourth quartile of inpatient ratio (more than 98%) (OR = 0.26). Age, group size, use of an office EHR, being hospital based, and loyalty were not significant predictors of adoption of CPOE by 6 months once adjusted for other variables

For the EH&P full model the QICu was 381.50, and for the reduced model the QICu was 377.53. While there was not much change in QICu, the reduced model was slightly better fitting. The likelihood of EH&P adoption was higher for the large academic hospital compared with the midsized community hospital (OR = 1.52) or small community hospital (OR = 2.94); higher for contracted physicians (OR = 20.5) and employed physicians (OR = 6.99) compared with= competing physicians; higher for women compared with men (OR = 1.7); and lower for physicians with discharges above the median (more than 70 discharges) compared with physicians with discharges in the fi rst quartile (ORs = 0.44 and 0.29).

For the EDS full model the QICu was 337.75, and for the reduced model the QICu was 328.47. Here, the QICu had a slightly larger change, indicating that the reduced model was better fitting. The likelihood of EDS adoption was higher for the large academic hospital compared with the mid-sized community hospital (OR = 2.9) and the small community hospital (OR = 1.3); higher for independent physicians (OR = 1.8), contracted physicians (OR = 12.7), and employed physicians (OR = 5.27) compared with competing physicians; higher for surgical specialists compared with medical specialists (OR = 2.3); and lower for hospital-based physicians compared with those who were not hospital based (OR = 0.45).

DISCUSSION

The combination of the diffusion of innovations theory and resource dependence theory provides a pragmatic model for the prediction of EHR adoption. Interestingly, organizational-level variables and level of physician-hospital alignment were more predictive than individual physician—level variables. The hospital type was signifi cantly associated with adoption of all 3 EHR functions. The direction of that association varied and may suggest that leadership, change management methods, social norms at each facility, or other environmental factors affect adoption differently for different functions in the EHR software.

Adjusted for other variables including hospital, the type of financial alignment was clearly the strongest predictor of adoption of all of the measures of EHR adoption. Employment and contractual alignment both increased the likelihood of adoption of all 3 EHR functions. Financial incentives or goals to meet the predetermined threshold of 80% use were known to be present in some of the contracts and employment agreements. Moreover, the lack of association between loyalty and adoption suggests financial relationships are more important than nonfinancial relationships. Additionally, admitting physicians employed by competing health systems may consider it an inconvenience to care for patients at the competing institutions, only admitting patients at the health system studied because of exclusive contracts with payers or because of patient request.

Figure

Another interesting finding is the difference in the speed and level of adoption of CPOE versus EH&P and EDS as shown in the . This suggests there is a difference in the way the physicians approach the “action” function of CPOE (telling people what to do) versus the “retrospective documentation” functions (telling people what was done) of EH&P and EDS. Another likely explanation may be the level of difficulty physicians experienced in using noncomputerized methods. Forms for written orders were removed from most areas of the hospital and verbal orders were strongly discouraged by the hospitals’ leadership, while the use of verbal dictation for the documentation of history, physical, and discharge summary information remained readily available. The effect of monitoring metrics and administrators’ attention to meeting goals may also explain some of the differences in adoption of CPOE compared with EH&P and EDS. Administrators emphasized CPOE compliance, evaluating use rates for each physician on a weekly basis. Use of the electronic documentation functions was encouraged but did not receive the same level of monitoring and feedback as CPOE.

Policy Implications

The adoption of CPOE at a level of 80% was suggested by experts as the hurdle for the receipt of incentive payments from the government. The fi nal Health Information Technology for Economic and Clinical Health (HITECH) Act reduced the level to 50%. This study shows the 80% rate is achievable and provides information about the association of physician alignment and other variables. No incentives are in place for the use of the documentation functions of the EHR, and physician adoption of those functions lags behind that of CPOE. Since alignment was signifi cantly associated with adoption, it may be valuable for the government to continue plans to integrate the payment for services for physician and hospital services, thereby encouraging physician-hospital alignment and consequently EHR adoption.

Administrative Implications

Hospital administrators may use the results of this study to perform an assessment of the likelihood of EHR adoption by the medical staff at individual hospitals. Understanding the variables that are associated with success or failure will enable them to devise strategies to improve the odds of high use and rapid adoption. The effect of the hospital environment was significant but much less powerful than the effects of alignment when adjusted for type of hospital. Understanding the association of financial alignment may encourage administrators to further their efforts to hire or contract with physicians. Actions may also include increasing attention to physicians predicted to be slow adopters to increase the odds that they buy into the system. Hospital administrators traditionally have relied on good relationships and physician loyalty as a predictor of future behavior. Loyalty, however, was not a signifi cant predictor of adoption or the rate of adoption. Administrators should be aware that strong informal relationships do not predict adoption by admitting physicians, although formal financial alignment is a strong predictor.

