Facilitating Cancer Epidemiologic Efforts in Cleveland via Creation of Longitudinal De-Duplicated Patient Data Sets.
Journal
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
ISSN: 1538-7755
Titre abrégé: Cancer Epidemiol Biomarkers Prev
Pays: United States
ID NLM: 9200608
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
11
07
2019
revised:
29
10
2019
accepted:
13
01
2020
pubmed:
29
1
2020
medline:
7
7
2021
entrez:
29
1
2020
Statut:
ppublish
Résumé
Cleveland, Ohio, is home to three major hospital systems serving approximately 80% of the Northeast Ohio population. The Cleveland Clinic, University Hospitals Health System, and MetroHealth are direct competitors for primary and specialty care, and patient overlap between these systems is high. Fragmentation of health data that exist in silos at these health systems produces an overestimation of disease burden due to double and sometimes triple counting of patients. As a result, longitudinal population-based studies across the Cleveland patient population are impeded unless accurate and actionable clinically derived health data sets can be created. The Cleveland Institute for Computational Biology has developed the De-Duplicate and De-Identify Research Engine (DeDeRE) that, without any exchange of personal health identifiers (PHI) between health systems, will effectively de-duplicate the patients between one or more health entities. The immediate utility of this software for cancer epidemiology is the increased accuracy in measuring cancer burden and the potential to perform longitudinal studies with de-duplicated, de-identified data sets. The DeDeRE software developed and tested here accomplishes its goals without exposing PHIs using a state-of-the-art, trusted privacy preservation network enabled by a hash-based matching algorithm. This paper will guide the reader through the functions currently developed in DeDeRE and how a healthcare organization (HCO) employing the release version of this technology can begin sharing data with one or more additional HCOs in a collaborative and noncompetitive manner to create a regional population health resource for cancer researchers.
Sections du résumé
BACKGROUND
Cleveland, Ohio, is home to three major hospital systems serving approximately 80% of the Northeast Ohio population. The Cleveland Clinic, University Hospitals Health System, and MetroHealth are direct competitors for primary and specialty care, and patient overlap between these systems is high. Fragmentation of health data that exist in silos at these health systems produces an overestimation of disease burden due to double and sometimes triple counting of patients. As a result, longitudinal population-based studies across the Cleveland patient population are impeded unless accurate and actionable clinically derived health data sets can be created.
METHODS
The Cleveland Institute for Computational Biology has developed the De-Duplicate and De-Identify Research Engine (DeDeRE) that, without any exchange of personal health identifiers (PHI) between health systems, will effectively de-duplicate the patients between one or more health entities.
RESULTS
The immediate utility of this software for cancer epidemiology is the increased accuracy in measuring cancer burden and the potential to perform longitudinal studies with de-duplicated, de-identified data sets.
CONCLUSIONS
The DeDeRE software developed and tested here accomplishes its goals without exposing PHIs using a state-of-the-art, trusted privacy preservation network enabled by a hash-based matching algorithm.
IMPACT
This paper will guide the reader through the functions currently developed in DeDeRE and how a healthcare organization (HCO) employing the release version of this technology can begin sharing data with one or more additional HCOs in a collaborative and noncompetitive manner to create a regional population health resource for cancer researchers.
Identifiants
pubmed: 31988074
pii: 1055-9965.EPI-19-0815
doi: 10.1158/1055-9965.EPI-19-0815
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
787-795Informations de copyright
©2020 American Association for Cancer Research.