Development of a Taxonomy for Medication-Related Patient Safety Events Related to Health Information Technology in Pediatrics

Taxonomy development methods regarding patient safety in health sciences - A systematic review

BACKGROUND

Taxonomies are needed for automated analysis of clinical data in healthcare. Few reviews of the taxonomy development methods used in health sciences are found. This systematic review aimed to describe the scope of the available taxonomies relative to patient safety, the methods used for taxonomy development, and the strengths and limitations of the methods. The purpose of this systematic review is to guide future taxonomy development projects.

METHODS

The CINAHL, PubMed, Scopus, and Web of Science databases were searched for studies from January 2012 to April 25, 2023. Two authors selected the studies using inclusion and exclusion criteria and critical appraisal checklists. The data were analysed inductively, and the results were reported narratively.

RESULTS

The studies (n = 13) across healthcare concerned mainly taxonomies of adverse events and medication safety but little for specialised fields and information technology. Critical appraisal indicated inadequate reporting of the used taxonomy development methods. Ten phases of taxonomy development were identified: (1) defining purpose and (2) the theory base for development, (3) relevant data sources' identification, (4) main terms' identification and definitions, (5) items' coding and pooling, (6) reliability and validity evaluation of coding and/or codes, (7) development of a hierarchical structure, (8) testing the structure, (9) piloting the taxonomy and (10) reporting application and validation of the final taxonomy. Seventeen statistical tests and seven software systems were utilised, but automated data extraction methods were used rarely. Multimethod and multi-stakeholder approach, code- and hierarchy testing and piloting were strengths and time consumption and small samples in testing limitations.

CONCLUSION

New taxonomies are needed on diverse specialities and information technology related to patient safety. Structured method is needed for taxonomy development, reporting and appraisal to strengthen taxonomies' quality. A new guide was proposed for taxonomy development, for which testing is required. Prospero registration number CRD42023411022.

Methods for studying medication safety following electronic health record implementation in acute care: a scoping review

OBJECTIVES

The objective of this scoping review is to map methods used to study medication safety following electronic health record (EHR) implementation. Patterns and methodological gaps can provide insight for future research design.

MATERIALS AND METHODS

We used the Joanna Briggs Institute scoping review methodology and a custom data extraction table to summarize the following data: (1) study demographics (year, country, setting); (2) study design, study period, data sources, and measures; (3) analysis strategy; (4) identified limitations or recommendations; (5) quality appraisal; and (6) if a Safety-I or Safety-II perspective was employed.

RESULTS

We screened 5879 articles. One hundred and fifteen articles met our inclusion criteria and were assessed for eligibility by full-text review. Twenty-seven articles were eligible for extraction.

DISCUSSION AND CONCLUSION

We found little consistency in how medication safety following EHR implementation was studied. Three study designs, 7 study settings, and 10 data sources were used across 27 articles. None of the articles shared the same combination of design, data sources, study periods, and research settings. Outcome measures were neither defined nor measured consistently. It may be difficult for researchers to aggregate and synthesize medication safety findings following EHR implementation research. All studies but one used a Safety-I perspective to study medication safety. We offer a conceptual model to support a more consistent approach to studying medication safety following EHR implementation.

Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage

BACKGROUND

Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of patient safety classifications and ontologies.

METHODS

We searched different databases and ontology/classification repositories to identify these classifications and ontologies. We included patient safety-related taxonomies, ontologies, classifications, and terminologies. We identified and extracted different concepts covered by these systems and mapped these concepts to international classification for patient safety (ICPS) and finally compared the content of these systems.

RESULTS

Finally, 89 papers (77 classifications or ontologies) were analyzed. Thirteen classifications have been developed to cover all medical domains. Among specific domain systems, most systems cover medication (16), surgery (8), medical devices (3), general practice (3), and primary care (3). The most common patient safety-related concepts covered in these systems include incident types (41), contributing factors/hazards (31), patient outcomes (29), degree of harm (25), and action (18). However, stage/phase (6), incident characteristics (5), detection (5), people involved (5), organizational outcomes (4), error type (4), and care setting (3) are some of the less covered concepts in these classifications/ontologies.

CONCLUSION

Among general systems, ICPS, World Health Organization's Adverse Reaction Terminology (WHO-ART), and Ontology of Adverse Events (OAE) cover most patient safety concepts and can be used as a gold standard for all medical domains. As a result, reporting systems could make use of these broad classifications, but the majority of their covered concepts are related to patient outcomes, with the exception of ICPS, which covers other patient safety concepts. However, the ICPS does not cover specialized domain concepts. For specific medical domains, MedDRA, NCC MERP, OPAE, ADRO, PPST, OCCME, TRTE, TSAHI, and PSIC-PC provide the broadest coverage of concepts. Many of the patient safety classifications and ontologies are not formally registered or available as formal classification/ontology in ontology repositories such as BioPortal. This study may be used as a guide for choosing appropriate classifications for various applications or expanding less developed patient safety classifications/ontologies. Furthermore, the same concepts are not represented by the same terms; therefore, the current study could be used to guide a harmonization process for existing or future patient safety classifications/ontologies.

