Surveying perceptions of the early impacts of an integrated electronic medical record across a hospital and healthcare service
Rebekah Eden![https://orcid.org/0000-0002-6228-7241](/media/client/orcid_16x16.png)
A School of Information Systems, Science and Engineering Faculty, Queensland University of Technology, 2 George Street, Brisbane, Qld 4000, Australia.
B UQ Business School, The University of Queensland, Blair Drive, St Lucia, Qld 4072, Australia. Email: abj@business.uq.edu.au
C Princess Alexandra Hospital, Metro South Hospital and Health Service, Ipswich Road, Woolloongabba, Qld 4102, Australia. Email: andrew.staib@health.qld.gov.au
D Metro North Hospital and Health Service, Herston Road, Herston, Qld 4006, Australia. Email: clair.sullivan@health.qld.gov.au
E Faculty of Medicine, The University of Queensland, Blair Drive, St Lucia, Qld 4072, Australia.
F Corresponding author. Email: rg.eden@qut.edu.au
Australian Health Review 44(5) 690-698 https://doi.org/10.1071/AH19157
Submitted: 19 July 2019 Accepted: 7 January 2020 Published: 10 September 2020
Abstract
Objective This study provides insights into the reported early impacts of the digital transformation of a large Australian hospital and healthcare service (HHS) by surveying staff perceptions of an integrated electronic medical record (ieMR).
Methods The information systems success model was used as a tool to evaluate perceptions of system quality, information quality, individual benefits and expected organisational benefits of the ieMR soon after its introduction at the HHS. A questionnaire was distributed to staff in all five hospitals in the HHS immediately after implementation. Overall staff perceptions were examined, in addition to how perceptions differed by site and profession.
Results Overall, staff held mildly positive early perceptions of system quality, information quality, individual benefits and expected organisational benefits. These views were largely consistent across sites. In terms of professions, allied health held more positive perceptions, followed by administrative and nursing professionals. Medical professionals held negative perceptions, but were neutral regarding their future expectations.
Conclusion On average, staff viewed the ieMR mildly positively immediately after implementation (despite significant changes to work practices), but differences exist across professional groups.
What is known about the topic? Hospitals globally are in the midst of a digital transformation. Yet, reported impacts are mixed and there have been few studies of the effects of comprehensive electronic medical record (EMR) implementations.
What does this paper add? This paper evaluates a comprehensive EMR immediately after go-live. We found positive early perceptions of system quality, information quality, individual benefits and expected organisational benefits. We also found that perceptions of medical professionals were largely negative, but they were neutral in terms of their future expectations.
What are the implications for practitioners? Health services may be unsure of the effect of implementing a comprehensive EMR because of conflicting reports in the literature, some touting major benefits, others stressing major costs. Our results paint a middle-ground picture immediately after implementation. Staff perceptions are mildly positive on average, which is reassuring given the results were obtained during the early disruptive period after implementation.
References
[1] Eden R, Burton-Jones A, Scott I, Staib A, Sullivan C. Effects of eHealth on hospital practice: synthesis of the current literature. Aust Health Rev 2018; 42 568–78.| Effects of eHealth on hospital practice: synthesis of the current literature.Crossref | GoogleScholarGoogle Scholar | 29986809PubMed |
[2] Bossen C, Jensen LG, Udsen FW. Evaluation of a comprehensive EHR based on the DeLone and McLean model for IS success: approach, results, success factors. Int J Med Inform 2013; 82 940–53.
| Evaluation of a comprehensive EHR based on the DeLone and McLean model for IS success: approach, results, success factors.Crossref | GoogleScholarGoogle Scholar | 23827768PubMed |
[3] Adler-Milstein J, Holmgren AJ, Kralovec P, Worzala C, Searcy T, Patel V. Electronic health record adoption in US hospitals: the emergence of a digital ‘advanced use’ divide. J Am Med Inform Assoc 2017; 24 1142–8.
| Electronic health record adoption in US hospitals: the emergence of a digital ‘advanced use’ divide.Crossref | GoogleScholarGoogle Scholar | 29016973PubMed |
[4] Queensland Health. 21st century healthcare: eHealth investment strategy. Brisbane: State of Queensland; 2015. Available at: https://www.health.qld.gov.au/__data/assets/pdf_file/0031/442939/ehealthinvestmentstrategy.pdf [verified 17 March 2020].
[5] Eden R, Burton-Jones A, Casey V, Draheim M. Digital transformation requires workforce transformation. MIS Q Exec 2019; 18 4
| Digital transformation requires workforce transformation.Crossref | GoogleScholarGoogle Scholar |
[6] Canada Health Infoway. Canada Health Infoway benefits evaluation indicators: technical report version 2.0. 2012. Available at: https://www.infoway-inforoute.ca/en/component/edocman/450-benefits-evaluation-indicators-technical-report-version-2-0/view-document?Itemid=0 [verified 8 June 2020].
[7] Delone WH, McLean ER. The DeLone and McLean model of information systems success: a ten-year update. J Manage Inf Syst 2003; 19 9–30.
| The DeLone and McLean model of information systems success: a ten-year update.Crossref | GoogleScholarGoogle Scholar |
[8] DeLone WH, McLean ER. Information systems success: the quest for the dependent variable. Inf Syst Res 1992; 3 60–95.
| Information systems success: the quest for the dependent variable.Crossref | GoogleScholarGoogle Scholar |
[9] Edmondson AC, McManus SE. Methodological fit in management field research. Acad Manage Rev 2007; 32 1246–64.
| Methodological fit in management field research.Crossref | GoogleScholarGoogle Scholar |
[10] Eden R, Burton-Jones A, Grant J, Collins R, Staib A, Sullivan C. Digitising an Australian university hospital: qualitative analysis of staff-reported impacts. Aust Health Rev 2019;
| Digitising an Australian university hospital: qualitative analysis of staff-reported impacts.Crossref | GoogleScholarGoogle Scholar | 31315788PubMed |
[11] Metro South Hospital and Health Service. Metro South Health: strategic plan 2015–2019. Brisbane: The State of Queensland, Metro South Hospital and Health Service; 2015.
