Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre.
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Journal articleKhanbhai M, Warren L, Symons J, et al., 2022,
Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care, International Journal of Medical Informatics, Vol: 157, Pages: 1-7, ISSN: 1386-5056
BackgroundPatient centred care necessitates that healthcare experiences and perceived outcomes be considered across all transitions of care. Information encoded within free-text patient experience comments relating to transitions of care are not captured in a systematic way due to the manual resource required. We demonstrate the use of natural language processing (NLP) to extract meaningful information from the Friends and Family Test (FFT).MethodsFree-text fields identifying favourable service (“What did we do well?”) and areas requiring improvement (“What could we do better?”) were extracted from 69,285 FFT reports across four care settings at a secondary care National Health Service (NHS) hospital. Sentiment and patient experience themes were coded by three independent coders to produce a training dataset. The textual data was standardised with a series of pre-processing techniques and the performance of six machine learning (ML) models was obtained. The best performing ML model was applied to predict the themes and sentiment from the remaining reports. Comments relating to transitions of care were extracted, categorised by sentiment, and care setting to identify the most frequent words/combinations presented as tri-grams and word clouds.ResultsThe support vector machine (SVM) ML model produced the highest accuracy in predicting themes and sentiment. The most frequent single words relating to transition and continuity with a negative sentiment were “discharge” in inpatients and Accident and Emergency, “appointment” in outpatients, and “home’ in maternity. Tri-grams identified from the negative sentiments such as ‘seeing different doctor’, ‘information aftercare lacking’, ‘improve discharge process’ and ‘timing discharge letter’ have highlighted some of the problems with care transitions. None of this information was available from the quantitative data.Conc
Journal articleNeves AL, van Dael J, O'Brien N, et al., 2021,
Use and impact of virtual primary care on quality and safety: The public's perspectives during the COVID-19 pandemic, Journal of Telemedicine and Telecare, ISSN: 1357-633X
IntroductionWith the onset of Coronavirus disease (COVID-19), primary care has swiftly transitioned from face-to-face to virtual care, yet it remains largely unknown how this has impacted the quality and safety of care. We aim to evaluate patient use of virtual primary care models during COVID-19, including change in uptake, perceived impact on the quality and safety of care and willingness of future use.MethodologyAn online cross-sectional survey was administered to the public across the United Kingdom, Sweden, Italy and Germany. McNemar tests were conducted to test pre- and post-pandemic differences in uptake for each technology. One-way analysis of variance was conducted to examine patient experience ratings and perceived impacts on healthcare quality and safety across demographic characteristics.ResultsRespondents (n = 6326) reported an increased use of telephone consultations ( + 6.3%, p < .001), patient-initiated services ( + 1.5%, n = 98, p < 0.001), video consultations ( + 1.4%, p < .001), remote triage ( + 1.3, p < 0.001) and secure messaging systems ( + 0.9%, p = .019). Experience rates using virtual care technologies were higher for men (2.4 ± 1.0 vs. 2.3 ± 0.9, p < .001), those with higher literacy (2.8 ± 1.0 vs. 2.3 ± 0.9, p < .001), and participants from Germany (2.5 ± 0.9, p < .001). Healthcare timeliness and efficiency were the dimensions most often reported as being positively impacted by virtual technologies (60.2%, n = 2793 and 55.7%, n = 2,401, respectively), followed by effectiveness (46.5%, n =
Journal articleO'Brien N, Durkin M, Lachman P, 2021,
The COVID-19 pandemic has been a challenge as well as an opportunity for healthcare. The pandemic has exposed the inherent weaknesses in health systems globally while, at the same time, revealing strengths on which post-pandemic health systems can be built. We propose lessons on improving quality and safety post-pandemic from a global perspective based on recent policy publications and our global experience. Nine possible lessons are discussed. These lessons can ensure that healthcare does not return to the old normal, but rather builds on what we have learnt as we deliver on the Sustainable Development Goals and universal health coverage. Quality and safety are an essential component of healthcare strategy. Post-pandemic systems require a transparent compassionate culture, with integration of care at its core. The workforce must be trained in the skills to improve care, and patient and healthcare worker protection (both physically and psychologically) needs to be a given. Any development of systems will best be co-produced with the people who receive and deliver care in an equal partnership. Finally, the new systems need to be conscious of emerging threats (such as the challenge of climate change), building sustainable health systems that also address the structural inequities that currently exist.
