AI in Healthcare

Artificial Intelligence (AI) is making its way into every industry, healthcare included. AI has already been woven into numerous healthcare operations at the effort of improving patient experience and providing reliable data and analytics for both healthcare providers and patients. As AI’s presence in healthcare continues to expand, it is important to have a decent understanding on how AI has and will improve healthcare in the United States and across the globe. Below are various aspects of the presence of AI in healthcare:

Intelligent Medical Devices & Machines

Smart devices are unavoidably a huge aspect of the consumer world, especially with the growing presence of internet of things (IoT) devices. Aside from just IoT, bringing intelligence to healthcare machines and devices ultimately helps to improve health-related outcomes, as well as reduce costs, both monetarily and health outcome related. Some intelligent healthcare machines and devices include various IoT devices, robotics, advanced tools, and imaging devices.

Internet of Things (IoT)

IoT devices are already heavily integrated into everyday life for many individuals. Devices such as smartphones, wearable trackers, and step trackers have made their way into most American homes. These devices provide numerous benefits to the healthcare sector, including:

1. Patient monitoring (heartbeat, steps, sleep, etc.)

2. Organizational asset tracking (staff, patients, and inventory)

Various IoT tools in healthcare have the capability of reliable and efficient asset tracking. One example of an IoT healthcare asset tracking tool is G.E. Healthcare’s AutoBed platform, which has the ability to monitor patient beds, process requests from bed patients, and even track where nurses are in relation to hospital beds. IoTs can help hospital organizations decrease wait room times, monitor equipment availability, and ensure that critical hardware is accessible when needed.

3. Collection and analyzing data capabilities

The large amounts of data collected from IoTs can be combined with patient entered data through various apps to provide a more in-depth perspective into the health of an individual, as well as the health of a population.

4. Telehealth: remote health and monitoring

Implementation of more telehealth opportunities can help minimize costs at healthcare clinics, specifically those in rural areas, as well as limit needs for patient visitation. Rural areas in particular can benefit from telehealth, as many patients in these areas have difficulty making appointments due to transportation issues, distance of the nearest health facility, etc. Telehealth helps to improve the quality of living for many patients with decreased inconvenience and cost that occurs from visiting a facility in person.

5. Enhanced drug management

Aside from wearable IoTs there are numerous other health related IoTs that provide useful functions for healthcare. These include IoTs that help manage drugs. One example of a drug management IoT is AdhereTech, which is a wireless pill bottle that helps patients monitor usage and provides support for prescription refills.

6. Addressing chronic disease

Wearables, like Fitbit for instance, that are used to monitor health information have the capability of being shared with a doctor. This type of information can help a doctor make more informed decisions regarding a patient’s condition and their risk for chronic disease. For example, if a patient is having recurring issues, these wearable devices and the data derived from them can help solve these problems more accurately and efficiently.

Robotics

Robotics are starting to be implemented into hospital settings to take on medical procedure tasks that improve safety and reduce costs for patients. Various robots are currently used to assist in surgery to obtain greater accuracy and reduce invasion. Another source of robotics in hospitals are robotic medical assistants. These robots monitor vitals, alert nurses, and automatically enter patient information into patient electronic health records (EHR).

Advanced Tools & Intelligent Imaging

Adding intelligence to tools has already helped to improve healthcare processes. Specifically, radiology tools have benefited from added intelligence by offering non-invasive ways to view and monitor internal body images. Examples of intelligent radiology tools include MRI machines, x-rays, and CT scanners. Greater enhancement of tools like these radiology tools will continue to improve accuracy and move to replace the need for invasive tissue samples.

Aside from intelligent tools, AI helps to increase precision for analytics of pathology images. Intelligent imaging tools provide a way to examine images on a smaller pixel level to provide insights that can show insights that the human eye cannot see. Pathology images are a key diagnostic tool for pathologists, and greater image capabilities help to improve productivity of identifying features of interest that lead to diagnoses.

Advanced tools such as radiology tools and pathology images allow for more enhanced diagnostic capabilities, while also minimizing risks like potential infection due to lack of invasion.

Electronic Health Record (EHR)

An EHR is essentially a digital medical record system used to collect and maintain patient health information. While EHRs aren’t a new concept, EHR developers are now integrating AI into these health records to automate processes and to create more interactive interfaces. EHRs are a helpful tool to manage patients, and it has turned into a reliable predictor of patient risk with the help of AI. AI processes have the capabilities to extract and analyze patient data in an EHR in a timely, accurate, and efficient manner, which is a challenge for developers and providers to do. Analyzation of EHR data can be used to score patient health risk for various outcomes, stratify patients, and identify connections between datasets that are timely and difficult for developers and providers. For example, EHR data can be used to contain antibiotic resistance risk by identifying infection patterns and feature at risk patients before symptoms arise.

Patient Reported Data & Outcomes

Data generated from patient devices, like IoTs, allow for patient-reported data and even patient-reported outcomes. These devices allow for greater insight into a patient that the health industry has never seen before. IoTs like a Fitbit, for example, generate patient data that can be used to provide greater insight into that patient to facilitate more accurate and timely diagnoses, improve quality of care and patient safety, and reduce healthcare costs. There is currently innovation for other tools and applications that can be used to generate patient-reported outcomes. For instance, MedStar is currently piloting an app that generates patient-reported outcomes that is integrated with EHR. The app uses AI algorithms to process patient-reported data to better understand patient habits like living habits, activity levels, appetite, medication compliance, etc. that can be used to access and monitor patient health status. Overall, patient-reported data allows for greater care management and more precise medicine and medical procedures.

Real-world Evidence

In short, real-world evidence refers to the clinical evidence regarding the usage, benefits, and risks of a medical product that results from analysis of real-world data (RWD). RWD is defined as data that is related to patient health status or functions of routine healthcare procedures. This data can originate from electronic health records (EHRs), claims and billing, product and disease registries, and patient-generated data from sources like mobile phones or equipment used at home. This data can be used to form real-world evidence that is helpful for controlling for external influences, such as genetics and environmental influences on delivered healthcare outcomes.

Personalized Patient Engagement

Implementation of AI technology into healthcare has not only helped to improve patient outcomes, but to also improve quality of patient care. AI can be used to personalize patient dashboards, care management, and overall personal health experience, which helps to increase patient engagement in their health. Various patient engagement tools are helpful to improve patient involvement in their health. IoTs are arguably the most prominent patient engagement technology; these devices support and encourage patients in their health. Data from devices like IoTs can be used to monitor patient behavior and engagement, allowing for personalization and dynamic content to further drive engagement and patient satisfaction.

Overall, AI’s presence in healthcare has drastically improved medicine as a whole. AI has helped to improve quality of patient care, improve diagnostic and imaging tools, and improve patient health engagement. One of the biggest takeaways of AI in healthcare is that this technology is working to improve overall patient health before, during, and after they see a doctor. While AI already holds a strong presence in healthcare, this technology will continue to provide avenues of innovation for the purpose of advancing and improving the healthcare industry.