Creating Accountability in Image Quality Analysis. Part 4: Quality Analytics

Abstract

Analytics has been defined as the study of historical data to research potential trends, to analyze the effects of certain decisions or events, or to evaluate the performance of a given tool or scenario, with the goal of improving outcomes through greater knowledge (http://www.businessdictionary.com/definition/analytics/html). While analytics have permeated all aspects of business to date, healthcare analytics represent an area of extremely high and untapped potential. The McKinsley Global Institute (MGI) estimates that big data analysis (i.e., analysis of large datasets) could save the U.S. healthcare system 300 billion dollars annually, with two thirds of that saving in the form of decreasing expenditures by 8 % (http://www.mckinsley.com/insights/mgi/research/technology_and_innovation/big_data). The data revolution is disrupting established healthcare industries and business models. Information technology (IT) companies such as Google, Microsoft, and IBM have all entered the arena of healthcare data analytics, with the goal of providing increased accessibility and understanding of data to patients and providers. Data is actually becoming a product and service deliverable in itself and is critical in new and burgeoning healthcare applications such as personalized medicine and disease state profiling. The healthcare companies, products, and services which can most creatively innovate through data analysis will triumph in the marketplace and effectively create brand differentiation through data equity. One potential drawback however is that the derived data analytics may not take into account the unique variations and challenges which occur in everyday clinical practice. To date, the commercialization of healthcare data and the derived analytics have primarily focused on historical data, and serve to improve decision-making through large-scale statistical analysis. In addition to this conventional approach, an additional data strategy would be prospective analytics, which utilize real-time data in combination with historical data to optimize performance. An example in medical imaging practice might consist of a CT scan where real-time quality analytics from the “scout image” is combined with historical data from the patient’s historical imaging database to create an optimal scan protocol, which attempts to simultaneously optimize image quality, radiation dose reduction, and clinical diagnosis based upon the specific attributes of the patient, clinical context, and technology being used. This “combined” approach to data analytics would utilize both prospective and retrospective data analysis at the point of care, taking into account “real world” (i.e., contemporaneous) circumstances. The analyses of these data could also serve as a potential source of collaborative innovation between service providers, technology producers, and IT companies. The ultimate goal is to improve healthcare outcomes by providing service providers with “best practice” data at the point if care, where clinical outcomes can be most critically affected.