Measuring Harm in Health Care: Optimizing Adverse Event Review.
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ABSTRACT:
SUBMITTER: Walsh KE
PROVIDER: S-EPMC5352561 | biostudies-literature | 2017 Apr
REPOSITORIES: biostudies-literature
Publications
Measuring Harm in Health Care: Optimizing Adverse Event Review.
Walsh Kathleen E KE Harik Polina P Mazor Kathleen M KM Perfetto Deborah D Anatchkova Milena M Biggins Colleen C Wagner Joann J Schoettker Pamela J PJ Firneno Cassandra C Klugman Robert R Tjia Jennifer J
Medical care 20170401 4
<h4>Objective</h4>The objective of this study was to identify modifiable factors that improve the reliability of ratings of severity of health care-associated harm in clinical practice improvement and research.<h4>Methods</h4>A diverse group of clinicians rated 8 types of adverse events: blood product, device or medical/surgical supply, fall, health care-associated infection, medication, perinatal, pressure ulcer, surgery. We used a generalizability theory framework to estimate the impact of num ...[more]
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Project description:BackgroundPatient safety is a critical concern in clinical oncology, but the ability to measure adverse events (AEs) across cancer care is limited by a narrow focus on treatment-related toxicities. The objective of this study was to assess the nature and extent of AEs among cancer patients across inpatient and outpatient settings.MethodsThis was a retrospective cohort study of 400 adult patients selected by stratified random sampling who had breast (n = 128), colorectal (n = 136), or lung cancer (n = 136) treated at a comprehensive cancer center in 2012. Candidate AEs, or injuries due to medical care, were identified by trained nurse reviewers over the course of 1 year from medical records and safety-reporting databases. Physicians determined the AE harm severity and the likelihood of preventability and harm mitigation.ResultsThe 400-patient sample represented 133,358 days of follow-up. Three hundred four AEs were identified for an overall rate of 2.3 events per 1000 patient days (91.2 per 1000 inpatient days and 0.9 per 1000 outpatient days). Thirty-four percent of the patients had 1 or more AEs (95% confidence interval, 29%-39%), and 16% of the patients had 1 or more preventable or mitigable AEs (95% confidence interval, 13%-20%). The AE rate for patients with breast cancer was lower than the rate for patients with colorectal or lung cancer (P ≤ .001). The preventable or mitigable AE rate was 0.9 per 1000 patient days. Six percent of AEs and 4% of preventable AEs resulted in serious harm. Examples included lymphedema, abscess, and renal failure.ConclusionsA heavy burden of AEs, including preventable or mitigable events, has been identified. Future research should examine risk factors and improvement strategies for reducing their burden. Cancer 2017;123:4728-4736. © 2017 American Cancer Society.
| S-EPMC7539686 | biostudies-literature
Project description:BackgroundPatients can provide valuable information missing from traditional sources of safety data, thus adding new insights about factors that lead to preventable harm. In this study, researchers determined associations between patient-reported contributory factors and patient-reported harms experienced after an adverse event (AE).MethodsA secondary analysis was conducted of a national sample of patient-reported AEs (surgical, medication, diagnostic, and hospital-acquired infection) gathered through an online questionnaire between January 2010 and February 2016. Generalized logit multivariable regression was used to assess the association between patient-reported contributory factors and patient-reported harms (grouped as nonphysical harm only, physical harm only, physical harm and emotional or financial harm, and all three harms) and adjusted for patient and AE characteristics.ResultsOne third of patients (32.6%) reported experiencing all three harms, 27.3% reported physical harms and one additional harm, 25.5% reported physical harms only, and 14.7% reported nonphysical harms only. Patients reporting all three harms were 2.5 times more likely to have filed a report with a responsible authority (95% confidence interval [CI] = 1.23-5.01) and 3.3 times more likely to have also experienced a surgical complication (95% CI = 1.42-7.51). Odds of reporting problems related to communication between clinician and patients/families or clinician-related behavioral issues was 13% higher in those experiencing all three harm types (95% CI = 1.07-1.19).ConclusionPatients' experiences are important to identify safety issues and reduce harm and should be included in patient safety measurement and improvement activities. These findings underscore the need for policy and practice changes to identify, address, and support harmed patients.
| S-EPMC7720932 | biostudies-literature
Project description:BACKGROUND:Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. OBJECTIVE:The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. METHODS:PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. RESULTS:A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. CONCLUSIONS:We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies.
