A systematic analysis of the optimization of computerized physician order entry and clinical decision support systems: A qualitative study in English hospitals

Abstract

This article analyzes the range of system optimization activities taking place over an extended period following the implementation of computerized physician order entry and clinical decision support systems. We undertook 207 qualitative semi-structured interviews, 24 rounds of non-participant observations of meetings and system use, and collected 17 organizational documents in five hospitals over three time periods between 2011 and 2016. We developed a systematic analysis of system optimization activities with eight sub-categories grouped into three main categories. This delineates the range of system optimization activities including resolving misalignments between technology and clinical practices, enhancing the adopted system, and improving user capabilities to utilize/further optimize systems. This study highlights the optimization efforts by user organizations adopting multi-user, organization-spanning information technologies. Hospitals must continue to attend to change management for an extended period (up to 5 years post-implementation) and develop a strategy for long-term system optimization including sustained user engagement, training, and broader capability development to ensure smoother and quicker realization of benefits.

Theoretical and methodological considerations in evaluating large-scale health information technology change programmes

“…Such systems tend to have distributed effects with hard-to-establish gradually emerging baselines (when compared to local discrete technologies implemented in specific settings, although the effects of these can also be hard to measure) [19]. Infrastructure renewal is a long term process where current achievements rest on earlier upgrades over long timeframes as systems are incrementally extended and optimised [20]. An example may be the implementation of EHRs and their integration with ancillary systems.…”

Protocol for a qualitative study to identify strategies to optimise hospital ePrescribing systems

“…These are misalignment, enhancement and developing user capabilities. 12 Misalignment is the mismatch of what an organisation needs the ePrescribing system to do and what it is capable of doing-its functionality. This can be addressed not only by configuring the system, adopting hardware and supporting infrastructure but also by changing existing ways of working.…”

“…Variability between different implementations has been attributed to the need for a process of convergence between technology and local organisational characteristics. 12 A qualitative study within four early adopter sites in the USA following the implementation of an ePrescribing system (referred to as computerised provider order entry systems in the US context) aimed to identify important lessons that future implementers should consider. 13 The findings indicated that successful implementation was accompanied by adequate user training, clear governance procedures and pre-emptive action with regard to user perceptions and the fear of change.…”

“…Optimisation, therefore, can be a process of balancing national policies, negotiations with EHR vendors and local and organisational factors. 12 The current situation of different vendors and informatics teams configuring systems in one or more hospitals raises questions of interoperability and shared information governance across the NHS. One of the recommendations made by the Wachter Review was not to overcorrect for the perceived drawbacks of the centralised approach of the NPfIT.…”

The Realization of Intelligent Algorithm of Knowledge Point Association Analysis in English Diagnostic Practice System

“…For example, the B/S structure-based college English online learning system developed by Wang et al [8], a personalized English learning system based on mobile devices researched and developed by Zhu [9], mainly discusses personalized learning for mobile devices. Wiegel et al [10] have studied the key technologies of personalized English learning system, including classification of English sentence reading difficulty, and proposed a text retrieval ranking model based on multiple similarities. e online English learning system developed by Gan et al [11] proposes the concept of setting diagnostic checkpoints, which is a relatively primitive diagnostic system.…”