Characterization of Safety Events Involving Technology in Primary and Community Care

BACKGROUND

The adoption of technology in health care settings is often touted as an opportunity to improve patient safety. While some adverse events can be reduced by health information technologies, technology has also been implicated in or attributed to safety events. To date, most studies on this topic have focused on acute care settings.

OBJECTIVES

To describe voluntarily reported safety events that involved health information technology in community and primary care settings in a large Canadian health care organization.

METHODS

Two years of safety events involving health information technology (2016-2018) were extracted from an online voluntary safety event reporting system. Events from primary and community care settings were categorized according to clinical setting, type of event, and level of harm. The Sittig and Singh sociotechnical system model was then used to identify the most prominent sociotechnical dimensions of each event.

RESULTS

Of 104 reported events, most (n = 85, 82%) indicated the event resulted in no harm. Public health had the highest number of reports (n = 45, 43%), whereas home health had the fewest (n = 7, 7%). Of the 182 sociotechnical concepts identified, many events (n = 61, 59%) mapped to more than one dimension. Personnel (n = 48, 46%), Workflow and Communication (n = 37, 36%), and Content (n = 30, 29%) were the most common. Personnel and Content together was the most common combination of dimensions.

CONCLUSION

Most reported events featured both technical and social dimensions, suggesting that the nature of these events is multifaceted. Leveraging existing safety event reporting systems to screen for safety events involving health information technology, and applying a sociotechnical analytic framework can aid health organizations in identifying, responding to, and learning from reported events.

Classification of Health Information Technology Safety Events in a Pediatric Tertiary Care Hospital

OBJECTIVE

State agencies have developed reporting systems of safety events that include events related to health information technology (HIT). These data come from hospital reporting systems where staff submit safety reports and nurses, in the role of safety managers, review, and code events. Safety managers may have varying degrees of experience with identifying events related to HIT. Our objective was to review events potentially involving HIT and compare those with what was reported to the state.

METHODS

We performed a structured review of 1 year of safety events from an academic pediatric healthcare system. We reviewed the free-text description of each event and applied a classification scheme derived from the AHRQ Health IT Hazard Manager and compared the results with events reported to the state as involving HIT.

RESULTS

Of 33,218 safety events for a 1-year period, 1247 included key words related to HIT and/or were indicated by safety managers as involving HIT. Of the 1247 events, the structured review identified 769 as involving HIT. In comparison, safety managers only identified 194 of the 769 events (25%) as involving HIT. Most events, 353 (46%), not identified by safety managers were documentation issues. Of the 1247 events, the structured review identified 478 as not involving HIT while safety managers identified and reported 81 of these 478 events (17%) as involving HIT.

CONCLUSIONS

The current process of reporting safety events lacks standardization in identifying health technology contributions to safety events, which may minimize the effectiveness of safety initiatives.

Using Community Detection Techniques to Identify Themes in COVID-19-Related Patient Safety Event Reports

OBJECTIVES

The COVID-19 pandemic has transformed how healthcare is delivered to patients. As the pandemic progresses and healthcare systems continue to adapt, it is important to understand how these changes in care have changed patient care. This study aims to use community detection techniques to identify and facilitate analysis of themes in patient safety event (PSE) reports to better understand COVID-19 pandemic's impact on patient safety. With this approach, we also seek to understand how community detection techniques can be used to better identify themes and extract information from PSE reports.

METHODS

We used community detection techniques to group 2082 PSE reports from January 1, 2020, to January 31, 2021, that mentioned COVID-19 into 65 communities. We then grouped these communities into 8 clinically relevant themes for analysis.

RESULTS

We found the COVID-19 pandemic is associated with the following clinically relevant themes: (1) errors due to new and unknown COVID-19 protocols/workflows; (2) COVID-19 patients developing pressure ulcers; (3) unsuccessful/incomplete COVID-19 testing; (4) inadequate isolation of COVID-19 patients; (5) inappropriate/inadequate care for COVID-19 patients; (6) COVID-19 patient falls; (7) delays or errors communicating COVID-19 test results; and (8) COVID-19 patients developing venous thromboembolism.

CONCLUSIONS

Our study begins the long process of understanding new challenges created by the pandemic and highlights how machine learning methods can be used to understand these and similar challenges. Using community detection techniques to analyze PSE reports and identify themes within them can help give healthcare systems the necessary information to improve patient safety and the quality of care they deliver.

Classification of Electronic Health Record-Related Patient Safety Incidents: Development and Validation Study

Background

It 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.

Objective

This 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.

Methods

The 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.

Results

The 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%.

Conclusions

This 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.