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AI Agents in Healthcare – EXPLAINED!

The healthcare and medical industry can vastly benefit from adopting advanced technologies like AI, ML, NLP, etc. Here, we’ll discuss AI agents in healthcare and the role of artificial intelligence in boosting the sector in various ways.  Artificial intelligence has become a part of many industries, including healthcare. In fact, AI has many uses in the medical and healthcare sector. According to Grand View Research, the global AI healthcare market is estimated to grow at a CAGR (compound annual growth rate) of 38.5% between 2024 and 2030. The AI healthcare market shot up by a whopping 233% between 2020 and 2023, the Covid-19 pandemic and post-pandemic years.  Another report shows that over 60% of people from healthcare in South America and the Asia Pacific regions believe AI will play a prominent role in decision-making in the next ten years, with the Middle East in the third place. The clinicians from North America and Europe aren’t as convinced but AI is majorly used (at 29%) in clinical decision support tools in the US.  AI in healthcare is helpful for doctors, surgeons, patients, medical/ lab technicians, and administrative teams. The popularity of generative AI and the development of AI agents has enhanced the use of new technologies in the industry. In this blog, we will read more about the uses, benefits, and need for AI agents in healthcare as well as the role of artificial intelligence in the medical sector.  AI Agents in Healthcare: Key Components, Uses, and Benefits An AI agent in healthcare is an intelligent software program trained on patient and medical data to provide virtual assistance by processing input and delivering relevant outcomes.  Key Components of AI Agents  The AI agents’ framework consists of five key components listed below:  AI agents use machine learning algorithms to identify patterns and discover trends or correlations in historical data to predict outcomes, diagnose diseases, recommend treatments, or determine the risk factors when considering a treatment/ surgery.  By using NLP, the AI agents can accurately understand and interpret human languages. This reduces the risk of error caused by misunderstanding the content or context. NLP helps in answering queries by patients, filling their forms/ medical records, and generating reports.  The AI agents’ architecture also includes technology to process audiovisual content like images and videos, X-rays, CT and MRI scan reports, etc., to identify patterns, detect early signs of diseases, and more. Tumors or cancerous cells can be identified before they affect the patient’s health adversely. Computer vision is also used when performing surgeries.  Another role of AI agents is to regularly interact with humans (be it doctors, nurses, admin staff, or patients) and assist them however necessary. For this, the software is trained to have a user-friendly interface with adaptability and responsiveness.  AI agents can be used to analyze datasets and derive actionable insights, future predictions, etc. From identifying the appropriate treatment method to post-care planning, the AI agent can help at every stage. It can provide the required insights and reports for the concerned parties to make the right decisions. Uses of AI Agents in Healthcare  Medical treatments are diverse and personalized based on the patient’s medical history and overall health condition. Moreover, concerns like allergies to active ingredients in drugs should be considered when prescribing medication. By digitalizing patient records, hospitals can use AI agents to create a quick and accurate report about the patient’s condition and provide it to the doctor. This allows the doctor to prescribe an appropriate drug or a treatment plan. Even if the patient forgets to inform during the appointment, the AI agent can ensure the doctor has all the necessary information to make the right decision.  IoT (Internet of Things) devices like sensors on wearables can be used to collect patient data in real-time and send alerts as necessary. For example, a patient recovering from a surgical procedure can be monitored through the AI agent that tracks their vitals like heartbeat, blood pressure, etc. The nurses can be quickly alerted in case of emergencies. Furthermore, patients recovering at home can also be monitored remotely through AI agents. The hospital can send someone to treat the patient or contact them immediately to provide support.  Administrative processes and revenue cycle management (RCM) can be streamlined using AI agents in healthcare. From automatically updating patient records in the central database to scheduling appointments and follow-ups, sending alerts to reduce waiting time, and tracking appointments to provide a better experience, AI agents have varied roles in increasing operational efficiency. The admin staff can use AI agents to ensure the claim submissions are accurate and get quick reimbursements from payers. Generating invoices and printing bills is also easier.  Sometimes, healthcare providers may have to come up with new treatments or drug combinations based on the patient’s condition and medical history. They can use AI agents to analyze the data and predict the possible outcomes of using a particular drug or procedure. This reduces the risk factor as they don’t have to rely only on guesswork or trial and error. It may lead to new discoveries and help more patients with similar conditions. Similarly, AI agents can provide insights about the benefits and potential side effects of a medicine on a patient.  AI agents can assist surgeries in many ways. One method is the support provided to robotic arms. Another method is through read-aloud devices or communicator tools that list the patient’s history to the surgeon and help them make proactive decisions during emergency situations. Instead of waiting for someone to gather the information, the AI virtual assistant will deliver the reports in real time. Saving a few precious minutes during complex surgical processes is a matter of life and death.  We cannot ignore the importance of providing mental health support to patients and medical staff. There are quite a few AI agents built on conversational AI that act as chatbots and virtual counselors. Patients can interact with these AI agents to share their worries/ troubles or get advice on how to

