How To Cut Costs and Scale Using Healthcare AI
10+ ways generative AI and large language models can reduce wasteful spending
Did you know that roughly 30% of healthcare spending is considered waste? Excess administrative spending and unnecessary medical care negatively impact healthcare organizations' bottom lines across the United States. As a result, healthcare executives are looking for innovative ways to scale their services and bring down costs. However, the question that often lingers is: How can this be successfully done without compromising quality of care, patient satisfaction and burning out your workforce? The answer to this question could lie with artificial intelligence (AI).
Healthcare AI explained
AI in healthcare is not particularly new. In the 1950s, AI researchers assessed how natural language processing (NLP) could be applied in healthcare. Even during the 1970s, there were tools that combined machine learning and NLP to help with diagnosis and treatment. However, when OpenAI's ChatGPT entered the market in late 2022, it introduced a new world of possibilities for healthcare organizations.
Today, healthcare executives can implement generative AI and large language models (LLM) in multiple ways to cut costs and improve patient care. However, before we dive into what that looks like, we'll clarify each type of tool.
What is generative AI?
Generative AI describes algorithms that can create different types of content, such as audio, imagery, code and text. It can take unstructured, complex data that’s often difficult for humans to analyze and make it meaningful.
What are large language models?
Large language models are a category of generative AI. They use NLP to understand and then create humanlike text-based content. Some of LLM’s key features include real-time interactions and responses, contextual awareness and deep learning architectures.
ChatGPT is an example of a powerful LLM that has several medical uses. This tool can assist with clinical decision support, create understandable patient education and even send medication reminders to patients.
Using Healthcare AI to cut wasteful spending
A 2020 study revealed that administrative complexity, clinical inefficiencies, overuse and missed prevention opportunities are some of the biggest categories of excess medical care spending. Here are several ways that AI can help.
Streamlining administrative functions
According to JAMA researchers, administrative complexity is the greatest healthcare waste source, mainly because the U.S. has multiple payers. Tasks like managing insurance claims and completing clinical coding and documentation are incredibly time-consuming. In addition, research shows that physicians spend almost 50% of their time handling administrative tasks, and the average US nurse spends 25% of their work time doing the same.
LLMs could help cut wasteful spending by assisting with the following tasks:
Automating medical data entry and aggregating medical information
Producing patient discharge summaries
Improving the efficiency and accuracy of electronic health records (EHR)
Using chatbots for patient interactions
Enabling diagnostic image ordering
Refilling prescriptions
Scheduling appointments
In addition, machine learning can help identify, analyze and fix coding issues and inaccurate claims. This is one of the most significant benefits of AI in healthcare, as it's a laborious and costly practice for multiple stakeholders.
Using healthcare AI to decrease errors and adverse events
Errors and adverse events not only cause patient safety issues but are also expensive for healthcare organizations. Medical errors cost about $20 billion annually, partly because organizations are reducing their nursing staff to decrease overhead. A better way to cut costs and maintain patient safety is to leverage AI to detect errors and reduce repetitive administrative tasks.
As a healthcare executive, you may consider deploying AI to assist with the following:
Use machine learning algorithms to alert surgeons to discrepancies in procedural steps
Assess fall risks based on clinical reports
Alleviate the frequency of adverse drug events by analyzing patient EHR data and multiple datasets
Use machine learning and fuzzy logic for early detection of healthcare-associated infections
Correctly triage patients who visit emergency departments
Employ machine learning to help interpret medical images and reduce diagnostic errors
Healthcare AI for clinical decision support
Overuse of medical services is another common, costly issue in healthcare organizations. It wastes scarce healthcare resources, has the potential to cause harm and ultimately provides no value to the patient. Using AI for clinical decision support may help with this.
A 2024 study revealed multiple instances where AI systems were used to predict diagnoses and provide recommendations. For medical imaging, AI has been able to spot skin cancer, identify mitosis in breast cancer histology images and diagnose diabetic retinopathy using retinal fundus pictures. It’s also been able to estimate the likelihood of patients' conditions advancing and help monitor cardiovascular patients in intensive care units.
Deep learning models may even be able to predict and diagnose Alzheimer's disease, mental health disorders and more. This can help reduce human errors and provide faster and more reliable information.
If you're looking to scale your organization's healthcare services and cut spending, using AI for clinical decision support may help. It can improve a clinician's performance and boost patient-provider interaction time. Patient satisfaction is primarily tied to how much time they spend with their doctor, so if that's increased, you’ll likely see happier patients with improved health outcomes.
Streamlining your virtual care delivery and clinical workflows
Although we still have much to learn about AI, the research done so far shows great promise. The future success of healthcare organizations will largely depend on how they harness technology to scale. The advent of telehealth has increased the amount of accessible data and analytics, and the power of AI can help companies deliver better care without significantly increasing costs.
If you’re interested in tackling wasteful healthcare spending, OpenLoop may be the answer. We have a full-stack of easy-to-use, innovative white-label telehealth solutions built with efficiency and affordability in mind—customized to meet your business needs.
Interested in what we can do for your organization? Get in touch here!
Here’s our full-suite of Telehealth Support Services to explore: