医院电子处方系统改善用药安全:一个多方法研究项目

Q4 Medicine
Aziz Sheikh, J. Coleman, Antony Chuter, Robin Williams, R. Lilford, A. Slee, Z. Morrison, K. Cresswell, Ann Robertson, S. Slight, H. Mozaffar, Lisa Lee, Sonal Shah, S. Pontefract, Abby King, V. Wiegel, S. Watson, Ndeshi Salema, David Bates, A. Avery, A. Girling, L. McCloughan, N. Watson
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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. 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引用次数: 1

摘要

这项研究是在少数早期采用者中进行的,主要集中在高风险的处方错误上,可能无法推广到其他医院。电子处方系统的实施具有挑战性。然而,当全面实施电子处方系统与减少临床重要的处方错误有关,我们的模型表明,当临床决策支持可用时,这种效果可能更具成本效益。仔细考虑临床过程和工作流程的系统配置对于实现这些潜在的好处是重要的,因此,我们的发现可能不能推广到所有的系统实现。对努力的形成性和总结性评价将是促进跨环境学习的核心。这项工作产生的其他优先事项包括从国际经验和商业部门学习的可能性。该项目由国家卫生和保健研究所(NIHR)应用研究方案资助,并将全文发表在应用研究方案资助上;第十卷第七期请参阅NIHR期刊图书馆网站了解更多项目信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electronic prescribing systems in hospitals to improve medication safety: a multimethods research programme
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. 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. We undertook a theoretically informed, mixed-methods, context-rich, naturalistic evaluation. 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. The implementation of ePrescribing systems with either computerised provider order entry or clinical decision support functionality. 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. 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. 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. 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. 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. 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.
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来源期刊
CiteScore
1.90
自引率
0.00%
发文量
9
审稿时长
53 weeks
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