Demands and influencing factors of digital transition care service in discharged older patients:a cross-sectional study

Objective:To identify the group classification of discharged older adults’digital transition care demands and analyze its influencing factors.Methods:From July to August 2022,we used stratified random sampling to recruit older patients who were discharged between July 2021 and July 2022 from tertiary hospitals in Shanghai.We used latent profile analysis to classify the older patients into distinct groups based on their service demands:low,medium,and high.We use multiple logistic regression to explore the factors influencing the different demand levels.Results:The degree of discharged older patients’demand was classified as low(Category 1(C1),34.2%),medium(Category 2(C2),49.5%),high-demand levels(Category 3(C3),16.3%).Compared to those have C2,older adults in C1 are more likely to be male(Odds Ratio(OR)=2.81,P=0.02),have 2 chronic diseases(OR=3.91,P=0.03),and are less likely to be junior high and below(OR=0.09,P=0.00),hospitalized for 1–2 times in the past year(1 times:OR=0.19,P=0.07;2 times:OR=0.14,P=0.02),living with children(OR=0.32,P=0.05),have less insurance(OR=0.48,P=0.03),less understanding of digital transitional care(OR=0.47,P=0.01),have less eHealth literacy(OR=0.80,P=0.00),have less degree of importance attributed by family(OR=0.52,P=0.03);Compared to those have medium demand level,older adults in high demand level are more likely to have self and spouse as primary income(self:OR=26.35,P=0.00;spouse:OR=24.06,P=0.02),walking to the nearest health facility(self:6.74,P=0.03),have higher eHealth literacy(OR=1.88,P=0.00),degree of importance within the family(OR=5.19,P=0.01),higher self’s influence on medical decisions-making(OR=5.69.P=0.01).They are less likely to be in 60–79 years group(OR=0.00–0.37,P=0.00–0.03),Household Annual Income<5,000 CNY(OR=0.05,P=0.02).Conclusion:Digital transitional care demands of discharged older patients can be divided into three categories.Constructing a digital transitional care service system that aligns with the demands of discharged older patients is essential.Communication,care plan development,and follow-up are the most fundamental services.Additionally,it is essential to understand the characteristics of high-demand populations to provide tailored services and identify vulnerable populations from health and social perspectives to offer cost-effective transitional care services.

Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy(DR)which is one of the four main reasons for sightlessness all over the globe.DR usually has no clear symptoms before the onset,thus making disease identication a challenging task.The healthcare industry may face unfavorable consequences if the gap in identifying DR is not lled with effective automation.Thus,our objective is to develop an automatic and cost-effective method for classifying DR samples.In this work,we present a custom Faster-RCNN technique for the recognition and classication of DR lesions from retinal images.After pre-processing,we generate the annotations of the dataset which is required for model training.Then,introduce DenseNet-65 at the feature extraction level of Faster-RCNN to compute the representative set of key points.Finally,the Faster-RCNN localizes and classies the input sample into ve classes.Rigorous experiments performed on a Kaggle dataset comprising of 88,704 images show that the introduced methodology outperforms with an accuracy of 97.2%.We have compared our technique with state-of-the-art approaches to show its robustness in term of DR localization and classication.Additionally,we performed cross-dataset validation on the Kaggle and APTOS datasets and achieved remarkable results on both training and testing phases.

Identifying Driver Genes Mutations with Clinical Significance in Thyroid Cancer

Advances in technology are enabling gene mutations in papillary thyroid carcinoma(PTC)to be analyzed and clinical outcomes,such as recurrence,to be predicted.To date,the most common genetic mutation in PTC is in BRAF kinase(BRAF).However,whether mutations in other genes coincide with those in BRAF remains to be clarified.The aim of this study was to find mutations in other genes that co-exist with mutated BRAF,and to analyze their frequency and clinical relevance in PTC.Clinical and genetic data were collected from 213 PTC patients with a total of 36,572 mutation sites in 735 genes.After matching with genes from PTC entries in a global database(NCBI Gene),69 genes with mutations in coding regions were chosen for further study.Through frequency-based analysis,we identified commonly mutated genes co-existing with mutated BRAF and,using the mutation count correlation matrix(MCCM)method,analyzed their incidence according to age and gender.We designed Chord diagrams to reveal gene relationships concerning age and gender,and found that mutations in ALK,ATM,COL1A1,MSTIR,PRKCA,and WNK1 most commonly coincide with mutated BRAF,followed by APC,AURKA,and AURKB.These findings provide further insight into the genetic profile of PTC.

