Dataset Information

Identifying no-harm incidents in home healthcare: a cohort study using trigger tool methodology.

ABSTRACT: BACKGROUND:Patient safety in home healthcare is largely unexplored. No-harm incidents may give valuable information about risk areas and system failures as a source for proactive patient safety work. We hypothesized that it would be feasible to retrospectively identify no-harm incidents and thus aimed to explore the cumulative incidence, preventability, types, and potential contributing causes of no-harm incidents that affected adult patients admitted to home healthcare. METHODS:A structured retrospective record review using a trigger tool designed for home healthcare. A random sample of 600 home healthcare records from ten different organizations across Sweden was reviewed. RESULTS:In the study, 40,735?days were reviewed. In all, 313 no-harm incidents affected 177 (29.5%) patients; of these, 198 (63.2%) no-harm incidents, in 127 (21.2%) patients, were considered preventable. The most common no-harm incident types were "fall without harm," "deficiencies in medication management," and "moderate pain." The type "deficiencies in medication management" was deemed to have a preventability rate twice as high as those of "fall without harm" and "moderate pain." The most common potential contributing cause was "deficiencies in nursing care and treatment, i.e., delayed, erroneous, omitted or incomplete treatment or care." CONCLUSION:This study suggests that it is feasible to identify no-harm incidents and potential contributing causes such as omission of care using record review with a trigger tool adapted to the context. No-harm incidents and potential contributing causes are valuable sources of knowledge for improving patient safety, as they highlight system failures and indicate risks before an adverse event reach the patient.

SUBMITTER: Lindblad M

PROVIDER: S-EPMC7137226 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Identifying no-harm incidents in home healthcare: a cohort study using trigger tool methodology.

Lindblad Marléne M Unbeck Maria M Nilsson Lena L Schildmeijer Kristina K Ekstedt Mirjam M

BMC health services research 20200406 1

<h4>Background</h4>Patient safety in home healthcare is largely unexplored. No-harm incidents may give valuable information about risk areas and system failures as a source for proactive patient safety work. We hypothesized that it would be feasible to retrospectively identify no-harm incidents and thus aimed to explore the cumulative incidence, preventability, types, and potential contributing causes of no-harm incidents that affected adult patients admitted to home healthcare.<h4>Methods</h4>A ...[more]

PMID: 32252755

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Project description:PurposeTo develop and validate a tool aiming to support ophthalmologists in identifying, during routine ophthalmologic visits, patients at higher risk of falling in the following year.MethodsA group of 141 subjects (age: 73.2 ± 11.4 years), recruited at our Eye Clinic, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. Moreover, visual disability was assessed by the Activity of Daily Vision Scale (ADVS). The subjects were followed up for 12 months in order to record prospective falls. A subject who reported at least one fall within one year from the baseline assessment was considered as faller, otherwise as non-faller. Different tree-based algorithms (i.e., C4.5, AdaBoost and Random Forests) were used to develop automatic classifiers and their performances were evaluated by the cross-validation approach.ResultsOver the follow-up, 25 falls were referred by 13 patients. The logistic regression analysis showed the following variables as significant predictors of prospective falls: pseudophakia and use of prescribed eyeglasses as protective factors, recent worsening of visual acuity as risk factor. Random Forest ranked best corrected visual acuity, number of sleeping hours and job type as the most important features. Finally, AdaBoost enabled the identification of subjects at higher risk of falling in the following 12 months with a sensitivity rate of 69.2% and a specificity rate of 76.6%.ConclusionsThe current study proposes a novel method, based on classification trees applied to self-reported factors and health information assessed by a standardized questionnaire during ophthalmological visits, to identify ophthalmic patients at higher risk of falling in the following 12 months. The findings of the current study pave the way to the validation of the proposed novel tool for fall risk screening on a larger cohort of patients with visual impairment referred to eye clinics.

Project description:BackgroundSelf-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.MethodsWe 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.ResultsMention-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.ConclusionsIt 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.

Dataset Information

Identifying no-harm incidents in home healthcare: a cohort study using trigger tool methodology.

Publications

Identifying no-harm incidents in home healthcare: a cohort study using trigger tool methodology.

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