Hematologic malignancies: an opportunity to fill a gap in cancer surveillance View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-08

AUTHORS

Lynne Penberthy, Donna McClish, Steven Peace, Laurel Gray, Jim Martin, Sandra Overton, Soundarya Radhakrishnan, Chris Gillam, Gordon Ginder

ABSTRACT

BACKGROUND: Reporting of hematologic malignancies is an increasingly important focus for cancer surveillance. As trends in cancer care are shifting to the outpatient setting, hospital-based data collection methods used for cancer surveillance will result in under-reporting of these cancers. This study describes the testing and validation of an automated system for capturing and reporting cancers from community oncology providers. METHODS: The system was evaluated in 5 oncology practices in two states processing claims data for a 4- or 8-month interval. Resulting cancers were matched with the state registries. A random sample of nonmatched cases was reabstracted to measure the accuracy of the claims data for reporting of hematologic malignancies. RESULTS: The overall match rate for the 1,935 hematologic malignancies reported during the study period was 58.2 % (range, 37.4 % for CLL to 71.2 % for Hodgkin's Lymphoma). The overall accuracy rate for billing-reported hematologic malignancies was 95 %. Accuracy among cases that did not match with the cancer registry was 88 %. The estimated number of missed cases for the five participating practices ranged from 0.8 leukemia cases/oncologist/year to 3.4 CLL cases/oncologist/year. The estimated total number of missed cases in the five participating practices was 292 with an interquartile range of 263-323. CONCLUSION: As cancer diagnosis and treatment continue migration into ambulatory physician practice settings unreported hematopoietic cases will become increasingly problematic. Leveraging the standardized electronic billing data for automated reporting of cancer cases from physician practices may be an efficient method to reduce this gap in cancer surveillance reporting. More... »

PAGES

1253-1264

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10552-012-0003-1

DOI

http://dx.doi.org/10.1007/s10552-012-0003-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039454187

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22674222


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10552-012-0003-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10552-012-0003-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10552-012-0003-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10552-012-0003-1'


 

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