Going digital: a narrative overview of the clinical and organisational impacts of eHealth technologies in hospital practice
Justin Keasberry A , Ian A. Scott A B D , Clair Sullivan A , Andrew Staib A and Richard Ashby CA Princess Alexandra Hospital, 199 Ipswich Road, Brisbane, Qld 4102, Australia. Email: justin.keasberry@health.qld.gov.au; clair.sullivan@health.qld.gov.au; andrew.staib@health.qld.gov.au
B Southern School of Medicine, University of Queensland, Translational Research Institute, 199 Ipswich Road, Brisbane, Qld 4102, Australia.
C Metro South Hospital and Health Service, Garden City Park, 2404 Logan Road, Brisbane, Qld 4113, Australia. Email: richard.ashby@health.qld.gov.au
D Corresponding author. Email: ian.scott@health.qld.gov.au
Australian Health Review 41(6) 646-664 https://doi.org/10.1071/AH16233
Submitted: 8 May 2016 Accepted: 4 November 2016 Published: 9 January 2017
Journal compilation © AHHA 2017 Open Access CC BY-NC-ND
Abstract
Objective The aim of the present study was to determine the effects of hospital-based eHealth technologies on quality, safety and efficiency of care and clinical outcomes.
Methods Systematic reviews and reviews of systematic reviews of eHealth technologies published in PubMed/Medline/Cochrane Library between January 2010 and October 2015 were evaluated. Reviews of implementation issues, non-hospital settings or remote care or patient-focused technologies were excluded from analysis. Methodological quality was assessed using a validated appraisal tool. Outcome measures were benefits and harms relating to electronic medical records (EMRs), computerised physician order entry (CPOE), electronic prescribing (ePrescribing) and computerised decision support systems (CDSS). Results are presented as a narrative overview given marked study heterogeneity.
Results Nineteen systematic reviews and two reviews of systematic reviews were included from 1197 abstracts, nine rated as high quality. For EMR functions, there was moderate-quality evidence of reduced hospitalisations and length of stay and low-quality evidence of improved organisational efficiency, greater accuracy of information and reduced documentation and process turnaround times. For CPOE functions, there was moderate-quality evidence of reductions in turnaround times and resource utilisation. For ePrescribing, there was moderate-quality evidence of substantially fewer medications errors and adverse drug events, greater guideline adherence, improved disease control and decreased dispensing turnaround times. For CDSS, there was moderate-quality evidence of increased use of preventive care and drug interaction reminders and alerts, increased use of diagnostic aids, more appropriate test ordering with fewer tests per patient, greater guideline adherence, improved processes of care and less disease morbidity. There was conflicting evidence regarding effects on in-patient mortality and overall costs. Reported harms were alert fatigue, increased technology interaction time, creation of disruptive workarounds and new prescribing errors.
Conclusion eHealth technologies in hospital settings appear to improve efficiency and appropriateness of care, prescribing safety and disease control. Effects on mortality, readmissions, total costs and patient and provider experience remain uncertain.
What is known about the topic? Healthcare systems internationally are undertaking large-scale digitisation programs with hospitals being a major focus. Although predictive analyses suggest that eHealth technologies have the potential to markedly transform health care delivery, contemporary peer-reviewed research evidence detailing their benefits and harms is limited.
What does this paper add? This narrative overview of 19 systematic reviews and two reviews of systematic reviews published over the past 5 years provides a summary of cumulative evidence of clinical and organisational effects of contemporary eHealth technologies in hospital practice. EMRs have the potential to increase accuracy and completeness of clinical information, reduce documentation time and enhance information transfer and organisational efficiency. CPOE appears to improve laboratory turnaround times and decrease resource utilisation. ePrescribing significantly reduces medication errors and adverse drug events. CDSS, especially those used at the point of care and integrated into workflows, attract the strongest evidence for substantially increasing clinician adherence to guidelines, appropriateness of disease and treatment monitoring and optimal medication use. Evidence of effects of eHealth technologies on discrete clinical outcomes, such as morbid events, mortality and readmissions, is currently limited and conflicting.
