Interventions to reduce the incidence of medical error and its financial burden in health care systems: A systematic review of systematic reviews

Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis

There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.

Dealing with complications in interventional radiology

It is widely accepted that most misadventures, which lead to harm have not occurred because of a single individual but rather due to a failure of process that results in healthcare workers making mistakes. This failure of process and the pervasiveness of adverse events is just as prevalent in Interventional Radiology (IR) as it is in other specialities. The true prevalence and prevailing aetiology of complications in IR are not exactly known as there is a paucity of investigative literature into this area; especially when compared with other more established disciplines such as Surgery. Some IR procedures have a higher risk profile than others. However, published data suggests that many adverse events in IR are preventable (55-84%) and frequently involve a device related complication such as improper usage or malfunction. This article aims to discuss factors that contribute to complications in IR along with tools and strategies for dealing with them to achieve optimal patient outcomes.

Patterns of adverse drug reactions in Jordan: a retrospective analysis of the National Pharmacovigilance Data Registry (2015-2021)

BACKGROUND

Post-marketing surveillance of drugs is a cornerstone of pharmacovigilance. This study was conducted to characterize patterns of adverse drug reactions (ADRs) reported in Jordan.

RESEARCH DESIGN AND METHODS

ADR reports submitted to the pharmacovigilance database of the Jordan Food and Drug Administration during 2015-2021 were retrospectively analyzed. The most commonly reported drugs, drug classes, ADRs, and ADRs consequences were explored. Logistic regression identified possible predictors of reporting serious ADRs.

RESULTS

A total of 2744 ADR reports were included, among which 28.4% were classified as serious. An annual increase in ADR reporting was observed. The most commonly implicated drug classes were antineoplastic and immunomodulating agents (24.0%), anti-infectives for systemic use (14.2%), and alimentary tract and metabolism (12.1%). Covid-19 vaccination was the most reported drug (22.8%). Fatigue (6.3%), injection site pain (6.1%), and headache (6.0%) were the top three common ADRs. Among ADRs with outcome information, 4.7% were fatal. Patient's age and intravenous medication use largely predicted reporting serious ADRs.

CONCLUSIONS

This study provides contemporary insights into the post-marketing surveillance of drugs in Jordan. The findings are foundational for future studies exploring drug-ADRs causality relationships. Efforts that promote pharmacovigilance concepts should be sustained and enhanced at the national level.

How Many People Experience Unsafe Medical Care in Thailand, and How Much Does It Cost under Universal Coverage Scheme?

Adverse events and medical harm comprise major health concerns for people all over the world, including Thailand. The prevalence and burden of medical harm must always be monitored, and a voluntary database should not be used to represent national value. The purpose of this study is to estimate the national prevalence and economic impact of medical harm in Thailand using routine administrative data from the inpatient department electronic claim database under the Universal Coverage scheme from 2016 to 2020. Our findings show that there are approximately 400,000 visits with potentially unsafe medical care per year (or 7% of all inpatient visits under the Universal Coverage scheme). The annual cost of medical harm is estimated to be approximately USD 278 million (approximately THB 9.6 billion), with an average of 3.5 million bed-days per year. This evidence can be used to raise safety awareness and support medical harm prevention policies. Future work should focus on improving medical harm surveillance using better data quality and more comprehensive data on medical harm.

Disclosing medical errors: how do we prepare our students?

PURPOSE

Despite patient safety initiatives, medical errors remain common and devastating. Disclosing errors is not only ethical, but also promotes restoration of the doctor-patient relationship. However, studies show active avoidance of error disclosure and the need for explicit training. In the South African setting, sparse information exists in terms of undergraduate medical training in error disclosure. To address this knowledge gap, the training of error disclosure in an undergraduate medical programme was examined, against the background of the available literature. The objective was to formulate a strategy to improve error disclosure teaching and practice, with the goal of improving patient care.

METHODS

Firstly, the literature was reviewed regarding the training of medical error disclosure. Secondly, the undergraduate medical training in error disclosure was probed, by looking at the pertinent findings from a broader study on undergraduate communication skills training. The design of the study was descriptive and cross-sectional. Anonymous questionnaires were distributed to all fourth- and fifth-year undergraduate medical students. Data were predominantly analysed quantitatively. Open-ended questions were analysed qualitatively using grounded theory coding.

RESULTS

Out of 132 fifth-year medical students, 106 participated (response rate 80.3%), while 65 out of 120 fourth-year students participated (response rate 54.2%). Of these participants, 48 fourth-year students (73.9%) and 64 fifth-year students (60.4%) reported infrequent teaching in the disclosure of medical errors. Almost half of the fourth-year students (49.2%) considered themselves novices in error disclosure, while 53.3% of fifth-year students rated their ability as average. According to 37/63 (58.7%) fourth-year students and 51/100 (51.0%) fifth-year students, senior doctors seldom or never modelled patient-centred care in the clinical training setting. These results resonated with the findings of other studies that showed lack of patient-centredness, as well as insufficient training in error disclosure, with resultant low confidence in this skill.

CONCLUSION

The study findings confirmed a dire need for more frequent experiential training in the disclosure of medical errors, in undergraduate medical education. Medical educators should view errors as learning opportunities to improve patient care and model error disclosure in the clinical learning environment.

Application of the program for artificial intelligence analytics of paper text and segmentation by specified parameters in clinical practice

The development of novel technologies using elements of artificial intelligence (AI) in medicine is addressed to practical clinical implementation and provision of key issues, including improvement in the use of routine clinical data, aimed at practical relevance, standardization, confidentiality and patient safety.

Aim. To evaluate the effectiveness of the RuPatient electronic heart record (EHR) system in real clinical practice for extracting and structuring medical data.

Material and methods. Extraction and recognition of data using EHR from various following sources: outpatient records, statements, routine medical reports, epicrisis and other structured and unstructured medical information based on the developed technology of intelligent text analytics, optical character recognition, for specified words and phrases, and the use of machine learning elements. A particular criterion for evaluating the effectiveness of EHR is the time spent on filling out electronic medical records compared to real clinical practice.

Results. The time of entering and processing information by the recognition system of medical documentation included in the RuPatient EHR was shorter than in standard practice (20,3±1,4 minutes, 25,1±1,5 minutes, respectively, p<0,001), the average time of recognition of documents was 30±4,3 seconds. During the ROC analysis, we determined that the threshold value that allows high accuracy to recognize images of discharge epicrisis using the RuPatient system was 83,5% with an area under the curve (AUC) value of 0,76.

Conclusions. The developed RuPatient EHR has a medical documentation recognition module for creating structured data based on AI technology elements and can be used in creating an electronic medical history and accumulation of structured data for the implementation of tasks for the practical and scientific use of big data and AI projects in medicine. When using the RuPatient system, the burden on medical staff during document management can be reduced and access to primary medical information simplified.

The ART of Electronic Prescribing

Medication errors are common. Electronic Health Records (EHR) reduce some of the roots with some while they create others. EHR need to evolve. A suggestion is made to deal with prescriptions that are new. being titrated, to support the process of prescribing and hopefully correcting errors linked to it.