Medical billing involves every step of the association between patients and healthcare providers. From the pre-registration process to the point of visit completion, the process consists of extracting patient data, verifying eligibility, checking medical billing codes, and approving healthcare insurance claims. However, for the most part, medical billing can be time-consuming for healthcare providers.

Automated medical billing is the way to ensure on-time reimbursement. Implementing automated medical billing software enables healthcare providers to generate medical bills and submit timely claims using AI and intelligent data extraction capabilities. AI in medical billing shortens the payment cycle and reduces operational billing costs. In this blog, we will explore how automated medical billing can reduce healthcare costs, expedite the reimbursement process, and improve cash flow.

Why Medical Billing Need Automation?

An automated medical billing system streamlines repetitive tasks like data entry, claims submission, and medical coding to speed up the reimbursement process. Below are some reasons medical billing automation is mandatory for healthcare providers.

1. Lack of Proper Documentation
   A recent report stated that 74% of healthcare providers use paper-based medical billing. On average, healthcare providers complete 20000 medical forms annually; each paper document filling costs $20. Given the extensive administrative work involved in medical billing, such as patient registration, eligibility checks, and claims validation, healthcare providers must prioritize proper documentation using automation solutions.
2. Incompatible Legacy Systems
   Numerous healthcare providers still depend on outdated billing systems, necessitating staff to manually enter data. This results in substantial investments of time and finances as employees must process paper forms, input data into digital systems, and meticulously cross-check numbers to prevent errors. In a recent healthcare payment report, around 67% of consumers want digital and electronic payment options. In the face of mounting financial constraints and changing customer preferences, medical billing automation is a win-win for healthcare providers and patients.
3. Medical Coding Errors
   A report estimated that 12% of healthcare insurance claims are submitted with the wrong medical coding. Medical coding involves translating procedures, surgeries, services, and treatments into standardized medical codes, providing hospitals with a shared billing language. However, the challenge arises when coders are presented with illegible handwritten notes from doctors or incomplete forms, halting the coding process.

What to Automate in Medical Billing Process?

1. Patient Registration
   A patient care journey starts with registration; automated medical billing software can simplify the registration process. Automation solutions enable medical billers to quickly extract and verify patient data from registration forms using intelligent data extraction and add it to the healthcare system.
2. Insurance Eligibility Check & Appointment
   After patient registration, healthcare providers must check the patient coverage details to ensure bills are correctly assigned and prescriptions are covered for the bill’s entity. AI bots can easily capture patient insurance details from their ID cards and check insurance eligibility by logging into the insurance portal and scheduling appointments.
3. Medical Coding & Compliance Check
   Automation and advanced algorithms can analyze medical documents and assign appropriate medical codes for billing. Automated medical billing systems ensure that medical codes comply with HIPAA regulations. It also facilitates the generation of insurance claims by compiling all relevant information, including patient demographics, provider details, and procedural codes. Once the claims are compiled, they are electronically submitted to insurance companies, expediting the reimbursement process and reducing the administrative burden on healthcare providers.
4. Claims Transmission & Adjudication
   As per HIPAA regulations, all healthcare providers must submit claims electronically. RPA in medical billing can be used to ensure claims are processed error-free. Besides claims transmission, an automated medical billing system provides an accurate claims adjudication process to inform patients whether a claim is accepted or rejected. Healthcare providers can quickly assess claims by comparing them against the pre-defined rules, policy terms, and historical data to evaluate coverage eligibility and determine payment amounts.
5. Patient Statement Generation
   Automated patient statements comprehensively outline all services provided by the healthcare provider. These statements include the bill for procedures, with patients responsible for any remaining payments after the payer settles the agreed-upon amount. Additionally, specific payers may provide an Explanation of Benefits (EOB) to clarify the coverage details for procedures, including explanations for any discrepancies in coverage. Healthcare providers can share these statements automatically using email automation with their patients.
6. Patient Follow Up
   Medical billing automation is vital in this process, as it can streamline bill generation, delivery, and payment reminders. Automated systems can schedule and send bills electronically, reducing the reliance on manual mailing processes. Additionally, they can send automated notifications to patients regarding upcoming payments or overdue balances. By implementing automation, healthcare providers can improve the efficiency of their billing operations and enhance their revenue cycle management.

