scholarly journals Nature of Blame in Patient Safety Incident Reports: Mixed Methods Analysis of a National Database

2017 ◽  
Vol 15 (5) ◽  
pp. 455-461 ◽  
Author(s):  
Jennifer Cooper ◽  
Adrian Edwards ◽  
Huw Williams ◽  
Aziz Sheikh ◽  
Gareth Parry ◽  
...  
Keyword(s):  
Patient Safety ◽  
Mixed Methods ◽  
National Database ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
Incident Reports

scholarly journals Sources of unsafe primary care for older adults: a mixed-methods analysis of patient safety incident reports

2017 ◽  
Vol 46 (5) ◽  
pp. 833-839 ◽  
Author(s):  
Alison Cooper ◽  
Adrian Edwards ◽  
Huw Williams ◽  
Huw P. Evans ◽  
Anthony Avery ◽  
...  
Keyword(s):  
Older Adults ◽  
Primary Care ◽  
Patient Safety ◽  
Mixed Methods ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
Incident Reports

scholarly journals Learning from patient safety incidents involving acutely sick adults in hospital assessment units in England and Wales: a mixed methods analysis for quality improvement

2021 ◽  
pp. 014107682110325
Author(s):  
Alexandra Urquhart ◽  
Sarah Yardley ◽  
Elin Thomas ◽  
Liam Donaldson ◽  
Andrew Carson-Stevens
Keyword(s):  
Patient Safety ◽  
Quality Improvement ◽  
Mixed Methods ◽  
Diagnostic Errors ◽  
Learning System ◽  
England And Wales ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
Incident Reports ◽  
National Analysis

Objective Six per cent of hospital patients experience a patient safety incident, of which 12% result in severe/fatal outcomes. Acutely sick patients are at heightened risk. Our aim was to identify the most frequently reported incidents in acute medical units and their characteristics. Design Retrospective mixed methods methodology: (1) an a priori coding process, applying a multi-axial coding framework to incident reports; and, (2) a thematic interpretative analysis of reports. Setting Patient safety incident reports (10 years, 2005–2015) collected from the National Reporting and Learning System, which receives reports from hospitals and other care settings across England and Wales. Participants Reports describing severe harm/death in acute medical unit were identified. Main outcome measures Incident type, contributory factors, outcomes and level of harm were identified in the included reports. During thematic analysis, themes and metathemes were synthesised to inform priorities for quality improvement. Results A total of 377 reports of severe harm or death were confirmed. The most common incident types were diagnostic errors ( n = 79), medication-related errors ( n = 61), and failures monitoring patients ( n = 57). Incidents commonly stemmed from lack of active decision-making during patient admissions and communication failures between teams. Patients were at heightened risk of unsafe care during handovers and transfers of care. Metathemes included the necessity of patient self-advocacy and a lack of care coordination. Conclusion This 10-year national analysis of incident reports provides recommendations to improve patient safety including: introduction of electronic prescribing and monitoring systems; forcing checklists to reduce diagnostic errors; and increased senior presence overnight and at weekends.

scholarly journals Health and social care-associated harm amongst vulnerable children in primary care: mixed methods analysis of national safety reports

2020 ◽  
Vol 105 (8) ◽  
pp. 731-737
Author(s):  
Adhnan Omar ◽  
Philippa Rees ◽  
Alison Cooper ◽  
Huw Evans ◽  
Huw Williams ◽  
...  
Keyword(s):  
Primary Care ◽  
Patient Safety ◽  
Mixed Methods ◽  
Social Care ◽  
National Database ◽  
Free Text ◽  
Vulnerable Children ◽  
Health And Social Care ◽  
Safety Incident ◽  
Incident Reports

