Publications by authors named "Meredith Zozus"

40 Publications

DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool.

Stud Health Technol Inform 2021 May;281:432-436

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Named Entity Recognition (NER) aims to identify and classify entities into predefined categories is a critical pre-processing task in Natural Language Processing (NLP) pipeline. Readily available off-the-shelf NER algorithms or programs are trained on a general corpus and often need to be retrained when applied on a different domain. The end model's performance depends on the quality of named entities generated by these NER models used in the NLP task. To improve NER model accuracy, researchers build domain-specific corpora for both model training and evaluation. However, in the clinical domain, there is a dearth of training data because of privacy reasons, forcing many studies to use NER models that are trained in the non-clinical domain to generate NER feature-set. Thus, influencing the performance of the downstream NLP tasks like information extraction and de-identification. In this paper, our objective is to create a high quality annotated clinical corpus for training NER models that can be easily generalizable and can be used in a downstream de-identification task to generate named entities feature-set.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210195DOI Listing
May 2021

Consolidated EHR Workflow for Endoscopy Quality Reporting.

Stud Health Technol Inform 2021 May;281:427-431

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone. We hypothesize that extracting comprehensive colonoscopy quality metrics from consolidated procedure documents using computational linguistic techniques, and integrating it with discrete EHR data can improve quality of screening and cancer detection rate. As a first step, we developed an algorithm that links colonoscopy, pathology and imaging documents by analyzing the chronology of various orders placed relative to the colonoscopy procedure. The algorithm was installed and validated at the University of Arkansas for Medical Sciences (UAMS). The proposed algorithm in conjunction with Natural Language Processing (NLP) techniques can overcome current limitations of manual data abstraction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210194DOI Listing
May 2021

Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research.

Stud Health Technol Inform 2021 May;281:397-401

University of Texas Health Science Center at San Antonio, San Antonio, Texas.

Direct extraction and use of electronic health record (EHR) data is a long-term and multifaceted endeavor that includes design, development, implementation and evaluation of methods and tools for semi-automating tasks in the research data collection process, including, but not limited to, medical record abstraction (MRA). A systematic mapping of study data elements was used to measure the coverage of the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard for a federally sponsored, pragmatic cardiovascular randomized controlled trial (RCT) targeting adults. We evaluated site-level implementations of the HL7® FHIR® standard to investigate study- and site-level differences that could affect coverage and offer insight into the feasibility of a FHIR-based eSource solution for multicenter clinical research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210188DOI Listing
May 2021

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures.

Stud Health Technol Inform 2021 May;281:183-187

Division of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Endoscopy procedures are often performed with either moderate or deep sedation. While deep sedation is costly, procedures with moderate sedation are not always well tolerated resulting in patient discomfort, and are often aborted. Due to lack of clear guidelines, the decision to utilize moderate sedation or anesthesia for a procedure is made by the providers, leading to high variability in clinical practice. The objective of this study was to build a Machine Learning (ML) model that predicts if a colonoscopy can be successfully completed with moderate sedation based on patients' demographics, comorbidities, and prescribed medications. XGBoost model was trained and tested on 10,025 colonoscopies (70% - 30%) performed at University of Arkansas for Medical Sciences (UAMS). XGBoost achieved average area under receiver operating characteristic curve (AUC) of 0.762, F1-score to predict procedures that need moderate sedation was 0.85, and precision and recall were 0.81 and 0.89 respectively. The proposed model can be employed as a decision support tool for physicians to bolster their confidence while choosing between moderate sedation and anesthesia for a colonoscopy procedure.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210145DOI Listing
May 2021

Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review.

Stud Health Technol Inform 2021 May;281:799-803

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic's impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI210285DOI Listing
May 2021

Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.

Informatics (MDPI) 2021 Mar 3;8(1). Epub 2021 Mar 3.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/informatics8010016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112729PMC
March 2021

Assessing clinical investigators' perceptions of relevance and competency of clinical trials skills: An international AIDS Malignancy Consortium (AMC) study.

J Clin Transl Sci 2020 Aug 7;5(1):e28. Epub 2020 Aug 7.

University of Texas Health Sciences Center, San Antonio, TX, USA.

