Free-text Entries Research Articles

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

BackgroundDiagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.MethodsThis cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.ResultsUndercounts were evident in all six diagnoses when using coded diagnoses alone (2.57–36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.ConclusionIn Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

Definition, Management, and Training in Impacted Fetal Head at Cesarean Birth: A National Survey of Maternity Professionals

ABSTRACT Impacted fetal head (IFH) is a complication that occurs in 10% of cesarean deliveries (CDs). Disimpacting the fetal head can have significant maternal risks, including trauma to the uterus, vagina, cervix, and bladder, postpartum hemorrhage, as well as neonatal risks, including skull fracture, intracranial hemorrhage, head and face trauma, low oxygen levels, admission to the neonatal intensive care unit, and death. Perinatal head injury due to IFH has resulted in increased litigation, with IFH identified as a contributing factor in nearly 10% of the most expensive maternity claims in the United Kingdom in 2018. International consensus on the most effective and safe management of IFH is lacking. Moreover, recent surveys of maternity professionals in the United Kingdom have reported a scarcity of training, lack of confidence, and underused techniques for IFH. Understanding how maternity professionals view the management of IFH during CD is helpful to improve care, training, and research. The aim of this study was to assess the views, understanding and practices to manage IFH during CD. This was a survey of health care professionals who provide maternity care or work in policy, research, or other areas of maternity care, using a secure online collaborative platform called Thiscovery. Participants included Thiscovery subscribers and members of clinical practice societies, such as the Royal College of Midwives, Royal College of Obstetricians and Gynecologists, and Royal College of Anesthetists. The survey analyzed responses for open-ended questions, with free-text entries, and closed-ended questions. A total of 419 participants were included in the analysis, including 144 midwives, 216 obstetricians, and 59 other maternity professionals. A high level of agreement was found among 79% of obstetricians who preferred a definition of IFH at CD that included “additional maneuvers and/or tocolysis to disimpact and deliver the fetal head.” About a third of free-text responses from 17 respondents noted a need to incorporate the difficulty or inability to get a hand below the fetal head for delivery. Approximately 95% of participants agreed or strongly agreed that management of IFH at CD requires a multiprofessional approach. When asked about communicating an IFH emergency, 55% preferred using the declaration, “This is an impacted fetal head.” Free-text responses from 57 participants emphasized the importance of clear communication and awareness of the emergency across all team members before and during the management of IFH as well as with the woman and birth partner. More than 70% found that a change of operator, manual cephalic extraction, tocolysis, operating changing hand, reserve breech extraction, Fetal Pillow, head-down tilt, and vaginal disimpaction were safe and effective techniques and adjunctive measures for managing IFH at CD. However, free-text responses from 56 participants found mixed views on the use of tocolysis, noting the risk of complications such as postpartum hemorrhage. The use of Fetal Pillow was positively accepted by 71 participants. Comparing obstetricians with midwives, approximately three quarters of obstetricians considered vaginal disimpaction before CD acceptable (72% preincision and 76% postincision). Fewer midwives favored this technique (25% preincision and 52% postincision), with 88% reporting that knowing the position of the fetal head would be helpful to know before undertaking vaginal disimpaction. Training in vaginal disimpaction was considered essential by 85% of midwives and obstetricians. Yet only 81% of midwives and 57% of obstetricians had received such training. The preferred method of training was hands-on training in simulation (91%), followed by animated video illustrating disimpaction techniques (73%) and small-group teaching (55%). Free text responses form 159 participants demonstrated a need and appetite for more training. The most frequent suggestions to improve care for IFH at CD included holding briefings and debriefings before and after the emergency and having robust plans for escalation and calling for help. This study identified high agreement among United Kingdom maternity professionals to define IFH at CD. The study authors suggested a standard definition as “a cesarean birth where the obstetrician is unable to deliver the fetal head with their usual delivering hand, and additional maneuvers and/or tocolysis are required to disimpact and deliver the fetal head.” In addition, using unambiguous language in the emergency, such as declaring “This is an impacted fetal head,” facilitates teamwork and collaboration and improves care.

