Clinical Record Interactive Search System Research Articles

Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records

ObjectivesTo assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).SettingThe Clinical Records...

Associated Mortality Risk of Atypical Antipsychotic Medication in Individuals With Dementia (AMRAAD): A Clinical Cohort Study

AimsAntipsychotic medications such as risperidone, olanzapine and aripiprazole are used to treat psychological and behavioural symptoms among dementia patients. Current evidence indicate prescription rates for antipsychotics vary and wider consensus to evaluate clinical epidemiological outcomes is limited. This study aims to investigate the potential impact of atypical antipsychotics on the mortality of patients with dementia.MethodsA retrospective clinical cohort study was developed to review United Kingdom Clinical Record Interactive Search system based data between January 1, 2013 to December 31, 2017. A descriptive statistical method was used to analyse the data. Mini Mental State Examination (MMSE) scores were used to assess the severity and stage of disease progression. A study specific cox proportional hazards model was developed to evaluate the relationship between survival following diagnosis and other variables.ResultsA total sample size of 1692 patients were identified using natural language processing of which, 587 were prescribed olanzapine, quetiapine, or risperidone (common group) whilst 893 (control group) were not prescribed any antipsychotics. Patients prescribed olanzapine and Risperidone showed similar risk of death [hazard ratio (HR) = 1.32; 95% confidence interval (CI): 1.08–1.60; P < 0.01], (HR = 1.35; 95%CI: 1.18–1.54; P < 0.001). Patients prescribed Quetiapine showed no significant association (HR = 1.09; 95%CI: 0.90–1.34; P = 0.38). Factors associated with a lower risk of death were elevated MMSE score at diagnosis (HR = 0.72; 95%CI: 0.62–0.83; P < 0.001) along with other demographic factors such as women (HR = 0.73; 95%CI: 0.64–0.82; P < 0.001) and being of a Caucasian British group (HR = 0.82; 95%CI: 0.72–0.94; P < 0.01).ConclusionA significant mortality risk was identified among those prescribed olanzapine and risperidone which contradicts previous findings although the study designs used were different. Comprehensive research should be conducted to better assess clinical epidemiological outcomes associated with diagnosis and therapies to improve clinical management of these patients.

Associated mortality risk of atypical antipsychotic medication in individuals with dementia.

BACKGROUNDAntipsychotic medications such as risperidone, olanzapine and aripiprazole are used to treat psychological and behavioural symptoms among dementia patients. Current evidence indicate prescription rates for antipsychotics vary and wider consensus to evaluate clinical epidemiological outcomes is limited. AIMTo investigate the potential impact of atypical antipsychotics on the mortality of patients with dementia.METHODSA retrospective clinical cohort study was developed to review United Kingdom Clinical Record Interactive Search system based data between January 1, 2013 to December 31, 2017. A descriptive statistical method was used to analyse the data. Mini Mental State Examination (MMSE) scores were used to assess the severity and stage of disease progression. A cox proportional hazards model was developed to evaluate the relationship between survival following diagnosis and other variables.RESULTSA total of 1692 patients were identified using natural language processing of which, 587 were prescribed olanzapine, quetiapine or risperidone (common group) whilst 893 (control group) were not prescribed any antipsychotics. Patients prescribed olanzapine showed an increased risk of death [hazard ratio (HR) = 1.32; 95% confidence interval (CI): 1.08-1.60; P < 0.01], as did those with risperidone (HR = 1.35; 95%CI: 1.18-1.54; P < 0.001). Patients prescribed quetiapine showed no significant association (HR = 1.09; 95%CI: 0.90-1.34; P = 0.38). Factors associated with a lower risk of death were: High MMSE score at diagnosis (HR = 0.72; 95%CI: 0.62-0.83; P < 0.001), identifying as female (HR = 0.73; 95%CI: 0.64-0.82; P < 0.001), and being of a White-British ethnic group (HR = 0.82; 95%CI: 0.72-0.94; P < 0.01).CONCLUSIONA significant mortality risk was identified among those prescribed olanzapine and risperidone which contradicts previous findings although the study designs used were different. Comprehensive research should be conducted to better assess clinical epidemiological outcomes associated with diagnosis and therapies to improve clinical management of these patients.

