Clinical care has evolved considerably for patients including lab testing, medications, and surgical procedures. Additionally, other technologies have replaced how medicine is practiced and documented. For example, the Electronic Health Record (EHR) has largely replaced paper charts. Healthcare technological upgrades are typically applied on a relatively incremental and fragmented scale. However, that changed in 2020 with the novel SARS-CoV-2 (COVID-19) pandemic. The role of technology in healthcare has recently shown to be critical for patient care. This three-part article series will review the role of artificial intelligence, machine learning, expert systems, and the ethical challenges that come with implementing these new and emerging technologies into healthcare.
Indeed, the COVID-19 pandemic caused worldwide disruption on a massive scale. Experts estimate the healthcare system made 10 years of technological upgrades in a few short months after the pandemic was declared by the World Health Organization (WHO). This is largely due to the response to local and national shutdowns that were implemented to slow the spread of the virus. Many healthcare facilities switched to telemedicine (phone or video call consultations) for non-emergent care to comply with new social distancing mandates. While telehealth appointments continue to be an integral part of the technological adaptations, there are many other technologies that have been implemented to improve patient care, increase access, and mitigate the spread of COVID-19 infections.
Artificial Intelligence (AI) is an umbrella term that describes a computer’s ability to copy human cognition, such as problem solving and learning. There are different types of AI processes. Machine Learning (ML) is a method of using data, computer algorithms, and mathematical modeling so a computer can learn without human input. It can improve independently, based on experience. Cardiac arrhythmia detection algorithms that are used in Automated Electronic Defibrillators (AEDs) is an example of ML technology. An Expert System (ES), which is different than ML, uses a rules-based system to replicate a human subject matter expert’s ability to interpret data and make decisions. It makes a recommendation based on a series of “if/than” statements. ES and ML systems are not necessarily mutually exclusive.
Remote Patient Monitoring (RPM) utilizes a series of AI cascade decision trees which may incorporate both ML and ES technologies for optimal outcomes. RPM uses digital technologies to capture real-time patient health information in one location and transmit it to a healthcare provider or system in a different location for assessment and management. It can be used for acute or chronic health problems and may cut down on in-person medical visits. It is estimated over 39 million people in the United States used one form of RPM in 2021, and that number is expected to grow to over 70 million in 2025. RPM devices are available for asthma, diabetes, high blood pressure, and small intestinal bacterial overgrowth (SIBO), among other health conditions. Benchmarks and thresholds can be preset in RPM systems to recommend an intervention based on static medical guidelines, though the physician still needs to use their clinical expertise for management.
Technology offers a variety of healthcare solutions as patient loads increase and the global pandemic continues. ES, ML, and RPM tools have the capacity to increase efficiencies and patient access if adopted on a large scale. The second article will take a deeper dive regarding specific applications where technology is already helping patients and providers.
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