Introduction: The AIDS Malignancy Consortium (AMC) conducts clinical trials of therapeutic and prevention strategies for cancer in people living with HIV. With its recent expansion to Sub-Saharan Africa and Latin America, there was a need to increase the competence of clinical investigators (CIs) to implement clinical trials in these regions.

Methods: AMC CIs were invited to complete a survey to assess role-relevance and self-perceived competence based on the Joint Task Force for Clinical Trials Competency domains.

Results: A total of 40 AMC CIs were invited to complete the questionnaire and 35 responded to the survey. The data management and informatics and engaging with communities' domains were lowest in the average proportion of CIs rating themselves high (scores of 3-4) for self-perceived competency (46.6% and 44.2%) and role-relevance (61.6% and 67.5%), whereas, the ethical and participant safety considerations domain resulted in the highest score for competency (86.6%) and role-relevance (93.3%). In the scientific concepts and research design domain, a high proportion rated for competency in evaluating study designs and scientific literature (71.4% and 74.3%) but a low proportion for competency for designing trials and specimen collection protocols (51.4% and 54.3%).

Conclusions: Given the complexity of AMC clinical research, these results provide evidence of the need to develop training for clinical research professionals across domains where self-perceived competence is low. This assessment will be used to tailor and prioritize the AMC Training Program in clinical trial development and management for AMC CIs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/cts.2020.520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057474PMC
August 2020

Evaluating the Coverage of the HL7 FHIR Standard to Support eSource Data Exchange Implementations for use in Multi-Site Clinical Research Studies.

AMIA Annu Symp Proc 2020 25;2020:472-481. Epub 2021 Jan 25.

University of Texas Health Science Center at San Antonio, San Antonio, TX.

The direct use of EHR data in research, often referred to as 'eSource', has long-been a goal for researchers because of anticipated increases in data quality and reductions in site burden. eSource solutions should rely on data exchange standards for consistency, quality, and efficiency. The utility of any data standard can be evaluated by its ability to meet specific use case requirements. The Health Level Seven (HL7 ) Fast Healthcare Interoperability Resources (FHIR ) standard is widely recognized for clinical data exchange; however, a thorough analysis of the standard's data coverage in supporting multi-site clinical studies has not been conducted. We developed and implemented a systematic mapping approach for evaluating HL7 FHIR standard coverage in multi-center clinical trials. Study data elements from three diverse studies were mapped to HL7 FHIR resources, offering insight into the coverage and utility of the standard for supporting the data collection needs of multi-site clinical research studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075534PMC
June 2021

API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research.

Healthc Inform Res 2021 Jan 31;27(1):39-47. Epub 2021 Jan 31.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Objectives: To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparate systems must be aggregated for analysis. Study participant records from various sources are linked together and to patient records when possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizes participant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programming interface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) to further de-identify PIDs. The tool, on-demand cohort and API participant identifier pseudonymization (O-CAPP), employs a pseudonymization method based on the type of incoming research data.

Methods: For images, pseudonymization of PIDs is done using API calls that receive PIDs present in Digital Imaging and Communications in Medicine (DICOM) headers and returns the pseudonymized identifiers. For non-imaging clinical research data, PIDs provided by study principal investigators (PIs) are pseudonymized using a nightly automated process. The pseudonymized PIDs (P-PIDs) along with other protected health information is further de-identified using POSDA.

Results: A sample of 250 PIDs pseudonymized by O-CAPP were selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process were validated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymization by API request based on the provided PID and P-PID mappings.

Conclusions: We developed a novel approach of an ondemand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.4258/hir.2021.27.1.39DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921568PMC
January 2021

A Rule-Based Data Quality Assessment System for Electronic Health Record Data.

Appl Clin Inform 2020 08 23;11(4):622-634. Epub 2020 Sep 23.

Department of Population Health Science, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States.

Objective: Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners.

Methods: We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring.

Results: We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information.

Discussion: The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set.

Conclusion: This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1055/s-0040-1715567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511263PMC
August 2020

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models.

Healthc Inform Res 2020 Jul 31;26(3):193-200. Epub 2020 Jul 31.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Objective: The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs).

Methods: A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs.

Results: At the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against: scores available in the 2015 quarter 1-3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate.

Conclusion: TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.4258/hir.2020.26.3.193DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438698PMC
July 2020

Rules Based Data Quality Assessment on Claims Database.

