Journal of registry management最新文献

{"title":"Letter from the Editor.","authors":"Nadine R Walker","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"52 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12244489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Workload and Staffing Study of Hospital Cancer Registries.","authors":"Susan A Chapman, Laurie Hailer, Jacqueline Miller","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The purpose of the workload and staffing study of hospital cancer registries conducted in 2024 was to update previous studies of workload and staffing guidelines. The population studied was composed of hospital cancer registries. Potential respondents for this study were hospital registry leads identified in the National Cancer Registrars Association (NCRA) membership database and their cancer registry staff. An online survey was sent to lead registrars along with directions for forwarding a separate cancer registrar survey to their staff. Postsurvey interviews were conducted with 11 experts in the field to review findings and discuss the future of the cancer registry workforce. There were 237 responses to the registry lead survey (RLS) and 290 responses to the cancer registrar survey (CRS). Results indicated a 22% increase in the mean budgeted full-time equivalents (FTEs) from 2019 in 2022; yet filled FTEs decreased slightly from 2019 to 2022. Registry leads reported that nearly half of their staff need additional training in data analysis and were very concerned about recruiting qualified staff, providing adequate compensation, and funding additional positions. Caseload continued to be the main predictor of staffing needs, as in the previous study. In staffing models, we found that for single-institution registries, for every 1,000 cases, staffing should increase by 1.8 to 2.1 FTEs. For multi-institution registries, staffing should increase by 1.6 to 1.9 FTEs for every 1,000 cases. Postsurvey interviewees were concerned about industry-wide vacancies and worker burnout. They mentioned contributing factors such as low wages, lack of recognition, isolated work, the lack of credentialed registrars, and pending retirements. They stressed that technological innovations and automation will not eliminate the registrar's role but will change it. These changes in registrar work may create more specialization in the role such as in data analytics and acting on real-time data and reporting. These changes may also create new career paths that would attract future students and workers to the cancer registry profession. Findings from this study may be useful for hospital registries to benchmark their own workload and staffing and to cancer registry organization leaders in planning for the future.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"52 1","pages":"16-21"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12244491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Related Processes and Challenges at Level I-IV Trauma Centers in Arkansas: Results of a Mixed-Methods Case Study.","authors":"Cara L Conner, Mersady C Redding, Emel Seker, Melody L Greer, Tremaine B Williams, Maryam Y Garza","doi":"","DOIUrl":"","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Clinical data registries provide a rich source of real-world data that can be leveraged by clinicians, researchers, and public health professionals to address some of the current health challenges faced in society today. The relative usefulness of a registry depends on the ability to gather data and the overall quality of the data. To assess existing datarelated processes, including barriers and facilitators to data collection and submission, we conducted an observational case study to evaluate sites submitting data to a prominent state-based trauma registry.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A mixed-methods approach was undertaken to evaluate existing processes and barriers to data collection for the Arkansas Trauma Registry (ATR). A series of interviews and observations were conducted with trauma registry personnel from level I-IV trauma centers across the state of Arkansas to collect data on current data-related processes. To facilitate observations, a think-aloud protocol was used to gather keystroke-level modeling (KLM) data. Additional observational data (qualitative) were collected regarding site processes and workflows pertaining to the collection and submission of registry data to the ATR. Following the observations, informal, semi-structured interviews were conducted to assess the participants' perspectives on current data-related processes, potential barriers to data collection or submission, and any recommendations for improvement. All sessions were recorded, and de-identified transcripts and session notes were used for analysis. Quantitative analyses were performed on the KLM data derived from observations to determine time spent performing end-to-end registry-related activities. Qualitative data from interviews were reviewed and coded by 2 independent reviewers. The qualitative codings were adjudicated by the reviewers using a consensus-driven approach. Themes were then extrapolated to