What are the implications for practitioners? eHealth technologies confer benefits in improving quality and safety of care with little evidence of major hazards. Whether EMRs and CPOE can affect clinical outcomes or overall costs in the absence of auxiliary support systems, such as ePrescribing and CDSS, remains unclear. eHealth technologies are evolving rapidly and the evidence base used to inform clinician and managerial decisions to invest in these technologies must be updated continually. More rigorous field research using appropriate evaluation methods is needed to better define real-world benefits and harms. Customisation of eHealth applications to the context of patient-centred care and management of highly complex patients with multimorbidity will be an ongoing challenge.
References
[1] Catwell L, Sheikh A. Evaluating eHealth interventions: the need for continuous systemic evaluation. PLoS Med 2009; 6 e1000126| Evaluating eHealth interventions: the need for continuous systemic evaluation.Crossref | GoogleScholarGoogle Scholar |
[2] Romer C, Berstein J. The job impact of the American Recovery and Reinvestment Plan. 2009. Available at: http://www.ampo.org/assets/library/184_obama.pdf [verified 19 April 2016].
[3] Joseph M. How President Obama shaped the future of digital health. Available at: https://techcrunch.com/2016/07/27/how-president-obama-shaped-the-future-of-digital-health/ [verified 30 July 2016].
[4] Grand View Research Inc. eHealth market will reach $308.0 billion by 2022: Grand View Research, Inc. 2015. Available at: https://globenewswire.com/news-release/2015/11/18/788256/0/en/eHealth-Market-Will-Reach-308-0-Billion-By-2022-Grand-View-Research-Inc.html [verified 19 April 2016].
[5] Queensland Health. 21st century healthcare. eHealth investment strategy. 2015. Available at: https://www.health.qld.gov.au/publications/portal/ehealth-investment-strategy/ehealthinvestmentstrategy.pdf [verified 19 April 2016].
[6] Black AD, Car J, Pagliari C, et al The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med 2011; 8 e1000387
| The impact of eHealth on the quality and safety of health care: a systematic overview.Crossref | GoogleScholarGoogle Scholar |
[7] Roshanov P, You JJ, Dhailwal J, et al Can computerized clinical decision support systems improve practitioners’ diagnostic test ordering behaviour. A systematic review. Implement Sci 2011; 6 88–99
| Can computerized clinical decision support systems improve practitioners’ diagnostic test ordering behaviour. A systematic review.Crossref | GoogleScholarGoogle Scholar |
[8] Moja L, Kwag KH, Lytras T, et al The effects of computerised decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health 2014; 104 e12–22.
| The effects of computerised decision support systems linked to electronic health records: a systematic review and meta-analysis.Crossref | GoogleScholarGoogle Scholar |
[9] Thompson G, O’Horo JC, Pickering BW, et al Impact of the electronic medical record on mortality, length of stay and cost in the hospital and ICU: a systematic review and meta-analysis. Crit Care Med 2015; 43 1276–82.
| Impact of the electronic medical record on mortality, length of stay and cost in the hospital and ICU: a systematic review and meta-analysis.Crossref | GoogleScholarGoogle Scholar |
[10] Balshem H, Helfand M, Schünemann HJ, et al GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol 2011; 64 401–6.
[11] Main C, Moxham T, Wyatt JC, et al Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic review of the effects and cost-effectiveness of systems. Health Technol Assess 2010; 14 48
| Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic review of the effects and cost-effectiveness of systems.Crossref | GoogleScholarGoogle Scholar |
[12] Sahota N, Lloyd R, Ramakrishna A, et al Computerised decision support systems for acute care management: a decision maker researcher partnership systematic review of effects on process of care and patient outcomes. Implement Sci 2011; 6 91–104.
| Computerised decision support systems for acute care management: a decision maker researcher partnership systematic review of effects on process of care and patient outcomes.Crossref | GoogleScholarGoogle Scholar |
[13] Roshanov P, Misra S, Gerstein HC, et al Computerised decision support systems for chronic disease management: systematic review. Implement Sci 2011; 6 92–107.
