Challenges in Retrieving Patterns from Generic Data Structures in Clinical Systems - A Technical Case Report.

INTRODUCTION: The secondary use of data in clinical environments offers significant opportunities to enhance medical research and practices. However, extracting data from generic data structures, particularly the Entity-Attribute-Value (EAV) model, remains challenging. This study addresses these challenges by developing a methodological approach to convert EAV-based data into a format more suitable for analysis. BACKGROUND: The EAV model is widely used in clinical information systems due to its adaptability, but it often complicates data retrieval for research purposes due to its vertical data structure and dynamic schema. OBJECTIVE: The objective of this study is to develop a methodological approach to address the handling of these generic data structures, Methods: We introduce a five-step methodological approach: 1) understanding the specific clinical processes to determine data collection points and involved roles; 2) analysing the data source to understand the data structure and metadata; 3) reversing a use-case-specific data structure to map the front-end data input to its storage format; 4) analysing the content to identify medical information and establish connections; and 5) managing schema changes to maintain data integrity. RESULTS: Applying this method to the hospital information system has shown that EAV-based data can be converted into a structured format, suitable for research. This conversion reduced data sparsity and improved the manageability of schema changes without affecting other classes of data. CONCLUSION: The developed approach provides a systematic method for handling complex data relationships and maintaining data integrity in clinical systems using EAV models. This approach facilitates the secondary use of clinical data, enhancing its utility for medical research and practice.

System interoperability and data linkage in the era of health information management: A bibliometric analysis.

Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

Derivation and external validation of a portable method to identify patients with pulmonary embolism from radiology reports: The READ-PE algorithm.

BACKGROUND: Identification of pulmonary embolism (PE) across a cohort currently requires burdensome manual review. Previous approaches to automate capture of PE diagnosis have either been too complex for widespread use or have lacked external validation. We sought to develop and validate the Regular Expression Aided Determination of PE (READ-PE) algorithm, which uses a portable text-matching approach to identify PE in reports from computed tomography with angiography (CTA). METHODS: We identified derivation and validation cohorts of final radiology reports for CTAs obtained on adults (≥ 18 years) at two independent, quaternary academic emergency departments (EDs) in the United States. All reports were in the English language. We manually reviewed CTA reports for PE as a reference standard. In the derivation cohort, we developed the READ-PE algorithm by iteratively combining regular expressions to identify PE. We validated the READ-PE algorithm in an independent cohort, and compared performance against three prior algorithms with sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and the F1 score. RESULTS: Among 2948 CTAs in the derivation cohort 10.8 % had PE and the READ-PE algorithm reached 93 % sensitivity, 99 % specificity, 94 % PPV, 99 % NPV, and 0.93 F1 score, compared to F1 scores ranging from 0.50 to 0.85 for three prior algorithms. Among 1206 CTAs in the validation cohort 9.2 % had PE and the algorithm had 98 % sensitivity, 98 % specificity, 85 % PPV, 100 % NPV, and 0.91 F1 score. CONCLUSIONS: The externally validated READ-PE algorithm identifies PE in English-language reports from CTAs obtained in the ED with high accuracy. This algorithm may be used in the electronic health record to accurately identify PE for research or surveillance. If implemented at other EDs, it should first undergo local validation and may require maintenance over time.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments.

INTRODUCTION AND PURPOSE: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security. METHODS AND RESULTS: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing. DISCUSSION AND CONCLUSIONS: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

Dermoscopy Differential Diagnosis Explorer (D3X) Ontology to Aggregate and Link Dermoscopic Patterns to Differential Diagnoses: Development and Usability Study.

BACKGROUND: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand. OBJECTIVE: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses. METHODS: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers. RESULTS: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain. CONCLUSIONS: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.

Integrated Real-World Data Warehouses Across 7 Evolving Asian Health Care Systems: Scoping Review.

