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

The strangeness form factors of the proton within nonrelativistic constituent quark model revisited

We reexamine, within the nonrelativistic constituent quark model (NRCQM), a recent claim that the current data on the strangeness form factors indicates that the uuds\bar{s} component in the proton is such that the uuds subsystem has the mixed spatial symmetry [31]_X and flavor spin symmetry [4]_{FS}[22]_F[22]_S, with \bar{s} in S state (configuration I). We find this claim to be invalid if corrected expressions for the contributions of the transition current to G_A^s and G_E^s are used. We show that, instead, it is the lowest-lying uuds\bar{s} configuration with uuds subsystem of completely symmetric spatial symmetry [4]_X and flavor spin symmetry [4]_{FS}[22]_F[22]_S, with \bar{s} in P state (configuration II), which could account for the empirical signs of all form factors G_E^s, G_M^s, and G_A^s. Further, we find that removing the center-of-mass motion of the clusters will considerably enhance the contributions of the transition current. We also demonstrate that it is possible to give a reasonable description of the existing form factors data with a tiny probability P_{s\bar{s}}=0.025% for the uuds\bar{s} component. We further see that with a small admixture of configuration I, the agreement of our prediction with data for G_A^s at low-q^2 region can be markedly improved. We find that without removing CM motion, P_{s\bar{s}} would be overestimated by about a factor of four in the case when transition current dominates. We also explore the consequence of a recent estimate reached from analyzing existing data on \bar{d} -\bar{u}, s +\bar{s}, and \bar{u} + \bar{d} - s -\bar{s}, that P_{s\bar{s}} lies between 2.4-2.9%. It would lead to a large size for the five-quark system and a small bump in both G_E^s+\eta G_M^s and G_E^s in the region of q^2<=0.1 GeV^2 within the considered model.Comment: 6 pages, 2 figure

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

Containership Flag Selection: The Opening of Direct Shipping between Taiwan and China

The signature of the cross-strait sea transport (CST) Agreement in 2008 has not only established the cross-strait direct shipping link, but also lifted the ban on the involvement of Taiwanese flagged ships to call at China’s ports. This paper focuses on the flag selection for Taiwanese container shipping companies under the provisions of the CST Agreement, and embraces the empirical investigation based on the Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results show Hong Kong is the optimal choice rather than China and Taiwan. Although cross-strait shipping is highly controlled by both sides of the strait, economic factors are still taken seriously in commercial activities. Further, to assist shipping companies to get direct shipping approvals from China and revising a package of financial measures under current shipping policies are recommended for the Taiwanese government

Mesozoic-Cenozoic mafic magmatism in Sanandaj-Sirjan Zone, Zagros Orogen (Western Iran): geochemical and isotopic inferences from Middle Jurassic and Late Eocene gabbros

One of the consequences of Neo-Tethys ocean subduction beneath the Central Iranian Micro-continent (CIMC) is the development of rare gabbroic intrusions in the Malayer-Boroujerd Plutonic Complex (MBPC) located in the Sanandaj-Sirjan Zone (SaSZ) of the Zagros Orogenic belt. The MBPC is a suite of extensive felsic and lesser mafic magmatic products in the northern SaSZ with geochemical signatures of arc-like magmatism during the Middle Jurassic (Ghorveh-Aligudarz arc) and intraplate type in the Late Eocene. Middle Jurassic gabbros (non-cumulate and cumulate) have low-Ti concentrations (&lt; 1 wt. %) and quite uniform isotopic compositions (initial 87Sr/86Sr: 0.7035‒0.70593 and εNd(t): - 6.18‒-0.7), enriched LILE relative to HFSE, variable fractionation between the LREE and HREE ((La/Yb)cn: 2.27‒7.45) and both negative to positive Eu anomalies. These distinctive features of arc-type magmatism are consistent with a subduction-modified mantle source for these rocks. Trace element and REE models indicate ~ 15% melting of a metasomatized amphibole-bearing garnet-spinel lherzolite (garnet:spinel ~ 7:3) in the sub-arc mantle wedge. The cumulate gabbros and non-cumulates belong to common liquid line of descent, with complementary trace element patterns. Much of the variation between samples can be modeled by fractional crystallization (FC) of a common parent; only one cumulate gabbro from this suite exhibits isotopic evidence of contamination, probably by Rb-depleted crustal materials. The Late Eocene gabbros have relatively high Ti (&gt;1 wt. %) and display isotopically depleted Sr-Nd values (initial 87Sr/86Sr: 0.7044-0.7087, εNd(t): 1.9-+3.2, barring one crustally contaminated sample). OIB-like trace element characteristics such as enriched HFSE, and only minor enrichment of LILE and LREE, reflect a within-plate character and asthenospheric source. Trace element modeling indicates small degree melting (fmelting: 0.05) of upper mantle lherzolite (garnet:spinel ~ 3:1) followed by higher degree melting (fmelting: 0.15) at shallower depths (garnet:spinel ~4.5:2). The Eocene parental magma underwent FC of olivine and clinopyroxene. We propose that Eocene asthenospheric upwelling was triggered by slab tearing in response to slab-rollback, which is elsewhere reported to have triggered a 'flareup' of extension-related magmatism across Iran. Three stages of tectonomagmatic evolution in the Ghorveh-Aligudarz arc segment of the N-SaSZ are represented by: 1) arc-like magmatism during active subduction of the Neo-Tethys seaway at Middle Jurassic, 2) magmatic quiescence during an interval of shallow-angle or highly oblique subduction during the Cretaceous‒Paleocene, and 3) asthenosphere melting during slab tearing shortly before the onset of the Arabia-Eurasia collision

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page