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Headline
This research programme found that the implementation of electronic prescribing systems was challenging, but when fully implemented, they were associated with a reduction in clinically important prescribing errors.
Abstract
Background:
There is a need to identify approaches to reduce medication errors. Interest has converged on ePrescribing systems that incorporate computerised provider order entry and clinical decision support functionality.
Objectives:
We sought to describe the procurement, implementation and adoption of basic and advanced ePrescribing systems; to estimate their effectiveness and cost-effectiveness; and to develop a toolkit for system integration into hospitals incorporating implications for practice from our research.
Design:
We undertook a theoretically informed, mixed-methods, context-rich, naturalistic evaluation.
Setting:
We undertook six longitudinal case studies in four hospitals (sites C, E, J and K) that did not have ePrescribing systems at the start of the programme (three of which went live and one that never went live) and two hospitals (sites A and D) with embedded systems. In the three hospitals that implemented systems, we conducted interviews pre implementation, shortly after roll-out and at 1 year post implementation. In the hospitals that had embedded systems, we conducted two rounds of interviews, 18 months apart. We undertook a three-round eDelphi exercise involving 20 experts to identify 80 clinically important prescribing errors, which were developed into the Investigate Medication Prescribing Accuracy for Critical error Types (IMPACT) tool. We elicited the cost of an ePrescribing system at one (non-study) site and compared this with the calculated ‘headroom’ (the upper limit that the decision-maker should pay) for the systems (sites J, K and S) for which effectiveness estimates were available. We organised four national conferences and five expert round-table discussions to contextualise and disseminate our findings.
Intervention:
The implementation of ePrescribing systems with either computerised provider order entry or clinical decision support functionality.
Main outcome measures:
Error rates were calculated using the IMPACT tool, with changes over time represented as ratios of error rates (as a proportion of opportunities for errors) using Poisson regression analyses.
Results:
We conducted 242 interviews and 32.5 hours of observations and collected 55 documents across six case studies. Implementation was difficult, particularly in relation to integration and interfacing between systems. Much of the clinical decision support functionality in embedded sites remained switched off because of concerns about over alerting. Getting systems operational meant that little attention was devoted to system optimisation or secondary uses of data. The prescriptions of 1244 patients were audited pre computerised provider order entry and 1178 post computerised provider order entry implementation of system A at sites J and K, and system B at site S. A total of 21,138 opportunities for error were identified from 28,526 prescriptions. Across the three sites, for those prescriptions for which opportunities for error were identified, the error rate was found to reduce significantly post computerised provider order entry implementation, from 5.0% to 4.0% (p < 0.001). Post implementation, the overall proportion of errors (per opportunity) decreased significantly in sites J and S, but remained similar in site K, as follows: 4.3% to 2.8%, 7.4% to 4.4% and 4.0% to 4.4%, respectively. Clinical decision support implementation by error type was found to differ significantly between sites, ranging from 0% to 88% across clinical contraindication, dose/frequency, drug interactions and other error types (p < 0.001). Overall, 43 out of 78 (55%) of the errors had some degree of clinical decision support implemented in at least one of the hospitals. For the site in which no improvement was detected in prescribing errors (i.e. site K), the ePrescribing system represented a cost to the service for no countervailing benefit. Cost-effectiveness rose in proportion to reductions in error rates observed in the other sites (i.e. sites J and S). When a threshold value of £20,000 was used to define the opportunity cost, the system would need to cost less than £4.31 per patient per year, even in site S, where effectiveness was greatest. We produced an ePrescribing toolkit (now recommended for use by NHS England) that spans the ePrescribing life cycle from conception to system optimisation.
Limitations:
Implementation delays meant that we were unable to employ the planned stepped-wedge design and that the assessment of longer-term consequences of ePrescribing systems was impaired. We planned to identify the complexity of ePrescribing implementation in a number of contrasting environments, but the small number of sites means that we have to infer findings from this programme with considerable care. The lack of transparency regarding system costs is a limitation of our method. As with all health economic analyses, our analysis is subject to modelling assumptions. The research was undertaken in a modest number of early adopters, concentrated on high-risk prescribing errors and may not be generalisable to other hospitals.
Conclusions:
The implementation of ePrescribing systems was challenging. However, when fully implemented the ePrescribing systems were associated with a reduction in clinically important prescribing errors and our model suggests that such an effect is likely to be more cost-effective when clinical decision support is available. Careful system configuration considering clinical processes and workflows is important to achieving these potential benefits and, therefore, our findings may not be generalisable to all system implementations.
Future work:
Formative and summative evaluations of efforts will be central to promote learning across settings. Other priorities emerging from this work include the possibility of learning from international experiences and the commercial sector.