BACKGROUND: Asia consists of diverse nations with extremely variable health care systems. Integrated real-world data (RWD) research warehouses provide vast interconnected data sets that uphold statistical rigor. Yet, their intricate details remain underexplored, restricting their broader applications. OBJECTIVE: Building on our previous research that analyzed integrated RWD warehouses in India, Thailand, and Taiwan, this study extends the research to 7 distinct health care systems: Hong Kong, Indonesia, Malaysia, Pakistan, the Philippines, Singapore, and Vietnam. We aimed to map the evolving landscape of RWD, preferences for methodologies, and database use and archetype the health systems based on existing intrinsic capability for RWD generation. METHODS: A systematic scoping review methodology was used, centering on contemporary English literature on PubMed (search date: May 9, 2023). Rigorous screening as defined by eligibility criteria identified RWD studies from multiple health care facilities in at least 1 of the 7 target Asian nations. Point estimates and their associated errors were determined for the data collected from eligible studies. RESULTS: Of the 1483 real-world evidence citations identified on May 9, 2023, a total of 369 (24.9%) fulfilled the requirements for data extraction and subsequent analysis. Singapore, Hong Kong, and Malaysia contributed to ≥100 publications, with each country marked by a higher proportion of single-country studies at 51% (80/157), 66.2% (86/130), and 50% (50/100), respectively, and were classified as solo scholars. Indonesia, Pakistan, Vietnam, and the Philippines had fewer publications and a higher proportion of cross-country collaboration studies (CCCSs) at 79% (26/33), 58% (18/31), 74% (20/27), and 86% (19/22), respectively, and were classified as global collaborators. Collaboration with countries outside the 7 target nations appeared in 84.2% to 97.7% of the CCCSs of each nation. Among target nations, Singapore and Malaysia emerged as preferred research partners for other nations. From 2018 to 2023, most nations showed an increasing trend in study numbers, with Vietnam (24.5%) and Pakistan (21.2%) leading the growth; the only exception was the Philippines, which declined by -14.5%. Clinical registry databases were predominant across all CCCSs from every target nation. For single-country studies, Indonesia, Malaysia, and the Philippines favored clinical registries; Singapore had a balanced use of clinical registries and electronic medical or health records, whereas Hong Kong, Pakistan, and Vietnam leaned toward electronic medical or health records. Overall, 89.9% (310/345) of the studies took >2 years from completion to publication. CONCLUSIONS: The observed variations in contemporary RWD publications across the 7 nations in Asia exemplify distinct research landscapes across nations that are partially explained by their diverse economic, clinical, and research settings. Nevertheless, recognizing these variations is pivotal for fostering tailored, synergistic strategies that amplify RWD's potential in guiding future health care research and policy decisions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/43741.

The 6I model: an expanded 4I framework to conceptualise interorganisational learning in the global health sector.

INTRODUCTION: An organisation's ability to learn and adapt is key to its long-term performance and success. Although calls to improve learning within and across health organisations and systems have increased in recent years, global health is lagging behind other sectors in attention to learning, and applications of conceptual models for organisational learning to this field are needed. LEVERAGING THE 4I FRAMEWORK: This article proposes modifications to the 4I framework for organisational learning (which outlines the processes of intuition, interpretation, integration and institutionalisation) to guide the creation, retention and exchange of knowledge within and across global health organisations. PROPOSED EXPANSIONS: Two expansions are added to the framework to account for interorganisational learning in the highly interconnected field: (1) learning pathways across organisations via formal or informal partnerships and communities of practice and (2) learning pathways to and from macro-level 'coordinating bodies' (eg, WHO). Two additional processes are proposed by which interorganisational learning occurs: interaction across partnerships and communities of practice, and incorporation linking global health organisations to coordinating bodies. Organisational politics across partnerships, communities of practice and coordinating bodies play an important role in determining why some insights are institutionalised while others are not; as such, the roles of the episodic influence and systemic domination forms of power are considered in the proposed additional organisational learning processes. DISCUSSION: When lessons are not shared across partnerships, communities of practice or the research community more broadly, funding may continue to support global health studies and programmes that have already been proven ineffective, squandering research and healthcare resources that could have been invested elsewhere. The '6I' framework provides a basis for assessing and implementing organisational learning approaches in global health programming, and in health systems more broadly.

Modelling of physicians' clinical information-seeking behaviour in Iran: a grounded theory study.

