Browsing by Subject "Data quality"

Now showing 1 - 6 of 6
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    Cancer reporting: timeliness analysis and process reengineering
    (2015-11-09) Jabour, Abdulrahman M.; Jones, Josette; Dixon, Brian; Haggstrom, David; Davide, Bolchini
    Introduction: Cancer registries collect tumor-related data to monitor incident rates and support population-based research. A common concern with using population-based registry data for research is reporting timeliness. Data timeliness have been recognized as an important data characteristic by both the Centers for Disease Control and Prevention (CDC) and the Institute of Medicine (IOM). Yet, few recent studies in the United States (U.S.) have systemically measured timeliness. The goal of this research is to evaluate the quality of cancer data and examine methods by which the reporting process can be improved. The study aims are: 1- evaluate the timeliness of cancer cases at the Indiana State Department of Health (ISDH) Cancer Registry, 2- identify the perceived barriers and facilitators to timely reporting, and 3- reengineer the current reporting process to improve turnaround time. Method: For Aim 1: Using the ISDH dataset from 2000 to 2009, we evaluated the reporting timeliness and subtask within the process cycle. For Aim 2: Certified cancer registrars reporting for ISDH were invited to a semi-structured interview. The interviews were recorded and qualitatively analyzed. For Aim 3: We designed a reengineered workflow to minimize the reporting timeliness and tested it using simulation. Result: The results show variation in the mean reporting time, which ranged from 426 days in 2003 to 252 days in 2009. The barriers identified were categorized into six themes and the most common barrier was accessing medical records at external facilities. We also found that cases reside for a few months in the local hospital database while waiting for treatment data to become available. The recommended workflow focused on leveraging a health information exchange for data access and adding a notification system to inform registrars when new treatments are available.
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    Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa
    (Cambridge University Press, 2023-11-03) Lotspeich, Sarah C.; Shepherd, Bryan E.; Kariuk, Marion Achieng; Wools-Kaloustian, Kara; McGowan, Catherine C.; Musick, Beverly; Semeere, Aggrey; Crabtree Ramírez, Brenda E.; Mkwashapi, Denna M.; Cesar, Carina; Ssemakadde, Matthew; Machado, Daisy Maria; Ngeresa, Antony; Ferreira, Flávia Faleiro; Lwali, Jerome; Marcelin, Adias; Wagner Cardoso, Sandra; Luque, Marco Tulio; Otero, Larissa; Cortés, Claudia P.; Duda, Stephany N.; Medicine, School of Medicine
    Introduction: Routine patient care data are increasingly used for biomedical research, but such "secondary use" data have known limitations, including their quality. When leveraging routine care data for observational research, developing audit protocols that can maximize informational return and minimize costs is paramount. Methods: For more than a decade, the Latin America and East Africa regions of the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium have been auditing the observational data drawn from participating human immunodeficiency virus clinics. Since our earliest audits, where external auditors used paper forms to record audit findings from paper medical records, we have streamlined our protocols to obtain more efficient and informative audits that keep up with advancing technology while reducing travel obligations and associated costs. Results: We present five key lessons learned from conducting data audits of secondary-use data from resource-limited settings for more than 10 years and share eight recommendations for other consortia looking to implement data quality initiatives. Conclusion: After completing multiple audit cycles in both the Latin America and East Africa regions of the IeDEA consortium, we have established a rich reference for data quality in our cohorts, as well as large, audited analytical datasets that can be used to answer important clinical questions with confidence. By sharing our audit processes and how they have been adapted over time, we hope that others can develop protocols informed by our lessons learned from more than a decade of experience in these large, diverse cohorts.
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    Leveraging Electronic Dental Record Data for Clinical Research in the National Dental PBRN Practices
    (Thieme, 2020-03) Thyvalikakath, Thankam Paul; Duncan, William D.; Siddiqui, Zasim; LaPradd, Michelle; Eckert, George; Schleyer, Titus; Rindal, Donald Brad; Jurkovich, Mark; Shea, Tracy; Gilbert, Gregg H.; Pediatrics, School of Medicine
    Objectives: The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR). Methods: Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location. Results: Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset. Conclusion: This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice.