| S-EPMC6000482 | biostudies-literature
Project description:PURPOSE: Although much is known about the safety of an anticancer agent at the time of initial marketing approval, sponsors customarily collect comprehensive safety data for studies that support supplemental indications. This adds significant cost and complexity to the study but may not provide useful new information. The main purpose of this analysis was to assess the amount of safety and concomitant medication data collected to determine a more optimal approach in the collection of these data when used in support of supplemental applications. METHODS: Following a prospectively developed statistical analysis plan, we reanalyzed safety data from eight previously completed prospective randomized trials. RESULTS: A total of 107,884 adverse events and 136,608 concomitant medication records were reviewed for the analysis. Of these, four grade 1 to 2 and nine grade 3 and higher events were identified as drug effects that were not included in the previously established safety profiles and could potentially have been missed using subsampling. These events were frequently detected in subsamples of 400 patients or larger. Furthermore, none of the concomitant medication records contributed to labeling changes for the supplemental indications. CONCLUSION: Our study found that applying the optimized methodologic approach, described herein, has a high probability of detecting new drug safety signals. Focusing data collection on signals that cause physicians to modify or discontinue treatment ensures that safety issues of the highest concern for patients and regulators are captured and has significant potential to relieve strain on the clinical trials system.
| S-EPMC3018355 | biostudies-literature
Project description:BackgroundMitigating or reducing the risk of medication harm is a global policy priority. But evidence reflecting preventable medication harm in medical care and the factors that derive this harm remain unknown. Therefore, we aimed to quantify the prevalence, severity and type of preventable medication harm across medical care settings.MethodsWe performed a systematic review and meta-analysis of observational studies to compare the prevalence of preventable medication harm. Searches were carried out in Medline, Cochrane library, CINAHL, Embase and PsycINFO from 2000 to 27 January 2020. Data extraction and critical appraisal was undertaken by two independent reviewers. Random-effects meta-analysis was employed followed by univariable and multivariable meta-regression. Heterogeneity was quantified using the I2 statistic, and publication bias was evaluated.ProsperoCRD42020164156.ResultsOf the 7780 articles, 81 studies involving 285,687 patients were included. The pooled prevalence for preventable medication harm was 3% (95% confidence interval (CI) 2 to 4%, I2 = 99%) and for overall medication harm was 9% (95% CI 7 to 11%, I2 = 99.5%) of all patient incidence records. The highest rates of preventable medication harm were seen in elderly patient care settings (11%, 95% 7 to 15%, n = 7), intensive care (7%, 4 to 12%, n = 6), highly specialised or surgical care (6%, 3 to 11%, n = 13) and emergency medicine (5%, 2 to 12%, n = 12). The proportion of mild preventable medication harm was 39% (28 to 51%, n = 20, I2 = 96.4%), moderate preventable harm 40% (31 to 49%, n = 22, I2 = 93.6%) and clinically severe or life-threatening preventable harm 26% (15 to 37%, n = 28, I2 = 97%). The source of the highest prevalence rates of preventable harm were at the prescribing (58%, 42 to 73%, n = 9, I2 = 94%) and monitoring (47%, 21 to 73%, n = 8, I2 = 99%) stages of medication use. Preventable harm was greatest in medicines affecting the 'central nervous system' and 'cardiovascular system'.ConclusionsThis is the largest meta-analysis to assess preventable medication harm. We conclude that around one in 30 patients are exposed to preventable medication harm in medical care, and more than a quarter of this harm is considered severe or life-threatening. Our results support the World Health Organisation's push for the detection and mitigation of medication-related harm as being a top priority, whilst highlighting other key potential targets for remedial intervention that should be a priority focus for future research.
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Project description:AimAdverse drug events (ADEs) are harmful and unintended consequences of medications. Their reporting is essential for drug safety monitoring and research, but it has not been standardized internationally. Our aim was to synthesize information about the type and variety of data collected within ADE reporting systems.MethodsWe developed a systematic search strategy, applied it to four electronic databases, and completed an electronic grey literature search. Two authors reviewed titles and abstracts, and all eligible full-texts. We extracted data using a standardized form, and discussed disagreements until reaching consensus. We synthesized data by collapsing data elements, eliminating duplicate fields and identifying relationships between reporting concepts and data fields using visual analysis software.ResultsWe identified 108 ADE reporting systems containing 1782 unique data fields. We mapped them to 33 reporting concepts describing patient information, the ADE, concomitant and suspect drugs, and the reporter. While reporting concepts were fairly consistent, we found variability in data fields and corresponding response options. Few systems clarified the terminology used, and many used multiple drug and disease dictionaries such as the Medical Dictionary for Regulatory Activities (MedDRA).ConclusionWe found substantial variability in the data fields used to report ADEs, limiting the comparability of ADE data collected using different reporting systems, and undermining efforts to aggregate data across cohorts. The development of a common standardized data set that can be evaluated with regard to data quality, comparability and reporting rates is likely to optimize ADE data and drug safety surveillance.
