215 results found
Smalley K, Aufegger L, Flott K, et al., 2022, The Self-Management Abilities Test (SMAT): a tool to identify the self-management abilities of adults with bronchiectasis, npj Primary Care Respiratory Medicine, ISSN: 2055-1010
Bronchiectasis is an increasingly common chronic respiratory disease which requires a high level of patient engagement in self-management. Whilst the need for self-management has been recognised, the knowledge and skills needed to do so – and the extent to which patients possess these – has not been well-specified. On one hand, understanding the gaps in people’s knowledge and skills can enable better targeting of self-management supports. On the other, clarity about what they do know can increase patients’ confidence to self-manage. This study aims to develop an assessment of patients’ ability to self-manage effectively, through a consensus-building process with patients, clinicians, and policymakers. The study employs a modified, online 3-round Delphi to solicit the opinions of patients, clinicians, and policymakers (N=30) with experience of bronchiectasis. The first round seeks consensus on the content domains for an assessment of bronchiectasis self-management ability. Subsequent rounds propose and refine multiple-choice assessment items to address the agreed domains. A group of 10 clinicians, 10 patients, and 10 policymakers provide both qualitative and quantitative feedback. Consensus is determined using content validity ratios. Qualitative feedback is analysed using the summative content analysis method. Overarching domains are: General Health Knowledge, Bronchiectasis-Specific Knowledge, Symptom Management, Communication, and Addressing Deterioration, each with two sub-domains. A final assessment tool of 20 items contains two items addressing each sub-domain. This study establishes that there is broad consensus about the knowledge and skills required to self-manage bronchiectasis effectively, across stakeholder groups. The output of the study is an assessment tool that can be used by patients and their healthcare providers to guide the provision of self-management education, opportunities, and support.
Khanbhai 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
Neves 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
Khanbhai M, Flott K, Manton D, et al., 2021, Identifying factors that promote and limit the effective use of real-time patient experience feedback: a mixed-methods study in secondary care, BMJ Open, Vol: 11, Pages: 1-7, ISSN: 2044-6055
Objectives:The Friends and Family Test (FFT) is commissioned by the National Health Service (NHS) in England to capture patient experience as a real-time feedback initiative for patient-centred quality improvement (QI). The aim of this study was to create a process map in order to identify the factors that promote and limit the effective use of FFT as a real-time feedback initiative for patient-centred QI. Setting:This study was conducted at a large London NHS Trust. Services include accident and emergency, inpatient, outpatient and maternity, which routinely collect FFT patient experience data. Participants:Healthcare staff and key stakeholders involved in FFT.Interventions:Semi-structured interviews were conducted on fifteen participants from a broad range of professional groups to evaluate their engagement with the FFT. Interview data were recorded, transcribed, and analysed for using deductive thematic analysis.Results:Concerns related to inefficiency in the flow of FFT data, lack of time to analyse FFT reports (with emphasis on high level reporting rather than QI), insufficient access to FFT reports and limited training provided to understand FFT reports for frontline staff. The sheer volume of data received was not amenable to manual thematic analysis resulting in inability to acquire insight from the free-text. This resulted in staff ambivalence towards FFT as a near real-time feedback initiative.Conclusions:The results state that there is too much FFT free text for meaningful analysis, and the output is limited to the provision of sufficient capacity and resource to analyse the data, without consideration of other options, such as text analytics and amending the data collection tool.
Neves 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
Hunter B, Reis S, Campbell D, et al., 2021, Development of a structured query language and natural language processing algorithm to identify lung nodules in a cancer centre, Frontiers in Medicine, Vol: 8, Pages: 1-10, ISSN: 2296-858X
Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.Objective: To automate lung nodule identification in a tertiary cancer centre.Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy.Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.