Limitations

The environment, EHR software, and cost are reasonably representative of many multihospital healthcare systems, or what they are in the process of becoming, but may not be representative of independent hospitals. Although the EHR software implemented was a version of the most commonly implemented EHR software, the results of this study may not be generalizable to other environments. The number of hospitals included in the study is limited; the hospital variable is intended to be used for statistical adjustment.

Globally, EHR development, implementation, and adoption have been slow due to cost and interoperability issues.30 It is clear that cost and interoperability are important issues even after the initial purchase of an EHR. Cost may limit healthcare systems’ ability to upgrade hardware or provide personnel, and a lack of interoperability limits the EHR’s usefulness during transitions of care. Seeking a pragmatic model, we were limited to information that could be collected without a survey and did not evaluate cost, interoperability, or perceptions. As more hospitals and physician groups implement EHRs, research regarding the effects of interoperability will be valuable. Finally, this study only focused on adoption and did not evaluate whether adoption resulted in improvements in patient care or patient outcomes.

CONCLUSIONS

This study evaluated the adoption of an EHR by admitting physicians using 3 functions of the EHR as dependent variables. The adoption of EHRs might relate to leadership influence, change management methods, and other organization- level variables. Physician-hospital alignment was a strong predictor of adoption. Individual characteristics of the adopter did not consistently predict adoption. Although consistently shown to be a predictor in extant literature, age had no significant effect on adoption.

There are differences in adoption for the action function of CPOE compared with the retrospective, documentation functions of EH&P and EDS. With the exception of alignment, the variables that strongly predicted adoption of the action versus documentation functions are the same, but the directions of the associations are different. Future research to explain these differences is recommended.

This study evaluated the installation of an EHR at a health system that was highly integrated and had high levels of physician-hospital alignment. The level of physician-hospital alignment and complexity of the EHR provided a unique opportunity to evaluate the effects of alignment on the adoption of the EHR and to create a predictive model for future use. Physician-hospital alignment had a consistent positive effect on physician adoption of the 3 EHR functions studied. The concept of loyalty, long held by healthcare administrators as a measure of the strength of the relationship between the hospital and the physician, and a good predictor of future physician cooperation, appears to have been upstaged by financial alignment. Knowing the results of this study may help administrators improve EHR installations.Author Affiliations: From Sentara Healthcare (JSH), Newport News, VA; Health Sciences (JAN, QZ), Psychology (MAP), Old Dominion University, Norfolk, VA; Mercer Consulting Inc (LTM), Portsmouth, VA. Funding Source: None.

Author Disclosures: The authors (JSH, JAN, MAP, QZ, LTM) report no relationship or financial interest with any entity that would pose a confl ict of interest with the subject matter of this article.

Authorship Information: Concept and design (JSH, JAN, QZ, LTM); acquisition of data (JSH); analysis and interpretation of data (JSH, MAP, QZ, LTM); drafting of the manuscript (JSH, MAP, LTM); critical revision of the manuscript for important intellectual content (JSH, JAN, LTM); statistical analysis (JSH, MAP); provision of study materials or patients (JSH); administrative, technical, or logistic support (JSH); supervision (JSH, JAN, QZ); and dissertation director (JAN).

Address correspondence to: John S. Hudson, RN, PhD, Director, Sentara Healthcare, 131 Linda Dr, Newport News, VA 23608. E-mail: jshudson1@aol.com.1 . Poon EG, Gandhi TK, Sequist TD, Murff HJ, Karson AS, Bates DW. Overcoming barriers to adopting and implementing computerized physician order entry systems in U.S. hospitals. Health Aff (Millwood). 2004;23(4):184-190.

2 . Furukawa MF, Raghu TS, Spaulding TJ, Vinze A. Adoption of health information technology for medication safety in U.S. hospitals, 2006. Health Aff (Millwood). 2008;27(3):865-875.

3 . Menachemi N, Ford EW, Beitsch LM, Brooks RG. Incomplete EHR adoption: late uptake of patient safety and cost control functions. Am J Med Qual. 2007;22(5):319-326.

4 . Wu RC, Abrams H, Baker M, Rossos PG. Implementation of a com puterized physician order entry system of medications at the University Health Network—physicians’ perspectives on the critical issues. Healthcare Q. 2006;9(1):106-109.

