Healthcare Data Integration Research Articles

Empowering Patients with AI-Driven Personalized Care: The Transformative Power of Generative AI and Healthcare Data Integration

Empowering Patients with AI-Driven Personalized Care: The Transformative Power of Generative AI and Healthcare Data Integration

Expert opinions on improving coercion data collection across Europe: a concept mapping study.

Coercion is frequently used in mental health practice. Since it overrides some patients' fundamental human rights, adequate use of coercion requires legal and ethical justifications. Having internationally standardised datasets to benchmark and monitor coercion reduction programs is desirable. However, only a few countries have specific, open, publicly accessible registries for this issue. This study aims to assemble expert opinions regarding strategies that might be feasible for promoting, developing, and implementing an integrated and differentiated coercion data collection system in Europe at national and international levels. A concept mapping methodology was followed, involving 59 experts from 27 countries in generating, sorting and rating strategies regarding relevance and feasibility. The experts were all researchers and/or practitioner members of an EU-COST-Action focused on coercion reduction Fostering and Strengthening Approaches to Reducing Coercion in European Mental Health Services (FOSTREN). A hierarchical cluster analysis revealed a conceptual map of 41 strategies organized in seven clusters. These clusters fit into two higher-order domains: "Advancing Global Health Research: Collaboration, Accessibility, and Technological Innovations/Advancing International Research" and "Strategies for Comprehensive Healthcare Data Integration, Standardization, and Collaboration." Regarding the action with the higher priority, relevance was generally rated higher than feasibility. No differences could be found regarding the two domains regarding the relevance rating or feasibility of the respective strategies in those domains. The following strategies were rated as most relevant: "Collection of reliable data", "Implementation of nationwide register, including data on coercive measures", and "Equal understanding of different coercive measures". In analysing the differences in strategies between countries and their health prosperity, the overall rating did not differ substantially between the groups. The strategy rated as most relevant was the collection of reliable data in the nationwide health register, ensuring that countries share a standard understanding/definition of different coercive measures. Respondents did not consider the feasibility of establishing a shared European database for coercive measures to be high, nor did they envision the unification of mental health legislation in the future. There is some consensus on the most suitable strategies that can be adopted to enable international benchmarking of coercion in mental health settings.

Advanced Framework for Multi-Modal Healthcare Data Integration: Leveraging HPC with GPU Computing and CNN Architecture in CDSS

In this study, we shall be looking at the challenges involved in integrating multi-modal healthcare data in the clinical decision support systems (CDSS). We propose the Automated Multi-Modal Data Integration (AMMI-CDSS) algorithm, which will utilize the latest high-performance computing (HPC) techniques such as the Convolutional Neural Network (CNN) architecture and the Graphics Processing Unit (GPU) computing to provide precise and rapid analysis. Which features will be extracted, multi-modal data will be merged, data will be prepared and algorithms developed in a distributed computing environment. We illustrate how AMMI-CDSS through the use of real world datasets such as wearable sensors data, medical imaging, genetic data, and electronic health records (EHRs), can improve the clinical decision support. By performing harmonization of the diverse data sources into a unique dataset after thorough data preprocessing and complex calculations, AMMI-CDSS provides the analysis with better quality and coherence.
 Our study allow us to make conclusion about how HPC-based CDSS models can be compared to conventional machine learning ones using their scalability and performance as key metrics. We enrich CDSS with the methodical framework for one-by-one testing and evaluation of proposed models and multi-modal healthcare data analysis. Future research might explore novel methods for integrating diverse types of healthcare data, as well as enhancing the HPC-based CDSS models by keeping them up-to-date.

A knowledge graph-based data harmonization framework for secondary data reuse

Background and objectiveThe adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and analysed. We propose a semantic-driven harmonization framework that (1) enables the meaningful sharing and integration of healthcare data across institutions and (2) facilitates the analysis and exploitation of the shared data. MethodsThe framework includes an ontology-based common data model (i.e. SCDM), a data transformation pipeline and a semantic query system. Heterogeneous datasets, mapped to different terminologies, are integrated by using an ontology-based infrastructure rooted in a top-level ontology. A graph database is generated by using these mappings, and web-based semantic query system facilitates data exploration. ResultsSeveral datasets from different European institutions have been integrated by using the framework in the context of the European H2020 Precise4Q project. Through the query system, data scientists were able to explore data and use it for building machine learning models. ConclusionsThe flexible data representation using RDF, together with the formal semantic underpinning provided by the SCDM, have enabled the semantic integration, query and advanced exploitation of heterogeneous data in the context of the Precise4Q project.

