Government and market-driven pressure for widespread adoption of health IT, including IoT solutions and mobile, cloud-based digital platforms, has grown exponentially over the last few years. Investments in health IT systems, including electronic medical records (EMRs) and health IT shared services continue to be major capital expenditures for healthcare organizations. Capturing accurate, high-quality clinical data, and ensuring access to its use, promise to deliver higher efficiencies and cost reductions.
Healthcare organizations increasingly rely on connecting clinical, social, behavioral and financial data to catalyze a shift from volume-based to value-based healthcare models. The emergence of the concept of the ‘learning healthcare system’, supported by predictive models of optimum clinical care and decision-making, necessitate a shift from retrospective to real-time data, and the development of robust digital platforms linked to sensor systems.
The health IT landscape is comprised of
• Technology enablers (biosensors and wearables, decision-support tools, interoperable standards) that reduce costs and produce meaningful self-management of health solutions
• Innovative models (business models, personalized and predictive behavior change models) that enable cost-effective, accessible delivery of self-management of health solutions to citizens and patients
• Value networks (ecosystems, smart health observatories, eHealth academies) that sustain and reinforce disruptive business models.
The redesign of healthcare systems involves the development of integrated care models more closely oriented to the needs of patients and older persons: multidisciplinary; well-coordinated; anchored in community and home care settings; shifting from a reactive to a proactive and patient-centered approach. While issues of interoperability, data privacy and security, and data integration across devices and platforms remain a challenge, the adoption of electronic medical records (EMRs) is near universal in the US. In this climate, several health IT priorities have emerged: data sharing; care coordination; patient engagement; and predictive analytics.
Although not exhaustive, the following topics are relevant to the Health IT track:
· Population health analytics and population health management of chronic epidemic diseases
· Big data and medical informatics
· mHealth models and technologies
· Wearables, embeddables and the ‘closed loop’ healthcare system
· IT solutions supporting patient empowerment, engagement and shared clinical decision-making
· Virtual reality applications to treat psychological trauma and speed recovery from surgery
· Predictive analytics (and decision support) to address quality issues in healthcare
· Artificial Intelligence in both the prevention and treatment of clinical conditions
· Contrasting models of Precision Medicine
· Leveraging IoT solutions to document, track, manage and predict clinical events, and resource use
· Telehealth models and practices and the efficacy of care delivery
· Computational biology and bioinformatics
· Clinical data access, security and privacy
· Applications of genomics to healthcare
· eHealth communication systems
· Technologies designed to promote medication adherence
· Regulation and government policy on health IT: FDA regulation of mobile health; software as a medical device; eHealth policies in Europe; National Digital Health Strategy in Australia
· Health IT/eHealth national case studies