An Information Systems Model of the Determinants of Electronic Health Record Use

P. Messeri; S. Khan; M. Millery; A. Campbell; J. Merrill; S. Shih; R. Kukafka

doi:10.4338/ACI-2013-01-RA-0005

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2013; 04(02): 185-200
DOI: 10.4338/ACI-2013-01-RA-0005

Research Article

Schattauer GmbH

An Information Systems Model of the Determinants of Electronic Health Record Use

P. Messeri

¹Mailman School of Public Health, Columbia Univiersity, Sociomedical Sciences, New York, New York, United States

,

S. Khan

²Columbia University, BIomedical Informatics, News York, United States

,

M. Millery

³Columbia University, Socomedical Sciences, New York, New York, United States

,

A. Campbell

⁴Pennsylvania State University, Population Research Institute, University Park, Pennsylvania, United States

,

J. Merrill

⁵Columbia University, Biomedical Informatics, New York, New York, United States

,

S. Shih

⁶NYC Department of Health and Mental Hygene, New York, New York, United States

,

R. Kukafka

⁵Columbia University, Biomedical Informatics, New York, New York, United States

› Institutsangaben

Abstract

Summary

Objectives: The prominence given to universal implementation of electronic health record (EHR) systems in U.S. health care reform, underscores the importance of devising reliable measures of factors that predict medical care providers’ use of EHRs. This paper presents an easily administered provider survey instrument that includes measures corresponding to core dimensions of DeLone and McClean’s (D & M) model of information system success.

Methods: Study data came from self-administered surveys completed by 460 primary care providers, who had recently begun using an EHR.

Results: Based upon assessment of psychometric properties of survey items, a revised D&M causal model was formulated that included four measures of the determinants of EHR use (system quality, IT support, ease of use, user satisfaction) and five indicators of provider beliefs about the impact on an individual’s clinical practice. A structural equation model was estimated that demonstrated a high level of inter-correlation between the four scales measuring determinants of EHR use. All four variables had positive association with each of the five individual impact measures. Consistent with our revised D&M model, the association of system quality and IT support with the individual impact measures was entirely mediated by ease of use and user satisfaction.

Conclusions: Survey research provides important insights into provider experiences with EHR. Additional studies are in progress to investigate how the variables constructed for this study are related to direct measures of EHR use.

Citation: Messeri P, Khan S, Millery M, Campbell A, Merrill J, Shih S, Kukafka R. An information systems model of the determinants of electronic health record use. Appl Clin Inf 2013; 4: 185–200

http://dx.doi.org/10.4338/ACI-2013-01-RA-0005

Keywords

Electronic health records - health information technology - EHR evaluation - provider assessment of EHR quality

Volltext

Referenzen

References
1 Institute of Medicine (U. S.) Committee on Data Standards for Patient Safety. Key capabilities of an electronic health record system : letter report. Washington, D.C.: The National Academies Press; 2003
2 Blumenthal D. Stimulating the Adoption of Health Information Technology. N Engl J Med 2009; 360 (15) 1477-1479.
3 Wang SJ. et al. A cost-benefit analysis of electronic medical records in primary care. Am J Med 2003; 114 (05) 397-403.
4 Bates DW. et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999; 6 (04) 313-321.
5 Kuperman GJ, Teich JM, Gandhi TK, Bates DW. Patient safety and computerized medication ordering at Brigham and Women’s Hospital. Jt Comm J Qual Patient Saf 2001; 27 (10) 509-521.
6 Keyhani S. et al. Electronic health record components and the quality of care. Med Care 2008; 46 (12) 1267-1272.
7 Zhou L. et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc 2009; 16 (04) 457-464.
8 Wolfstadt JI. et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med 2008; 23 (04) 451-458.
9 Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003; 163 (12) 1409-1416.
10 Bates DW. et al. What proportion of common diagnostic tests appear redundant?. Am J Med 1998; 104 (04) 361-368.
11 Kellermann AL, Jones SS. What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Affairs 2013; 32 (01) 63-68.
12 DesRoches CM. et al. Small, nonteaching, and rural hospitals continue to be slow in adopting electronic health record systems. Health Affairs 2012; 31 (05) 1092-1099.
13 Hsiao CJ, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001–2012. Hyattsville, MD: National Center for Health Statistics,; 2012
14 American Recovery and Reinvestment Act. 2009
15 Electronic Health Record Incentive Program, Proposed rule Fed Regist. 2010
16 DeLone WH, McLean ER. Information systems success: The quest for the dependent variable. Inform Syst Res 1992; 3 (01) 60-95.
17 DeLone WH, McLean ER. The DeLone and McLean model of information systems success: A ten-year Update. JIMS 2003; 19 (04) 9-30.
18 Iivari J. An empirical test of the DeLone-McLean model of information system success. SIGMIS Database 2005; 36 (02) 8-27.
19 van der Meijden MJ, Tange HJ, Troost J, Hasman A. Determinants of success of inpatient clinical information systems: A literature review. J Am Med Inform Assoc 2003; 10 (03) 235-243.
20 Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 2008; 77 (05) 291-304.
21 Otieno OG. et al. Nurses’ views on the use, quality and user satisfaction with electronic medical records: questionnaire development. J Adv Nurs 2007; 60 (02) 209-219.
22 Davis FD. Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly 1989; 13 (03) 319-340.
23 Petter S, DeLone W, McLean ER. Measuring information systems success: models, dimensions, measures, and interrelationships. EJIS 2008; 17: 236-263.
24 Fan-Yun P, Kai-I H. Applying the technology acceptance model to the introduction of healthcare information systems. Technol Forecast Soc 2011; 78 (04) 650-660.
25 Palm JP, Colombet I, Sicotte C, Degoulet P. Determinants of user satisfaction with a clinical information system. AMIA Annu Symp Proc 2006: 614-618.
26 Wixom BH, Todd PA. A theoretical integration of user satisfaction and technology acceptance. Inform Syst Res 2005; 16 (01) 82-105.
27 Mostashari F, Tripathi M, Kendall M. A tale of two large community electronic health record extension projects. Health Affairs 2009; 28 (02) 345-356.
28 Hardin JW, Hilber JM. Generalized estimating equations. New York: Chapman & Hall/CRC; 2003
29 van Buuren S, Boshuizen HC, Knook DJ. Multiple imputation of missing blood pressure covariates in a survival analysis. Stat Med 1999; 18: 681-694.
30 Rubin D. Multiple imputation for nonresponse in surveys. New York: John Wiley & Sons; 1987
31 Kirakowski J. SUMI: the Software Usability Measurement Inventory. Brit J Educ Technol 1993; 24 (03) 210-212.
32 Doll WJ, Torkzadeh K. The measurement of end-user computing satisfaction. MIS Quarterly 1988; 12 (12) 259-274.
33 Venkatesh V, Davis FD. A model of the antecedents of perceived ease of use: Development and Test. Decision Sci 1996; 27 (03) 451-481.
34 O’Connell RT, Cho C, Shah N, Brown K, Shiffman Rn.. Takes note(s): Differential EHR satisfaction with two implementations under one roof. J Am Med Inform Assoc 2004; 11 (01) 43-49.
35 Simon SR. et al. Correlates of electronic health record adoption in office practices: a statewide survey. J Am Med Inform Assoc 2007; 14 (01) 110-117.
36 Laerum H, Faxvaag A. Task-oriented evaluation of electronic medical records systems: development and validation of a questionnaire for physicians. BMC Med Inform Decis Mak 2004; 4: 1.
37 MacKinnon D. Introduction to Statistical Mediation Analysis. New York: Taylor & Francis Group; 2008
38 Yusof MM, Kuljis J, Papazafeiropoulou A, Stergioulas LK. An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit). International Journal of Medical Informatics 2008; 77 (06) 377-385.
39 Lau F, Price M, Keshavjee K. From benefits evaluation to clinical adoption: making sense of health information system success in Canada. Healthcare Quarterly 2001; 14 (01) 39-45.