Dataset Information

An analysis of computer-related patient safety incidents to inform the development of a classification.

ABSTRACT:

Objective

To analyze patient safety incidents associated with computer use to develop the basis for a classification of problems reported by health professionals.

Design

Incidents submitted to a voluntary incident reporting database across one Australian state were retrieved and a subset (25%) was analyzed to identify 'natural categories' for classification. Two coders independently classified the remaining incidents into one or more categories. Free text descriptions were analyzed to identify contributing factors. Where available medical specialty, time of day and consequences were examined.

Measurements

Descriptive statistics; inter-rater reliability.

Results

A search of 42,616 incidents from 2003 to 2005 yielded 123 computer related incidents. After removing duplicate and unrelated incidents, 99 incidents describing 117 problems remained. A classification with 32 types of computer use problems was developed. Problems were grouped into information input (31%), transfer (20%), output (20%) and general technical (24%). Overall, 55% of problems were machine related and 45% were attributed to human-computer interaction. Delays in initiating and completing clinical tasks were a major consequence of machine related problems (70%) whereas rework was a major consequence of human-computer interaction problems (78%). While 38% (n=26) of the incidents were reported to have a noticeable consequence but no harm, 34% (n=23) had no noticeable consequence.

Conclusion

Only 0.2% of all incidents reported were computer related. Further work is required to expand our classification using incident reports and other sources of information about healthcare IT problems. Evidence based user interface design must focus on the safe entry and retrieval of clinical information and support users in detecting and correcting errors and malfunctions.

SUBMITTER: Magrabi F

PROVIDER: S-EPMC3000751 | biostudies-literature | 2010 Nov-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An analysis of computer-related patient safety incidents to inform the development of a classification.

Magrabi Farah F Ong Mei-Sing MS Runciman William W Coiera Enrico E

Journal of the American Medical Informatics Association : JAMIA 20101101 6

<h4>Objective</h4>To analyze patient safety incidents associated with computer use to develop the basis for a classification of problems reported by health professionals.<h4>Design</h4>Incidents submitted to a voluntary incident reporting database across one Australian state were retrieved and a subset (25%) was analyzed to identify 'natural categories' for classification. Two coders independently classified the remaining incidents into one or more categories. Free text descriptions were analyze ...[more]

PMID: 20962128

Similar Datasets

Project description:BackgroundIt is assumed that the implementation of health information technology introduces new vulnerabilities within a complex sociotechnical health care system, but no international consensus exists on a standardized format for enhancing the collection, analysis, and interpretation of technology-induced errors.ObjectiveThis study aims to develop a classification for patient safety incident reporting associated with the use of mature electronic health records (EHRs). It also aims to validate the classification by using a data set of incidents during a 6-month period immediately after the implementation of a new EHR system.MethodsThe starting point of the classification development was the Finnish Technology-Induced Error Risk Assessment Scale tool, based on research on commonly recognized error types. A multiprofessional research team used iterative tests on consensus building to develop a classification system. The final classification, with preliminary descriptions of classes, was validated by applying it to analyze EHR-related error incidents (n=428) during the implementation phase of a new EHR system and also to evaluate this classification's characteristics and applicability for reporting incidents. Interrater agreement was applied.ResultsThe number of EHR-related patient safety incidents during the implementation period (n=501) was five-fold when compared with the preimplementation period (n=82). The literature identified new error types that were added to the emerging classification. Error types were adapted iteratively after several test rounds to develop a classification for reporting patient safety incidents in the clinical use of a high-maturity EHR system. Of the 427 classified patient safety incidents, interface problems accounted for 96 (22.5%) incident reports, usability problems for 73 (17.1%), documentation problems for 60 (14.1%), and clinical workflow problems for 33 (7.7%). Altogether, 20.8% (89/427) of reports were related to medication section problems, and downtime problems were rare (n=8). During the classification work, 14.8% (74/501) of reports of the original sample were rejected because of insufficient information, even though the reports were deemed to be related to EHRs. The interrater agreement during the blinded review was 97.7%.ConclusionsThis study presents a new classification for EHR-related patient safety incidents applicable to mature EHRs. The number of EHR-related patient safety incidents during the implementation period may reflect patient safety challenges during the implementation of a new type of high-maturity EHR system. The results indicate that the types of errors previously identified in the literature change with the EHR development cycle.

