J L Cruz-Bermúdez1, C Parejo2, F Martínez-Ruíz3, J C Sánchez-González2, A Ramos Martín-Vegue4, A Royuela5, A Rodríguez-González6, E Menasalvas-Ruiz7, M Provencio8. 1. Information Technologies Department, Puerta de Hierro-Majadahonda University Hospital, Universidad Politécnica de Madrid, Madrid, Spain. jlcruz@idiphim.org. 2. Medical Oncology Department, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain. 3. Facultad de Matemáticas, Universidad de Valencia, Valencia, Spain. 4. Admission and Clinical Documentation Department, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain. 5. Biostatistics Department, Biomedical Sciences Research Institute Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain. 6. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain. 7. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain. ernestina.menasalvas@ctb.upm.es. 8. Medical Oncology Department, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain. mariano.provencio@salud.madrid.org.
Abstract
PURPOSE: Our primary goal was to study the use of outpatient attendances by lung cancer patients in Hospital Universitario Puerta de Hierro Majadahonda (HUPHM), Spain, by leveraging our Electronic Patient Record (EPR) and structured clinical registry of lung cancer cases as well as assessing current Data Science methods and tools. METHODS/PATIENTS: We applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) to integrate and analyze activity data extracted from the EPR (9.3 million records) and clinical data of lung cancer patients from a previous registry that was curated into a new, structured database based on REDCap. We have described and quantified factors with an influence in outpatient care use from univariate and multivariate points of view (through Poisson and negative binomial regression). RESULTS: Three cycles of CRISP-DM were performed resulting in a curated database of 522 lung cancer patients with 133 variables which generated 43,197 outpatient visits and tests, 1538 ER visits and 753 inpatient admissions. Stage and ECOG-PS at diagnosis and Charlson Comorbidity Index were major contributors to healthcare use. We also found that the patients' pattern of healthcare use (even before diagnosis), the existence of a history of cancer in first-grade relatives, smoking habits, or even age at diagnosis, could play a relevant role. CONCLUSIONS: Integrating activity data from EPR and clinical structured data from lung cancer patients and applying CRISP-DM has allowed us to describe healthcare use in connection with clinical variables that could be used to plan resources and improve quality of care.
PURPOSE: Our primary goal was to study the use of outpatient attendances by lung cancerpatients in Hospital Universitario Puerta de Hierro Majadahonda (HUPHM), Spain, by leveraging our Electronic Patient Record (EPR) and structured clinical registry of lung cancer cases as well as assessing current Data Science methods and tools. METHODS/PATIENTS: We applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) to integrate and analyze activity data extracted from the EPR (9.3 million records) and clinical data of lung cancerpatients from a previous registry that was curated into a new, structured database based on REDCap. We have described and quantified factors with an influence in outpatient care use from univariate and multivariate points of view (through Poisson and negative binomial regression). RESULTS: Three cycles of CRISP-DM were performed resulting in a curated database of 522 lung cancerpatients with 133 variables which generated 43,197 outpatient visits and tests, 1538 ER visits and 753 inpatient admissions. Stage and ECOG-PS at diagnosis and Charlson Comorbidity Index were major contributors to healthcare use. We also found that the patients' pattern of healthcare use (even before diagnosis), the existence of a history of cancer in first-grade relatives, smoking habits, or even age at diagnosis, could play a relevant role. CONCLUSIONS: Integrating activity data from EPR and clinical structured data from lung cancerpatients and applying CRISP-DM has allowed us to describe healthcare use in connection with clinical variables that could be used to plan resources and improve quality of care.
Entities:
Keywords:
CRISP-DM; Data Science; Data mining; Healthcare use; Lung cancer
Authors: Mariano Provencio; Isabel Millán; Pilar España; Antonio C Sánchez; José J Sánchez; Blanca Cantos; Juan A Vargas; Carmen Bellas; Vanesa García; Pilar Sabin; Félix Bonilla Journal: Clin Cancer Res Date: 2008-08-15 Impact factor: 12.531
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317