scholarly journals Mapping European research networks providing health data: results from the InfAct Joint Action on health information

2022 ◽  
Vol 80 (1) ◽  
Author(s):  
Brigid Unim ◽  
Elsi Haverinen ◽  
Eugenio Mattei ◽  
Flavia Carle ◽  
Andrea Faragalli ◽  
...  
Keyword(s):  
Data Collection ◽  
Quality Assessment ◽  
Health Information ◽  
Information Exchange ◽  
Ad Hoc ◽  
Health Information System ◽  
Data Access ◽  
Health Data ◽  
Quality Data ◽  
Research Networks

Abstract Background Research networks offer multidisciplinary expertise and promote information exchange between researchers across Europe. They are essential for the European Union’s (EU) health information system as providers of health information and data. The aim of this mapping exercise was to identify and analyze EU research networks in terms of health data collection methods, quality assessment, availability and accessibility procedures. Methods A web-based search was performed to identify EU research networks that are not part of international organizations (e.g., WHO-Europe, OECD) and are involved in collection of data for health monitoring or health system performance assessment. General characteristics of the research networks (e.g., data sources, representativeness), quality assessment procedures, availability and accessibility of health data were collected through an ad hoc extraction form. Results Fifty-seven research networks, representative at national, international or regional level, were identified. In these networks, data are mainly collected through administrative sources, health surveys and cohort studies. Over 70% of networks provide information on quality assessment of their data collection procedures. Most networks share macrodata through articles and reports, while microdata are available from ten networks. A request for data access is required by 14 networks, of which three apply a financial charge. Few networks share data with other research networks (8/49) or specify the metadata-reporting standards used for data description (9/49). Conclusions Improving health information and availability of high quality data is a priority in Europe. Research networks could play a major role in tackling health data and information inequalities by enhancing quality, availability, and accessibility of health data and data sharing across European networks.

scholarly journals Health data collection methods and procedures across EU member states: findings from the InfAct Joint Action on health information

2022 ◽  
Vol 80 (1) ◽  
Author(s):  
Brigid Unim ◽  
Eugenio Mattei ◽  
Flavia Carle ◽  
Hanna Tolonen ◽  
Enrique Bernal-Delgado ◽  
...  
Keyword(s):  
Data Collection ◽  
Quality Assessment ◽  
Health Information ◽  
Population Health ◽  
Policy Making ◽  
Health Data ◽  
Data Sources ◽  
Eu Member States ◽  
Collection Methods ◽  
The Eu

Abstract Background Health-related data are collected from a variety of sources for different purposes, including secondary use for population health monitoring (HM) and health system performance assessment (HSPA). Most of these data sources are not included in databases of international organizations (e.g., WHO, OECD, Eurostat), limiting their use for research activities and policy making. This study aims at identifying and describing collection methods, quality assessment procedures, availability and accessibility of health data across EU Member States (MS) for HM and HSPA. Methods A structured questionnaire was developed and administered through an online platform to partners of the InfAct consortium form EU MS to investigate data collections applied in HM and HSPA projects, as well as their methods and procedures. A descriptive analysis of the questionnaire results was performed. Results Information on 91 projects from 18 EU MS was collected. In these projects, data were mainly collected through administrative sources, population health interview or health examination surveys and from electronic medical records. Tools and methods used for data collection were mostly mandatory reports, self-administered questionnaires, or record linkage of various data sources. One-third of the projects shared data with EU research networks and less than one-third performed quality assessment of their data collection procedures using international standardized criteria. Macrodata were accessible via open access and reusable in 22 projects. Microdata were accessible upon specific request and reusable in 15 projects based on data usage licenses. Metadata was available for the majority of the projects, but followed reporting standards only in 29 projects. Overall, compliance to FAIR Data principles (Findable, Accessible, Interoperable, and Reusable) was not optimal across the EU projects. Conclusions Data collection and exchange procedures differ across EU MS and research data are not always available, accessible, comparable or reusable for further research and evidence-based policy making. There is a need for an EU-level health information infrastructure and governance to promote and facilitate sharing and dissemination of standardized and comparable health data, following FAIR Data principles, across the EU.