Khanbhai M, Symons J, Flott K, et al., 2021, Enriching the value of patient experience feedback: interactive dashboard development using co-design and heuristic evaluation, JMIR Human Factors, ISSN: 2292-9495
Background:There is an abundance of patient experience data held within healthcare organisations but stakeholders and staff are often unable to use the output in a meaningful and timely way to improve care delivery. Dashboards, which use visualised data to summarise key patient experience feedback, have the potential to address these issues.Objective:The aim of this study was to develop a patient experience dashboard with an emphasis on FFT reporting as per the national policy drive. An iterative process involving co-design involving key stakeholders was used to develop the dashboard, followed by heuristic usability testing.Methods:A two staged approach was employed; participatory co-design involving 20 co-designers to develop a dashboard prototype followed by iterative dashboard testing. Language analysis was performed on free-text patient experience data from the Friends and Family Test (FFT) and the themes and sentiment generated was used to populate the dashboard with associated FFT metrics. Heuristic evaluation and usability testing were conducted to refine the dashboard and assess user satisfaction using the system usability score (SUS).Results:Qualitative analysis from the co-design process informed development of the dashboard prototype with key dashboard requirements and a significant preference for bubble chart display. Heuristic evaluation revelated the majority of cumulative scores had no usability problem (n=18), cosmetic problem only (n=7), or minor usability problem (n= 5). Mean SUS was 89.7 (SD 7.9) suggesting an excellent rating.Conclusions:The growing capacity to collect and process patient experience data suggests that data visualisation will be increasingly important in turning the feedback into improvements to care. Through heuristic usability we demonstrated that very large FFT data can be presented into a thematically driven, simple visual display without loss of the nuances and still allow for exploration of the original free-text comments. T
Fiorentino 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
Fiorentino 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
Glampson B, Brittain J, Kaura A, et al., 2021, North West London Covid-19 Vaccination Programme: Real-world evidence for Vaccine uptake and effectiveness: Retrospective Cohort Study, JMIR Public Health and Surveillance, Vol: 7, Pages: 1-17, ISSN: 2369-2960
Background:On March 11, 2020 the World Health Organisation declared the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causing Coronavirus Disease 2019 (COVID-19) syndrome, as a pandemic. The UK mass vaccination programme commenced on December 08, 2020 vaccinating groups of the population deemed to be most vulnerable to severe COVID-19 infection.Objective:To assess the early vaccine administration coverage and outcome data across an integrated care system in North West London (NWL), leveraging a unique population-level care dataset. Vaccine effectiveness of a single dose of the Oxford/Astrazeneca and Pfizer/BioNtech vaccines were compared.Methods:A retrospective cohort study identified 2,183,939 individuals eligible for COVID-19 vaccination between December 08, 2020 and February 24, 2021 within a primary, secondary and community care integrated care dataset. These data were used to assess vaccination hesitancy across ethnicity, gender and socio-economic deprivation measures (Pearson Product-Moment Correlations); investigated COVID-19 transmission related to vaccination hubs; and assessed the early effectiveness of COVID-19 vaccination (after a single dose) using time to event analyses with multivariable Cox regression analysis to investigate if vaccination independently predicted positive SARS-CoV-2 in those vaccinated compared to those unvaccinated.Results: In the study 5.88% (24,332/413,919) of individuals declined and did not receive a vaccination. Black or Black British individuals had the highest rate of declining a vaccine at 16.14% (4,337/26,870). There was a strong negative association between socio-economic deprivation and rate of declining vaccination (r=-0.94, P=.002) with 13.5% (1980/14571) of individuals declining vaccination in the most deprived areas compared to 0.98% (869/9609) in the least. In the first six days after vaccination 344 of 389587 individuals tested positive for SARS-CoV-2 (0.09%). The rate increased to 0.13% (525/389,243)
Neves AL, Pereira Rodrigues P, Mulla A, et al., 2021, Using electronic health records to develop and validate a machine learning tool to predict type 2 diabetes outcomes: a study protocol, BMJ Open, Vol: 11, Pages: 1-5, ISSN: 2044-6055
