Clinical AI in Healthcare

Researchers believe that using clinical AI in healthcare has the potential to save $150 billion for the economy over the next few years.
Application Innovation / Automation - AI, ML, & RPA / Data & Analytics / Digital Optimization Strategy / Healthcare / IT Consulting

Clinical AI in Healthcare

Advanced technologies like artificial intelligence (AI) and machine learning systems have enabled computers to analyze data faster and more accurately than humans. With the large amount of information collected by healthcare providers, using AI in clinical settings can help interpret patient data for a more accurate, early diagnosis of issues. It also holds the promise of identifying more successful treatments and helping overcome the predicted shortfall of 9.9 million healthcare professionals. While many AI-enabled solutions are already being used in the industry, for example, real-time intervention, data collaboration, personalized care and prescription auditing, there will be more opportunities to integrate tech solutions with healthcare. Researchers believe that using AI in clinical healthcare has the potential to save $150 billion for the economy over the next few years.


Real-Time Intervention

Delivering real-time insights has been one of the biggest challenges of AI-enabled care. This struggle is primarily based on the tech available and the level of adoption. For example, while voice recognition technology is a valuable tool for dictation, it fails to provide a more robust decision-support system. If voice recognition technology could provide additional insights for healthcare providers regarding the diagnosis and treating illnesses, it would be a much more powerful tool.

Although some tech solutions developed that offer real-time insights, they lack a wide level of adoption. One example is the smartwatch-based COVID diagnosis app developed at Stanford University. The app, created with the help of Amazon, can analyze heart rates and track other abnormalities in patients who may be infected with COVID-19. A tool like this would be beneficial if it could be scaled up and used to push real-time alerts to patients.


Data Collaboration

Collaborations between large tech organizations and healthcare providers provide the promise of a better AI-enabled healthcare future. One example includes Truveta, which involves 14 different health systems. The goal is to combine patient data from all the systems and develop advanced analytics to improve patient outcomes.

Other significant partnerships have been announced between Google and Mayo Clinic, Ascension Health and Highmark. The possibilities with these collaborations include data analysis for measures, benchmarking, and administrative reporting.

Over and above their partnership with Google, the Mayo Clinic is also pursuing data collaboration with AI startups to collect information from remote monitoring devices. Additionally, Highmark has created a 10-year partnership with Christiana Care. They will be sharing medical and claims data to create better outcomes for patients.

Using datasets to improve patient experiences and enhance efficiencies in the healthcare system is one of the strongest reasons to use AI-enabled care.


Other Clinical AI Use Cases

There are other ways that clinical AI can enhance healthcare and improve outcomes for patients, such as:

Automated diagnosis – Using tools like chatbots, patients will be better positioned to self-diagnose or offer more assistance to physicians and healthcare providers with diagnosis. Some smartphone apps are already capable of providing patients with health and triage information based on their symptoms. The benefit that chatbots hold for the industry will increase as more data is collected and accuracy improves.

Prescription auditing – AI can be applied to help patients manage their prescriptions. Audit systems utilizing AI technology will be able to minimize prescription errors for patients, providing safer healthcare.

Personalized care – Creating personalized treatment plans for patients using AI technology will enable healthcare providers to cut back on costs while improving the efficiency of patient care. For example, providers can use machine learning to analyze information and discover the best patient treatments for each condition. By analyzing big data, AI tools present an opportunity to identify solutions that create a more personalized and successful healthcare experience.

Surgical robots – Using robots to assist in surgical procedures can help physicians improve accuracy and avoid exhaustion. These tools are designed to help with procedures that use repetitive movements. An additional benefit of robot-assisted surgeries is the machine’s ability to recognize patterns of procedures, which can then be used to identify best practices. This knowledge can also be used to improve the accuracy of the robot.


Is the Patient Ready?

As technology advances and becomes more sophisticated, the question of whether patients will welcome these tools in their healthcare journeys remains a hurdle. There is still a level of mistrust in machines when it comes to something as personal as one’s health. Some healthcare providers are still reluctant to adopt AI-enabled tools for patient care, particularly for those with more complex health issues.

Moreover, many patients are reluctant to trust a chatbot for medical advice. The overuse of these tools by other industries for marketing and sales purposes has led to a mistrust of the technology.

However, the administrative aspect of AI technology has proven to be one of the most successful applications. Using solutions that can quickly and accurately sort through large amounts of data is a boon for most healthcare organizations.


As AI and machine learning systems continue to mature, the use cases for the technology in healthcare will also expand. From improving patient outcomes through collaboration, customer-facing technologies, and tools to assist healthcare providers, AI-enabled clinical solutions hold promise to overcome challenges in the industry, such as rising costs of patient healthcare and forecasted short staffing issues.  In the end, it will come down to encouraging a wider acceptance of the tools by providers and patients.