Incidence of suicidality in people with depression over a 10-year period treated by a large UK mental health service provider

Summary We describe the incidence of suicidality (2007–2017) in people with depression treated by secondary mental healthcare services at South London and Maudsley NHS Trust (n = 26 412). We estimated yearly incidence of ‘suicidal ideation’ and ‘high risk of suicide’ from structured and free-text fields of the Clinical Record Interactive Search system. The incidence of suicidal ideation increased from 0.6 (2007) to 1 cases (2017) per 1000 population. The incidence of high risk of suicide, based on risk forms, varied between 0.06 and 0.50 cases per 1000 adult population (2008–2017). Electronic health records provide the opportunity to examine suicidality on a large scale, but the impact of service-related changes in the use of structured risk assessment should be considered.

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

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. (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. 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. 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. 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.

Identifying perinatal self-harm in electronic healthcare records using natural language processing

Aims1.To generate a Natural Language Processing (NLP) application that can identify mentions of perinatal self-harm among electronic healthcare records (EHRs)2.To use this application to estimate the prevalence of perinatal self-harm within a data-linkage cohort of women accessing secondary mental healthcare during the perinatal period.MethodData source: the Clinical Record Interactive Search system. This is a database of de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS Foundation Trust (SLaM). CRIS has pre-existing ethical approval via the Oxfordshire Research Ethics Committee C (ref 18/SC/0372) and this project was approved by the CRIS Oversight Committee (16-069). After developing a list of synonyms for self-harm and piloting coding rules, a gold standard dataset of EHRs was manually coded using Extensible Human Oracle Suite of Tools (eHOST) software. An NLP application to detect perinatal self-harm was then developed using several layers of linguistic processing based on the spaCy NLP library for Python. Evaluation of mention-level performance was done according to the attributes of mentions the application was designed to identify (span, status, temporality and polarity), by comparing application performance against the gold standard dataset. Performance was described as precision, recall, F-score and Cohen's kappa. Most service-users had more than one EHR in their period of perinatal service use. Performance was therefore also measured at “service-user level” with additional performance metrics of likelihood ratios and post-test probabilities. Linkage with the Hospital Episode Statistics datacase allowed creation of a cohort of women who accessed SLaM during the perinatal period. By deploying the application on the EHRs of the women in the cohort, we were able to estimate the prevalence of perinatal self-harm.ResultMention-level performance: micro-averaged F-score, precision and recall for span, polarity and temporality all &gt;0.8. Kappa for status 0.68, temporality 0.62, polarity 0.91. Service-user level performance: F-score, precision, recall all 0.69, overall F-score 0.81, positive likelihood ratio 9.4 (4.8–19), post-test probability 68.9% (95%CI 53–82).Cohort prevalence of self-harm in pregnancy was 15.3% (95% CI 14.3–16.3); self-harm in the postnatal year was 19.7% (95% CI 18.6–20.8). Only a very small proportion of women self-harmed in both pregnancy and the postnatal year (3.9%, 95% CI 3.3–4.4).ConclusionNLP can be used to identify perinatal self-harm within EHRs. The hardest attribute to classify was temporality. This is in line with the wider literature indicating temporality as a notoriously difficult problem in NLP. As a result, the application probably over-estimates prevalence, to a degree. However, overall performance, given the difficulty of the task, is good.Bearing in mind the limitations, our findings suggest that self-harm is likely to be relatively common in women accessing secondary mental healthcare during the perinatal period.Funding: KA is funded by a National Institute for Health Research Doctoral Research Fellowship (NIHR-DRF-2016-09-042). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. RD is funded by a Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences which also party funds AB. AB's work was also part supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities, as well as the Maudsley Charity.Acknowledgements: Professor Louise M Howard, who originally suggested using NLP to identify perinatal self-harm in EHRs. Professor Howard is the primary supervisor of KA's Fellowship.

