Detecting adverse events for patient safety research: a review of current methodologies

Interprofessional education interventions for healthcare professionals to improve patient safety: a scoping review

BACKGROUND

Patient safety incidents, such as adverse events and medical errors, are often caused by ineffective communication and collaboration. Interprofessional education is an effective method for promoting collaborative competencies and has attracted great attention in the context of patient safety. However, the effectiveness of interprofessional education interventions on patient safety remains unclear. This scoping review aimed to synthesize existing studies that focused on improving patient safety through interprofessional education interventions for healthcare professionals.

METHODS

Six databases, including Medline (via PubMed), Embase, Cochrane Library, CINAHL (via EBSCO), Scopus and Web of Science, were last searched on 20 December 2023. The search records were independently screened by two researchers. The Joanna Briggs Institute Critical Appraisal Tool for Quasi-Experimental Studies was used for quality appraisal. The data were extracted by two researchers and cross-checked. Finally, a narrative synthesis was performed. The protocol for this scoping review was not registered.

RESULTS

Thirteen quasi-experimental studies with moderate methodological quality were included. The results revealed that the characteristics of current interprofessional education interventions were diverse, with a strong interest in simulation-based learning strategies and face-to-face delivery methods. Several studies did not assess the reduction in patient safety incidents involving adverse events or medical errors, relying instead on the improvements in healthcare professionals' knowledge, attitude or practice related to patient safety issues. Less than half of the studies examined team performance, based primarily on the self-evaluation of healthcare professionals and observer-based evaluation. There is a gap in applying newer tools such as peer evaluation and team-based objective structured clinical evaluation.

CONCLUSION

Additional evidence on interprofessional education interventions for improving patient safety is needed by further research, especially randomized controlled trials. Facilitating simulation-based interprofessional education, collecting more objective outcomes of patient safety and selecting suitable tools to evaluate teamwork performance may be the focus of future studies.

Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning-Based Design Thinking Study

BACKGROUND

The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events (AEs), and various methods exist for investigating and measuring AEs. However, some methods have a limited scope, data extraction, and the need for data standardization. In Brazil, there are few studies on the application of trigger tools, and this study is the first to create automated triggers in ambulatory care.

OBJECTIVE

This study aims to develop a machine learning (ML)-based automated trigger for outpatient health care settings in Brazil.

METHODS

A mixed methods research will be conducted within a design thinking framework and the principles will be applied in creating the automated triggers, following the stages of (1) empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) ideation, where various solutions to the problem are generated; (3) prototyping, involving the construction of a minimal representation of the best solutions; (4) testing, where user feedback is obtained to refine the solution; and (5) implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, ML methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field.

RESULTS

This protocol describes a research study in its preliminary stages, prior to any data gathering and analysis. The study was approved by the members of the organizations within the institution in January 2024 and by the ethics board of the University of São Paulo and the institution where the study will take place. in May 2024. As of June 2024, stage 1 commenced with data gathering for qualitative research. A separate paper focused on explaining the method of ML will be considered after the outcomes of stages 1 and 2 in this study.

CONCLUSIONS

After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of AEs more promptly, providing valuable information. This technological innovation not only promotes advances in clinical practice but also contributes to the dissemination of techniques and knowledge related to patient safety. Additionally, health care professionals can adopt evidence-based preventive measures, reducing costs associated with AEs and hospital readmissions, enhancing productivity in outpatient care, and contributing to the safety, quality, and effectiveness of care provided. Additionally, in the future, if the outcome is successful, there is the potential to apply it in all units, as planned by the institutional organization.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/55466.

Accuracy assessment of patient safety incident (PSI) codes and present-on-admission (POA) indicators: a cross-sectional analysis using the Patient Safety Incidents Inquiry (PSII) in Korea

BACKGROUND

Among the various methods used, administrative data collected for claims and billing purposes, such as diagnosis codes and present-on-admission (POA) indicators, can easily be employed to assess patient safety status. However, it is crucial that administrative data be accurate to generate valid estimates of adverse event (AE) occurrence. Thus, we aimed to evaluate the accuracy of diagnosis codes and POA indicators in patients with confirmed AEs in the hospital admission setting.

METHODS

We analysed the diagnosis codes of 1,032 confirmed AE cases and 6,754 non-AE cases from the 2019 Patient Safety Incidents Inquiry, which was designed as a cross-sectional study, to determine their alignment with the Korean Patient Safety Incidents (PSIs) Code Classification System. The unit of analysis was the individual case rather than the patient, because two or more AEs may occur in one patient. We examined whether the primary and secondary diagnostic codes had PSIs codes matching the AE type and checked each PSI code for whether the POA indicator had an 'N' tag. We reviewed the presence of PSI codes in patients without identified AEs and calculated the correlation between the AE incidence rate and PSI code and POA indicator accuracy across 15 hospitals.

RESULTS

Ninety (8.7%) of the AE cases had PSI codes with an 'N' tag on the POA indicator compared to 294 (4.4%) of the non-AE cases. Infection- (20.4%) and surgery/procedure-related AEs (13.6%) had relatively higher instances of correctly tagged PSI codes. We did not identify any PSI codes for diagnosis-related incidents. While we noted significant differences in AE incidence rates, PSI code accuracy, and POA indicator accuracy among the hospitals, the correlations between these variables were not statistically significant.

CONCLUSION

Currently, PSI codes and POA indicators in South Korea appear to have low validity. To use administrative data in medical quality improvement activities such as monitoring patient safety levels, improving the accuracy of administrative data should be a priority. Possible strategies include targeted education on PSI codes and POA indicators and introduction of new evaluation indicators regarding the accuracy of administrative data.

BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study

BACKGROUND

Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls.

OBJECTIVE

This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model.

METHODS

A cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture.

RESULTS

To address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F1-score model (F1=64.4). Our proposed method outperformed 3 classical ML algorithms and an International Classification of Diseases code-based algorithm for fall detection, showing its potential for improved performance in diverse clinical settings.

CONCLUSIONS

The developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals.

Trigger tool-based description of adverse events in helicopter emergency medical services in Qatar

INTRODUCTION

Adverse events (AEs) in helicopter emergency medical services (HEMS) remain poorly reported, despite the potential for harm to occur. The trigger tool (TT) represents a novel approach to AE detection in healthcare. The aim of this study was to retrospectively describe the frequency of AEs and their proximal causes (PCs) in Qatar HEMS.

METHODS

Using the Pittsburgh Adverse Event Tool to identify AEs in HEMS, we retrospectively analysed 804 records within an existing AE TT database (21-month period). We calculated outcome measures for triggers, AEs and harm per 100 patient encounters, plotted measures on statistical process control charts, and conducted a multivariate analysis to report harm associations.

RESULTS

We identified 883 triggers in 536 patients, with a rate of 1.1 triggers per patient encounter, where 81.2% had documentation errors (n=436). An AE and harm rate of 27.7% and 3.5%, respectively, was realised. The leading PC was actions by HEMS Crew (81.6%; n=182). The majority of harm (57.1%) stemmed from the intervention and medication triggers (n=16), where deviation from standard of care was common (37.9%; n=11). Age and diagnosis-adjusted odds were significant in the patient condition (6.50; 95% CI 1.71 to 24.67; p=0.01) and interventional (11.85; 95% CI 1.36 to 102.92; p=0.03) trigger groupings, while age and diagnosis had no effect on harm.

CONCLUSION

The TT methodology is a robust, reliable and valid means of AE detection in the HEMS domain. While an AE rate of 27.7% is high, more research is required to understand prehospital clinical decision-making and reasons for guideline deviance. Furthermore, focused quality improvement initiatives to reduce AEs and documentation errors should also be addressed in future research.

Incidence and characteristics of adverse events in paediatric inpatient care: a systematic review and meta-analysis

BACKGROUND

Adverse events (AEs) cause suffering for hospitalised children, a fragile patient group where the delivery of adequate timely care is of great importance.

OBJECTIVE

To report the incidence and characteristics of AEs, in paediatric inpatient care, as detected with the Global Trigger Tool (GTT), the Trigger Tool (TT) or the Harvard Medical Practice Study (HMPS) method.