Introduction: Type 2 diabetes (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as socio-demographic determinants, self-management ability, or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability.Objective: The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient level characteristics retrieved from a population health linked dataset.Sample and design: Retrospective cohort study of patients with diagnosis of T2DM on Jan 1st, 2015, with a 5-year follow-up. Anonymised electronic health care records from the Whole System Integrated Care (WSIC) database will be used. Preliminary outcomes: Outcome variables of clinical deterioration will include retinopathy, chronic renal disease, myocardial infarction, stroke, peripheral arterial disease, or death. Predictor variables will include sociodemographic and geographic data, patients’ ability to self-manage disease, clinical and metabolic parameters and healthcare service usage. Prognostic models will be defined using multi-dependence Bayesian networks (BN). The derivation cohort, comprising 80% of the patients, will be used to define the prognostic models. Model parameters will be internally validated by comparing the area under the receiver operating characteristic (ROC) curve (AUC) in the derivation cohort with those calculated from a leave-one-out and a 10 times 2-fold cross-validation. Ethics and dissemination: The study has received approvals from the Information Governance Committee at the Whole Systems Integrated Care. Results will be made available to people with type 2 diabetes
Glampson B, Brittain J, Kaura A, et al., 2021, Assessing COVID-19 vaccine uptake and effectiveness through the north west London vaccination program: retrospective cohort study, Publisher: JMIR Publications
Background:Real world data supporting the effectiveness of the COVID-19 vaccination strategy in the UK population is needed to guide health policy. This real-word data-driven evidence study of the UK COVID-19 Vaccination Programme in the Northwest London (NWL) population used a unique dataset established as part of the Gold Command Covid-19 response in NWL (iCARE https://imperialbrc.nihr.ac.uk/facilities/icare/), which included the pre-established Whole System Integrated Care (WSIC) data collated for the purposes of population health in the sector.Objective:To assess the early vaccine administration coverage and vaccine effectiveness and outcome data across an integrated care system of eight CCGs leveraging a unique population-level care datasetMethods:Design - Retrospective cohort study. Setting - Individuals eligible for COVID 19 vaccination in North West London based on linked primary and secondary care data. Participants - 2,183,939 individuals eligible for COVID 19 vaccinationResults:During the NWL vaccine programme study time period 5.88% of individuals declined and did not receive a vaccination. Black or black British individuals had the highest rate of declining a vaccine at 16.14% (4,337). There was a strong negative association between deprivation and rate of declining vaccination (r=-0.94, p<0.01) with 13.5% of individuals declining vaccination in the most deprived postcodes compared to 0.98% in the least deprived postcodes. In the first six days after vaccination 344 of 389587 individuals tested positive for COVID-19 (0.09%). The rate increased to 0.13% (525/389,243) between days 7 and 13, before then gradually falling week on week. At 28 days post vaccination there was a 74% (HR 0.26 (0.19-0.35)) and 78% (HR 0.22 (0.18-0.27)) reduction in risk of testing positive for COVID -19 for individuals that received the Oxford/Astrazeneca and Pfizer/BioNTech vaccines respectively, when compared with unvaccinated individuals. After vaccination very low rates of
van Dael J, Smalley K, Gillespie A, et al., 2021, Getting the whole story: integrating patient complaints and staff reports of unsafe care, Journal of Health Services Research and Policy, ISSN: 1355-8196
Objective: It is increasingly recognized that patient safety requires heterogeneous insights from a range of stakeholders, yet incident reporting systems in health care still primarily rely on staff perspectives. This paper examines the potential of combining insights from patient complaints and staff incident reports for a more comprehensive understanding of the causes and severity of harm. Methods: Using five years of patient complaints and staff incident reporting data at a large multi-site hospital in London (in the United Kingdom), this study conducted retrospective patient-level data linkage to identify overlapping reports. Using a combination of quantitative coding and in-depth qualitative analysis, we then compared level of harm reported, identified descriptions of adjacent events missed by the other party and examined combined narratives of mutually identified events. Results: Incidents where complaints and incident reports overlapped (n=446, 8.5% of all complaints and 0.6% of all incident reports) represented a small but critical area of investigation, with significantly higher rates of Serious Incidents and severe harm. Linked complaints described greater harm from safety incidents in 60% of cases, reported many surrounding safety events missed by staff (n=582), and provided contesting stories of why problems occurred in 46% cases, and complementary accounts in 26% cases.Conclusions: This study demonstrates the value of using patient complaints to supplement, test, and challenge staff reports, including to provide greater insight on the many potential factors that may give rise to unsafe care. Accordingly, we propose that a more holistic analysis of critical safety incidents can be achieved through combining heterogeneous data from different viewpoints, such as better integration of patient complaints and staff incident reporting data.
