Comprehensive stem cell transplant (SCT) outcome data submission.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 293-293
Author(s):  
Roy B. Jones ◽  
Charles Martinez ◽  
J. Douglas Rizzo ◽  
Dianne Reeves
Keyword(s):  
Data Entry ◽  
Marrow Transplant ◽  
Outcome Data ◽  
Extended Model ◽  
Cell Transplant ◽  
Electronic Data ◽  
Common Data Elements ◽  
Cancer Data ◽  
Standard Data ◽  
Data Elements

293 Background: All U.S. transplant centers must report comprehensive SCT outcome data to a federal registry. The current electronic data capture method requires manual data entry of 700+ unique data elements into an internet application, FormsNet (Center for International Blood and Marrow Transplant Research). Data is routinely copied from program databases or the EMR by manual transcription, an inefficient, inaccurate and expensive process. A method was needed to allow electronic transmission of outcome data directly from these databases to the mandated SCT Outcomes Database (SCTOD). Methods: We designed an interface engine (IE) to transmit structured data through a caGRID subnet (AGNIS) directly to the SCTOD from a proprietary MDACC SCT database using a secure, auditable method. To make this method applicable to other centers, we collaborated with the NCI and others to expand the Biomedical Research Informatics Domain Group (BRIDG) standard data model to support a full set of granular data elements (>1,900) required to describe SCT outcomes. The IE was modified to transmit SCT data from the expanded BRIDG database to the SCTOD. The IE and expanded BRIDG database model will be made available to all centers without charge. In this way centers interfacing data to this new structure can transmit data to the SCTOD without transcription. Results: The BRIDG oversight committee has approved the extended model and made its structure and content publically available. The IE has been used to transmit >4,000 data forms from MDACC to the SCTOD. The full set of SCT common data elements (CDE) has been published in the Cancer Data Standards Repository of the NCI. The American Society of Blood and Marrow Transplantation is publishing an RFA to identify 1-3 vendors qualified to interface data from center-specific systems to the BRIDG database. Conclusions: Comprehensive and direct electronic data transmission to the SCTOD is feasible and can be done without modifying individual center’s legacy applications. The plan will make appropriate tools available to all transplant centers. This paradigm should be applicable to other areas of oncology.

A Generic Data Harmonization Process for Cross-linked Research and Network Interaction

2015 ◽  
Vol 54 (05) ◽  
pp. 455-460 ◽  
Author(s):  
M. Ganzinger ◽  
T. Muley ◽  
M. Thomas ◽  
P. Knaup ◽  
D. Firnkorn
Keyword(s):  
Lung Cancer ◽  
Research Networks ◽  
Data Set ◽  
Common Data Elements ◽  
Cancer Data ◽  
Data Harmonization ◽  
Wide Range ◽  
Harmonization Process ◽  
Cancer Phenotype ◽  
Data Elements

Summary Objective: Joint data analysis is a key requirement in medical research networks. Data are available in heterogeneous formats at each network partner and their harmonization is often rather complex. The objective of our paper is to provide a generic approach for the harmonization process in research networks. We applied the process when harmonizing data from three sites for the Lung Cancer Phenotype Database within the German Center for Lung Research. Methods: We developed a spreadsheet-based solution as tool to support the harmonization process for lung cancer data and a data integration procedure based on Talend Open Studio. Results: The harmonization process consists of eight steps describing a systematic approach for defining and reviewing source data elements and standardizing common data elements. The steps for defining common data elements and harmonizing them with local data definitions are repeated until consensus is reached. Application of this process for building the phenotype database led to a common basic data set on lung cancer with 285 structured parameters. The Lung Cancer Phenotype Database was realized as an i2b2 research data warehouse. Conclusion: Data harmonization is a challenging task requiring informatics skills as well as domain knowledge. Our approach facilitates data harmonization by providing guidance through a uniform process that can be applied in a wide range of projects.

scholarly journals TBI surveillance using the common data elements for traumatic brain injury: a population study

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Latha Ganti Stead ◽  
◽  
Aakash N Bodhit ◽  
Pratik Shashikant Patel ◽  
Yasamin Daneshvar ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Population Study ◽  
Common Data Elements ◽  
The Common ◽  
Data Elements

How Did a Multi-Institutional Trial Show Feasibility of Electronic Data Capture in Older Patients With Cancer? Results From a Multi-Institutional Qualitative Study (Alliance A171902)