METHOD

MEDLINE, Embase, Web of Science and Google Scholar were searched from inception to June 2021, without language restrictions. Studies using manual record review were included if paediatric data were reported separately. We excluded studies reporting: AEs for a specific disease/diagnosis/treatment/procedure, or deceased patients; study protocols with no AE outcomes; conference abstracts, editorials and systematic reviews; clinical incident reports as the primary data source; and studies focusing on specific AEs only. Methodological risk of bias was assessed using a tool based on the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Primary outcome was the percentage of admissions with ≥1 AEs. All statistical analyses were stratified by record review methodology (GTT/TT or HMPS) and by type of population. Meta-analyses, applying random-effects models, were carried out. The variability of the pooled estimates was characterised by 95% prediction intervals (PIs).

RESULTS

We included 32 studies from 44 publications, conducted in 15 countries totalling 33 873 paediatric admissions. The total number of AEs identified was 8577. The most common types of AEs were nosocomial infections (range, 6.8%-59.6%) for the general care population and pulmonary-related (10.5%-36.7%) for intensive care. The reported incidence rates were highly heterogeneous. The PIs for the primary outcome were 3.8%-53.8% and 6.9%-91.6% for GTT/TT studies (general and intensive care population). The equivalent PI was 0.3%-33.7% for HMPS studies (general care). The PIs for preventable AEs were 7.4%-96.2% and 4.5%-98.9% for GTT/TT studies (general and intensive care population) and 10.4%-91.8% for HMPS studies (general care). The quality assessment indicated several methodological concerns regarding the included studies.

CONCLUSION

The reported incidence of AEs is highly variable in paediatric inpatient care research, and it is not possible to estimate a reliable single rate. Poor reporting standards and methodological differences hinder the comparison of study results.

Practice-Based Learning and Improvement: Improving Morbidity and Mortality Review Using Natural Language Processing

INTRODUCTION

Practice-Based Learning and Improvement, a core competency identified by the Accreditation Council for Graduate Medical Education, carries importance throughout a physician's career. Practice-Based Learning and Improvement is cultivated by a critical review of complications, yet methods to accurately identify complications are inadequate. Machine-learning algorithms show promise in improving identification of complications. We compare a manual-supplemented natural language processing (ms-NLP) methodology against a validated electronic morbidity and mortality (MM) database, the Morbidity and Mortality Adverse Event Reporting System (MARS) to understand the utility of NLP in MM review.

METHODS

The number and severity of complications were compared between MARS and ms-NLP of surgical hospitalization discharge summaries among three academic medical centers. Clavien-Dindo (CD) scores were assigned to cases with identified complications and classified into minor (CD I-II) or major (CD III-IV) harm.

RESULTS

Of 7774 admissions, 987 cases were identified to have 1659 complications by MARS and 1296 by ms-NLP. MARS identified 611 (62%) cases, whereas ms-NLP identified 670 (68%) cases. Less than one-third of cases (299, 30.3%) were detected by both methods. MARS identified a greater number of complications with major harm (457, 46.30%) than did ms-NLP (P < 0.0001).

CONCLUSIONS

Both a prospectively maintained MM database and ms-NLP review of discharge summaries fail to identify a significant proportion of postoperative complications and overlap 1/3 of the time. ms-NLP more frequently identifies cases with minor complications, whereas prospective voluntary reporting more frequently identifies major complications. The educational benefit of reporting and analysis of complication data may be supplemented by ms-NLP but not replaced by it at this time.

Reducing preventable adverse events in obstetrics by improving interprofessional communication skills - Results of an intervention study

BACKGROUND

Progress in medicine involves the structured analysis and communication of errors. Comparability between the individual disciplines is only possible to a limited extent and obstetrics plays a special role: the expectation of a self-determined and joyful event meets with possibly serious complications in highly complex care situations. This must be managed by an interdisciplinary team with an increasingly condensed workload. Adverse events cannot be completely controlled. However, taking controllable risk factors into account and with a focused communication a reduction of preventable adverse events is possible. In the present study, the effect of interprofessional team training on preventable adverse events in an obstetric department was investigated.

METHODS

The training consisted of a 4-h interdisciplinary training session based on psychological theories. Preventable adverse events were defined in six categories according to potential patterns of causation. 2,865 case records of a refence year (2018) and 2,846 case records of the year after the intervention (2020) were retrospectively evaluated. To determine the communication training effect, the identified preventable adverse events of 2018 and 2020 were compared according to categories and analyzed for obstetrically relevant controllable and uncontrollable risk factors. Questionnaires were used to identify improvements in self-reported perceptions and behaviors.

RESULTS

The results show that preventable adverse events in obstetrics were significantly reduced after the intervention compared to the reference year before the intervention (13.35% in the year 2018 vs. 8.83% in 2020, p < 0.005). Moreover, obstetrically controllable risk factors show a significant reduction in the year after the communication training. The questionnaires revealed an increase in perceived patient safety (t(28) = 4.09, p < .001), perceived communication behavior (t(30) = -2.95, p = .006), and self-efficacy to cope with difficult situations (t(28) = -2.64, p = .013).

CONCLUSIONS

This study shows that the communication training was able to reduce preventable adverse events and thus increase patient safety. In the future, regular trainings should be implemented alongside medical emergency trainings in obstetrics to improve patient safety. Additionally, this leads to the strengthening of human factors and ultimately also to the prevention of second victims. Further research should follow up implementing active control groups and a randomized-controlled trail study design.

TRIAL REGISTRATION

The study was approved by the Ethics Committee of University Hospital  (protocol code 114/19-FSt/Sta, date of approval 29 May 2019), study registration: NCT03855735 .

Development of the Korean Patient Safety Incidents Code Classification System

OBJECTIVES

Attempts to understand patient safety using administrative data in Korea have been rare. This study develops a Korean patient safety incident code classification system and identifies its characteristics to boost diagnosis code usage for assessing patient safety.

METHODS

Based on existing literature, we selected Korean Standard Classification of Diseases 7 codes for characterizing patient safety incidents using diagnosis codes. We conducted 2 rounds of review to evaluate the codes applicability to different patient safety incidents using the Delphi method. The verified diagnosis codes were then classified by incident type.

RESULTS

Of the 54,259 Korean Standard Classification of Diseases 7 codes, 4509 were applicable for Korean patients, which were divided into 2435 code groups and 2074 candidate groups. The codes were classified into 6 categories (diagnosis, medication, patient care, operation or procedure, infection related, and other) and then further classified into 35 subcategories. The major categories of patient safety incidents, in the order of frequency, involved medication, fluid and blood related (1719, 38.1%), operation and procedure related (1339, 29.7%), and patient care related (991, 22.0%). Meanwhile, there were only 2 codes related to diagnosis.

CONCLUSIONS

Our study provides a basis for estimating patient safety incidents using diagnosis codes. We suggest that gradually increasing the utilization and accuracy of the patient safety incident codes will help develop effective patient safety indicators in Korea similar to other countries. Moreover, clinicians are also needed to be aware of using the developed code classification system.

Occurrence of patient safety incidents during cancer screening: A cross-sectional investigation of the general public

This study aimed to explore the various types and frequency of patient safety incidents (PSIs) during a cancer screening health examination for the general public of Ulsan Metropolitan City, South Korea. Furthermore, the associated elements and responses to PSIs during a cancer screening were examined. The survey, conducted in the five districts of Ulsan, was completed by residents aged 19 years and older who agreed to participate. Descriptive analysis, Chi-square or Fisher exact test, and multivariable logistic regression were performed to analyze the data. A total of 620 participants completed the survey, with 11 (1.8%) individuals who experienced PSIs themselves and 11 (1.8%) by their family members. The highest type of PSIs was those related to procedures. The multivariable logistic regression analysis showed no significant variables associated with experiencing PSIs during cancer screening. However, there was a significant association between the judgment of medical error occurrence and level of patient harm both in experience by family members and total experience of PSIs (P < .05). There was also a significant difference between with and without an experience of PSIs disclosure (P < .001). This study comprehensively analyzed the types and extent of PSIs experienced by Korean individuals and their family members in Ulsan. These findings suggest that patient safety issues during cancer screening should not be overlooked. Furthermore, an investigation system to regularly monitor PSIs in cancer screening should be developed and established.