Stud Health Technol Inform 2020 Jun;272:350-353

University of Arkansas for Medical Sciences, Littlerock, Arkansas, USA.

Data quality problems in coded clinical and administrative data have persisted ever since diagnoses and procedures were first coded and used for healthcare billing. These data are used in clinical decision-making introducing a route for iatrogenesis. As we share data on regional Health Information Exchanges (HIEs) and include them in electronic health records the potential for harm may be increased. To study this problem we applied rules-based data quality checks that have been previously tested on Electronic Health Records (EHR) data on a limited set of aggregated claims data. Medicaid claims data was used exclusively. CMS has clear guidelines for claims submitted for Medicaid patients and penalties are incurred for erroneous claims, which should ensure a high quality data source, however reports of low and varying sensitivity, specificity, positive and negative predictive value of coded diagnoses are common. To identify data quality defects in claims data in a state All Payer Claims Dataset (APCD) we applied and evaluated a recently developed rules-based data quality assessment and monitoring system for Electronic Health Record (EHR) data to test effectiveness in claims data. These rules, that are feasible for "All Payer Claims data" and Medicaid data are identified, applied and the Data Quality issue results are produced.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI200567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899162PMC
June 2020

The Impact of Information Quality on Retracted Bioinformatics Literature.

Stud Health Technol Inform 2020 Jun;270:1203-1204

University of Arkansas at Little Rock.

One of the biggest challenges facing biomedical research today is the lack of reproducibility in findings. In response, a growing body of literature has emerged to address this. However, much of this focuses on bias and methods, while little addresses the issue of information quality. The purpose of this poster is to determine the role of information quality for retracted bioinformatics literature.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI200363DOI Listing
June 2020

Analysis of Professional Competencies for the Clinical Research Data Management Profession.

Stud Health Technol Inform 2020 Jun;270:1199-1200

University of Arkansas for Medical Sciences, Little Rock Arkansas, USA.

Objective: This job analysis was conducted to compare, assess and refine the competencies of the clinical research data management profession.

Materials And Methods: Two questionnaires were administered in 2015 and 2018 to collect information from data managers on professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge.

Results: In 2018 survey, 67 professional competencies were identified. Job tasks differed between early- to mid-career and mid- to late-career practitioners. A large variation in the types of studies conducted and variation in the data managed by the participants was observed.

Discussion: Clinical research data managers managed different types of data with variety of research settings, which indicated a need for training in methods and concepts that could be applied across therapeutic areas and types of data.

Conclusion: The competency survey reported here serves as the foundation for the upcoming revision of the Certified Clinical Data Manager (CCDMTM) exam.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI200361DOI Listing
June 2020

eSource-Enabled vs. Traditional Clinical Trial Data Collection Methods: A Site-Level Economic Analysis.

Stud Health Technol Inform 2020 Jun;270:961-965

University of Texas Health Science Center at San Antonio, TX, United States.

Directly extracting data from site electronic health records for updating clinical trial databases (eSource) can reduce site data collection times and errors. We conducted a study to determine clinical trial characteristics that make eSource vs. traditional data collection methods more and less economically attractive. The number of patients a site enrolls, the number of study data elements, study coordinator data collection times, and the percent of study data elements that can be extracted via eSource software all impact eSource economic attractiveness. However, these factors may not impact all clinical trial designs in the same way.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI200304DOI Listing
June 2020

Near Real Time EHR Data Utilization in a Clinical Study.

Stud Health Technol Inform 2020 Jun;270:337-341

University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Extraction and use of Electronic Health Record (EHR) data is common in retrospective observational studies. However, electronic extraction and use of EHR data is rare during longitudinal prospective studies. One of the reasons is the amount of processing needed to assess data quality and assure consistency in meaning and format across multiple investigational sites. We report a case study of and lessons learned from acquisition and processing of EHR data in an ongoing basis during a clinical study.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI200178DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898242PMC
June 2020

Document Oriented Graphical Analysis and Prediction.