Conclusion

As technologies continue to evolve, the capabilities of automated medical billing will only expand. Implementing medical billing automation can ensure interoperability between different healthcare IT systems, ensuring accurate medical billing and a better return on investment. AutomationEdge’s CareFlo ICD Coding bot enables healthcare providers to reduce manual effort in coding search and allow/reject updating the ICD codes identified by the AI system. Contact us to learn more about AutomationEdge CareFlo.

In the healthcare industry, every second counts. Whether managing patient records, initiating healthcare insurance claims, or scheduling appointments, administrative tasks can overshadow the primary goal of offering value-added patient care. What if there was a way to alleviate the administrative task of data management and give healthcare providers enough time to focus on patient care? Healthcare document automation is emerging as a valuable solution to healthcare data interoperability challenges.

The Challenge of Moving Data, Not Documents

In the healthcare sector, the central challenge lies not in mere document transfer between systems but in meaningful and efficient data movement. Forms are omnipresent in healthcare operations, particularly within healthcare providers, where every patient service entails submitting billing claims.

Healthcare providers need to fill an average of 20,000 forms every year.

These forms are fundamental to the healthcare ecosystem, often standardized and approved by organizations like the American Medical Association (AMA). However, manual filling remains prevalent despite standardization, leading to extensive paperwork. The sheer volume of forms in healthcare is immense and varies depending on the services provided. Thus, healthcare providers must adopt healthcare document automation to create better data interoperability.

Document automation in healthcare involves using intelligent document processing and AI capabilities to extract, analyze and process data seamlessly with minimal human efforts.

Digitizing Top Healthcare Documents to Simplify Data Processing

The types of healthcare documents to automate using automation and AI technologies are-

1. Registration Form

   AI algorithms can analyze patient demographics and extract patient data from pre-populated forms to speed up registration. Healthcare providers can also integrate the registration data with insurance providers to automatically verify insurance eligibility.

2. Patient Records

   Managing patient records is complex, especially in extensive healthcare facilities. AI-enabled electronic health record (EHR) management can automatically extract data from doctors’ notes, prescriptions, and test results and create a centralized repository for easy access. Natural language processing technologies can categorize medical information effortlessly.

3. Medical Transcription

   Transcribing dictated medical notes and reports manually is time-consuming and prone to errors. Medical document automation can automatically transcribe these voices into texts. Healthcare providers can also train AI models to learn from feedback and corrections provided by healthcare professionals, improving transcription accuracy over time and adapting to different accents and speech patterns.

4. Billing Forms

   Healthcare document automation solutions can analyze patient data and diagnoses to automatically assign the correct medical codes for billing, reducing errors and delays. Medical document automation can validate the extracted data against predefined rules and criteria to ensure accuracy and completeness. This helps identify errors or discrepancies in the billing information and flag them for further review or correction.

5. Healthcare Insurance Claims

   Processing healthcare insurance claims manually leads to reimbursement delays. Healthcare insurance document automation can automate claims processing by verifying patient eligibility, checking for errors, and submitting claims electronically. AI bots can also analyze historical claims data to identify trends and patterns, helping insurers make more accurate predictions and improve risk management.

6. Prescription

   Document automation in healthcare can assist healthcare providers in generating electronic prescriptions, checking for drug interactions, and ensuring compliance with regulatory guidelines. By analyzing patient data and medical histories, healthcare providers can personalize treatment plans and medication recommendations, improving patient safety and outcomes.

7. Test Results

   Automating the processing and analysis of test results using AI can significantly reduce turnaround times and improve patient care. AI algorithms can interpret diagnostic tests such as medical imaging scans or laboratory tests, assisting healthcare professionals in diagnosing conditions accurately and efficiently.

Way Forward

Adopting healthcare document automation enables healthcare providers to address all the data interoperability challenges and focus on driving value-added patient care. In future healthcare, document automation will involve advancements like telemedicine integration, predictive analytics, and large language learning models like Generative AI to simplify data interoperability.

As we stand on the cusp of a new era in healthcare, embracing the opportunities that AI and machine learning can improve the efficiency and effectiveness of healthcare delivery. AutomationEdge’s healthcare solution can help make this goal achievable. AutomationEdge healthcare automation includes technologies like intelligent document processing, AI chatbot, ready solution workflow and service desk automation that can simplify healthcare data management.