Enhancing health-care data integration via automated semantic mapping

PurposeThis paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.Design/methodology/approachA design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.FindingsThe study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.Originality/valueThe originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

Three-Dimensional Disease Outbreak Surveillance System in a Tertiary Hospital in Singapore: A Proof of Concept

ObjectiveTo develop an electronic surveillance system that provides prompt in-depth situational infectious disease risk and linkage analysis for inpatients in a tertiary hospital. Patients and MethodsAll patients admitted to Singapore General Hospital (SGH), a 1900-bedded tertiary care hospital, are included in routine surveillance. The 3-Dimensional Disease Outbreak Surveillance System (3D-DOSS) was developed to spatiotemporally represent inpatient surveillance data on a “digital twin” of SGH and evaluated for performance in surveillance, contact tracing, and outbreak investigations. This study was conducted over a 12 month period (October 1, 2020 to September 30, 2021). ResultsThe 3D-DOSS surveillance module identified an influenza cluster of 10 inpatients in November 2018, mapping retrospective data between September 2018 and December 2018. Seventy-six clusters of 2 or more linked patients with health care–associated Klebsiella pneumoniae carbapenemase–type carbapenemase-producing Enterobacteriaceae were detected in SGH in 2 years (2018 and 2019). The 3D-DOSS contact tracing module promptly identified 44 primary and 162 secondary inpatient contacts, after exposure to a health care worker with coronavirus disease 2019 in April 2021. For outbreak mapping, 24 patients with OXA-48 were mapped on October 22, 2020, using 3D-DOSS to determine their spatiotemporal distribution. ConclusionThe integration of health care data and representation on a virtual hospital digital twin is a useful tool in an outbreak alert and response framework. Infectious disease surveillance systems, which are syndrome-based, that can access real-time data, and can incorporate movement networks, can potentially enhance health care–associated infection prevention and preparedness for disease X.

ETL Processes for Integrating Healthcare Data - Tools and Architecture Patterns.

Improving the interoperability of healthcare information systems is a crucial clinical care issue involving disparate but coexisting information systems. However, healthcare organizations are also facing the dilemma of choosing the right ETL tool and architecture pattern as data warehouse enterprises. This article gives an overview of current ETL tools for healthcare data integration. In addition, we demonstrate three ETL processes for clinical data integration using different ETL tools and architecture patterns, which map data from various data sources (e.g. MEONA and ORBIS) to diverse standards (e.g. FHIR and openEHR). Depending on the project's technical requirements, we choose our ETL tool and software architecture pattern to boost team efficiency.

Integration of EHR and PHR leveraging cloud services for approving treatments

Historically clinicians have been prescribing treatment to patients based on their visit to the hospital without referring to previous health records of the patients. This is primarily because clinicians do not have access to patient’s medical records. Though, with digital revolution in healthcare domain, patient’s medical history is available via Patient Health Records using Electronic Health Records (EHR) and Personal Health Records (PHR) systems. However, as EHR and PHR are maintained as separate systems in isolated manner, efficient accessibility of these systems is still very limited. To provide a unified view of patient data to the providers and the patients, integrated data resulting from such system not only allow to access past health records of patients, but further leverage PHRs data to provide quality treatment by examining the effect of treatment on patient’s health. Data integration techniques such as ETL and Cloud based technologies can be used to develop a system which provides real-time integration of healthcare data sourced & streamed from various healthcare systems. It will leverage the recommendations of quality treatments to the patients as well as the clinicians from cohesive view of integrated EHR and PHR data.

Healthcare data integration using machine learning: A case study evaluation with health information-seeking behavior databases

BackgroundThe amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be used to conduct different types of analyses. However, with increasing numbers of variables, manual mapping of a dataset can become inefficient. Another approach is to use text classification through machine learning to classify the variables to a schema. ObjectivesOur aim was to create and evaluate the use of machine learning methods for the integration of data from datasets across health information-seeking behavior (HISB) databases. MethodsFour online databases relevant to the research field were selected for integration. Two experiments were designed for dataset mapping: intra-database mapping using the one data source, and inter-database mapping to map datasets between the four databases. We compared logistic regression (LR), a random forest classifier (RFC), and neural network (NN) models by F1-score for two methods of integration. A third experiment was an ablation study that used all the available data to create a model for classifying HISB variables in a dataset. ResultsIn intra-database mapping, the mean F1 score for an LR classifier (0.787) was better than the RFC score (0.767) and fully connected NN (0.735). In inter-database mapping, the LR (0.245) scored best, however, this was dependent on which database was used as a training source. Using all the databases, these top three models were able to correctly classify 90–91% of the variables. Removing one dataset improved scores and resulted in a model able to correctly classify 95–96% of the HISB variables. ConclusionsAs part of data integration, a neural network can be used as an approach to map the variables of a dataset. The developed models can be used to classify the HISB terms in a database.

4 ． Research Use of Public Health and Medical Data Using the “Healthcare Data Integration System” Promoted by Kobe City Government