2021 ◽  
pp. 442-449
Author(s):  
Nichole A. Martin ◽  
Elizabeth S. Harlos ◽  
Kathryn D. Cook ◽  
Jennifer M. O'Connor ◽  
Andrew Dodge ◽  
...  
Keyword(s):  
Older Patients ◽  
Electronic Devices ◽  
Data Entry ◽  
Data Capture ◽  
Data Availability ◽  
Electronic Data Capture ◽  
Wireless Internet ◽  
Electronic Data ◽  
Patients With Cancer ◽  
Patient Reported

PURPOSE New technology might pose problems for older patients with cancer. This study sought to understand how a trial in older patients with cancer (Alliance A171603) was successful in capturing electronic patient-reported data. METHODS Study personnel were invited via e-mail to participate in semistructured phone interviews, which were audio-recorded and qualitatively analyzed. RESULTS Twenty-four study personnel from the 10 sites were interviewed; three themes emerged. The first was that successful patient-reported electronic data capture shifted work toward patients and toward study personnel at the beginning of the study. One interviewee explained, “I mean it kind of lost all advantages…by being extremely laborious.” Study personnel described how they ensured electronic devices were charged, wireless internet access was up and running, and login codes were available. The second theme was related to the first and dealt with data filtering. Study personnel described high involvement in data gathering; for example, one interviewee described, “I answered on the iPad, whatever they said. They didn't even want to use it at all.” A third theme dealt with advantages of electronic data entry, such as prompt data availability at study completion. Surprisingly, some remarks described how electronic devices brought people together, “Some of the patients, you know, it just gave them a chance to kinda talk about, you know, what was going on.” CONCLUSION High rates of capture of patient-reported electronic data were viewed favorably but occurred in exchange for increased effort from patients and study personnel and in exchange for data that were not always patient-reported in the strictest sense.

Abstract TP374: Data Linkage is Effective for Improving the Available Data for Stroke: An Example from the Australian Stroke Clinical Registry.

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Monique F Kilkenny ◽  
Helen M Dewey ◽  
Natasha A Lannin ◽  
Vijaya Sundararajan ◽  
Joyce Lim ◽  
...  
Keyword(s):  
Hospital Discharge ◽  
Hospital Discharge Data ◽  
Hospital Data ◽  
High Quality ◽  
Clinical Registry ◽  
Large Hospital ◽  
Common Data Elements ◽  
Discharge Data ◽  
Data Elements ◽  
Data Collections

Introduction: Multiple data collections can be a burden for clinicians. In 2009, the Australian Stroke Clinical Registry (AuSCR) was established by non-government and research organizations to provide quality of care data unavailable for acute stroke admissions. We show here the reliability of linking complimentary registry data with routinely collected hospital discharge data submitted to governmental bodies. Hypothesis: A high quality linkage with a > 90% rate is possible, but requires multiple personal identifiers common to each dataset. Methods: AuSCR identifying variables included date of birth (DoB), Medicare number, first name, surname, postcode, gender, hospital record number, hospital name and admission date. The Victorian Department of Health emergency department (ED) and hospital discharge linked dataset has most of these, with first name truncated to the first 3 digits, but no surname. Common data elements of AuSCR patients registered at a large hospital in Melbourne, Victoria (Australia) between 15 June 2009 and 31 December 2010 were submitted to undergo stepwise deterministic linkage. Results: The Victorian AuSCR sample had 818 records from 788 individuals. Three steps with 1) Medicare number, postcode, gender and DoB (80% matched); 2) hospital number/admit date; and 3) ED number/visit date were required to link AuSCR data with the ED and hospital discharge data. These led to an overall high quality linkage of >99% (782/788) of AuSCR patients, including 731/788 for ED records and 736/788 for hospital records. Conclusion: Multiple personal identifiers from registries are required to achieve reliable linkage to routinely collected hospital data. Benefits of these linked data include the ability to investigate a broader range of research questions than with a single dataset. Characters with spaces= 1941 (limit is 1950)

scholarly journals Preclinical common data elements (CDEs) for epilepsy: A joint ILAE/AES and NINDS translational initiative

2018 ◽  
Vol 3 ◽  
pp. 9-12 ◽  
Author(s):  
Helen E. Scharfman ◽  
Aristea S. Galanopoulou ◽  
Jacqueline A. French ◽  
Asla Pitkänen ◽  
Vicky Whittemore ◽  
...  
Keyword(s):  
Common Data Elements ◽  
Data Elements
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