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Revenue Cycle Management in Healthcare: Your 2025 Playbook

RCM is a key process in the healthcare sector and impacts revenue and patient management. Here, we’ll discuss the top practices to enhance revenue cycle management in healthcare and the role of RCM analytics in the industry. There is an increase in demand for big data analytics in the healthcare industry to handle the increasing generation of new data every day. Technology has become an integral part of the industry for effectively managing large data volumes and providing data-driven decision-making analytics. Different types of services, like patient management, revenue cycle management, financial management, data management, etc., are offered by data analytics companies to help healthcare centers streamline their internal processes.  RCM analytics is similar to healthcare analytics but focuses on the financial aspect of the business. Statistics show that the global healthcare analytics market was $44.8 billion in 2024 and is projected to reach $284.5 billion by 2035 at a CAGR (compound annual growth rate) of 18.3%.  RCM, aka revenue cycle management, is one of the core functions in the healthcare sector. Setting up a comprehensive RCM system can reduce the risk of errors in patient data, reduce claim denials by insurance companies, and increase patient satisfaction with the healthcare center. In this blog, we’ll explore the role of RCM analytics and the best practices to follow when adopting the process. What is Revenue Cycle Management?  Revenue cycle management is managing the revenue cycle in a healthcare center. A revenue cycle can be broadly classified into three stages – patient registration, preparing to claim insurance, and collecting the payment. These are further divided into multiple steps.  But what is the use of RCM in medical billing? RCM combines various tasks and departments to create a pathway for the patient’s journey with the hospital. From the time the patient arrives at the hospital and asks for an appointment to the final bill payment after the treatment ends (and any other revisits), RCM ensures the patient’s data is accurately collected, recorded, stored, and shared with relevant parties to provide medical treatment and manage insurance/ payment for the services provided. RCM analysis minimizes the risk of mismanagement of these functions and increases overall efficiency. What is an Example of RCM? Let’s say, a patient with an ear infection visits the hospital for treatment. The front office staff will gather the patient’s details and give an appointment with the specialist. The report (tests and medication) recommended by the specialist has to be updated in the patient’s file. If surgery is necessary, the patient’s insurance details are collected so that the insurance company can contact them and file a claim to reimburse the expenses. When the patient goes home, the bill is settled by tallying the claim amount with the bill amount. The patient pays the balance, if any, to end the transaction.  These are a series of tasks performed by different people over a period. With revenue cycle management in medical billing, these can be streamlined and accelerated using technology. Effective RCM enhances patient satisfaction while reducing the stress on hospital staff. What are the 12 Steps of the RCM Cycle? Now that you know what the revenue cycle process is, let’s look at the various stages of revenue cycle management.  8 Top RCM Practices to Follow in 2025 To get the best of RCM analytics, the healthcare center should follow the industry-wide best practices and be open to accepting new technology and modern processes.  1. Invest in Technology  In a world where technology gives a business a competitive edge, not utilizing it can lead to chaos and decline. Considering the complex stages of revenue cycle management, healthcare RCM analytics include a range of tools and technologies.  For example, a data warehouse has to be set up (on-premises or cloud) to store all the data in a central database. This data warehouse is integrated with input sources to collect patient data and connected with AI tools for data analytics and data visualization dashboards. More third-party integrations can be added to send the details to insurance companies for claim submissions. Invoice generation and payment management software are also integrated with the data warehouse to create an interconnected RCM system that provides real-time information to the hospital staff.  2. Collect Patient Data Early  Patients are a key part of the healthcare system. Collecting all the necessary information about the patient will create a better profile and lead to more effective treatment. Similarly, asking patients about their insurance details right during the initial registration will allow the hospital to get the required pre-authorization for claim submission.  Instead of waiting until bill generation, ask for the information when they register the first time. By storing this data in the central repository, the staff can access it whenever necessary. The claims can be submitted quickly, and patients can be informed in advance about any extra payments they would have to make if the insurance payout doesn’t cover the entire bill amount.  3. Optimize Patient Registration Process Collecting patient data upfront is the first step. This information has to be verified to avoid complications later on. For example, a simple clerical mistake in adding the insurance policy number can result in claim denial and loads of paperwork. Verifying the insurance details as soon as possible will prevent such delays and decrease the claim denial rate.  Similarly, RCM performance analytics can be used to optimize the registration process and simplify it for the patients. After all, people don’t want to fill out a dozen applications just to get an appointment with a doctor. Identify the bottlenecks in patient registration and eliminate unwanted steps. Keep the forms straightforward but also include enough fields to collect the required information.  4. Aggregate Patient Data (Digitally) Digitalizing the process ensures patient records are moved to the data warehouse and can be accessed by doctors and admin whenever necessary. This is also important to process insurance claims as the details should be shared with the insurer.  Thankfully, today’s revenue cycle management technology is advanced and

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