Automated Medical Coding: The Best Solution to Avoid Denials and Plug Revenue Leakage

Role of Medical Coding in Healthcare

Medical coding is a vital aspect of the healthcare industry, ensuring accurate billing and reimbursement for medical services. It streamlines the billing process, reduces errors, and improves efficiency by facilitating data exchange among healthcare professionals, payers, and patients. It supports the financial sustainability of healthcare organizations by ensuring fair and timely payment for services. Medical coding also contributes to medical knowledge and innovation by organizing data for analysis and research. It offers a rewarding career path for those interested in medicine, coding, and technology, and provides opportunities for professional growth and development by adapting to changes in coding systems, rules, and guidelines.

Challenges with Coding Systems in Healthcare

1. Payment Accessibility

This is a common challenge with most billing software for doctors. Inaccessibility to payment modes causes significant delays in the code completion process and overall profit of the healthcare organization. An automated health system software needs to be incorporated with payment options that increase the accessibility to payment by providers, insurance companies, and patients alike.

2. Non-responsive Customer Support

Automation in healthcare that fails to offer efficient customer support can do more damage than good. Medical coding is complex and requires consistent and clear customer support tools for smooth functioning and higher patient satisfaction. If the patient cannot get their doubts addressed, automation in medical billing can be considered redundant.

3. Filling Out Multiple Claims

Healthcare billing efficiency is dependent on accurate claims processing. When multiple claims have been involved, the complexity of this process is drastically increased. Physician billing companies face a great challenge in filling out various claims, owing to a large amount of paperwork involved. This makes the process error-prone and affects the bottom line of the organization. Automated healthcare solutions must be provisioned with the capacity to address multiple claims simultaneously.

4. Implementation Hurdles

Even if the healthcare organization has chosen cost-efficient medical automation systems, healthcare process automation that is not easily implemented can cost more in modifications. Implementing success is a primary factor to qualify among the best-automated health services. A simple medical automated system that offers minimal implementation hurdles is ideal. Over years of experience, it has been repeatedly observed that the best billing software is free of implementation hurdles toward health care management.

5. Patient Education

A medical coding practice software involves patient, provider, and payor usage. While automating healthcare processes, patient education must be taken into account. Educating the patient on the billing process and accurate and timely information filling are essential factors toward patient education. The efficiency of automation in hospitals is dependent on seamless patient education with patient engagement systems that can simplify the process.

6. On-time Payments

Anyone involved in the healthcare space can attest to the challenges of receiving on-time payments. Medical billing management is continually facing the challenge of late payments that affect the organization’s overall profit. The complexity of medical coding plays a significant role in delayed payments. The right medical coding solution should have ample provisions to ensure on-time payments through integrated healthcare solutions.

Medical Coding Automation Tools

1. Computer-assisted coding (CAC)

Computer-assisted coding (CAC) is a natural language processing (NLP) solution that aids in the faster and easier coding of unstructured data. It functions like a spell checker for medical records, analyzing documents to identify words and phrases related to classification. CAC can distinguish between phrases with the same keywords but different meanings due to their context, suggesting a code when a diagnosed condition is detected but not in the patient’s family history.

2. Artificial intelligence

Medical coding AI is enhancing the capabilities of CAC by incorporating machine learning. It can remember corrections made by human coders and apply them in future cases. AI can also analyze legacy documentation to identify patterns and make predictions, such as evaluating the likelihood of readmission. Applying AI to data processing solutions results in faster, more accurate, and autonomous software for medical coding and billing, which helps with unstructured data like doctor’s notes. Although AI-powered medical coding solutions have not achieved total autonomy, they are expected to become essential tools in the future. However, complex machine learning systems require more computing power, high-quality data, and data science experts. An affordable solution is available for healthcare facilities operating on tight budgets without the need for medical data analysis experts.

3. Robotic Process Automation

Medical coding is an ideal candidate for Robotic Process Automation (RPA) due to its repeatability, rules-based nature, and high volume of structured data. RPA systems can efficiently handle simple workflows and heavy amounts of textual data, making them an ideal choice for medical documentation. Despite lacking AI’s decision-making and learning skills, RPA outperforms AI in efficiency. It is also a relatively affordable solution, requiring only qualified personnel for deployment and maintenance, and can be easily operated by non-technical personnel once deployed.

Data Mining and Analysis for Automated Medical Coding

Data analysis is analyzing large chunks of data to discover meaningful patterns and trends through complicated mathematical calculations that offer predictable outcomes. The process creates a possibility to decipher coding complexities that remain beyond the bounds of manual analysis.   

The healthcare industry deals with data in large volumes. More and more organizations are opting for medical informatics to gain insights into their workings. Data mining is now more accessible to medical coding vendors, with everything from servicing to IT infrastructure being outsourced. From overcoming business challenges to increasing the efficiency of everyday workings, the benefits of data mining in healthcare remain unprecedented. Data mining and analysis in automated healthcare solutions can offer the below features:  

1. The automated revelation of patterning

This is done by creating models that use algorithms to determine a specific set of data. These models create discoveries by analyzing large chunks of data. They are created in a way that can lend themselves to various sets of data and can also be customized based on selective data through healthcare cloud computing.

2. Analysis of possible outcomes

The discoveries that these models offer extend into future predictions, including predictive incomes, revenues, sales, etc. The probability factor of these predictions is also easily gaugeable.