Stud Health Technol Inform 2020 Jun;270:183-187

Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

In general, small-mid size research laboratories struggle with managing clinical and secondary datasets. In addition, faster dissemination, correlation and prediction of information from available datasets is always a bottleneck. To address these challenges, we have developed a novel approach, Document Oriented Graphical Analysis and Prediction (DO-GAP), a hybrid tool, merging strengths of Not only SQL (NoSQL) document oriented and graph databases. DO-GAP provides flexible and simple data integration mechanism using document database, data visualization and knowledge discovery with graph database. We demonstrate how the proposed tool (DO-GAP) can integrate data from heterogeneous sources such as Genomic lab findings, clinical data from Electronic Health Record (EHR) systems and provide simple querying mechanism. Application of DO-GAP can be extended to other diverse clinical studies such as supporting or identifying weakness of clinical diagnosis in comparison to molecular genetic analysis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI200147DOI Listing
June 2020

Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.

Yearb Med Inform 2019 Aug 16;28(1):140-151. Epub 2019 Aug 16.

University of Arkansas for Medical Sciences, Arkansas, USA.

Objectives: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).

Methods: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.

Results: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.

Conclusions: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1055/s-0039-1677912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697506PMC
August 2019

Adapting Scoring Based Classification to Simplify and Automate Phenotype Creation for Cohort Identification in Clinical Data.

AMIA Jt Summits Transl Sci Proc 2019 6;2019:488-494. Epub 2019 May 6.

University of Arkansas for Medical Sciences College of Medicine, Department of Biomedical Informatics, Little Rock, Arkansas, USA.

EHR-based phenotype development and validation are extremely time-consuming and have considerable monetary cost. The creation of a phenotype currently requires clinical experts and experts in the data to be queried. The new approach presented here demonstrates a computational alternative to the classification of patient cohorts based on automatic weighting of ICD codes. This approach was applied to data from six different clinics within the University of Arkansas for Medical Science (UAMS) health system. The results were compared with phenotype algorithms designed by clinicians and informaticians for asthma and melanoma. Relative to traditional phenotype development, this method shows potential to considerably reduce time requirements and monetary costs with comparable results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568072PMC
May 2019

Factors impacting physician use of information charted by others.

JAMIA Open 2019 Apr 28;2(1):107-114. Epub 2018 Dec 28.

Duke University Center for Health Informatics, Durham, North Carolina, USA.

Objectives: To identify factors impacting physician use of information charted by others.

Materials And Methods: A 4-round Delphi process was conducted with physicians and non-physicians publishing in the healthcare data quality literature to identify and characterize factors impacting physician use of information charted by others (other people or devices), either within or external to their organization. Factors with high average importance and reliability were categorized according to similarity of topic.

Results: Thirty-nine factors were ultimately identified as impacting physician use of information charted by others. Five categories of factors included aspects of: the information source, the information itself, the information user, the information system, and aspects of healthcare as an institution. In addition, 4 themes were identified: (1) value of narrative text in providing context, (2) importance of mental models and personal heuristics in deciding whether, and how to use information, (3) loss of confidence in, and decreased use of information due to errors encountered, and (4) existence of a trust hierarchy potentially influencing information use.

Discussion: Five similarly focused studies have recently probed clinician willingness to use information in decision-making. Our results mostly confirmed factors identified by prior studies, and uniquely identified aspects of the information user as important.

Conclusion: According to the participants in this study, information quality is prominent among factors impacting physician use of information charted by others. Based on this and similar studies, it appears that despite concerns about information quality, physicians use information charted by others.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamiaopen/ooy041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447025PMC
April 2019

Training as an Intervention to Decrease Medical Record Abstraction Errors Multicenter Studies.

Stud Health Technol Inform 2019 ;257:526-539

National Institutes of Health, Bethesda, MD.

Studies often rely on medical record abstraction as a major source of data. However, data quality from medical record abstraction has long been questioned. Electronic Health Records (EHRs) potentially add variability to the abstraction process due to the complexity of navigating and locating study data within these systems. We report training for and initial quality assessment of medical record abstraction for a clinical study conducted by the IDeA States Pediatric Clinical Trials Network (ISPCTN) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Neonatal Research Network (NRN) using medical record abstraction as the primary data source. As part of overall quality assurance, study-specific training for medical record abstractors was developed and deployed during study start-up. The training consisted of a didactic session with an example case abstraction and an independent abstraction of two standardized cases. Sixty-nine site abstractors from thirty sites were trained. The training was designed to achieve an error rate for each abstractor of no greater than 4.93% with a mean of 2.53%, at study initiation. Twenty-three percent of the trainees exceeded the acceptance limit on one or both of the training test cases, supporting the need for such training. We describe lessons learned in the design and operationalization of the study-specific, medical record abstraction training program.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692114PMC
August 2019

Data Profiling in Support of Entity Resolution of Multi-Institutional EHR Data.

