What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Implementing a Biomedical Data Warehouse From Blueprint to Bedside in a Regional French University Hospital Setting: Unveiling Processes, Overcoming Challenges, and Extracting Clinical Insight

Background

Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use.

Objective

In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW.

Methods

We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights.

Unlabelled

More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements.

Conclusions

Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.

Correlation Aware Relevance-Based Semantic Index for Clinical Big Data Repository

In this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a methodology that categorizes medical data based on the semantics of data elements and merges them into an integrated repository. We developed a data integration system for medical data sources that combines heterogeneous medical data and provides access to knowledge-based database repositories to different users. In this research work, we designed an indexing system using semantic tags extracted from clinical data sources and medical ontologies that retrieves relevant data from database repositories and speeds up the process of data retrieval. Our objective is to provide an integrated biomedical database repository that can be used by radiologists as a reference, or for patient care, or by researchers. In this paper, we focus on designing a technique that performs data processing for data integration, learn the semantic properties of data elements, and develop a correlation-aware topic index that facilitates efficient data retrieval. We generated semantic tags by identifying key elements from integrated clinical cases using topic modeling techniques. We investigated a technique that identifies tags for merged categories and provides an index to fetch data from an integrated database repository. We developed a topic coherence matrix that shows how well a topic is supported by a corpus from clinical cases and medical ontologies. We were able to find more relevant results using an annotation index from an integrated database repository, and there was a 61% increase in a recall. We evaluated results with the help of experts and compared them with naive index (index with all terms from the corpus). Our approach improved data retrieval quality by providing most relevant results and reduced data retrieval time as we applied correlation-aware index on an integrated data repository. Topic indexing approach proposed in this research work identifies tags based on a correlation between different data elements, improves data retrieval time, and provides most relevant cases as an outcome of this system.

Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses

Introduction

The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes.

Materials and methods

Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network.

Results

In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria's national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested.

Discussion

The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.

"In conferences, everyone goes 'health data is the future' ": an interview study on challenges in re-using EHR data for research in Clinical Data Warehouses

More and more hospital Clinical Data Warehouses (CDWs) are developed to gain access to EHR data. The rapid growth of investments in CDWs suggest a real potential for innovation in healthcare. However, it is still not confirmed that CDWs will deliver on their promises as researchers working with CDWs face many challenges. To gain a better understanding of these challenges and how to overcome them, we conducted a series of semi-structured interviews with EHR data experts. In this article, we share some initial results from the ongoing interview study. Two main themes emerged from the analysis of the transcripts of the interviews: the importance of infrastructures in terms of data and how it is generated, and the difficulty to make care, clinical research, and data science work together. Finally, based on the experts' experience, several recommendations were identified when using a CDW.

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

BACKGROUND

In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible.

OBJECTIVE

The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks.

METHODS

This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English.

RESULTS

We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%).

CONCLUSIONS

CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

Integrated Real-World Study Databases in 3 Diverse Asian Health Care Systems in Taiwan, India, and Thailand: Scoping Review

BACKGROUND

The use of real-world data (RWD) warehouses for research in Asia is on the rise, but current trends remain largely unexplored. Given the varied economic and health care landscapes in different Asian countries, understanding these trends can offer valuable insights.

OBJECTIVE

We sought to discern the contemporary landscape of linked RWD warehouses and explore their trends and patterns in 3 Asian countries with contrasting economies and health care systems: Taiwan, India, and Thailand.

METHODS

Using a systematic scoping review methodology, we conducted an exhaustive literature search on PubMed with filters for the English language and the past 5 years. The search combined Medical Subject Heading terms and specific keywords. Studies were screened against strict eligibility criteria to identify eligible studies using RWD databases from more than one health care facility in at least 1 of the 3 target countries.

