Investigating Adverse Event Free Admissions in Medicare Inpatients as a Patient Safety Indicator

Publication Trends of Research on Adverse Event and Patient Safety in Nursing Research: A 8-Year Bibliometric Analysis

BACKGROUND

Adverse events (AEs), which are associated with medical system instability, poor clinical outcomes, and increasing socioeconomic burden, represent a negative outcome of the healthcare system and profoundly influence patient safety. However, research into AEs remains at a developmental stage according to the existing literature, and no previous studies have systematically reviewed the current state of research in the field of AEs. Therefore, the aims of this study were to interpret the results of published research in the field of AEs through bibliometric analysis and to analyze the trends and patterns in the data, which will be important for subsequent innovations in the field.

METHODS

A statistical and retrospective visualization bibliometric analysis was performed on July 28, 2022. The research data were extracted from the Web of Science Core Collection, and bibliometric citation analysis was performed using Microsoft Excel, VOSviewer 1.6.18, CiteSpace 6.1.R2, and the Online Analysis Platform of Literature Metrology ( http://bibliometric.com/ ).

RESULTS

A total of 1035 publications on AEs were included in the analysis. The number of articles increased annually from 2014 to 2022. Among them, the United States (n = 318) made the largest contribution, and Chung-Ang University (n = 20) was the affiliation with the greatest influence in this field. Despite notable international cooperation, a regional concentration of research literature production was observed in economically more developed countries. In terms of authors, Stone ND (n = 9) was the most productive author in the research of AEs. Most of the publications concerning AEs were cited from internationally influential nursing journals, and the Journal of Nursing Management (n = 62) was the most highly published journal. Regarding referencing, the article titled "Medical error-the third leading cause of death in the US" received the greatest attention on this topic (51 citations).

CONCLUSIONS

After systematically reviewed the current state of research in the field of AEs through bibliometric analysis, and AEs highlighted medication errors, patient safety, according reporting, and quality improvement as essential developments and research hotspots in this field. Furthermore, thematic analysis identified 2 new directions in research, concerned with psychological safety, nurse burnout, and with important research value and broad application prospects in the future.

Evaluation of a preoperative personalized risk communication tool: a prospective before-and-after study

PURPOSE

Patients want personalized information before surgery; most do not receive personalized risk estimates. Inadequate information contributes to poor experience and medicolegal complaints. We hypothesized that exposure to the Personalized Risk Evaluation and Decision Making in Preoperative Clinical Assessment (PREDICT) app, a personalized risk communication tool, would improve patient knowledge and satisfaction after anesthesiology consultations compared with standard care.

METHODS

We conducted a prospective clinical study (before-after design) and used patient-reported data to calculate personalized risks of morbidity, mortality, and expected length of stay using a locally calibrated National Surgical Quality Improvement Program risk calculator embedded in the PREDICT app. In the standard care (before) phase, the application's materials and output were not available to participants; in the PREDICT app (after) phase, personalized risks were communicated. Our primary outcome was knowledge score after the anesthesiology consultation. Secondary outcomes included patient satisfaction, anxiety, feasibility, and acceptability.

RESULTS

We included 183 participants (90 before; 93 after). Compared with standard care phase, the PREDICT app phase had higher post-consultation: knowledge of risks (14.3% higher; 95% confidence interval [CI], 6.5 to 22.0; P < 0.001) and satisfaction (0.8 points; 95% CI, 0.1 to 1.4; P = 0.03). Anxiety was unchanged (- 1.9%; 95% CI, - 4.2 to 0.5; P = 0.13). Acceptability was high for patients and anesthesiologists.

CONCLUSION

Exposure to a patient-facing, personalized risk communication app improved knowledge of personalized risk and increased satisfaction for adults before elective inpatient surgery.

TRIAL REGISTRATION

www.clinicaltrials.gov (NCT03422133); registered 5 February 2018.

Incident Reporting System in an Italian University Hospital: A New Tool for Improving Patient Safety

Clinical risk management constitutes a central element in the healthcare systems in relation to the reverberation that it establishes, and as regards the optimization of clinical outcomes for the patient. The starting point for a right clinical risk management is represented by the identification of non-conforming results. The aim of the study is to carry out a systematic analysis of all data received in the first three years of adoption of a reporting system, revealing the strengths and weaknesses. The results emerged showed an increasing trend in the number of total records. Notably, 86.0% of the records came from the medical category. Moreover, 41.0% of the records reported the possible preventive measures that could have averted the event and in 30% of the reports are hints to be put in place to avoid the repetition of the events. The second experimental phase is categorizing the events reported. Implementing the reporting system, it would guarantee a virtuous cycle of learning, training and reallocation of resources. By sensitizing health workers to a correct use of the incident reporting system, it could become a virtuous error learning system. All this would lead to a reduction in litigation and an implementation of the therapeutic doctor–patient alliance.

Care complexity individual factors associated with adverse events and in-hospital mortality

Introduction

Measuring the impact of care complexity on health outcomes, based on psychosocial, biological and environmental circumstances, is important in order to detect predictors of early deterioration of inpatients. We aimed to identify care complexity individual factors associated with selected adverse events and in-hospital mortality.

Methods

A multicenter, case-control study was carried out at eight public hospitals in Catalonia, Spain, from January 1, 2016 to December 31, 2017. All adult patients admitted to a ward or a step-down unit were evaluated. Patients were divided into the following groups based on the presence or absence of three adverse events (pressure ulcers, falls or aspiration pneumonia) and in-hospital mortality. The 28 care complexity individual factors were classified in five domains (developmental, mental-cognitive, psycho-emotional, sociocultural and comorbidity/complications). Adverse events and complexity factors were retrospectively reviewed by consulting patients’ electronic health records. Multivariate logistic analysis was performed to identify factors associated with an adverse event and in-hospital mortality.

