Healthcare Artificial Intelligence: Elevating Outpatient Medical Coding Automation
Artificial intelligence (AI) and machine learning (ML) algorithms are transforming almost every industry, and healthcare is no exception. Many healthcare organizations are leveraging AI to better allocate resources, predict patient outcomes, and schedule personnel. AI and ML can help to augment medical coders – coding outpatient services efficiently and accurately – allowing coders to devote more time to complex tasks.
How does Artificial Intelligence help outpatient medical coding?
Manual outpatient coding has several drawbacks, including lower productivity, lower case review rates, and a longer physician response time, all of which slow reimbursement. The Diagnostic Related Grouping (DRG) assignment is not optimized through manual coding, and the staff will be unable to track queries effectively. AI is a powerful automation tool that addresses the flaws in manual outpatient coding processes. This can aid HIMs optimize the coding quality, ensuring prompt reimbursement, better managing hospital finances, and improving patient care. Experienced coders don’t need to waste time coding simple charts when they can focus on more complex tasks that machines can’t handle.
Benefits of AI in Outpatient Medical Coding
- Tailored Patient Care
Healthcare AI analyzes the outpatient data collected from physician’s records, diagnostic results, and lab tests and compares them with medical protocols, recommendations, and clinical procedures. Medical staff can use the results to determine whether any additional testing is required and the best course of treatment for the patient. AI allows medical treatment to be tailored to outpatient care.
- Computer Assisted Coding (CAC)
Computer-Assisted Coding, or CAC, amalgamates various features of AI and Natural Language Processing (NLP). AI-powered CAC software can evaluate and interpret physician notes, assign modifiers, detect errors, and recognize coding edits, freeing medical coders to focus on other tasks. Its dedicated algorithms can extract clinical facts and assign the appropriate E/M code. Identifying, extracting, and feeding it into the system are no longer concerns for physicians. Additionally, AI-powered CAC provides real-time feedback to the healthcare system and physicians about the precision of E/M codes used during outpatient visits. Cloud-based systems are not only secure, but they also offer easy access to data, enhance data scalability, and reduce process downtime.
- Computer-Assisted Clinical Documentation Improvement (CDI)
NLP-enabled CDI synched with EHRs (Electronic Health Records) help medical staff fill gaps in clinical data. This tool can examine documentation in real-time, enabling CDIs, coders, and physicians to work concurrently in improving document quality. Charts can be prioritized based on suggested queries and held in a work queue prior to coding to ensure queries are not missed. Each chart can be predictively coded based on current documentation and queries suggested based on potential coding opportunities or missing support.
- Real-time feedback
Real-time feedback helps coders improve faster. Assume a new coder makes a mistake in coding an outpatient chart. The AI assistant will immediately flag the error, suggest a solution, and notify the coder of the change’s repercussions. This way, the accuracy issue is caught the same day while the case is new and before it is sent to billing.
- Improved Billing Procedures
Healthcare AI technology allows medical billing staff to improve the efficacy and efficiency of the outpatient coding and billing process. Many companies are adopting AI applications to simplify manual coding labor. AI in healthcare can significantly reduce working hours and human error, in addition to processing codes and large data volumes.
- Interaction analysis
Interaction analysis systems for outpatient care are typically done manually. These prove to be time-consuming, costly, and complex. The computerized algorithms of AI can make this process more cost-effective and straightforward. We can also go beyond the established boundaries of patient-physician communication with the help of technology.
On a large scale, AI/ML solutions can pinpoint common mistakes in outpatient medical coding, tighten the floodgates against coding errors, and improve documentation. Through real-time feedback, coders can enhance their skills. Top-tier coders can devote more time to complex cases rather than mundane tasks. AGS recommends this hybrid approach as coders are also able to validate the computer’s proposed codes – ensuring accuracy and maximizing reimbursements.