A Mixed Methods Analysis of Standardized Documentation of Serious Illness Conversations Within an Electronic Health Record Module During Hospitalization

Background Analysis of documented Serious Illness Conversations (SICs) in the inpatient setting can help clinicians align management to address patient and caregiver needs. Methods We conducted a mixed methods analysis of the first instance of standardized documentation of a SIC within a structured module among hospitalized general medicine patients from 2018 to 2019. Percentage of documentations that included a description of patient or family understanding of the patient’s medical condition and use of radio buttons to answer the “prognostic information shared,” “hopes,” and “worries” modules are reported. Using grounded theory approach, physicians analyzed free text entries to: “What is important to the patient/family?” and “Recommendations or next steps planned.” Results Out of 5142 patients, 59 patients had a documented SIC. Patient or family understanding of the medical condition(s) was reported in 56 (95%). For “prognostic information shared,” the most frequently selected radio buttons were: 49 (83%) incurable disease and 28 (48%) prognosis of weeks to months while those for “hopes” were: 52 (88%) be comfortable and 27 (46%) be at home and for “worries” were: 49 (83%) other physical suffering and 36 (61%) pain. Themes generated from entries to “What’s important to patient/family?” included being with loved ones; comfort; mentally and physically present; and reliable care while those for “Recommendations” were coordinating support services; symptom management; and support and communication. Conclusions SIC content indicated concern about pain and reliable care suggesting the complex, intensive nature of caring for seriously ill patients and the need to consider SICs earlier in the life course of patients.

Making Use of Natural Language Processing to Better Understand Medical Students' Self-Assessment of Clinical Skills.

Reflective practice is necessary for self-regulated learning. Helping medical students develop these skills can be challenging since they are difficult to observe. One common solution is to assign students' reflective self-assessments, which produce large quantities of narrative assessment data. Reflective self-assessments also provide feedback to faculty regarding students' understanding of content, reflective abilities, and areas for course improvement. To maximize student learning and feedback to faculty, reflective self-assessments must be reviewed and analyzed, activities that are often difficult for faculty due to the time-intensive and cumbersome nature of processing large quantities of narrative assessment data. The authors collected narrative assessment data (2,224 students' reflective self-assessments) from 344 medical students' reflective self-assessments. In academic years 2019-2020 and 2021-2022, students at the University of Cincinnati College of Medicine responded to 2 prompts (aspects that surprised students, areas for student improvement) after reviewing their standardized patient encounters. These free-text entries were analyzed using TopEx, an open-source natural language processing (NLP) tool, to identify common topics and themes, which faculty then reviewed. TopEx expedited theme identification in students' reflective self-assessments, unveiling 10 themes for prompt 1 such as question organization and history analysis, and 8 for prompt 2, including sensitive histories and exam efficiency. Using TopEx offered a user-friendly, time-saving analysis method without requiring complex NLP implementations. The authors discerned 4 education enhancement implications: aggregating themes for future student reflection, revising self-assessments for common improvement areas, adjusting curriculum to guide students better, and aiding faculty in providing targeted upcoming feedback. The University of Cincinnati College of Medicine aims to refine and expand the utilization of TopEx for deeper narrative assessment analysis, while other institutions may model or extend this approach to uncover broader educational insights and drive curricular advancements.

Every CFTR variant counts – Target-capture based next-generation-sequencing for molecular diagnosis in the German CF Registry

BackgroundIn times of genotype guided therapy options, a total of 3.2 % of people with CF (pwCF) in the German CF Registry[1] only have one or no CFTR-variant detected after genetic analysis. Additionally, genetic data in the Registry can be documented as free text and can therefore be prone to error. In order to allow the greatest possible amount of pwCF access to modern therapies, we conducted a re-evaluation of free text entries and established a custom-whole-CFTR-locus NGS-approach for all pwCF who remained without genetic confirmation afterwards. MethodsTo this end, we assembled 731 free text variants of 655 pwCF in the German CF Registry. All variants were evaluated using ClinVar, HGMD and CFTR1/2, corrected in the Registries’ database and uploaded to ClinVar. PwCF whose diagnosis remained uncertain as well as additional pwCF or pwCFTR-RD that were assembled through a nationwide call for testing of unclear cases were offered genetic analysis. Samples were analysed using a target-capture based NGS-custom-design-panel covering the entire CFTR-locus. ResultsEvaluation of free text variants led to the discovery of 43 variants not formerly reported in the context of CF. The Registries’ dropdown list was extended by 497 variants and over 500 pwCF were provided with their most up-to-date genotype. Samples of 47 pwCF/pwCFTR-RD were sequenced via NGS with an overall success rate of 61.7 %, resulting in implementation of entire CFTR-genotyping into routine diagnostics. ConclusionEntire CFTR-genotyping can greatly increase the genetic diagnostic rate of pwCF/pwCFTR-RD and should be considered after inconspicuous CFTR screening panels in CFTR-diagnostics.

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study.

Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data. In the Netherlands, where general practitioners (GPs) serve as the first point of contact for health care, EHRs generated by these primary care providers contain a wealth of potentially valuable information. Nonetheless, the unstructured nature of free-text entries in EHRs poses challenges in identifying trends, detecting disease outbreaks, or accurately pinpointing COVID-19 cases. This study aims to develop and validate a BERT model for detecting COVID-19 consultations in general practice EHRs in the Netherlands. The BERT model was initially pretrained on Dutch language data and fine-tuned using a comprehensive EHR data set comprising confirmed COVID-19 GP consultations and non-COVID-19-related consultations. The data set was partitioned into a training and development set, and the model's performance was evaluated on an independent test set that served as the primary measure of its effectiveness in COVID-19 detection. To validate the final model, its performance was assessed through 3 approaches. First, external validation was applied on an EHR data set from a different geographic region in the Netherlands. Second, validation was conducted using results of polymerase chain reaction (PCR) test data obtained from municipal health services. Lastly, correlation between predicted outcomes and COVID-19-related hospitalizations in the Netherlands was assessed, encompassing the period around the outbreak of the pandemic in the Netherlands, that is, the period before widespread testing. The model development used 300,359 GP consultations. We developed a highly accurate model for COVID-19 consultations (accuracy 0.97, F1-score 0.90, precision 0.85, recall 0.85, specificity 0.99). External validations showed comparable high performance. Validation on PCR test data showed high recall but low precision and specificity. Validation using hospital data showed significant correlation between COVID-19 predictions of the model and COVID-19-related hospitalizations (F1-score 96.8; P<.001; R2=0.69). Most importantly, the model was able to predict COVID-19 cases weeks before the first confirmed case in the Netherlands. The developed BERT model was able to accurately identify COVID-19 cases among GP consultations even preceding confirmed cases. The validated efficacy of our BERT model highlights the potential of NLP models to identify disease outbreaks early, exemplifying the power of multidisciplinary efforts in harnessing technology for disease identification. Moreover, the implications of this study extend beyond COVID-19 and offer a blueprint for the early recognition of various illnesses, revealing that such models could revolutionize disease surveillance.

A Web-Based, Mail-Order Sexually Transmitted Infection Testing Program: Qualitative Analysis of User Feedback.

The incidence of sexually transmitted infections (STIs) is increasing in the United States. The COVID-19 pandemic resulted in significant reductions in access to health care services, including STI testing and treatment, leading to underreporting of STI cases and a need for alternatives to clinic-based testing. Moreover, concerns around confidentiality, accessibility, and stigma continue to limit access to clinic-based STI testing, particularly for high-priority populations. IWantTheKit (IWTK) is a web-based platform that mails free, confidential, self-administered sample collection kits for testing for gonorrhea, chlamydia (both genital and extragenital sites), and vaginal trichomonas. Individuals visiting the IWTK website may select genital, pharyngeal, and rectal samples for chlamydia and gonorrhea testing. Vaginal samples are tested for trichomoniasis. Self-collected samples are processed in a College of American Pathologists-accredited laboratory, and results are posted to an individual's secure digital account. This study aimed to (1) describe users' experience with the IWTK service through analysis of routine data and (2) optimize retention among current users and expand reach among high-priority populations by responding to user needs through programmatic and functional changes to the IWTK service. Free-text entries were submitted by IWTK users via a confidential "Contact Us" page on the IWTK website from May 17, 2021, to January 31, 2022. All entries were deidentified prior to analysis. Two independent analysts coded these entries using a predefined codebook developed inductively for thematic analysis. A total of 254 free-text entries were analyzed after removing duplicates and nonsensical entries. Themes emerged regarding the functionality of the website and personal experiences using IWTK's services. Users' submissions included requests related to order status, address changes, replacement of old kits, clinical information (eg, treatment options and symptom reports), and reported risk behaviors. This analysis demonstrates how routine data can be used to propose potential programmatic improvements. IWTK implemented innovations on the website based on the study results to improve users' experience, including a tracking system for orders, address verification for each order, a physical drop box, additional textual information, direct linkage to care navigation, and printable results. Web-based, mail-order STI testing programs can leverage user feedback to optimize implementation and retention among current users and potentially expand reach among high-priority populations. This analysis is supported by other data that demonstrate how comprehensive support and follow-up care for individuals testing positive are critical components of any self-testing service. Additional formal assessments of the IWTK user experience and efforts to optimize posttesting linkage to care may be needed.

Improving Access to Cancer Clinical Trials for Patients From Culturally and Linguistically Diverse Backgrounds in Australia: A Survey of Clinical and Research Professionals.

