Views and experiences of women, peer supporters and healthcare professionals on breastfeeding peer support: A systematic review of qualitative studies.

OBJECTIVE: To examine and synthesise qualitative evidence of women's, peer supporters' and healthcare professionals' views and experiences of breastfeeding peer support. DESIGN: The Joanna Briggs Institute (JBI) approach to systematic reviews of qualitative studies was followed. Seven databases: CINAHL, MEDLINE, EMBASE, PsycINFO, Scopus, Maternal & Infant Care, and Web of Science were searched. Included papers were critically appraised using the JBI Critical Appraisal Checklist for Qualitative Research. JBI's meta-aggregation approach was used to synthesise findings. JBI's ConQual process was followed to assess confidence of evidence. PARTICIPANTS AND SETTING: Primiparous and multiparous women, lay breastfeeding peer supporters, and healthcare professionals based in high, middle, and low income countries. FINDINGS: Twenty-three papers presenting findings from 22 studies were included. The synthesised findings included: (1) Positive characteristics, approaches and benefits of peer support(ers); (2) Relationships between healthcare professionals and peer supporters; (3) Improving women's access to peer support services; (4) Barriers and enablers to provide peer support. KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Breastfeeding peer support increased women's self-esteem and confidence in breastfeeding while reducing social isolation. Peer supporters valued the experience, which gave them a sense of purpose and confidence, and felt good about helping the women they supported. Women appreciated peer supporters who were caring, spent time with them, shared experiences, provided realistic information, practical and emotional support. Although there were tensions between some healthcare professionals and peer supporters, many valued the mutual support offered. Embedding peer supporters in healthcare systems for them to work alongside healthcare professionals, combined with good communications and building trusty relationships could be a useful strategy to reduce tensions between them.

Medical students in the pre-hospital environment - An untapped resource for undergraduate acute care and interprofessional education.

BACKGROUND: The King's College London Pre-hospital Care Programme (KCL PCP) is a student-run programme that provides undergraduate medical students with the opportunity to attend observer shifts with the local ambulance service. This study evaluates the contribution of pre-hospital exposure to medical students' clinical and professional development. METHODS: Students were asked to complete a Likert-scale based survey on self-reported exposure and confidence in various aspects of acute patient assessment, communication and interprofessional education, both before and after the programme; additional qualitative questions querying their experience were asked post-programme. Pre and post-programme Likert-scale responses were matched and statistically analysed, alongside a thematic analysis of qualitative responses. RESULTS: Exposure to ambulance service clinicians, confidence assessing acutely unwell patients, and confidence making clinical handovers all increased with statistical significance. Key areas of learning identified from the thematic analysis include increased confidence communicating with patients and families, and an enriched understanding of the work done by pre-hospital clinicians. CONCLUSIONS: Time spent in the pre-hospital environment shadowing ambulance service clinicians positively contributes to acute care knowledge, inter-personal skills and interprofessional understanding. Rotating medical students through the pre-hospital environment could bridge education gaps in these areas in a manner that complements traditional pre-clinical and clinical teaching.

Developing a Natural Language Processing tool to identify perinatal self-harm in electronic healthcare records.

BACKGROUND: Self-harm occurring within pregnancy and the postnatal year ("perinatal self-harm") is a clinically important yet under-researched topic. Current research likely under-estimates prevalence due to methodological limitations. Electronic healthcare records (EHRs) provide a source of clinically rich data on perinatal self-harm. AIMS: (1) To create a Natural Language Processing (NLP) tool that can, with acceptable precision and recall, identify mentions of acts of perinatal self-harm within EHRs. (2) To use this tool to identify service-users who have self-harmed perinatally, based on their EHRs. METHODS: We used the Clinical Record Interactive Search system to extract de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS Foundation Trust. We developed a tool that applied several layers of linguistic processing based on the spaCy NLP library for Python. We evaluated mention-level performance in the following domains: span, status, temporality and polarity. Evaluation was done against a manually coded reference standard. Mention-level performance was reported as precision, recall, F-score and Cohen's kappa for each domain. Performance was also assessed at 'service-user' level and explored whether a heuristic rule improved this. We report per-class statistics for service-user performance, as well as likelihood ratios and post-test probabilities. RESULTS: Mention-level performance: micro-averaged F-score, precision and recall for span, polarity and temporality >0.8. Kappa for status 0.68, temporality 0.62, polarity 0.91. Service-user level performance with heuristic: F-score, precision, recall of minority class 0.69, macro-averaged F-score 0.81, positive LR 9.4 (4.8-19), post-test probability 69.0% (53-82%). Considering the task difficulty, the tool performs well, although temporality was the attribute with the lowest level of annotator agreement. CONCLUSIONS: It is feasible to develop an NLP tool that identifies, with acceptable validity, mentions of perinatal self-harm within EHRs, although with limitations regarding temporality. Using a heuristic rule, it can also function at a service-user-level.

Generation and evaluation of artificial mental health records for Natural Language Processing.

A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data. In this work, we present an approach to generate artificial clinical documents. We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit. We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generated text based on a human evaluation task. Furthermore, we perform an extrinsic evaluation by studying the impact of using artificial text in a downstream NLP text classification task. We found that using this artificial data as training data can lead to classification results that are comparable to the original results. Additionally, using only a small amount of information from the original data to condition the generation of the artificial data is successful, which holds promise for reducing the risk of these artificial data retaining rare information from the original data. This is an important finding for our long-term goal of being able to generate artificial clinical data that can be released to the wider research community and accelerate advances in developing computational methods that use healthcare data.

Temporal information extraction from mental health records to identify duration of untreated psychosis.

BACKGROUND: Duration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge about when psychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Electronic health records (EHRs) represent a useful resource for retrospective clinical studies on DUP, but the core information underlying this construct is most likely to lie in free text, meaning it is not readily available for clinical research. Natural Language Processing (NLP) is a means to addressing this problem by automatically extracting relevant information in a structured form. As a first step, it is important to identify appropriate documents, i.e., those that are likely to include the information of interest. Next, temporal information extraction methods are needed to identify time references for early psychosis symptoms. This NLP challenge requires solving three different tasks: time expression extraction, symptom extraction, and temporal "linking". In this study, we focus on the first step, using two relevant EHR datasets. RESULTS: We applied a rule-based NLP system for time expression extraction that we had previously adapted to a corpus of mental health EHRs from patients with a diagnosis of schizophrenia (first referrals). We extended this work by applying this NLP system to a larger set of documents and patients, to identify additional texts that would be relevant for our long-term goal, and developed a new corpus from a subset of these new texts (early intervention services). Furthermore, we added normalized value annotations ("2011-05") to the annotated time expressions ("May 2011") in both corpora. The finalized corpora were used for further NLP development and evaluation, with promising results (normalization accuracy 71-86%). To highlight the specificities of our annotation task, we also applied the final adapted NLP system to a different temporally annotated clinical corpus. CONCLUSIONS: Developing domain-specific methods is crucial to address complex NLP tasks such as symptom onset extraction and retrospective calculation of duration of a preclinical syndrome. To the best of our knowledge, this is the first clinical text resource annotated for temporal entities in the mental health domain.

Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms.

For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used. Development and evaluation of NLP systems requires manually annotated corpora. We present a corpus of mental health records annotated with temporal relations for psychosis symptoms. We propose a methodology for document selection and manual annotation to detect symptom onset information, and develop an annotated corpus. To assess the utility of the created corpus, we propose a pilot NLP system. To the best of our knowledge, this is the first temporally-annotated corpus tailored to a specific clinical use-case.