Dataset Information

Using the Normalization Process Theory to qualitatively explore sense-making in implementation of the Enhanced Recovery After Surgery programme: "It's not rocket science".

ABSTRACT: The Enhanced Recovery After Surgery programme (ERAS) is an approach to the perioperative care of patients encompassing multiple interventions and involving a wide range of different actors. It can thus be defined as a complex intervention. Despite the strength of the evidence-base in its support, the implementation of ERAS has been slow. This paper specifically explores the utility of Normalization Process Theory (NPT) as a methodological framework to aid exploration of ERAS implementation, with a focus on the core NPT construct coherence.The study employed qualitative methods guided by NPT. Semi-structured interviews were conducted with twenty-six healthcare professionals working in three specialities (thoracic, colorectal, head and neck) in a UK hospital. Data were analysed using an adapted Framework Approach.Coherence, or sense-making work, was key to successful implementation and demonstrated in the importance of participants believing in ERAS both as an individual and as a team. In order to invest in ERAS individuals needed to be able to differentiate its practices favourably with those enacted pre-implementation (differentiation). Participants also needed to understand their specific tasks and responsibilities (individual specification) and build a shared understanding (communal specification), resolving differences in planning meetings. Belief in the worth of ERAS was often aligned to evidence for its effectiveness or benefit to patients (internalization), so implementing ERAS therefore 'made sense'. Sense-making work had strong links with aspects of implementation related to other NPT constructs including resource issues such as funding for data collection and feedback (reflexive monitoring: systemization) and failure to replace key staff members (collective action: skill set workability).NPT was found to be a valuable heuristic device to employ in the exploration of ERAS implementation processes. NPT was useful in facilitating recognition of the importance of coherence work to successful implementation. However despite participants' strong beliefs in the worth of ERAS, it was in translating these beliefs into action that barriers were encountered, highlighting the interconnectedness of NPT constructs and the complicated nature of implementing complex interventions.

SUBMITTER: Sutton E

PROVIDER: S-EPMC5906013 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using the Normalization Process Theory to qualitatively explore sense-making in implementation of the Enhanced Recovery After Surgery programme: "It's not rocket science".

Sutton Eileen E Herbert Georgia G Burden Sorrel S Lewis Stephen S Thomas Steve S Ness Andy A Atkinson Charlotte C

PloS one 20180418 4

<h4>Introduction</h4>The Enhanced Recovery After Surgery programme (ERAS) is an approach to the perioperative care of patients encompassing multiple interventions and involving a wide range of different actors. It can thus be defined as a complex intervention. Despite the strength of the evidence-base in its support, the implementation of ERAS has been slow. This paper specifically explores the utility of Normalization Process Theory (NPT) as a methodological framework to aid exploration of ERAS ...[more]

PMID: 29668717

Similar Datasets

Project description:ObjectiveTo compare cognitive testing scores in neurosurgeons and aerospace engineers to help settle the age old argument of which phrase-"It's not brain surgery" or "It's not rocket science"-is most deserved.DesignInternational prospective comparative study.SettingUnited Kingdom, Europe, the United States, and Canada.Participants748 people (600 aerospace engineers and 148 neurosurgeons). After data cleaning, 401 complete datasets were included in the final analysis (329 aerospace engineers and 72 neurosurgeons).Main outcome measuresValidated online test (Cognitron's Great British Intelligence Test) measuring distinct aspects of cognition, spanning planning and reasoning, working memory, attention, and emotion processing abilities.ResultsThe neurosurgeons showed significantly higher scores than the aerospace engineers in semantic problem solving (difference 0.33, 95% confidence interval 0.13 to 0.52). Aerospace engineers showed significantly higher scores in mental manipulation and attention (-0.29, -0.48 to -0.09). No difference was found between groups in domain scores for memory (-0.18, -0.40 to 0.03), spatial problem solving (-0.19, -0.39 to 0.01), problem solving speed (0.03, -0.20 to 0.25), and memory recall speed (0.12, -0.10 to 0.35). When each group's scores for the six domains were compared with those in the general population, only two differences were significant: the neurosurgeons' problem solving speed was quicker (mean z score 0.24, 95% confidence interval 0.07 to 0.41) and their memory recall speed was slower (-0.19, -0.34 to -0.04).ConclusionsIn situations that do not require rapid problem solving, it might be more correct to use the phrase "It's not brain surgery." It is possible that both neurosurgeons and aerospace engineers are unnecessarily placed on a pedestal and that "It's a walk in the park" or another phrase unrelated to careers might be more appropriate. Other specialties might deserve to be on that pedestal, and future work should aim to determine the most deserving profession.

