Front-end User Research Articles

Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework.

BackgroundAn estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse.ObjectiveThe aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse.MethodsWe developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation).ResultsOur technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network.ConclusionsThe proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.

A novel fuzzy credit risk assessment decision support system based on the python web framework

ABSTRACT When a financial institute constructs a credit risk assessment model, qualitative, and quantitative data must be considered at the same time due to differences in the attributes of each form of data. While qualitative data are usually relied on professional appraisers’ judgment, 2-tuple fuzzy linguistic representation could assist to organize appraisers’ scores to avoid any loss of information. In practice, a financial institute needs to regularly update its credit risk assessment model to maintain correct assessment results. However, every update involves a lot of numerical experiments using multiple systems or even software packages for evaluating the effect of different sampling methods and classifiers to construct a suitable model for the updated dataset. Such an assessment process is time-consuming with many repetitive processes. This study applied the latest web-based technology to develop a fuzzy decision support system (DSS) that used logistic regression as the classifier combined with different sampling methods and model threshold settings to make data preprocessing and model fitting process more structured and efficient. This DSS was written by Django, a Python web framework, using the RESTful architecture that has good database imaging mechanism and good interaction between the front-end (user side) and the back-end (service side). After verification of multiple actual data set, the average time spent on constructing one model combination per dataset is less than 1 min, which indeed is significantly shorter than the original time required for credit risk assessment and provided a practical tool for the related work of credit risk assessment.

Design and Usability of an Electronic Health Record-Integrated, Point-of-Care, Clinical Decision Support Tool for Modeling and Simulation of Antihemophilic Factors.

With the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers. The aim of this study is to develop and examine the usability of a CDS tool for antihemophilic factor dose individualization. Our development strategy was based on the features associated with successful CDS tools and driven by a formal requirements analysis. The back-end code was based on algorithms developed for manual individualization and unit tested with 23,000 simulated patient profiles created from the range of patient-derived pharmacokinetic parameter estimates defined in children and adults. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users were recruited to participate in traditional usability testing under an institutional review board approved protocol. CDS software was developed to systematically walk the point-of-care clinician through dose individualization after seamlessly importing the requisite patient data from the EHR. Classical and population pharmacokinetic approaches were incorporated with clearly displayed estimates of reliability and uncertainty. Users can perform simulations for prophylaxis and acute bleeds by providing two of four therapeutic targets. Testers were highly satisfied with our CDS and quickly became proficient with the tool. With early and broad stakeholder engagement, we developed a CDS tool for hematology provider that affords seamless transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent dose selection.

HastaLaVista, a web-based user interface for NMR-based untargeted metabolic profiling analysis in biomedical sciences: towards a new publication standard

Metabolic profiling has been shown to be useful to improve our understanding of complex metabolic processes. Shared data are key to the analysis and validation of metabolic profiling and untargeted spectral analysis and may increase the pace of new discovery. Improving the existing portfolio of open software may increase the fraction of shared data by decreasing the amount of effort required to publish them in a manner that is useful to others. However, a weakness of open software, when compared to commercial ones, is the lack of user-friendly graphical interface that may discourage inexperienced researchers. Here, a web-browser-oriented solution is presented and demonstrated for metabolic profiling analysis that combines the power of R for back-end statistical analyses and of JavaScript for front-end visualisations and user interactivity. This unique combination of statistical programming and web-browser visualisation brings enhanced data interoperability and interactivity into the open source realm. It is exemplified by characterizing the extent to which bariatric surgery perturbs the metabolisms of rats, showing the value of the approach in iterative analysis by the end-user to establish a deeper understanding of the system perturbation. HastaLaVista is available at: (https://github.com/jwist/hastaLaVista, https://doi.org/10.5281/zenodo.3544800) under MIT license. The approach described in this manuscript can be extended to connect the interface to other scripting languages such as Python, and to create interfaces for other types of data analysis.

GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data

The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein, the source code at: https://github.com/uc-bd2k/grein, and the Docker container at: https://hub.docker.com/r/ucbd2k/grein.

