Evaluation of Research Accessibility and Data Elements of HIV Registries

2019 ◽  
Vol 17 (4) ◽  
pp. 258-265
Author(s):  
Craig S. Mayer ◽  
Nick Williams ◽  
Kin Wah Fung ◽  
Vojtech Huser
Keyword(s):  
Registry Data ◽  
Collection System ◽  
Patient Registries ◽  
Common Data Elements ◽  
Health Records ◽  
Real World Evidence ◽  
Data Elements ◽  
Research Contract ◽  
Term Data

Background:: Patient registries represent a long-term data collection system that is a platform for performing multiple research studies to generate real-world evidence. Many of these registries use common data elements (CDEs) and link data from Electronic Health Records. Objective:: This study evaluated HIV registry features that contribute to the registry’s usability for retrospective analysis of existing registry data or new prospective interventional studies. Methods:: We searched PubMed and ClinicalTrials.gov (CTG) to generate a list of HIV registries. We used the framework developed by the European Medical Agency (EMA) to evaluate the registries by determining the presence of key research features. These features included information about the registry, request and collaboration processes, and available data. We acquired data dictionaries and identified CDEs. Results: We found 13 HIV registries that met our criteria, 11 through PubMed and 2 through CTG. The prevalence of the evaluated features ranged from all 13 (100%) having published key registry information to 0 having a research contract template. We analyzed 6 data dictionaries and identified 14 CDEs that were present in at least 4 of 6 (66.7%) registry data dictionaries. Conclusion:: The importance of registries as platforms for research data is growing and the presence of certain features, including data dictionaries, contributes to the reuse and secondary research capabilities of a registry. We found some features such as collaboration policies were in the majority of registries while others such as, ethical support, were in a few and are more for future development.

71 Challenges to the Standardization of Trauma Data Collection: A Call for Common Data Elements for Acute and Long-Term Trauma Databases

2018 ◽  
Vol 39 (suppl_1) ◽  
pp. S40-S41
Author(s):  
L C Simko ◽  
L A Chen ◽  
R Friedman ◽  
D Amtmann ◽  
K Kowalske ◽  
...  
Keyword(s):  
Data Collection ◽  
Common Data Elements ◽  
Data Elements

scholarly journals Measuring What Matters Across International Long-Term Care Settings

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 160-160
Author(s):  
Kirsten Corazzini ◽  
Michael Lepore
Keyword(s):  
Long Term Care ◽  
Assessment Tool ◽  
Term Care ◽  
Common Data Elements ◽  
Person Centered Care ◽  
Care Assessment ◽  
Centered Care ◽  
Data Elements ◽  
What Matters

Abstract Measuring what matters most to residents, relatives and staff in residential long-term care settings is critical, yet underdeveloped in our predominantly frailty and deficits-focused measurement frameworks. The Worldwide Elements to Harmonize Research in Long-Term Care Living Environments (WE-THRIVE) consortium has previously prioritized measurement concepts in the areas of care outcomes, workforce and staffing, person-centered care, and care context. These concepts include knowing the resident and what matters most to the resident, and outcomes such as quality of life, and personhood. We present findings of our currently recommended measures, including both general population and dementia-specific measures, such as the Person-Centered Care Assessment Tool (PCAT), the Personhood in Dementia Questionnaire (PDQ), and the ICEpop CAPability Measure for Older People (ICECAP-O). We also describe remaining gaps in existing measures that will need to be addressed to fully specify common data elements focused on measuring what matters most to residents, relatives and staff.

scholarly journals Laying Down the Groundwork for an International Measurement Infrastructure

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 145-145
Author(s):  
Katherine McGilton ◽  
Franziska Zuniga ◽  
Michael Lepore ◽  
Kirsten Corazzini ◽  
Charlene Chu
Keyword(s):  
Staff Training ◽  
Staff Retention ◽  
Policy And Practice ◽  
Term Care ◽  
Common Data Elements ◽  
Supervisor Effectiveness ◽  
Data Elements ◽  
Measurement Infrastructure ◽  
International Measurement

Abstract The COVID-19 epidemic has brought to light the significant problems in the long-term care (LTC) sector, specifically the lack of an infrastructure to collect and aggregate data between LTC sectors in different countries. This talk will briefly describe goals of the WE-THRIVE initiative, and focus on exploring the development of “workforce and staffing” common data elements for LTC. We will describe how the subgroup is “laying down the groundwork” within this domain with various methodologies to develop CDEs related to workforce and staffing. The CDEs aim to measure staff retention and turnover, evaluating nursing supervisor effectiveness, and staff training in LTC. Anticipated challenges of this international work will also be highlighted. International research on LTC can valuably inform LTC policy and practice, and the proposed CDEs can facilitate data sharing and aggregation internationally, including low-, middle-, and high-income countries.

