Health intelligence: how artificial intelligence transforms population and personalized health

Abstract

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.

Physician attitudes towards—and adoption of—mobile health

“…1,2 An underdeveloped opportunity exists to integrate the unprecedented and accelerating stream of patient data collected from these technologies into medical practice. [3][4][5][6] Devices have been developed to target various aspects of healthcare, such as encouraging healthy lifestyles, assisting with diagnoses, and improving patient care following treatment. [7][8][9][10] When fully realized, mHealth has the potential to reduce costs, disseminate health information, extend care to resource-limited settings, and provide continuous information on individual biometrics to precisely diagnose and intervene in both acute and chronic disease.…”

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

“…The particular social-economic-political context in which social media data are recorded therefore plays an important role in analysis. Given these potential population biases, mining social media for healthcare information relevant to the broader human population requires a careful consideration of the multilevel complexity of human health (55), in which social and behavioral contexts play a critical role (177).…”

Developing an Artificial Intelligence–Enabled Health Care Practice: Rewiring Health Care Professions for Better Care

“…Examples of NLP applications can be seen in surveillance and outbreak predictions using data from electronic health records and online media and social media sources [6]. Data can be plotted on a map using geocoding information, which can help epidemiologists accurately monitor and control the spread of diseases [9]. Another example of this includes the automated speech recognition technology being used for clinical intake to assist physicians in taking notes for input into EMRs.…”