Investigating the association between physical health comorbidities and disability in individuals with severe mental illness.

Research suggests that an increased risk of physical comorbidities might have a key role in the association between severe mental illness (SMI) and disability. We examined the association between physical multimorbidity and disability in individuals with SMI. Data were extracted from the clinical record interactive search system at South London and Maudsley Biomedical Research Centre. Our sample (n=13,933) consisted of individuals who had received a primary or secondary SMI diagnosis between 2007 and 2018 and had available data for Health of Nations Outcome Scale (HoNOS) as disability measure. Physical comorbidities were defined using Chapters II-XIV of the International Classification of Diagnoses (ICD-10). More than 60 % of the sample had complex multimorbidity. The most common organ system affected were neurological (34.7%), dermatological (15.4%), and circulatory (14.8%). All specific comorbidities (ICD-10 Chapters) were associated with higher levels of disability, HoNOS total scores. Individuals with musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders were found to be associated with significant difficulties associated with more than five HoNOS domains while others had a lower number of domains affected. Individuals with SMI and musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders are at higher risk of disability compared to those who do not have those comorbidities. Individuals with SMI and physical comorbidities are at greater risk of reporting difficulties associated with activities of daily living, hallucinations, and cognitive functioning. Therefore, these should be targeted for prevention and intervention programs.

Real-world effectiveness of admissions to a tertiary treatment-resistant psychosis service: 2-year mirror-image study.

Treatment-resistant schizophrenia is a major disabling illness which often proves challenging to manage in a secondary care setting. The National Psychosis Unit (NPU) is a specialised tertiary in-patient facility that provides evidence-based, personalised, multidisciplinary interventions for complex treatment-resistant psychosis, in order to reduce the risk of readmission and long-term care costs. This study aimed to assess the long-term effectiveness of treatment at the NPU by considering naturalistic outcome measures. Using a mirror image design, we compared the numbers of psychiatric and general hospital admissions, in-patient days, acuity of placement, number of psychotropic medications and dose of antipsychotic medication prescribed before and following NPU admission. Data were obtained from the Clinical Records Interactive Search system, an anonymised database sourced from the South London and Maudsley NHS Trust electronic records, and by means of anonymous linkage to the Hospital Episode Statistics system. Compared with the 2 years before NPU admission, patients had fewer mental health admissions (1.65 ± 1.44 v. 0.87 ± 0.99, z = 5.594, P < 0.0001) and less mental health bed usage (335.31 ± 272.67 v. 199.42 ± 261.96, z = 5.195 P < 0.0001) after NPU admission. Total in-patient days in physical health hospitals and total number of in-patient days were also significantly reduced (16.51 ± 85.77 v. 2.83 ± 17.38, z = 2.046, P = 0.0408; 351.82 ± 269.09 v. 202.25 ± 261.05, z = 5.621, P < 0.0001). The reduction in level of support required after treatment at the NPU was statistically significant (z = -8.099, P < 0.0001). This study demonstrates the long-term effectiveness of a tertiary service specialising in treatment-resistant psychosis.

Dementia in People from Ethnic Minority Backgrounds: Disability, Functioning, and Pharmacotherapy at the Time of Diagnosis