Funding:
This project was funded by the National Institute for Health and Care Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 10, No. 7. See the NIHR Journals Library website for further project information.
Contents
- Plain English summary
- Scientific summary
- SYNOPSIS
- Work package 1: implementation, adoption and connectivity
- Work package 2: assessing the impact on prescribing safety
- Work package 3: health economics
- Work package 4: integration of work packages and implications for future NHS ePrescribing deployments and research
- Patient and public involvement
- What was successful and not successful in the programme?
- Recommendations for future research
- Implications for practice
- Acknowledgements
- References
- Appendix 1. Outline of work package 1 research design
- Appendix 2. Summary of the case study sites
- Appendix 3. Summary of work package 1 data collection
- Appendix 4. Work package 2 pilot work on the frequency of alerts for prescription indicators at University Hospitals Birmingham NHS Foundation Trust
- Appendix 5. Work package 2 final prescription indicators
- Appendix 6. Information about the IMPACT tool
- Appendix 7. Work package 2 standardised data collection process
- Appendix 8. Work package 2: documentation errors under paper-based (pre) and electronic (post) ePrescribing system implementation
- Appendix 9. Work package 2: summary of indicator errors
- Appendix 10. Level of clinical decision support
- Appendix 11. Comparison of paper- and ePrescribing-based systems
- Appendix 12. Comparison of error rates on pre and post implementation grouped by level of clinical decision support
- Appendix 13. Aggregated prior density for the relative risk of a mediation error with an ePrescribing system compared with paper-based prescribing from the expert elicitation work shop and individual expert priors densities
- Appendix 14. Plot showing prior density (dashed line) and posterior density (solid line) for the relative risk of experiencing a medication error with an ePrescribing system compared with paper-based prescribing
- Appendix 15. Conference 1 programme
- Appendix 16. Evaluation summary from conference 1
- Appendix 17. Conference 2 programme
- Appendix 18. Evaluation summary from conference 2
- Appendix 19. Conference 3 programme
- Appendix 20. Evaluation summary from conference 3
- Appendix 21. Conference 4 programme
- Appendix 22. Evaluation summary from conference 4
- Appendix 23. Example screenshots from the ePrescribing toolkit
- Appendix 24. The original (later amended) patient and public involvement strategy for the ePrescribing programme
- Appendix 25. Patient and public involvement ePrescribing jargon buster
- Appendix 26. Patient and public involvement in health information technology research: top 10 tips for researchers
- Appendix 27. Unpublished lay summaries
- Appendix 28. Patient and public involvement induction day documents role guide
- Appendix 29. Patient and public involvement induction day documents expenses guide
- List of abbreviations
- List of supplementary material
About the Series
Declared competing interests of authors: David Bates is a co-inventor on patent number 6029138, which is held by Brigham and Women’s Hospital (Boston, MA, USA), on the use of decision support software for medical management, held by Medicalis Corporation (Kitchener, ON, Canada). He also holds a minority equity position in the privately held company Medicalis Corporation, which develops web-based decision support for radiology test ordering, and served on the board for SEA Medical Systems (Emerald Hills, CA, USA), which makes intravenous pump technology (2012–20). David Bates is on the clinical advisory board for Zynx Health Inc. (Los Angeles, CA, USA), which develops evidence-based algorithms, consults for EarlySense (Ramat Gan, Israel), which makes patient safety monitoring systems, and received cash and equity from QPID Inc. (Boston, MA, USA), a company focused on intelligence systems for electronic health records. He also receives cash compensation from CDI Ltd (Negev, Israel), which is a non-for-profit incubator for health IT start-ups, receives equity from Enelgy (Northbridge, CA, USA), which makes software to support clinical decision-making in intensive care, and receives equity from MDClone (Tel Aviv-Yafo, Israel), which takes clinical data and produces deidentified version of them. David Bates receives equity from ValeraHealth (Brooklyn, NY, USA), which makes software to help patients with chronic diseases, and equity from Intensix (Los Angeles, CA, USA), which makes software to support clinical decision-making in intensive care. David Bates’ financial interests have been reviewed by Brigham and Women’s Hospital and Partners HealthCare (now Mass General Brigham) (Boston, MA, USA) in accordance with their institutional policies. Lisa Slee reports personal fees from NHS England and PCS Health Ltd (Chester, UK) outside the submitted work. Sarah Slight is a member of the Health Technology Assessment Primary Care, Community and Preventive Interventions Panel.
Article history
The research reported in this issue of the journal was funded by PGfAR as project number RP-PG-1209-10099. The contractual start date was in November 2011. The final report began editorial review in February 2018 and was accepted for publication in September 2020. As the funder, the PGfAR programme agreed the research questions and study designs in advance with the investigators. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The PGfAR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.
Last reviewed: February 2018; Accepted: September 2020.
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