Generic data storage-based dynamic mobile app for standardised electronic health records database

Standardisation plays an important role in making healthcare application worldwide adaptable. It uses archetypes for semantic interoperability. In addition to the interoperability, a mechanism to handle future evolution is the primary concern for market sustainability. An application should possess dynamism in terms of the front end (user interface) as well as the back end (database) to build a future proof system. Current research aims to extend the functionality of prior work on HEALTHSURANCE with a search efficient generic storage and validation support. At application level, graphical user interface is dynamically built using knowledge provided by standards in terms of archetypes. At the database level, generic storage structure is provided with improved searching capabilities to support faster access, to capture dynamic knowledge evolution and to handle sparseness. A standardised format and content helps to uplift the credibility of data and maintains a uniform and specific set of constraints used to evaluate user's health. Architecture proposed in current research enables implementation of mobile app based on an archetype paradigm that can avoid reimplementation of the systems, supports migrating databases and allows the creation of future-proof systems.

Generic data storage-based dynamic mobile app for standardised electronic health records database

Standardisation plays an important role in making healthcare application worldwide adaptable. It uses archetypes for semantic interoperability. In addition to the interoperability, a mechanism to handle future evolution is the primary concern for market sustainability. An application should possess dynamism in terms of the front end (user interface) as well as the back end (database) to build a future proof system. Current research aims to extend the functionality of prior work on HEALTHSURANCE with a search efficient generic storage and validation support. At application level, graphical user interface is dynamically built using knowledge provided by standards in terms of archetypes. At the database level, generic storage structure is provided with improved searching capabilities to support faster access, to capture dynamic knowledge evolution and to handle sparseness. A standardised format and content helps to uplift the credibility of data and maintains a uniform and specific set of constraints used to evaluate user's health. Architecture proposed in current research enables implementation of mobile app based on an archetype paradigm that can avoid reimplementation of the systems, supports migrating databases and allows the creation of future-proof systems.

Development of a Maths Learning System for High School Students using the Java Desktop Platform

This paper presents the development of a math learning system for high school math students. The development process involved the creation of the front-end graphical user interface and the back-end database, with both being implemented to near-commercial standard. The front-end consists of two main screens; a separate one each for the teacher and the student. The teacher's screen displays and allows the editing of all data in the database through the use of tables. The student is presented with all the math questions from the database on their screen in a structured form for the practice of math problems of various difficulty levels. Three types of final tests were performed to examine the effectiveness of math learning system. The results showed that the system is consistent and usable for learning math at high school.

A New User Front End for EAST Remote Participation

The Web-based remote participation system for experimental advanced superconducting tokamak (EAST RPS) has been developed to provide a high-efficient and low-cost way to meet international collaboration requirements. The EAST RPS team focused on the extension, update, and optimization for the RPS during last two years. In the first version, EAST RPS has established Apache-Flex based front-end components to provide a platform-independent user interface. However, some Web browsers, such as Firefox, Google Chrome, and Microsoft Edge, and operation systems (iOS and android) stop supporting or disable the flash player plugin by default, and the Flex technology will become less relevant in the future. The purpose of this paper is to provide an update of the RPD in EAST. The front-end migration should be a priority to update the EAST RPS. The open source, cross-platform, maintainability, and life cycle are the key features that the front-end platform must have. The technical solutions for the new user front end for EAST RPS are offered in this paper.

A NAT traversal mechanism for cloud video surveillance applications using WebSocket

This paper describes a novel Video Surveillance as a Service (VSaaS) architecture. The proposed solution uses an add-on component, named WS-Gateway (WebSocket-based gateway), installed in the client’s private network (along with IP-cameras network). The WebSocket protocol is used to establish a bi-directional communication among the actors in the system. The main advantage of the solution is the overcoming of reachability problems caused by the presence of NATs or Firewalls in the network. A prototype system including one IP-camera, a WS-Gateway running on Android smartphone, a WS-Server built on a Windows system, and a Web-page implementing an user front-end has been tested. The obtained experimental results are compared, in term of latency time, frame loss rate and other implementation features, to other existing solutions, and to the traditional HTTP-Polling used in conjunction with the SSH reverse tunneling to traverse the NAT.