Jay APM, Aldiwani M, O'Callaghan ME, et al., 2021, Features and Management of Late Relapse of Nonseminomatous Germ Cell Tumour, EUROPEAN UROLOGY OPEN SCIENCE, Vol: 29, Pages: 82-88, ISSN: 2666-1691
Fankhauser CD, Afferi L, Stroup SP, et al., 2021, Perioperative safety and short-term oncological outcomes of minimally invasive retroperitoneal lymph node dissection, Publisher: ELSEVIER, Pages: S911-S912, ISSN: 0302-2838
Espinosa-Gonzalez AB, Neves AL, Fiorentino F, et al., 2021, Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool, JMIR RESEARCH PROTOCOLS, Vol: 10, ISSN: 1929-0748
Glampson B, Brittain J, Kaura A, et al., 2021, North West London Covid-19 vaccination programme: real-world evidence for vaccine uptake and effectiveness, Publisher: Cold Spring Harbor Laboratory
Objective To assess the early vaccine administration coverage and vaccine effectiveness and outcome data across an integrated care system of eight CCGs leveraging a unique population-level care datasetDesign Retrospective cohort study.Setting Individuals eligible for COVID 19 vaccination in North West London based on linked primary and secondary care data.Participants 2,183,939 individuals eligible for COVID 19 vaccinationResults During the NWL vaccine programme study time period 5.88% of individuals declined and did not receive a vaccination. Black or black British individuals had the highest rate of declining a vaccine at 16.14% (4,337). There was a strong negative association between deprivation and rate of declining vaccination (r=-0.94, p<0.01) with 13.5% of individuals declining vaccination in the most deprived postcodes compared to 0.98% in the least deprived postcodes.In the first six days after vaccination 344 of 389587 individuals tested positive for COVID-19 (0.09%). The rate increased to 0.13% (525/389,243) between days 7 and 13, before then gradually falling week on week.At 28 days post vaccination there was a 74% (HR 0.26 (0.19-0.35)) and 78% (HR 0.22 (0.18-0.27)) reduction in risk of testing positive for COVID-19 for individuals that received the Oxford/Astrazeneca and Pfizer/BioNTech vaccines respectively, when compared with unvaccinated individuals.After vaccination very low rates of hospital admission were seen in individuals testing positive for COVID-19 (0.01% of all patients vaccinated).Conclusions This study provides further evidence that a single dose of either the Pfizer/BioNTech vaccine or the Oxford/Astrazeneca vaccine is effective at reducing the risk of testing positive for COVID-19 up to 60 days across all adult age groups, ethnic groups, and risk categories in an urban UK population. There was no difference in effectiveness up to 28 days between the Oxford/Astrazeneca and Pfizer/BioNtech vaccines.In those declining vaccination higher
Khanbhai M, Anyadi P, Symons J, et al., 2021, Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review, BMJ Health & Care Informatics, Vol: 28, ISSN: 2632-1009
Objectives Unstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.Methods Databases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.Results Nineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.Conclusion NLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.
Freise L, Neves AL, Flott K, et al., 2021, Assessment of patients' ability to review electronic health record information to identify potential errors: cross-sectional web-based survey, JMIR Formative Research, Vol: 5, ISSN: 2561-326X
Background: Sharing personal health information positively impacts quality of care across several domains, and particularly, safety and patient-centeredness. Patients may identify and flag up inconsistencies in their electronic health records (EHRs), leading to improved information quality and patient safety. However, in order to identify potential errors, patients need to be able to understand the information contained in their EHRs.Objective: The aim of this study was to assess patients’ perceptions of their ability to understand the information contained in their EHRs and to analyze the main barriers to their understanding. Additionally, the main types of patient-reported errors were characterized.Methods: A cross-sectional web-based survey was undertaken between March 2017 and September 2017. A total of 682 registered users of the Care Information Exchange, a patient portal, with at least one access during the time of the study were invited to complete the survey containing both structured (multiple choice) and unstructured (free text) questions. The survey contained questions on patients’ perceived ability to understand their EHR information and therefore, to identify errors. Free-text questions allowed