PurposePatient safety failures are recognised as a global threat to public health, yet remain a leading cause of death internationally. Vulnerable children are inversely more in need of high-quality primary health and social-care but little is known about the quality of care received. Using national patient safety data, this study aimed to characterise primary care-related safety incidents among vulnerable children.MethodsThis was a cross-sectional mixed methods study of a national database of patient safety incident reports occurring in primary care settings. Free-text incident reports were coded to describe incident types, contributory factors, harm severity and incident outcomes. Subsequent thematic analyses of a purposive sample of reports was undertaken to understand factors underpinning problem areas.ResultsOf 1183 reports identified, 572 (48%) described harm to vulnerable children. Sociodemographic analysis showed that included children had child protection-related (517, 44%); social (353, 30%); psychological (189, 16%) or physical (124, 11%) vulnerabilities. Priority safety issues included: poor recognition of needs and subsequent provision of adequate care; insufficient provider access to accurate information about vulnerable children, and delayed referrals between providers.ConclusionThis is the first national study using incident report data to explore unsafe care amongst vulnerable children. Several system failures affecting vulnerable children are highlighted, many of which pose internationally recognised challenges to providers aiming to deliver safe care to this at-risk cohort. We encourage healthcare organisations globally to build on our findings and explore the safety and reliability of their healthcare systems, in order to sustainably mitigate harm to vulnerable children.

scholarly journals Can Patient Safety Incident Reports Be Used to Compare Hospital Safety? Results from a Quantitative Analysis of the English National Reporting and Learning System Data

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0144107 ◽  
Author(s):  
Ann-Marie Howell ◽  
Elaine M. Burns ◽  
George Bouras ◽  
Liam J. Donaldson ◽  
Thanos Athanasiou ◽  
...  
Keyword(s):  
Patient Safety ◽  
Quantitative Analysis ◽  
Learning System ◽  
Hospital Safety ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
System Data ◽  
Incident Reports

scholarly journals Patient safety incidents in advance care planning for serious illness: a mixed-methods analysis

2019 ◽  
pp. bmjspcare-2019-001824
Author(s):  
Toby Dinnen ◽  
Huw Williams ◽  
Sarah Yardley ◽  
Simon Noble ◽  
Adrian Edwards ◽  
...  
Keyword(s):  
Patient Safety ◽  
Mixed Methods ◽  
Advance Care Planning ◽  
Learning System ◽  
Care Planning ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
Advance Care ◽  
National Analysis ◽  
Patient Safety Incidents

ObjectivesAdvance care planning (ACP) is essential for patient-centred care in the last phase of life. There is little evidence available on the safety of ACP. This study characterises and explores patient safety incidents arising from ACP processes in the last phase of life.MethodsThe National Reporting and Learning System collates patient safety incident reports across England and Wales. We performed a keyword search and manual review to identify relevant reports, April 2005–December 2015. Mixed-methods, combining structured data coding, exploratory and thematic analyses were undertaken to describe incidents, underlying causes and outcomes, and identify areas for improvement.ResultsWe identified 70 reports in which ACP caused a patient safety incident across three error categories: (1) ACP not completed despite being appropriate (23%, n=16). (2) ACP completed but not accessible or miscommunicated between professionals (40%, n=28). (3) ACP completed and accessible but not followed (37%, n=26). Themes included staff lacking the knowledge, confidence, competence or belief in trustworthiness of prior documentation to create or enact ACP. Adverse outcomes included cardiopulmonary resuscitation attempts contrary to ACP, other inappropriate treatment and/or transfer or admission.ConclusionThis national analysis identifies priority concerns and questions whether it is possible to develop strong system interventions to ensure safety and quality in ACP without significant improvement in human-dependent issues in social programmes such as ACP. Human-dependent issues (ie, varying patient, carer and professional understanding, and confidence in enacting prior ACP when required) should be explored in local contexts alongside systems development for ACP documentation.

scholarly journals An Analysis of Patient Safety Incident Reports Associated with Electronic Health Record Interoperability