| Computerised decision support systems for chronic disease management: systematic review.Crossref | GoogleScholarGoogle Scholar |
[14] Hemens B, Holbrook A, Tonkin M, et al Computerised decision support systems for drug prescribing and management: systematic review. Implement Sci 2011; 6 89–105.
| Computerised decision support systems for drug prescribing and management: systematic review.Crossref | GoogleScholarGoogle Scholar |
[15] Nieuwlaat R, Connolly SJ, Mackay JA, et al Computerised decision support systems for therapeutic drug monitoring and dosing: systematic review. Implement Sci 2011; 6 90–103.
| Computerised decision support systems for therapeutic drug monitoring and dosing: systematic review.Crossref | GoogleScholarGoogle Scholar |
[16] Nirantharakumar R, Chen YF, Marshall T, et al Computerised decision support systems in the care of in patients with diabetes in non-critical care setting: systematic review. Diabet Med 2012; 29 698–708.
| Computerised decision support systems in the care of in patients with diabetes in non-critical care setting: systematic review.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtFSru7nL&md5=8f3626a296fbdc70a20f6f9f98d6109bCAS |
[17] Bright T, Wong A, Dhurjati R, et al Effects of computerised decision support systems. Ann Intern Med 2012; 157 29–43.
| Effects of computerised decision support systems.Crossref | GoogleScholarGoogle Scholar |
[18] Anchala R, Pinto MP, Shroufi A, et al The role of decision support system (DSS) in prevention of cardiovascular disease: a systematic review and meta-analysis. PLoS One 2012; 7 e47064
| The role of decision support system (DSS) in prevention of cardiovascular disease: a systematic review and meta-analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhsFOkurnP&md5=84b8f03e102ae74553379468f9b01debCAS |
[19] Gillaizeau F, Chan E, Trinquart L, et al Computerized advice on drug dosage to improve prescribing practice. Cochrane Database Syst Rev 2013; 11 CD002894
| Computerized advice on drug dosage to improve prescribing practice.Crossref | GoogleScholarGoogle Scholar |
[20] Ontario Health Technology Assessment Electronic tools for health information exchange: an evidence based analysis Ont Health Technol Assess Ser 2013; 13 1–76.
[21] Nuckols T, Smith-Spangler C, Morton SC, et al The effectiveness of computerised physician order entry at reducing preventable adverse drug events and medication errors in hospital settings. Syst Rev 2014; 3 56–67.
| The effectiveness of computerised physician order entry at reducing preventable adverse drug events and medication errors in hospital settings.Crossref | GoogleScholarGoogle Scholar |
[22] Murphy CDS. Effectiveness in improving quality processes and clinical outcomes and factors that may influence success. Yale J Biol Med 2014; 87 187–97.
[23] Goldzweig C, Orshansky G, Paige NM, et al Electronic health record based interventions for improving appropriate diagnostic imaging. Ann Intern Med 2015; 162 557–65.
| Electronic health record based interventions for improving appropriate diagnostic imaging.Crossref | GoogleScholarGoogle Scholar |
[24] Goldzweig CL, Orshansky G, Paige NM, Electronic health record based interventions for reducing inappropriate imaging in the clinical setting: a systematic review of the evidence. Veteran Affairs ESP Project #05-226; 2014.
[25] Campanella P, Lovato E, Marone C, et al The impact of electronic health records on healthcare quality: systematic review. Eur J Public Health 2016; 26 60–4.
| The impact of electronic health records on healthcare quality: systematic review.Crossref | GoogleScholarGoogle Scholar |
[26] Fraccaro P, Casteleiro MA, Ainsworth J, et al Adoption of computerised decision support in multimorbidity: a systematic review. JMIR Med Inform 2015; 3 e4–17.
| Adoption of computerised decision support in multimorbidity: a systematic review.Crossref | GoogleScholarGoogle Scholar |
[27] Lau F, Kuziemsky C, Price M, Gardner J. A review on systematic reviews of health information system studies. J Am Med Inform Assoc 2010; 17 637–45.
| A review on systematic reviews of health information system studies.Crossref | GoogleScholarGoogle Scholar |