Patients with cancer from racial and ethnic minorities, referred to as culturally and linguistically diverse (CALD) in Australia, are significantly under-represented in cancer clinical trials. We performed a national survey of the Australian cancer clinical trials workforce to determine barriers and preferred solutions to address this inequity. A 15-item online survey containing both closed- and open-ended purpose-designed questions was created using REDCap. The survey was emailed to members of the Clinical Oncology Society of Australia, Medical Oncology Group of Australia, and Australian cancer cooperative trial groups, and promoted via Twitter. Descriptive analyses summarized quantitative data, and free-text entries underwent thematic analysis with NVivo Version 12. Ninety one respondents completed the survey-with representation across Australia. Eighty-seven percent were directly involved in clinical trial recruitment. Sixty-eight percent were clinicians. Seventy-four percent of respondents did not collect routine data on CALD patient enrollment to cancer clinical trials. Communication (eg, lack of translated materials) and opportunity-related barriers (eg, exclusionary trial protocols) were the most frequently perceived barriers to recruitment. Additionally, qualitative analysis indicated that insufficient consultation time and difficulties accessing interpreters for patients with non-English language preference were significant barriers. Trial navigators and a generic cancer trial pamphlet available in multiple languages were judged the most likely solutions to improve recruitment. This study articulates the Australian clinical trials workforce's perspective on current barriers and potential solutions to the under-representation of patients from CALD backgrounds on cancer clinical trials. The insights and solutions from this survey provide steps toward achieving equity in Australian cancer clinical trials.

Quality of Life and Real-time Patient Experience During Neoadjuvant Therapy: A Prospective Cohort Study.

To use a customized smartphone application to prospectively measure QOL and the real-time patient experience during neoadjuvant therapy (NT). NT is increasingly used for patients with localized gastrointestinal (GI) cancers. There is little data assessing patient experience and quality of life (QOL) during NT for GI cancers. Patients with GI cancers receiving NT were instructed on using a customized smartphone application through which the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire, a validated measure of health-related QOL, was administered at baseline, every 30 days, and at the completion of NT. Participants also tracked their moods and symptoms and used free-text journaling functionalities in the application. Mean overall and subsection health-related QOL scores were calculated during NT. Among 104 enrolled patients, the mean age was 60.5 ± 11.5 years and 55% were males. Common cancer diagnoses were colorectal (40%), pancreatic (37%), and esophageal (15%). Mean overall FACT-G scores did not change during NT ( P = 0.987). While functional well-being scores were consistently the lowest and social well-being scores the highest, FACT subscores similarly did not change during NT (all P > 0.01). The most common symptoms reported during NT were fatigue, insomnia, and anxiety (39.3%, 34.5%, and 28.3% of patient entries, respectively). Qualitative analysis of free-text journaling entries identified anxiety, fear, and frustration as the most common themes, but also the importance of social support systems and confidence in health care providers. While patient symptom burden remains high, results of this prospective cohort study suggest QOL is maintained during NT for localized GI cancers.

Can digital health researchers make a difference during the pandemic? Results of the single-arm, chatbot-led Elena+: Care for COVID-19 interventional study.

The current paper details findings from Elena+: Care for COVID-19, an app developed to tackle the collateral damage of lockdowns and social distancing, by offering pandemic lifestyle coaching across seven health areas: anxiety, loneliness, mental resources, sleep, diet and nutrition, physical activity, and COVID-19 information. The Elena+ app functions as a single-arm interventional study, with participants recruited predominantly via social media. We used paired samples T-tests and within subjects ANOVA to examine changes in health outcome assessments and user experience evaluations over time. To investigate the mediating role of behavioral activation (i.e., users setting behavioral intentions and reporting actual behaviors) we use mixed-effect regression models. Free-text entries were analyzed qualitatively. Results show strong demand for publicly available lifestyle coaching during the pandemic, with total downloads (N = 7'135) and 55.8% of downloaders opening the app (n = 3,928) with 9.8% completing at least one subtopic (n = 698). Greatest areas of health vulnerability as assessed with screening measures were physical activity with 62% (n = 1,000) and anxiety with 46.5% (n = 760). The app was effective in the treatment of mental health; with a significant decrease in depression between first (14 days), second (28 days), and third (42 days) assessments: F2,38 = 7.01, p = 0.003, with a large effect size (η2G = 0.14), and anxiety between first and second assessments: t54 = 3.7, p = <0.001 with a medium effect size (Cohen d = 0.499). Those that followed the coaching program increased in net promoter score between the first and second assessment: t36 = 2.08, p = 0.045 with a small to medium effect size (Cohen d = 0.342). Mediation analyses showed that while increasing number of subtopics completed increased behavioral activation (i.e., match between behavioral intentions and self-reported actual behaviors), behavioral activation did not mediate the relationship to improvements in health outcome assessments. Findings show that: (i) there is public demand for chatbot led digital coaching, (ii) such tools can be effective in delivering treatment success, and (iii) they are highly valued by their long-term user base. As the current intervention was developed at rapid speed to meet the emergency pandemic context, the future looks bright for other public health focused chatbot-led digital health interventions.