An Interactive and Comprehensive Working Environment for High-Energy Physics Software with Python and Jupyter Notebooks

Today’s analyses for high-energy physics (HEP) experiments involve processing a large amount of data with highly specialized algorithms. The contemporary workflow from recorded data to final results is based on the execution of small scripts – often written in Python or ROOT macros which call complex compiled algorithms in the background – to perform fitting procedures and generate plots. During recent years interactive programming environments, such as Jupyter, became popular. Jupyter allows to develop Python-based applications, so-called notebooks, which bundle code, documentation and results, e.g. plots. Advantages over classical script-based approaches is the feature to recompute only parts of the analysis code, which allows for fast and iterative development, and a web-based user frontend, which can be hosted centrally and only requires a browser on the user side. In our novel approach, Python and Jupyter are tightly integrated into the Belle II Analysis Software Framework (basf2), currently being developed for the Belle II experiment in Japan. This allows to develop code in Jupyter notebooks for every aspect of the event simulation, reconstruction and analysis chain. These interactive notebooks can be hosted as a centralized web service via jupyterhub with docker and used by all scientists of the Belle II Collaboration. Because of its generality and encapsulation, the setup can easily be scaled to large installations.

Azure-Based Smart Monitoring System for Anemia-Like Pallor

Increasing costs of diagnostic healthcare have necessitated the development of hardware independent non-invasive Point of Care (POC) systems. Although anemia prevalence rates in global populations vary between 10% and 60% in various demographic groups, smart monitoring systems have not yet been developed for screening and tracking anemia-like pallor. In this work, we present two cloud platform-hosted POC applications that are directed towards smart monitoring of anemia-like pallor through eye and tongue pallor site images. The applications consist of a front-end graphical user interface (GUI) module and two different back-end image processing and machine learning modules. Both applications are hosted on a browser accessible tomcat server ported to an Azure Virtual Machine (VM). We observe that the first application spatially segments regions of interest from pallor site images with higher pallor classification accuracy and relatively longer processing times when compared to the lesser accurate yet faster second application. Also, both applications achieve 65%–98% accuracy in separating normal images from images with pallor or abnormalities. The optimized front-end module is significantly light-weight with a run-through time ratio of 10−5 with respect to the back-end modules. Thus, the proposed applications are portable and hardware independent, allowing for their use in pallor monitoring and screening tasks.

From the Lab to the Real World: Re-identification in an Airport Camera Network

Over the past ten years, human re-identification has received increased attention from the computer vision research community. However, for the most part, these research papers are divorced from the context of how such algorithms would be used in a real-world system. This paper describes the unique opportunity our group of academic researchers had to design and deploy a human re-identification system in a demanding real-world environment: a busy airport. The system had to be designed from the ground up, including robust modules for real-time human detection and tracking, a distributed, low-latency software architecture, and a front-end user interface designed for a specific scenario. None of these issues are typically addressed in re-identification research papers, but all are critical to an effective system that end users would actually be willing to adopt. We detail the challenges of the real-world airport environment, the computer vision algorithms underlying our human detection and re-identification algorithms, our robust software architecture, and the ground-truthing system required to provide the training and validation data for the algorithms. Our initial results show that despite the challenges and constraints of the airport environment, the proposed system achieves very good performance while operating in real time.

Real-time Miscarriage Prediction with SPARK

Mobile phones and sensors have become very useful to understand and analyze human lifestyle because of the huge amount of data they collect every second. This triggered the idea of taking advantage of such technologies to predict miscarriages and help pregnant women react in advance to a probable miscarriage. To achieve this, we propose to combine benefits and advantages of both machine learning and Big Data tools applied to smartphone/sensors real time generated data. Kmeans clustering algorithm is used for miscarriage prediction and predicted clusters (partitions) are transmitted to the pregnant woman in her front-end user interface in the mobile application, so that she can make quick decisions in case of miscarriage or probable miscarriage. We used real-world data to validate our system and assess its performance and effectiveness. All data management and processing tasks are conducted over Apache Spark Databricks.

