Real-World Examples and Applications of AI in Healthcare
Discover where health leaders are taking the leap in AI
Artificial intelligence (AI) and its long-term role in healthcare is currently uncertain. Much of the AI technology out there is still in its infancy, but healthcare leaders have already made note of its advantages. One of the biggest being increased efficiency, which has proven to be a large bottleneck to innovation within healthcare.
A lot of what's been written about AI in healthcare has been theory or prediction, but what does its adoption actually look like in practice? We’ll be highlighting some real-world examples and scenarios where healthcare organizations are taking the leap into AI and the companies offering those solutions.
Clinician workflow and administrative tasks
According to Mckinsey & Company, nearly a quarter of U.S. national health expenditure goes toward administrative costs, which could be reduced through technology. Additionally, the U.S Department of Health and Human Services predicts that there will be a nationwide shortage of 90,000 physicians by 2025. As a result, AI has emerged as a potential solution to these healthcare hurdles.
Chatbots and LLMs
If you’ve been following AI innovation, chatbots and Large Language Models are likely familiar to you. This type of AI technology has gained a lot of attention from healthcare leaders for use in clinical documentation and workflow. The ability of LLMs to learn, adapt and automate complex processes presents an enticing solution to clinical efficiency.
Essentially, an advanced language learning model (LLM) refers to a sophisticated computational system designed to comprehend, analyze and generate human language with a high level of precision and contextual understanding. By utilizing AI and machine learning techniques, particularly deep learning, these models are tailored to process and interpret healthcare-related language data in a manner that closely mirrors human linguistic capabilities.
They can be trained to label text using medical ontologies and easily transform documents into structured tables. Of course, as with any new technology, some leaders are concerned about data security and the use of PHI. However, others have chosen to brave the new AI frontier. Here are a few ways AI can improve workflow and examples of companies that are leading the charge in AI innovation.
Automated documentation and transcription
AI-powered speech recognition and natural language processing (NLP) can automate the transcription of clinical notes and dictations, saving clinicians time on documentation. For instance, Nuance's Dragon Medical One uses AI to transcribe spoken words into text in real-time, allowing clinicians to quickly document patient encounters without manual typing.
Clinical coding and billing
Algorithms can assist in accurately assigning medical codes for billing purposes by analyzing clinical documentation. Cerner's AI-driven coding assistant analyzes EHR data to suggest appropriate medical codes, reducing the time and effort required for manual coding by healthcare professionals. Microsoft and Epic are also entering the chat with their Azure OpenAI Service.
Appointment scheduling and patient communication
AI-powered chatbots and virtual assistants can handle appointment scheduling, answer basic patient inquiries and send automated reminders, reducing the administrative burden on staff. AI platforms can automate administrative tasks such as scheduling appointments and verifying insurance eligibility, allowing healthcare staff to focus on patient care.
Prior authorization assistance
AI can streamline the prior authorization process by analyzing clinical data and insurance requirements to determine the necessity of certain procedures or treatments. For example, CoverMyMeds' Prior Authorization Support System uses AI to automate prior authorization requests, helping healthcare providers obtain approvals more efficiently and reducing administrative burdens.
Clinical decision support
Clinical decision support systems powered by AI can provide evidence-based recommendations to clinicians during patient encounters, reducing the time spent searching for information and making treatment decisions. For instance, IBM Healthcare offers clinical decision support solutions that leverage AI to provide personalized treatment recommendations based on patient data and medical literature.
Predictive analytics for resource allocation
AI-driven predictive analytics can forecast patient volumes, predict disease outbreaks and optimize resource allocation within healthcare facilities, improving efficiency and reducing administrative overhead. For example, GE Healthcare's Command Center uses AI to analyze real-time data from various hospital systems to predict patient admissions, bed capacity and staffing needs, enabling proactive management of resources.
Clinical judgment and diagnosis
Similar to streamlining clinical workflows and reducing administrative burdens, AI technology (LLMs, NLPs) has the potential to significantly enhance clinical judgment and diagnosis in healthcare by leveraging vast amounts of data to assist healthcare professionals in making more accurate and timely decisions. Here are a few ways AI can contribute to this improvement along with real-world examples.
Early disease detection
Predictive analytics driven by AI can analyze patient data to identify patterns and trends indicative of the early stages of diseases. Google's DeepMind Health developed an AI system that analyzes retinal images to detect early signs of diabetic retinopathy, a leading cause of blindness.