Hospital Acquired Complications in South Australian major public hospitals

The prevalence of Hospital Acquired Complications (HACs) within major hospitals and intensive care units (ICUs) is often used as an indication of care quality. We performed a retrospective cohort study of acute care separations from four adult public hospitals in the state of South Australia, Australia. Data were derived from the Integrated South Australian Activity Collection (ISAAC) database, subdivided into those admitted to ICU or non-ICU (Ward) in tertiary referral or (other major) metropolitan hospitals. During the five-year study period (1 July 2013 to 30 June 2018), there were 471,934 adult separations with 65,133 HAC events reported in 43,987 (9.32%) at a mean rate of 13.8 (95% confidence interval (CI) 13.7 to 13.9) HAC events per 100 separations and 18.5 (95% CI 18.4 to 18.7) per 1000 bed days. The Ward cohort accounted for the majority (430,583 (91.2%)) of separations, in-hospital deaths (6928 (66.4%)) and HAC events (29,826 (67.8%)). The smaller ICU cohort (41,351 (8.76%)) had a higher mortality rate (8.46% versus 1.61%; P < 0.001), longer length of stay (median 10.0 (interquartile range (IQR) 6.0-18.0) days versus 4.0 (IQR 3.0-8.0) days P < 0.001), and higher HAC prevalence (62.1 (95% CI 61.3 to 62.9) versus 9.16 (95% CI 9.07 to 9.25) per 100 separations P < 0.001). Both ICU and Ward HAC prevalence rates were higher in tertiary referral than major metropolitan hospitals (P < 0.001). In conclusion, higher HAC prevalence rates in the ICU and tertiary referral cohorts may be due to high-risk patient cohorts, variable provision of care, or both, and warrants urgent clinical investigation and further research.

Feasibility of Capturing Adverse Events From Insurance Claims Data Using International Classification of Diseases, Tenth Revision, Codes Coupled to Present on Admission Indicators

OBJECTIVE

The aim of the study was to investigate the feasibility of using administrative data to screen adverse events in Korea.

METHODS

We used a diagnosis-related groups claims data set and the information of the checklist of healthcare quality improvement (a part of the value incentive program) to verify adverse events in fiscal year 2018. Adverse events were identified using patient safety indicator (PSI) clusters and a present on admission indicator (POA). The PSIs consisted of 19 clusters representing subcategories of adverse events, such as hospital-acquired infection. Among the adverse events identified using PSI clusters, "POA = N," which means not present at the time of admission, was only deemed as the case in the final stage. We compared the agreement on the occurrence of adverse events from claims data with a reference standard data set (i.e., checklist of healthcare quality improvement) and presented them by PSI cluster and institution.

RESULTS

The cases of global PSI for any adverse event numbered 27,320 (2.32%) among all diagnostic codes in 2018. In terms of institutional distribution, considerable variation was observed throughout the clusters. For example, only 13.2% of institutions (n = 387) reported any global PSI for any adverse event throughout the whole year. The agreement between the reference standard and the claims data was poor, in the range of 2.2% to 10.8%, in 3 types of adverse events. The current claims data system (i.e., diagnostic codes coupled to POA indicators) failed to capture a large majority of adverse events identified using the reference standard.

CONCLUSIONS

Our results imply that the coding status of International Classification of Diseases, Tenth Revision, codes and POA indicators should be refined before using them as quality indicators.

Type and impact of clinical incidents identified by a voluntary reporting system covering 130 small animal practices in mainland Europe

BACKGROUND

Veterinary healthcare can be a complex process and may lead to unwanted, potentially harmful patient safety incidents as a consequence, negatively impacting both the practice team and client satisfaction. The aim of this study was to identify how such incidents impact cats and dogs by analysing reports gathered in a large-scale voluntary incident reporting system.

METHODS

Descriptive statistical analysis was used to study a total of 2155 incident reports, submitted by 130 practices on mainland Europe.

RESULTS

Incidents caused harm in more than 40% of reports. Medication-related incidents were the most frequent type of incident recorded (40%). Treatment-related incidents were the most common type of incident causing patient harm (55%). Anaesthesia-related incidents were the most severe type of incident, resulting in patient death in 18% of these reports. Most incidents were reported from hospital wards, and a significantly higher proportion of cats were harmed by incidents compared to dogs.

CONCLUSION

This study demonstrates that patients are regularly harmed by incidents, with medication-related incidents being most common. In depth understanding of incident data can help develop interventions to reduce the risk of incident recurrence.

Detecting Patient Safety Errors by Characterizing Incidents Reported by Medical Imaging Staff

The objectives of the study were to characterize events related to patient safety reported by medical imaging personnel in Finland in 2007–2017, the number and quality of reported injuries, the risk assessment, and the planned improvement of operations. The information was collected from a healthcare patient safety incident register system. The data contained information on the nature of the patient safety errors, harms and near-misses in medical imaging, the factors that lead to the events, the consequences for the patient, the level of risks, and future measures. The number of patient safety incident reports included in the study was 7,287. Of the incident reports, 75% concerned injuries to patients and 25% were near-misses. The most common consequence of adverse events and near-misses were minor harm (37.2%) related to contrast agent, or no harm (27.9%) related to equipment malfunction. Supervisors estimated the risks as low (47.7%) e.g., data management, insignificant (35%) e.g., verbal communication or moderate (15.7%) e.g., the use of contrast agent. The most common suggestion for learning from the incident was discussing it with the staff (58.1%), improving operations (5.7%) and submitting it to a higher authority (5.4%). Improving patient safety requires timely, accurate and clear reporting of various patient safety incidents. Based on incident reports, supervisors can provide feedback to staff, develop plans to prevent accidents, and monitor the impact of measures taken. Information on the development of occupational safety should be disseminated to all healthcare professionals so that the same mistakes are not repeated.

Retrospective identification of medication related adverse events in the emergency medical services through the analysis of a patient safety register

Adverse drug events encompass a wide range of potential unintended and harmful events, from adverse drug reactions to medication errors, many of which in retrospect, are considered preventable. However, the primary challenge towards reducing their burden lies in consistently identifying and monitoring these occurrences, a challenge faced across the spectrum of healthcare, including the emergency medical services. The aim of this study was to identify and describe medication related adverse events (AEs) in the out-of-hospital setting. The medication components of a dedicated patient safety register were analysed and described for the period Jan 2017–Sept 2020. Univariate descriptive analysis was used to summarize and report on basic case and patient demographics, intervention related AEs, medication related AEs, and AE severity. Multivariable logistic regression was used to assess the odds of AE severity, by AE type. A total of 3475 patient records were assessed where 161 individual medication AEs were found in 150 (4.32%), 12 of which were categorised as harmful. Failure to provide a required medication was found to be the most common error (1.67%), followed by the administration of medications outside of prescribed practice guidelines (1.18%). There was evidence to suggest a 63% increase in crude odds of any AE severity [OR 1.63 (95% CI 1.03–2.6), p = 0.035] with the medication only AEs when compared to the intervention only AEs. Prehospital medication related adverse events remain a significant threat to patient safety in this setting and warrant greater widespread attention and future identification of strategies aimed at their reduction.

Challenges using electronic nursing routine data for outcome analyses: A mixed methods study

OBJECTIVES

To explore the challenges of secondary use of routinely collected data for analyzing nursing-sensitive outcomes in Austrian acute care hospitals.

METHOD

A convergent parallel mixed methods design was performed. We conducted a quantitative representative survey with nursing managers from 32 Austrian general acute care hospitals and 11 qualitative semi-structured interviews with nursing quality management experts. Both results were first analyzed independently and afterward merged in the discussion.

RESULTS

On average, 76% of nursing documentation is already electronically supported in the surveyed Austrian hospitals. However, existing nursing data is seldom used for secondary purposes such as nursing-sensitive outcome analyses. This is due to four major reasons: First, hospitals often do not have a data strategy for the secondary use of routine data. Second, hospitals partly lack the use of standardized and uniform nursing terminologies, especially for nursing evaluation. Third, routine nursing data is often not documented correctly and completely. Fourth, data on nursing-sensitive outcomes is usually collected in specific documentation forms not integrated into routine documentation.

CONCLUSION

The awareness of the possibilities for secondary use of nursing data for nursing-sensitive outcome analyses in Austrian hospitals is still in its infancy. Therefore, nursing staff and nursing management must be trained to understand how to collect and process nursing data for nursing-sensitive outcome analyses. Further studies would be interesting in order to determine the factors that influence the decision-making processes for the secondary use of nursing data for outcome analyses.