respondents to expand on the reasoning for their structured responses and provide more detail about their perceptions of EHRs and identifying errors within them. Qualitative data were systematically reviewed by 2 independent researchers using the framework analysis method in order to identify emerging themes.Results: A total of 210 responses were obtained. The majority of the responses (123/210, 58.6%) reported understanding of the information. The main barriers identified were information-related (medical terminology and knowledge and interpretation of test results) and technology-related (user-friendliness of the portal, information display). Inconsistencies relating to incomplete and incorrect information were reported in 12.4% (26/210) of the res
Kowa J-Y, Soneji N, Sohaib SA, et al., 2021, Detection and staging of radio-recurrent prostate cancer using multiparametric MRI., British Journal of Radiology, Vol: 94, Pages: 1-10, ISSN: 0007-1285
OBJECTIVE: We determined the sensitivity and specificity of multiparametric magnetic resonance imaging (MP-MRI) in detection of locally recurrent prostate cancer and extra prostatic extension in the post-radical radiotherapy setting. Histopathological reference standard was whole-mount prostatectomy specimens. We also assessed for any added value of the dynamic contrast enhancement (DCE) sequence in detection and staging of local recurrence. METHODS: This was a single centre retrospective study. Participants were selected from a database of males treated with salvage prostatectomy for locally recurrent prostate cancer following radiotherapy. All underwent pre-operative prostate-specific antigen assay, positron emission tomography CT, MP-MRI and transperineal template prostate mapping biopsy prior to salvage prostatectomy. MP-MRI performance was assessed using both Prostate Imaging-Reporting and Data System v. 2 and a modified scoring system for the post-treatment setting. RESULTS: 24 patients were enrolled. Using Prostate Imaging-Reporting and Data System v. 2, sensitivity, specificity, positive predictive value and negative predictive value was 64%, 94%, 98% and 36%. MP-MRI under staged recurrent cancer in 63%. A modified scoring system in which DCE was used as a co-dominant sequence resulted in improved diagnostic sensitivity (61%-76%) following subgroup analysis. CONCLUSION: Our results show MP-MRI has moderate sensitivity (64%) and high specificity (94%) in detecting radio-recurrent intraprostatic disease, though disease tends to be under quantified and under staged. Greater emphasis on dynamic contrast images in overall scoring can improve diagnostic sensitivity. ADVANCES IN KNOWLEDGE: MP-MRI tends to under quantify and under stage radio-recurrent prostate cancer. DCE has a potentially augmented role in detecting recurrent tumour compared with the de novo setting. This has relevance in the event of any future modified MP-MRI scoring system for the irradiated gl
Smalley KR, Lound A, Gardner C, et al., 2021, CO-DESIGNING A DIGITAL SELF-MANAGEMENT PLAN FOR BRONCHIECTASIS, Publisher: BMJ PUBLISHING GROUP, Pages: A170-A171, ISSN: 0040-6376
Smalley K, Aufegger L, Flott K, et al., 2021, Can self-management programmes change healthcare utilisation in COPD?: A systematic review and framework analysis, Patient Education and Counseling, Vol: 104, Pages: 50-63, ISSN: 0738-3991
ObjectiveThe study aims to evaluate the ability of self-management programmes to change the healthcare-seeking behaviours of people with Chronic Obstructive Pulmonary Disease (COPD), and any associations between programme design and outcomes.MethodsA systematic search of the literature returned randomised controlled trials of SMPs for COPD. Change in healthcare utilisation was the primary outcome measure. Programme design was analysed using the Theoretical Domains Framework (TDF).ResultsA total of 26 papers described 19 SMPs. The most common utilisation outcome was hospitalisation (n = 22). Of these, 5 showed a significant decrease. Two theoretical domains were evidenced in all programmes: skills and behavioural regulation. All programmes evidenced at least 5 domains. However, there was no clear association between TDF domains and utilisation. Overall, study quality was moderate to poor.ConclusionThis review highlights the need for more alignment in the goals, design, and evaluation of SMPs. Specifically, the TDF could be used to guide programme design and evaluation in future.Practice implicationsPractices have a reasonable expectation that interventions they adopt will provide patient benefit and value for money. Better design and reporting of SMP trials would address their ability to do so.
