About this Research Topic
A crucial question is: How to measure the impacts of Electronic Health Records (EHR)? Answering this question is not easy, as there are many ways to assess their relevance, and plenty of examples can be found in the scientific literature.
EHR represent complex, heterogeneous data, whose nature and impacts are context-dependent. Context here may mean, for instance, the environment, the type of health facility, or also the application domain.
Concerning applications, big data offer opportunities to be used in the context of cost-effectiveness analyses, evaluation of policy impacts, and similar topics. Moreover, interest goes to which technology currently in use may establish standards whose value can be assessed in terms of both interoperability between the generated information sources and the desired integrability between the different outsourced evidences.
At present, it seems more realistic to ask whether we are starting to assist a limitless progression towards the natural effects of the announced transformations, or instead some of the strategies enabled by data-driven health need to be questioned or revised.
Relevant questions to be addressed by this Research Topic are:
Are we working with similar systems, measures, policies, and problems for which it makes sense to use EHR for testing new ideas and models within an interdisciplinary approach?
How to collect, assimilate, analyse, and integrate data to avoid a simplistic “reinvention” of healthcare instead of proposing concrete and harmonized advances?
Finally, institutions, academics, and industry share the awareness that successful healthcare depends on having access to the right data at the right time, through effective use of technology for enhanced patient care. However, even after having minimized the constraints determined by digital capabilities and associated costs, and being inspired by better policies, the use of health information to its full potential still needs to be fully exploited.
Contributing papers should address some of the medical and health challenges related to big data, in particular public health informatics for policy development and use of big-data analytics in health systems for improved clinical decision making, enhanced efficiency of care provisions, and policy implementation. Methodological contributions related to predictive modeling and concerning longitudinal studies are welcome.
Keywords: Healthcare Policies, Electronic Health Records, Big Data Analytics, Clinical Decision Support Systems, Modeling and Analysis of Longitudinal Data, Comorbidities, Risk Factors Assessment
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.