A Peta-Scale Data Movement and Analysis in Data Warehouse (APSDMADW)

In this research paper so as to handle Information warehousing as well as online synthetic dispensation OLAP are necessary aspects of conclusion support which takes more and more turn into a focal point of the data source business.This paper offers an outline of information warehousing also OLAP systems with a highlighting on their latest necessities.All of us explain backside end tackle for extract clean-up and load information into an Data warehouse multi dimensional data model usual of OLAP frontend user tools for query and facts evaluation server extension for useful query dispensation and apparatus for metadata managing and for supervision the stockroom. Insights centered on complete data on customer actions manufactured goods act and souk performance are powerful advance and opposition in the internet gap .In this research conclude the company inspiration and the program and efficiency of servers working in a data warehouse through use of some new techniques and get better and efficient results. Data in petabyte scale. This test shows the data dropping rate in data warehouse. The locomotive is in creation at Yahoo! since 2007 and presently manages more than half a dozen peta bytes of data.

LAND USER AND LAND COVER MAPS OF EUROPE: A WEBGIS PLATFORM

This paper presents the methods and implementation processes of a WebGIS platform designed to publish the available land use and land cover maps of Europe at continental scale. The system is built completely on open source infrastructure and open standards. The proposed architecture is based on a server-client model having GeoServer as the map server, Leaflet as the client-side mapping library and the Bootstrap framework at the core of the front-end user interface. The web user interface is designed to have typical features of a desktop GIS (e.g. activate/deactivate layers and order layers by drag and drop actions) and to show specific information on the activated layers (e.g. legend and simplified metadata). Users have the possibility to change the base map from a given list of map providers (e.g. OpenStreetMap and Microsoft Bing) and to control the opacity of each layer to facilitate the comparison with both other land cover layers and the underlying base map. In addition, users can add to the platform any custom layer available through a Web Map Service (WMS) and activate the visualization of photos from popular photo sharing services. This last functionality is provided in order to have a visual assessment of the available land coverages based on other user-generated contents available on the Internet. It is supposed to be a first step towards a calibration/validation service that will be made available in the future.

LAND USER AND LAND COVER MAPS OF EUROPE: A WEBGIS PLATFORM

Abstract. This paper presents the methods and implementation processes of a WebGIS platform designed to publish the available land use and land cover maps of Europe at continental scale. The system is built completely on open source infrastructure and open standards. The proposed architecture is based on a server-client model having GeoServer as the map server, Leaflet as the client-side mapping library and the Bootstrap framework at the core of the front-end user interface. The web user interface is designed to have typical features of a desktop GIS (e.g. activate/deactivate layers and order layers by drag and drop actions) and to show specific information on the activated layers (e.g. legend and simplified metadata). Users have the possibility to change the base map from a given list of map providers (e.g. OpenStreetMap and Microsoft Bing) and to control the opacity of each layer to facilitate the comparison with both other land cover layers and the underlying base map. In addition, users can add to the platform any custom layer available through a Web Map Service (WMS) and activate the visualization of photos from popular photo sharing services. This last functionality is provided in order to have a visual assessment of the available land coverages based on other user-generated contents available on the Internet. It is supposed to be a first step towards a calibration/validation service that will be made available in the future.

Social media analytics and research testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages

The multilevel model of meme diffusion conceptualizes how mediated messages diffuse over time and space. As a pilot application of implementing the meme diffusion, we developed the social media analytics and research testbed to monitor Twitter messages and track the diffusion of information in and across different cities and geographic regions. Social media analytics and research testbed is an online geo-targeted search and analytics tool, including an automatic data processing procedure at the backend and an interactive frontend user interface. Social media analytics and research testbed is initially designed to facilitate (1) searching and geo-locating tweet topics and terms in different cities and geographic regions; (2) filtering noise from raw data (such as removing redundant retweets and using machine learning methods to improve precision); (3) analyzing social media data from a spatiotemporal perspective; and (4) visualizing social media data in diagnostic ways (such as weekly and monthly trends, trend maps, top media, top retweets, top mentions, or top hashtags). Social media analytics and research testbed provides researchers and domain experts with a tool that can efficiently facilitate the refinement, formalization, and testing of research hypotheses or questions. Three case studies (flu outbreaks, Ebola epidemic, and marijuana legalization) are introduced to illustrate how the predictions of meme diffusion can be examined and to demonstrate the potentials and key functions of social media analytics and research testbed.