RESULTS

Our search yielded 2277 studies, of which 833 (36.6%) met our criteria. Overall, single-country studies (SCS) dominated at 89.4% (n=745), with cross-country collaboration studies (CCCS) being at 10.6% (n=88). However, the country-wise breakdown showed that of all the SCS, 623 (83.6%) were from Taiwan, 81 (10.9%) from India, and 41 (5.5%) from Thailand. Among the total studies conducted in each country, India at 39.1% (n=133) and Thailand at 43.1% (n=72) had a significantly higher percentage of CCCS compared to Taiwan at 7.6% (n=51). Over a 5-year span from 2017 to 2022, India and Thailand experienced an annual increase in RWD studies by approximately 18.2% and 13.8%, respectively, while Taiwan's contributions remained consistent. Comparative effectiveness research (CER) was predominant in Taiwan (n=410, or 65.8% of SCS) but less common in India (n=12, or 14.8% of SCS) and Thailand (n=11, or 26.8% of SCS). CER percentages in CCCS were similar across the 3 countries, ranging from 19.2% (n=10) to 29% (n=9). The type of RWD source also varied significantly across countries, with India demonstrating a high reliance on electronic medical records or electronic health records at 55.6% (n=45) of SCS and Taiwan showing an increasing trend in their use over the period. Registries were used in 26 (83.9%) CCCS and 31 (75.6%) SCS from Thailand but in <50% of SCS from Taiwan and India. Health insurance/administrative claims data were used in most of the SCS from Taiwan (n=458, 73.5%). There was a consistent predominant focus on cardiology/metabolic disorders in all studies, with a noticeable increase in oncology and infectious disease research from 2017 to 2022.

CONCLUSIONS

This review provides a comprehensive understanding of the evolving landscape of RWD research in Taiwan, India, and Thailand. The observed differences and trends emphasize the unique economic, clinical, and research settings in each country, advocating for tailored strategies for leveraging RWD for future health care research and decision-making.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/43741.

Biomedical heterogeneous data categorization and schema mapping toward data integration

Data integration is a well-motivated problem in the clinical data science domain. Availability of patient data, reference clinical cases, and datasets for research have the potential to advance the healthcare industry. However, the unstructured (text, audio, or video data) and heterogeneous nature of the data, the variety of data standards and formats, and patient privacy constraint make data interoperability and integration a challenge. The clinical text is further categorized into different semantic groups and may be stored in different files and formats. Even the same organization may store cases in different data structures, making data integration more challenging. With such inherent complexity, domain experts and domain knowledge are often necessary to perform data integration. However, expert human labor is time and cost prohibitive. To overcome the variability in the structure, format, and content of the different data sources, we map the text into common categories and compute similarity within those. In this paper, we present a method to categorize and merge clinical data by considering the underlying semantics behind the cases and use reference information about the cases to perform data integration. Evaluation shows that we were able to merge 88% of clinical data from five different sources.

Contemporary Databases in Real-world Studies Regarding the Diverse Health Care Systems of India, Thailand, and Taiwan: Protocol for a Scoping Review

BACKGROUND

Real-world data (RWD) related to patient health status or health care delivery can be broadly defined as data collected outside of conventional clinical trials, including those from databases, treatment and disease registries, electronic medical records, insurance claims, and information directly contributed by health care professionals or patients. RWD are used to generate real-world evidence (RWE), which is increasingly relevant to policy makers in Asia, who use RWE to support decision-making in several areas, including public health policy, regulatory health technology assessment, and reimbursement; set priorities; or inform clinical practice.

OBJECTIVE

To support the achievement of the benefits of RWE in Asian health care strategies and policies, we sought to identify the linked contemporary databases used in real-world studies from three representative countries-India, Thailand, and Taiwan-and explore variations in results based on these countries' economies and health care reimbursement systems by performing a systematic scoping review. Herein, we describe the protocol and preliminary findings of our scoping review.

