Financial services � Key-management-related data element � Application and usage of ISO 8583-1 data elements for encryption

2019 ◽  
Keyword(s):  
Financial Services ◽  
Key Management ◽  
Data Element ◽  
Related Data ◽  
Data Elements

Developing Data Link User-Interface Designs Using Pilot Conceptual Networks

1998 ◽  
Vol 42 (1) ◽  
pp. 77-79
Author(s):  
Kevin W. Williams ◽  
Kurt M. Joseph
Keyword(s):  
User Interface ◽  
User Interfaces ◽  
General Aviation ◽  
Data Element ◽  
Data Link ◽  
Service Station ◽  
Related Data ◽  
Conceptual Network ◽  
Data Elements ◽  
Interface Designs

One hundred and forty-eight pilots were asked to categorize a list of flight-related data elements that could be sent via data link from an FAA automated flight service station to an aircraft or vice versa. The categorization was used to construct a matrix of proximity values for each of the data-element pairs so that a conceptual network of the elements could be constructed using the Pathfinder algorithm developed by Schvaneveldt, Durso, and Dearholt (1985). Additionally, pilots were asked to judge each data element according to how useful the element was for the functions of communication, navigation, and surveillance within the general aviation (GA) flight environment. Elements scoring high on each of these flight-related functions were then subjected to a Pathfinder analysis. The conceptual networks that were created as a result of these analyses are discussed in relation to the development of data link user interfaces for the GA cockpit.

scholarly journals From OpenEHR to FHIR and OMOP Data Model for Microbiology Findings

2021 ◽  
Author(s):  
Eugenia Rinaldi ◽  
Sylvia Thun
Keyword(s):  
Infection Control ◽  
Data Model ◽  
Data Exchange ◽  
Control Group ◽  
Common Data Model ◽  
University Hospitals ◽  
Data Set ◽  
Related Data ◽  
National Initiative ◽  
Data Elements

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.

How Customer Analytics and Digital Technologies Drive Banks to Understand Consumer Awareness Towards Financial Inclusion

2018 ◽  
pp. 66-85
Author(s):  
Yuvraj Sharma
Keyword(s):  
Financial Institutions ◽  
Financial Services ◽  
Digital Technologies ◽  
Financial Inclusion ◽  
Primary Data ◽  
Related Data ◽  
Customer Analytics ◽  
Clear Communication ◽  
Increasing Demand

In today's switching economy, customers' needs are changing and they are demanding more transparency, higher involvement, and clear communication in day-to-day banking processes. The rationale behind carrying out the present research is to identify the role of customer analytics in the new digital customer journey in terms of enhancing their engagement, loyalty, and satisfaction. The present research emphasizes opportunities that would accrue to financial institutions after demonetization and collecting large amount of demographics, customer transaction, and account-related data. Primary data was collected from 300 customers through a structured questionnaire to know their perceptions about the role of customer analytics and digital technologies to build their confidence and capability to use financial services. This study brings out the customer analytics trends and identifies the reasons due to which banks are struggling to keep pace with the increasing demand of both digital savvy and traditional consumers.

XTEngine

2011 ◽  
pp. 174-213
Author(s):  
Kamal Taha
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Marked Improvement ◽  
Data Models ◽  
Data Element ◽  
Xml Data ◽  
Building Relationships ◽  
Search Techniques ◽  
Data Elements

There has been extensive research in XML Keyword-based and Loosely Structured querying. Some frameworks work well for certain types of XML data models while fail in others. The reason is that the proposed techniques overlook the context of elements when building relationships between the elements. The context of a data element is determined by its parent, because a data element is generally a characteristic of its parent. Overlooking the contexts of elements may result in relationships between the elements that are semantically disconnected, which lead to erroneous results. We present in this chapter a context-driven search engine called XTEngine for answering XML Keyword-based and Loosely Structured queries. XTEngine treats each set of elements consisting of a parent and its children data elements as one unified entity, and then uses context-driven search techniques for determining the relationships between the different unified entities. We evaluated XTEngine experimentally and compared it with three other search engines. The results showed marked improvement.