scholarly journals Maturity assessment of Kenya’s health information system interoperability readiness

2021 ◽  
Vol 28 (1) ◽  
pp. e100241
Author(s):  
Job Nyangena ◽  
Rohini Rajgopal ◽  
Elizabeth Adhiambo Ombech ◽  
Enock Oloo ◽  
Humphrey Luchetu ◽  
...  
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Working Group ◽  
Data Exchange ◽  
Enterprise Architecture ◽  
Health Information Exchange ◽  
Digital Health ◽  
Health Information System ◽  
Maturity Level ◽  
Technical Standards

BackgroundThe use of digital technology in healthcare promises to improve quality of care and reduce costs over time. This promise will be difficult to attain without interoperability: facilitating seamless health information exchange between the deployed digital health information systems (HIS).ObjectiveTo determine the maturity readiness of the interoperability capacity of Kenya’s HIS.MethodsWe used the HIS Interoperability Maturity Toolkit, developed by MEASURE Evaluation and the Health Data Collaborative’s Digital Health and Interoperability Working Group. The assessment was undertaken by eHealth stakeholder representatives primarily from the Ministry of Health’s Digital Health Technical Working Group. The toolkit focused on three major domains: leadership and governance, human resources and technology.ResultsMost domains are at the lowest two levels of maturity: nascent or emerging. At the nascent level, HIS activities happen by chance or represent isolated, ad hoc efforts. An emerging maturity level characterises a system with defined HIS processes and structures. However, such processes are not systematically documented and lack ongoing monitoring mechanisms.ConclusionNone of the domains had a maturity level greater than level 2 (emerging). The subdomains of governance structures for HIS, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for operations and maintenance of hardware attained higher maturity levels. These findings are similar to those from interoperability maturity assessments done in Ghana and Uganda.

scholarly journals Alberta's Data and Analytic Strategy: Leveraging Linked Data to Drive Innovation

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Larry Svenson
Keyword(s):  
Health System ◽  
Health Information ◽  
Data Warehouse ◽  
Data Access ◽  
Health Data ◽  
Direct Access ◽  
Safe Haven ◽  
For Profit ◽  
Secondary Use ◽  
Not For Profit

BackgroundThe Province of Alberta, Canada, maintains a mature data environment with linkable administrative and clinical data dating back up to 30 years. Alberta has a single payer, publicly funded and administered, universal health system, which maintains multiple administrative data sets. Main AimThe main aim of the strategy is to fully maximize the data assets in the province to drive health system health system innovation, with a focus on improving health outcomes and quality of life. Methods/ApproachThe Alberta Ministry of Health has created the Secondary Use Data Access (SUDA) initiative to leverage its administrative health data. SUDA envisions strengthening partnerships between the public and private sectors through two main data access approaches. The first is direct access to de-identified data held within the Alberta Health data warehouse by key health system stakeholders (e.g. academic institutions, professional associations, regulatory colleges). The second is indirect access to private and not-for-profit organizations, using a data access safe haven (DASH) approach. Indirect access is achieved through private sector investments to a trusted third party that hires analysts placed within the Ministry of Health offices. ResultsStaffing agreements and privacy impact assessments are in place. Indirect access includes a multiple stakeholder steering committee to vet and prioritize projects. Private and not-for-profit stakeholders do not have access to raw data, but rather receive access to aggregated data and statistical models. All data disclosures are done by Ministry staff to ensure compliance with Alberta's Health Information Act. Direct access has been established for one professional organization and one academic institution, with access restricted to de-identified data. ConclusionThe Secondary Use Data Access initiative uses a safe haven approach to leveraging data to provide a more secure approach to data access. It reduces the need to provision data outside of the data warehouse while improving timely access to data. The approach provides assurances that people's health information is held secure, while also being used to create health system improvements.