METHODS

The PubMed search strategy covered 3 concepts. Concept 1 was designed to identify potential RWE and RWD studies by applying various Medical Subject Headings (MeSH) terms ("Treatment Outcome," "Evidence-Based Medicine," "Retrospective Studies," and "Time Factors") and related keywords (eg, "real-world," "actual life," and "actual practice"). Concept 2 introduced the three countries-India, Taiwan, and Thailand. Concept 3 focused on data types, using a combination of MeSH terms ("Electronic Health Records," "Insurance, Health," "Registries," "Databases, Pharmaceutical," and "Pharmaceutical Services") and related keywords (eg, "electronic medical record," "electronic healthcare record," "EMR," "EHR," "administrative database," and "registry"). These searches were conducted with filters for language (English) and publication date (publications in the last 5 years before the search). The retrieved articles will undergo 2 screening phases (phase 1: review of titles and abstracts; phase 2: review of full texts) to identify relevant and eligible articles for data extraction. The data to be extracted from eligible studies will include the characteristics of databases, the regions covered, and the patient populations.

RESULTS

The literature search was conducted on September 27, 2022. We retrieved 3,172,434, 1,094,125, and 672,794 articles for concepts 1, 2, and 3, respectively. After applying all 3 concepts and the language and publication date filters, 2277 articles were identified. These will be further screened to identify eligible studies. Based on phase 1 screening and our progress to date, approximately 44% (1003/2277) of articles have undergone phase 2 screening to judge their eligibility. Around 800 studies will be used for data extraction.

CONCLUSIONS

Our research will be crucial for nurturing advancement in RWD generation within Asia by identifying linked clinical RWD databases and new avenues for public-private partnerships and multiple collaborations for expanding the scope and spectrum of high-quality, robust RWE generation in Asia.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/43741.

Artificial intelligence for oral and maxillo-facial surgery: A narrative review

Artificial Intelligence (AI) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the exponential production and the routine collection of data have led to the rapid development of AI in the health sector. In this review, we propose to provide surgeons with the essential technical elements to help them understand the possibilities offered by AI and to review the current applications of AI for oral and maxillofacial surgery (OMFS). The review of the literature reveals a real research boom of AI in all fields in OMFS. The algorithms used are related to machine learning, with a strong representation of the convolutional neural networks specific to deep learning. The complex architecture of these networks gives them the capacity to extract and process the elementary characteristics of an image, and they are therefore particularly used for diagnostic purposes on medical imagery or facial photography. We identified representative articles dealing with AI algorithms providing assistance in diagnosis, therapeutic decision, preoperative planning, or prediction and evaluation of the outcomes. Thanks to their learning, classification, prediction and detection capabilities, AI algorithms complement human skills while limiting their imperfections. However, these algorithms should be subject to rigorous clinical evaluation, and ethical reflection on data protection should be systematically conducted.

Semantic interoperability in health records standards: a systematic literature review

The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient’s history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.

Real-World Data from a Refractory Triple-Negative Breast Cancer Cohort Selected Using a Clinical Data Warehouse Approach

Simple Summary

Patients with metastatic triple-negative breast cancer (mTNBC) have a very poor prognosis. We assume that some mTNBC patients have worse treatment outcomes and defined them as cases of refractory TNBC. We tried to investigate the characteristics and treatment outcomes of our refractory mTNBC cohort selected using a clinical data warehouse (CDW) approach. Between January 1997 and December 2019, TNBC patients were searched for in the breast cancer registry and, among them, pathologically confirmed mTNBC patients were selected as the study cohort (n = 451). Refractory TNBC was defined as cases of TNBC with confirmed distant metastasis within one year after adjuvant treatment. The refractory mTNBC group was younger and had a higher proportion of Ki-67 ≥ 3+ than the nonrefractory group. In addition, a much poorer prognosis existed among mTNBC patients, with an overall survival (OS) of 14.3 months and progression-free survival (PFS) of 4.2 months after first-line palliative chemotherapy compared to an OS of 24.8 months and PFS of 6.2 months in the nonrefractory TNBC group (p < 0.001).

