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Machine Learning in Healthcare: Examples, Tips & Resources for Implementing into Your Care Practice

Health informatics professionals stand at the entryway of opportunity, playing a key role in enabling machine learning’s integration into healthcare and medical processes.

Their in-depth knowledge of technology and how it can be applied to improve patient care and outcomes offers enormous value to an evolving healthcare industry increasingly reliant on data.

The deep-learning algorithms of machine learning can trim the time it takes to review patient and medical data, leading to faster diagnosis and speedier patient recovery.

As healthcare organizations seek to integrate machine learning into healthcare and medical processes, a primary responsibility of health informatics professionals—to ensure that healthcare data is reliable—becomes a high priority.

From counting steps to monitoring heart rhythms, various types of consumer wearable technologies provide information that can help people become more fit.

Other wearable technologies can provide doctors with vital information about patient health, including heart rhythm, blood pressure, temperature and heart rate.

As more people embrace wearable technologies, health informatics professionals can help improve the communication and accuracy of data shared between these devices and health information systems that doctors use.

As genome sequencing becomes more affordable and machine learning becomes smarter, health informatics professionals can help advance genomic medicine to treat the world’s deadliest diseases.

According to the National Nanotechnology Initiative, nanotechnology is defined as “the understanding and control of matter at the nanoscale, at dimensions between approximately 1 and 100 nanometers.” Nanotechnology application in healthcare is referred to as nanomedicine.

For example, future nanotechnology medicine includes drug delivery methods that “enable site-specific targeting to avoid the accumulation of drug compounds in healthy cells or tissues,” according to Engineering.com.

AI Leadership for Healthcare

Kenyon Crowley, PhD, MBA, CPHIMS Kenyon Crowley serves as managing director of the Center for Health Information and Decision Systems (CHIDS), where he manages a broad portfolio of innovation activities aimed at improving the design, integration and effective use of digital systems and analytics in healthcare.  He has served as a scientific reviewer for the National Institutes of Health and the National Science Foundation and has published peer-reviewed articles and book chapters on health data analytics, mobile health applications for chronic disease, and health IT implementation.

Michelle Dugas, PhD Michelle Dugas is a senior research scientist at CHIDS where she applies her background as a social psychologist to better understand how technology and analytics can be leveraged to help people achieve their health goals, address bias and disparities in care, and reduce burdens on providers and patients.

Artificial intelligence in healthcare: transforming the practice of medicine

for healthcare: improve population health, improve the patient's experience of care, enhance caregiver experience and reduce the rising cost of care.1–3 Ageing populations, growing burden of chronic diseases and rising costs of healthcare globally are challenging governments, payers, regulators and providers to innovate and transform models of healthcare delivery.

Indeed, we observe that many technology providers are increasingly seeking to partner with healthcare organisations to drive AI-driven medical innovation enabled by cloud computing and technology-related transformation (Box ​(Box22).6–8 Here, we summarise recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective AI systems and discuss the possible future direction of AI augmented healthcare systems.