ObjectivesIncreasingly, older populations in the United Kingdom and other well-resourced settings are ethnically diverse. Despite a concern that the prevalence of dementia is expected to rise, very little is known about the association of ethnicity and dementia among aging older adults. The current study aimed to compare ethnic group differences in symptom profile, functioning and pharmacotherapy at dementia diagnosis. DesignCross-sectional study of patient characteristics at the point of dementia diagnosis. Setting and ParticipantsIn total, 12,154 patients aged 65 years or older diagnosed with dementia in Southeast London between 2007 and 2015. MethodsData were extracted from the Clinical Record Interactive Search system, which provides anonymized access to the electronic health records of a large mental healthcare provider in Southeast London. Patients from ethnic minority backgrounds were compared with white British individuals on mental and physical well-being, functional scales and medications prescribed at dementia diagnosis, as well as subtype of dementia documented anywhere in the record. ResultsCompared with white British patients, Black African and Black Caribbean patients were more likely to present with psychotic symptoms and were less likely to have an antidepressant prescribed; white Irish patients had higher rates of substance/alcohol use and depressive symptoms were more prevalent in South Asian patients; all ethnic minority groups had higher odds of polypharmacy; and vascular dementia diagnoses were more common in Black and Irish ethnic minority groups. Conclusions and ImplicationsAt dementia diagnosis, there are substantial differences in noncognitive mental health symptoms and pharmacotherapy across ethnic minority groups and compared with the white British majority population. Some of these differences might reflect access/treatment inequalities or implicit unconscious bias related to ethnicity, influencing both. They need to be taken into consideration to optimize pathways into care and personalize assessment and management.

Clozapine treatment and risk of COVID-19 infection: retrospective cohort study.

Clozapine, an antipsychotic with unique efficacy in treatment-resistant psychosis, is associated with increased susceptibility to infection, including pneumonia. To investigate associations between clozapine treatment and increased risk of COVID-19 infection in patients with schizophrenia-spectrum disorders who are receiving antipsychotic medications in a geographically defined population in London, UK. Using information from South London and Maudsley NHS Foundation Trust (SLAM) clinical records, via the Clinical Record Interactive Search system, we identified 6309 individuals who had an ICD-10 diagnosis of schizophrenia-spectrum disorders and were taking antipsychotics at the time of the COVID-19 pandemic onset in the UK. People who were on clozapine treatment were compared with those on any other antipsychotic treatment for risk of contracting COVID-19 between 1 March and 18 May 2020. We tested associations between clozapine treatment and COVID-19 infection, adjusting for gender, age, ethnicity, body mass index (BMI), smoking status and SLAM service use. Of 6309 participants, 102 tested positive for COVID-19. Individuals who were on clozapine had increased risk of COVID-19 infection compared with those who were on other antipsychotic medication (unadjusted hazard ratio HR = 2.62, 95% CI 1.73-3.96), which was attenuated after adjusting for potential confounders, including clinical contact (adjusted HR = 1.76, 95% CI 1.14-2.72). These findings provide support for the hypothesis that clozapine treatment is associated with an increased risk of COVID-19 infection. Further research will be needed in other samples to confirm this association. Potential clinical implications are discussed.

Impact of matching error on linked mortality outcome in a data linkage of secondary mental health data with Hospital Episode Statistics (HES) and mortality records in South East London: a cross-sectional study

ObjectivesLinkage of electronic health records (EHRs) to Hospital Episode Statistics (HES)-Office for National Statistics (ONS) mortality data has provided compelling evidence for lower life expectancy in people with severe mental...

Understanding geographical patterning of self-harm prevalence within a diverse urban population: a mixed methods spatial analysis and qualitative study

