You are currently viewing Artificial Intelligence (AI) in Healthcare

Artificial Intelligence (AI) in Healthcare

Applying Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) into healthcare is becoming increasingly common. The use of AI has the potential to help healthcare providers with both patient care and administrative processes, leading to improved solutions and quicker problem-solving. 

In the healthcare industry, there are different types of AI that offer various benefits. Machine learning, natural language processing (NLP), rule-based expert systems, diagnosis and treatment applications and administration are some of the most commonly used AI technologies in healthcare. 

Machine learning is widely used in precision medicine, allowing healthcare providers to predict treatment success based on patient information and treatment history. Machine learning can also be applied to identify and correct coding issues associated with health claims.. 

NLP is used to extract meaningful information from unstructured medical records, understand patient feedback, offer patients 24/7 access to health information and support via chatbots, and automate the process of assigning codes to medical procedures and diagnoses.

Expert systems are used in electronic health record systems (EHRs) for clinical decision support, but they can become challenging to manage as the number of rules grows. Moreover, if the  number of rules is too large, i.e. exceeding several thousand, the rules can begin to conflict with each other and fall apart. 

Diagnosis and treatment of diseases have also been a focus of AI in healthcare, but integrating AI into clinical workflows and EHRs can present challenges. Moreover, much of the AI and healthcare capabilities for diagnosis and treatment form medical software vendors are standalone and address only a certain area of care. 

AI can also be used to improve efficiencies in healthcare administration including claims processing, clinical documentation, and revenue cycle management. 

However, using AI in healthcare is not without challenges. Access to large amounts of data is necessary for AI to be effective, and there is a risk of bias if the data used to train algorithms is not representative of the population. Additionally, there is a lack of standardization across different AI systems, making it difficult for healthcare providers to choose the right technology for their needs. To overcome these challenges, it is important for healthcare providers to leverage the capabilities of third-party vendors like Synegys that have AI capabilities in health systems and can integrate bespoke systems.