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

Adaptation to water level variation: Responses of a floating-leaved macrophyte Nymphoides peltata to terrestrial habitats

A straightforward experimental approach was carried out to study the adaptation responses of a typical floating-leaved aquatic plant Nymphoides peltata to changes in water availability. N. peltata grown in terrestrial habitat was approximately 88.77% lower in total biomass, 62.75% higher in root biomass allocation, 80.9% higher in root-shoot ratio, and 54.5% longer in leaf longevity compared with N. peltata grown in aquatic habitats. Anatomical analyses suggest that aquatic-grown N. peltata exhibits a well-developed lacunal system in leaf, petiole, and coarse root. Moreover, aquatic-grown N. peltata had approximately a higher in lacunal system in leaf, petiole, and coarse root by 28.57%, 56.41% and 82.35%, respectively, than those of terrestrial-grown N. peltata. These results indicated that N. peltata was well adapted to the terrestrial habitat because of its biomass allocation, morphological, and anatomical strategies that depended on the increase in root biomass allocation and leaf longevity, as well as the decrease in the lacunal system volume in leaf, petiole, and coarse root. This indicates that N. peltata can develop multiple morphological and anatomical strategies, an integrated approach to enhance survival in dynamic and unpredictable environments

High oscillator strength interlayer excitons in two-dimensional heterostructures for mid-infrared photodetection

The development of infrared photodetectors is mainly limited by the choice of available materials and the intricate crystal growth process. Moreover, thermally activated carriers in traditional III-V and II-VI semiconductors enforce low operating temperatures in the infrared photodetectors. Here we demonstrate infrared photodetection enabled by interlayer excitons (ILEs) generated between tungsten and hafnium disulfide, WS2/HfS2. The photodetector operates at room temperature and shows an even higher performance at higher temperatures owing to the large exciton binding energy and phonon-assisted optical transition. The unique band alignment in the WS2/HfS2 heterostructure allows interlayer bandgap tuning from the mid- to long-wave infrared spectrum. We postulate that the sizeable charge delocalization and ILE accumulation at the interface result in a greatly enhanced oscillator strength of the ILEs and a high responsivity of the photodetector. The sensitivity of ILEs to the thickness of two-dimensional materials and the external field provides an excellent platform to realize robust tunable room temperature infrared photodetectors.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)National Research Foundation (NRF)Accepted versionThe work is supported by the Agency for Science, Technology and Research (A*STAR) under the 2D Materials Pharos Program (grant no. 152 700014 and grant no. 152 700017). Q.J.W. acknowledges funding from the National Research Foundation Competitive Research Program (NRF-CRP18-2017-02 and NRF–CRP19–2017–01). K.S.T. acknowledges support from the Center for Nanostructured Graphene (CNG) under the Danish National Research Foundation (project DNRF103) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 773122, LIMA). G.L. acknowledges the supported under the grant MOE2017-T2-1-114. H.L. acknowledges the support by the Ministry of Science and Technology (MOST) in Taiwan under grant no. MOST 109-2112-M-001-014-MY3. J.T. and S. Lukman thank C. W. Lee, A. Ngo and M. Zhao for their valuable inputs and K Hippalgaonkar for sharing tools in the device fabrication