A Systematic Review of Methods for Medical Record Analysis to Detect Adverse Events in Hospitalized Patients

OBJECTIVE

In this systematic review, we evaluate 2 of the most used trigger tools according to the criteria of the World Health Organization for evaluating methods.

METHODS

We searched Embase, PubMed, and Cochrane databases for studies (2000-2017). Studies were included if medical record review (MRR) was performed with either the Global Trigger Tool or the Harvard Medical Practice Study in a hospital population. Quality assessment was performed in duplicate. Fifty studies were included, and results were reported for every criterion separately.

RESULTS

Medical record review reveals more adverse events (AEs) than any other method. However, at the same time, it detects different AEs. The costs of an AE were on average €4296. Considerable efforts have been made worldwide in health care to improve safety and to reduce errors. These have resulted in some positive effects. The literature showed that MRR is focused on several domains of quality of care and seems suitable for both small and large cohorts. Furthermore, we found a moderate to substantial agreement for the presence of a trigger and a moderate to good agreement for the presence of an AE.

CONCLUSIONS

Medical record review with a trigger tool is a reasonably well-researched method for the evaluation of the medical records for AEs. However, looking at the World Health Organization criteria, much research is still lacking or of moderate quality. Especially for the cost of detecting AEs, valuable information is missing. Moreover, knowledge of how MRR changes quality and safety of care should be evaluated.

The Incidence and Nature of Adverse Medical Device Events in Dutch Hospitals: A Retrospective Patient Record Review Study

OBJECTIVES

Despite widespread use of medical devices and their increasing complexity, their contribution to unintended injury caused by healthcare (adverse events, AEs) remains relatively understudied. The aim of this study was to gain insight in the incidence and types of AEs involving medical devices (AMDEs).

METHODS

Data from two patient record studies for the identification of AEs were used. Identification of AMDEs was part of these studies. Patient records of 6894 admissions of a random sample of 20 hospitals in 2011/2012 and 19 hospitals in 2015/2016 were reviewed for AMDEs by trained nurses and physicians.

RESULTS

In 98.7% of the admissions, a medical device was used. Adverse events involving medical devices were present in 2.8% of the admissions, with 24% of the AMDEs being potentially preventable. Of all AEs, in 40%, medical devices were involved. Of all potentially preventable AEs, in 44%, medical devices were involved. Implants were most often involved in potentially preventable AMDEs.

CONCLUSIONS

Medical devices are substantially involved in potentially preventable AEs in hospitals. Research into AMDEs is of great importance because of the increasing use and complexity of medical devices. Based on patient records, most improvements could be made for placement of implants and prevention of infections related to medical devices. Safety and safe use of medical devices should be a subject of attention and further research.

Evaluation of a Broad-Spectrum Partially Automated Adverse Event Surveillance System: A Potential Tool for Patient Safety Improvement in Hospitals With Limited Resources

OBJECTIVE

The aim of the study was to evaluate the sensitivity and resource efficiency of a partially automated adverse event (AE) surveillance system for routine patient safety efforts in hospitals with limited resources.

METHODS

Twenty-eight automated triggers from the hospital information system's clinical and administrative databases identified cases that were then filtered by exclusion criteria per trigger and then reviewed by an interdisciplinary team. The system, developed and implemented using in-house resources, was applied for 45 days of surveillance, for all hospital inpatient admissions (N = 1107). Each trigger was evaluated for its positive predictive value (PPV). Furthermore, the sensitivity of the surveillance system (overall and by AE category) was estimated relative to incidence ranges in the literature.

RESULTS

The surveillance system identified a total of 123 AEs among 283 reviewed medical records, yielding an overall PPV of 52%. The tool showed variable levels of sensitivity across and within AE categories when compared with the literature, with a relatively low overall sensitivity estimated between 21% and 44%. Adverse events were detected in 23 of the 36 AE categories defined by an established harm classification system. Furthermore, none of the detected AEs were voluntarily reported.

CONCLUSIONS

The surveillance system showed variable sensitivity levels across a broad range of AE categories with an acceptable PPV, overcoming certain limitations associated with other harm detection methods. The number of cases captured was substantial, and none had been previously detected or voluntarily reported. For hospitals with limited resources, this methodology provides valuable safety information from which interventions for quality improvement can be formulated.

Hospital-acquired complications in critically ill patients

Background: The national hospital-acquired complications (HAC) system has been promoted as a method to identify health care errors that may be mitigated by clinical interventions. Objectives: To quantify the rate of HAC in multiday stay adults admitted to major hospitals. Design: Retrospective observational analysis of 5-year (July 2014 - June 2019) administrative dataset abstracted from medical records. Setting: All 47 hospitals with on-site intensive care units (ICUs) in the State of Victoria. Participants: All adults (aged ≥ 18 years) stratified into planned or unplanned, surgical or medical, ICU or other ward, and by hospital peer group (tertiary referral, metropolitan, regional). Main outcome measures: HAC rates in ICU compared with ward, and mixed-effects regression estimates of the association between HAC and i) risk of clinical deterioration, and ii) admission hospital site (intraclass correlation coefficient [ICC] > 0.3). Results: 211 120 adult ICU separations with mean hospital mortality of 7.3% (95% CI, 7.2-7.4%) reported 110 132 (42.6%) HAC events (commonly, delirium, infection, arrhythmia and respiratory failure) in 62 945 records (29.8%). Higher HAC rates were reported in elective (cardiac [50.3%] and non-cardiac [40.6%]) surgical subgroups compared with emergency medical subgroup (23.9%), and in tertiary (35.4%) compared with non-tertiary (22.7%) hospitals. HAC was strongly associated with on-admission patient characteristics (P < 0.001), but was weakly associated with hospital site (ICC, 0.08; 95% CI, 0.05-0.11). Conclusions: Critically ill patients have a high burden of HAC events, which appear to be associated with patient admission characteristics. HAC may an indicator of hospital admission complexity rather than hospital-acquired complications.

Adverse Events Detection Through Global Trigger Tool Methodology: Results From a 5-Year Study in an Italian Hospital and Opportunities to Improve Interrater Reliability

OBJECTIVE

Global Trigger Tool (GTT) has been proposed as a low-cost method to detect adverse events (AEs). The validity of the methodology has been questioned because of moderate interrater agreement. Continuous training has been suggested as a means to improve consistency over time. We present the main findings of the implementation of the Italian version of the GTT and evaluate efforts to improve the interrater reliability over time.

METHODS

The Italian version of the GTT was developed and implemented at the San Bonifacio Hospital, a 270-bed secondary care acute hospital in Verona, Italy. Ten clinical records randomly selected every 2 weeks were reviewed from 2009 to 2014. Two-stage interrater reliability assessment between team members was conducted on 2 subsamples of 50 clinical records before and after the implementation of specific review rules and staff training.

RESULTS

Among 1320 medical records reviewed, a total of 366 AEs were found with at least 1 AE on 20.2% of all discharges, 27.7 AEs/100 admissions, and 30.6 AEs/1000 patient-days. Adverse events with harm score E and F were respectively 58.2% (n = 213) and 38.8% (n = 142). First round interrater reliability was comparable with other international studies. The interrater agreement improved significantly after intervention (κ interrater I = 0.52, κ interrater II = 0.80, P < 0.001).

CONCLUSIONS

Despite the improvements in the interrater consistency, overall results did not show any significant trend in AEs over time. Future studies may be directed to apply and adapt the GTT methodology to more specific settings to explore how to improve its sensitivity.

Life-Threatening and Fatal Adverse Drug Events in a Danish University Hospital

OBJECTIVES

Detection of adverse drug events (ADEs) in Danish hospitals relies on health care professionals' incident reporting to a national database for adverse events, but the method is incomplete; thus, fatal and life-threatening ADEs may remain unrecognized.The objectives of this study were to examine the occurrence of life-threatening and fatal ADEs in population of hospitalized patients with suspected adverse outcome and to compare these findings with the actual number of reported ADEs in the study period of 3 months.

METHODS

Study was designed as a cross-sectional study of adult population, hospitalized for more than 24 hours, having an unplanned transfer to an intensive care unit (ICU), or having unexpected death. Medical records were retrospectively screened by the Global Trigger Tool. All positive triggers were assessed for ADEs by a clinical pharmacologist.

