Health Informatics—Ambitions and Purpose

Abstract

The current transformation of the digital health landscape is not only technological, it's also social, cognitive, and political, with the end goal participatory health—a partnership with digital devices collecting data and generating insights with new models of care evolving through partnerships of clinicians, patients, and carers. Most people are likely aware of the enormous challenges facing the world to deliver equitable, affordable healthcare to a world population that is growing, aging, undergoing increasing urbanisation, and suffering more chronic preventable diseases. Consumer expectations of their health practitioners are growing, and the cost of devices and medications are constantly rising. Increasing antibiotic resistance may challenge the very existence of hospital based care. Added to this, the looming impacts of climate change on communicable disease patterns, social disruption, and migration patterns together with the cost of climate mitigation will reduce the financial pool available for health care (1). How then can we use new technologies to keep up, or better yet, get in front of the curve, to reduce cost, improve safety, increase equity of access, reduce the burden on limited hospital facilities, and increase the overall health and well-being of the community at large? Regardless of the social or funding model, reducing avoidable complications through evidence based, personalised, connected care is essential. Increasing patient focus and patient control are necessary elements of the individual responsibility for health and wellness. Managing social determinants of health in our population also has the potential for increasing the return on investment of our health expenditure (2). For those of us working in health, the current convergence of wireless communications, miniaturisation of physiological sensors, Electronic Medical/Health Records (EMRs), mobile computing interfaces (including smartphones), advanced analytics, personalised medicine, and connected data repositories provides a source of optimism that dramatic transformation and improved cost effectiveness can be achieved in the foreseeable future. Examples include prediction return to work after rehabilitation or understanding factors that may lead to complications in health procedures (3, 4). There remain significant hurdles to overcome in our quest. System disruption from consumer wearable devices improves consumer-directed care, but this increasing independence may paradoxically isolate the patient from their care providers. Connection between data systems is dramatically increasing, but data aggregation and analysis is still limited by different vendor EMR data structures, competing terminologies and ontologies, jurisdictional data sharing, and privacy legislative differences. Telehealth has improved access to many forms of clinical support, however rural and remote communities still suffer from physical isolation and access to interventional services. As acute hospitals reduce the need for inpatient care, do we have the trained workforce to care for patients at home? Will telemedicine, remote monitoring, GP and community sectors be able to assume the load? How will the economic and regulatory enablers be established to promote appropriate outcomes? What then will be the role of digital technologies in identifying and supporting patients most at risk? As the EMR becomes more embedded, some jurisdictions have discovered that unparalleled ability to capture data including repetitive clinical documentation for compliance purposes may reduce the capacity of the system overall through staff burnout and clinical resistance. A necessary step in improvement is understanding that our most dramatic increase in capability is derived from our closure of the data cycle. The advent of EMR, connected Acute and family medicine—General Practice data systems, real-time pathology and radiology results, device-generated physiology and mobility data enables rapid cycle individual outcomes assessment and population level reporting, as well as algorithmic notification of early disease markers, medication safety, conformance with evidence-based care, and pragmatic assessment of changes in health system delivery. These enhancements can collectively be seen as Learning Health Systems and will be essential in providing safe, effective, and evidence based care, regardless of the economic and political framework. At a research level, we now have numerous examples of machine learning and artificial intelligence impacting on the routine clinical diagnostic process. However, these algorithms must become trusted partners in a sustainable health system. The algorithms must be validated prior to implementation, embedded in the workflow and enhance the operational capacity of the human sponsors. Re-tasking and supporting human resources in the most effective manner may enable more humanistic interactions between clinicians and patients, allow development of newer techniques or simply just enable us to keep up with the ever increasing workload. Regardless of which scenarios play out in the future, the social dimensions of these changes will be far reaching, and are just as worthy of study as the technical achievements themselves. Health Informatics is the practice of acquiring, studying and managing health data, and applying medical concepts in conjunction with information technology systems to help clinicians provide better healthcare. We believe useful insights can be gained by viewing Health Informatics in general, and electronic healthcare records in particular, as a “public good.” A public good is any service or resource that cannot be withheld from an individual due to inalienable characteristics relating to citizens' rights (5). Examples of public good resources include city parks, street lighting or freeways, which are funded by the state but available to all. Economists have extensively studied public...