Chapter 2: What Do AI Applications Look Like in Healthcare?

From customer service chatbots to virtual health assistants, artificial intelligence can take various forms in healthcare. On the medical side, it can help diagnose diseases and create crucial treatment plans. On the administrative side, it can optimize financial processes, improve accuracy and enhance revenue collection.

In this chapter, we’ll discuss the types of AI used in this industry, the benefits it can provide and how you can leverage it for revenue recovery.

AI in Healthcare

Artificial intelligence uses computers and machine processes to simulate human intelligence and perform complex automated tasks. The emergence of AI in healthcare has reshaped how providers diagnose, treat and monitor patients. It has also drastically improved healthcare research and outcomes by providing more accurate diagnoses and allowing more personalized treatments.

Essentially, AI can be used for the following tasks: 

• Predicting revenue outcomes
• Identifying and fixing errors
• Automating claims processing
• Providing efficient customer support

AI can help automate your processes and improve decision-making. It can also help elevate successful clinical outcomes by enhancing the customer experience and providing predictive analytics. AI in the healthcare industry promotes smarter, faster and more efficient care to patients. Ultimately, this groundbreaking technology can be used to improve your patient’s health outcomes and reduce costs.

Types of AI Used in Healthcare

AI is an umbrella term covering distinct but interrelated processes. The most common types of AI used in the healthcare industry include: 

• Machine learning: This involves training algorithms using data sets like health records to create models that can perform tasks like categorizing information and predicting outcomes. The analysis tool can predict the most ideal treatment for patients and streamline healthcare procedures. To improve the revenue cycle, machine learning can be used to learn and predict how individual patients prefer to pay and contact them based on their preferences.  
• Rule-based expert systems: Expert systems refer to the collection of “if-then” rules for clinical decision support purposes.
• Deep learning: Deep learning refers to the subset of machine learning involved in even greater volumes of data, training times and layers of algorithms to complete complex tasks. Most EHR providers use these sets of rules within their systems today.
• Robotic process automation (RPA): RPA helps automate administrative and clinical workflows. Many healthcare organizations use this type of AI to improve the patient experience and efficiency in daily tasks at their facilities. For instance, RPA can record early authorizations, update medical records and assist in billing. 
• Physical robots: Initially, physical robots were designed to help deliver hospital supplies. Today, these robots can be trained to do specific tasks. Surgical robots have been provided to help surgeons see better and create more precise and minimally invasive incisions. 
• Natural language processing (NLP): NLP is a type of AI used to understand human language, whether verbal or written. Conversational AI for healthcare includes interpreting documentation, notes, reports and published research for more efficiency. NLP can analyze patient records and give suggestions to improve the quality of methods used. 

7 AI Applications in Healthcare

AI presents various opportunities for the healthcare industry, from diagnosing diseases to identifying the best treatment plans for patients with critical illnesses. Specific applications of AI in healthcare include:

1. Diagnostics

With AI machine learning tools, pathologists can make more accurate diagnoses. Artificial intelligence can reduce errors during the process of cancer diagnosis while offering various techniques for personalized medical treatment. Increased accuracy can lead to improved cancer patient outcomes, such as monitoring health or even being cured at a stage before it can turn fatal, saving lives.

2. Customer Service

Many healthcare organizations leverage AI for patient engagement. By providing customers with individualized recommendations and information, AI chatbots can help make patient interactions more seamless.

These bots might offer solutions, products or services, empowering patients to make more informed decisions on their health. Patients might also access their medical records and learn more about their care and treatment options using AI.

Ultimately, artificial intelligence benefits patient health outcomes while allowing providers to focus on other crucial tasks.  

3. Targeted Treatment

Deep learning AI allows your healthcare organization to provide targeted treatment to patients at the right time. That means delivering treatment at the right time, and achieving better target selection of patients while offering insights.

AI can also analyze data to identify trends and patterns, such as patients who are at risk of developing a specific disease. Using this technology can help you provide patients with care and treatment before they develop a disease. 