OBJECTIVES: Exploring clinical information-seeking behaviour (CISB) and its associated factors contributes to its theoretical advancement and offers a valuable framework for addressing physicians' information needs. This study delved into the dimensions, interactions, strategies and determinants of CISB among physicians at the point of care. DESIGN: A grounded theory study was developed based on Strauss and Corbin's approach. Data were collected by semistructured interviews and then analysed through open, axial and selective coding. SETTING: The study was conducted at academic centres affiliated with Isfahan University of Medical Sciences. PARTICIPANTS: This investigation involved recruiting 21 specialists and subspecialists from the academic centres. RESULTS: The findings revealed that physicians' CISB encompassed multiple dimensions when addressing clinical inquiries. Seven principal themes emerged from the analysis: 'clinical information needs', 'clinical question characteristics', 'clinical information resources', 'information usability', 'factors influencing information seeking', 'action/interaction encountering clinical questions' and 'consequences of CISB'. The core category identified in this study was 'focused attention'. CONCLUSIONS: The theoretical explanation demonstrated that the CISB process was interactive and dynamic. Various stimuli, including causal, contextual and intervening conditions, guide physicians in adopting information-seeking strategies and focusing on resolving clinical challenges. However, insufficient stimuli may hinder physicians' engagement in CISB. Understanding CISB helps managers, policy-makers, clinical librarians and information system designers optimally implement several interventions, such as suitable training methods, reviewing monitoring and evaluating information systems, improving clinical decision support systems, electronic medical records and electronic health records, as well as monitoring and evaluating these systems. Such measures facilitate focused attention on clinical issues and promote CISB among physicians.

A framework for understanding an open scientific community using automated harvesting of public artifacts.

Background: The Observational Health Data Sciences and Informatics (OHDSI) community has emerged as a leader in observational research on real-world clinical data for promoting evidence for healthcare and decision-making. The community has seen rapid growth in publications, citations, and the number of authors. Components of its successful uptake have been attributed to an open science and collaborative culture for research and development. Investigating the adoption of OHDSI as a field of study provides an opportunity to understand how communities embrace new ideas, onboard new members, and enhance their impact. Objective: To track, study, and evaluate an open scientific community's growth and impact. Method: We present a modern architecture leveraging open application programming interfaces to capture publicly available data (PubMed, YouTube, and EHDEN) on open science activities (publication, teaching, and engagement). Results: Three interactive dashboard were implemented for each publicly available artifact (PubMed, YouTube, and EHDEN). Each dashboard provides longitudinal summary analysis and has a searchable table, which differs in the available features related to each public artifact. Conclusion: We discuss the insights enabled by our approach to monitor the growth and impact of the OHDSI community by capturing artifacts of learning, teaching, and creation. We share the implications for different users based on their functional needs. As other scientific networks adopt open-source frameworks, our framework serves as a model for tracking the growth of their community, driving the perception of their development, engaging their members, and attaining higher impact.

Search still matters: information retrieval in the era of generative AI.

OBJECTIVE: Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process? PROCESS: This perspective explores the use of generative AI in the context of the motivations, considerations, and outcomes of the IR process with a focus on the academic use of such systems. CONCLUSIONS: There are many information needs, from simple to complex, that motivate use of IR. Users of such systems, particularly academics, have concerns for authoritativeness, timeliness, and contextualization of search. While LLMs may provide functionality that aids the IR process, the continued need for search systems, and research into their improvement, remains essential.

Reducing the residue of retractions in evidence synthesis: ways to minimise inappropriate citation and use of retracted data.

The incorporation of publications that have been retracted is a risk in reliable evidence synthesis. Retraction is an important mechanism for correcting the literature and protecting its integrity. Within the medical literature, the continued citation of retracted publications occurs for a variety of reasons. Recent evidence suggests that systematic reviews and meta-analyses often unwittingly cite retracted publications which, at least in some cases, may significantly impact quantitative effect estimates in meta-analyses. There is strong evidence that authors of systematic reviews and meta-analyses may be unaware of the retracted status of publications and treat them as if they are not retracted. These problems are difficult to address for several reasons: identifying retracted publications is important but logistically challenging; publications may be retracted while a review is in preparation or in press and problems with a publication may also be discovered after the evidence synthesis is published. We propose a set of concrete actions that stakeholders (eg, scientists, peer-reviewers, journal editors) might take in the near-term, and that research funders, citation management systems, and databases and search engines might take in the longer term to limit the impact of retracted primary studies on evidence syntheses.

Factors Associated With Transition From Community to Permanent Residential Aged Care Following Stroke: A Linked Registry Data Study.

