The Emergence of Generative Artificial Intelligence in Healthcare RCM

Health care, already grappling with struggles that range from staff shortages and clinician burnout to declining margins and worsening health outcomes – particularly in underserved areas — faces a maze of complex challenges. Therefore, it has become important to make the best use of new technologies and at the same time maintain healthcare quality. As generative AI models in healthcare can solve these pain points, democratizing knowledge, and boosting interoperability and discovery to new levels of speed and accuracy while enabling real personalization.

Generative AI in healthcare was recently reported to have the power to save billions of dollars from waste, where over $1 trillion is wasted every year on ineffective treatment and care. Generative AI in healthcare presents a valuable opportunity to enhance healthcare outcomes and provide a personalized approach to patient care, given the significant cost containment.

The Healthcare Market Landscape is Changing

The healthcare system is currently faced with a critical situation as a result of the interconnectedness of operational, talent, financial, and value crises in the modern world. We operate in an intricate place wherein the healthcare system confronts a combination of operational, talent, financial and value crises that are all interrelated, forming a critical predicament. Although healthcare providers have been trying to tackle these issues step by step, they still haven’t delivered truly equitable, high-quality care. There are few challenges that need to be quickly fixed in healthcare:

Labor Shortage

Every sector of the healthcare industry is short on employees. Workforce challenges are currently the number one concern for healthcare providers, according to a recent survey. Assistance is required by even the most elite healthcare systems to address the growing demand for healthcare services. Numerous organizations depend on contract labour due to a shortage of 1.1 million nurses.

Burnout among clinicians

81% of clinicians report experiencing high or moderate levels of burnout as a result of the increasing administrative burdens and obligations they encounter. A significant amount of manual labor is necessary for administrative duties, including patient scheduling, electronic health record management, and follow-up with patients. Clinicians are advocating for the implementation of technology and automation to enable them to concentrate on patient care. Nevertheless, only 45% of frontline clinicians have confidence in their leadership’s ability to prioritize patient care.

The absence of patient care

Healthcare professionals typically engage in administrative tasks such as data entry, documentation, and paperwork. They waste precious time and focus directed towards patient care. Administration is getting in the way of provider-patient relationships and increasing burnout.

Lower AI Adoption

In many cases, the healthcare sector is considered later adopters of AI technology compared to other sectors due in part to technical challenges and difficulty interpreting outputs from machines but also because most data are text rather than numbers. These included previous NLP techniques, which were limited in their ability to understand the context of the medical text. The stakes of healthcare, combined with dangers like hyperparameters obscenely high on the sensitivity in AI implementation.

The emergence of generative AI in the healthcare sector

Healthcare is currently the world’s largest data repository, accounting for 30% of annual production and 80% of healthcare data in unstructured formats. This implies that it is not meticulously organized in spreadsheets or databases. The profundity of healthcare data and the ongoing advancements suggest that generative AI has a promising future. In healthcare, generative AI has the potential to provide immediate benefits in terms of efficacy, efficiency, and personalization.

Healthcare Applications of Generative Artificial Intelligence

Generative AI is well-suited to the functional requirements of healthcare that may be disregarded by conventional AI and ML models. Generative AI in healthcare billing has the potential to replace tasks and roles related to data entry, classification, and generation in specific areas, while also augmenting functions that require empathy, innovation, and complex decision-making. The following are a few potential applications of generative AI in the healthcare sector:

EHR Management

The primary focus of conventional AI models in electronic health record management is data entry and classification. Although presentations like these have automated some record-keeping procedures, the type of information healthcare providers need to capture and maintain has become increasingly complex. Through its capacity to comprehend and produce human text, generative AI can summarize patient notes by extracting relevant data, etc., thereby enabling a holistic healthcare record management system.

Medical Scribe

Due to the manual process of traditional transcription methods, it can be error- and time consuming. Other challenges for providers Few healthcare providers would say they love to document patient encounters, assist physicians with administrative tasks, and maintain accurate medical records. If you combined the two technologies, generative AI could be seamlessly integrated into a patient’s healthcare system to generate real-time, accurate medical notes (Capgemini Research Institute).

Generative AI steps in to help process and document the conversation, so that medical scribes can spend more time focusing on patient care.

Patient Scheduling

Direct the scheduling of a patient (i.e., who they see and when). This involves how appointments can be coordinated and cancellations managed to minimize clinics falling behind their set times. Generative AI adds a layer of adaptability and personalization, while traditional AI models can assist with appointment reminders and scheduling algorithms. Generative AI can analyse patient preferences, historical data, and clinic resources to recommend optimal appointment times, predict no-shows, and alter real-time schedules.

Personalized Patient Experience

Patients frequently endure extended interactions with Interactive Voice Response (IVR) systems and other automated systems in order to address their concerns. Numerous agents are required to effectively manage the high volume of inquiries. Generative AI provides a solution by customizing responses to the preferences and requirements of consumers.

It even assists in conducting live agent recap inquiries or at times, as with Hubspot, applying real-time personalized guidance to those queries. Faster resolution time means happier customers, better agent productivity and lower operational costs achieved thanks to generative AI.

Claims Denial Management

Healthcare providers face increased costs due to denied claims on an annual basis. Around 60% of the denied claims are recoverable, yet only 2% were appealed. It rapidly indexes and retrieves relevant content from vast policy databases to provide context for claim appeals, thereby offering a solution in the form of generative AI within healthcare. It can also pull relevant patient information from the EHR and generate case-specific appeal letters. These capabilities provide dramatic value to healthcare systems, enabling them to recoup billions of dollars in appeals and reduce the time-consuming nature of filing claims.

Pros of Generative AI in Healthcare

Healthcare implications: This means the generative AI in healthcare can touch everything from patient care to healthcare’s multiple domain functions. Some of its benefits are:

• By using unstructured data for healthcare insurance claims and revenue cycle management, generative AI can automate back-office tasks. In fact, combining Generative AI with chatbots can also allow enterprises to handle daily IT queries like password resets and HR inquiries while heightening the employee experience and reducing administrative costs.
• For the workflow side of healthcare operations, it can be discharges, care coordination notes, checklists — in real time.
• The natural language understanding skills of generative AI could help improve Electronic Health Records (EHRs) in a variety of ways, such as automatically populating visit summaries, prompting for documentation updates, and displaying decision support research.

Conclusion

Generative AI offers newer capabilities in healthcare that were not possible earlier, and which could revolutionise the sector completely with its advanced functionality. The evolution of Generative AI could enhance various emerging technologies such as virtual and augmented reality, among other types of AI, to reform healthcare delivery.

Although these concepts appear in the future, they illustrate practical potential as Generative AI continues to evolve. Nevertheless, the use of this technology by healthcare providers must be made in a responsible and ethical manner.

Through Velan’s collaboration, Generative AI has been integrated smoothly into its solutions for expanding processes like patient scheduling, voice notes, healthcare insurance claims management and data analytics. Furthermore, Velan Solution offers healthcare providers a revolutionary chatbot with built-in gyro AI that really helps you understand patient questions and answer immediately from the institutional knowledge base. This integration is meant to improve the experiences of patients interacting with clinic or hospital support systems.