Promises and Challenges of Medical Patient Healthcare Portals in Underserved Communities

As a safety-net medical center that serves many underserved communities, Einstein Medical Center Philadelphia (EMCP) faces many challenges in providing healthcare to its communities. To improve those services, EMCP has released a new IT healthcare portal (app). This chapter describes some of the promises and challenges EMCP is currently facing in their attempts to convince communities in its catchment area to adopt that healthcare portal. The challenges are discussed in the contexts of poor social determinants of health (SDOH), unique social factors, as well as the importance of managing community trust in EMCP within the broader contexts of underserved communities of which the new portal is only part of the story. This is not a typical case of IT adoption. The challenges at hand are not only technical but to a large degree social, dealing in part with issues of cultural diversity, perceived lack of respect, and poor health literacy.

Secure Federated Aggregate-Count Queries on Medical Patient Databases Using Fully-Homomorphic Cryptography

Electronic health records (EHR) are often siloed across a network of hospitals, but researchers may wish to perform aggregate count queries on said records in entirety---e.g. How many patients have diabetes? Prior work has established a strong approach to answering these queries in the form of probabilistic sketching algorithms like LogLog and HyperLogLog; however, it has remained somewhat of an open question how these algorithms should be made truly private. While many works in the computational biology community---as well as the computer science community at large---have attempted to solve this problem using differential privacy, these methods involve adding noise and still reveal some amount of non-trivial information. Here, we prototype a new protocol using fully homomorphic encryption that is trivially secured even in the setting of quantum-capable adversaries, as it reveals no information other than that which can be trivially gained from final numerical estimation. Simulating up to 16 parties on a single CPU thread takes no longer than 20 minutes to return an estimate with expected 6% approximation error; furthermore, the protocol is parallelizable across both parties and cores, so, in practice, with optimized code, we might expect sub-minute processing time for each party.

Automated Transformation of UML/SysML Behavioral Diagrams for Stochastic Error Propagation Analysis of Autonomous Systems

Modern technical systems consist of heterogeneous components, including mechanical parts, hardware, and the extensive software part that allows the autonomous system operation. The heterogeneity and autonomy require appropriate models that can describe the mutual interaction of the components. UML and SysML are widely accepted candidates for system modeling and model-based analysis in early design phases, including the analysis of reliability properties. UML and SysML models are semi-formal. Thus, transformation methods to formal models are required. Recently, we introduced a stochastic Dual-graph Error Propagation Model (DEPM). This model captures control and data flow structures of a system and allows the computation of advanced risk metrics using probabilistic model checking techniques. This article presents a new automated transformation method of an annotated State Machine Diagram, extended with Activity Diagrams, to a hierarchical DEPM. This method will help reliability engineers to keep error propagation models up to date and ensure their consistency with the available system models. The capabilities and limitations of transformation algorithm is described in detail and demonstrated on a complete model-based error propagation analysis of an autonomous medical patient table.

Leveraging digital transformation to empower clinical governance: enhancement in intermediate clinical endpoints and patients’ survival after implementation of a continuous quality improvement program in a large dialysis network

Background Treatment of end-stage kidney disease patients is extremely challenging given the inter-connected functional derangements and comorbidities characterizing the disease. Continuous Quality Improvement (CQI) in healthcare is a structured clinical governance process helping physicians adhere to best clinical practices. The digitization of patient medical records and data warehousing technologies has standardized and enhanced the efficiency of the CQÍs evidence generation process. There is limited evidence that ameliorating intermediate outcomes would translate into better patient-centered outcomes. We sought to evaluate the relationship between Fresenius Medical Care (FME) medical patient review CQI (MPR-CQI) implementation and patients’ survival in a large historical cohort study. Methods We included all incident adult patients with 6 months survival on chronic dialysis registered in the EMEA region between 2011-2018. We compared medical Key Performance Indicator (KPI) target achievements and 2-year mortality for patients enrolled prior and after to MPR-CQI policy onset (Cohort A and Cohort B). We adopted a structural equation model where MPR-CQI policy was the exogenous explanatory variable, KPI target achievements the mediator variable, and survival was the outcome of interest. Results 4.270 patients (Cohort A: 2.397; Cohort B: 1.873) met the inclusion criteria. We observed an increase in KPI target achievements after MPR-CQI policy implementation. Mediation analysis demonstrated a significant reduction in mortality due to indirect effect of MPR-CQI implementation through improvement in KPI target achievement occurring in the post-implementation era (OR: 0.70; 95%CI: 0.65-0.76; p < 0.0001) Conclusions Our study suggests that MPR-CQI achieved by standardized clinical practice and periodical, structured, medical patient review may improve patients’ survival through improvement in medical KPIs.