Neves AL, Lawrence-Jones A, Naar L, et al., 2020, Multidisciplinary teams must work together to co-develop inclusive digital primary care for older people, British Journal of General Practice, Vol: 70, Pages: 582-582, ISSN: 0960-1643
Neves AL, Freise L, Laranjo L, et al., 2020, Impact of providing patients access to electronic health records on quality and safety of care: a systematic review and meta-analysis, BMJ Quality and Safety, Vol: 29, Pages: 1019-1032, ISSN: 2044-5415
Objective To evaluate the impact of sharing electronic health records (EHRs) with patients and map it across six domains of quality of care (ie, patient-centredness, effectiveness, efficiency, timeliness, equity and safety).Design Systematic review and meta-analysis.Data sources CINAHL, Cochrane, Embase, HMIC, Medline/PubMed and PsycINFO, from 1997 to 2017.Eligibility criteria Randomised trials focusing on adult subjects, testing an intervention consisting of sharing EHRs with patients, and with an outcome in one of the six domains of quality of care.Data analysis The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Title and abstract screening were performed by two pairs of investigators and assessed using the Cochrane Risk of Bias Tool. For each domain, a narrative synthesis of the results was performed, and significant differences in results between low risk and high/unclear risk of bias studies were tested (t-test, p<0.05). Continuous outcomes evaluated in four studies or more (glycated haemoglobin (HbA1c), systolic blood pressure (SBP) and diastolic blood pressure (DBP)) were pooled as weighted mean difference (WMD) using random effects meta-analysis. Sensitivity analyses were performed for low risk of bias studies, and long-term interventions only (lasting more than 12 months).Results Twenty studies were included (17 387 participants). The domain most frequently assessed was effectiveness (n=14), and the least were timeliness and equity (n=0). Inconsistent results were found for patient-centredness outcomes (ie, satisfaction, activation, self-efficacy, empowerment or health literacy), with 54.5% of the studies (n=6) demonstrating a beneficial effect. Meta-analyses showed a beneficial effect in effectiveness by reducing absolute values of HbA1c (unit: %; WMD=−0.316; 95% CI −0.540 to −0.093, p=0.005, I2=0%), which remained significant in the sensitivity analyses for low risk of bias s
van Dael J, Gillespie AT, Reader TW, et al., 2020, Patient and staff perceptions of safety and risk: triangulating patient complaints and staff incident reports towards a dual perspective on adverse events, Society for Social Medicine & Population Health, Publisher: BMJ PUBLISHING GROUP, Pages: A40-A40, ISSN: 0143-005X
Background Incident reporting systems in healthcare are historically based on staff descriptions of adverse events. An increasing body of literature suggests patients provide critical insights to risk and error, but their potential has not sufficiently been investigated at the incident level. This study aims to examine to what extent patient complaints and staff incident reports discuss identical incidents, and how their perspectives could be integrated for more comprehensive safety analysis.Methods Deterministic data linkage was performed on all complaints (n=5,265) and staff incident reports (‘PSIs’) (n=81,077) between April 2014 and March 2019 at a multisite hospital in London. A total of 402 complaints covered at least one incident also identified in the PSIs, and were included in the study. All incidents reported in complaints and staff incident reports were codified based on problem domain; problem severity; stage of care; staff group implicated; reported harm; and descriptive level (eg, description of human factors and root causes); adapted from the Healthcare Complaints Analysis Tool (HCAT) and the National Reporting and Learning System (NRLS). Aggregated coding outputs informed targeted qualitative analysis of free text incident reports for an in-depth exploration of key overlap and discrepancies in patient and staff descriptions of unsafe care.Results Our preliminary results indicate staff and patients reported similar problem themes for 81.1% of overlapping incidents (of which 66.5% clinical, followed by 27.1% institutional, and 6.4% relational), but commonly differed in their description of contributing factors and root causes (eg, different time points in patient journey). Alongside overlapping incidents, patients reported an average of 1.4 additional incidents in their complaint, of which 23.6% were high severity. Additional patient-reported incidents included blind spot clinical issues (36.7%; eg care continuity; care omissions) or relatio
Neves AL, Smalley K, Freise L, et al., 2020, Sharing electronic health records with patients - Who is using the Care Information Exchange portal? A cross-sectional study., Publisher: JMIR Preprints
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, adoption rates vary widely across countries and, within countries, across regions and health systems. 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. Individual-level data from patients registered in the CIE portal were collected, including age, gender, ethnicity, educational level, health status, postcode, and digital literacy (using the eHEALS tool). Registered individuals were defined as having an account created in the portal, independent of their actual use of the platform, and 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 have 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
van Dael J, Reader T, Gillespie A, et al., 2020, Learning from complaints in healthcare: a realist review of academic literature, policy evidence, and frontline insights, BMJ Quality and Safety, Vol: 29, Pages: 684-695, ISSN: 2044-5415
Introduction A global rise in patient complaints has been accompanied by growing research to effectively analyse complaints for safer, more patient-centric care. Most patients and families complain to improve the quality of healthcare, yet progress has been complicated by a system primarily designed for case-by-case complaint handling.Aim To understand how to effectively integrate patient-centric complaint handling with quality monitoring and improvement.Method Literature screening and patient codesign shaped the review’s aim in the first stage of this three-stage review. Ten sources were searched including academic databases and policy archives. In the second stage, 13 front-line experts were interviewed to develop initial practice-based programme theory. In the third stage, evidence identified in the first stage was appraised based on rigour and relevance, and selected to refine programme theory focusing on what works, why and under what circumstances.Results A total of 74 academic and 10 policy sources were included. The review identified 12 mechanisms to achieve: patient-centric complaint handling and system-wide quality improvement. The complaint handling pathway includes (1) access of information; (2) collaboration with support and advocacy services; (3) staff attitude and signposting; (4) bespoke responding; and (5) public accountability. The improvement pathway includes (6) a reliable coding taxonomy; (7) standardised training and guidelines; (8) a centralised informatics system; (9) appropriate data sampling; (10) mixed-methods spotlight analysis; (11) board priorities and leadership; and (12) just culture.Discussion If healthcare settings are better supported to report, analyse and use complaints data in a standardised manner, complaints could impact on care quality in important ways. This review has established a range of evidence-based, short-term recommendations to achieve this.