BackgroundPublic health understanding of variations in self-harm prevalence between populations largely relies on routinely collected service-use data. An unexpectedly low number of hospital inpatient admissions following self-harm has been observed in inner London despite established associations of area deprivation and social fragmentation with self-harm. We aimed to measure associations between rates of attendance at an Emergency Department following self-harm and area characteristics, and to explore the processes that might underlie the incidence of self-harm observed in inner inner-city areas. MethodFor the spatial analysis, we created a dataset of Emergency Department attendances following self-harm from 2009 to 2016 by residents of four southeast London boroughs who were older than 11 years using linked electronic patient record data and Hospital Episode Statistics. Age-standardised and sex-standardised small-area rates of self-harm attendance (SARs) were spatially smoothed using Bayesian models and mapped. Associations with area deprivation (assessed using the Index of Multiple Deprivation) and social fragmentation (Congdon Index) were analysed. For the qualitative study, we identified a case-study area from within the four boroughs studied of four contiguous census lower super output areas (LSOAs; approximately 2600 households that were both in the most deprived quintile of areas in the UK and had below average SARs for self-harm). We undertook semi-structured interviews with 14 individuals working in community organisations and two focus groups of 12 people older than 16 years and resident in the study area, recruited through community organisations. Ethical approval for data access and linkage was granted by the Oxford Research Ethics Committee C, Clinical Records Interactive Search system Oversight Committee, and the NHS Health Research Authority Confidentiality Advisory Group. The qualitative study was given ethical approval by the King's College London, Psychiatry, Nursing and Midwifery Subcommittee. FindingsWe identified 20 750 Emergency Department attendances following self-harm by 12 577 individuals. The ratio of SARs in the highest versus the lowest 5% of areas was 2·87 (95% credible interval [CrI] 2·65–3·13). Associations with self-harm were found for area deprivation (most vs least deprived quintile: rate ratio 2·07, 95% CrI 1·88–2·26) and social fragmentation (1·39, 1·23–1·56); however, we identified clusters of deprived areas with a low incidence of self-harm Emergency Department attendance. Preliminary qualitative analysis suggests that individuals in the case-study population have substantial cumulative stressors and mental health difficulties despite a low incidence of Emergency Department presentation for self-harm (SARs for the four LSOAs ranged from 0·80 to 0·94 before adjustment for deprivation and 0·68–0·81 after adjustment). Frequent experiences of violence and an emphasis on appearing tough combine with discomfort with talking about self-harm and fear of negative treatment by mental health and other services, particularly within the Caribbean and African population who make up 44% of the local population. These factors might make people more likely to respond to distress in ways not defined by researchers as self-harm (even if they are also damaging); equally, these pressures might make individuals who self-harm less likely to identify themselves to services. InterpretationRelying on service-use data for self-harm risks underestimating the extent of distress and subsequent harmful behaviours in some deprived urban populations already disadvantaged by high levels of violence and minority ethnic and migrant status. FundingNational Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.

Adverse life experiences and triggers for self-harm in young people aged 12–26 years in inner south London: a retrospective observational study

BackgroundThe prevalence of self-harm among children and young people in the UK is increasing, with few accessing formal support. Drivers of self-harm remain poorly understood, and evidence-based opportunities to intervene are scarce. We aimed to identify factors associated with self-harm in young people to inform local action. MethodsIn this retrospective, observational study, we identified 196 residents of the south London Borough of Lambeth, aged 12–26 years, presenting to two emergency departments for injuries related to self-harm in the 2015–16 financial year (April 1, 2015, to March 31, 2016). A pragmatic sample size of 100 was selected via computer randomisation for further analysis. Electronic mental health records were retrospectively analysed using a data collection protocol that was validated with researchers before data collection. Demographics and psychiatric history were obtained from pre-coded databases linking electronic mental health records and Hospital Episode Statistics data on emergency department attendance for self-harm. Presentation details and social risk factors were derived from assessment of case notes pertaining to the 2 weeks after first presentation; we noted any risk factors that had been positively recorded. Data relating to the individual's first presentation within the study period were used; subsequent attendances were not included in the sample. We analysed the data using descriptive statistics. Ethical approval for data access and linkage was granted by the Oxford Research Ethics Committee C (18/SC/0372), Clinical Records Interactive Search system Oversight Committee (project numbers 14–026 and 19–004), and the National Health Service (NHS) Health Research Authority Confidentiality Advisory Group (reference ECC 3–04(f)/2011). This study received approval as an audit project through Southwark Operational Directorate in the South London and Maudsley Foundation NHS Trust. Findings48 (48%) of the 100 selected participants had documented experience of psychological trauma, including exposure to violence. 18 (18%) lived in unstable accommodation, and 20 (20%) were known to social services. Difficulties at home (16 [16%]) and stress relating to housing, school, or financial pressures (13 [13%]) were common documented triggers for the self-harm episode at presentation. Peer influence was rarely noted, although 27 (27%) had recorded experience of bullying. InterpretationAdverse life experiences were prevalent and align with wider population prevalence estimates. Common triggers for self-harm relate to home and school environment and wider socioeconomic stressors, supporting the link between wider determinants of health and self-harm, and contributing to the case for public health action. Risk factors and triggers identified provide potential areas for additional targeted support to prevent development or escalation of self-harm behaviours. FundingUK National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, and King's College London.