Stud Health Technol Inform 2019 ;257:479-483

University of Arkansas for Medical Sciences, Little Rock, Arkansas.

Information Quality (IQ) is a core tenant of contemporary data management practices. Across many disciplines and industries, it has become a necessary process to improve value and reduce liability in data driven processes. Information quality is a multifaceted discipline with many degrees of complexity in implementation, especially in healthcare. Data profiling is one of the simpler tasks that an organization can perform to understand and monitor the intrinsic quality of its data. This case study demonstrates the application of core concepts of data profiling to entity resolution of multi-institutional Electronic Health Record (EHR) data. We discuss the benefits of using data profiling to better understand quality issues and their impact on entity resolution and how data profiling might be augmented to increase utility to clinical data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692113PMC
August 2019

Analysis of Anesthesia Screens for Rule-Based Data Quality Assessment Opportunities.

Stud Health Technol Inform 2019 ;257:473-478

University of Arkansas for Medical Sciences.

A rules-based data quality assessment system in electronic health record was explored through compilation of over six thousand data quality rules and twenty-two rule templates. To overcome the lack of knowledge sources and identify additional rules or rule templates, thirty-three anesthesia (perioperative period) EHR screens were reviewed. We analyzed the data elements appearing on anesthesia screens and relationships between them to identify new data quality rules and rule templates relevant to anesthesia care. We present the review process as well as new rules and rule templates identified. We found decomposition and analysis of EHR screens a viable mechanism for acquisition of new data quality rules and proved the number of rules likely tractable and their management scalable.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692112PMC
August 2019

Cancer Phenotype Development: A Literature Review.

Stud Health Technol Inform 2019 ;257:468-472

University of Arkansas for Medical Sciences, Little Rock, Arkansas.

EHR-based, computable phenotypes can be leveraged by healthcare organizations and researchers to improve the cohort identification process. The ability to identify patient cohorts using aspects of care and outcomes based on clinical characteristics or diagnostic conditions and/or risk factors presents opportunities to researchers targeting specific populations for drug development and disease interventions. The objective of this review was to summarize the literature describing the development and use of phenotypes for cohort identification of cancer patients. A survey of the literature indexed in PubMed was performed to identify studies using EHR-based phenotypes for use in cancer studies. Specific search criteria were formulated by leveraging a phenotype identification guideline developed by the Phenotypes, Data Standards, and Data Quality Core of the NIH Health Care Systems Research Collaboratory. The final set of articles was examined further to identify 1) the cancer of interest and 2) the different approaches used for phenotype development, validation and implementation. The articles reviewed were specific to breast cancer, colorectal cancer, ovarian cancer, and lung cancer. The approaches taken for phenotype development and validation varied slightly among the relevant publications. Four studies relied on chart review, three utilized machine learning techniques, one took an ontological approach, and one utilized natural language processing (NLP).
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2019

Rule-Based Data Quality Assessment and Monitoring System in Healthcare Facilities.

Stud Health Technol Inform 2019 ;257:460-467

University of Arkansas for Medical Sciences.

Measuring and managing data quality in healthcare has remained largely uncharted territory with few notable exceptions. A rules-based approach to data error identification was explored through compilation of over 6,000 data quality rules used with healthcare data. The rules were categorized based on topic and logic yielding twenty-two rule templates and associated knowledge tables used by the rule templates. This work provides a scalable framework with which data quality rules can be organized, shared among facilities and reused. The ten most frequent data quality problems based on the initial rules results are identified. While there is significant additional work to be done in this area, the exploration of the rule template and associated knowledge tables approach here shows rules-based data quality assessment and monitoring to be possible and scalable.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692115PMC
August 2019

A Qualitative Evidence Synthesis of Adverse Event Detection Methodologies.

Stud Health Technol Inform 2019 ;257:346-351

University of Arkansas for Medical Sciences.