Design and Testing of an EHR-Integrated, Busulfan Pharmacokinetic Decision Support Tool for the Point-of-Care Clinician.

Background: Busulfan demonstrates a narrow therapeutic index for which clinicians routinely employ therapeutic drug monitoring (TDM). However, operationalizing TDM can be fraught with inefficiency. We developed and tested software encoding a clinical decision support tool (DST) that is embedded into our electronic health record (EHR) and designed to streamline the TDM process for our oncology partners.Methods: Our development strategy was modeled based on the features associated with successful DSTs. An initial Requirements Analysis was performed to characterize tasks, information flow, user needs, and system requirements to enable push/pull from the EHR. Back-end development was coded based on the algorithm used when manually performing busulfan TDM. The code was independently validated in MATLAB using 10,000 simulated patient profiles. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users (n = 28) were recruited to participate in traditional usability testing under an IRB approved protocol.Results: Decision support software was developed to systematically walk the point-of-care clinician through the TDM process. The system is accessed through the EHR which transparently imports all of the requisite patient data. Data are visually inspected and then curve fit using a model-dependent approach. Quantitative goodness-of-fit are converted to single tachometer where “green” alerts the user that the model is strong, “yellow” signals caution and “red” indicates that there may be a problem with the fitting. Override features are embedded to permit application of a model-independent approach where appropriate. Simulations are performed to target a desired exposure or dose as entered by the clinician and the DST pushes the user approved recommendation back into the EHR. Usability testers were highly satisfied with our DST and quickly became proficient with the software.Conclusions: With early and broad stake-holder engagement we developed a clinical DST for the non-pharmacologist. This tools affords our clinicians the ability to seamlessly transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent prescription order entry.

A virtualization mechanism for real-time multimedia-assisted mobile food recognition application in cloud computing

The integration of multimedia-assisted healthcare systems with could-computing services and mobile technologies has led to increased accessibility for healthcare providers and patients. Utilizing cloud computing infrastructures and virtualization technologies allows for the transformation of traditional healthcare systems that demand manual care and monitoring to more salient, automatic and cost effective systems. The goal of this paper is to develop a multimedia-assisted mobile healthcare application using cloud-computing virtualization technologies. We consider calorie measurement as an example healthcare application that can benefit from cloud-computing virtualization technology. The key functionalities of our application entail image segmentation, image processing and deep learning algorithms for food classification and recognition. Client side devices (e.g. smartphones, tablets etc.) have limitations in handling time sensitive and computationally intensive algorithms pertained to our application. Image processing and deep learning algorithms, used in food recognition and calorie measurement, consume devices' batteries quickly, which is inconvenient for the user. It is also very challenging for client side devices to scale for large number of data and images, as needed for food recognition. The entire process is time-consuming and inefficient and discomforting from users' perspective and may deter them from using the application. In this paper, we address these challenges by proposing a virtualization mechanism in cloud computing that utilizes the Android architecture. Android allows for parting an application into activities run by the front-end user and services run by the back-end tasks. In the proposed virtualization mechanism, we use both the hosted and the hypervisor models to publish our Android-based food recognition and calorie measurement application in the cloud. By so doing, the users of our application can control their virtual smartphone operations through a dedicated client application installed on their smartphones, while the processing of the application continue to run on the virtual Android image even if the user is disconnected due to any unexpected event. We have performed several experiments to validate our mechanism. Specifically, we have run our deep learning and image processing algorithms for food recognition on different configuration platforms on both the cloud and local server connected to the mobile. The results show that the accuracy of the system with the virtualization mechanism is more than 94.33 % compared to 87.16 % when we run the application locally. Also, with our virtualization mechanism the results are processed 49 % faster than the case of running the application locally.