How Does AI Elevate a Patients' Quality of Life?
AI will be the driving source of patient life in the near future with its increased capability to monitor them.
FREMONT, CA: Artificial Intelligence (AI) is now reducing the needs for the physical presence of doctors in monitoring their patients. The proliferation of medical devices and consumer wearables built with AI capabilities is being employed to detect early stages of diseases by physicians and other caregivers. This enables better monitoring while identifying life-threatening episodes at earlier and treatable stages. AI revolutionizes the healthcare industry by delivering operations efficiently, rapidly, and at a lower cost. It embraces the patients to stay awake and updated with their health conditions throughout their period of life to remain healthy.
AI is already used in cancer detection, especially breast cancer. While the probability of yielding false results by mammograms is high, where 1 or 2 women undergone mammograms were predicted to have cancer, the use of AI enables the translation and reviewing of mammograms. This, in turn, eradicates error, thereby producing accurate results and reducing the need for unnecessary biopsies.
Well of Opportunities
AI and machine learning combine to transform healthcare functions into data-driven services to improve health outcomes. Ownership of connected devices is growing with almost 46 percent of the US broadband households owning at least one device and 19 percent using at least two. The accessories like smartwatches and trackers are conventional in use in recent years. The benefits of AI are applied in these devices by using machine learning to monitor personal health data and behavioral activity for delivering personal healthcare services. Other extended opportunities created by AI include remote patient management, chronic disease management, and independent living.
Remote Patient Management
Remote patient management (RPM) can improve the quality of life and provide clinicians more directly with relevant patient data that eases the possibility of burnout while lowering cost and increasing efficiency. It can be applied to a broad spectrum of the patient population comprising aging adults, those with chronic diseases, and recently discharged patients after major surgeries.
The mission of RPM is to reduce the readmissions, hospitalizations, and length of stay in hospitals aimed at improving quality of life and reducing cost. Some common examples of remote patient monitoring devices include digital blood pressure cuffs that monitor the blood pressure and remotely send the data to the concerned physicians. RPM is worthwhile when used for patients with chronic care conditions like coronary artery disease, congestive heart failure, diabetes, chronic obstructive pulmonary disease, hypertension, or asthma.
Machine learning technology can generate alerts and notifications through improved communications when it detects danger, which can help clinicians provide better patient care at a lower cost than traditional healthcare delivery. It can also be used to train monitoring systems utilizing large data sets for predictive analytics that possibly estimates the future health outcomes based on patterns in the historical data.
Chronic Disease Management
The high cost for managing chronic diseases insists on healthcare companies investing in AI technologies. AI tools show promising results in detecting conditions like pneumonia, eye diseases, Parkinson’s disease, obesity, heart diseases, and breast and skin cancer. According to US health records, 57 percent of the population is said to have at least one chronic condition, and 28 percent have two or more.
The proliferation of smartphones and portable medical devices provides opportunities for physicians to help manage their patients living with chronic conditions remotely for improved health and safety. Data from these personal medical devices, sensors, and electronic health records provide insights into activities, including diet, stress levels, and exercise, giving a patient’s complete health status.
According to estimation, 20 percent of Americans will be over the age of 25 by 2020, and 84 percent of them choose to live independently in their homes. When the aged population prefers to live independently, they are subjected to various diseases influenced by the age factor. To address their needs, many healthcare companies utilizing AI and machine learning tools to bring new devices in the markets to support healthcare.
The data collected from various sensors jointly help in tracking a person’s state of motion, food, and other activities like gait patterns and factors that could lead to fall risk along with daily activities including personal hygiene and sleep patterns. This continuous tracking ensures personalized care by building a contextual understanding of each person’s normal daily activities, and helps minimize hospital readmission rates.
These types of applications apply machine learning and predictive analytics to data gathered by smart home sensors, connected medical devices, health and fitness wearables, security systems, personal emergency response systems (PERS), and so on. The machine learning application, in turn, converts the data into behavioral activities that is unique for each patient. These behavioral studies can identify anomalies and issue alerts for trends that are not normal or healthy.
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