Approximately 97% of the data handled by the insurance industry is unstructured, and the most valuable information lies within the adjuster’s notes, comprising unique words, acronyms, and abbreviations. Surprisingly, insurance firms utilize less than 3% of this data for decision-making, a revelation that highlights the untapped potential in this sector. Intelligent document processing (IDP) presents a solution to process unstructured data effectively, leading to improved customer satisfaction, operational efficiency, and overall business performance.

In the insurance domain, every step of the process heavily relies on documents, often in handwritten, PDF, or image formats. Human processing of these documents introduces the risk of errors, delays, and false assumptions, which can lead to significant issues. Intelligent document processing emerges as the optimal choice for handling sensitive documents, as it ensures better compliance and outcomes.

Before delving into how Intelligent document processing addresses bottlenecks in the insurance industry, it is crucial to understand the impact of Intelligent document processing by examining the conventional claim processing model in this sector.

Steps Involved in Insurance Claims Processing

The insurance industry comprises various types of insurance, each with its unique process flow. The standard claim processing procedure typically starts with policyholders reporting an event and submitting supporting documents to raise a claim request. Most of these documents are unstructured which makes the claims processing more complex. The steps involved in Claims processing are-

1. Reporting – Once All the event-related documents are submitted, and the claim request is raises, an insurance agent is assigned to look into the claim. From here, all the process is done manually.
2. Initiation of the investigation – Once the claim has been initiated, the insurance agent investigates and determines the claim amount.‍
3. Policy review and evaluation – Following an investigation, insurers review policies, seek expert advice, and determine the extent of damage covered by insurance.‍
4. Payment – Once a claim is initiated by the claim officer, the policyholder is notified.

Challenges with Manual Insurance Claims Processing

According to the Accenture report, poor claims experience can put up to $170B of global insurance claims at risk. Some of the challenges that come with manual claims processing are-

1. Slow Processing Times

   Manual claims processing involves the physical handling of documents, which can be time-consuming. It requires manual verification, data entry, and routing, leading to delays in claim approval and settlement.

2. Human Error

   The reliance on human operators for data entry and processing increases the likelihood of errors. Even simple mistakes in data entry can lead to claim processing inaccuracies and delays.
   

3. Data Inconsistency

   Manual data entry can lead to inconsistencies in how information is recorded and stored. This can cause difficulties in data retrieval and analysis, affecting decision-making and reporting.

4. Difficulties in Retrieving Information

   Manually managed documents may not be easily searchable or retrievable. Finding specific information within a pile of physical documents can be time-consuming and frustrating.

5. Data Security

   Physical handling of documents can pose security risks, including loss, theft, or unauthorized access to sensitive customer information. Protecting data privacy and security becomes more challenging with manual processes.

6. Limited Data Analysis

   Manual processing hinders the ability to perform in-depth data analysis on claims trends and patterns. Analyzing data is crucial for identifying fraud, improving processes, and making data-driven decisions.

How Intelligent Document Processing Solution Fast Track Claims Processing?

Intelligent Document Processing (IDP) plays a significant role in streamlining and improving the efficiency of insurance claims processing. This solution combines technologies like Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning (ML) to automate and optimize the handling of these documents.

Source Here’s how Intelligent Document Processing can help in insurance claims processing:

1. Data Extraction

   Intelligent Document Processing can automatically extract relevant information from various types of documents, eliminating the need for manual data entry. This includes extracting policyholder details, claim amounts, incident descriptions, dates, and other critical data points required for processing the claim.

2. Faster Processing

   With Intelligent Document Processing, the claims processing time is significantly reduced since manual data entry is eliminated. This leads to faster claim approvals or rejections, ultimately enhancing customer satisfaction and improving the overall claims handling time.

3. Accuracy and Error Reduction

   Human involvement in data entry is prone to errors, but Intelligent Document Processing systems can achieve high accuracy rates in data extraction and analysis. By reducing human error, insurers can avoid costly mistakes that might lead to claim delays or incorrect payouts.

4. Fraud Detection

   Intelligent Document Processing can be integrated with fraud detection algorithms to flag suspicious claims based on historical patterns and anomalies found in the data. This helps insurers identify potentially fraudulent claims early in the process and take appropriate actions.

5. Claim Validation

   Intelligent Document Processing can cross-reference data extracted from various documents against existing policy information and other databases to validate the legitimacy of the claim. This ensures that only valid claims are processed, reducing the risk of payouts for fraudulent or ineligible claims.

