Blaming your problems on clinical coding ignores the real culprit – your clinical documentation.
“We need to get the coding right!” is a comment we often hear from hospital executives who are looking to improve the accuracy of their data or reduce their loss of missed revenue.
But improving these processes is not just the responsibility of the health information management team – and it usually isn’t resolved by employing ‘better’ coders.
It may surprise you to learn that issues with data integrity begin before the record even reaches the clinical coder.
Clinical Coding 101
To understand how data issues arise, it’s essential to have a basic understanding of clinical coding.
In a standard inpatient admission, a patient presents to hospital, is admitted, receives treatment, and is discharged home. Following discharge, the patient’s record is collated either physically or digitally, and is handed over to the clinical coders.
The coders then analyse the entire inpatient record to determine and assign ICD-10-AM codes for the patient’s principal diagnosis and additional diagnoses. The principal diagnosis is the main reason the patient came to hospital, and additional diagnoses are complications and co-morbidities that contribute to the admission. A coder will also assign Australian Classification of Health Interventions (ACHI) codes for any procedures performed.
ICD-10 is the International Statistical Classification of Diseases. It is a medical classification system developed and updated by the World Health Organisation (WHO), and Australia has its own version, the ICD-10-AM (Australian Modification). It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. There are around 17,000 disease codes!
Hospitals and policy makers rely on ICD-10-AM codes to track healthcare outcomes, disease burden, health service quality, and mortality.
Diagnosis Related Groups and Reimbursement
Another use of the coded ICD-10-AM data is to calculate hospital funding. In Australia, most hospitals are funded by calculating the complexity of individual patient admissions. For most public patients, and many private ones, this is determined by the patient’s Diagnosis Related Group (DRG).
Each patient is assigned a DRG based on their principal diagnosis and relevant procedure codes. The DRG groups patients into categories that are clinically meaningful and consume similar amounts of resources.
However, we all know that patients can vary wildly in their complexity, and the funding system has a method to account for this. Based on additional diagnoses, patients are assigned to a complexity level within their DRG. The hospital generally receives greater reimbursement for patients that are more complex.
But often patients are coded and reimbursed as though they’re simple when, in reality, they’re extremely complex. This creates a real problem for hospital revenue. To understand why it’s happening, we need to look at the strict rules that bind coders.
The Australian Coding Standards
Clinical coders are bound by a rigid set of rules and standards that dictate how they operate. The purpose of these rules is to standardise the process of generating the ICD-10-AM codes from clinical records. Let’s explore why these standards are so important.
Some clinical coders have been coding for twenty years or more! That’s a lot of time reading records, and they’ll have gleaned significant clinical knowledge in that time. Others may have previously been healthcare professionals, such as a doctors or nurses. Their significant experience enables them to have a deeper understanding of a patient’s record.
These coders might recognise that a patient’s file is ambiguous or missing specific diagnosis information. In other words, they can clearly read between the lines and see the reason why a patient may have had a treatment – even if that reason hasn’t been documented. But what about a coder with no previous clinical experience? Or just not as much time in the coding industry? These coders could read the same chart and not realise that there is a diagnosis missing.
If these two groups of coders were allowed to fill in the gaps, different coders would assign different codes for the same record. And this is clearly not a good thing for a role that is, essentially, one of data abstraction and management.
So the rules are very strict. Even if a coder recognises that a specific diagnosis is missing or reads between the lines to interpret why a patient may have had a treatment, they are unable to code any diagnoses that aren’t specified.
As a result, clinical coders can’t assign codes from treatments or results – they can only code from explicitly stated diagnoses. Despite this, it’s not uncommon for clinicians to describe clinical conditions or imply diagnoses through treatments or results. They often leave a lot of patient information to be inferred. As a result, a huge number of diagnoses cannot be coded and are, therefore, missed.
Consider the following examples:
In both scenarios, there are diagnoses missing from the patient’s record.
It is possible for coders to send requests to clinicians for clarification or elaboration, but it’s a complicated, lengthy process that isn’t sustainable.
Moving Forward
So now you can clearly see that the problem is not your coders – it’s your coded data.
And why is your coded data the way it is?
Your clinical documentation!
To improve the accuracy of coded data, the information that you’re feeding to your clinical coders needs to be accurate. Good quality documentation is the foundation of better patient care, accurate funding, and sustainable hospitals.
Now that you’ve identified the issue, it might feel insurmountable to overcome. Clinicians who have been working for years can be very set in their ways.
The effective solution is to develop a robust and sustainable clinical documentation improvement (CDI) program that educates clinicians about the importance of documenting diagnoses.
All things considered; it becomes clear that hospitals can’t afford not to have a CDI program. So instead of “we need to get the coding right” let’s make it “we need to get our CDI program right!”
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