How to Utilize Data Visualization Method to Analyze Information Systems Related Medical Errors

Medical errors, such as misdiagnosis, incorrect drug dispensing, surgical injuries, even patient name errors, or misuse computer information systems in healthcare systems can cause serious consequences to patients. A meta-analysis estimates that medical errors cause approximately 22,000 preventable deaths in the United State each year (Rodwin et al., 2020). To help reduce medical errors and enhance patient safety, The Agency for Healthcare Research and Quality (AHRQ), one of twelve agencies within the United States Department of Health and Human Services, have created repository of medical error cases, which are often reports and case studies authored by clinical professionals (e.g., physicians, nurses, and hospital managers). These narratives provide patients and peer healthcare professionals with valuable lessons regarding the causes, contexts, and consequences of various medical errors, and some are following up with comments or suggestions from experienced professionals. In addition, those medical reports often have many attributes subjectively tagged by the report authors such as Computer Information Systems related, EHR related, or Telemedicine related. Some research has applied machine-learning methods (e.g., BERT) to mine medical error reports (Xu et al. 2021). The present study focuses on the attributes of medical error reports and seeks to identify and visualize the correlation between types of medical errors and the types of information systems related errors, and hope to provide patients, clinical professionals, healthcare administrators, and policy makers with straightforward illustrations to understand the medical error issues. We have acquired over 500 medical error reports from AHRQ, from 2003 to 2021. The attributes of these reports include case title, error types, clinical area, safety target, target audience, setting of care, approach to improve safety, etc. Medical errors are categorized into seven major types: Active Errors, Cognitive Errors ( Mistakes ), Epidemiology of Errors and Adverse Events, Latent Errors, Near Miss, Non-cognitive Errors ( Slips & Lapses ), and Other. Our preliminary study explores all the medical error reports with one analysis focusing on the error cases which can be improved by clinical information systems related approaches, such as Computerized Adverse Event Detection (CAED), Computer-Assisted Therapy (CAT), Clinical Information Systems (CIS), Computerized Decision Support (CDS), Computerized Provider Order Entry (CPOE), Electronic Health Records (EHR), and Telemedicine. The preliminary result (see the chart below) shows that in each of the seven error types, EHR is a major type of approach to improve in six out of seven error categories

Forecasting Chinese EPU based on financial uncertainty in emerging market economies (EMEs): evidence from six selected East Asian economies

While the influential role of Economic Policy Uncertainty (EPU) on economic activity and financial markets is well-documented, little is known about how to forecast EPU, especially in the framework of an emerging market economy (EME). We forecast the newly developed EPU index of China based on financial uncertainty (measured by a realised volatility) of the selected East Asian Economies (EAEs) including ASEAN5 and Hong Kong, having close trade linkages with China, by using LR and DT methods. After controlling for macroeconomic variables, it is evident that the realised volatility of regional EAEs significantly forecasts the EPU of China, except for Thailand. Moreover, comparing the performance of both models based on the accuracy classification score test, LR performs better than DT. Policymakers, who aim to keep and maintain a low level of EPU to achieve effective investment policies and avoid reduced consumer spending, should take into account the findings of this study

Uncertainty shocks and monetary policy: evidence from the troika of China’s economy

Growth in China’s economy is driven by the troika: consumption, investment and export. This paper examines the effect of uncertain events such as the global financial crisis in 2008, and the COVID-19 pandemic on the troika. Based on the construction of a new uncertainty index of China’s economy, the relationship between uncertainty and growth in the troika is examined by using a TVP-VAR model. Results show that fluctuations in the uncertainty index during the COVID-19 epidemic had the greatest negative impact on consumption and investment at a magnitude of 0.27, notably greater than that during the period of the global financial crisis. The negative impact on export reached 0.73, smaller than that during the global financial crisis. Against a backdrop of the novel coronavirus epidemic, it is also found that expansionary monetary policies can have a relatively large impact on investment and export, reaching 1.75 and 1.57 respectively, while short-term impact on consumption is relatively weak, averaging at 0.51

