8 Key Use Cases of AI in Healthcare Administration and Their Benefits

Healthcare providers can reduce administrative burden and cost (around $265 billion annually as per McKinsey report) if their workflow is more streamlined. AI can help! We have mentioned 8 examples of AI in healthcare administration that are the rich targets of the industry. Let's explore them.

How AI is Reducing the Administrative Burden in Healthcare 

1. Revenue Cycle Management (RCM) and Billing

Billing and coding are major drivers of increased administrative expenses in the healthcare industry. There is much to do and manage, from managing bills, claims, and denials to handling disputes. 

On top of this manual process, healthcare providers need skilled staff to perform these tasks as per the complex regulations and stay updated with the frequent changes in billing codes. Hiring a skilled workforce means more spending on salaries. Artificial intelligence can help you balance both by: 

• Automatically reviewing claims, cross-checking them with payer requirements, and flagging errors before submission. 
• Translating physician notes into accurate medical codes reduces errors and ensures faster billing.
• Predicting which claim can be denied based on historical data saves staff time manually reviewing each claim. 

2. Discharge Instructions

Paper-based discharge instructions cost healthcare providers additional paper, ink, printing, and storage costs. Manually preparing instructions for each patient takes more time for the staff. 

Using AI in healthcare management can help in rapidly preparing discharge instructions for each patient based on their treatment. 

It can take information from EHRs and prepare electronically accessible instructions, saving time and printing costs. It can also remind patients about follow-up care and post-discharging care plans, reducing readmission rates.

3. Appointment Scheduling

Scheduling appointments and managing communication between doctors and patients is a real headache. This is less hectic for a small hospital with a low patient volume. 

However, the workload is greater in large healthcare settings as more staff is required to meet the complex scheduling needs and manage communication. Scheduling inefficiencies become common in such environments, leading to no-shows, booking clashes, and mismatching of clinician and patients availability. 

AI-based scheduling tools can tackle this issue head-on.

• By matching patient-clinicians' timing to book appointments at suitable times and days, this efficient matching reduces the chances of no-shows and cancellations. 
• By guiding patients on appropriate bookings for their conditions and answering their common inquiries about time, hospital services, and follow-ups.

4. Prior Authorizations (PA)

Clinicians must manually analyze the treatment plan and medication best according to the patient's current conditions and take prior authorizations. Doing this with utmost care and precision can delay the needed care patients need when done manually. 

It takes around 15 hours per week to get 30 prior authorizations, making PA an extremely high-burden task, as per the American Medical Association. AI can save clinicians these hours by automating the process smartly, checking a patient's health plan and medical benefits. 

It can help clinicians decide in real time whether prior authorization is required. If PA is needed, the AI-driven system can help with the documentation and send it to the payer. Faster document processing speeds up the approval process, reducing unnecessary delays due to workload or documentation. This ensures patients receive care at the right time. 

5. Licensure Verification

Verifying clinicians' licenses is an important but time-consuming and resource-intensive job. Healthcare providers must ensure that every clinician is well-trained and certified to pursue the profession. This requires primary source verification, telephonic verification, and contacting credential verification organizations (CVOs), state medical boards, and medical specialty boards. 

Using secure healthcare software and complying with regulations are also important parts of license verification. If discrepancies are found, the entire process can take weeks or more, delaying patient care. 

However, manually screening each document and cross-verifying the clinicians' credentials and practice history is challenging. The rigorous verification process makes it even more difficult to identify tampered documents and false identities. 

How can AI speed up the process?

AI techniques like image recognition, facial recognition, pattern identification, and behavior analysis can help detect faulty licenses faster. They also bring more accuracy to the process. 

6. Quality Measure Reporting

Again, providing value-based care to patients and ensuring they receive reimbursements quickly for their healthcare expenses is time-consuming. Compiling all the records and submitting the necessary data for reporting can be labor-intensive. AI is streamlining this process. 

AI systems can reduce the effort and documentation. It can automatically extract relevant quality measures based on each record and determine which measures meet the requirements of specific health plans or reimbursement models. AI can also automate the data submission and generate quick reports with all the insights. 

7. Admission Procedures

Patient admission is a complex process. The admission process involves many steps, from registration, admission interview, bed confirmation, and bio-data collection to patient identification. 

This can delay or forego the needed care. AI can speed up by prepopulating online admission forms for repeated patients. AI bots can act as an initial window for streamlining registration, admission interviews, bed confirmation, and patient verification for new patients. 

These bots can examine patients' initial conditions and submit them to the EHR for further processing. By integrating with different departments, AI can notify clinicians and doctors about patients' arrival. 

8. Customer Service Responses

Answering patients' urgent queries outside the business hours of hospitals is a common challenge in the healthcare industry. AI-powered chatbots and virtual assistants can solve this by serving patients 24x7. 

AI-driven chatbots allow patients to book appointments, ask for general health advice, or check the status of their claims, reducing the administrative burden. 

These are the key use cases and benefits of using AI in healthcare. 

Five Traits That Show AI is Perfect for Healthcare Administration 

As per a recent survey, healthcare executives choose to automate administrative tasks. However, identifying which tasks are the perfect fit and will be more beneficial for the staff can be challenging. 

There are a few characteristics that will help you identify those tasks. Use AI in healthcare administration for

• Manual tasks
• Repetitive tasks
• Transactional tasks
• Tasks with limited or unstructured data 
• Tasks that generate data in abundance 

Conclusion

Administrative tasks in the healthcare industry account for 25-30% of overall spending and consume 15-20% of clinicians' time. Even a simple job like patient admission can consume significant staff hours daily. 

Despite appropriate software, administrative tasks are still a burden and exhaustive, causing unnecessary costs, delays, and burnout among the staff and clinicians. The above AI use cases in healthcare can reduce all three, bringing more accuracy, precision, and quality patient care. Besides these benefits of AI in healthcare, hospitals can improve their financial performance while complying with standard regulations. 

A healthcare solution development company can build specific AI solutions for all the above use cases or any custom requirements. Feel free to contact us for more information.