Neves AL, Freise L, Flott K, et al., 2020, Patients’ ability to review electronic health record information to identify potential errors: a pilot qualitative study, Publisher: JMIR Preprints
Sharing personal health information positively impacts quality of care across several domains, and particularly safety and patient-centeredness. Patients when reading their electronic health records (EHRs) may identify and flag up inconsistencies, leading to improved information quality and patient safety. However, in order to identify potential errors, patients need to be able to understand the information contained in their electronic records.Objective:This study assesses patients’ ability to identify errors present in their EHRs. Specifically, it evaluates the degree to which patients comprehend the information in their EHRs, what barriers exist to their understanding, and what, if any, errors patients can identify when given access to their EHRs.Methods:A cross-sectional online survey was undertaken between March 2017 and September 2017. A total of 682 registered users of the Care Information Exchange patient portal, with at least one access during the time of the study, were invited to complete the survey containing both structured (multiple choice) and unstructured (free-text) questions. The survey contained questions on patients’ perceived ability to understand their EHR information and therefore to identify errors. Free-text questions allowed respondents to expand on the reasoning behind their structured responses and provide more detail about their perceptions of EHRs and identifying errors within them. Qualitative data was systematically reviewed by two independent researchers using the framework analysis method, in order to identify emerging themes.Results:A total of 160 participants completed the survey (response rate=23.5%). The majority of participants (68.7%) reported they understood the information. The main barriers identified were information-related (medical terminology and knowledge, and interpretation of test results) and technology-related (user-friendliness of the portal, information display). Participants identified inconsistencie
Nicol D, Huddart R, Reid A, et al., 2020, OUTCOMES WITH MINIMALLY INVASIVE RETROPERITONEAL LYMPH NODE DISSECTION (MI-RPLND) AND SINGLE DOSE CARBOPLATIN IN CLINICAL STAGE 2 SEMINOMA, Annual Meeting of the American-Urological-Association (AUA), Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: E136-E137, ISSN: 0022-5347
Dilley J, Singh H, Pratt P, et al., 2020, Visual behaviour in robotic surgery – demonstrating the validity of the simulated environment, International Journal of Medical Robotics and Computer Assisted Surgery, Vol: 16, ISSN: 1478-5951
BackgroundEye metrics provide insight into surgical behaviour allowing differentiation of performance, however have not been used in robotic surgery. This study explores eye metrics of robotic surgeons in training in simulated and real tissue environments.MethodsFollowing the Fundamentals of Robotic Surgery (FRS), training curriculum novice robotic surgeons were trained to expert‐derived benchmark proficiency using real tissue on the da Vinci Si and the da Vinci skills simulator (dVSS) simulator. Surgeons eye metrics were recorded using eye‐tracking glasses when both “novice” and “proficient” in both environments. Performance was assessed using Global Evaluative Assessment of Robotic skills (GEARS) and numeric psychomotor test score (NPMTS) scores.ResultsSignificant (P ≤ .05) correlations were seen between pupil size, rate of change and entropy, and associated GEARS/NPMTS in “novice” and “proficient” surgeons. Only number of blinks per minute was significantly different between pupilometrics in the simulated and real tissue environments.ConclusionsThis study illustrates the value of eye tracking as an objective physiological tool in the robotic setting. Pupilometrics significantly correlate with established assessment methods and could be incorporated into robotic surgery assessments.
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