CANCELLED - Using Real-Time Cloud-Based EHR Data to Monitor Influenza Treatment Patterns

BACKGROUND: Cloud-based health information technologies are an efficient, accurate and rich data source for population health monitoring and research.  More specifically, the design of modern electronic health records (EHR) and billing systems provide public health officials and researchers a timely view of local and national healthcare trends.

METHODS: Automated extracts from athenahealth’s EHR database (athenanet) were developed to produce provider-level data on influenza related treatment patterns. athenanet is the centralized database for athenahealth’s EHR and billing application. Because this application is a common web-based application that all clients use, detailed documentation of medical visits is instantly and centrally recorded.  From this system, we developed weekly and daily data that summarizes, for each provider, measures of treatment and diagnoses activity related to influenza, including: number of patient visits, visits with influenza-like illness (ILI) diagnoses (using ICD-9 codes), visits with influenza vaccinations (using CPT codes), influenza lab test orders and antiviral prescriptions. These measures are based on data from a network of 13,000 primary care providers in ambulatory settings, who account for approximately 700,000 visits per week across the United States.  

RESULTS: We produced reports of ILI rates that are similar in pattern to the CDC’s FluView (e.g. identical outbreak peaks, and parallel ILI rates), but also included additional information such as treatment patterns, disaggregated by patient and provider characteristics (e.g. patient age, geography, provider specialty).   Our data shows that orders for influenza lab tests and antiviral prescriptions were particularly high for the 2014-2015 season, compared to the previous two seasons. However, as a result of an already high antiviral prescription rate, there is a muted response to a December 4, 2014 CDC advisory that suggests the use of antivirals for influenza. 

CONCLUSIONS: Cloud-based EHRs provide timely data for understanding healthcare trends. The accessibility of the data enables real time disease surveillance at a detailed level (e.g. ILI rates within a county, for a particular age group, in the past week), as well as measurement of response to public health advisories (i.e. measurement of “campaign effectiveness”). The large volume and breadth of data allows researchers to understand treatment patterns across time, geography and cohorts (e.g. influenza treatment for patients with risk factors for complications, by specific geographies). Overall, this source of data can be a valuable planning and research asset for public health officials.