Abstract

Purpose: Triple-negative breast cancer (TNBC) is well known for its aggressive course and poor prognosis. In this study, we sought to investigate clinical, demographic, and pathologic characteristics and treatment outcomes of patients with refractory, metastatic TNBC selected by a clinical data warehouse (CDW) approach. Patients and methods: Data were extracted from the real-time breast cancer registry integrated into the Data Analytics and Research Window for Integrated Knowledge C (DARWIN-C), the CDW of Samsung Medical Center. Between January 1997 and December 2019, a TNBC cohort was searched for in the breast cancer registry, which includes records from more than 40,000 patients. Among them, cases of pathologically confirmed metastatic TNBC (mTNBC) were selected as the cohort group (n = 451). The extracted data from the registry via the CDW platform included clinical, pathological, laboratory, and chemotherapy information. Refractory TNBC was defined as confirmed distant metastasis within one year after adjuvant treatment. Results: This study comprised a total of 451 patients with mTNBC, including 69 patients with de novo mTNBC, 131 patients in the nonrefractory TNBC group with confirmed stage IV disease after one year of adjuvant treatment, and 251 patients with refractory mTNBC, whose disease recurred as stage IV within one year after completing adjuvant treatment. The refractory mTNBC cohort was composed of patients with disease that recurred at stage IV after surgery (refractory mTNBC after surgery) (n = 207) and patients in whom metastasis was confirmed during neoadjuvant chemotherapy (unresectable TNBC due to progression during neoadjuvant chemotherapy) (n = 44). Patients in the refractory mTNBC group were younger than those in the nonrefractory group (median age 46 vs. 51 years; p < 0.001). Considering the pathological findings, the refractory group had a greater proportion of cases with Ki-67 ≥ 3+ than did the nonrefractory group (71% vs. 47%; p = 0.004). During a median 8.4 years of follow-up, the overall survival was 24.8 months in the nonrefractory mTNBC group and 14.3 months in the refractory mTNBC group (p < 0.001), and the median progression-free survival periods were 6.2 months and 4.2 months, respectively (p < 0.001). The median disease-free survival period was 30.1 months in the nonrefractory mTNBC group and only 7.6 months in the refractory mTNBC group. Factors related to metastatic sites affecting overall survival were liver metastasis at diagnosis (p < 0.001) and leptomeningeal involvement (p = 0.001). Conclusions: We revealed that patients with refractory mTNBC had a much poorer prognosis among all mTNBC cases and described the characteristics of this patient group.

Assessment of Inter-Institutional Post-Operative Hypoparathyroidism Status Using a Common Data Model

Post-thyroidectomy hypoparathyroidism may result in various transient or permanent symptoms, ranging from tingling sensation to severe breathing difficulties. Its incidence varies among surgeons and institutions, making it difficult to determine its actual incidence and associated factors. This study attempted to estimate the incidence of post-operative hypoparathyroidism in patients at two tertiary institutions that share a common data model, the Observational Health Data Sciences and Informatics. This study used the Common Data Model to extract explicitly specified encoding and relationships among concepts using standardized vocabularies. The EDI-codes of various thyroid disorders and thyroid operations were extracted from two separate tertiary hospitals between January 2013 and December 2018. Patients were grouped into no evidence of/transient/permanent hypoparathyroidism groups to analyze the likelihood of hypoparathyroidism occurrence related to operation types and diagnosis. Of the 4848 eligible patients at the two institutions who underwent thyroidectomy, 1370 (28.26%) experienced transient hypoparathyroidism and 251 (5.18%) experienced persistent hypoparathyroidism. Univariate logistic regression analysis predicted that, relative to total bilateral thyroidectomy, radical tumor resection was associated with a 48% greater likelihood of transient hypoparathyroidism and a 102% greater likelihood of persistent hypoparathyroidism. Moreover, multivariate logistic analysis found that radical tumor resection was associated with a 50% greater likelihood of transient hypoparathyroidism and a 97% greater likelihood of persistent hypoparathyroidism than total bilateral thyroidectomy. These findings, by integrating and analyzing two databases, suggest that this analysis could be expanded to include other large databases that share the same Observational Health Data Sciences and Informatics protocol.

The Role of Electronic Health Records in Advancing Genomic Medicine

Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care.