Abstract
Dogs provide an ideal model for study as they have the most phenotypic diversity and known naturally occurring diseases of all non-human land mammals. Thus, data related to dog health present many opportunities to discover insights into health and disease outcomes. Here, we describe several sources of veterinary medical big data that can be used in research. These sources include medical records from primary medical care centers or referral hospitals, medical claims data from animal insurance companies, and datasets constructed specifically for research purposes. No data source provides information that is without limitations, but large-scale, prospective, longitudinally collected data from dog populations are ideal for further research as they offer many advantages over other data sources.
Highlights
The era of big data has opened up many new opportunities in preventive care, chronic disease management, and treatment optimization
It has allowed for a new model of medicine to be employed, that of personalized or precision medicine. In this model of human healthcare, decisions about medical interventions and other treatments are tailored to an individual patient based upon their predicted risk of developing disease or their predicted response to therapy [1]
This has been enabled primarily through the adoption of electronic health record systems by healthcare providers, which allow for the construction of detailed longitudinal data about large populations of patients over long periods of time. These data frameworks can be interrogated to discover the combinations of risk factors that lead to disease outcomes and deliver personalized disease risk profiles for individual patients [2]
Summary
The era of big data has opened up many new opportunities in preventive care, chronic disease management, and treatment optimization. In this model of human healthcare, decisions about medical interventions and other treatments are tailored to an individual patient based upon their predicted risk of developing disease or their predicted response to therapy [1] This has been enabled primarily through the adoption of electronic health record systems by healthcare providers, which allow for the construction of detailed longitudinal data about large populations of patients over long periods of time. There are a number of domain-specific challenges encountered in human healthcare disciplines that are unique to these data frameworks One example of this is the use of unstructured data, such as patient notes and interpretations of diagnostic tests, which contain rich information that can provide valuable insights at both the individual patient and population levels, but the heterogeneity, variability, and diversity of these data make them difficult to access if analyzed in a controlled manner [3]. We will describe the veterinary medical data sources available for research as well as the opportunities and challenges related to each
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