4. Automation

AI tools in healthcare play a significant role in automating redundant, time-consuming tasks. For instance, artificial intelligence can be used for verifying the eligibility of medical claims, clinical documentation, transferring medical data to the respective medical professionals, revenue cycle management, and various other crucial tasks that can take up your staff’s time.

For example, clinicians must track the moment a patient enters an operating room. However, manually inputting that data can be burdensome. Instead, AI can automate the process, tracking that movement so clinicians can spend more time delivering crucial bedside care to their patients.

5. Managing Medical Records

Without connecting significant data points, it becomes much more difficult to develop proper diagnoses and new medicines. Thankfully, AI makes the process of managing medical records much easier. The technology can conduce data, connecting it to required data that had earlier taken years to process.

6. Error Prevention 

Just a single dose of a medicine can drastically impact a patient’s body, making it important that the patient takes the right amount of medicine prescribed to them. With the help of AI, the industry can reduce the margin of errors in how a patient self-administers medications.

7. Patient Engagement Intelligence

Engagement intelligence tools use machine learning to analyze patient behaviors and trends. That way, you can determine the best solutions for each patient to target them effectively and encourage payment.

For instance, one patient might pay their bills faster with a text reminder, while another might not need reminders. Engagement intelligence can ensure each patient has what they need to pay successfully.

Advantages of Using AI in Healthcare

There are many potential advantages of using AI in healthcare, such as:

• Streamline processes: AI can streamline many crucial healthcare processes, such as verifying medical claims, transferring medical data and improving revenue. Streamlining these processes means more time to focus on patient care while removing inefficiencies and reducing human error. With the extra time, your organization can increase productivity and increase the patient payment collection rate.

• More accurate diagnoses: AI can be immensely helpful when it comes to diagnosing diseases, including those in the skin, liver, brain and heart. AI can analyze larger volumes of patient data in real-time to uncover patterns that could be overlooked by humans. For example, deep learning AI has been shown to identify Alzheimer’s disease to attain an average classification accuracy of 99.95%, aiding in preventative care and resource planning.
• Better clinical decisions: With more accurate diagnoses being made, AI can also help doctors make better clinical decisions. By processing and analyzing more information much faster, this technology offers physicians more evidence-based treatment suggestions. Physicians can deliver more effective, individualized care, improving patient outcomes.
• More satisfied customers: AI can enhance customer engagement, providing a seamless experience for patients. AI-based tools like health apps, chatbots and telemedicine platforms can provide instant, personalized services for patients. The more people that proactively participate in their own care and well-being, the better the outcomes. With real-time health tracking and advice, you’ll have higher overall patient satisfaction rates. 

Using AI for Revenue Recovery

From a business perspective, AI makes a lot of sense when utilized in the healthcare industry. You’ll find plenty of opportunities to use AI to improve your revenue cycle management when you’re dealing with key issues like ineffective tools, a lack of direction and an insurmountable volume of work.

From streamlining administrative processes to enhancing decision support and patient engagement, AI can be used from the beginning to the end of the revenue cycle to overcome these hurdles:

• Give Revenue Cycle Analysts a clear direction: Many analysts lack direction at the beginning of the revenue recovery process, making it impossible to differentiate potentially valuable accounts from those with a low likelihood of collection. AI offers clear guidance on accounts to pursue by analyzing the individual’s propensity to pay. 
• Automate redundant processes: AI tools reduce your team’s reliance on manual processes, maximizing productivity and reducing the workload on team members. Overall, AI can help boost morale and efficiency. 

Engagement intelligence software can help you increase patient collection through targeted engagement modeling which determines the best engagement solution for each patient. At Millennia, we offer various AI revenue recovery features, from machine learning propensity-to-pay options to quality assurance automation. Our tools can help you increase patient engagement while enhancing your organization’s recovery solution. 

Take Advantage of AI With Millennia 

Artificial intelligence presents numerous opportunities to improve patient care outcomes, streamline processes and improve your patient collections. The Millennia Patient Payment Solution boosts your patient engagement to improve revenue. Our revenue cycle management technology focuses on educating, informing and helping patients understand their responsibility — leading to successful payments. 

Provide cutting-edge solutions to your patients with our patient-friendly technology. Schedule a consultation to learn how our technology can benefit your practice.