5 . Menachemi N, Brooks R. Reviewing the benefits and costs of electronic health records and associated patient safety technologies. J Med Syst. 2006;30(3):159-168.

6 . Callen JL, Westbrook JI, Braithwaite J. The effect of physicians’ long-term use of CPOE on their test management work practices. J Am Med Inform Assoc. 2006;13(6):643-652.

7 . Ohsfeldt RL, Ward MM, Schneider JE, et al. Implementation of hospital computerized physician order entry systems in a rural state: feasibility and financial impact. J Am Med Inform Assoc. 2005;12(1):20-27.

8 . Saginur MD. Technologies to Improve Medication Safety in Hospitals: A Study of Their Effectiveness and Use in Canada. Ottawa, Canada: University of Ottawa; 2005.

9 . Stone WM, Smith BE, Shaft JD, Nelson RD, Money SR. Impact of a computerized physician order-entry system. J Am Coll Surg. 2009; 208(5):960-967.

10. Miller RH, Sim I. Physicians’ use of electronic medical records: barriers and solutions. Health Aff (Millwood). 2004;23(2):116-126.

11. Conn J. Failure, de-installation of EHRs abound: study. Modern Healthcare. http://www.modernhealthcare.com/article/20071030/ INFO/310300002. Published October 30, 2007. Accessed March 6, 2012.

12. Dephillips HA III. Initiatives and barriers to adopting health information technology. Dis Manage Health Outcomes. 2007;15(1):1-6.

13. Abdolrasulnia M, Menachemi N, Shewchuk RM, Ginter PM, Duncan WJ, Brooks RG. Market effects on electronic health record adoption by physicians. Health Care Manage Rev. 2008;33(3):243-252.

14. McKinnon K. HIV risk behaviors. Psychiatr Serv. 1997;48(12): 1592-1593.

15. 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.

16. Anderson JG. Social, ethical and legal barriers to E-health. Int J Med Inform. 2007;76(5/6):480-483.

17. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, effi ciency, and costs of medical care. Ann Intern Med. 2006;144(10):E12-W18.

18. Aziz J, McKenzie K, Clark M. The impact of health information technology on the quality of medical and health care: a systematic review. Health Inform Manag J. 2009;38(3):26-37.

19. Berner ES, Detmer D, Simborg D. Will the wave finally break? a brief view of the adoption of electronic medical records in the United States. J Am Med Inform Assoc. 2005;12(1):3-7.

20. Clamp S, Keen J. The Value of Electronic Health Records: A Literature Review. White paper. Leeds, UK: Yorkshire Centre for Health Informatics, University of Leeds; December 2005. http://www.ychi.leeds.ac.uk/ychi/pubs.aspx. Accessed March 6, 2012.

21. Tang PC, Hripcsak G. HIT Policy Committee. Meaningful Use Workgroup Presentation to HIT Policy Committee. November 9, 2011. healthit.hhs.gov/.../TangMUWG_Presentation-11-9-11_HITPC.pdf. Accessed March 6, 2012.

22. Agency for Healthcare Research and Quality (AHRQ). Percentage of Orders Entered by Authorized Providers Using CPOE. Rockville, MD: AHRQ; 2009.

23. Healthcare Information Management Systems Society. EMR adoption model. http://www.himssanalytics.org/hc_providers/emr_adoption. asp. Published 2009. Accessed October 13, 2009.

24. Lindenauer PK, Ling D, Pekow PS, et al. Physician characteristics, attitudes, and use of computerized order entry. J Hosp Med. 2006; 1(4):221-230.

25. Roukema J, Los RK, Bleeker SE, van Ginneken AM, van der Lei J, Moll HA. Paper versus computer: feasibility of an electronic medical record in general pediatrics. Pediatrics. 2006;117(1):15-21.

26. SAS Institute. SAS/STAT [computer program]. Cary, NC: SAS Institute, Inc; 2008.

27. Pan W. Akaike’s information criterion in generalized estimating equations. Biometrics. 2001;57(1):120-125.

28. Hardin JW, Hilbe JM. Generalized Estimating Equations. New York: Chapman & Hall/CRC; 2003.

29. Steiger J, Fouladi R. R2 software download. Version 1.1. 1992. http:// www.statpower.net/Software.html. January 12, 2011.

30. Arnold S, Wagner J, Hyatt SJ, Klein GM; Global EHR Task Force Members. Electronic Health Records: A Global Perspective. The Healthcare information and Management Systems Society. http://www.himss.org/content/fi les/DrArnold20011207EISPresentationWhitePaper.pdf. Published 2007. Accessed November 29, 2011.