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A data infrastructure for the assessment of health care performance: lessons from the BRIDGE-health project.

ABSTRACT: The integration of different administrative data sources from a number of European countries has been shown useful in the assessment of unwarranted variations in health care performance. This essay describes the procedures used to set up a data infrastructure (e.g., data access and exchange, definition of the minimum common wealth of data required, and the development of the relational logic data model) and, the methods to produce trustworthy healthcare performance measurements (e.g., ontologies standardisation and quality assurance analysis). The paper ends providing some hints on how to use these lessons in an eventual European infrastructure on public health research and monitoring. Although the relational data infrastructure developed has been proven accurate, effective to compare health system performance across different countries, and efficient enough to deal with hundred of millions of episodes, the logic data model might not be responsive if the European infrastructure aims at including electronic health records and carrying out multi-cohort multi-intervention comparative effectiveness research. The deployment of a distributed infrastructure based on semantic interoperability, where individual data remain in-country and open-access scripts for data management and analysis travel around the hubs composing the infrastructure, might be a sensible way forward.

SUBMITTER: Bernal-Delgado E

PROVIDER: S-EPMC5784587 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A data infrastructure for the assessment of health care performance: lessons from the BRIDGE-health project.

Bernal-Delgado Enrique E Estupiñán-Romero Francisco F

Archives of public health = Archives belges de sante publique 20180124

The integration of different administrative data sources from a number of European countries has been shown useful in the assessment of unwarranted variations in health care performance. This essay describes the procedures used to set up a data infrastructure (e.g., data access and exchange, definition of the minimum common wealth of data required, and the development of the relational logic data model) and, the methods to produce trustworthy healthcare performance measurements (e.g., ontologies ...[more]

PMID: 29416858

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Project description:Relatively little attention has been focused on standardization of data exchange in clinical research studies and patient care activities. Both are usually managed locally using separate and generally incompatible data systems at individual hospitals or clinics. In the past decade there have been nascent efforts to create data standards for clinical research and patient care data, and to some extent these are helpful in providing a degree of uniformity. Nonetheless, these data standards generally have not been converted into accepted computer-based language structures that could permit reliable data exchange across computer networks. The National Cardiovascular Research Infrastructure (NCRI) project was initiated with a major objective of creating a model framework for standard data exchange in all clinical research, clinical registry, and patient care environments, including all electronic health records. The goal is complete syntactic and semantic interoperability. A Data Standards Workgroup was established to create or identify and then harmonize clinical definitions for a base set of standardized cardiovascular data elements that could be used in this network infrastructure. Recognizing the need for continuity with prior efforts, the Workgroup examined existing data standards sources. A basic set of 353 elements was selected. The NCRI staff then collaborated with the 2 major technical standards organizations in health care, the Clinical Data Interchange Standards Consortium and Health Level Seven International, as well as with staff from the National Cancer Institute Enterprise Vocabulary Services. Modeling and mapping were performed to represent (instantiate) the data elements in appropriate technical computer language structures for endorsement as an accepted data standard for public access and use. Fully implemented, these elements will facilitate clinical research, registry reporting, administrative reporting and regulatory compliance, and patient care.

Project description:BackgroundTo avoid or mitigate potential project-related adverse health effects, the Trident copper project in Kalumbila, northwestern Zambia, commissioned a health impact assessment. HIV was identified a priority health issue based on the local vulnerability to HIV transmission and experience from other mining projects in Africa. Hence, an HIV/AIDS management plan was developed, including community and workplace interventions, with HIV testing and counselling (HTC) being one of the key components. We present trends in HTC data over a 4-year period.MethodsIn 13 communities affected by the Trident project, HTC was implemented from 2012 onwards, using rapid diagnostic tests, accompanied by pre- and post-test counselling through trained personnel. In addition, HTC was initiated in the project workforce in 2013, coinciding with the launch of the mine development. HTC uptake and HIV positivity rates were assessed in the study population and linked to demographic factors using regression analysis.ResultsIn total, 11,638 community members and 5564 workers have taken up HTC with an increase over time. The HIV positivity rate in the community was 3.0% in 2012 and 3.4% in 2015, while positivity rate in the workforce was 5.2% in 2013 and 4.3% in 2015. Females showed a significantly higher odds of having a positive test result than males (odds ratio (OR) = 1.96, 95% confidence interval (CI): 1.55-2.50 among women in the community and OR = 2.90, 95% CI: 1.74-4.84 among women in the workforce). HTC users in the 35-49 years age group were most affected by HIV, with an average positivity rate of 6.6% in the community sample and 7.9% in the workforce sample. These study groups had 4.50 and 4.95 higher odds of being positive, respectively, compared to their younger counterparts (15-24 years).ConclusionsWhile HTC uptake increased five-fold in the community and almost three-fold in the workplace, the HIV positivity rates were insignificantly higher in 2015 compared to 2012. Our data can be used alongside other surveillance data to track HIV transmission in this specific context. Guided by the health impact assessment, the HIV prevention and control programme was readily adapted to the current setting through the identification of socioeconomic and environmental determinants of health.

Project description:BACKGROUND:Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. OBJECTIVE:This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. METHODS:The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. RESULTS:The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. CONCLUSIONS:To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.

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A data infrastructure for the assessment of health care performance: lessons from the BRIDGE-health project.

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A data infrastructure for the assessment of health care performance: lessons from the BRIDGE-health project.

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