Journal articleNeves AL, Smalley K, Freise L, et al., 2021,
Sharing electronic health records with patients: Who is using the Care Information Exchange portal? A cross-sectional study, Jornal of Medical Internet Research, Vol: 13, Pages: 1-12, ISSN: 1438-8871
Background: Sharing electronic health records with patients has been shown to improve patient safety and quality of care, and patient portals represent a powerful and convenient tool to enhance patient access to their own healthcare data. However, the success of patient portals will only be possible through sustained adoption by its end-users: the patients. A better understanding of the characteristics of users and non-users is critical to understand which groups remain underserved or excluded from using such tools.Objective: To identify the determinants of usage of the Care Information Exchange (CIE), a shared patient portal program in the United Kingdom.Methods: A cross-sectional study was conducted, using an online questionnaire. Information collected included age, gender, ethnicity, educational level, health status, postcode and digital literacy. Registered individuals were defined as having had an account created in the portal, independent of their actual use of the platform; users were defined as having ever used the portal. Multivariate logistic regression was used to model the probability of being a user. Statistical analysis was performed in R, and Tableau ® was used to create maps of the proportion of CIE users by postcode area.Results: A total of 1,083 subjects replied to the survey (+186% of the estimated minimum target sample). The proportion of users was 61.6% (n=667), and within these, the majority (57.7%, n=385) used the portal at least once a month. To characterise the users and non-users of the system, we performed a sub-analysis of the sample, including only participants that had provided at least information regarding gender and age category. The sub-analysis included 650 individuals (59.8% women, 84.8% over 40 years). The majority of the subjects were white (76.6%, n=498), resident in London (64.7%, n=651), and lived in North West London (55.9%, n=363). Individuals with a higher educational degree (undergraduate/professional or postgraduat
Journal articleElliott P, Haw D, Wang H, et al., 2021,
Exponential growth, high prevalence of SARS-CoV-2 and vaccine effectiveness associated with Delta variant, Science, Vol: 374, Pages: 1-11, ISSN: 0036-8075
SARS-CoV-2 infections were rising during early summer 2021 in many countries associated with the Delta variant. We assessed RT-PCR swab-positivity in the REal-time Assessment of Community Transmission-1 (REACT-1) study in England. We observed sustained exponential growth with average doubling time (June-July 2021) of 25 days driven by complete replacement of Alpha variant by Delta, and by high prevalence at younger less-vaccinated ages. Unvaccinated people were three times more likely than double-vaccinated people to test positive. However, after adjusting for age and other variables, vaccine effectiveness for double-vaccinated people was estimated at between ~50% and ~60% during this period in England. Increased social mixing in the presence of Delta had the potential to generate sustained growth in infections, even at high levels of vaccination.
Journal articleJones MD, Clarke J, Feather C, et al., 2021,
Use of pediatric injectable medicines guidelines and associated medication administration errors: a human reliability analysis, Annals of Pharmacotherapy, Vol: 55, Pages: 1333-1340, ISSN: 1060-0280
Background:In a recent human reliability analysis (HRA) of simulated pediatric resuscitations, ineffective retrieval of preparation and administration instructions from online injectable medicines guidelines was a key factor contributing to medication administration errors (MAEs).Objective:The aim of the present study was to use a specific HRA to understand where intravenous medicines guidelines are vulnerable to misinterpretation, focusing on deviations from expected practice (discrepancies) that contributed to large-magnitude and/or clinically significant MAEs.Methods:Video recordings from the original study were reanalyzed to identify discrepancies in the steps required to find and extract information from the NHS Injectable Medicines Guide (IMG) website. These data were combined with MAE data from the same original study.Results:In total, 44 discrepancies during use of the IMG were observed across 180 medication administrations. Of these discrepancies, 21 (48%) were associated with an MAE, 16 of which (36% of 44 discrepancies) made a major contribution to that error. There were more discrepancies (31 in total, 70%) during the steps required to access the correct drug webpage than there were in the steps required to read this information (13 in total, 30%). Discrepancies when using injectable medicines guidelines made a major contribution to 6 (27%) of 22 clinically significant and 4 (15%) of 27 large-magnitude MAEs.Conclusion and Relevance:Discrepancies during the use of an online injectable medicines guideline were often associated with subsequent MAEs, including those with potentially significant consequences. This highlights the need to test the usability of guidelines before clinical use.
Journal articleHay AD, Moore M, Taylor J, et al., 2021,
Journal articleShaw A, O'Brien N, Flott K, et al., 2021,
How to improve patient safety in fragile, conflict-affected, and vulnerable settings: a Delphi study protocol, BMJ Open, Vol: 11, Pages: 1-5, ISSN: 2044-6055
Introduction There is a high burden of adverse events and poor outcomes in fragile, conflict-affected and vulnerable (FCV) settings. To improve outcomes, there is a need to better identify which interventions can improve patient safety in these settings, as well as to develop strategies to optimise their implementation.Objective This study intends to generate a consensus on the most relevant patient safety interventions from experts with experience on FCV settings, including frontline clinicians and managers/administrators, non-governmental organisations, policymakers and researchers.Methods and analysis The study uses an online Delphi research approach (eDelphi). Participants will include experts from a range of backgrounds, including those working in a variety of FCV settings. Participants will be established contacts known to the research team or recruited via snowball sampling, and will be asked to identify and rank the importance of a variety of patient safety interventions. Consensus will be defined as >70% of participants agreeing/strongly agreeing or disagreeing/strongly disagreeing with a statement. Data analysis will be completed in Microsoft Excel and NVivo. The primary outcome of the study will be a list of the most relevant and applicable patient safety interventions for FCV settings.Ethics and dissemination The study has received approval from Imperial College London Ethics Committee (reference number 20IC665). Anonymous results will be made available to the public, academic organisations and policymakers.
Journal articleFiorentino F, Prociuk D, Espinosa Gonzalez AB, et al., 2021,
An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan, JMIR Research Protocols, Vol: 10, ISSN: 1929-0748
Background:Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.Objective:We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death.Methods:After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets
Journal articleFiorentino F, Prociuk D, Espinosa Gonzalez AB, et al., 2021,
An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan, JMIR Research Protocols, Vol: 10, Pages: e30083-e30083
<jats:sec> <jats:title>Background</jats:title> <jats:p>Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient’s clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>After the data have been collected, we will assess the degree of missingness and use a combination
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