Quality of Routine Health Information System Data and Associated Factors Among Departments in Public Health Facilities of Harari Region, Ethiopia

2021 ◽  
Author(s):  
Adisu Tafari Shama ◽  
Hirbo Shore Roba ◽  
Admas Abera ◽  
Negga Baraki
Keyword(s):  
Public Health ◽  
Information System ◽  
Data Quality ◽  
Health Information ◽  
Health Information System ◽  
Health Facilities ◽  
Quality Data ◽  
Public Health Facilities ◽  
System Data

Abstract Background: Despite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia.Methods: A cross-sectional study was conducted in all public health facilities in Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation check-lists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P-value <0.05. Result: The study found a good quality data in 51.35% (95% CI, 44.6-58.1) of the departments in public health facilities in Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to departments found in the health posts. The presence of trained staffs able to fill reporting formats (AOR=2.474; 95%CI: 1.124-5.445) and provision of feedback (AOR=3.083; 95%CI: 1.549-6.135) were also significantly associated with data quality. Conclusion: The level of good data quality in the public health facilities was less than the expected national level. Training should be provided to increase the knowledge and skills of the health workers.

hData - A Simple XML Framework for Health Data Exchange

2009 ◽  
Author(s):  
Gerald Beuchelt ◽  
Harry Sleeper ◽  
Andrew Gregorowicz ◽  
Robert Dingwell
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Data Exchange ◽  
Health Information Exchange ◽  
Health Data ◽  
Care Providers ◽  
Future Health ◽  
Data Interoperability ◽  
History Of ◽  
Electronic Health

Health data interoperability issues limit the expected benefits of Electronic Health Record (EHR) systems. Ideally, the medical history of a patient is recorded in a set of digital continuity of care documents which are securely available to the patient and their care providers on demand. The history of electronic health data standards includes multiple standards organizations, differing goals, and ongoing efforts to reconcile the various specifications. Existing standards define a format that is too complex for exchanging health data effectively. We propose hData, a simple XML-based framework to describe health information. hData addresses the complexities of the current HL7 Clinical Document Architecture (CDA). hData is an XML design that can be completely validated by modern XML editors and is explicitly designed for extensibility to address future health information exchange needs. hData applies established best practices for XML document architectures to the health domain, thereby facilitating interoperability, increasing software developer productivity, and thus reducing the cost for creating and maintaining EHR technologies.

scholarly journals Indicators of Organizational Support in Implementing Maternal and Child Health Information System

2016 ◽  
Vol 5 (3) ◽  
pp. 280
Author(s):  
Heru Santoso Wahito Nugroho ◽  
Stefanus Supriyanto ◽  
Hari Basuki Notobroto
Keyword(s):  
Information System ◽  
Child Health ◽  
Health Information ◽  
Maternal And Child Health ◽  
Organizational Support ◽  
Health Information System ◽  
Supervisor Support ◽  
Quality Data ◽  
Work Condition ◽  
And Child Health

<p style="color: #000000; font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">Nowadays, the implementation of health informtion system in Indonesia still encounters a number of obstacles e.g. redundant data, activities duplication, data quality, data not in harmony with the necessities, report not submitted on time, unoptimized feedback, low information utilization, and inefficient resources. This research aimed to analyze the indicators of organizational support which were suspected as one of the obstacles of the implementation of Maternal and Child Health Information System in Health Office of Ngawi Regency. The population of this cross sectional research was all village midwives administratively in duty in the areas of Ngawi Regency in 2015. Data was taken from all member of populaton through questionnaire filling, which was then analyzed by using confirmatory factor analysis (CFA). The result of data analysis suggested that the coefficient value that has been standardized from each indicators were as follows: supervisor support = 0.82, work condition = 0.80, and reard = 0.90. Indicators of organizational support<br />in implementing Maternal and Child Health Information System at Ngawi Regency Health Office, respectively from the most important are: reward, supervisor support, and work condition.</p>

scholarly journals Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Alan Katz ◽  
Jennifer Enns ◽  
Sabrina T Wong ◽  
Tyler Williamson ◽  
Alexander Singer ◽  
...  
Keyword(s):  
British Columbia ◽  
Social Services ◽  
Administrative Data ◽  
Data Access ◽  
Health Data ◽  
Quality Data ◽  
Data Governance ◽  
Quality Health Care ◽  
Data Repositories ◽  
Administrative Health Data

