The study's data proved that the intention of using an EMR system was the most influential and predictor of the actual use of the system. The study further utilised the Artificial Neural Networks (ANN) algorithm and the Partial Least Squares Structural Equation Modeling (PLS-SEM) in the analysis of the data collected. The collection of data was through a cross-section design and survey questionnaires as the tool for data collection among 259 participants from 15 healthcare facilities in Dubai. This study aims at investigating aspects that predict and explain an EMR system adoption in the healthcare system in the UAE through an integrated approach of the Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) using various external factors. Future studies should explore how implementation frameworks can be systematically embedded in addressing EHR-related burden.Īn Electronic Medical Record (EMR) has the capability of promoting knowledge and awareness regarding healthcare in both healthcare providers and patients to enhance intercon-nectivity within various government bodies, and quality healthcare services. This study demonstrates the value of applying well-established implementation frameworks, such as the i-PARIHS framework, in mitigating challenges related to documentation burden. Through an analysis using the i-PARIHS framework, key considerations were mapped across the four components of the framework. Open-ended responses and follow-up interviews explored challenges and concerns on using SRT in practice. As part of pre-adoption implementation activities for Speech Recognition Technology (SRT), a cross-sectional survey was conducted with physicians, residents, and fellows at an academic mental health hospital to explore their perceptions on SRT. This case study demonstrates how the i-PARIHS framework can be applied to support the implementation of interventions in reducing documentation and EHR-related burden in a mental health context.
SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.ĭocumentation burden continues to be a critical issue in the adoption of comprehensive electronic health record systems.
SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. Fourteen studies met the inclusion criteria and were retained. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Experimental and non-experimental designs were considered. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes.
To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care.Ī systematic review of existing literature from 2000 was undertaken.