scholarly journals Toward Common Data Elements for International Research in Long-term Care Homes: Advancing Person-Centered Care

2019 ◽  
Vol 20 (5) ◽  
pp. 598-603 ◽  
Author(s):  
Kirsten N. Corazzini ◽  
Ruth A. Anderson ◽  
Barbara J. Bowers ◽  
Charlene H. Chu ◽  
David Edvardsson ◽  
...  
Keyword(s):  
International Research ◽  
Long Term Care ◽  
Care Homes ◽  
Term Care ◽  
Common Data Elements ◽  
Person Centered Care ◽  
Centered Care ◽  
Data Elements

Common data elements for substance use disorders in electronic health records: the NIDA Clinical Trials Network experience

Addiction ◽  
2012 ◽  
Vol 108 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Udi E. Ghitza ◽  
Robert E. Gore-Langton ◽  
Robert Lindblad ◽  
David Shide ◽  
Geetha Subramaniam ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Substance Use ◽  
Electronic Health Records ◽  
Substance Use Disorders ◽  
Common Data Elements ◽  
Health Records ◽  
Electronic Health ◽  
Data Elements

scholarly journals Understanding Contextual Differences in Residential LTC Provision for Cross-National Research: Identifying Internationally Relevant CDEs

2019 ◽  
Vol 5 ◽  
pp. 233372141984059 ◽  
Author(s):  
Elena O. Siegel ◽  
Annica Backman ◽  
Yi Cai ◽  
Claire Goodman ◽  
Oscar Noel Ocho ◽  
...  
Keyword(s):  
The United States ◽  
Term Care ◽  
Common Data Elements ◽  
Working Definition ◽  
Centered Care ◽  
Definition Of ◽  
Data Elements ◽  
Measurement Infrastructure ◽  
Contextual Differences

Long-term care (LTC) reflects a growing emphasis on person-centered care (PCC), with services oriented around individuals’ needs and preferences. Addressing contextual and cultural differences across countries offers important insight into factors that facilitate or hinder application of PCC practices within and across countries. This article takes an international lens to consider country-specific contexts of LTC, describing preliminary steps to develop common data elements that capture contextual differences across LTC settings globally. Through an iterative series of online, telephone, and in-person sessions, we engaged in in-depth discussions with 11 colleague experts in residential LTC and coauthors from six countries (China and Hong Kong, England, Sweden, Thailand, Trinidad and Tobago, and the United States). Our discussions yielded rich narrative describing a vast range in types of LTC settings, leading to our development of a working definition of residential LTC. Scope of services, funding, ownership, and regulations varied greatly across countries and across different residential LTC settings within countries. Moving forward, we recommend expanding our activities to countries that reflect different stages of residential LTC development. Our goal is to contribute to a larger initiative underway by the WE-THRIVE consortium to establish a global research measurement infrastructure that advances PCC internationally.

scholarly journals MLTI-05. IDENTIFYING BRAIN METASTATIC CASES FROM FREE TEXT CLINICAL NARRATIVES WITH REFINEMENT OF SEMANTIC HETEROGENEITY USING MACHINE LEARNING

2019 ◽  
Vol 1 (Supplement_1) ◽  
pp. i15-i15
Author(s):  
Michael Wells ◽  
Adam Robin ◽  
Laila Poisson ◽  
Houtan Noushmehr ◽  
James Snyder
Keyword(s):  
Machine Learning ◽  
Control Measure ◽  
Word Embedding ◽  
Free Text ◽  
Semantic Heterogeneity ◽  
Test Environment ◽  
Common Data Elements ◽  
Real World Evidence ◽  
Demographic Representation ◽  
Data Elements