Social practices of nurse care coordination using sensor technologies e Challenges with an alert system adoption in assisted living communities for older adults

Objectives:From the view of everyday practices and the socio-technical coordination lens,this study aimed to analyz the gap between creators’intention and the users’implementation(mainly nursing staff and social workers)of an alert system in assisted living communities.Methods:Qualitative methods were employed by way of five user interviews and focus groups with six system developers.Modeling instruments were applied for data collection to analyze the different clinical workflows versus the expectations of the system development team.Results:Results indicate that the clinical workflow changed over time,which led to a mismatch of nurse care coordination,social practices,and technology use.The results show different mental models of the socio-technical practice.Applying the coordination theory,the following recommendations could be developed to overcome the mismatch.First,it is recommended that nursing staff set goals together.Second,a communication rhythmwith the nursing staff and developer teams should be established,with guided questions to facilitate the conversation,to shed light on the different workflows and the difference in social practices when using sensor technologies or alert systems.Third,a checklist for new employees should be created so they know how and on which devices to use the alert system.Fourth,the user experience with the alert system should be improved(e.g.,an improved user interface).Conclusions:This work indicates recommendations to close the mental model gap to overcome the mismatch between optimal use of the alert system and how the nursing staff is actually using it.

The Impact of Information Technology on Admission Decisions

Information systems have been adopted in many fields including the health industry. Indeed, keeping abreast of the advances of the technological age, many medical organizations have invested heavily in information technologies （IT）, aiming at improving medical decision-making and increasing its efficiency. Despite their obvious advantages, the systems do not always immediately provide the vital medical information required for critical decision-making, and the decisions that are based on this partial information may result in a decreased level of quality of care and unnecessary costs. The objective of this research is to evaluate the contribution of IT to decision-makers （physicians） at the point of care of emergency departments （EDs） by investigating whether the information systems （IS） have improved the medical outcomes, in the complex and highly stressful environment of the ED, with time constraints and overcrowding. The authors evaluated the contribution of the medical information to admission decisions by using the track log-file analysis method. The results were obtained using a unique database containing referrals to the ED from seven main hospitals in Israel. The authors＇ results lead to the major conclusion that viewing medical history contributes to admission decisions and clearly reduces the number of avoidable admissions.

COVID-19 and thyroid disease: An infodemiological pilot study

BACKGROUND Google Trends searches for symptoms and/or diseases may reflect actual disease epidemiology.Recently,Google Trends searches for coronavirus disease 2019(COVID-19)-associated terms have been linked to the epidemiology of COVID-19.Some studies have linked COVID-19 with thyroid disease.AIM To assess COVID-19 cases per se vs COVID-19-associated Google Trends searches and thyroid-associated Google Trends searches.METHODS We collected data on worldwide weekly Google Trends searches regarding“COVID-19”,“severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)”,“coronavirus”,“smell”,“taste”,“cough”,“thyroid”,“thyroiditis”,and“subacute thyroiditis”for 92 wk and worldwide weekly COVID-19 cases'statistics in the same time period.The study period was split in half(approximately corresponding to the preponderance of different SARS-COV-2 virus variants)and in each time period we performed cross-correlation analysis and mediation analysis.RESULTS Significant positive cross-correlation function values were noted in both time periods.More in detail,COVID-19 cases per se were found to be associated with no lag with Google Trends searches for COVID-19 symptoms in the first time period and in the second time period to lead searches for symptoms,COVID-19 terms,and thyroid terms.COVID-19 cases per se were associated with thyroidrelated searches in both time periods.In the second time period,the effect of“COVID-19”searches on“thyroid”searches was significantly mediated by COVID-19 cases(P=0.048).CONCLUSION Searches for a non-specific symptom or COVID-19 search terms mostly lead Google Trends thyroid-related searches,in the second time period.This time frame/sequence particularly in the second time period(noted by the preponderance of the SARSCOV-2 delta variant)lends some credence to associations of COVID-19 cases per se with(apparent)thyroid disease(via searches for them).