ELECTRONIC RECORDS FOR DEMENTIA RESEARCH: DO BEHAVIOURAL DISTURBANCES, ANTIHYPERTENSIVES AND ANTIDEPRESSANTS INFLUENCE DECLINE TRAJECTORIES?

Background: Understanding the drivers of heterogeneous progression in dementia has huge implications for recruitment to clinical trails and care planning. One way to investigate the factors contributing to this heterogeneity is to stratify subjects by similar patterns of change and compare the characteristics of the sub-populations identified. We applied this approach to routinely collected electronic health records from the South London and Maudesly NHS Foundation Trust (SLAM). Methods: This retrospective study includes 3441 patients with at least three MMSE scores recorded and available for research through the SLAM clinical record interactive search system (CRIS). A Latent Class Growth Analysis was used to identify key trajectories of decline. Information on age, gender, ethnicity, qualification levels, cohabiting status, retirement status, Health of Nation Outcome Scales (HoNOS) and medications were obtained to describe characteristics of the identified trajectory sub-populations in a multivariable multinomial regression analysis. Results: We identified six trajectories of cognitive decline. Four of these trajectories differed in initial MMSE score, and showed increased rate of decline with lower initial MMSE. Two trajectories had very similar initial MMSE scores but differed in the rate of decline. Exploring these trajectories further, the severity of cognitive problems at baseline and prescription of Donepezil and Amlodipine was significantly higher in the slower declining trajectory. In the faster declining trajectory, severity of behavioral problems and prescription of sertraline was significantly higher. Conclusions: Most of the trajectories differed by initial MMSE and thus likely represented patients at different disease stages, however differences in behavioral problems, antihypertensive, antidepressant and dementia medication prescription may be indicators of future rate of cognitive decline. Further information is required on depression and hypertensive comorbidities to explore these findings in greater detail.

Using routine clinical and administrative data to produce a dataset of attendances at Emergency Departments following self-harm.

BackgroundSelf-harm is a significant public health concern in the UK. This is reflected in the recent addition to the English Public Health Outcomes Framework of rates of attendance at Emergency Departments (EDs) following self-harm. However there is currently no source of data to measure this outcome. Routinely available data for inpatient admissions following self-harm miss the majority of cases presenting to services.We aimed to investigate (i) if a dataset of ED presentations could be produced using a combination of routinely collected clinical and administrative data and (ii) to validate this dataset against another one produced using methods similar to those used in previous studies.MethodsUsing the Clinical Record Interactive Search system, the electronic health records (EHRs) used in four EDs were linked to Hospital Episode Statistics to create a dataset of attendances following self-harm. This dataset was compared with an audit dataset of ED attendances created by manual searching of ED records. The proportion of total cases detected by each dataset was compared.ResultsThere were 1932 attendances detected by the EHR dataset and 1906 by the audit. The EHR and audit datasets detected 77 % and 76 % of all attendances respectively and both detected 82 % of individual patients. There were no differences in terms of age, sex, ethnicity or marital status between those detected and those missed using the EHR method. Both datasets revealed more than double the number of self-harm incidents than could be identified from inpatient admission records.ConclusionsIt was possible to use routinely collected EHR data to create a dataset of attendances at EDs following self-harm. The dataset detected the same proportion of attendances and individuals as the audit dataset, proved more comprehensive than the use of inpatient admission records, and did not show a systematic bias in those cases it missed.