[12] Sullivan C, Staib A, Ayre S, Daly M, Collins R, Draheim M, Ashby R. Pioneering digital disruption: Australia’s first integrated digital tertiary hospital. Med J Aust 2016; 205 386–9.
| Pioneering digital disruption: Australia’s first integrated digital tertiary hospital.Crossref | GoogleScholarGoogle Scholar | 27809727PubMed |
[13] Wu I-L, Kuo Y-Z. A balanced scorecard approach in assessing IT value in healthcare sector: an empirical examination. J Med Syst 2012; 36 3583–96.
| A balanced scorecard approach in assessing IT value in healthcare sector: an empirical examination.Crossref | GoogleScholarGoogle Scholar | 22366918PubMed |
[14] Erlirianto LM, Ali AHN, Herdiyanti A. The implementation of the Human, Organization, and Technology–Fit (HOT–Fit) framework to evaluate the electronic medical record (EMR) system in a hospital. Proc Comput Sci 2015; 72 580–7.
| The implementation of the Human, Organization, and Technology–Fit (HOT–Fit) framework to evaluate the electronic medical record (EMR) system in a hospital.Crossref | GoogleScholarGoogle Scholar |
[15] Lau F. eHealth economic evaluation framework. In: Lau F, Kuziemsky C, editors. Handbook of eHealth evaluation: an evidence-based approach. Victoria, BC, Canada: University of Victoria; 2015. pp. 93–108.
[16] Ojo AI. Validation of the DeLone and McLean information systems success model. Healthc Inform Res 2017; 23 60–6.
| Validation of the DeLone and McLean information systems success model.Crossref | GoogleScholarGoogle Scholar | 28261532PubMed |
[17] Tilahun B, Fritz F. Modeling antecedents of electronic medical record system implementation success in low resource setting hospitals. BMC Med Inform Decis Mak 2015; 15 61–9.
| Modeling antecedents of electronic medical record system implementation success in low resource setting hospitals.Crossref | GoogleScholarGoogle Scholar | 26231051PubMed |
[18] Top M, Yilmaz A, Karabulut E, Otieno OG, Saylam M, Bakır S, Top S. Validation of a nurses’ views on electronic medical record systems (EMR) questionnaire in Turkish health system. J Med Syst 2015; 39 67
| Validation of a nurses’ views on electronic medical record systems (EMR) questionnaire in Turkish health system.Crossref | GoogleScholarGoogle Scholar | 25957164PubMed |
[19] Lau F, Price M, Keshavjee K. From benefits evaluation to clinical adoption: making sense of health information system success in Canada. Healthc Q 2011; 14 39–45.
| From benefits evaluation to clinical adoption: making sense of health information system success in Canada.Crossref | GoogleScholarGoogle Scholar | 21301238PubMed |
[20] Gable GG, Sedera D, Chan T. Re-conceptualizing information system success: the IS-impact measurement model. J Assoc Inf Syst 2008; 9 18
| Re-conceptualizing information system success: the IS-impact measurement model.Crossref | GoogleScholarGoogle Scholar |
[21] VanGeest JB, Johnson TP, Welch VL. Methodologies for improving response rates in surveys of physicians: a systematic review. Eval Health Prof 2007; 30 303–21.
| Methodologies for improving response rates in surveys of physicians: a systematic review.Crossref | GoogleScholarGoogle Scholar | 17986667PubMed |
[22] MacKenzie SB, Podsakoff PM, Podsakoff NP. Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Q 2011; 35 293–334.
| Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques.Crossref | GoogleScholarGoogle Scholar |
[23] Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. Upper Saddle River: Pearson Education; 2009.
[24] Morgan GA, Leech NL, Gloeckner GW, Barrett KC. SPSS for introductory statistics: use and interpretation. Mahwah: Psychology Press; 2004.
[25] Byrne BM. Structural equation modeling with Mplus: basic concepts, applications, and programming. New York: Routledge; 2013.
[26] Wang J, Wang X. Structural equation modeling: applications using Mplus. Chichester: John Wiley & Sons; 2012.
[27] Little RJ, Rubin DB. The analysis of social science data with missing values. Sociol Methods Res 1989; 18 292–326.
| The analysis of social science data with missing values.Crossref | GoogleScholarGoogle Scholar |
[28] Hulland J, Baumgartner H, Smith KM. Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. J Acad Mark Sci 2018; 46 92–108.
| Marketing survey research best practices: evidence and recommendations from a review of JAMS articles.Crossref | GoogleScholarGoogle Scholar |
[29] Vodanovich S, Sundaram D, Myers M. Research commentary – digital natives and ubiquitous information systems. Inf Syst Res 2010; 21 711–23.
| Research commentary – digital natives and ubiquitous information systems.Crossref | GoogleScholarGoogle Scholar |
[30] Venkatesh V, Brown SA, Bala H. Bridging the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems. MIS Q 2013; 37 21–54.
| Bridging the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems.Crossref | GoogleScholarGoogle Scholar |
[31] Sullivan C, Staib A. Digital disruption ‘syndromes’ in a hospital: important considerations for the quality and safety of patient care during rapid digital transformation. Aust Health Rev 2018; 42 294–8.
| Digital disruption ‘syndromes’ in a hospital: important considerations for the quality and safety of patient care during rapid digital transformation.Crossref | GoogleScholarGoogle Scholar | 28514640PubMed |