The Use of a Mobile Application to Increase Access to Interpreters for Cancer Patients With Limited English Proficiency: A Pilot Study.

BACKGROUND: Language barriers can influence the quality of health care and health outcomes of limited English proficient patients with cancer. The use of medical interpretation services can be a valuable asset for improving communications in emergency care settings. OBJECTIVE: To evaluate whether a mobile translation application increased call frequency to interpreter services among providers in an Urgent Care Center at a comprehensive cancer center and to assess provider satisfaction of the mobile application. RESEARCH DESIGN: Prospective pre-post nonrandomized intervention of a mobile translation application with access to an over the phone interpreter (OPI) service at the push of a button and poststudy satisfaction survey. SUBJECTS: Sixty-five clinicians working at the Urgent Care Center in a cancer center in New York City. MEASURES: Mean call frequency to OPI services, tested by the nonparametric Wilcoxon Mann Whitney test, and self-reported provider satisfaction descriptives. RESULTS: The mobile application contributed to increasing the frequency of phone calls to OPI services during the intervention period (mean=12.8; P=0.001) as compared with the preintervention period (mean=4.3), and showed continued use during the postintervention period (mean=5.7). Most clinicians were satisfied with the use of the mobile application and access to the OPI services. CONCLUSIONS: The results suggest that mobile application tools contribute to increasing the use and ease of access to language services. This has the potential to improve the quality of communication between medical providers and limited English proficient patients in the delivery of cancer care in urgent care settings.

Using search queries for malaria surveillance, Thailand.

BACKGROUND: Internet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region. METHODS: Official Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models' accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC). RESULTS: Each model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand. CONCLUSIONS: Models built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005-2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health.