Background Little is known about the specific information parents of children with cancer search for online. Understanding the content of parents’ searches over time could offer insight into what matters most to parents and identify knowledge gaps that could inform more comprehensive approaches to family education and support. Methods We describe parents’ health‐related Google searches starting six months before cancer diagnosis and extending through the date of study enrollment, which was at least one month after initiating cancer treatment. Searches were obtained retrospectively and grouped into health‐related and non‐health‐related categories. The median time to parent enrollment from date of cancer diagnosis was 264 days. Results Parents searched for health‐related topics more frequently than the general population (13% vs 5%). Health‐related searches increased in the months preceding the child's cancer diagnosis and most commonly pertained to symptoms and logistics, “directions to hospital.” Health‐related search volume peaked about a month after cancer diagnosis when general health‐related searches were present in addition to cancer‐specific searches. Eighteen percent of health‐related searches were cancer specific, and of these cancer‐specific searches, 54% pertained to support, for example “cancer quote for son.” Conclusions Google search content offers insight into what matters to parents of cancer patients. Understanding search content could inform more comprehensive approaches to family education and support initiatives.

Background Widespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies. Methods This study used EHR‐derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype‐defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90‐day period, two chemotherapy exposures, and three hematology‐oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype‐identified patients compared to a control group with at least three hematology‐oncology encounters. Results The computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C‐statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C‐statistic of 0.99. Conclusion The EHR‐based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.