(I-BusinessNews.Com, July 15, 2021 ) The global computer-assisted coding market is valued at USD 2.76 billion in 2017 and is expected to register a CAGR of 11.5% to reach to USD 4.75 billion by 2022. Factors driving market growth include the growing need within the global healthcare system to curtail increasing healthcare costs, improve coding accuracy, and streamline the revenue process. In addition, the eventual conversion from ICD-9 to ICD-10 is slated to increase the number and complexity of medical codes. This is expected to result in an exponential increase in the volume of structured clinical documentation, thereby, increasing the demand for CAC solutions to manage this data. The increasing consolidation among healthcare providers, increasing consolidation among the players operating in the computer-assisted coding market and evolution of new coding technology for computer-assisted coding in the U.S are the key trends in the computer-assisted coding market.
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By product and service, the CAC solutions segment accounted for the largest share of the market in 2016
On the basis of product and service, the market is categorized into solutions and services. The solutions segment is further divided into standalone and integrated software. The standalone computer-assisted coding software considered is of two types natural language processing (NLP) software and structured input software. Similarly, the services segment is divided into support & maintenance and education & training services. In 2016, the CAC solutions segment accounted for the largest share of the computer-assisted coding market. The large share can be attributed to the need to shift from ICD-9 to ICD-10 coding standards, government initiatives for eHealth, and the rising need to adhere to regulatory requirements.
By mode of delivery, the cloud-based solutions is expected to grow at the highest CAGR
By mode of delivery, the global computer-assisted coding market is segmented into web-based solutions, cloud-based solutions, and on-premise solutions. In 2016, the cloud-based solutions segment dominated the global computer-assisted coding market. This growth can be attributed to the advantage of its flexible, scalable, and affordable nature and additional advantage of a separate cloud for the client along with a public cloud server and easy access to information for users operating in different time zones and geographic locations.
By application, the automated computer-assisted encoding segment is projected to grow at the highest CAGR during the forecast period
Based on application, the market is categorized into three types automated computer-assisted encoding, management reporting and analytics, and clinical code auditing. The automated computer-assisted encoding application is expected to grow at the highest CAGR during the forecast period. This can be attributed to the rise in the volume of patient data and codes processed, rising adoption of electronic health records, and the need to conform to the ICD-10 standards.
By end users, the hospitals segment held the largest market share in 2016
The major end users of computer-assisted coding are hospitals, physiciansÃ‚Â’ practices, clinical laboratories, academic medical centers, and other healthcare institutions. In 2016, the hospitals segment accounted for the largest share of the global computer-assisted coding market. The high growth rate of the hospitals segment can be primarily attributed to the successful and high adoption of computer-assisted coding to curtail healthcare delivery costs while maintaining compliance.
North America dominated the market in 2016
In 2016, North America dominated the global computer-assisted coding market. Increasing government support for improving healthcare infrastructure, growing need for reducing healthcare costs, and advancements in healthcare facilities are driving the growth of the computer-assisted coding market in this region.
Key Players of Market:
The key players in the computer-assisted coding market are 3M Health Information Systems (U.S.), Optum, Inc. (U.S.), Nuance Communications, Inc. (U.S.), McKesson Corporation (U.S.), Cerner Corporation (U.S.), Dolbey Systems, Inc. (U.S.), Precyse Solutions, LLC (U.S.), Craneware plc (U.K.), Artificial Medical Intelligence (U.S.), and TruCode (U.S.).
Mr. Aashish Mehra