RESULTS

Of the 26,176 patients admitted in the study period, 105 had an unplanned transfer to the ICU and 36 died unexpectedly. In total, 15 positive triggers were identified in 10 patients. Life-threatening ADEs accounted for 7.6% (8/105) of patients transferred to the ICU, and fatal ADEs constituted 5.5% (2/36) of the deceased patients. Life-threatening and fatal ADEs corresponded to an overall prevalence of 0.04% (10/26,176). Most ADEs were related to hemorrhages and respiratory problems. No serious or fatal ADEs were reported in the incident reporting system in the study period.

CONCLUSIONS

Ten life-threatening and fatal ADEs were uncovered as not reported in the incident reporting system. Further steps are needed for recognition and prevention of this patient safety challenge.

Validation of an Automated System for Identifying Complications of Serious Pediatric Emergencies

BACKGROUND

Illness complications are condition-specific adverse outcomes. Detecting complications of pediatric illness in administrative data would facilitate widespread quality measurement, however the accuracy of such detection is unclear.

METHODS

We conducted a cross-sectional study of patients visiting a large pediatric emergency department. We analyzed those <22 years old from 2012 to 2019 with 1 of 14 serious conditions: appendicitis, bacterial meningitis, diabetic ketoacidosis (DKA), empyema, encephalitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, ovarian torsion, sepsis, septic arthritis, stroke, and testicular torsion. We applied a method using disposition, diagnosis codes, and procedure codes to identify complications. The automated determination was compared with the criterion standard of manual health record review by using positive predictive values (PPVs) and negative predictive values (NPVs). Interrater reliability of manual reviews used a κ.

RESULTS

We analyzed 1534 encounters. PPVs and NPVs for complications were >80% for 8 of 14 conditions: appendicitis, bacterial meningitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, sepsis, and testicular torsion. Lower PPVs for complications were observed for DKA (57%), empyema (53%), encephalitis (78%), ovarian torsion (21%), and septic arthritis (64%). A lower NPV was observed in stroke (68%). The κ between reviewers was 0.88.

CONCLUSIONS

An automated method to measure complications by using administrative data can detect complications in appendicitis, bacterial meningitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, sepsis, and testicular torsion. For DKA, empyema, encephalitis, ovarian torsion, septic arthritis, and stroke, the tool may be used to screen for complicated cases that may subsequently undergo manual review.

Potential adverse drug events and its predictors among hospitalized patients at medical center in Ethiopia: a prospective observational study

Potential adverse drug event (PADE) is a medication error with the potential to cause associate degree injury however that does not cause any injury, either due to specific circumstances, chance, or as a result of the error being intercepted and corrected. This study aimed to assess the incidence, contributing factors, predictors, severity, and preventability of PADEs among hospitalized adult patients at Jimma Medical Center. A prospective observational study was conducted among hospitalized adult patients at a tertiary hospital in Ethiopia. Logistic regression was performed to identify factors predicting PADE occurrence. P-value < 0.05 was considered for statistical significance. A total of 319 patients were included. About 50.5% of them were females. The mean ± SD age of the participants was 43 ± 17.6 years. Ninety-four PADEs were identified. Number of medications (adjusted OR = 5.12; 95% CI: 2.01–13.05; p = 0.001), anticoagulants (adjusted OR = 2.51; 95% CI: 1.22–5.19; p = 0.013), anti-seizures (adjusted OR = 21.96; 95% CI: 6.57–73.39; p < 0.0001), anti-tuberculosis (adjusted OR = 2.2; 95% CI: 1.002–4.59, p = 0.049), and Elixhauser comorbidity Index ≤ 15 (adjusted OR = 6.24; 95% CI: 1.48–26.25, p = 0.013) were independent predictors of PADEs occurrence. About one-third of patients admitted to the hospital experienced PADEs.

The incidence and nature of adverse events in rehabilitation inpatients with acquired brain injuries

INTRODUCTION

Patient safety is important in all healthcare settings. Few studies have examined the state of patient safety in rehabilitation and none have examined patient safety in the setting of acquired brain injury (ABI) rehabilitation.

OBJECTIVES

To determine the incidence, most common types, and severities of adverse events among inpatients undergoing ABI rehabilitation.

DESIGN

Retrospective case series descriptive study.

SETTING

The inpatient ABI rehabilitation program at an academic, tertiary rehabilitation hospital in Canada.

PARTICIPANTS

108 consecutive inpatients with acquired brain injuries.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

Patient charts and incident reports from the hospital's voluntary reporting system were reviewed by three board-certified physiatrists to determine the incidence, type, severity and preventability of adverse events. Adverse events were identified and classified for severity and type using the WHO International Classification for Patient Safety. Preventability was rated on a 6-point Likert scale.

RESULTS

During the study period, the incidence of adverse events was 17.42 ± 3.86 per 1000 patient days. Adverse events affected 52.8% of patients. Most adverse events identified were mild in severity (98.51%) and the rest were of moderate severity. The two most common types of adverse events were 1) patient incidents (56.72%) such as falls, pressure ulcers and skin tears, and 2) patient behaviors such as missing patient, assault, or sexual behaviors (16.42%). Of the 80 adverse events identified in the study, 44.78% were preventable. The hospital's voluntary reporting system did not capture 57.9% of the adverse events identified.

CONCLUSIONS

Future efforts to improve patient safety in ABI rehab should focus on reducing falls, skin injuries and behaviors, and removing barriers to voluntary incident reporting. Detecting adverse events through chart reviews provide a more complete understanding of patient safety risks in ABI rehab than relying on incident reporting alone. This article is protected by copyright. All rights reserved.

Evaluating resampling methods and structured features to improve fall incident report identification by the severity level

OBJECTIVE

This study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.

MATERIALS AND METHODS

We present an incident report classification (IRC) framework to classify the in-hospital fall incident severity level by addressing the imbalanced class problem and incorporating structured attributes. After text preprocessing, bag-of-words features, structured text features, and structured clinical features were extracted from the reports. Next, resampling techniques were incorporated into the training process. Machine learning algorithms were used to build classification models. IRC systems were trained, validated, and tested using a repeated and randomly stratified shuffle-split cross-validation method. Finally, we evaluated the system performance using the F1-measure, precision, and recall over 15 stratified test sets.

RESULTS

The experimental results demonstrated that the classification system setting considering both data imbalance issues and structured features outperformed the other system settings (with a mean macro-averaged F1-measure of 0.733). Considering the structured features and resampling techniques, this classification system setting significantly improved the mean F1-measure for the rare class by 30.88% (P value < .001) and the mean macro-averaged F1-measure by 8.26% from the baseline system setting (P value < .001). In general, the classification system employing the random forest algorithm and random oversampling method outperformed the others.

CONCLUSIONS

Structured features provide essential information for categorizing the fall incident severity level. Resampling methods help rebalance the class distribution of the original incident report data, which improves the performance of machine learning models. The IRC framework presented in this study effectively automates the identification of fall incident reports by the severity level.

National and Institutional Trends in Adverse Events Over Time: A Systematic Review and Meta-analysis of Longitudinal Retrospective Patient Record Review Studies

OBJECTIVE

This study aimed to determine if the implementation of large-scale patient safety initiatives have been successful in reducing overall and preventable adverse event rates in hospital inpatients.

DESIGN

The design used in this study was systematic review and meta-analysis.

DATA RESOURCES

We followed our published protocol (PROSPERO [CRD42019140058]) and searched the following databases: PubMed, CINAHL, PsycINFO, Cochrane Library, and Embase from inception to February 2020. The reference lists of eligible studies were also searched.

ELIGIBILITY

All longitudinal retrospective record review studies that examined adverse event rates before and after the introduction of patient safety initiatives in hospital inpatients were included.

DATA EXTRACTION

Data extraction, quality, and risk of bias assessment were carried out by 2 independent reviewers. Information on study design, setting, demographics, interventions, and safety outcome measures was extracted.

RESULTS

A total of 3894 articles were screened, and 7 articles met the eligibility criteria for our systematic review with 5 of these providing sufficient information for inclusion in the meta-analysis. The degree of heterogeneity was high among studies. The meta-analysis demonstrated a minimal risk reduction in overall adverse event rates of 0.017 (95% confidence interval, 0.002-0.032) when the lower-quality studies were excluded, with one adverse event being prevented for every 59 hospital admissions.