AN EVALUATION OF SWINE FLU (INFLUENZA A H3N2V) VIRUS PREDICTION USING DATA MINING AND CONVENTIONAL NEURAL NETWORK TECHNIQUES

Nowadays, The health care commercial enterprise collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. Data mining plays a significant role in predicting diseases. The database report of medical patient is not more efficient, currently we made an Endeavour to detect the most widely spread disease in all over the world named Swine flu. Swine flu is a respiratory disease which has Numeral number of tests must be requisite from the patient for detecting a disease. Advanced data mining techniques gives us help to remedy this situation. In this work we describes about a prototype using data mining techniques, namely Naive Bayes Classifier. The Data mining is an emerging research trend which helps in finding accurate solutions in many fields. This paper highlights the various data mining technique and Convolution Neural Network used for predicting swine flu diseases.

Telemedicine and digital narrative medicine for the customization of the diagnostic-therapeutic path at the time of COVID 19

The Covid-19 issues have placed the telemedicine into the limelight, for its ability to reach remote patients affected by COVID-19, offering them support, expert advice, home hospitalization. At the same time, it gives the many fragile patients, who should be submitted to therapeutic checks or adjustments, the opportunity to be followed appropriately, avoiding travelling and associated risks of contagion. The current situation has accelerated its use in diabetological care: but with the risk of reducing the medical-patient empathic relationship. Narrative Medicine (NBM) integrates with Evidence-Based Medicine (EBM) and, taking into account the plurality of perspectives, makes clinical-care decisions more complete, personalized, effective and appropriate. The stories told by patient and by those who take care, are an essential element of contemporary medicine, based on the active participation of the subjects who are involved in the choices. People, through their own stories, become protagonists of the process of care”. The ability of mixing the technology achievements with the humanistic vision of the care process characterizes DNM, the first digital platformentirely designed for the development of narrative medicine projects in the clinical practice. It was conceived by a team of anthropologists and psychologists with the advice of doctors and experts in narrative medicine of OMNI the Observatory of Narrative Medicine Italy. The DNM features aim to maximize the potential of the digital process, at the same time preserving patient privacy and health data confidentiality. KEY WORDS telemedicine; narrative medicine; digital narrative medicine.

Efektifitas Penyelenggaraan E-Puskesmas Di Puskesmas Lubuk Buaya Kota Padang ¬

This research was done in Puskesmas Lubuk Buaya Padang City whichpurpose to discover about to effectiveness of organized e-puskesmas in Puskesmas Lubuk Buaya Padang City. This research applies a descriptive qualitative method, namely a method by providing a comprehensive description of the research. Informants selection of informants in this reseacrhusing purposive sampling technique. Prosess of aggregation data colth pass through observation technique, study documentation and interviews, while technique for validity testing data that using resource triangulation with data analysis such as a data collection, data reduction, data presentation, along with pulling conclusions. The results of the research conducted by researchers was explain effectiveness of organized e-puskesmas in puskesmas Lubuk Buaya Padang City was not yet effective, because in case still presence obstacle inside administration that e-puskesmas, that e-puskesmas namely network problems are not always good, sometimes disconnected when used so that it can slow down patient services in medical treatment, and other obstacles, sometimes the patient data input report for each clinic is also done manually till case mentioned needed much time in data in data input medical patient and be able pulling conclusion its not yet effective organized e-puskesmas in that Lubuk Buaya.

FORECASTING OF BREAST CANCER USING VOTING CLASSIFIER

Breast cancer is the most diagnosed and the leading cause of death in women. among women is breast cancer. Between 1 in 8 and 1 in 12 women in the developed world will experience breast cancer throughout their lives. The risk of breast cancer is of two primary types. The first type is that a person is likely to develop breast cancer over a specified time period. The second type reflects the likelihood of a high-risk gene mutation. Earlier work has shown that it has been better to predict the risk of breast cancer by adding input into the wide-spread Gail model. The main objective is to predict analytics model to diagnose breast cancer stages of patients. The main objective of this work is to detect and analyze breast cancer. It predicts the stages of the cancer and gives as the accurate result. In this work, to investigate a dataset of medical patient records for hospital sector using machine learning technique and to identify patients having breast cancer stages from given dataset attributes. Then the accurate result is found by naive Bayesian algorithm with precision, recall, F1score.