2017 ◽  
Vol 08 (02) ◽  
pp. 593-602 ◽  
Author(s):  
Katharine Adams ◽  
Jessica Howe ◽  
Allan Fong ◽  
Joseph Puthumana ◽  
Kathryn Kellogg ◽  
...  
Keyword(s):  
Patient Safety ◽  
Electronic Health Record ◽  
Health Record ◽  
Health It ◽  
Specific Patient ◽  
It Systems ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
Incident Reports ◽  
Electronic Health

SummaryBackground: With the widespread use of electronic health records (EHRs) for many clinical tasks, interoperability with other health information technology (health IT) is critical for the effective delivery of care. While it is generally recognized that poor interoperability negatively impacts patient care, little is known about the specific patient safety implications. Understanding the patient safety implications will help prioritize interoperability efforts around architectures and standards.Objectives: Our objectives were to (1) identify patient safety incident reports that reflect EHR interoperability challenges with other health IT, and (2) perform a detailed analysis of these reports to understand the health IT systems involved, the clinical care processes impacted, whether the incident occurred within or between provider organizations, and the reported severity of the patient safety events.Methods: From a database of 1.735 million patient safety event (PSE) reports spanning multiple provider organizations, 2625 reports that were indicated as being health IT related by the event reporter were reviewed to identify EHR interoperability related reports. Through a rigorous coding process 209 EHR interoperability related events were identified and coded.Results: The majority of EHR interoperability PSE reports involved interfacing with pharmacy systems (i.e. medication related), followed by laboratory, and radiology. Most of the interoperability challenges in these clinical areas were associated with the EHR receiving information from other health IT systems as opposed to the EHR sending information to other systems. The majority of EHR interoperability challenges were within a provider organization and while many of the safety events reached the patient, only a few resulted in patient harm.Conclusions: Interoperability efforts should prioritize systems in pharmacy, laboratory, and radiology. Providers should recognize the need to improve EHRs interfacing with other health IT systems within their own organization.Citation: Adams KT, Howe JL, Fong A, Puthumana JS, Kellogg KM, Gaunt M, Ratwani RM. An analysis of patient safety incident reports associated with electronic health record interoperability. Appl Clin Inform 2017; 8: 593–602 https://doi.org/10.4338/ACI-2017-01-RA-0014

scholarly journals Using convolutional neural networks to identify patient safety incident reports by type and severity

2019 ◽  
Vol 26 (12) ◽  
pp. 1600-1608 ◽  
Author(s):  
Ying Wang ◽  
Enrico Coiera ◽  
Farah Magrabi
Keyword(s):  
Patient Safety ◽  
Test Data ◽  
Reporting System ◽  
Word Embedding ◽  
Support Vector ◽  
Data Sets ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
Incident Reports ◽  
Severity Levels

Abstract Objective To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports. Materials and Methods A CNN with word embedding was applied to identify 10 incident types and 4 severity levels. Model training and validation used data sets (n_type = 2860, n_severity = 1160) collected from a statewide incident reporting system. Generalizability was evaluated using an independent hospital-level reporting system. CNN architectures were examined by varying layer size and hyperparameters. Performance was evaluated by F score, precision, recall, and compared to binary support vector machine (SVM) ensembles on 3 testing data sets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent = 6000/5950). Results A CNN with 6 layers was the most effective architecture, outperforming SVMs with better generalizability to identify incidents by type and severity. The CNN achieved high F scores (> 85%) across all test data sets when identifying common incident types including falls, medications, pressure injury, and aggression. When identifying common severity levels (medium/low), CNN outperformed SVMs, improving F scores by 11.9%–45.1% across all 3 test data sets. Discussion Automated identification of incident reports using machine learning is challenging because of a lack of large labelled training data sets and the unbalanced distribution of incident classes. The standard classification strategy is to build multiple binary classifiers and pool their predictions. CNNs can extract hierarchical features and assist in addressing class imbalance, which may explain their success in identifying incident report types. Conclusion A CNN with word embedding was effective in identifying incidents by type and severity, providing better generalizability than SVMs.

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