From research to practice: Factors affecting implementation of prospective targeted injury-detection systems
RTI International, Research Triangle Park, NC 27709-2194, USA.BMJ quality & safety (Impact Factor: 3.99). 02/2011; 20(6):527-33. DOI: 10.1136/bmjqs.2010.045039
AIM This paper describes key factors that shaped implementation of prospective targeted injury-detection systems (TIDS) for adverse drug events (ADEs) and nosocomial pressure ulcers (PrU). METHODS Using case-study methodology, the authors conducted semistructured interviews with implementation champions and TIDS users at five hospitals. Interviews focused on implementation experiences, assessment of TIDS' effectiveness and utility, and plans for sustainability. The authors used content analysis techniques to compare implementation experiences within and across organisations and triangulated data for explanation and confirmation of common themes. FINDINGS Participating hospitals were more successful in implementing the low-complexity PrU-TIDS, as compared with high-complexity ADE-TIDS. This pattern reflected the greater complexity of ADE-TIDS, its higher costs and poorer alignment with existing workflows. Complexity affected the innovations' perceived usability, the time needed to learn and install the trigger systems, and their costs. Local factors affecting implementation and sustainability of both innovations included turnover affecting champions and other staff, shifting organisational priorities, changing information infrastructures, and institutional constraints on adapting existing IT to the electronic TIDS. CONCLUSIONS To facilitate implementation of complex healthcare innovations such as ADE-TIDS, staff in adopting organisations should give high priority to innovation implementation; allocate sufficient resources; effectively communicate with and involve local champions and users; and align innovations with workflows and information systems. In addition, they should monitor local factors, such as changes in organisational priorities and IT, availability of implementation staff and champions, and external regulations and constraints that may pose barriers to innovation implementation and sustainability.
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ABSTRACT: The implementation of clinical information systems and electronic medical records does not have a good track record. It is estimated that more than 50% of implementations fail. A review of electronic health information system (EHIS) models incorporating clinical information systems and electronic medical records was undertaken to determine the models developed and applied in health. Twenty one health and five non-health models were identified. The non-health models were included as a number of health models were derived form these. The findings and evaluation of the models has identified varying contents and results. The models identified were assessed to determine how these related to each other, whether models were tested and how, if benefits were identified and if costsavings were projected or realised. This review of EHIS implementation models has identified a need for clear definition of terms used, careful categorisation and for models to be comprehensive, extensive and rigorous if successful outcomes are to occur.Studies in health technology and informatics 07/2012; 178:117-23. DOI:10.3233/978-1-61499-078-9-117
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ABSTRACT: Trigger tools, both paper and automated, have been viewed as a promising technology for patient record content analysis and identification of patient safety adverse events. The requirements and potential barriers for implementation of each line of tools have been explored by means of a literature review focusing on two interconnected subject areas: the Institute of Healthcare Improvement's paper-based Global Trigger Tool, which is currently taken up by several national level patient safety programs, and automated trigger tools, because of their increased feasibility as electronic health record (EHR) adoption grows. This paper provides an overview of the existing evidence on the strengths and weaknesses of each approach, and discusses the implications of the findings from the perspectives of healthcare organizations' management and staff, and from the viewpoint of demands on EHR systems.Studies in health technology and informatics 08/2012; 180:786-90. DOI:10.3233/978-1-61499-101-4-786
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ABSTRACT: /st>Getting greater levels of evidence into practice is a key problem for health systems, compounded by the volume of research produced. Implementation science aims to improve the adoption and spread of research evidence. A linked problem is how to enhance quality of care and patient safety based on evidence when care settings are complex adaptive systems. Our research question was: according to the implementation science literature, which common implementation factors are associated with improving the quality and safety of care for patients? /st>We conducted a targeted search of key journals to examine implementation science in the quality and safety domain applying PRISMA procedures. Fifty-seven out of 466 references retrieved were considered relevant following the application of exclusion criteria. Included articles were subjected to content analysis. Three reviewers extracted and documented key characteristics of the papers. Grounded theory was used to distil key features of the literature to derive emergent success factors. /st>Eight success factors of implementation emerged: preparing for change, capacity for implementation-people, capacity for implementation-setting, types of implementation, resources, leverage, desirable implementation enabling features, and sustainability. Obstacles in implementation are the mirror image of these: for example, when people fail to prepare, have insufficient capacity for implementation or when the setting is resistant to change, then care quality is at risk, and patient safety can be compromised. /st>This review of key studies in the quality and safety literature discusses the current state-of-play of implementation science applied to these domains.International Journal for Quality in Health Care 05/2014; 26(2). DOI:10.1093/intqhc/mzu047 · 1.76 Impact Factor
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