Over the last 30 years, public investments in Canada and many other countries have created clinical and administrative health data repositories to support research on health and social services, population health and health policy. However, there is limited capacity to share and use data across jurisdictional boundaries, in part because of inefficient and cumbersome procedures to access these data and gain approval for their use in research. A lack of harmonization among variables and indicators makes it difficult to compare research among jurisdictions. These challenges affect the quality, scope, and impact of work that could be done. The purpose of this paper is to compare and contrast the data access procedures in three Canadian jurisdictions (Manitoba, Alberta and British Columbia), and to describe how we addressed the challenges presented by differences in data governance and architecture in a Canadian cross-jurisdictional research study. We characterize common stages in gaining access to administrative data among jurisdictions, including obtaining ethics approval, applying for data access from data custodians, and ensuring the extracted data is released to accredited individuals in secure data environments. We identify advantages of Manitoba’s flexible ‘stewardship’ model over the more restrictive ‘custodianship’ model in British Columbia, and highlight the importance of communication between analysts in each jurisdiction to compensate for differences in coding variables and poor quality data. Researchers and system planners must have access to and be able to make effective use of administrative health data to ensure that Canadians continue to have access to high-quality health care and benefit from effective health policies. The considerable benefits of collaborative population-based research that spans jurisdictional borders have been recognized by the Canadian Institutes for Health Research in their recent call for the creation of a National Data Platform to resolve many of the issues in harmonization and validation of administrative data elements.

scholarly journals Canadian Approaches to Optimizing Quality of Administrative Data for Health System Use, Research, and Linkage

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Catherine Eastwood ◽  
Keith Denny ◽  
Maureen Kelly ◽  
Hude Quan
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Quality Assessment ◽  
Administrative Data ◽  
Information Quality ◽  
Health Data ◽  
Data Sources ◽  
University Of Calgary ◽  
Quality Assessment And Improvement

Theme: Data and Linkage QualityObjectives: To define health data quality from clinical, data science, and health system perspectives To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data. To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives) To highlight how data linkage can be used to provide new insights into the quality of original data sources To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards. Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture. Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11. Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality. Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data. Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost. This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use. CIHI content will include: Methods for optimizing the value of clinical-administrative data CIHI Information Quality Framework Reabstraction studies (e.g. physician documentation/coders’ experiences) Linkage analytics for data quality University of Calgary content will include: Defining/measuring health data quality Automated methods for quality assessment and improvement ICD-11 features and coding practices Electronic health record initiatives

Monitoring, Evaluation, and Quality Improvement in Global Health

2021 ◽  
pp. 245-268
Author(s):  
Joia S. Mukherjee
Keyword(s):  
Quality Improvement ◽  
Global Health ◽  
Health Information ◽  
Health Surveys ◽  
Program Planning ◽  
Health Data ◽  
Quality Data ◽  
Electronic Systems ◽  
Health Delivery ◽  
Use Of Data

Quality data are necessary to make good decisions in health delivery, for both individuals and populations. Data can be used to improve care and achieve equity. However, the collection of health data has been weak in most impoverished countries, where health data are compiled in stacks of poorly organized paper records. Efforts to streamline and improve health information discussed in this chapter include patient-held booklets, demographic health surveys, and the use of common indicators. This chapter also focuses on the evolution of medical records, including electronic systems. The use of data for monitoring, evaluation, and quality improvement is explained. Finally, this chapter reviews the use of frameworks—such as logic models and log frames—for program planning, evaluation, and improvement.

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