Clinical decision support systems
AI-powered clinical decision support systems can provide evidence-based recommendations to clinicians at the point of care. IBM's Watson for Oncology, for example, analyzes patient data and medical literature to provide personalized treatment recommendations for cancer patients, aiding oncologists in making informed decisions.
Natural Language Processing (NLP) for Electronic Health Records (EHRs)
NLP algorithms can extract valuable insights from unstructured clinical notes in EHRs, facilitating better clinical decision-making. Bringing it back, Nuance's Dragon Medical One uses NLP to convert speech into structured clinical documentation, enabling faster and more accurate patient records.
Genomic analysis
Algorithms can analyze genomic data to identify genetic variations associated with diseases and guide personalized treatment plans. Foundation Medicine's FoundationOne CDx is an AI-powered genomic profiling test that helps oncologists identify targeted therapies and clinical trial options for cancer patients based on their genomic profile.
Remote monitoring and telemedicine
AI-enabled remote monitoring devices and telemedicine platforms can continuously collect and analyze patient data, allowing healthcare providers to remotely monitor patients' health status and intervene as needed. Biofourmis' FDA-cleared Biovitals platform uses AI to analyze physiological data from wearable sensors to detect early signs of patient deterioration.
Medical imaging
Like much of the healthcare sector, medical imaging could use an update. The current process for image analysis can be quite time-consuming for providers.
According to Time, if the first few decades of radiology focused on refining the resolution of the images, then these next decades are going to focus on the interpretation of data. AI is poised to offer healthcare providers and organizations a more functional type of imaging. This in turn would support higher precision care for patients and allow for more informed decisions.
Image reconstruction and enhancement
Algorithms can reconstruct and enhance medical images to improve image quality, reduce noise and enhance diagnostic accuracy. Canon Medical Systems' Advanced intelligent Clear-IQ Engine (AiCE) , which uses deep learning algorithms to reconstruct images from noisy data, is producing high-resolution images with improved clarity in CT scans.
Automated image analysis and detection
Additionally, AI algorithms can analyze medical images to detect abnormalities, lesions or early signs of diseases, assisting radiologists in diagnosis. Aidoc's AI-powered platform, for example, analyzes medical images such as CT scans and MRI scans to automatically flag critical findings, helping radiologists prioritize urgent cases and reduce interpretation time.
Quantitative image analysis
Healthcare professionals can use AI to perform quantitative analysis of medical images to extract precise measurements and biomarkers for diagnostic purposes. Siemens Healthineers' AI-Rad Companion Chest CT software analyzes chest CT images to provide automated measurements of lung nodules, airways and lung parenchyma, aiding in the diagnosis and monitoring of pulmonary diseases.
Personalized treatment planning
Algorithms can analyze medical images along with clinical data to personalize treatment planning for patients. For example, Elekta ProKnow's AI-driven software analyzes medical images from radiation therapy treatments to automatically contour organs at risk and target volumes, optimizing treatment plans for individual patients and reducing radiation exposure to healthy tissues.
Image-guided interventions
AI can assist in image-guided interventions by providing real-time feedback and guidance to healthcare professionals. Activ Surgical's ActivEdge platform uses AI to enhance surgical vision during minimally invasive procedures by analyzing real-time video feeds and providing augmented reality guidance to surgeons, improving accuracy and patient outcomes.
Quality assurance and workflow optimization
AI algorithms can analyze medical images to ensure quality assurance and optimize workflow efficiency in radiology departments. Subtle Medical's SubtlePET AI software reduces PET scan acquisition time, improving patient throughput and reducing radiation exposure, while maintaining image quality for accurate interpretation by radiologists.
Google’s AI imaging study
Using complex AI algorithms to detect slight variances in medical imaging can offer early detection for diseases like cancer, stroke, embolisms, cardiovascular abnormalities and more. It can be the difference between life and death for some patients.
In a breast cancer study sponsored by Google in 2020, researchers from Northwestern University in Chicago and two British Medical Centers found that computers trained to interpret images and recognize patterns performed better than radiologists. Tests done in the United States saw a 9.4% reduction in false negatives and lowered false positives by 5.7%. In Britain, they saw similar results with a 2.7% reduction in false negatives and a 1.2% reduction in false positives.
Although the program showed that AI can help in identifying breast cancer in patients, there were some cases in the study where the radiologists got it right and the algorithm got it wrong. Just another indication that AI isn’t meant to replace clinical oversight, but instead be a tool radiologists can use to provide better patient outcomes.
Powering innovative virtual healthcare solutions
There is still a lot unknown about healthcare AI and what it might bring to the future of the industry. But there is no doubt that artificial intelligence will be an integral part of patient care in the future.
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