Project description:BackgroundCurrently, there is no holistic theoretical approach available for guiding classification development. On the basis of our recent classification development research in the area of patient safety in health information technology, this focus area would benefit from a more systematic approach. Although some valuable theoretical and methodological approaches have been presented, classification development literature typically is limited to methodological development in a specific domain or is practically oriented.ObjectiveThe main purposes of this study are to fill the methodological gap in classification development research by exploring possible elements of systematic development based on previous literature and to promote sustainable and well-grounded classification outcomes by identifying a set of recommended elements. Specifically, the aim is to answer the following question: what are the main elements for systematic classification development based on research evidence and our use case?MethodsThis study applied a qualitative research approach. On the basis of previous literature, preliminary elements for classification development were specified, as follows: defining a concept model, documenting the development process, incorporating multidisciplinary expertise, validating results, and maintaining the classification. The elements were compiled as guiding principles for the research process and tested in the case of patient safety incidents (n=501).ResultsThe results illustrate classification development based on the chosen elements, with 4 examples of technology-induced errors. Examples from the use case regard usability, system downtime, clinical workflow, and medication section problems. The study results confirm and thus suggest that a more comprehensive and theory-based systematic approach promotes well-grounded classification work by enhancing transparency and possibilities for assessing the development process.ConclusionsWe recommend further testing the preliminary main elements presented in this study. The research presented herein could serve as a basis for future work. Our recently developed classification and the use case presented here serve as examples. Data retrieved from, for example, other type of electronic health records and use contexts could refine and validate the suggested methodological approach.

Project description:ObjectivesPrisoners use healthcare services three times more frequently than the general population with poorer health outcomes. Their distinct healthcare needs often pose challenges to safe healthcare provision. This study aimed to characterise patient safety incidents reported in prisons to guide practice improvement and identify health policy priorities.Design: We carried out an exploratory multi-method analysis of anonymised safety incidents from prisons.SettingSafety incidents had been reported to the National Reporting and Learning System by prisons in England between April 2018 and March 2019.ParticipantsReports were reviewed to identify any unintended or unexpected incident(s) which could have, or did, lead to harm for prisoners receiving healthcare.Main outcome measuresFree-text descriptions were examined to identify the type and nature of safety incidents, their outcomes and harm severity. Analysis was contextualised with subject experts through structured workshops to explain relationships between the most common incidents and contributory factors.ResultsOf 4112 reports, the most frequently observed incidents were medication-related (n = 1167, 33%), specifically whilst administering medications (n = 626, 54%). Next, were access-related (n = 559,15%), inclusive of delays in patients accessing healthcare professionals (n = 236, 42%) and managing medical appointments (n = 171, 31%). The workshops contextualised incidents involving contributing factors (n = 1529, 28%) into three key themes, namely healthcare access, continuity of care and the balance between prison and healthcare priorities.ConclusionsThis study highlights the importance of improving medication safety and access to healthcare services for prisoners. We recommend staffing level reviews to ensure healthcare appointments are attended, and to review procedures for handling missed appointments, communication during patient transfers and medication prescribing.

Project description:Objectives(1) To describe the epidemiology of patient safety (PS) incidents registered in an electronic notification system in primary care (PC) health centres; (2) to define a risk map; and (3) to identify the critical areas where intervention is needed.DesignDescriptive analytical study of incidents reported from 1 January to 31 December 2018, on the TPSC Cloud™ platform (The Patient Safety Company) accessible from the corporate website (Intranet) of the regional public health service.Setting24 Catalan Institute of Health PC health centres of the Tarragona region (Spain).ParticipantsProfessionals from the PC health centres and a Patient Safety Functional Unit.MeasurementsData obtained from records voluntarily submitted to an electronic, standardised and anonymised form. Data recorded: healthcare unit, notifier, type of incident, risk matrix, causal and contributing factors, preventability, level of resolution and improvement actions.ResultsA total of 1544 reports were reviewed and 1129 PS incidents were analysed: 25.0% of incidents did not reach the patient; 66.5% reached the patient without causing harm, and 8.5% caused adverse events. Nurses provided half of the reports (48.5%), while doctors reported more adverse events (70.8%; p < 0.01). Of the 96 adverse events, 46.9% only required observation, 34.4% caused temporary damage that required treatment, 13.5% required (or prolonged) hospitalization, and 5.2% caused severe permanent damage and/or a situation close to death. Notably, 99.2% were considered preventable. The main critical areas were: communication (27.8%), clinical-administrative management (25.1%), care delivery (23.5%) and medicines (18.4%); few incidents were related to diagnosis (3.6%).ConclusionsPS incident notification applications are adequate for reporting incidents and adverse events associated with healthcare. Approximately 75% and 10% of incidents reach the patient and cause some damage, respectively, and most cases are considered preventable. Adequate and strengthened risk management of critical areas is required to improve PS.

Dataset Information

An analysis of computer-related patient safety incidents to inform the development of a classification.

Objective

Design

Measurements

Results

Conclusion

Publications

An analysis of computer-related patient safety incidents to inform the development of a classification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets