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Opening the Door to Data Analytics in Medical Coding

Opening the Door to Data Analytics in Medical Coding

Medical codes are not just alphanumeric codes that are used in reimbursement and claims activities. They in fact tell the story of a claim. Let us take an example:  a 42-year old woman presents to the emergency department with an open fracture to her right toe. She was checking inventory boxes in a large supermarket when several large boxes fell from the storage racks onto her feet. The medical codes used to document this incident will impact her medical reimbursement. It will also decide how she is treated and how recovery is planned. The place of occurrence as well as what the patient was doing at the time of injury, is significant. ICD-10-CM Chapter 20: External Causes of Morbidity codes is where you’ll find the right code to tell this story accurately. In fact this code might also determine if the employee is eligible for any workers’ compensation. 

Medical coders should be aware that the codes they assign routinely have a much larger role to play like in the data analysis of disease management and best care practices. Since medical codes are by nature available in a standard format they can be used to analyze information in a structured way. They give insights to a plethora of valuable information like diagnoses, procedures, medications, and much more. While most of this data is protected health information as defined by HIPPA, the information sans the personal patient information is a veritable mine of information that can provide valuable insights that could improve future care. 

Since the key is the medical code, it’s natural that any data analytics in this field should start with analyzing the productivity and the accuracy of medical coders. Coding productivity is dependent on many factors like the EHR systems used, the number of different systems accessed during coding, turnaround time for query resolution and other tasks.

You can improve coder productivity by tracking and analyzing these parameters.

  • The start time and end time for each record
  • The average number of charts completed by a coder for a unit of time ( example per hour)
  • The percentage of charts that take a longer time than the average
  • The category of cases processed by each coder

Data analytics can also be employed to determine coder accuracy of your coders.  It helps you identify patterns and trends that lead to errors in the coding process. This in turn helps you strategize and improve coding accuracy. Analytics can also pinpoint the areas where coding is taking a longer time and help you optimize the process. It also helps you develop best practices that reduce overall coding costs by ironing out repetitive processes and identifying redundancies. Analytics can help you identify where you can ideally use AI powered coding assistants that can enhance productivity and reduce errors. Ideally the data analytics findings should flow back to coding managers to analyze the team’s data and pinpoint where most coders are struggling. This can then become the blueprint  for knowledge sharing tools like coding hotlines and FAQs(frequently asked questions). 

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