PACS is only in the Beginning of Being Used as Clinical Research Tools

The PACS concept was introduced in 19821, after more than twenty years of technical advancements;it has become an integrated component of today’s healthcare delivery system.PACS is now in the beginning of being used as a clinical research tool.2Among others,this paper describes four PACS-based research activities: medical imaging informatics, medical imaging Data Grid, combining PACS and teleradiology operations, and computer-assisted detection and diagnosis(CAD). In medical imaging informatics (MII), we first introduce its infrastructure and the five layer of software architecture. The description of a new MII training program supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National institutes of Health (NIH), USA is followed. The training program accepts candidates with medical and or biomedical engineering background. The goal is to cross-train multi-disciplinary individuals to be future leaders in the field of medical imaging informatics. Grid computing is a new paradigm combining computing, networking, information and storage technologies to advance the conventional distributing computing to the next level. One resource in Grid Computing is the Data Grid. We describe the use of Data Grid concept in medical imaging applications based on the five layer of the open source Globus toolkit 4 (GT4). Three examples are given. First, a Data Grid specifically designed for PACS image backup and disaster recovery developed at the Imaging and Informatics Laboratory (IPI), USC is illustrated. The second application is for image-based clinical trials using three international sites at IPI, USC, USA; the PACS Lab, Hong Kong Polytechnic University, Hong Kong; and the Heart Institute, Sao Paulo, Brazil In combining PACS and teleradiology operations , a Data Grid model is proposed to combinetwo disjoint ,and yet ,daily used PACS and telera-diology operations as one integrated system in a large-scale enterprise level . Methods of combining workflows ,storage ,and reading of PACS and teleradiology i mages are detailed .The last work-in-progress research is the in-tegration of CAD results with daily PACS workflow. The integration methods are based on DICOMScreen Captured and Structured Report Stan-dards ; and several IHE (Integrating the Healthcare Enterprise) Workflow Profiles .

Methodology in improving antibiotic implementation policies

The basic requirements of antibiotic prescribing are components of methodology; knowledge, logical reasoning, and analysis. Antimicrobial drugs are valuable but limited resources, different from other drugs and they are among the most commonly prescribed drugs all over the world. They are the only drugs which do not intentionally affect the patient. They affect the pathogens which invade the host. The emergence and spread of antibiotic-resistant pathogens are accelerated by heavy antibiotic usage. The effective antimicrobial stewardship and infection control program have been shown to limit the emergence of antimicrobial-resistant bacteria. In this respect, education for antibiotic prescribing could be designed by going through the steps of scientific methodology. A defined leadership and a coordinated multidisciplinary approach are necessary for optimizing the indication, selection, dosing, route of administration, and duration of antimicrobial therapy. In scenarios, knowledge is also as important as experience for critical decision making as is designated. In this setting, the prevalence and resistance mechanisms of antimicrobials, and their interactions with other drugs need to be observed. In this respect, infectious disease service should play an important role in improving antimicrobial use by giving advice on the appropriate use of antimicrobial agents, and implementing evidencebased guidelines.

Similarities between "Big Data" and Traditional Chinese Medicine information

In this paper,we analyze the four distinct characteristics of information on Traditional Chinese Medicine(TCM), namely epistemological information,phenomenon information, overall information, and time information. These characteristics bear to some extent strong similarity to the three characteristics of "Big Data", namely integrity data, fuzzy data and correlation data, so the advent of the age of "Big Data" is bound to create good opportunities for the development of TCM informatics and is also be expected to provide methods and techniques for processing and analysis of TCM "comprehensive data".

Evidence-based Practice Method of Integrative Chinese and Western Medicine Based on Literature Retrieval through PICO Question and Complementary and Alternative Medicine Topics

An evidence-based practice method according to literature retrieval through PICO （Patients,Intervention,Comparison,Outcome） questions and complementary and alternative medicine （CAM） topics,which can obtain helpful evidence for guiding clinical practice,was introduced with a practical example in this paper.The knowledge of diseases and Western medicine treatment can be acquired by literature retrieval through PICO question,while searching by CAM topics may provide evidence for Chinese medicine （CM）.Thus the author held that literature retrieval through both PICO question and CAM topics was an ideal evidencebased practice method for integrative Chinese and Western medicine （ICWM）.However,since the standard in CM evidence hierarchy is still under study,the value of the CAM thematic retrieval method remains very limited.In the future,studies on the definition and hierarchy of CM evidences and the herb-drug interaction between Western and Chinese medicine during a combination therapy should be strengthened to improve the status of ICWM evidence-based practice.