CONCLUSIONS

These findings are significant when the large numbers of admissions to a hospital every year are considered. Given the low numbers of large-scale implementation studies, there is a need for more research on the effectiveness of patient safety initiatives to further assess the impact of such initiatives on adverse events.

Patient Safety Incidents Reported by the General Public in Korea: A Cross-Sectional Study

OBJECTIVES

Previous studies have demonstrated that the general public can report various patient safety incidents (PSIs) that are not identified by other methods. In this study, we investigated the characteristics of PSIs that the general public experience in Korea.

METHODS

In face-to-face surveys, participants were asked to report the frequency and type of PSIs, level of patient harm, and whether the PSIs were perceived as a medical error. We conducted logistic regression analysis to identify the sociodemographic factors of participants associated with their PSI experiences. Additionally, we analyzed relationships between the perception of PSIs as a medical error and both the type of PSIs and level of patient harm.

RESULTS

Among the 700 participants surveyed, 24 (3.4%) and 37 (5.3%) individuals reported that they or their family members experienced PSIs, respectively. Participants with at least a college degree were more likely to report PSI experiences than those with a lower educational level (odds ratio, 3.54; 95% confidence interval, 1.86-6.74). Whereas approximately half of participants (48.2%) involved in PSI experiences that caused no harm thought that there were medical errors in their PSIs, all participants (100%) who experienced PSIs with severe harm responded that medical errors occurred in their PSIs.

CONCLUSIONS

The general public can report their experiences with PSIs. Periodic surveys that target the general public will provide additional data that reflect the level of patient safety from the viewpoint of the general public.

Applying the Global Trigger Tool in German Hospitals: A Pilot in Surgery and Neurosurgery

The aim of the study was to assess the feasibility and potential of the Global Trigger Tool (GTT) for identifying adverse events (AEs) in different specialties in German hospitals.

Mitigating imperfect data validity in administrative data PSIs: a method for estimating true adverse event rates

QUESTION

Are there ways to mitigate the challenges associated with imperfect data validity in Patient Safety Indicator (PSI) report cards?

FINDINGS

Applying a methodological framework on simulated PSI report card data, we compare the adjusted PSI rates of three hospitals with variable quality of data and coding. This framework combines (i) a measure of PSI rates using existing algorithms; (ii) a medical record review on a small random sample of charts to produce a measure of hospital-specific data validity and (iii) a simple Bayesian calculation to derive estimated true PSI rates. For example, the estimated true PSI rate, for a theoretical hospital with a moderately good quality of coding, could be three times as high as the measured rate (for example, 1.4% rather than 0.5%). For a theoretical hospital with relatively poor quality of coding, the difference could be 50-fold (for example, 5.0% rather than 0.1%).

MEANING

Combining a medical chart review on a limited number of medical charts at the hospital level creates an approach to producing health system report cards with estimates of true hospital-level adverse event rates.

Adverse Events Among Emergency Department Patients With Cardiovascular Conditions: A Multicenter Study

STUDY OBJECTIVE

We aim to determine incidence and type of adverse events (adverse outcomes related to emergency care) among emergency department (ED) patients discharged with recent-onset atrial fibrillation, acute heart failure, and syncope.

METHODS

This 5-year prospective cohort study included high-acuity adult patients discharged with the 3 sentinel diagnoses from 6 tertiary care Canadian EDs. We screened all ED visits for eligibility and performed telephone interviews 14 days postdischarge to identify flagged outcomes: death, hospital admission, return ED visit, health care provider visit, and new or worsening symptoms. We created case summaries describing index ED visit and flagged outcomes, and trained emergency physicians reviewed case summaries to identify adverse events. We reported adverse event incidence and rates with 95% confidence intervals and contributing factor themes.

RESULTS

Among 4,741 subjects (mean age 70.2 years; 51.2% men), we observed 170 adverse events (3.6 per 100 patients; 95% confidence interval 3.1 to 4.2). Patients discharged with acute heart failure were most likely to experience adverse events (5.3%), followed by those with atrial fibrillation (2.0%) and syncope (0.8%). We noted variation in absolute adverse event rates across sites from 0.7 to 6.0 per 100 patients. The most common adverse event types were management issues, diagnostic issues, and unsafe disposition decisions. Frequent contributing factor themes included failure to recognize underlying causes and inappropriate management of dual diagnoses.

CONCLUSION

Among adverse events after ED discharge for patients with these 3 sentinel cardiovascular diagnoses, we identified quality improvement opportunities such as strengthening dual diagnosis detection and evidence-based clinical practice guideline adherence.

A patient and family reporting system for perceived ambulatory note mistakes: experience at 3 U.S. healthcare centers

OBJECTIVE

The study sought to test a patient and family online reporting system for perceived ambulatory visit note inaccuracies.

MATERIALS AND METHODS

We implemented a patient and family electronic reporting system at 3 U.S. healthcare centers: a northeast urban academic adult medical center (AD), a northeast urban academic pediatric medical center (PED), and a southeast nonprofit hospital network (NET). Patients and families reported potential documentation inaccuracies after reading primary care and subspecialty visit notes. Results were characterized using descriptive statistics and coded for clinical relevance.

RESULTS

We received 1440 patient and family reports (780 AD, 402 PED, and 258 NET), and 27% of the reports identified a potential inaccuracy (25% AD, 35% PED, 28% NET). Among these, patients and families indicated that the potential inaccuracy was important or very important in 58% of reports (55% AD, 55% PED, 71% NET). The most common types of potential inaccuracies included description of symptoms (21%), past medical problems (21%), medications (18%), and important information that was missing (15%). Most patient- and family-reported inaccuracies resulted in a change to care or to the medical record (55% AD, 67% PED, data not available at NET).

DISCUSSION

About one-quarter of patients and families using an online reporting system identified potential documentation inaccuracies in visit notes and more than half were considered important by patients and clinicians, underscoring the potential role of patients and families as ambulatory safety partners.

CONCLUSIONS

Partnering with patients and families to obtain reports on inaccuracies in visit notes may contribute to safer care. Mechanisms to encourage greater use of patient and family reporting systems are needed.

Relationship between adverse drug reactions and unlicensed/off-label drug use in hospitalized children (EREMI): A study protocol

INTRODUCTION

To date, few studies have shown a significant association between off-label drug use and adverse drug reactions (ADRs). The main aims of this study is to evaluate the relationship between adverse drug reactions and unlicensed or off-label drugs in hospitalized children and to provide more information on prescribing practice, the amplitude, consequences of unlicensed or off-label drug use in pediatric inpatients.

METHODS

In this multicenter prospective study started from 2013, we use the French summaries of product characteristics in Theriaque (a prescription products guide) as a primary reference source for determining pediatric drug labeling. The detection of ADRs is carried out spontaneously by health professionals and actively by research groups using a trigger tool and patients' electronic health records. The causality between suspected ADRs and medication is evaluated using the Naranjo and the French methods of imputability independently by pharmacovigilance center. All suspected ADRs are submitted for a second evaluation by an independent pharmacovigilance experts.

STRENGTH AND LIMITATIONS OF THIS STUDY

For our best knowledge, EREMI is the first large multicenter prospective and objective study in France with an active ADRs monitoring and independent ADRs validation. This study identifies the risk factors that could be used to adjust preventive actions in children's care, guides future research in the field and increases the awareness of physicians in off-label drug use and in detecting and declaring ADRs. As data are obtained through extraction of information from hospital database and medical records, there is likely to be some under-reporting of items or missing data. In this study the field specialists detect all adverse events, experts in pharmacovigilance centers assess them and finally only the ADRs assessed by the independent committee are confirmed. Although we recruit a high number of patients, this observational study is subject to different confounders.

The Irish National Adverse Event Study-2 (INAES-2): longitudinal trends in adverse event rates in the Irish healthcare system

Objectives

To quantify the prevalence and nature of adverse events in acute Irish hospitals in 2015 and to assess the impact of the National Clinical Programmes and the National Clinical Guidelines on the prevalence of adverse events by comparing these results with the previously published data from 2009.

Design and methods

A retrospective chart review of 1605 admissions to eight Irish hospitals in 2015, using identical methods to those used in 2009.

