Visual analytics in healthcare - opportunities and research challenges

As medical organizations modernize their operations, they are increasingly adopting electronic health records (EHRs) and deploying new health information technology systems that create, gather, and manage their information. As a result, the amount of data available to clinicians, administrators, and researchers in the healthcare system continues to grow at an unprecedented rate. However, despite the substantial evidence showing the benefits of EHR adoption, e-prescriptions, and other components of health information exchanges, healthcare providers often report only modest improvements in their ability to make better decisions by using more comprehensive clinical information. The large volume of clinical data now being captured for each patient poses many challenges to (a) clinicians trying to combine data from different disparate systems and make sense of the patient’s condition within the context of the patient’s medical history, (b) administrators trying to make decisions grounded in data, (c) researchers trying to understand differences in population outcomes, and (d) patients trying to make use of their own medical data. In fact, despite the many hopes that access to more information would lead to more informed decisions, access to comprehensive and large-scale clinical data resources has instead made some analytical processes even more difficult. Visual analytics is an emerging discipline that has shown significant promise in addressing many of these information overload challenges. Visual analytics is the science of analytical reasoning facilitated by advanced interactive visual interfaces. In order to facilitate reasoning over, and interpretation of, complex data, visual analytics techniques combine concepts from data mining, machine learning, human computing interaction, and human cognition. As the volume of healthrelated data continues to grow at unprecedented rates and new information systems are deployed to those already overrun with too much data, there is a need for exploring how visual analytics methods can be used to avoid information overload. Information overload is the problem that arises when individuals try to analyze a number of variables that surpass the limits of human cognition. Information overload often leads to users ignoring, overlooking, or misinterpreting crucial information. The information overload problem is widespread in the healthcare domain and can result in incorrect interpretations of data, wrong diagnoses, and missed warning signs of impending changes to patient conditions. The multi-modal and heterogeneous properties of EHR data together with the frequency of redundant, irrelevant, and subjective measures pose significant challenges to users trying to synthesize the information and obtain actionable insights. Yet despite these challenges, the promise of big data in healthcare remains. There is a critical need to support research and pilot projects to study effective ways of using visual analytics to support the analysis of large amounts of medical data. Currently new interactive interfaces are being developed to unlock the value of large-scale clinical databases for a wide variety of different tasks. For instance, visual analytics could help provide clinicians with more effective ways to combine the longitudinal clinical data with the patient-generated health data to better understand patient progression. Patients could be supported in understanding personalized wellness plans and comparing their health measurements against similar patients. Researchers could use visual analytics tools to help perform population-based analysis and obtain insights from large amounts of clinical data. Hospital administrators could use visual analytics to better understand the productivity of an organization, gaps in care, outcomes measurements, and patient satisfaction. Visual analytics systems—by combining advanced interactive visualization methods with statistical inference and correlation models—have the potential to support intuitive analysis for all of these user populations while masking the underlying complexity of the data. This special focus issue of JAMIA is dedicated to new research, applications, case studies, and approaches that use visual analytics to support the analysis of complex clinical data.