How Electronic Health Records differ from Billing Records — an Illustrative Approach

Lately, many cases regarding serious apprehensions among people who have been moving over to the electronic form of storing their health records have come to the fore. In many of these scenarios, a common pattern can be traced — Electronic Health Record services like Google Health have a tendency to recommend diagnostic medical conditions that are slightly inaccurate. Often, EHRs derive their diagnosis from the medical billing records that are saved as a part of the individual’s medical history. Most of these billing records use a system of medical coding wherein specific codes represent very precise medical conditions. The fact is that the electronic medium has many self-automated functions and it tends to interpret every bit of information within its realm of programming. However, the codes used in medical billing records are mechanically coded for insurance companies and are not meant to be used for conclusive clinical observations. Thus, when EHR service providers, like Google Health, tap into this coded data, there is a scope of deriving at mistaken results.

A simple example can illustrate how this predicament is created: if an individual has been suffering from a clinical condition in the past but suffers from a new set of diagnosis currently, services such as Google Health will tend to incorporate both sets of medical conditions to create a diagnostic recommendation. How is this inaccurate?
Simply because the previous medical condition does exist in the form of codes that are present in the billing records but these codes, do not highlight the End Date or the fact that certain medical problems have now ceased to exist. This is where it is critical to understand the scope of Electronic Health Records that consist of billing records besides other medical data such as scanned images.

Electronic Health Records are also sometimes called Personal Health Records but unlike the billing records, they are more wide-ranging in their format and therefore, there is a scope for a certain degree of inaccuracy seeping-in. Another example amply illustrates the problems that could be created due to wrongful diagnostic conditions concluded by the EHR services based upon data taken from the medical billing records — if an individual is suffering from a life-threatening situation and an erroneous health condition is being highlighted as a part of his electronically-accessible health record, then it could mean that certain life-saving drugs cannot be administered to prevent contradictory metabolic reactions.

However, it would be a misplaced notion in saying that the entire fault lies in the way which various EHR platforms function. The coding language and the entire coding format that is used to represent various health conditions as a part of the medical billing procedure is equally perplexing and often borders on being used unsystematically, thereby catalyzing the generation of wrongful analysis in the EHR. The most common example of this is when a confirmative diagnostic test is labeled with the disease for which it is being conducted. There are many similar flaws that exist in the way in which medical technology and codes are used by medical billers.

From an overall perspective, it can still be stated that having a 24x7 accessible medical information resource like Google Health should not be overly criticized just because there is a some scope for erroneous data interpretation. Instead, a solution should be sought in having a more precise system of creating Electronic Health Records wherein the records indicate the kind of data that was used for making a diagnosis. The accuracy of EHRs will also increase if a more methodical approach is taken in the medical coding industry.