Clinician perspectives on barriers and solutions to symptom management in cystic fibrosis

People living with cystic fibrosis (PLwCF) experience high symptom burden. 146 clinicians completed online surveys regarding barriers and solutions to symptom management between September and October 2020. The surveys contained both closed-ended and free-text entries. The symptom management specialists that CF clinicians most wished to consult included mental health (88, 65%), palliative care (59, 41%), and pain specialists (48, 33%). Barriers to symptom management included concerns about controlled substances prescribed for symptom control causing addiction and precluding transplantation, a lack of trust and collaboration among clinical specialties, a lack of symptom management specialists with CF expertise, and a worry about the affordability of specialist-level symptom management care. Potential solutions included non-pharmacological approaches, expanding access to affordable specialist symptom management care, the creation of clinical care guidelines for symptom management in CF, and having CF clinicians and symptom management specialists work alongside each other in CF clinic to build interdisciplinary trust and education.

Translating clinical notes into quantitative measures-a real-world observation on the response to cholinesterase inhibitors or selective serotonin reuptake inhibitors prescribed to outpatients with dementia using electronic medical records.

Objective: Cholinesterase inhibitors (CEIs) are prescribed for dementia to maintain or improve memory. Selective serotonin reuptake inhibitors (SSRIs) are also prescribed to manage psychiatric symptoms seen in dementia. What proportion of outpatients actually responds to these drugs is still unclear. Our objective was to investigate the responder rates of these medications in an outpatient setting using the electronic medical record (EMR). Methods: We used the Johns Hopkins EMR system to identify patients with dementia who were prescribed a CEI or SSRI for the first time between 2010 and 2021. Treatment effects were assessed through routinely documented clinical notes and free-text entries in which healthcare providers record clinical findings and impressions of patients. Responses were scored using a three-point Likert scale named the NOte-based evaluation method for Treatment Efficacy (NOTE) in addition to the Clinician's Interview-Based Impression of Change Plus caregiver input (CIBIC-plus), a seven-point Likert scale used in clinical trials. To validate NOTE, the relationships between NOTE and CIBIC-plus and between NOTE and change in MMSE (Mini-Mental State Examination) before and after medication were examined. Inter-rater reliability was evaluated using Krippendorff's alpha. The responder rates were calculated. Results: NOTE showed excellent inter-rater reliability and correlated well with CIBIC-plus and changes in MMSEs. Out of 115 CEI cases, 27.0% reported improvement and 34.8% reported stable symptoms in cognition; out of 225 SSRI cases, 69.3% reported an improvement in neuropsychiatric symptoms. Conclusion: NOTE showed high validity in measuring the pharmacotherapy effects based on unstructured clinical entries. Although our real-world observation included various types of dementia, the results were remarkably similar to what was reported in controlled clinical trials of Alzheimer's disease and its related neuropsychiatric symptoms.

Using Natural Language Processing to identify patients with Multiple Sclerosis (P3-3.012)

<h3>Objective:</h3> We used text comments from social media sources and the UK MS register (UKMSR) to determine the possibility of identifying MS subjects using natural language processing (NLP). <h3>Background:</h3> Multiple sclerosis (MS) has a wide range of impacts that result in symptoms often being dismissed in the earliest disease phase potentially leading to a delay in diagnosis. Predictive classifiers from natural language processing (NLP) are a potential aid to early identification of symptoms of a range of conditions. These classifiers take as input, patient experience, in text format. Identifying people with MS symptoms earlier could enable them to be directed to the correct resources to confirm a diagnosis. <h3>Design/Methods:</h3> Classifiers were trained using combined social media text data with data from the UKMSR, a database of people clinically diagnosed with MS, where free text entries are collected about their unique experiences. Two control groups were identified: generic social media users and people with self-reported diagnoses of diabetes on social media. Words related to the diagnosis and relevant treatments were removed. We explored three ways to represent text as word vectors and trained seven different machine learning models, including logistic regression, k-nearest neighbors, Naïve Bayes and deep learning approaches. <h3>Results:</h3> 6021 comments from 3037 patients with MS, 6021 comments from 3037 diabetes patients and 6021 comments from 3037 generic social media users were gathered. From the seven models, we achieved a maximum accuracy of 94.3% when classifying experiences of MS patients against generic social media users and a maximum accuracy of 92.4% against diabetes patients. <h3>Conclusions:</h3> The results suggest that NLP-based methods used here can identify people with MS with high accuracy. Further work on more datasets and conditions is required to confirm the utility of these findings but they have the potential to assist accelerating the diagnosis of MS. <b>Disclosure:</b> Mr. Jimulia has received research support from Imperial College London. Dr. Lala has received research support from Imperial College London. Dr. Nicholas has nothing to disclose. The institution of Mr. Middleton has received research support from MS Society.