3. Summarized information for action

By grouping the data into meaningful sections, data mining can offer a summarized view of information that can be actionable toward practice management solutions.Even though simpler data techniques and statistics analysis use data for intelligent coding, their capabilities don’t even come close to the complex abilities of data mining. This makes the latter superior to conventional statistical analysis for medical coding software. The automated nature of data mining models reduces the dependence on manual entries, and much larger amounts of data can be used.

How Does EHR Help with Medical Coding?

Medical coding services have become more efficient with the widespread adoption of Electronic Health Records (EHRs). EHR systems allow physicians and providers to translate diagnoses and procedures into codes during office visits, such as ICD-10 and CPT codes. This also makes medical billing solutions easier, as billing specialists can refine codes based on each payer’s rules. EHR and EPM systems like NextGen allow data from patient charts to flow directly into billing, reducing data entry errors and saving practice time and money. Overall, EHRs have made medical coding more efficient and efficient.

Predictive Analysis in Medical Billing and Coding Software

Predictive analysis features in coding solutions can go a long way to manage reimbursement cuts and control patient claims efficiently. These analytical tools will help predict coding errors and therefore increase the likelihood of efficient functioning while avoiding unnecessary financial costs. These tools also aid in identifying areas of billing errors and substantially reduce the risk of subsequent inefficiencies.   

Future predictions allow software for medical billing and coding to adopt strategies that diminish the likelihood of rejected or denied claims. The evidence gained from predictive analysis allows medical coders and billers to incorporate strong and efficient categories into practice early.   

Predictive Data Analysis uses the following information to make intelligent predictions through electronic data interchange:  

• A comprehensive record of bills submitted by healthcare providers  
• A quantum of data related to the billing and coding of each practice  
• Supporting documents related to a particular or a group of claims  
• An analysis of claims submitted  

While it is virtually impossible to identify misdoings before they occur definitively, predictive data analytics efficiently points computer-assisted coding software vendors in the right direction, wherein qualitative investigation can occur to minimize its susceptibility to wrongdoing. 

Fraud Detection in Medical Coding Solutions

With the ongoing instances of fraud in medical billing and coding continually rising, medical software solutions are now being looked at to address and identify frauds. With the intelligent capturing capability of advanced telehealth solutions, fraud can be identified, and there are provisional ways to eradicate the possibility of them taking place completely.   

Medical coding and billing software accumulate data to prevent fraudsters from accomplishing their goal. Data mining technology is used to gather the data through expert techniques within the analytics system. This data is then converted into meaningful analogies and standard measurements, culminating into an Enterprise Data Warehouse (EDW). EDW then works as the basis for further data investigations that can identify fraud.   

Through this EDW, data mining identifies healthcare providers whose:  

• Coding and billing strategies and actions vary from their regular practices  
• Coding and billing systems that differ significantly from their competitors  

This is done through the analysis of the healthcare providers:  

• Area of practice  
• Location   
• Type of healthcare service offered  
• Frequency of billing  
• Size of operations  

Prescriptive Analysis in Automated Medical Coding

Just as it works wonders toward remote health monitoring systems, predictive analysis tools can go a long way to manage reimbursement cuts and control patient claims efficiently. These tools also aid in identifying areas of billing errors and substantially reduce the risk of subsequent inefficiencies.   

There is a noticeable increase in value that the medical billing and coding companies will notice from mining their data. The future predictions can lend coding companies to adopt strategies that will diminish the likelihood of reduced productivity and increase overall performance through intelligent evaluations. The evidence gained from predictive analysis allows medical coders and billers to incorporate strong and efficient categories into practice early.   

Predictive Data Analysis uses the following information to make intelligent predictions:  

• A comprehensive record of bills submitted by healthcare providers  
• A quantum of data related to the billing and coding of each practice  
• Supporting documents related to a particular or a group of claims  
• An analysis of claims submitted  

While it is virtually impossible to identify misdoings before they occur definitively, predictive data analytics efficiently points the medical billing and coding industry in the right direction, wherein qualitative investigation can occur to minimize its susceptibility to wrongdoing.

Medical Coding Best Practices

1. Build Medical Coding into Your Workflows: Medical coding should be a standard procedure for complex office visits, focusing on high-dollar and complex cases. Real-time coding improves accuracy, reduces after-hours work, and prevents physician burnout. It also ensures quick prescriptions and referrals, and clean claims submission for the practice. Therefore, medical coding should be a priority for the medical coding team.

2. Automate Billing Processes Where Possible: Automating all the billing tasks is essential for improved operational efficiency. EHR and EPM automate billing tasks, saving time and money. The Background Business Processor allows overnight batch claims submission and automatic claims run against the claims edit library to identify potential errors. This frees medical coding staff to focus on complex cases and process improvements, reducing strain on the system.

3. Customize Your EHR: Customization options in EHR systems allow practice to tailor their workflows and clinical templates. These include coding rules for common payers, and ensuring accurate data collection during office visits. Additionally, the EHR can build a claims library for efficient claim scrubbers.

Conclusion

Healthcare organizations can reap immense benefits from agile and efficient automated coding software. Automated medical coding solutions relieve healthcare staff to focus on clinical processes and medical treatment, focusing on increasing patient satisfaction. The benefits of adopting an automated medical coding system completely outweigh manual coding processes. There is no doubt that the healthcare industry will witness an increasing reliance on data mining for medical billing and coding purposes. However, it is important to remember that these techniques keep evolving. Therefore, medical experts need to make an added effort to keep up to date with the ever-changing technologies to obtain maximum gain from them.