Results

The percentage of admissions associated with one or more adverse events was unchanged (p=0.48) at 14% (95% CI=10.4% to 18.4%) in 2015 compared with 12.2% (95% CI=9.5% to 15.5%) in 2009. Similarly, the prevalence of preventable adverse events was unchanged (p=0.3) at 7.4% (95% CI=5.3% to 10.5%) in 2015 compared with 9.1% (95% CI=6.9% to 11.9%) in 2009. The incidence densities of preventable adverse events were 5.6 adverse events per 100 admissions (95% CI=3.4 to 8.0) in 2015 and 7.7 adverse events per 100 admissions (95% CI=5.8 to 9.6) in 2009 (p=0.23). However, the percentage of preventable adverse events due to hospital-associated infections decreased to 22.2% (95% CI=15.2% to 31.1%) in 2015 from 33.1% (95% CI=25.6% to 41.6%) in 2009 (p=0.01).

Conclusion

Adverse event rates remained stable between 2009 and 2015. The percentage of preventable adverse events related to hospital-associated infection decreased, which may represent a positive impact of the related national programmes and guidelines.

Translation, Cross-Cultural Adaptation, and Measurement Properties of the Portuguese Version of the Global Trigger Tool for Adverse Events

Purpose

To adapt and validate the Global Trigger Tool (IHI-GTT), which identifies and analyzes adverse events (AE) in hospitalized patients and their measurement properties in the Portuguese context.

Methods

A retrospective cross-sectional study was based on a random sample of 90 medical records. The stages of translation and cross-cultural adaptation of the IHI-GTT were based on the Cross-Cultural Adaptation Protocol that originated from the Portuguese version, GTT-PT, for the hospital context in medical-surgical departments. Internal consistency, reliability, reproducibility, diagnostic tests, and discriminatory predictive value were investigated.

Results

The final phase of the GTT-PT showed insignificant inconsistencies. The pre-test phase confirmed translation accuracy, easy administration, effectiveness in identifying AEs, and relevance of integrating it into hospital risk management. It had a sensitivity of 97.8% and specificity of 74.8%, with a cutoff point of 0.5, an accuracy of 83%, and a positive predictive value of 69.8% and a negative predictive value of 0.98%.

Conclusion

The GTT-PT is a reliable, accurate, and valid tool to identify AE, with robust measurement properties.

The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis

Background

As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site.

Objective

The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions.

Methods

The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized.

Results

Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit.

Conclusions

The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe’s fit at new sites.

Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation

BACKGROUND

Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives can be challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but its application for medical AE detection has been limited.

METHOD

In this study, we developed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following primary total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives. We benchmarked these proposed models with traditional machine learning based NLP (ML-NLP) models, and also assessed the accuracy of International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes in capturing these hip dislocation AEs in a multi-center orthopaedic registry.

RESULTS

All DL-NLP models outperformed all of the ML-NLP models, with a convolutional neural network (CNN) model achieving the best overall performance (Kappa = 0.97 for radiology notes, and Kappa = 1.00 for follow-up telephone notes). On the other hand, the ICD/CPT codes of the patients who sustained a hip dislocation AE were only 75.24% accurate.

CONCLUSIONS

We demonstrated that a DL-NLP model can be used in largescale orthopaedic registries for accurate and efficient detection of hip dislocation AEs. The NLP model in this study was developed with data from the most frequently used electronic medical record (EMR) system in the U.S., Epic. This NLP model could potentially be implemented in other Epic-based EMR systems to improve AE detection, and consequently, quality of care and patient outcomes.

Patient safety in dental care: an integrative review

Adverse events pose a serious problem for quality of healthcare. Dental practice is eminently invasive and involves close and routine contact with secretions; as such, it is potentially prone to the occurrence of adverse events. Various patient safety studies have been developed in the last two decades, but mostly in the hospital setting due to the organizational complexity, severity of the cases, and diversity and specificity of the procedures. The objective was to identify and explore studies on patient safety in Dentistry. An integrative literature review was performed in MEDLINE via PubMed, Scopus via Portal Capes, and the Regional Portal of the Virtual Health Library, using the terms patient safety and dentistry in English, Spanish, and Portuguese, starting in 2000. The research cycle in patient safety was used, as proposed by the World Health Organization to classify studies. We analyzed 91 articles. The most common adverse events were allergies, infections, diagnostic delay or failure, and technical error. Measures to mitigate the problem highlight the need to improve communications, encourage reporting, and search for tools to assist the management of care. The authors found a lack of studies on implementation and assessment of the impact of proposals for improvement. Dentistry has made progress in patient safety but still needs to transpose the results into practice, where efforts are crucial to prevent adverse events.

Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study

BACKGROUND

Falls are common adverse events in hospitals, frequently leading to additional health costs due to prolonged stays and extra care. Therefore, reliable fall detection is vital to develop and test fall prevention strategies. However, conventional methods-voluntary incident reports and manual chart reviews-are error-prone and time consuming, respectively. Using a search algorithm to examine patients' electronic health record data and flag fall indicators offers an inexpensive, sensitive, cost-effective alternative.

OBJECTIVE

This study's purpose was to develop a fall detection algorithm for use with electronic health record data, then to evaluate it alongside the Global Trigger Tool, incident reports, a manual chart review, and patient-reported falls.

METHODS

Conducted on 2 campuses of a large hospital system in Switzerland, this retrospective diagnostic accuracy study consisted of 2 substudies: the first, targeting 240 patients, for algorithm development and the second, targeting 298 patients, for validation. In the development study, we compared the new algorithm's in-hospital fall rates with those indicated by the Global Trigger Tool and incident reports; in the validation study, we compared the algorithm's in-hospital fall rates with those from patient-reported falls and manual chart review. We compared the various methods by calculating sensitivity, specificity, and predictive values.

RESULTS

Twenty in-hospital falls were discovered in the development study sample. Of these, the algorithm detected 19 (sensitivity 95%), the Global Trigger Tool detected 18 (90%), and incident reports detected 14 (67%). Of the 15 falls found in the validation sample, the algorithm identified all 15 (100%), the manual chart review identified 14 (93%), and the patient-reported fall measure identified 5 (33%). Owing to relatively high numbers of false positives based on falls present on admission, the algorithm's positive predictive values were 50% (development sample) and 47% (validation sample). Instead of requiring 10 minutes per case for a full manual review or 20 minutes to apply the Global Trigger Tool, the algorithm requires only a few seconds, after which only the positive results (roughly 11% of the full case number) require review.

CONCLUSIONS

The newly developed electronic health record algorithm demonstrated very high sensitivity for fall detection. Applied in near real time, the algorithm can record in-hospital falls events effectively and help to develop and test fall prevention measures.

Operational measurement of diagnostic safety: state of the science

Reducing the incidence of diagnostic errors is increasingly a priority for government, professional, and philanthropic organizations. Several obstacles to measurement of diagnostic safety have hampered progress toward this goal. Although a coordinated national strategy to measure diagnostic safety remains an aspirational goal, recent research has yielded practical guidance for healthcare organizations to start using measurement to enhance diagnostic safety. This paper, concurrently published as an Issue Brief by the Agency for Healthcare Research and Quality, issues a "call to action" for healthcare organizations to begin measurement efforts using data sources currently available to them. Our aims are to outline the state of the science and provide practical recommendations for organizations to start identifying and learning from diagnostic errors. Whether by strategically leveraging current resources or building additional capacity for data gathering, nearly all organizations can begin their journeys to measure and reduce preventable diagnostic harm.

Validation of an algorithm based on administrative data to detect new onset of atrial fibrillation after cardiac surgery

Introduction

Postoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery associated with important morbidity, mortality, and costs. To assess the effectiveness of preventive interventions, an important prerequisite is to have access to accurate measures of POAF incidence. The aim of this study was to develop and validate such a measure.

Methods

A validation study was conducted at two large Canadian university health centers. First, a random sample of 976 (10.4%) patients who had cardiac surgery at these sites between 2010 and 2016 was generated. Then, a reference standard assessment of their medical records was performed to determine their true POAF status on discharge (positive/negative). The accuracy of various algorithms combining diagnostic and procedure codes from: 1) the current hospitalization, and 2) hospitalizations up to 6 years before the current hospitalization was assessed in comparison with the reference standard. Overall and site-specific estimates of sensitivity, specificity, positive (PPV), and negative (NPV) predictive values were generated, along with their 95%CIs.

