scholarly journals A Framework for Policy-Based Data Integration in Palliative Health Care

10.5772/29623 ◽  
2012 ◽  
Author(s):  
Benjamin Eze
Keyword(s):  
Health Care ◽  
Data Integration

Privacy Preserving Integration of Health Care Data

2012 ◽  
pp. 94-107 ◽  
Author(s):  
Xiaoyun He ◽  
Jaideep Vaidya ◽  
Basit Shafiq ◽  
Nabil Adam ◽  
Tom White
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Privacy Preserving ◽  
Cross Linking ◽  
Health Care Information ◽  
Multiple Sources ◽  
Rule Based ◽  
Related Research ◽  
Health Care Data ◽  
Care Information

For health care related research studies the medical records of patients may need to be retrieved from multiple sites with different regulations on the disclosure of health information. Given the sensitive nature of health care information, privacy is a major concern when patients’ health care data is used for research purposes. In this paper, the authors propose approaches for integration and querying of health care data from multiple sources in a secure and privacy preserving manner. In particular, the first approach ensures secure data integration based on unique identifiers, and the second one considers data integration based on quasi identifiers, for which a rule-based framework is proposed for cross-linking data records, including secure character matching.

scholarly journals Trends in Integrated Clinical Workstations

1996 ◽  
Vol 05 (01) ◽  
pp. 101-107
Author(s):  
T. Timmers ◽  
E. M. van Mulligen
Keyword(s):  
Health Care ◽  
Information Systems ◽  
Software Engineering ◽  
Data Integration ◽  
Building Blocks ◽  
Legacy Systems ◽  
Health Care Institutions ◽  
Methods And Techniques ◽  
Communication Layer ◽  
Clinical Workstation

AbstractDuring the last decade, several projects aiming at integrated clinical workstations have been described and several prototypes have been demonstrated. In most of these projects, the clinical workstation accesses information and functionality provided by the present proprietary legacy systems of health-care institutions. We discuss trends in integrated clinical workstations from the viewpoints of software engineering and integration, considering that the clinical workstation itself basically consists of three layers: a presentation layer, a data integration layer, and a communication layer. The software engineering view on clinical workstations focuses on the development of basic building blocks from which clinical workstations, specific to a particular medical domain, can be composed. The integration view on clinical workstations addresses methods and techniques to deal with the, in general, intrinsically closed information systems in health-care institutions.

scholarly journals Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices

10.2196/34493 ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. e34493
Author(s):  
Ieuan Clay ◽  
Christian Angelopoulos ◽  
Anne Lord Bailey ◽  
Aaron Blocker ◽  
Simona Carini ◽  
...  
Keyword(s):  
Health Care ◽  
Best Practices ◽  
Data Integration ◽  
Health Outcomes ◽  
Large Scale ◽  
Care Delivery ◽  
Sensor Data ◽  
Digital Medicine ◽  
Industry Collaboration ◽  
Stakeholder Needs

Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.

Data Integration in Health Care

2021 ◽  
pp. 497-505
Author(s):  
Maya Leventer-Roberts ◽  
Ran Balicer
Keyword(s):  
Health Care ◽  
Data Integration

scholarly journals Data Integration for Future Medicine (DIFUTURE)

2018 ◽  
Vol 57 (S 01) ◽  
pp. e57-e65 ◽  
Author(s):  
Fabian Prasser ◽  
Oliver Kohlbacher ◽  
Ulrich Mansmann ◽  
Bernhard Bauer ◽  
Klaus Kuhn
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Data Sharing ◽  
Data Protection ◽  
State Of The Art ◽  
Data Access ◽  
Use Cases ◽  
Working Environment ◽  
Security And Privacy ◽  
Use Case

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers. Objectives: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing. Governance and Policies: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change. Architectural Framework and Methodology: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing. Use Cases: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios. Discussion: Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture.

scholarly journals Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices (Preprint)

2021 ◽  
Author(s):  
Ieuan Clay ◽  
Christian Angelopoulos ◽  
Anne Lord Bailey ◽  
Aaron Blocker ◽  
Simona Carini ◽  
...  
Keyword(s):  
Health Care ◽  
Best Practices ◽  
Data Integration ◽  
Health Outcomes ◽  
Large Scale ◽  
Care Delivery ◽  
Sensor Data ◽  
Digital Medicine ◽  
Industry Collaboration ◽  
Stakeholder Needs

UNSTRUCTURED Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.

Privacy Preserving Integration of Health Care Data

2010 ◽  
Vol 1 (2) ◽  
pp. 22-36 ◽  
Author(s):  
Xiaoyun He ◽  
Jaideep Vaidya ◽  
Basit Shafiq ◽  
Nabil Adam ◽  
Tom White
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Medical Records ◽  
Information Privacy ◽  
Privacy Preserving ◽  
Health Care Information ◽  
Multiple Sources ◽  
Rule Based ◽  
Health Care Data ◽  
Care Information

For health care related research studies the medical records of patients may need to be retrieved from multiple sites with different regulations on the disclosure of health information. Given the sensitive nature of health care information, privacy is a major concern when patients’ health care data is used for research purposes. In this paper, the authors propose approaches for integration and querying of health care data from multiple sources in a secure and privacy preserving manner. In particular, the first approach ensures secure data integration based on unique identifiers, and the second one considers data integration based on quasi identifiers, for which a rule-based framework is proposed for cross-linking data records, including secure character matching.

Data Integration in Health Care

2017 ◽  
pp. 121-129 ◽  
Author(s):  
Maya Leventer-Roberts ◽  
Ran Balicer
Keyword(s):  
Health Care ◽  
Data Integration

scholarly journals Patient Clinical Data Integration in Integrated Electronic Medical Record System for Health Care Facilities in Indonesia

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Arif Kurniadi ◽  
Retno Pratiwi
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Data ◽  
Data Exchange ◽  
Clinical History ◽  
Health Care Facilities ◽  
Portal System ◽  
Care Facilities

Complete patient service requires continuous support of clinical history. This can be realized by integrating electronic medical record data. The limitation is the wide variety of software, formats, and data dictionaries used in healthcare facilities. This was a descriptive analysis study with cross sectional approach to find open source electronic medical record integration model for clinical data exchange between health care facilities. Respondents were doctors, nurses, pharmacists, laboratory staffs, and person in charge of hospital information system as informant for content analysis. From the study, we managed a web-based service portal to implement clinical data integration that can be accessed by clinician registered within the Ministry of Health. The patients clinical history is stored in the hospital database and requires unique OpenIDRM code on the Health Service Server to integrate it. OpenIDRM contains all of the patients medical record number, as one patient may have several different medical record numbers in several hospitals. In conclusion, clinician can access the patients clinical history by opening a web portal system through a unique OpenIDRM code.

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