BACKGROUND: Understanding factors that influence the transition to permanent residential aged care following a stroke or transient ischemic attack may inform strategies to support people to live at home longer. We aimed to identify the demographic, clinical, and system factors that may influence the transition from living in the community to permanent residential care in the 6 to 18 months following stroke/transient ischemic attack. METHODS: Linked data cohort analysis of adults from Queensland and Victoria aged ≥65 years and registered in the Australian Stroke Clinical Registry (2012-2016) with a clinical diagnosis of stroke/transient ischemic attack and living in the community in the first 6 months post-hospital discharge. Participant data were linked with primary care, pharmaceutical, aged care, death, and hospital data. Multivariable survival analysis was performed to determine demographic, clinical, and system factors associated with the transition to permanent residential care in the 6 to 18 months following stroke, with death modeled as a competing risk. RESULTS: Of 11 176 included registrants (median age, 77.2 years; 44% female), 520 (5%) transitioned to permanent residential care between 6 and 18 months. Factors most associated with transition included the history of urinary tract infections (subhazard ratio [SHR], 1.41 [95% CI, 1.16-1.71]), dementia (SHR, 1.66 [95% CI, 1.14-2.42]), increasing age (65-74 versus 85+ years; SHR, 1.75 [95% CI, 1.31-2.34]), living in regional Australia (SHR, 31 [95% CI, 1.08-1.60]), and aged care service approvals: respite (SHR, 4.54 [95% CI, 3.51-5.85]) and high-level home support (SHR, 1.80 [95% CI, 1.30-2.48]). Protective factors included being dispensed antihypertensive medications (SHR, 0.68 [95% CI, 0.53-0.87]), seeing a cardiologist (SHR, 0.72 [95% CI, 0.57-0.91]) following stroke, and less severe stroke (SHR, 0.71 [95% CI, 0.58-0.88]). CONCLUSIONS: Our findings provide an improved understanding of factors that influence the transition from community to permanent residential care following stroke and can inform future strategies designed to delay this transition.

Repurposing of H1-receptor antagonists (levo)cetirizine, (des)loratadine, and fexofenadine as a case study for systematic analysis of trials on clinicaltrials.gov using semi-automated processes with custom-coded software.

To gain a comprehensive overview of the landscape of clinical trials for the H1-receptor antagonists (H1R antagonists) cetirizine, levocetirizine, loratadine, desloratadine, and fexofenadine and their potential use cases in drug repurposing (the use of well-known drugs outside the scope of the original medical indication), we analyzed trials from clincialtrials.gov using novel custom-coded software, which itself is also a key emphasis of this paper. To automate data acquisition from clincialtrials.gov via its API, data processing, and storage, we created custom software by leveraging a variety of open-source tools. Data were stored in a relational database and annotated facilitating a specially adapted web application. Through the data analysis, we identified use cases for repurposing and reviewed backgrounds and results in the scientific literature. Even though we found very few trials with published results for repurpose indications, extended literature research revealed some prominent use cases: Cetirizine seems promising in mitigating infusion-associated reactions and is also more effective than placebo in the treatment of androgenetic alopecia. Loratadine may be beneficial in the prophylaxis of G-CSF-related bone pain. In COVID-19, H1R antagonists may be helpful, but placebo-controlled scientific evidence is needed. For asthma, the effect of H1R antagonists only seems to be secondary by alleviating allergy symptoms. Our novel method to find potential use cases for repurposing of H1R antagonists allows for high automation, reduces human error, and was successful in revealing potential areas of interest. The software could be used for similar research questions and analyses in the future.

Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews.

BACKGROUND: Evidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction. METHODS: We evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies. Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models. Using human annotation as a reference, we assessed the ability of the models to identify biomedical terms of interest (entities) and their relations. We also pretrained BERT on a corpus of 100,000 open access clinical publications and/or enhanced context-dependent entity classification with a conditional random field (CRF) model. Performance was measured using the F1 score, a metric that combines precision and recall. We defined successful matches as partial overlap of entities of the same type. RESULTS: For entity recognition, the pretrained BERT+CRF model had the best performance, with an F1 score of 73% in SLR 1 and 70% in SLR 2. Entity types identified with the highest accuracy were metrics for progression-free survival (SLR 1, F1 score 88%) or for patient age (SLR 2, F1 score 82%). Treatment arm dosage was identified less successfully (F1 scores 60% [SLR 1] and 49% [SLR 2]). The best-performing model for relation extraction, pretrained BERT relation classification, exhibited F1 scores higher than 90% in cases with at least 80 relation examples for a pair of related entity types. CONCLUSIONS: The performance of BERT is enhanced by pretraining with biomedical literature and by combining with a CRF model. With refinement, machine learning may assist with manual data extraction for SLRs.

Organizing and Sharing Medical Knowledge Among Anesthesiology and Intensive care Residents: Evaluating Existing Practices and the Feasibility of Implementing a Dedicated Multiplatform Application.

Treating patients with up-to-date medical knowledge is an ongoing goal for healthcare workers and implies efficient knowledge management at the point of care. Widely available mobile wireless technologies influence practices but a significant gap remains between technological possibilities and actual usage. The purpose of this study was to analyze residents' baseline practices in managing medical knowledge and to evaluate the use and impact of an innovative multiplatform application dedicated to anesthesiology and intensive care residents. This study took place in Rennes Teaching Hospital and comprised two distinct surveys. First, in April 2018, all residents received a ten-items online survey focusing on managing medical knowledge. Then, through a second online survey constituted of ten items, we sought to assess the use of a new multiplatform cloud-based application named "DansMaBlouse", dedicated to sharing and indexing medical knowledge, in anesthesiology and intensive care residents. Among 148 residents that answered the evaluation survey, the most sought out pieces of information in clinical setting were a phone or fax number (74%), drugs' characteristics (68%) and expert guidelines (57%). The main sources were senior staff (68%), medical databases (60%) and an Internet search engine (59%). Computers and smartphones were more frequently used than bound paper notebooks. After implementation of the multiplatform application DansMaBlouse, fifty-nine (82%) of the 72 residents that answered the evaluation survey reported using the application and 39% used it more than ten times. Among application users, 90% found it easy to use and 92% agreed that it improved point-of-care access to knowledge. Accessing appropriate medical knowledge at the point of care remains an issue for residents and can be improved by a multiplatform application combining personal and shared up-to-date resources.

Ethics information retrieval in HTA: state of current practice.

OBJECTIVES: Though there have been longstanding discussions on the value of ethics in health technology assessment (HTA), less awareness exists on ethics information retrieval methods. This study aimed to scope available evidence and determine current practices for ethics information retrieval in HTA. METHODS: Literature searches were conducted in Ovid MEDLINE, LISTA, Scopus, and Google Scholar. Once a list of relevant articles was determined, citation tracking was conducted via Scopus. HTA agency websites were searched for published guidance on ethics searching, and for reports which included ethical analyses. Methods sections of each report were analyzed to determine the databases, subject headings, and keywords used in search strategies. The team also reached out to information specialists for insight into current search practices. RESULTS: Findings from this study indicate that there is still little published guidance from HTA agencies, few HTAs that contain substantial ethical analysis, and even less information on the methodology for ethics information retrieval. The researchers identified twenty-five relevant HTAs. Ten of these reports did not utilize subject-specific databases outside health sciences. Eight reports published ethics searches, with significant overlap in subject headings and text words. CONCLUSIONS: This scoping study of current practice in HTA ethics information retrieval highlights findings of previous studies-while ethics analysis plays a crucial role in HTA, methods for literature searching remain relatively unclear. These findings provide insight into the current state of ethics searching, and will inform continued work on filter development, database selection, and grey literature searching.

The NYU Data Catalog: a modular, flexible infrastructure for data discovery.

OBJECTIVE: Researchers at New York University (NYU) Grossman School of Medicine contacted the Health Sciences Library for help with locating large datasets for reuse. In response, the library developed and maintained the NYU Data Catalog, a public-facing data catalog that has supported not only faculty acquisition of data but also the dissemination of the products of their research in various ways. MATERIALS AND METHODS: The current NYU Data Catalog is built upon the Symfony framework with a tailored metadata schema reflecting the scope of faculty research areas. The project team curates new resources, including datasets and supporting software code, and conducts quarterly and annual evaluations to assess user interactions with the NYU Data Catalog and opportunities for growth. RESULTS: Since its launch in 2015, the NYU Data Catalog underwent a number of changes prompted by an increase in the disciplines represented by faculty contributors. The catalog has also utilized faculty feedback to enhance support of data reuse and researcher collaboration through alterations to its schema, layout, and visibility of records. DISCUSSION: These findings demonstrate the flexibility of data catalogs as a platform for enabling the discovery of disparate sources of data. While not a repository, the NYU Data Catalog is well-positioned to support mandates for data sharing from study sponsors and publishers. CONCLUSION: The NYU Data Catalog makes the most of the data that researchers share and can be harnessed as a modular and adaptable platform to promote data sharing as a cultural practice.