Beyond Medicine: How AI Copilots are Revolutionizing the Healthcare Industry

10 Healthcare Applications for AI Copilots

AI Copilots in Health Insurance

1. Detecting Fraud in Health Insurance Claims

The most data-driven health insurance companies are leveraging AI to analyze claims data at scale and identify suspicious patterns or anomalies that could be indicative of benefits fraud.

By ramping up their ability to detect fraud, health insurance companies can identify healthcare providers with problematic or dishonest business practices, ensure that plan members play by the rules, lower administrative costs, and save employers millions of dollars each year in fraudulent benefits paid.

2. Health Trend Forecasting and Predictive Modeling

Health insurance providers can analyze claims and other data using artificial intelligence to forecast emerging healthcare trends and predict how plan members might use their benefits in the future. AI can help health insurance providers answer questions like:

• What conditions or diseases are becoming more prevalent?
• Where is there increased/decreased demand for specific treatments?
• What treatments are plan members asking about via our customer service lines?

Forecasting health trends with help from AI is helping insurance providers develop new product offerings that align with the evolving needs of their customers and plan members.

3. Personalized Pricing and Risk Assessment

Health insurance companies are leveraging AI to develop powerful underwriting algorithms that can deliver personalized risk assessments and plan rate recommendations by analyzing electronic health records and historical claims data along with information about the applicant’s lifestyle and health status. 

AI-powered underwriting algorithms are empowering health insurers to automate and streamline the underwriting process and accelerate timelines for quoting on new business while offering more competitive rates to plan members.

AI Copilots in Clinical Management

4. Clinical Data Entry

Clinical data entry has historically been a significant pain point for physicians practicing medicine in a clinical setting. Studies have shown that physicians spend 35-50% or more of each shift on clinical data entry tasks, such as transcribing voice notes, digitizing clinical notes and reports, and updating patient EHR. In contrast, just 27% of the average physician’s workday is spent seeing patients.

But with help from artificial intelligence, clinicians are leveraging innovative technologies to accelerate clinical data entry so they can spend less time in front of computer screens and more time in front of patients. 

The most efficient clinicians are now using AI-powered voice assistant applications (think “Siri” or “Alexa”, but for Doctors) to record and capture voice notes during patient visits, automatically transcribe the contents to text, parse the data, and update patient EHR in real time. Powered by Natural Language Processing (NLP) technology, the most advanced of these applications can parse out important medical information from the rapport-building small-talk that happens during a clinical visit, and/or allow physicians to access patient data using voice commands.

These capabilities can save physicians hours of time each day, allowing them to see more patients and focus on more valuable activities that drive positive health care outcomes.

AI Copilots in Research

5. Medical Research Scraping

Healthcare researchers spend huge amounts of time analyzing existing research papers to understand existing treatments and best practices, compare data on various aspects of clinical research, and develop research questions and hypotheses for future studies.

To accelerate this process, healthcare researchers are now leveraging AI-driven web scrapers to automate the process of collecting and aggregating research materials from across the public Internet. These tools can compile valuable and relevant data from clinical trials, medical journals, patient reviews, and drug databases in just seconds, helping researchers save hours of time and focus on more valuable tasks like analysis and experimental design.

6. Clinical Trial Matching

A common strategy when allocating patients into experimental groups for clinical trials is to match patients with similar demographic profiles and medical history, then split the matched pairs into the control and experimental groups. 

This process normally requires clinical investigators to manually parse through patient EHR records to identify potential matches, but it’s now possible to automatically parse through EHR at scale and identify high-quality patient matches with help from AI. Leveraging AI to match patients for clinical trial design helps trial sponsors save time and reduce costs while effectively controlling for confounding variables.

7. Drug Discovery, Repurposing, and Optimization

Pharmaceutical companies are investing heavily in AI-driven methods for supporting drug discovery, repurposing, and optimization. This includes a wide range of applications, such as:

• Identifying drug targets (target proteins) in the human body,
• Predicting and modeling drug-target interactions and bonding affinity,
• Designing new molecules through generative modeling,
• Optimizing molecular properties, and
• Searching drug databases to identify repurposing opportunities.

These AI applications have the potential to accelerate the discovery and development of new drugs that can enhance patient care outcomes.

8. Clinical Trial Data Analysis

The most technologically advanced pharma companies are now using artificial intelligence to analyze the massive amounts of data they generate in clinical trials. 

A 2023 literature analysis reveals that AI-driven data analysis is being used in clinical investigations across multiple therapeutic areas (e.g. cardiovascular, oncology, neurology, etc.) to identify key risk factors, improve handling of missing data (e.g. by extrapolation), automate data extraction with reduced human error, and enable more insightful analysis of experimental results.

Using artificial intelligence to analyze clinical trial data helps improve evidence generation so pharma companies can earn FDA approval and get their products to market.

AI Copilots in Hospital Administration

9. Healthcare Staffing Optimization

Since the start of the COVID pandemic, healthcare organizations across North America have experienced high turnover rates and critical staff shortages, especially in nursing. The current turnover rate for nurses in the US varies between 8.8% and 37% depending on the location and nursing specialty. Reasons for high attrition of nurses include demanding work schedules, high stress, relatively low pay, high patient-to-nurse ratios, and lack of support.

Data-driven healthcare organizations are turning to artificial intelligence, machine learning, and predictive analytics applications to address the nursing crisis in 2023. 

AI can help hospitals identify and prioritize the most effective interventions to reduce nursing attrition rates, predict and manage staffing and scheduling, and streamline or automate administrative tasks so nurses can spend more time focused on patient care.

10. Healthcare Supply Chain Management

The COVID pandemic exposed supply chain vulnerabilities across the healthcare sector, and ongoing global instability is likely to drive additional supply chain challenges for healthcare organizations.

To keep critical resources flowing, healthcare organizations are deploying AI-driven supply chain analytics tools that can provide greater visibility into current inventor levels, predict future demand for critical healthcare supplies and resources, and anticipate when an unfolding crisis (e.g. an adverse weather event, port congestion, political unrest, or conflict) might cause a supply disruption.