Abstract INTRODUCTION: Brain metastatic disease (BM) is ripe for discovery using computational tools like machine learning (ML) due to disease complexity and multidimensional critical data (imaging, genomics, primary disease, drug exposures)1. Leveraging real-world-evidence’ (RWE) from routine health data to inform clinical management is hindered by fragmented unstructured data and semantic heterogeneity2. Clinical data in EHR and institutional registries are typically free text narratives absent common data elements (CDE). Curating existing data into CDE with machine learning (ML) may inform contemporary approaches (RWE, N-of-1 trials, and precision medicine) that are dependent on large high-quality datasets. Harvesting existing institutional registries may expand demographic representation, confirm benchmarks of established treatments, and provide test environment for prospective ML applications. METHOD: An R-based deep convoluted neural network (DNN) using keras and an API for Tensorflow python was trained on physician narratives of 2000 BM cases and 8000 other CNS conditions labeled by diagnosis spanning 17 years3,4. The ML model was tested with 405 non-labeled narratives to: A) Identify BM from other CNS conditions (i.e. glioma, meningioma, non-tumor). B) Evaluate word embedding using GLoVe5 to standardize abbreviations and misspellings by assigning terms to CDE by training the model to plot “mets”, “metastases” and “spine” with the 20 most similar contextual words. RESULTS: DNN architecture achieved 97% accuracy in distinguishing BM (n=178) for others (n=227). “Mets” and “metastasis” have a connected contextual network suggesting shared meaning, whereas spine did not share a network. CONCLUSIONS: ML can identify BM cases in free-text registries which can serve as a quality control measure and aid data aggregation. Standardizing shorthand terminology to CDE with DNN trained in word embedding can possibly address semantic heterogeneity and facilitate data automation. Solutions are needed to compile and automate quality BM data across institutions to achieve the volume and complexity required for contemporary analysis using ML.

scholarly journals Measuring What Matters in Long-Term Care: Common Data Elements in Brazil, China, and the United States

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 160-160
Author(s):  
Michael Lepore ◽  
Kirsten Corazzini ◽  
Sheryl Zimmerman
Keyword(s):  
Healthy Aging ◽  
Long Term Care ◽  
The United States ◽  
Term Care ◽  
Common Data Elements ◽  
Low Resource ◽  
Person Centered Care ◽  
Centered Care ◽  
Data Elements

Abstract Internationally sharable common data elements on residential long-term care (LTC) settings, such as nursing homes and assisted living facilities, can facilitate comparisons across diverse LTC settings for valuable insights on LTC regulation and oversight, practice and operations, infrastructure development, human resources issues, and quality and safety. However, such insights are predicated on the premise that data elements capture information that matters to the full LTC community, including residents, relatives and staff, and are able to be collected across diverse care settings, including low-resource contexts. A critique of much current LTC measurement is its focus on deficits and loss, rather than thriving, person-centered care, and healthy aging, which have been established as important to LTC communities internationally. Further, measurement burden, cultural differences in perceptions of data sharing, and data infrastructure differences are key issues for international data. An international collaborative of LTC researchers—Worldwide Elements to Harmonize Research in Long-Term Care Living Environments (WE-THRIVE)—has developed a set of common data elements that are recommended for parsimoniously assessing key outcomes, workforce and staffing, person-centered care, and the contexts within which LTC settings operate. The studies in this symposium provide insights into the validation and implementation of WE-THRIVE recommended measures in diverse, low-resource LTC contexts, including LTC settings in Brazil, China, and rural Midwest US. Study findings validate WE-THRIVE measures, and provide new knowledge to inform capacity-building for the measurement of person-centered care and healthy aging outcomes in diverse, low-resource, LTC settings.

scholarly journals Common Data Elements for Workforce and Staffing Measurements in Long-Term Care

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 144-145
Author(s):  
Charlene Chu ◽  
Franziska Zúñiga ◽  
Kirsten Corazzini
Keyword(s):  
Long Term Care ◽  
Quality Care ◽  
Policy And Practice ◽  
High Quality ◽  
Term Care ◽  
Common Data Elements ◽  
Centered Care ◽  
Care Outcomes ◽  
Data Elements

Abstract The workforce in residential long-term care (LTC) is key in providing high-quality, person-centered care for residents. However, low staffing and adverse staffing outcomes such as turnover or job dissatisfaction hinder the provision of high-quality care. International research can add valuable insights for policy and practice by learning from different settings and cultures. The initiative “To Harmonize Research In long-term care liVing Environments (WE-THRIVE)”, is led by an international group of LTC researchers to identify common data elements (CDE) for cross-comparative research that support older adults thriving in LTC. In this symposium, we will present an overview of the WE-THRIVE initiative with a specific focus on CDEs and measurement. The first talk will provide the context for the WE-THRIVE initiative, and discuss the collaborative and iterative processes required to develop the initial CDEs in the area of workforce and staffing. In the second talk, we will discuss which staff should be “in the house” to meet the needs of residents during and after a pandemic, and what type of workforce data system should be available to ensure the best quality outcomes for residents and carers. Next, current issues in the measurement of staffing in LTC based on a review of reviews of staffing’s relationship to quality of care will be discussed. Finally, we extend the debate to consider theoretical and empirical explanations for the relationship between staffing and quality in LTC and the promotion of person-centred care outcomes.

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