Results

A total of 183,677 adult admissions were studied. Of these, 3,973 (2.2%) patients experienced an adverse event during hospitalization (1,673 [0.9%] pressure ulcers; 1,217 [0.7%] falls and 1,236 [0.7%] aspiration pneumonia). In-hospital mortality was recorded in 3,996 patients (2.2%). After adjustment for potential confounders, the risk factors independently associated with both adverse events and in-hospital mortality were: mental status impairments, impaired adaptation, lack of caregiver support, old age, major chronic disease, hemodynamic instability, communication disorders, urinary or fecal incontinence, vascular fragility, extreme weight, uncontrolled pain, male sex, length of stay and admission to a medical ward. High-tech hospital admission was associated with an increased risk of adverse events and a reduced risk of in-hospital mortality. The area under the ROC curve for both outcomes was > 0.75 (95% IC: 0.78–0.83).

Conclusions

Several care complexity individual factors were associated with adverse events and in-hospital mortality. Prior identification of complexity factors may have an important effect on the early detection of acute deterioration and on the prevention of poor outcomes.

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury

BACKGROUND

More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients' data to extract the temporal features using their structural temporal patterns, that is, trends.

OBJECTIVE

This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI).

METHODS

Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation.

RESULTS

Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001).

CONCLUSIONS

Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

Validation of new ICD-10-based patient safety indicators for identification of in-hospital complications in surgical patients: a study of diagnostic accuracy

OBJECTIVE

Administrative data systems are used to identify hospital-based patient safety events; few studies evaluate their accuracy. We assessed the accuracy of a new set of patient safety indicators (PSIs; designed to identify in hospital complications).

STUDY DESIGN

Prospectively defined analysis of registry data (1 April 2010-29 February 2016) in a Canadian hospital network. Assignment of complications was by two methods independently. The National Surgical Quality Improvement Programme (NSQIP) database was the clinical reference standard (primary outcome=any in-hospital NSQIP complication); PSI clusters were assigned using International Classification of Disease (ICD-10) codes in the discharge abstract. Our primary analysis assessed the accuracy of any PSI condition compared with any complication in the NSQIP; secondary analysis evaluated accuracy of complication-specific PSIs.

PATIENTS

All inpatient surgical cases captured in NSQIP data.

ANALYSIS

We assessed the accuracy of PSIs (with NSQIP as reference standard) using positive and negative predictive values (PPV/NPV), as well as positive and negative likelihood ratios (±LR).

RESULTS

We identified 12 898 linked episodes of care. Complications were identified by PSIs and NSQIP in 2415 (18.7%) and 2885 (22.4%) episodes, respectively. The presence of any PSI code had a PPV of 0.55 (95% CI 0.53 to 0.57) and NPV of 0.93 (95% CI 0.92 to 0.93); +LR 6.41 (95% CI 6.01 to 6.84) and -LR 0.40 (95% CI 0.37 to 0.42). Subgroup analyses (by surgery type and urgency) showed similar performance. Complication-specific PSIs had high NPVs (95% CI 0.92 to 0.99), but low to moderate PPVs (0.13-0.61).

CONCLUSION

Validation of the ICD-10 PSI system suggests applicability as a first screening step, integrated with data from other sources, to produce an adverse event detection pathway that informs learning healthcare systems. However, accuracy was insufficient to directly identify or rule out individual-level complications.

Analysis of Human Performance Deficiencies Associated With Surgical Adverse Events

IMPORTANCE

Potentially preventable adverse events remain a formidable cause of patient harm and health care expenditure despite advances in systems-based risk-reduction strategies.

OBJECTIVE

To analyze and describe the incidence of human performance deficiencies (HPDs) during the provision of surgical care to identify opportunities to enhance patient safety.

DESIGN, SETTING, AND PARTICIPANTS

This quality improvement study used a new taxonomy to inform the development and implementation of an HPD classifier tool to categorize HPDs into errors associated with cognitive, technical, and team dynamic functions. The HPD classifier tool was then used to concurrently analyze surgical adverse events in 3 adult hospital affiliates-a level I municipal trauma center, a quaternary care university hospital, and a US Veterans Administration hospital-from January 2, 2018, to June 30, 2018. Surgical trainees presented data describing all adverse events associated with surgical services at weekly hospital-based morbidity and mortality conferences. Adverse events and HPDs were classified in discussion with attending faculty and residents. Data were analyzed from July 9, 2018, to December 23, 2018.

MAIN OUTCOMES AND MEASURES

The incidence and primary and secondary causes of HPDs were classified using an HPD classifier tool.

RESULTS

A total of 188 adverse events were recorded, including 182 adverse events (96.8%) among 5365 patients who underwent surgical operations and 6 adverse events (3.2%) among patients undergoing nonoperative treatment. Among these 188 adverse events, 106 (56.4%) were associated with HPDs. Among these 106 HPD adverse events, a total of 192 HPDs (mean [SD], 1.8 [0.9] HPDs per HPD event) were identified. Human performance deficiencies were categorized as execution (98 HPDs [51.0%]), planning or problem solving (55 HPDs [28.6%]), communication (24 HPDs [12.5%]), teamwork (9 HPDs [4.7%]), and rules violation (6 HPDs [3.1%]). Human performance deficiencies most commonly presented as cognitive errors in execution of care or in case planning or problem solving (99 of 192 HPDs [51.6%]). In contrast, technical execution errors without other associated HPDs were observed in 20 of 192 HPDs (10.4%).

CONCLUSIONS AND RELEVANCE

Human performance deficiencies were identified in more than half of adverse events, most commonly associated with cognitive error in the execution of care. These data provide a framework and impetus for new quality improvement initiatives incorporating cognitive training to mitigate human error in surgery.