The detection of adverse events (AE) and their relationship to data quality issues through processes or medical error is not currently understood. In order to study the relationship between adverse events and data quality it is necessary to capture as many AE as possible and computational methods will be necessary to handle the large volumes of patient data. The need for adverse event detection methodology has been repeatedly noted but standard AE detection methods are not in place in the US. At present, there are several widely enforced strategies for AE detection but none are both highly successful and computational. In order to maximize AE detection, we have conducted a qualitative evidence synthesis of these approaches. The categorization of the circumstances of the event as well as the resulting patient safety problem and the method of detection provide a means to synthesize AE detection solutions. This has resulted in a set of 130 AE detection algorithms in 9 circumstances categories and 41 patient safety problem categories. This work begins the effort of consolidation of current safety metrics in an effort to produce a common set of safety measures.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2019

Evaluative Outcomes in Direct Extraction and Use of EHR Data in Clinical Trials.

Stud Health Technol Inform 2019 ;257:333-340

University of Arkansas for Medical Sciences, Little Rock, AR.

Use of electronic health record (EHR) data in clinical trials has long been a goal for researchers. However, few demonstrations and fewer evaluative studies have been published. The variability in outcome choice and measurement hinders synthesis of the extant literature. In collaboration with a contemporaneous systematic review of EHR data use in clinical trial data collection, we analyze reported outcomes and recommend a standardized measure set for the evaluation of human safety, data quality, operational efficiency and cost of eSource solutions.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2019

Development of Data Validation Rules for Therapeutic Area Standard Data Elements in Four Mental Health Domains to Improve the Quality of FDA Submissions.

Stud Health Technol Inform 2019 ;257:125-132

University of Arkansas for Medical Sciences, Little Rock, Arkansas.

Data standards are now required for many submissions to the United States Food and Drug Administration (FDA). The required standard for submission of clinical data is the Clinical Data Interchange Standards Consortium (CDISC) Submission Data Tabulation Model (SDTM). Currently, 45 business rules and 115 associated validation rules exist for SDTM data. However, such rules have not yet been developed for therapeutic area data standards developed under the last reauthorization of the Prescription Drug User Fee Act (PDUFA V). The objective of this effort was to develop data validation rules for new therapeutic area data standards in four mental health domains, assess the metadata required to associate such rules with standard data elements, and assess the level of data validation possible for therapeutic area data elements.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2019

eSource for Standardized Health Information Exchange in Clinical Research: A Systematic Review.

Stud Health Technol Inform 2019 ;257:115-124

University of Arkansas for Medical Sciences, Little Rock, Arkansas.

The availability of research and outcomes data is the primary limitation to evidence-based practice. Today, only a fraction of clinical decisions are based upon evidence derived from randomized control trials (RCTs), the gold-standard of knowledge discovery. At the same time, clinical trial complexity has steadily increased as has the effort required at clinical investigational sites. Direct use of electronic health record (EHR) data for clinical trials has the potential to address some of these needs, improving data quality and reducing cost.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2019

Factors Associated with Increased Adoption of a Research Data Warehouse.

Stud Health Technol Inform 2019 ;257:31-35

Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR.

The increased demand of clinical data for the conduct of clinical and translational research incentivized repurposing of the University of Arkansas for Medical Sciences' enterprise data warehouse (EDW) to meet researchers' data needs. The EDW was renamed the Arkansas Clinical Data Repository (AR-CDR), underwent content enhancements, and deployed a self-service cohort estimation tool in late of 2016. In an effort to increase adoption of the AR-CDR, a team of physician informaticist and information technology professionals conducted various informational sessions across the UAMS campus to increase awareness of the AR-CDR and the informatics capabilities. The restructuring of the data warehouse resulted in four-fold utilization increase of the AR-CDR data services in 2017. To assess acceptance rates of the AR-CDR and quantify outcomes of services provided, Everett Rogers' diffusion of innovation (DOI) framework was applied, and a survey was distributed. Results show the factors that had impact on increased adoption were: presence of physician informaticist to mediate interactions between researchers and analysts, data quality, communication with and engagement of researchers, and the AR-CDR's team responsiveness and customer service mindset.
View Article and Find Full Text PDF

Download full-text PDF

Source
August 2019