The research interest, capacity and culture of NHS staff in South East Scotland and changes in attitude to research following the pandemic: a cross-sectional survey

BackgroundThe UK National Health Service (NHS) is ideally placed to undertake research. The UK Government recently launched its vision of research within the NHS to improve research culture and activity amongst its staff. Currently, little is known about the research interest, capacity and culture of staff in one Health Board in South East Scotland and how their attitudes to research may have changed as a result of the SARS-CoV-2 pandemic.MethodsWe used the validated Research Capacity and Culture tool in an online survey of staff working in one Health Board in South East Scotland to explore attitudes to research at the organisation, team and individual level together with involvement in, barriers to and motivators to engage in research. Questions included changes in attitude to research as a result of the pandemic. Staff were identified by professional group: nurses/midwives, medical/dental, allied health professionals (AHP), other therapeutic and administrative roles. Median scores and interquartile ranges were reported and differences between groups assessed using the Chi-square and Kruskal-Wallis tests with P < 0.05 accepted as statistical significance. Free-text entries were analysed using content analysis.ResultsReplies were received from 503/9145 potential respondents (5.5% response), of these 278 (3.0% response) completed all sections of the questionnaire. Differences between groups were noted in the proportions of those with research as part of their role (P = 0.012) and in being research-active (P < 0.001). Respondents reported high scores for promoting evidence-based practice and for finding and critically reviewing literature. Low scores were returned for preparing reports and securing grants. Overall, medical and other therapeutic staff reported higher levels of practical skills compared with other groups. Principal barriers to research were pressure of clinical work and lack of time, backfill and funds. 171/503 (34%) had changed their attitude to research as a result of the pandemic with 92% of 205 respondents more likely to volunteer for a study themselves.ConclusionWe found a positive change in attitude to research arising from the SARS-CoV-2 pandemic. Research engagement may increase after addressing the barriers cited. The present results provide a baseline against which future initiatives introduced to increase research capability and capacity may be assessed.

International Cross-Sectional Survey of Bullying, Undermining, and Harassment in the Vascular Workplace

Bullying, undermining behaviour, and harassment (BUH) may exist in healthcare settings, impacting on patient care. The aim of this international study was to evaluate the characteristics of BUH experienced by physicians treating vascular diseases at various career stages. This was an anonymous international structured non-validated cross-sectional survey distributed via relevant professional societies in collaboration with the Research Collaborative in Peripheral Artery Disease. The survey was disseminated through societies' newsletters, emails, and social media. Data were collected online, allowing free text entries alongside structured multiple choice questions based on previous surveys. Demographics, geographical information, and data relating to stage/environment of training were collected. Of 587 respondents from 28 countries, 86% were working in vascular surgery, mostly at a university hospital (56%); 81% were aged between 31 and 60 years, 57% were working as a consultant, and 23% as a resident. Respondents were mostly white (83%), male (63%), heterosexual (94%), and without disability (96%). Overall, 253 (43%) reported experiencing BUH personally, 75% had witnessed BUH toward colleagues, and 51% witnessed these in the last 12 months. Female sex and non-white ethnicity were associated with BUH (53% vs. 38% and 57% vs. 40% respectively; p < .001 in both cases). While working as consultant, 171 (50%) reported experiencing BUH, more often among females, non-heterosexuals, those who were not working in their country of birth, and non-white people. Specialty and hospital type were not associated with BUH. BUH remains a major problem in the vascular workplace. Female sex, non-heterosexuality, and non-white ethnicity are associated with BUH at various career stages.

The reality of advanced airway management during out of hospital cardiac arrest; why did paramedics deviate from their allocated airway management strategy during the AIRWAYS-2 randomised trial?