6. Seamless Data Integration

   Intelligent Document Processing solutions can be integrated into existing insurance claims systems, allowing for a seamless transition to automated document processing without the need for a complete overhaul of the existing infrastructure.

[Also Read: How Automated Claims Processing Accelerates the Customer Experience in Insurance Industry?]

How can AutomationEdge Help?

AutomationEdge DocEdge is an IDP tool, is designed to automate the extraction and processing of data from unstructured documents. It leverages technologies such as Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning (ML) to intelligently recognize and extract information from various types of documents, such as invoices, purchase orders, insurance claims, bank statements, and more. Irrespective of industries, intelligent document processing can be used in banking, healthcare, insurance, and many others. To know more about how you can automate data extraction and processing across your enterprises, do contact us here.

Ever since the government has introduced the AADHAAR card as a unique identification number, it is considered an important document for the proof of identification for all citizens.

Its regulations are governed by UIDAI, this 12-digit unique AADHAAR number is used by citizens to avail many government schemes and use it as ID proof for opening bank accounts, KYC, income tax return, loan processing, and other banking purposes.

However, with sharing comes the risk of data breaches and security. And what if some fraudster catches hold of it, and misuses it? Hence, in line with the prevention of money laundering cases and security of AADHAAR card data, AADHAAR masking is mandated by RBI.

In May 2019, RBI updated the master direction on KYC guidelines. One such update makes it mandatory for RBI regulated entities to mask the AADHAAR card of all the customers from the AADHAAR image as a part of the KYC process.

What is AADHAAR Masking?

The AADHAAR card masking process includes the concealing of the first 8 digits of the AADHAAR number captured in the application form as a part of the proof of identity and keeps the rest of 4-digits visible

Other demographic information such as name, DOB, gender, address, and QR code remain the same such as. It is digitally signed by the competent authority UIDAI and it is masked at the point of capture before it’s stored permanently in a database system

AADHAAR Masking Challenges for BFSI Industries

As per the government guidelines, one needs to submit an AADHAAR card in multiple instances like opening a bank account, insurance, fixed deposit, video KYC, and in the onboarding process.

And the KYC documents that are presented at the time of verification are kept as scanned copies.

Banking and financial companies will have a bulk of customer-proof images in their database and mainstream systems that will not be segregated and classified.

Moreover, the existing KYC process is encompassed with various stages of human intervention including collection & submission of relevant documents at branches, scan & upload of hard copy in the business process management system, and manual verification of image quality.

And extracting particular data from an image is a time-consuming and costly process. Hence, to accelerate the customer experience and employee productivity it is crucial to classify and segregate the KYC documents.

The advanced technologies like RPA and AI can detect 12 digit AADHAAR card numbers from images and videos accurately. Even in the KYC guidelines mandated by RBI, the regulator allows the use of AI and automation technology for such tasks

[Also Read : Intelligent Document Processing: The Complete Guide]

Solution to AADHAAR Card Masking – Automation

AutomationEdge brings an end to this time-consuming and mundane task by introducing AADHAAR Card Masking Bot.

It provides the customer an easy solution of masking AADHAAR data by default within a few seconds.

With an AADHAAR card masking bot, BFSI companies can now automate AADHAAR card classification, extraction, and mask the first eight-digit of an AADHAAR card in minimal time. This automated process will-

• Enhance customer experience
• Cost-effective & time saving
• Prevent misuse of customer data
• Maintain confidentiality of data
• Enable employees to visually inspect bad images & correct manually

“AutomationEdge AADHAAR Masking Bot can mask 1000 images in just 22-25 minutes.”

This bot can also be customized to mask name, DOB, complete AADHAAR number, QR code, and any other information present on the AADHAAR Card. Request a Bot

Regardless of industries, AutomationEdge’s personalized AADHAAR masking enables its customer to upload the AADHAAR details directly to your web and can view the output folder for masked images and output log files

AutomationEdge uses AI and intelligent document processing abilities like OCR, NLP to generate high-quality storage efficient images and modify them to suit the customer’s needs for style and masking.

Considering the manual intervention and time-consuming stages included in the KYC process, the value of the AADHAAR card masking solution is quite evident in the Banking, Financial Services and Insurance (BFSI) companies.

AutomationEdge’s Bot store accelerates the robotic process automation initiative by providing ready action connectors and plug-and-play bots to automate complex business processes into simplified norms in a cost-effective and timely manner. Visit Bot Store