The use of Artificial intelligence (AI) may have stated as a competitive necessity. But the way it transformed business, industries and lives over the years, its importance and acceptance became universal. Its use has turned out to be a massive success in the healthcare sector, mostly among providers.
The potential and application of AI in providing care is not limited to filtering records of chronically ill patients in need of urgent and critical care. AI is the steppingstone for providers to get closer to a comprehensive approach for management of chronic and acute diseases. In the treatment of chronic diseases, it helps clinicians to strategize long-term care plans much more effectively.
A Deloitte dossier has pointed out that AI adoption in healthcare is in the initial stages. But it is “quickly gaining traction and ultimately Al is expected to have a huge transformational impact on the business of healthcare and on how care is delivered”.
It highlights how providers can use AI to improve “every aspect of patient engagement”, right from appointments and accessing medical records to maintaining contact with the staff and care coordination teams.
1. Easy Access for Patients
AI can be a boon for patients who often struggle to manage their care owing to a complex process to book appointments and access medical records. The report underlines the following points on how AI can help patients overcome these barriers.
- AI makes complex medical information easier for patients to understand. Natural language processing can parse complicated medical information/data into meaningful insights for patients—and then communicate those insights to them, increasing their health literacy.
- It streamlines communication between health care workers by filtering out extraneous information. AI and machine learning solutions can improve internal communication between health care workers by enabling systems to collect and share relevant information only with those who need it.
- It accelerates and improves database searches. AI-enabled databases can execute queries faster and more accurately, reducing the time required to find information and improving database reliability.
- It makes chatbots smarter. Natural language processing and machine learning can train chatbots to perform better on a wide range of tasks, such as addressing patient questions, scheduling appointments and calls, and referring patients to other departments. Chatbots and call automation can also be used for outpatient follow-ups and check-ins.
- It creates and executes personalized plans for engaging with patients. Prescriptive analytics can suggest personalized next best actions for patients, with appropriate “nudging” and other tailored engagement activities.
2. High-precision Diagnosis
Another important use of AI by providers is diagnosing medical conditions — which is usually considered to be a complex task — with high precision. The dossier points out multiple factors that make diagnoses a complicated process, like unavailability of “genetic background, lifestyle, and detailed medical history”. Applying AI in diagnosing medical conditions can go a long way in helping clinicians.
- AI can analyze vast quantities of medical data. It can analyze vast amounts of data from a wide range of sources and then connect the dots, uncovering complex patterns and disease characteristics that humans might not be looking for.
- AI can provide recommendations to medical practitioners. Through focused application of AI technologies such as deep neural networks, machine learning, and categorization, medical practitioners can rely on AI for more accurate and efficient analysis of patient data.
3. Operational Management
Besides care management, AI comes in quite handy in operational and administrative processes to ensure a smooth functioning of hospitals. The report says providers can press predictive AI into service to “forecast peaks and valleys” in patient volume that can help them arrange staff and resources accordingly. AI can help hospitals:
- Predict future resource needs based on historical data and real-time situation analysis. Data mining, modeling, and AI can help organizations make predictions based on historical data and real-time situation analysis. AI-based prescriptive analytics can provide indications of future resource needs for different scenarios, like determining the optimal inventory to satisfy an uptick in hospital readmission, or what new machinery or supplies are needed to meet seasonal demand.
- Comprehensively analyze enormous amounts of detailed data. AI and machine learning can analyze all available data comprehensively and in detail can provide a much clearer picture of health status.
Identify high-impact patterns and trends. Thorough AI-enabled analysis of various data sources can reveal hidden trends and patterns with the potential for large-scale impact, like areas at elevated risk of supply shortages.
4. Medical Billing: The AI is here the way billing and medical coding is handled. The sun has set on manual billing. With ever increasing piles of billable codes, the AI is the answer to the speedy and effective services.
- Bill processing: They say at present, there are over 70,000 billable codes in the healthcare industry. It is an uphill task for coders who try to match the codes with corresponding medical visits. To assist the new coders and find alternatives to the aging workforce, the providers need a more intelligent backend system for quick and error free processes.
- Handles Volume: It frees up the human source from repetitive but important tasks to organize, rearrange and match all the documents related to the billing. From speech recognition for clinical documentation to machine learning for data extraction, the tech is improving medical financing.
- Combat team burn out: Translating the EHR data into codes accurately and quickly is a process that takes years of experience to carry out effectively. The AI can be employed in the billing, be it to reduce the pandemic-induced fatigue, aging workforce or handle the increasing demand of human resources to handle finance.
- Administrative Automation: Medical coding solutions use EHR notes to translate health services into billing codes, and AI-powered RPA platforms can extract data from EHRs to populate claims forms. Plus, it lets users automatically conduct audits while narrowing margins of error.
5. Medical Coding: According to IBM, With the exponential growth of healthcare data and complexity expected to continue, the expertise of medical coders remains in high demand. The US Bureau of Labor Statistics projects 8% growth for the profession between 2019 and 2029, which is faster than the estimated growth rate for other professions
- Address code complexity: The overwhelming increase in codes has accelerated the incorporation of AI when it comes to accurate code assigning. After the successful use of the computer-based coding system, the AI-based coding systems will enhance code validation and identification. Available with real-time reports, which will in turn lead to accuracy and efficiency of coders.
- Better coding practice: AI coding helps to contextualize the data, extract information from multiple sources and rearrange them. It omits the setbacks of existing coding practice. The result is a seamless medical billing and coding process.
Conclusion: There are already plenty of examples across the globe to give us a sense of how AI could transform patient care and diagnoses. From more focus on care delivery and improved patient engagement to reduced time and cost, and addressing patient needs, reasons galore for healthcare providers to embrace advanced AI.