BackgroundAIRWAYS-2 was a large multi-centre cluster randomised controlled trial investigating the effect on functional outcome of a supraglottic airway device (i-gel) versus tracheal intubation (TI) as the initial advanced airway during out-of-hospital cardiac arrest. We aimed to understand why paramedics deviated from their allocated airway management algorithm during AIRWAYS-2. MethodsThis study employed a pragmatic sequential explanatory design utilising retrospective study data collected during the AIRWAYS-2 trial. Airway algorithm deviation data were analysed to categorise and quantify the reasons why paramedics did not follow their allocated strategy of airway management during AIRWAYS-2. Recorded free text entries provided additional context to the paramedic decision-making related to each category identified. ResultsIn 680 (11.7%) of 5800 patients the study paramedic did not follow their allocated airway management algorithm. There was a higher percentage of deviations in the TI group (399/2707; 14.7%) compared to the i-gel group (281/3088; 9.1%). The predominant reason for a paramedic not following their allocated airway management strategy was airway obstruction, occurring more commonly in the i-gel group (109/281; 38.7%) versus (50/399; 12.5%) in the TI group. ConclusionThere was a higher proportion of deviations from the allocated airway management algorithm in the TI group (399; 14.7%) compared to the i-gel group (281; 9.1%). The most frequent reason for deviating from the allocated airway management algorithm in AIRWAYS-2 was obstruction of the patient’s airway by fluid. This occurred in both groups of the AIRWAYS-2 trial, but was more frequent in the i-gel group.

Pain Widespreadedness, and Not Primary Pain Location, is Associated With Comorbid Symptoms in Children With Chronic Pain

Pediatric chronic pain represents heterogeneous diagnoses; often, primary pain location informs research classifications and treatment. In contrast, recent research has highlighted the role of widespread pain and this perspective has been adopted in assessments in specialty pediatric pain clinics. The lack of direct comparison between these 2 methods of categorizing pediatric chronic pain may hinder the adoption of evidence-based practices across the spectrum of care. Therefore, this study aimed to compare whether primary pain location or pain widespreadedness is more informative for pain-related symptoms in pediatric chronic pain. Youth (n=223) between the ages of 8 to 23 years (M=15.93, SD=2.11, 83% female) completed surveys upon intake at the pediatric chronic pain clinic. Free-text entries of primary pain location were coded into categories: headache, abdominal pain, and musculoskeletal pain. Additional domains assessed included widespread pain, pain interference, kinesiophobia, catastrophizing, anxiety, depression, sleep, and fatigue. Differences based on primary pain location only emerged for kinesiophobia, F(2150)=8.20, P<0.001, with the highest scores among those with musculoskeletal pain. In contrast, controlling for sex, age, and pain intensity, pain widespreadedness was associated with pain interference, pain catastrophizing, fatigue, anxiety, and depression (P<0.05). Pain widespreadedness was more consistently associated with pain-related outcomes among pediatric chronic pain patients than primary pain location, and body maps may be useful in determining a nociplastic pain mechanism to inform treatment. Improved assessment of pediatric pain mechanisms may help advance more precise treatment delivery.

Perceived workplace support and mental health, well-being and burnout among health care professionals during the COVID-19 pandemic: a cohort analysis.

Little is known about the relationship between workplace support and mental health and burnout among health care professionals (HCPs) during the COVID-19 pandemic. In this cohort study, we sought to evaluate the association between perceived level of (and changes to) workplace support and mental health and burnout among HCPs, and to identify what constitutes perceived effective workplace support. Online surveys at baseline (July-September 2020) and follow-up 4 months later assessed the presence of generalized anxiety disorder (using the 7-item Generalized Anxiety Disorder scale [GAD-7]), clinical insomnia, major depressive disorder (using the 9-item Patient Health Questionnaire), burnout (emotional exhaustion and depersonalization) and mental well-being (using the Short Warwick-Edinburgh Mental Wellbeing Score). Both surveys assessed self-reported level of workplace support (single-item Likert scale). For baseline and follow-up, independently, we developed separate logistic regression models to evaluate the association of the level of workplace support (tricohotomized as unsupported, neither supported nor unsupported and supported) with mental health and burnout. We also developed linear regression models to evaluate the association between the change in perceived level of workplace support and the change in mental health scores from baseline and follow-up. We used thematic analyses on free-text entries of the baseline survey to evaluate what constitutes effective support. At baseline (n = 1422) and follow-up (n = 681), HCPs who felt supported had reduced risk of anxiety, depression, clinical insomnia, emotional exhaustion and depersonalization, compared with those who felt unsupported. Among those who responded to both surveys (n = 681), improved perceived level of workplace support over time was associated with significantly improved scores on measures of anxiety (adjusted β -0.13, 95% confidence interval [CI] -0.25 to -0.01), depression (adjusted β -0.17, 95% CI -0.29 to -0.04) and mental well-being (adjusted β 0.19, 95% CI 0.10 to 0.29), independent of baseline level of support. We identified 5 themes constituting effective workplace support, namely concern or understanding for welfare, information, tangible qualities of the workplace, leadership and peer support. We found a significant association between perceived level of (and changes in) workplace support and mental health and burnout of HCPs, and identified potential themes that constitute perceived workplace support. Collectively, these findings can inform changes in guidance and national policies to improve mental health and burnout among HCPs. Trial registration: ClinicalTrials.gov, no. NCT04433260.

