scholarly journals 2286

2017 ◽  
Vol 1 (S1) ◽  
pp. 14-14
Author(s):  
William G. Adams ◽  
Michael Mendis ◽  
Shiby Thomas ◽  
David Center ◽  
Sara Curran
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Community Health ◽  
Comparative Effectiveness ◽  
Large Scale ◽  
Safety Net ◽  
Services Research ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

OBJECTIVES/SPECIFIC AIMS: The primary objective of this effort is to develop and distribute an easy to use i2b2 component that is capable of evaluating diverse complex relationships for a wide variety of exposures and outcomes over time. In this manner we are able to leverage the unique design of the i2b2 database to support health services research, comparative effectiveness, and quality improvement using a single tool. Furthermore, our novel database redesign has the potential to provide user-friendly access to individual and group CHC data for CER. METHODS/STUDY POPULATION: For this project we used software experts, clinical informatics specialists, and the existing i2b2 open-source software to convert our legacy HOME Cell into a web-client version. The tool will be used to study health outcomes within a network of Boston based Community Health Centers and the largest safety-net hospital in New England, Boston Medical Center. RESULTS/ANTICIPATED RESULTS: The new web-client HOME Cell will allow i2b2 users to model virtually any exposure (including therapeutic interventions such as medications or tests) in i2b2 against any outcome accounting for complex temporal relationships and other factors. In addition we plan to use our new Community Health Center views to enhance our community engagement activities by allowing direct access to their data for our partners. DISCUSSION/SIGNIFICANCE OF IMPACT: Our project addresses multiple national priorities related to data sharing, clinical research informatics, and comparative effectiveness. The web-client version of the HOME Cell substantially improves our community’s access to HOME Cell functionality and is a novel, sharable resource for use within the CTSA/NCATS community. Our approach provides a new way to perform large-scale collaborative research without the need to actually move patient-level data and has demonstrated that CER, health services research, and quality measurement can share a common framework. In addition, and as demonstrated in our earlier pilot work, the HOME Cell also has the potential to support large-scale multivariate analyses in a distributed manner that does not require sharing of patient-level data. We believe our approach has great promise for supporting the reuse of clinical data for rapid, transparent, health outcome assessments on a national scale. Our efforts support multiple strategic goals including: (1) support for building national clinical and translational research capacity by enhancing a broadly adopted informatics tool (i2b2); (2) enhanced consortium-wide collaborations by offering a tool that can be easily shared within the CTSA network to support multi-institutional collaboration; and (3) improving the health of our communities by offering a tool that has the potential to provide new insights into health care processes and outcomes that could drive innovation and improvement activities.

scholarly journals Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

2021 ◽  
Vol 09 (02) ◽  
pp. E233-E238
Author(s):  
Rajesh N. Keswani ◽  
Daniel Byrd ◽  
Florencia Garcia Vicente ◽  
J. Alex Heller ◽  
Matthew Klug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Medical Center ◽  
Academic Medical Center ◽  
Procedure Time ◽  
Image Capture ◽  
Common Procedure ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

Abstract Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

scholarly journals Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Chuan Hong ◽  
Everett Rush ◽  
Molei Liu ◽  
Doudou Zhou ◽  
Jiehuan Sun ◽  
...  
Keyword(s):  
Feature Selection ◽  
Electronic Health Record ◽  
Large Scale ◽  
Knowledge Extraction ◽  
Knowledge Map ◽  
Health Record ◽  
Clinical Knowledge ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

AbstractThe increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

scholarly journals Opportunities for diversifying and enriching our article mix

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Bruce Rosen ◽  
Stephen C. Schoenbaum ◽  
Avi Israeli
Keyword(s):  
Health Policy ◽  
Health Services Research ◽  
Health Services ◽  
Large Scale ◽  
Policy Research ◽  
National Policy ◽  
Administrative Databases ◽  
Services Research ◽  
Health Policy Analysis ◽  
Access To Data

AbstractAs 2020 comes to a close, the Israel Journal of Health Policy Research (IJHPR) will soon be starting its tenth year of publication. This editorial compares data from 2012 (the journal’s first year of publication) and 2019 (the journal’s most recent full year of publication), regarding the journal’s mix of article types, topics, data sources and methods, with further drill-downs regarding 2019.The analysis revealed several encouraging findings, including a broad and changing mix of topics covered. However, the analysis also revealed several findings that are less encouraging, including the limited number of articles which assessed national policy changes, examined changes over time, and/or made secondary use of large-scale survey data. These findings apparently reflect, to some extent, the mix of studies being carried out by Israeli health services researchers.As the senior editors of the IJHPR we are interested in working with funders, academic institutions, the owners and principal users of relevant administrative databases, and individual scholars to further understand the factors influencing the mix of research being carried out, and subsequently published, by Israel’s health services research community. This deeper understanding could then be used to develop a joint plan to diversify and enrich health services research and health policy analysis in Israel. The plan should include a policy of ensuring improved access to data, to properly support information-based research.

