Development of CancerLinQ, a Health Information Learning Platform From Multiple Electronic Health Record Systems to Support Improved Quality of Care.
Danielle M PotterRaven BrothersAndrej KolacevskiJacob E KoskimakiAmy McNuttRobert S MillerJatin NagdaAnil NairWendy S RubinsteinAndrew K StewartIris J TriebGeorge A KomatsoulisPublished in: JCO clinical cancer informatics (2021)
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.
Keyphrases
- electronic health record
- health information
- healthcare
- clinical trial
- primary care
- quality improvement
- palliative care
- clinical decision support
- social media
- adverse drug
- phase ii
- high throughput
- genome wide
- current status
- big data
- open label
- copy number
- gene expression
- risk assessment
- machine learning
- human health
- dna methylation
- pain management
- phase iii
- chronic pain
- climate change