神戸市では，科学的根拠に基づく保健事業の推進による市民サービスの向上を目指し，医療・介護のレセプトデータや健診データ等を連結・匿名化した「ヘルスケアデータ連携システム」を新たに整備し，2020年11月に運用を開始した．本システムには，国保・後期高齢者・生保医療レセプトデータ，国保・後期高齢者・生保健診データ，介護レセプトデータ，介護認定調査票，予防接種データ等，市民約60 万人分の公的データが，個人単位で連結された状態で格納されている．これらの連結されたデータは，研究目的であれば，学術機関に限定して無償で利用可能である．データ利用にあたっては，神戸市の倫理審査委員会の審査で承認を得る必要がある．提供されるデータセットは，研究内容に応じて必要最小限に形成され，個人が特定できないように匿名化処理が施されている． 神戸市はヘルスケアデータ連携システムに格納されたデータを，二つの用途で利用している．一つ目は，市民全体の健康状態や課題の把握，保健事業の効果検証である．比較的悉皆性の高いデータであることと，既存事業で収集されるデータであることが，市民の健康状態を永続的に追跡していく運用を可能にしている．二つ目は，学術機関へのデータの研究利用目的での無償提供である．公益性の高い研究が神戸市のデータを元に実施されれば，結果から得られる新しい知見を保健事業に反映させることができる．ひいては，科学的根拠に基づく保健事業の推進につながり，更なる市民サービスの向上が期待される．

Utility of an Electronic Health Record Report to Identify Patients with Delays in Testing for Poorly Controlled Diabetes.

Reducing hemoglobin A1c (HbA1c) is essential for patients with poorly controlled diabetes. However, delays in HbA1c testing are common, and incomplete electronic health record (EHR) reports hinder identification of patients who are overdue. This study sought to quantify how often an EHR report correctly identifies patients with HbA1c testing delays and to describe potential contributing factors. Using an EHR report, the researchers identified adult patients who had an HbA1c > 9.0% between October 2017 and March 2018 and a suspected delay (for example, another HbA1c had not resulted within six months). A retrospective chart review of 200 randomly selected records was performed to confirm delays in testing. Secondary measures were collected from 93 charts to evaluate associated factors. A total of 685 patients with suspected delays were identified. On chart review (N = 200), 82.0% were confirmed. Nine percent of patients had a timely repeat result, but the result was not in a discrete field within the EHR. Another 8.5% were never expected to return. Among a subset of confirmed delays, patients often received lifestyle counseling, but less than half had documented discussions about repeat glycemic testing. Also, 74.2% had a timely follow-up appointment scheduled but the majority (85.5%) were missed. Most suspected delays in HbA1c testing were confirmed; however, a substantial minority were misclassified due to missing data or follow-up care outside the health system. Current solutions to improve data quality for HbA1c are labor intensive and highlight the need for better integration of health care data. Missed appointments were commonly noted among patients with delays in care and are a potential target for improvement.

Barriers and challenges to Primary Health Care Information System (PHCIS) adoption from health management perspective: A qualitative study

Abstract Enactment of a National Health Information System regulation in 2014 by the Indonesian government enabled the integration of healthcare data using electronic systems in the country. However, limited information was gained regarding the barriers from the healthcare management point of view that might cause slowness of adoption. We evaluated the implementation of the Primary Health Care Information System (PHCIS) in order to explore and describe the barriers and challenges during the adoption of Primary Health Care (PHC) from a health management perspective, and propose a PHCIS design to minimize the barriers. A qualitative form of research was conducted in an urban area of Banten Province from February–April 2018, as that area has gained experience of PHCIS implementation for more than five years. An in-depth interview was recorded to explore and describe the barriers during PHCIS adoption. Four themes of the barriers have been identified from a strategic and operational level perspective, namely: human resources, infrastructure, organizational support, and processing. Our analysis suggests that PHCIS adoption could be more effective if there were greater interaction between human resources, infrastructure, organizational support, and process factors. Hence, involvements including: strengthening staff competency, improving technology infrastructure, increasing organizational support with more investment for high-quality PHCIS, and re-designing the PHCIS to accommodate the basic process of PHC , might be beneficent to improve PHCIS adoption.

Design and Application of a Containerized Hybrid Transaction Processing and Data Analysis Framework

This article describes how rapidly growing data volumes require systems that have the ability to handle massive heterogeneous unstructured data sets. However, most existing mature transaction processing systems are built upon relational databases with structured data. In this article, the authors design a hybrid development framework, to offer greater scalability and flexibility of data analysis and reporting, while keeping maximum compatibility and links to the legacy platforms on which transaction business logics run. Data, service and user interfaces are implemented as a toolset stack, for developing applications with functionalities of information retrieval, data processing, analyzing and visualizing. A use case of healthcare data integration is presented as an example, where information is collected and aggregated from diverse sources. The workflow and simulation of data processing and visualization are also discussed, to validate the effectiveness of the proposed framework.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

Healthcare Data Integration and Informatics in the Cloud

An information integration and informatics framework for healthcare applications leverages the parallel computing capability of a cloud-based, large-scale distributed batch-processing infrastructure built with commodity hardware. The result is new flexibility for developers of advanced healthcare applications.

Privacy Preserving Integration of Health Care Data

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.

Federated identity management to link and protect healthcare data

Healthcare data should be linked and aggregated across data sources from different organisations to manage performance and provide integrated services. The internet offers the possibility of providing near real-time integration, but the central issue is how to integrate federated identity management with healthcare data integration to protect patient identity and ensure compliance with privacy legislation. In this paper, we use a healthcare scenario to analyse three proposed approaches. Each approach addresses some aspects of the problem while failing to address others. We then present a single framework that combines the three approaches into a single, more comprehensive approach.