Analysis of Novel Care Management Programs in Primary Care: An Example of Mixed Methods in Health Services Research

2016 ◽  
Vol 13 (1) ◽  
pp. 85-112 ◽  
Author(s):  
Jodi Summers Holtrop ◽  
Georges Potworowski ◽  
Lee A. Green ◽  
Michael Fetters
Keyword(s):  
Mixed Methods ◽  
Health Services Research ◽  
Health Services ◽  
Quantitative Data ◽  
Mixed Methods Research ◽  
Care Management ◽  
Large Scale ◽  
Qualitative Comparative Analysis ◽  
Data Transformation ◽  
Services Research

While health services researchers are using mixed methods research in large-scale studies with “big data” and incorporating data transformation for merging qualitative and quantitative data sets, these developments are not widely known to the broader mixed methods research community. Our purpose in this article is to introduce health services research to the broader mixed methods audience, to examine the potential for novel innovations in mixed methods research procedures, and to illustrate these points through a project on care management that used a convergent mixed methods design. In addition to traditional analytical procedures, we illustrate two qualitative to quantitative data transformation procedures, one using normalization process theory and a second, fuzzy set qualitative comparative analysis.

scholarly journals Clinical Knowledge Extraction via Sparse Embedding Regression (KESER) with Multi-Center Large Scale Electronic Health Record Data

2021 ◽  
Author(s):  
Chuan Hong ◽  
Everett Rush ◽  
Molei Liu ◽  
Doudou Zhou ◽  
Jiehuan Sun ◽  
...  
Keyword(s):  
Feature Selection ◽  
Electronic Health Record ◽  
Large Scale ◽  
Knowledge Extraction ◽  
Knowledge Map ◽  
Health Record ◽  
Clinical Knowledge ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

ABSTRACTObjectiveThe increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Even with a working knowledge of EHR, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions to establish a cooperative and integrated knowledge network. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease or condition of interest.MethodWe constructed large-scale code embeddings for a wide range of codified concepts, including diagnosis codes, medications, procedures, and laboratory tests from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis based on the trained code embeddings. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions.ResultsThe features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Additionally, features identified automatically via KESER used in the development of phenotype algorithms resulted in comparable performance to those built upon features selected manually or identified via existing feature selection methods with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data.ConclusionAnalysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among diseases, treatment, procedures, and laboratory measurement. This approach automates the grouping of clinical features facilitating studies of the condition. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

scholarly journals Implementation and data-related challenges in the Deepening our Understanding of Quality in Australia (DUQuA) study: implications for large-scale cross-sectional research

2020 ◽  
Vol 32 (Supplement_1) ◽  
pp. 75-83
Author(s):  
Gaston Arnolda ◽  
Teresa Winata ◽  
Hsuen P Ting ◽  
Robyn Clay-Williams ◽  
Natalie Taylor ◽  
...  
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Large Scale ◽  
Organizational Capacity ◽  
Quality And Safety ◽  
Governance System ◽  
Services Research ◽  
Cross Sectional ◽  
Healthcare Administrators ◽  
Safety Systems

Abstract Healthcare organisations vary in the degree to which they implement quality and safety systems and strategies. Large-scale cross-sectional studies have been implemented to explore whether this variation is associated with outcomes relevant at the patient level. The Deepening our Understanding of Quality in Australia (DUQuA) study draws from earlier research of this type, to examine these issues in 32 Australian hospitals. This paper outlines the key implementation and analysis challenges faced by DUQuA. Many of the logistical difficulties of implementing DUQuA derived from compliance with the administratively complex and time-consuming Australian ethics and governance system designed principally to protect patients involved in clinical trials, rather than for low-risk health services research. The complexity of these processes is compounded by a lack of organizational capacity for multi-site health services research; research is expected to be undertaken in addition to usual work, not as part of it. These issues likely contributed to a relatively low recruitment rate for hospitals (41% of eligible hospitals). Both sets of issues need to be addressed by health services researchers, policymakers and healthcare administrators, if health services research is to flourish. Large-scale research also inevitably involves multiple measurements. The timing for applying these measures needs to be coherent, to maximise the likelihood of finding real relationships between quality and safety systems and strategies, and patient outcomes; this timing was less than ideal in DUQuA, in part due to administrative delays. Other issues that affected our study include low response rates for measures requiring recruitment of clinicians and patients, missing data and a design that necessarily included multiple statistical comparisons. We discuss how these were addressed. Successful completion of these projects relies on mutual and ongoing commitment, and two-way communication between the research team and hospital staff at all levels. This will help to ensure that enthusiasm and engagement are established and maintained.

scholarly journals Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research

2020 ◽  
pp. 0272989X2095440
Author(s):  
Glen B. Taksler ◽  
Jarrod E. Dalton ◽  
Adam T. Perzynski ◽  
Michael B. Rothberg ◽  
Alex Milinovich ◽  
...  
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Electronic Health Records ◽  
Large Scale ◽  
Data Access ◽  
Data Sets ◽  
Services Research ◽  
Health Records ◽  
Electronic Health ◽  
Derived Data

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

Sign in / Sign up

Export Citation Format

Share Document