scholarly journals Completeness, accuracy, and computability of National Quality Forum-specified eMeasures

2014 ◽  
Vol 22 (2) ◽  
pp. 409-416 ◽  
Author(s):  
Andy Amster ◽  
Joseph Jentzsch ◽  
Ham Pasupuleti ◽  
K G Subramanian
Keyword(s):  
Data Availability ◽  
Data Mapping ◽  
Data Element ◽  
Clinical Practices ◽  
National Quality Forum ◽  
Post Discharge ◽  
Data Set ◽  
Clinical Text ◽  
National Quality ◽  
Data Elements

Abstract Objective To analyze the completeness, computability, and accuracy of specifications for five National Quality Forum-specified (NQF) eMeasures spanning ambulatory, post-discharge, and emergency care within a comprehensive, integrated electronic health record (EHR) environment. Materials and methods To evaluate completeness, we assessed eMeasure logic, data elements, and value sets. To evaluate computability, we assessed the translation of eMeasure algorithms to programmable logic constructs and the availability of EHR data elements to implement specified data criteria, using a de-identified clinical data set from Kaiser Permanente Northwest. To assess accuracy, we compared eMeasure results with those obtained independently by existing audited chart abstraction methods used for external and internal reporting. Results One measure specification was incomplete; missing applicable LOINC codes rendered it non-computable. For three of four computable measures, data availability issues occurred; the literal specification guidance for a data element differed from the physical implementation of the data element in the EHR. In two cases, cross-referencing specified data elements to EHR equivalents allowed variably accurate measure computation. Substantial data availability issues occurred for one of the four computable measures, producing highly inaccurate results. Discussion Existing clinical workflows, documentation, and coding in the EHR were significant barriers to implementing eMeasures as specified. Implementation requires redesigning business or clinical practices and, for one measure, systemic EHR modifications, including clinical text search capabilities. Conclusions Five NQF eMeasures fell short of being machine-consumable specifications. Both clinical domain and technological expertise are required to implement manually intensive steps from data mapping to text mining to EHR-specific eMeasure implementation.

Electronic calculation of quality measures in the EMR and dependence on data capture in structured fields: The OCM quality reporting experience.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18278-e18278
Author(s):  
James Hamrick ◽  
Cindy Revol ◽  
Rohit Parulkar ◽  
Olaf Lemmers ◽  
James Gippetti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Quality Measures ◽  
Quality Measure ◽  
Data Element ◽  
Adjuvant Trastuzumab ◽  
Quality Reporting ◽  
Her2 Breast Cancer ◽  
Feasible Alternative ◽  
Data Elements

e18278 Background: Reporting of quality measures is ideally achieved electronically. This is dependent on presence of data elements in structured fields. We report on the variability of quality measure reporting in the OCM for quality measures OCM 8 (adjuvant chemotherapy in stage 3 colon cancer), 9 (adjuvant chemotherapy in HR- breast cancer), 10 (adjuvant trastuzumab for Her2+ breast cancer), and 11 (adjuvant hormonal therapy for HR+ breast cancer). Methods: Quality measures were calculated for 30,524 patients across 35 practices participating in the OCM. We examined the calculation of quality metrics OCM-8, 9, 10, and 11 based on presence or absence of data elements in the medical record. Results: For quality measures OCM-8 and 9 that require documentation of date of surgery, only 1.3% and 1.6%, respectively, of patients could be included in the calculation due to absence of surgical date in a structured field. For quality measures OCM 9 and 11 that require attestation of surgical staging of breast cancer, this data element was documented in 0% of patients. For OCM 9 and 10 that require stage documentation of breast cancer, this data element was documented in 63% and 75%, respectively, of patients (range 12-100%). In cases where staging criteria were not met, 58% and 44%, respectively, of patients were removed due to total absence of staging data in structured fields. Conclusions: Quality measure calculation and reporting is highly dependent on documentation of data elements in structured fields. Lack of documentation of key elements distorts calculation of the measure. Modification of physician workflow or restructuring of measure calculation to optimize use of well documented structured elements will improve generalizability of quality measure reporting. Manual abstraction of data does not represent a feasible alternative.

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