Why does the future of healthcare include AI?

Healthcare can significantly benefit of the ethical use of AI.

Increased wellbeing, prevention of serious diseases and patient safety are some of the primary healthcare objectives. How do healthcare services and healthcare technology organizations go about tracking and improving healthcare outcomes and patient safety? The traditional approach includes models that are clunky, require a lot of administrative work and subjective judgment of what works. This created ineffective and hardly used models. The current healthcare system does not rely on outcomes, which are crucial for improved quality of care. 

Enter Artificial Intelligence (AI). AI can’t replace the hands-on experience of a medical professional, but can add objectivity and data-driven processes while reducing the likelihood that patients will be negatively impacted by human error or a provider’s lack of knowledge. In addition to the potential positive impact on improving outcomes and patient safety, the implementation of AI can also help streamline administrative tasks, automate a lot of processes and provide greater control to the patients over their healthcare experience (e.g. seeing if they are getting the outcomes they need from their providers). 

All this talk about AI, but what is it really? Simply put, AI is an algorithm implemented in various technological solutions that analyzes data, learns about trends and provides insights that clinicians, service providers and patients can use to improve the outcome of the care they provide and receive. An example of how the implementation of AI can improve healthcare is the optimization of physician workloads so they can effectively provide better care with lower wait times for patients. Additionally, integrating AI into lab processes can eliminate some points of human input to allow for shorter wait times for test results or automated detection of abnormal cells. This can reduce strain on patients and improve their confidence in the integrity of results.

Optimizing care is particularly important in underserved communities where facilities may need to prioritize critical resources and specialists due to funding concerns. Despite a high initial cost to invest in AI tools, the long-term benefit to healthcare entities results in a net gain due to increased efficiency and a reduction in medical errors. It’s imperative for healthcare organizations to articulate these potential benefits so that healthcare leaders and providers can make informed decisions about how to translate cost savings into meaningful value for patients.

Artificial intelligence allows for personalized attention. From algorithms that detect unusual heart rhythms to symptom databases that provide initial diagnostic recommendations to physicians, AI drives superior patient care by placing each individual at the center of their own support team. Having automatic access to information about lesser-known illnesses empowers patients to recognize the need for specialist input or a second opinion instead of relying on the personal knowledge of a single provider.

Creating entry points for users to interact with their health information is a significant opportunity for health technology organizations as patients continue to search for more autonomy and choice. Cloud-based programs and electronic case-management software enable patients to participate in their own care, something that isn’t always feasible with traditional models of practice.

Hardware plays a role in improving quality of care as well. Since AI monitoring devices can track everything from routine vital signs to cardiac events, patients are more equipped to understand health changes over time. Wearing a smart device eliminates the need to take manual measurements or generate reports. Some programs send out alerts or offer custom programming for users to add their own setpoints for off-normal ranges. This is particularly beneficial for those with chronic conditions that must be managed through a combination of self-monitoring, home care, and in-person treatment.

Due to the inherent reliance on data in many predictive and diagnostic functions, AI is a natural complement to these essential aspects of medicine. Artificial intelligence can be used to collect information about a person’s symptoms and provide potential diagnoses to clinicians. From mobile apps to check-in software for in-person appointments, these tools act as open-ended spaces for patients to explain their concerns, report pain, or request additional attention.

There are also passive AI products that don’t require direct patient input. In a unit specifically dedicated to AI, researchers at Cedars-Sinai are refining a technique to predict heart attacks and other severe cardiac conditions such as hypertrophic cardiomyopathy. Similar advancements are being made with neural networks that can anticipate a patient’s risk of disease and estimate the likelihood that a treatment will be successful. Unlike generalized data, these predictions are tailored to each individual’s medical history and based on their unique health profile.

Artificial intelligence and deep learning also have the potential to improve outcomes in diagnostic imaging, particularly in the field of radiomics, which targets data beyond the scope of what can be detected by the unaided human eye. AI mitigates the shortcomings of human perception by analyzing images in search of lesions and other signs of cancer. Early detection is likely to provide oncology patients with additional treatment options that are more effective and less invasive. 

In terms of predictive capabilities in larger clinical settings, AI tools can increase safety for staff, visitors, and patients by tracking interpersonal exposures. Maintaining a running record of close interactions allows facilities to get ahead of infectious outbreaks and predict patterns of spread based on the movements of affected individuals.

Although AI creates many opportunities to improve the quality of care and make specialized medicine accessible to a broader audience, there are several notable drawbacks compared to traditional analog frameworks. As healthcare organizations continue to create new products, further innovation is also needed to close gaps related to the implementation of artificial intelligence.

Privacy and data breaches pose potential threats to patient data. With so much personally identifiable information stored electronically, a cyber-security event could have disastrous consequences for patient privacy. Mitigating cyber risk on a large scale requires additional security measures, protocols, and training. Users may also need personal assistance if they’re unfamiliar with how to interact with a facility’s tools in a secure manner.

The potential applications and advantages of implementing AI into medical processes are limitless. With informed user-AI relationships and proper protections in place, patients should experience superior outcomes while maintaining decision-making control over their health. As patients share more data with AI tools to derive these benefits, healthcare-technology organizations receive source information to analyze for trending and predictive purposes.

Advancements in the medical use of artificial intelligence have led to incredible breakthroughs in multiple disciplines, including cancer detection and interpreting complex radiological imaging. There’s no doubt that each successful instance of using AI diagnostics or robotic surgical equipment adds to the body of research about what’s possible in the future; however, in questioning how AI can further be used to improve patient outcomes, there’s one group that should never be overlooked in favor of providers, hospital administrators, or even leaders in the healthcare sector—the patients themselves.