Gaining Access to Data Sources in Statutory Social Work Agencies: The Long and Winding Road

2005 ◽  
Vol 35 (7) ◽  
pp. 1193-1202 ◽  
Author(s):  
D. Hayes
Keyword(s):  
Social Work ◽  
Data Sources ◽  
Access To Data

scholarly journals Ontology-Based Data Access: A Survey

2018 ◽  
Author(s):  
Guohui Xiao ◽  
Diego Calvanese ◽  
Roman Kontchakov ◽  
Domenico Lembo ◽  
Antonella Poggi ◽  
...  
Keyword(s):  
Data Access ◽  
Data Sources ◽  
Relational Data ◽  
Data Repositories ◽  
The Past ◽  
Future Challenges ◽  
Access To Data ◽  
User Friendly ◽  
Theoretical Results

We present the framework of ontology-based data access, a semantic paradigm for providing a convenient and user-friendly access to data repositories, which has been actively developed and studied in the past decade. Focusing on relational data sources, we discuss the main ingredients of ontology-based data access, key theoretical results, techniques, applications and future challenges.

scholarly journals A Future Trend in Sensing Enterprise Systems

2018 ◽  
Author(s):  
Majid Zamiri ◽  
Andreia Artifice ◽  
Elsa Marcelino-Jesus ◽  
Joao Sarraipa ◽  
Ricardo Jardim-Goncalves
Keyword(s):  
Business Models ◽  
Business Processes ◽  
Enterprise Systems ◽  
Business Environment ◽  
Future Trend ◽  
Data Sources ◽  
Free Access ◽  
Blockchain Technology ◽  
Intelligent Knowledge Management ◽  
Access To Data

The widespread use of sensorial technologies has created new opportunities for enterprises, as these new sources of data assist in increasing the context-awareness in which enterprises operate and enable them to better anticipate and adapt to changes in the business environment. This leads to better decision-making and greater profitability, as well as to reduced operating risks. However, if enterprises are to get any benefit from these new data sources, there is a need for tools, which will enable them to deal with the complexity of the multitude of data sources, both from physical and virtual objects, as well as a means to extract relevant and correct information and knowledge from it. Having access to data is not enough. The real value to enterprises comes from being able to process it, interpret it and being able to make accurate forecasts upon which they can base their business decisions. This paper presents the blockchain technology which is intended to support the development of sensing enterprise systems for intelligent knowledge management. Since the creation of the internet, blockchain is the most important technology created that is in constant development and has still much more to develop. In a simple way, blockchain is a computational technology for register of operations, decentralized, free-access, transparent, global, continuous. It is a public database that is accessible for everyone and is much more secure and reliable than other forms currently known to perform similar operations. Thus, it is intended to demonstrate the importance of this recent technology in business processes and the future trends of its use in sensing enterprise business processes. The proposed framework intends to demonstrate and serve as the foundation for new business models supported by the new capabilities provided by sensorial technologies in the support of enterprise applications.

scholarly journals Tokenized Ecosystem of Personal Data — Exemplified on the Context of the Smart City

2017 ◽  
Vol 9 (2) ◽  
pp. 110-133 ◽  
Author(s):  
Jan Thomas Frecè ◽  
Thomas Selzam
Keyword(s):  
Data Protection ◽  
Smart City ◽  
Smart Cities ◽  
Personal Data ◽  
Privacy Preserving ◽  
Data Sources ◽  
Data Driven ◽  
Access To Data

Data driven businesses, services, and even smart cities of tomorrow depend on access to data not only from machines, but also personal data of consumers, clients, citizens. Sustain-able utilization of such data must base on legal compliancy, ethical soundness, and consent. Data subjects nowadays largely lack empowerment over utilization and monetization of their personal data. To change this, we propose a tokenized ecosystem of personal data (TokPD), combining anonymization, referencing, encryption, decentralization, and functional layering to establish a privacy preserving solution for processing of personal data. This tokenized ecosys-tem is a more generalized variant of the smart city ecosystem described in the preceding publi-cation "Smart Cities of Self-Determined Data Subjects" (Frecè & Selzam 2017) with focus to-wards further options of decentralization. We use the example of a smart city to demonstrate, how TokPD ensures the data subjects’ privacy, grants the smart city access to a high number of new data sources, and simultaneously handles the user-consent to ensure compliance with mod-ern data protection regulation.

scholarly journals TargetDB: A target information aggregation tool and tractability predictor

2020 ◽  
Author(s):  
Stephane P. De Cesco ◽  
John B. Davis ◽  
Paul E. Brennan
Keyword(s):  
Drug Targets ◽  
Public Information ◽  
Information Aggregation ◽  
Data Sources ◽  
Test Cases ◽  
Potential Drug ◽  
Target Information ◽  
Multiple Targets ◽  
Access To Data ◽  
Potential Drug Targets

ABSTRACTWhen trying to identify new potential therapeutic targets, access to data and knowledge is increasingly important. In a field where new resources and data sources become available every day, it is crucial to be able to take a step back and look at the wider picture in order to identify potential drug targets. While this task is routinely performed by bespoke researchers, it is often time-consuming and lacks uniformity when one wants to compare multiple targets at the same time. Therefore we developed TargetDB, a tool that aggregates public information available on given target(s) (Links to disease, safety, 3D structures, ligandability, novelty,…) and assembles it in an easy to read output ready for the researcher to analyze. In this manuscript, we will present the methodology used to develop TargetDB as well as test cases.

scholarly journals Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services

2017 ◽  
Vol 25 (1) ◽  
pp. 47-53 ◽  
Author(s):  
Suranga N Kasthurirathne ◽  
Joshua R Vest ◽  
Nir Menachemi ◽  
Paul K Halverson ◽  
Shaun J Grannis
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Social Work ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Social Service ◽  
Health Data ◽  
Data Sources ◽  
Decision Models ◽  
Predictive Values

Abstract Introduction A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. Materials and Methods We integrated patient clinical data and community-level data representing patients’ social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. Results Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. Discussion The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.

Dysphagia in Hospice Care: The Roles of Social Work and Speech-Language Pathologists

2014 ◽  
Vol 23 (4) ◽  
pp. 173-186 ◽  
Author(s):  
Deborah Hinson ◽  
Aaron J. Goldsmith ◽  
Joseph Murray
Keyword(s):  
Social Work ◽  
End Of Life ◽  
Case Studies ◽  
End Of Life Care ◽  
Hospice Care ◽  
Patient Communication ◽  
Life Care ◽  
Speech Language Pathologists ◽  
Four Levels

This article addresses the unique roles of social work and speech-language pathologists (SLPs) in end-of-life and hospice care settings. The four levels of hospice care are explained. Suggested social work and SLP interventions for end-of-life nutrition and approaches to patient communication are offered. Case studies are used to illustrate the specialized roles that social work and SLP have in end-of-life care settings.

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