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Project description:Introduction: Achieving equity in health care remains a challenge for health care systems worldwide and marked inequities in access and quality of care persist. Identifying health care equity indicators is an important first step in integrating the concept of equity into assessments of health care system performance, particularly in emergency care. Methods: We conducted a systematic review of administrative data-derived health care equity indicators and their association with socioeconomic determinants of health (SEDH) in emergency care settings. Following PRISMA-Equity reporting guidelines, Ovid MEDLINE, EMBASE, PubMed, and Web of Science were searched for relevant studies. The outcomes of interest were indicators of health care equity and the associated SEDH they examine. Results: Among 29 studies identified, 14 equity indicators were identified and grouped into four categories that reflect the patient emergency care pathway. Total emergency department (ED) visits and ambulatory care-sensitive condition-related ED visits were the two most frequently used equity indicators. The studies analyzed equity based on seven SEDH: social deprivation, income, education level, social class, insurance coverage, health literacy, and financial and nonfinancial barriers. Despite some conflicting results, all identified SEDH are associated with inequalities in access to and use of emergency care. Conclusion: The use of administrative data-derived indicators in combination with identified SEDH could improve the measurement of health care equity in emergency care settings across health care systems worldwide. Using a combination of indicators is likely to lead to a more comprehensive, well-rounded measurement of health care equity than using any one indicator in isolation. Although studies analyzed focused on emergency care settings, it seems possible to extrapolate these indicators to measure equity in other areas of the health care system. Further studies elucidating root causes of health inequities in and outside the health care system are needed.
| S-EPMC8742300 | biostudies-literature
Project description:Adverse events are poor patient outcomes caused by medical care. Their identification requires the peer-review of poor outcomes, which may be unreliable. Combining physician ratings might improve the accuracy of adverse event classification.To evaluate the variation in peer-reviewer ratings of adverse outcomes; determine the impact of this variation on estimates of reviewer accuracy; and determine the number of reviewers who judge an adverse event occurred that is required to ensure that the true probability of an adverse event exceeded 50%, 75% or 95%.Thirty physicians rated 319 case reports giving details of poor patient outcomes following hospital discharge. They rated whether medical management caused the outcome using a six-point ordinal scale. We conducted latent class analyses to estimate the prevalence of adverse events as well as the sensitivity and specificity of each reviewer. We used this model and Bayesian calculations to determine the probability that an adverse event truly occurred to each patient as function of their number of positive ratings.The overall median score on the 6-point ordinal scale was 3 (IQR 2,4) but the individual rater median score ranged from a minimum of 1 (in four reviewers) to a maximum median score of 5. The overall percentage of cases rated as an adverse event was 39.7% (3798/9570). The median kappa for all pair-wise combinations of the 30 reviewers was 0.26 (IQR 0.16, 0.42; Min?=?-0.07, Max?=?0.62). Reviewer sensitivity and specificity for adverse event classification ranged from 0.06 to 0.93 and 0.50 to 0.98, respectively. The estimated prevalence of adverse events using a latent class model with a common sensitivity and specificity for all reviewers (0.64 and 0.83 respectively) was 47.6%. For patients to have a 95% chance of truly having an adverse event, at least 3 of 3 reviewers are required to deem the outcome an adverse event.Adverse event classification is unreliable. To be certain that a case truly represents an adverse event, there needs to be agreement among multiple reviewers.
| S-EPMC3406022 | biostudies-literature
Project description:ObjectiveWe conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients.Materials and methodsWe searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS).ResultsTwenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated.ConclusionsSeveral challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.
| S-EPMC10114128 | biostudies-literature
Project description:BACKGROUND:The use of eHealth has increased tremendously in recent years. eHealth is generally considered to have a positive effect on health care quality and to be a promising alternative to face-to-face health care contacts. Surprisingly little is known about possible adverse effects of eHealth apps. OBJECTIVE:We conducted a scoping review on empirical research into adverse effects of eHealth apps that aim to deliver health care at a distance. We investigated whether adverse effects were reported and the nature and quality of research into these possible adverse effects. METHODS:For this scoping review, we followed the five steps of Arksey and O'Malley's scoping review methodology. We searched specifically for studies into eHealth apps that replaced or complemented the face-to-face contact between a health professional and a patient in the context of treatment, health monitoring, or supporting self-management. Studies were included when eHealth and adverse effects were mentioned in the title or abstract and when empirical data on adverse effects were provided. All health conditions, with the exception of mental health conditions, all ages, and all sample sizes were included. We examined the literature published between December 2012 and August 2017 in the following databases: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and the Cochrane Library. The methodological quality of the studies was assessed using the Critical Appraisal Skills Programme (CASP) checklists. RESULTS:Our search identified 79 papers that were potentially relevant; 11 studies met our inclusion criteria after screening. These studies differed in many ways and the majority were characterized by small research populations and low study quality. Adverse effects are rarely subject to systematic scientific research. So far, information on real adverse effects is mainly limited to incidental reporting or as a bycatch from qualitative pilot studies. Despite the shortage of solid research, we found some indications of possible negative impact on patient-centeredness and efficiency, such as less transparency in the relationship between health professionals and patients and time-consuming work routines. CONCLUSIONS:There is a lack of high-quality empirical research on adverse effects of eHealth apps that replace or complement face-to-face care. While the development of eHealth apps is ongoing, the knowledge with regard to possible adverse effects is limited. The available research often focuses on efficacy, added value, implementation issues, use, and satisfaction, whereas adverse effects are underexplored. A better understanding of possible adverse effects could be a starting point in improving the positive impact of eHealth-based health care delivery.
| S-EPMC6421717 | biostudies-literature