Revisiting Medical Errors: Collaborative Errors

Medical error is a label used to refer to preventable adverse events in the healthcare setting. Errors in medical practice and service can occur at various timepoints and contexts, driven by both human and non-human factors. As healthcare continuously evolves, particularly against the backdrop of a digital landscape, it has become even more of a necessity to conduct a comprehensive examination of the causes and potential solutions for the wide array of medical errors that can occur. Conventionally, medical errors have been studied from the clinical perspective to prevent and remedy errors such as diagnostic errors, medication errors, surgical errors, and errors in medical protocol. The digitalization of healthcare practice provides new opportunities to conduct longitudinal analysis, but also presents challenges relatively new to medical error research, but familiar in the world of data quality, including data that is siloed across different timepoints and entities. As the field moves towards prevention-focused care practice, we anticipate that longitudinal data about managed care bundled by patients will become more available. This study conducts an exploratory literature review of the factors contributing to medical errors, emphasizing the interdisciplinary nature and collaborative mode in defining and mitigating errors. The medical and healthcare literature discusses the medical practice and service within a visit, test, surgery, and transfer extensively. The error research literature identifies human errors, such as, slips and mistakes, and others from individual episodes. Other literature focuses on specific types, causes, and contexts of medical errors, such as culture, leadership, training, and systems. Many empirical medical error studies are available for certain service or project period. Other studies focus on transfers of patients. We also reviewed literature on non-medical errors, such as, nuclear plants and airlines. We reviewed many organizational process literatures that discusses errors stem from knowledge sharing and boundary shifting. We also reviewed data quality literature that embeds various contexts in quality of data. We aim to review and synthesize the literature across disciplines for studying the medical errors based on a patient over time, cross multiple services, visits, and transfers in order to account for the interdisciplinary phenomenon of medical errors and collaborative errors. Based on this review, we propose a longitudinal framework and concepts to understand collaborative medical errors based on patients’ experience over time. We present several propositions on how specialized collaborative efforts might contribute to creating and solving medical errors. In addition, this review also explores the role of automation, technology, role-based communication, and evidence-based approaches in mitigating errors. This research significantly contributes to the field by challenging traditional perspectives on medical errors, expanding the scope of error analysis, and offering practical strategies for error reduction. It underscores the critical role of interdisciplinary collaboration in healthcare and provides a solid foundation for future studies in the pursuit of safer and higher-quality patient care

Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

Clustering has become a common trend in very long instruction words (VLIW) architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC) VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC) VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file

Activation detection in functional near-infrared spectroscopy by wavelet coherence

Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction

Predictive values of PD‑L1 expression for survival outcomes in patients with cervical cancer: a systematic review and meta-analysis

Objectives: Cervical cancer is one of the most common cancers in women worldwide. Although mortality has declined over the past 30 years in high-income areas, it remains a problem in several countries. Given that the prognosis of patients with recurrent or metastatic disease is poor, it is necessary to identify valuable predictive indicators to estimate survival outcomes in patients with cervical cancer.Material and methods: We searched electronic databases such as PubMed, Web of Science, Embase, Ovid MEDLINE, and the Cochrane Central Register of Controlled Trials, and investigated the relationship between Programmed death-ligand 1(PD-L1) expression and prognosis. Chi squared tests and I2 were utilized to assess study heterogeneity, and publication bias was estimated using Begg’s funnel plot and Egger linear regression test.Results: Thirteen eligible studies with 1422 patients were included. Generally, high PD-L1 expression was conclusively associated with poor overall survival (OS) (HR: 1.31; 95% CI 1.03–1.66, p = 0.025). However, PD-L1 expression demonstrated no association with progression-free survival (HR: 0.93; 0.73–1.19, p = 0.57). High PD-L1 expression with a sample size over 100 indicated a shorter OS (HR: 1.51; 95% CI 1.13–2.01). High expression of PD-L1 in Asians represented a lower OS (HR: 1.52; 1.14–2.03). Overexpression of PD-L1 in tumor cells (HR: 1.57; 1.29–2.10) and tumor-infiltrating immune cells (HR: 1.75; 1.02-2.99) predicted poor OS. High PD-L1 expression (HR: 4.04; 2.58–6.31) showed a lower effect on OS with a cut-off value of 5%.Conclusions: Our results indicate that high PD-L1 expression could be a valuable biomarker for predicting clinical outcomes in patients with cervical cancer

Adult Attachment Styles Associated with Brain Activity in Response to Infant Faces in Nulliparous Women: An Event-Related Potentials Study

Adult attachment style is a key for understanding emotion regulation and feelings of security in human interactions as well as for the construction of the caregiving system. The caregiving system is a group of representations about affiliative behaviors, which is guided by the caregiver’s sensitivity and empathy, and is mature in young adulthood. Appropriate perception and interpretation of infant emotions is a crucial component of the formation of a secure attachment relationship between infant and caregiver. As attachment styles influence the ways in which people perceive emotional information, we examined how different attachment styles associated with brain response to the perception of infant facial expressions in nulliparous females with secure, anxious, and avoidant attachment styles. The event-related potentials of 65 nulliparous females were assessed during a facial recognition task with joy, neutral, and crying infant faces. The results showed that anxiously attached females exhibited larger N170 amplitudes than those with avoidant attachment in response to all infant faces. Regarding the P300 component, securely attached females showed larger amplitudes to all infant faces in comparison with avoidantly attached females. Moreover, anxiously attached females exhibited greater amplitudes than avoidantly attached females to only crying infant faces. In conclusion, the current results provide evidence that attachment style differences are associated with brain responses to the perception of infant faces. Furthermore, these findings further separate the psychological mechanisms underlying the caregiving behavior of those with anxious and avoidant attachment from secure attachment