![]() We conducted 242 interviews and 32.5 hours of observations and collected 55 documents across six case studies. The implementation of ePrescribing systems with either computerised provider order entry or clinical decision support functionality.Įrror 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 organised four national conferences and five expert round-table discussions to contextualise and disseminate our findings. 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 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. In the hospitals that had embedded systems, we conducted two rounds of interviews, 18 months apart. ![]() In the three hospitals that implemented systems, we conducted interviews pre implementation, shortly after roll-out and at 1 year post implementation. 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. We undertook a theoretically informed, mixed-methods, context-rich, naturalistic evaluation. ![]() 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. Interest has converged on ePrescribing systems that incorporate computerised provider order entry and clinical decision support functionality. There is a need to identify approaches to reduce medication errors. ![]()
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