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    Quantifying Electronic Health Record Data Quality in Telehealth and Office-Based Diabetes Care
    (Thieme, 2022) Wiley, Kevin K.; Mendonca, Eneida; Blackburn, Justin; Menachemi, Nir; De Groot, Mary; Vest, Joshua R.; Health Policy and Management, School of Public Health
    Objective: Data derived from the electronic health record (EHR) are commonly reused for quality improvement, clinical decision-making, and empirical research despite having data quality challenges. Research highlighting EHR data quality concerns has largely been examined and identified during traditional in-person visits. To understand variations in data quality among patients managing type 2 diabetes mellitus (T2DM) with and without a history of telehealth visits, we examined three EHR data quality dimensions: timeliness, completeness, and information density. Methods: We used EHR data (2016-2021) from a local enterprise data warehouse to quantify timeliness, completeness, and information density for diagnostic and laboratory test data. Means and chi-squared significance tests were computed to compare data quality dimensions between patients with and without a history of telehealth use. Results: Mean timeliness or T2DM measurement age for the study sample was 77.8 days (95% confidence interval [CI], 39.6-116.4). Mean completeness for the sample was 0.891 (95% CI, 0.868-0.914). The mean information density score was 0.787 (95% CI, 0.747-0.827). EHR data for patients managing T2DM with a history of telehealth use were timelier (73.3 vs. 79.8 days), and measurements were more uniform across visits (0.795 vs. 0.784) based on information density scores, compared with patients with no history of telehealth use. Conclusion: Overall, EHR data for patients managing T2DM with a history of telehealth visits were generally timelier and measurements were more uniform across visits than for patients with no history of telehealth visits. Chronic disease care relies on comprehensive patient data collected via hybrid care delivery models and includes important domains for continued data quality assessments prior to secondary reuse purposes.
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    The IeDEA harmonist data toolkit: A data quality and data sharing solution for a global HIV research consortium
    (Elsevier, 2022) Lewis, Judith T.; Stephens, Jeremy; Musick, Beverly; Brown, Steven; Malateste, Karen; Ostinelli, Cam Ha Dao; Maxwell, Nicola; Jayathilake, Karu; Shi, Qiuhu; Brazier, Ellen; Kariminia, Azar; Hogan, Brenna; Duda, Stephany N.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    We describe the design, implementation, and impact of a data harmonization, data quality checking, and dynamic report generation application in an international observational HIV research network. The IeDEA Harmonist Data Toolkit is a web-based application written in the open source programming language R, employs the R/Shiny and RMarkdown packages, and leverages the REDCap data collection platform for data model definition and user authentication. The Toolkit performs data quality checks on uploaded datasets, checks for conformance with the network's common data model, displays the results both interactively and in downloadable reports, and stores approved datasets in secure cloud storage for retrieval by the requesting investigator. Including stakeholders and users in the design process was key to the successful adoption of the application. A survey of regional data managers as well as initial usage metrics indicate that the Toolkit saves time and results in improved data quality, with a 61% mean reduction in the number of error records in a dataset. The generalized application design allows the Toolkit to be easily adapted to other research networks.
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    The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope
    (Oxford University Press, 2022) Hogan, William R.; Shenkman, Elizabeth A.; Robinson, Temple; Carasquillo, Olveen; Robinson, Patricia S.; Essner, Rebecca Z.; Bian, Jiang; Lipori, Gigi; Harle, Christopher; Magoc, Tanja; Manini, Lizabeth; Mendoza, Tona; White, Sonya; Loiacono, Alex; Hall, Jackie; Nelson, Dave; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium (“OneFlorida”). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97–0.99, recall 0.75), thereby linking patients’ EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network (“PCORnet”), where OneFlorida is 1 of 9 clinical research networks. The Data Trust’s robust, centralized, statewide data are a valuable and relatively unique research resource.