generate the final set of results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Seven unique staff members (trauma registrars, coordinators, supervisors, and directors) participated in the study, having completed both observation and interview sessions. These participants were from 5 unique trauma centers (one level I, one level II, two level III, and one level IV). Through the observations, we were able to characterize the typical operational flow for level I-IV trauma centers participating in the ATR, confirming a primarily manual process was used across all sites. Furthermore, the KLM analysis demonstrated that, on average, site staff would need close to 26 total hours to identify, abstract, and transcribe a single, relatively complex trauma registry case (meaning all 288 registry data elements were captured). Results from interviews further emphasized the exhaustive nature of the current data collection processes across sites, regardless of trauma level classification. Five common themes were identified across all 7 interviews: da","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"52 1","pages":"6-15"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12248252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Journal of Registry Management</i> Continuing Education Quiz-SPRING 2025: WORKLOAD AND STAFFING STUDY OF HOSPITAL CANCER REGISTRIES.","authors":"Vida Cari","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This quiz is derived from the article, \"Workload and Staffing Study of Hospital Cancer Registries\" by Susan A. Chapman, PhD, MPH, RN, and coauthors. <b>After reading the article and completing the quiz, participants will be able to:</b> Recognize the need for updated workload and staffing data guidelinesDifferentiate the 2 surveys provided to the registry manager/lead and cancer registrarsDescribe the implications for the registry workforce based on the qualitative assessment of the study.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"52 1","pages":"23"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12244488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Ambiguity of \"Ambiguous Terminology\".","authors":"Frederick L Greene, Shirley F Dalrymple","doi":"","DOIUrl":"","url":null,"abstract":"<p><p><i>Ambiguous</i> having more than one possible interpretation or meaningdifficult to understand or classify; obscure <i>Collins English Dictionary</i>, HarperCollins Publishers.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"52 1","pages":"4-5"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12244490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Summary of Veterans Health Administration Cancer Data Sources.","authors":"Leah L Zullig, Shelley A Jazowski, Neetu Chawla, Christina D Williams, David Winski, Christopher G Slatore, Alecia Clary, Kelli M Rasmussen, Leann M Ticknor, Michael J Kelley","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"51 1","pages":"21-28"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancer Registry Enrichment via Linkage with Hospital-Based Electronic Medical Records: A Pilot Investigation.","authors":"Monique N Hernandez, Lydia Voti, Jason D Feldman, Stacey L Tannenbaum, Wendy Scharber, Jill A MacKinnon, David J Lee, Youjie X Huang","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"51 1","pages":"41-48"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Journal of Registry Management</i> Continuing Education Quiz-FALL 2024: EVALUATION OF MELANOMA AND BLADDER CANCERS IN NEW HAMPSHIRE TO IDENTIFY REGISTRAR TRAINING NEEDS.","authors":"Susanna Mitchell, Cari Vida","doi":"","DOIUrl":"","url":null,"abstract":"<p><p><b>After reading the article and completing the quiz, participants will be able to:</b> Understand data quality evaluation field accuracy ratesDescribe the method of data quality evaluationUnderstand the implications for educational opportunities based on this data quality evaluation.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"51 3","pages":"128"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11781283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a 5-Step Electronic Medical Record-Based Algorithm to Identify Patients with Opioid Use Disorder in Pregnancy.","authors":"Kimberly Fryer, Chinyere N Reid, Chaitanya Chaphalkar, Jennifer Marshall, Laura Szalacha, Kimberly Johnson, Tanner Wright, Caitlin Read, Ayesha Khan, Anna Wilson, Meera Ratani, Kaitlyn Cox, Angela Tavolieri, Melanny Sampayo, Rachel Su, Kelly Campbell, Jason L Salemi","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion and conclusion: </strong>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.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"51 2","pages":"69-74"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142056848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol for the Evaluation of Data-Related Processes and Challenges at Level 1-4 Trauma Centers in Arkansas: A Mixed-Methods Case Study.","authors":"Cara L Conner, Mersady C Redding, Emel Seker, Melody L Greer, Maryam Y Garza","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"51 1","pages":"12-18"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}