Project description:BackgroundA concise visualization framework of related reports would increase readability and improve patient management. To this end, temporal referrals to prior comparative exams are an essential connection to previous exams in written reports. Due to unstructured narrative texts' variable structure and content, their extraction is hampered by poor computer readability. Natural language processing (NLP) permits the extraction of structured information from unstructured texts automatically and can serve as an essential input for such a novel visualization framework.ObjectiveThis study proposes and evaluates an NLP-based algorithm capable of extracting the temporal referrals in written radiology reports, applies it to all the radiology reports generated for 10 years, introduces a graphical representation of imaging reports, and investigates its benefits for clinical and research purposes.MethodsIn this single-center, university hospital, retrospective study, we developed a convolutional neural network capable of extracting the date of referrals from imaging reports. The model's performance was assessed by calculating precision, recall, and F1-score using an independent test set of 149 reports. Next, the algorithm was applied to our department's radiology reports generated from 2011 to 2021. Finally, the reports and their metadata were represented in a modulable graph.ResultsFor extracting the date of referrals, the named-entity recognition (NER) model had a high precision of 0.93, a recall of 0.95, and an F1-score of 0.94. A total of 1,684,635 reports were included in the analysis. Temporal reference was mentioned in 53.3% (656,852/1,684,635), explicitly stated as not available in 21.0% (258,386/1,684,635), and omitted in 25.7% (317,059/1,684,635) of the reports. Imaging records can be visualized in a directed and modulable graph, in which the referring links represent the connecting arrows.ConclusionsAutomatically extracting the date of referrals from unstructured radiology reports using deep learning NLP algorithms is feasible. Graphs refined the selection of distinct pathology pathways, facilitated the revelation of missing comparisons, and enabled the query of specific referring exam sequences. Further work is needed to evaluate its benefits in clinics, research, and resource planning.

Project description:Following several pilot projects, in 2020, the Department for Education (DfE) in England committed funding of £220M p. a to its Holiday Activities and Food (HAF) programme to support all 153 upper-tier local authorities, comprising City Councils, County Councils and Metropolitan Borough Councils, to provide an activity and food programme for children who are in receipt of means-tested free school meals. In this study, qualitative interviews were conducted with representatives from three Local Authorities in the North East of England who were responsible for overseeing the implementation and delivery of HAF programmes in their Local Authority area to examine how the summer HAF programme was implemented during summer 2021. Interviews were conducted with eight participants prior to the implementation of the HAF programme, and four interviews were conducted after the programme had been delivered. Using a directed content analysis approach, an interpretative framework was co-developed, based on the four constructs (and selected sub-constructs) of Normalization Process Theory. This framework guided data coding. The analysis aimed to identify and understand the barriers and opportunities in relation to HAF implementation within local authorities. Participants did not perceive HAF as a totally new initiative as many had either commissioned or delivered holiday clubs in the past. However, the increased scale and scope of HAF was perceived as highly complex, involving multiple local authority departments and stakeholders. Nonetheless, HAF funding enabled local authorities to improve the quality and reach of their holiday programmes. Strong networks and good communication between all stakeholders supported successful delivery, despite tight delivery timescales. However, the rigidity of some of the DfE guidance was a barrier for some providers, particularly the recommended delivery model of 4 h a day, 4 days a week for 4 weeks, with many individual holiday clubs struggling to meet this level of delivery, and local authority leads interpreting the guidance at a club level rather than an individual child access level. Furthermore, participants considered the HAF eligibility criteria too restrictive. Many councils were developing long-term plans for HAF delivery, integrated into planning across several departments, and all local authorities were actively seeking ways to engage with and embed HAF within local communities.

Project description:BACKGROUND:In Ontario, Canada, healthcare for transgender individuals is accessed through primary care; however, there are a limited number of practitioners providing transgender care, and patients are often on waiting lists and/or traveling great distances to receive care. Understanding how primary care is implemented and delivered to transgender individuals is key to improving access and eliminating healthcare barriers. The purpose of this study is to understand how the implementation of primary care services for transgender individuals compares across various models of primary care delivery in Ontario. METHODS:A qualitative, exploratory, multiple-case study guided by Normalization Process Theory (NPT) was used to compare transgender care delivery and implementation across three primary care models. Three cases known to provide transgender primary care and represent different primary care models in Ontario, Canada (i.e., family health team, community health centre, fee-for service physician) were explored. The NoMAD survey, a tool to measure implementation processes, and qualitative interviews with primary care practitioners and allied healthcare staff were administered. RESULTS:Using the NPT framework to guide analysis, key themes emerged about successful implementation of primary care services for transgender individuals. These themes include creating a safe space for patients, identifying gaps in services, understanding practitioners' roles, and the need for more training and education in transgender care for practitioners. CONCLUSIONS:Primary care services for transgender individuals can and should be delivered in all models of primary care. Training and awareness for healthcare practitioners are needed to develop capacity in providing primary care to transgender individuals. A greater number of practitioners and organizations are needed to take on this work, embedding and normalizing transgender care into routine practice to address barriers to access and improve quality of care for transgender individuals.

Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

Dataset Information

Using the Normalization Process Theory to qualitatively explore sense-making in implementation of the Enhanced Recovery After Surgery programme: "It's not rocket science".

Publications

Using the Normalization Process Theory to qualitatively explore sense-making in implementation of the Enhanced Recovery After Surgery programme: "It's not rocket science".

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets