scholarly journals Drug Hypersensitivity Reactions Documented in Electronic Health Records within a Large Health System

2019 ◽  
Vol 7 (4) ◽  
pp. 1253-1260.e3 ◽  
Author(s):  
Adrian Wong ◽  
Diane L. Seger ◽  
Kenneth H. Lai ◽  
Foster R. Goss ◽  
Kimberly G. Blumenthal ◽  
...  
PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244004
Author(s):  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez Inhiesto ◽  
Teresa Acaiturri Ayesta ◽  
Jose A. Lozano

The aim of this paper is to analyze the sequence of actions in the health system associated with a particular disease. In order to do that, using Electronic Health Records, we define a general methodology that allows us to: (i) identify the actions in the health system associated with a disease; (ii) identify those patients with a complete treatment for the disease; (iii) and discover common treatment pathways followed by the patients with a specific diagnosis. The methodology takes into account the characteristics of the EHRs, such as record heterogeneity and missing information. As an example, we use the proposed methodology to analyze breast cancer disease. For this diagnosis, 5 groups of treatments, which fit in with medical practice guidelines and expert knowledge, were obtained.


2013 ◽  
Vol 20 (2) ◽  
pp. 238-244 ◽  
Author(s):  
Juan Eugenio Hernández-Ávila ◽  
Lina Sofia Palacio-Mejía ◽  
Agustín Lara-Esqueda ◽  
Eva Silvestre ◽  
Marcela Agudelo-Botero ◽  
...  

2020 ◽  
pp. 929-937
Author(s):  
Danielle Potter ◽  
Raven Brothers ◽  
Andrej Kolacevski ◽  
Jacob E. Koskimaki ◽  
Amy McNutt ◽  
...  

PURPOSE ASCO, through its wholly owned subsidiary, CancerLinQ LLC, developed CancerLinQ, a learning health system for oncology. A learning health system is important for oncology patients because less than 5% of patients with cancer enroll in clinical trials, leaving evidence gaps for patient populations not enrolled in trials. In addition, clinical trial populations often differ from the overall cancer population with respect to age, race, performance status, and other clinical parameters. MATERIALS AND METHODS Working with subscribing practices, CancerLinQ accepts data from electronic health records and transforms the local representation of a patient’s care into a standardized representation on the basis of the Quality Data Model from the National Quality Forum. CancerLinQ provides this information back to the subscribing practice through a series of tools that support quality improvement. CancerLinQ also creates de-identified data sets for secondary research use. RESULTS As of March 2020, CancerLinQ includes data from 63 organizations across the United States that use nine different electronic health records. The database includes 1,426,015 patients with a primary cancer diagnosis, of which 238,680 have had additional information abstracted from unstructured content. CONCLUSION As CancerLinQ continues to onboard subscribing practices, the breadth of potential applications for a learning health care system widen. Future practice-facing tools could include real-world data visualization, recommendations for treatment of patients with actionable genetic variations, and identification of patients who may be eligible for clinical trials. Feeding these insights back into oncology practice ensures that we learn how to treat patients with cancer not just on the basis of the selective experience of the 5% that enroll in clinical trials, but from the real-world experience of the entire spectrum of patients with cancer in the United States.


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