Healthcare: AI-led Medical Coding and Billing Favoring Sectoral Growth

August 27, 2021 3 min read By Cogito Tech. 1001 views

The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026.

Countries like the US spend a major chunk of their GDP on healthcare. The sector is technologically advanced and futuristic and the amount spent per person annually is higher in comparison to any other country in the world. There are general acute care hospitals, government hospitals, specialty, non-profit, and privately owned hospitals as well. Healthcare funding includes private, governmental, and public insurance claims and finance processes. The US private healthcare is dominated by private medicare facilities, in which the costs are borne by the patients majorly.

Underlying Challenges in the Sector

ai led medical coding and billing

The Healthcare sector has its challenges to deal with. Depending on legacy apps and following conventional procedures for treating patients has resulted in a lot of revenue losses. Hospital revenues have taken a setback and even though the EHR (electronic health record) system is implemented yet granular information in a physician’s clinical summary is often difficult to record and maintain.

Then, the medical billing and claim procedure is yet another tough turf to manage. On part of the healthcare institutions, maintaining a seamless patient experience has become crucial. Additionally, the process of translating a patient’s medical details and history into coding allows healthcare institutions and payers to track and monitor a patient’s medical current condition, manage history and update correct records. The process is lengthy and the slightest human error can result in discrepancies in the patient’s history and financial transactions for the treatment received. It can further disrupt the claims and disbursal process for all future transactions, posing risk to tracking a patient’s current medical condition. Not merely this, it can create additional hassles for medical practitioners, healthcare institutions, and insurance providers to process and settle claims.

Moreover, in terms of tracking and providing appropriate treatment, health practitioners and institutions, on the other hand, are faced with the persistent challenge of collating patient’s data from multiple sources and analyzing it manually.

Also Read: Top Five Best Usages of Artificial Intelligence in Healthcare Medical Imaging

Building Medical AI Models with Reliable Training Data

The healthcare sector deals with gigantic data, that is sensitive to patient’s health and also impacts physician’s credibility.

For a long time, health institutions have invested significantly in managing patient records and relied on software and costly legacy applications, which have their own limitations. Meanwhile, hiring of trained professionals or medical coders and outsourcing of service platforms have always added to the spending. Implementation of EHR systems has improved the fundamental processes yet, the technical limitations have made it difficult for the medical sector to rely on them, entirely. This has led to delays in accessing patients’ treatment history, suggesting effective treatment & care, billing, and processing of medical claims; eventually, hurting revenue growth for the health institutions and other players in the chain.

To handle all such scenarios, the healthcare community has aggressively adopted Artificial Intelligence enabled with machine learning with NLP for automation of key processes. AI and machine learning programs are automating procedures like medical coding and reducing the cycle of patient care. In more than 100 countries the clinical summaries are converted into codes. AI and NLP or natural language processing-based programs trained with structured medical training data are helping doctors access patient’s history based on the medical codes, instantly without delay. The effectiveness of AI-based results are reducing patient visits, stress on the doctors, and improving the entire lifecycle of patient experience with doctors, health service providers, and medical claim payers.

Also Read: Reasons to Outsource Medical Data Annotation Services.

In addition to this, the AI-enabled processes are also easing out the pressure on all the stakeholders in the loop, with tasks like revealing out-of-pocket expenses to patients before availing of the healthcare services. This has helped patients plan their expenditure, beforehand. It has also eased out pre-authorization procedures and fastened the entire cycle of patient care by the healthcare provider. Firms like Cogito are actively developing medical coding and billing training data with the help of a specialized team of in-house medical practitioners to deliver cutting-edge data labeling services. In terms of medical billing, AI programs powered with machine learning and structured training data are ensuring efficient revenue cycles and preventing the claim denials based on incorrect or missing information of the patient.


Recent AI implementations have helped healthcare institutions provide proactive support to patients and tackle significant revenue loss, in the process. For the healthcare domain, Artificial Intelligence is letting patients claim payers, and healthcare service providers work in tandem, accelerating overall sectoral growth. AI-led automation powered with NLP is saving time and costs up to 70%; including overall costs. From availing a health service to identifying the right health institution for treatment, both patients and doctors are gaining immense value from the transformation.

Also Read: Role of Medical Image Annotation in the AI Medical Image Diagnostics for Healthcare

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