Results

Upon manual review, 324 (33.2%) patients were POAF-positive. Our best-performing algorithm combining data from both sites used a look-back window of 6 years to exclude patients previously known for AF. This algorithm achieved 70.4% sensitivity (95%CI: 65.1–75.3), 86.0% specificity (95%CI: 83.1–88.6), 71.5% PPV (95%CI: 66.2–76.4), and 85.4% NPV (95%CI: 82.5–88.0). However, significant site-specific differences in sensitivity and NPV were observed.

Conclusion

An algorithm based on administrative data can identify POAF patients with moderate accuracy. However, site-specific variations in coding practices have significant impact on accuracy.

Variability in the Reporting Terminology of Adverse Events and Complications in Ankle Fracture Fixation: A Systematic Review

BACKGROUND

Classification systems for the reporting of surgical complications have been developed and adapted for many surgical subspecialties. The purpose of this systematic review was to examine the variability and frequency of reporting terms used to describe adverse events and complications in ankle fracture fixation. We hypothesized that the terminology used would be highly variable and inconsistent, corroborating previous results that have suggested a need for standardized reporting terminology in orthopedics.

METHODS

Ankle fracture outcome studies meeting predetermined inclusion and exclusion criteria were selected for analysis by 2 independent observers. Terms used to define adverse events and complications were identified and recorded. Discrepancies were resolved by consensus with the aid of a third observer. All terms were then compiled and assessed for variability and frequency of use throughout the studies involved. Reporting terminology was subsequently grouped into 10 categories.

RESULTS

In the 48 studies analyzed, 301 distinct terms were utilized to describe complications or adverse events. Of these terms, 74.4% (224/301) were found in a single study each. Only 1 term, "infection," was present in 50% of studies, and only 19 of 301 terms (6.3%) were used in at least 10% of papers. The category that was most frequently reported was "infection," with 89.6% of studies reporting on this type of adverse event using 25 distinct terms. Other categories were "wound healing complications" (72.9% of papers, 38 terms), "bone/joint complications" (66.7% of papers, 35 terms), "hardware/implant complications" (56.3% of papers, 47 terms), "revision" (56.3% of papers, 35 terms), "cartilage/soft tissue injuries" (45.8% of papers, 31 terms), "reduction/alignment issues" (45.8% of papers, 29 terms), "medical complications" (43.8% of papers, 32 terms), "pain" (29.2% of papers, 16 terms), and "other complications" (20.8% of papers, 13 terms). There was a 78.6% interobserver agreement in the identification of terms across the 48 studies included.

CONCLUSION

The reporting terminology utilized to describe complications and adverse events in ankle fracture fixation was found to be highly variable and inconsistent. This variability prevents accurate reporting of complications and adverse events and makes the analysis of potential outcomes difficult. The development of standardized reporting terminology in orthopedics would be instrumental in addressing these challenges and allow for more accurate and consistent outcome reporting.

LEVEL OF EVIDENCE

Level III; systematic review of Level III studies and above.

Comparing Rates of Adverse Events and Medical Errors on Inpatient Psychiatric Units at Veterans Health Administration and Community-based General Hospitals

OBJECTIVE

There is limited knowledge about how general hospitals and Veterans Health Administration (VHA) hospitals fare relative to each other on a broad range of inpatient psychiatry-specific patient safety outcomes.This research compares data from 2 large-scale epidemiological studies of adverse events (AEs) and medical errors (MEs) in inpatient psychiatric units, one in VHA hospitals and the other in community-based general hospitals.

METHOD

Retrospective medical record reviews assessed the prevalence of AEs and MEs in a sample of 4371 discharges from 14 community-based general hospitals (derived from 69,081 discharges at 85 hospitals) and a sample of 8005 discharges from 40 VHA hospitals (derived from 92,103 discharges at 105 medical centers). Rates of AEs and MEs across hospital systems were calculated, controlling for relevant patient and hospital characteristics.

RESULTS

The overall rate of AEs and MEs in inpatient psychiatric units of VHA hospitals was 7.11 and 1.49 per 100 patient discharges; at community-based acute care hospitals, these rates were 13.48 and 3.01 per 100 patient discharges. The adjusted odds ratio of a patient experiencing an AE and a ME at community-based hospitals as compared with VHA hospitals was 2.11 and 2.08, respectively.

CONCLUSION

Although chart reviews may not document the complete nature and outcomes of care, even after controlling for differences in patient and hospital characteristics, psychiatric inpatients at community-based hospitals were twice as likely to experience AEs or MEs as inpatients at VHA hospitals. While community-based hospitals may lag behind VHA hospitals, both hospital systems should continue to pursue evidence-based improvements in patient safety. Future research aimed at changing hospital practices should draw on established strategies for bridging the gap from research to practice in order to improve the quality of care for this vulnerable patient population.

Concept of error and nature of nursing error detectors in military hospitals: a qualitative content analysis

BACKGROUND AND OBJECTIVES

Nursing errors can cause irreparable consequences. Understanding the concept of error and the nature of nursing error detectors can significantly reduce this type of errors. The present study was conducted to explain the concept of error and the nature of nursing error detectors in military hospitals.

MATERIALS AND METHODS

The present study was conducted on eight nurses working in different wards of military hospitals using a qualitative approach to content analysis proposed by Graneheim and Lundman. Data were collected through in-depth semistructured interviews.

FINDINGS

'The concept of error' and 'the nature of error detectors' in military hospitals were the two main categories extracted from data analysis. The present findings showed that the nature of errors in military hospitals is inevitable, a threat to job position and bipolar. Nurses use different resources to identify errors, including personal, environmental and organisational factors of detection.

DISCUSSION AND CONCLUSION

Given the military nature of the study hospitals, organisational factors of detection played a key role in identifying errors. Moreover, given the perception of military nurses of errors, they were not inclined to personal detectors. The managers of military hospitals are therefore recommended to pursue a justice-oriented and supportive culture to help nurses play a more active role in identifying errors.

Study of a multisite prospective adverse event surveillance system

Background

We have designed a prospective adverse event (AE) surveillance method. We performed this study to evaluate this method’s performance in several hospitals simultaneously.

Objectives

To compare AE rates obtained by prospective AE surveillance in different hospitals and to evaluate measurement factors explaining observed variation.

Methods

We conducted a multicentre prospective observational study. Prospective AE surveillance was implemented for 8 weeks on the general medicine wards of five hospitals. To determine if population factors may have influenced results, we performed mixed-effects logistic regression. To determine if surveillance factors may have influenced results, we reassigned observers to different hospitals midway through surveillance period and reallocated a random sample of events to different expert review teams.

Results

During 3560 patient days of observation of 1159 patient encounters, we identified 356 AEs (AE risk per encounter=22%). AE risk varied between hospitals ranging from 9.9% of encounters in Hospital D to 35.8% of encounters in Hospital A. AE types and severity were similar between hospitals—the most common types were related to clinical procedures (45%), hospital-acquired infections (21%) and medications (19%). Adjusting for age and comorbid status, we observed an association between hospital and AE risk. We observed variation in observer behaviour and moderate agreement between clinical reviewers, which could have influenced the observed rate difference.

Conclusion

This study demonstrated that it is possible to implement prospective surveillance in different settings. Such surveillance appears to be better suited to evaluating hospital safety concerns within rather than between hospitals as we could not definitively rule out whether the observed variation in AE risk was due to population or surveillance factors.

Limited external reproducibility restricts the use of medical record review for benchmarking

Background

Medical record review (MRR) is used to assess the quality and safety in hospitals. It is increasingly used to compare institutions. Therefore, the external reproducibility should be high. In the current study, we evaluated this external reproducibility for the assessment of an adverse event (AE) in a sample of records from two university medical centres in the Netherlands, using the same review method.

Methods

From both hospitals, 40 medical records were randomly chosen from patient files of deceased patients that had been evaluated in the preceding years by the internal review committees. After reviewing by the external committees, we assessed the overall and kappa agreement by comparing the results of both review rounds (once by the own internal committee and once by the external committee). This was calculated for the presence of an AE, preventability and contribution to death.

Results

Kappa for the presence of AEs was moderate (k=0.47). For preventability, the agreement was fair (k=0.39) and poor for contribution to death (k=−0.109).

Conclusion

We still believe that MRR is suitable for the detection of general issues concerning patient safety. However, based on the outcomes of this study, we would advise to be careful when using MRR for benchmarking.