Electronic Medical Billing Software For Healthcare Clinics and Chiropractic Offices - Naive Taxonomy
By Yuval Lirov 
Gerber's E-Myth theory applies well to billing: most billing services fail because the founders are "technicians" who are inspired to start a business without knowledge of how successful businesses run. Typically billing "technicians," who are skilled at billing and may enjoy coding, start their own billing operation and continue doing the work they are skilled at. However without access to solid technology and industrial-grade processes, these "technicians" soon find themselves unable to scale up. Rather than working "on" the business, they work "in" the business, merely owning a job instead of a business.
Billing is especially hard because of coding complexity and payer adversity. The shear number of codes and rules create an environment, where a coder is unable to perform consistently. It includes more than 8,500 procedure codes and modifiers, over 16,000 diagnosis codes, and millions of rules for medical necessity, correct coding initiative (CCI), local medical review policy (LMRP) and bundling. Even highly trained coders have difficulty maintaining coding consistency. Their CPT choices are inconsistent fifteen percent of the time, while their ICD-9 codes disagree with their own earlier choices almost half the time (Perspectives in Health Information Management, Fall 2006).
Billing complexity also generates opportunities for providers to commit fraud and for payers to benefit at the provider's expense. An in-house billing operation and a naive outsourced billing office owner are both helpless against insurance companies armed with a powerful three-pronged system to keep providers' money: solid business strategy, well-documented and professionally managed processes, and leading-edge technology.t
A scalable billing service requires disciplined performance measurement, process consistency, and industrial-grade technology. Provider-side billing technology can be roughly divided into three categories: encoders, revenue-cycle management tools, and artificial intelligence systems:
- Encoders help coding personnel improve consistency. An encoder is a database-driven software, which prints a list of codes in return to a procedure name or problem description. Typical encoders include ICD-9, CPT, and HCPCS books. Additionally, encoders provide access to transmittals and bulletins from Medicare, advice from the AMA's CPT Assistant, CCI, and details from national coverage decisions (NCDs). The database must be continuously updated, which can be accomplished with ongoing Internet research.
- Revenue-Cycle Management tools scrub claims and manage the claim payment cycle. Scrubbing starts with missing field identification and testing of medical necessity rules, typically including CCI, LMRP, and, most importantly, local payer rules. The knowledge base, consisting of rules like "For payer X and CPT code Y, do Z," must be continuously updated. However, in contrast to encoders, up-to-date maintenance of the knowledge base cannot be accomplished by research alone. Since some payers do not publish their payment policies, the rule base must be built through a live trial-and-error process. In this process, a delay or a denial triggers a dialog with the payer, the discovery of a missing rule, and software changes to encode the new rule. This procedure is much more time consuming and expensive for both the vendor and the provider that happens to experience the inconsistency.
- Artificial Intelligence systems typically use natural language processing algorithms to make human-computer interaction more intuitive and easy. Such tools accept an electronic medical record of the encounter and return a coded superbill, which include diagnosis and procedures codes.
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