Dynamic reaction picklist for improving allergy reaction documentation: A usability study

ObjectiveTo assess novel dynamic reaction picklists for improving allergy reaction documentation compared to a static reaction picklist. Materials and methodsWe developed three web-based user interfaces (UIs) mimicking the Mass General Brigham’s EHR allergy module: the first and second UIs (i.e., UI-1D, UI-2D) implemented two dynamic reaction picklists with different ranking algorithms and the third UI (UI-3S) implemented a static reaction picklist like the one used in the current EHR. We recruited 18 clinicians to perform allergy entry for 10 test cases each via UI-1D and UI-3S, and another 18 clinicians via UI-2D and UI-3S. Primary measures were the number of free-text entries and time to complete the allergy entry. Clinicians were also interviewed using 30 questions before and after the data entry. Results and discussionsAmong 36 clinicians, less than half were satisfied with the current EHR reaction picklists, due to their incomprehensiveness, inefficiency, and lack of intuitiveness. The clinicians used significantly fewer free-text entries when using UI-1D or UI-2D compared to UI-3S (p < 0.05). The clinicians used on average 51 s (15 %) less time via UI-1D and 50 s (16 %) less time via UI-2D in completing the allergy entries versus UI-3S, and there was not a statistically significant difference in documentation time for either group between the dynamic and static UIs. Overall, 15–17 (83–94 %) clinicians rated UI-1D and 13–15 (72–83 %) clinicians rated UI-2D as efficient, easy to use, and useful, while less than half rated the same for UI-3S. Most clinicians reported that the dynamic reaction picklists always or often suggested appropriate reactions (n = 30, 83 %) and would decrease the free-text entries (n = 26, 72 %); nearly all preferred the dynamic picklist over the static picklist (n = 32, 89 %). ConclusionWe found that dynamic reaction picklists significantly reduced the number of free-text entries and could reduce the time for allergy documentation by 15%. Clinicians preferred the dynamic reaction picklist over the static picklist.

EPH34 Long COVID Symptoms and Diagnosis in Primary Care: A Cohort Study Using the Thin Database Including Unstructured Text

Long COVID is a widely recognised consequence of COVID-19 infection, but there is currently little information about the symptoms that patients present with in primary care, as these are typically recorded only in free text clinical notes. To compare the occurrence of symptoms in patients with and without a history of COVID-19 infection. We used data from the The Health Improvement Network (THIN, a Cegedim Database). We included patients aged 18 or over registered with participating practices in England, Scotland or Wales. We compared COVID-19 cases (defined by Read-coded diagnoses) with unexposed controls. We extracted baseline and symptom information from both the Read codes and free text, which was analysed by natural language processing. We calculated hazard ratios (adjusted for age, sex, baseline medical conditions and prior record of symptoms) for each of 89 symptoms from 12 weeks after the COVID-19 diagnosis. We compared 10,229 patients with confirmed COVID-19 and 22,654 unexposed controls. Patients had a mean age of 52.4 years and 62.5% were female. Around 80% of the symptom mentions in the primary care were only in the free text. A wide range of symptoms were associated with previous COVID-19 infection, including shortness of breath (hazard ratio (HR) 3.2, 95% confidence interval (CI) 2.8, 3.5), chest pain (HR 2.35, 95% CI 2.03, 2.72) and fatigue (HR 3.4, 95% CI 3.0, 3.8). There were 603 free text entries of ‘Long Covid’, but none recorded using Read codes. These preliminary results show that there are numerous symptoms which are more commonly recorded after COVID-19 infection. There is a lack of structured recording of symptoms and Long COVID diagnoses, showing the importance of free text in health records for studying these topics