Journal of registry management最新文献

Objectives: The Veterans Health Administration (VHA) is a leader in generating transformational research across the cancer care continuum. Given the extensive body of cancer-related literature utilizing VHA data, our objectives are to: (1) describe the VHA data sources available for conducting cancer-related research, and (2) discuss examples of published cancer research using each data source.

Methods: We identified commonly used data sources within the VHA and reviewed previously published cancer-related research that utilized these data sources. In addition, we reviewed VHA clinical and health services research web pages and consulted with a multidisciplinary group of cancer researchers that included hematologist/oncologists, health services researchers, and epidemiologists.

Results: Commonly used VHA cancer data sources include the Veterans Affairs (VA) Cancer Registry System, the VA Central Cancer Registry (VACCR), the Corporate Data Warehouse (CDW)-Oncology Raw Domain (subset of data within the CDW), and the VA Cancer Care Cube (Cube). While no reference standard exists for cancer case ascertainment, the VACCR provides a systematic approach to ensure the complete capture of clinical history, cancer diagnosis, and treatment. Like many population-based cancer registries, a significant time lag exists due to constrained resources, which may make it best suited for historical epidemiologic studies. The CDW-Oncology Raw Domain and the Cube contain national information on incident cancers which may be useful for case ascertainment and prospective recruitment; however, additional resources may be needed for data cleaning.

Conclusions: The VHA has a wealth of data sources available for cancer-related research. It is imperative that researchers recognize the advantages and disadvantages of each data source to ensure their research questions are addressed appropriately.

Background: Hospital electronic medical record (EMR) systems are becoming increasingly integrated for management of patient data, especially given recent policy changes issued by the Centers for Medicaid and Medicare Services. In addition to data management, these data provide evidence for patient-centered outcomes research for a range of diseases, including cancer. Integrating EMR patient data with existing disease registries strengthens all essential components for assuring optimal health outcomes.

Objectives: To identify the mechanisms for extracting, linking, and processing hospital EMR data with the Florida Cancer Data System (FCDS); and to assess the completeness of existing registry treatment data as well as the potential for data enhancement.

Methods: A partnership among the Florida Department of Health, FCDS, and a large Florida hospital system was established to develop methods for hospital EMR extraction and transmission. Records for admission years between 2007 and 2010 were extracted using ICD-9-CM codes as the trigger and were linked with the cancer registry for patients with invasive cancers of the breast.

Results: A total of 11,506 unique patients were linked with a total of 12,804 unique breast tumors. Evaluation of existing registry treatment data against the hospital EMR produced a total of 5% of registry records with updated surgery information, 1% of records with updated radiation information, and 7% of records updated with chemotherapy information. Enhancement of registry treatment information was particularly affected by the availability of chemotherapy medications data.

Conclusion: Hospital EMR linkages to cancer disease registries is feasible but challenged by lack of standards for data collection, coding and transmission, comprehensive description of available data, and the exclusion of certain hospital datasets. The FCDS standard treatment data variables are highly robust and complete but can be enhanced by the addition of detailed chemotherapy regimens that are commonly used in patient centered outcomes research.

After reading the article and completing the quiz, participants will be able to: Understand data quality evaluation field accuracy ratesDescribe the method of data quality evaluationUnderstand the implications for educational opportunities based on this data quality evaluation.

Objective: Nontyphoidal Salmonella infection is one of the most common foodborne illnesses, and its oncogenic potential has been documented in animal models. The primary goal of this study was to examine whether individuals who were exposed to enteric Salmonella infection are more likely to develop colorectal cancer (CRC) than the general population through the linkage of 2 statewide public health surveillance databases.

Materials and methods: We designed a 2-stage probabilistic linkage, starting with 17,587 records of enteric salmonellosis reported to Michigan Department of Health and Human Services between 1992 and 2020. These records did not include unique identifiers (such as Social Security number [SSN]). The initial linkage to LexisNexis address history was conducted to obtain information to calculate each person's time in Michigan as well as SSN for the second linkage. The linkage to the state cancer registry was performed to obtain the observed number of CRC cases, while the expected number of CRC cases was calculated according to corresponding state CRC incidence by age, sex, and calendar year.

Results: Ninety-three percent of the initially identified salmonellosis records were sent to LexisNexis linkage, which returned address history, death, and SSN for 97% of the records. Further linkage to the statewide cancer registry identified 98 incident CRC cases. Overall, the observed-to-expected (O/E) ratio was not different from unity (0.833; 95% CI, 0.627-1.003).

Conclusions: While the new linkage strategy was found effective and should be applicable to other health conditions, we cannot rule out bias due to incomplete or underreporting of the infection in estimating the risk of CRC.

Objectives: This study aimed to develop and validate an algorithm for the identification of opioid use disorder (OUD) in pregnant patients using electronic medical record (EMR) data.

Materials and methods: A cohort of pregnant patients from a single institution was used to develop and validate the algorithm. Five algorithm components were used, and chart reviews were conducted to confirm OUD diagnoses based on established criteria. Positive predictive values (PPV) of each of the algorithm's components were assessed.

Results: Of the 334 charts identified by the algorithm, 256 true cases were confirmed. The overall PPV of the algorithm was 76.6%, with 100% accuracy for outpatient medication lists, and high PPVs ranging from 81.3% to 93.4% across other algorithm components.

Discussion and conclusion: The study highlights the significance of a multifaceted approach in identifying OUD among pregnant patients, aiming to improve patient care and target interventions for patients at risk.

Background: In the following manuscript, we describe the detailed protocol for a mixed-methods, observational case study conducted to identify and evaluate existing data-related processes and challenges currently faced by trauma centers in a rural state. The data will be utilized to assess the impact of these challenges on registry data collection.

Methods: The study relies on a series of interviews and observations to collect data from trauma registry staff at level 1-4 trauma centers across the state of Arkansas. A think-aloud protocol will be used to facilitate observations to gather keystroke-level modeling data and insight into site processes and workflows for collecting and submitting data to the Arkansas Trauma Registry. Informal, semi-structured interviews will follow the observation period to assess the participant's perspective on current processes, potential barriers to data collection or submission to the registry, and recommendations for improvement. Each session will be recorded, and de-identified transcripts and session notes will be used for analysis. Keystroke level modeling data derived from observations will be extracted and analyzed quantitatively to determine time spent performing end-to-end registry-related activities. Qualitative data from interviews will be reviewed and coded by 2 independent reviewers following a thematic analysis methodology. Each set of codes will then be adjudicated by the reviewers using a consensus-driven approach to extrapolate the final set of themes.

Discussion: We will utilize a mixed methods approach to understand existing processes and barriers to data collection for the Arkansas Trauma Registry. Anticipated results will provide a baseline measure of the data collection and submission processes at various trauma centers across the state. We aim to assess strengths and limitations of existing processes and identify existing barriers to interoperability. These results will provide first-hand knowledge on existing practices for the trauma registry use case and will provide quantifiable data that can be utilized in future research to measure outcomes of future process improvement efforts. The potential implications of this study can form the basis for identifying potential solutions for streamlining data collection, exchange, and utilization of trauma registry data for clinical practice, public health, and clinical and translational research.