Simply put, one of the biggest and most bothersome issues faced by providers and payers in the healthcare industry is the onerous procedure of pre-authorization. These pre-authorizations for medication and treatments are time-consuming and costly.
Physicians spend an estimated 20 hours per week on pre-authorization activities that cost around $83,000 in annual overheads per physician. These pre-authorizations are expected to mitigate costs for those paying for them, but is that really true? Let’s explore!
Do Pre-Authorizations Help Keep Costs Low?
There are several costs incurred by physicians interacting with payers, and these comprise mostly of the following:
- Submission of claims
- Accuracy verification of payments
- Submission of data
- Review of quality performance reports
- Contract negotiation
- Credential costs
These costs are at times deemed redundant and can be effectively avoided. These administrative costs are considered redundant under the following three conditions:
- When costs exceed benefits
- Inefficiencies in well-structured interactions between physician and payer
- Inefficiently structured interactions
There has been an inverse relationship identified between administrative costs and the quality of healthcare provided. Not only this, but insurers are also obliged to bear the partial burden of these pre-authorization activities, with their share comprising 1.3 percent. This 1.3 percent is added to the overall administrative costs of the physician under the category ‘provider and medical management.’
Moreover, there is no standardized criterion for coverage limitation in case of certain medications and procedures which is, again, inevitable to control.
This means that these costs are not helping any one party but are simply adding to redundancies. There are inefficiencies that need addressing and fixation, but how can this dilemma be solved? Here’s how!
Use Artificial Intelligence
The main issue with administrative complexity has to do with dispersed data sets. The processes require unification of data in order to create AI machine learning processes to provide improved and rational outcomes. This data may be used to establish algorithms that may then be used by all parties involved to streamline and simplify processes. AI will tend to focus on the main parties involved in the following ways:
Healthcare providers require AI assistance so that their manual authorization tasks are automated, allowing them to work for fewer hours each week while ensuring premium service to all patients alike.
These AI-assisted processes will have to be embedded in the workflows that are being used by the provider. With the help of AI, the EHR will generate a standard clinical summary message and an HL7 order message to the data source.
AI will automate the authorization analysis process in real time, and the so-called inefficient interactions will be reduced eventually.
AI will automate authorization of medication and procedures with ample consideration given to the payer’s contracting and formulary preferences. With the help of AI, clinical documentation may be filled in automatically, making it all the more easy for payers.
Moreover, this AI-assisted authorization ought to be more cost-effective for the payers to avoid administrative costs that they are currently facing. AI can effectively reduce the administrative burden of staffing and managing authorizations while keeping the overall cost of healthcare low. This can be sufficiently ensured with the aid of a periodic A/B testing procedure.
AI is expected to revolutionize the entire pre-authorization process for the betterment of patients. Without the involvement of patients in this now-automated process, a solution may never be found to treat this dilemma.
Al must ensure that patients have access to actual data being used by insurers and healthcare providers. Their recommendations and objections must be accounted for in each of these steps to allow for the AI-assisted processes to work.
Artificial intelligence has and will be used to automate processes in the future related to healthcare and other industries, but all of this requires patience. With a revolution in technology, there may be further developments in AI that could be used to further cut down costs and achieve optimal results for medical administrators, payers and patients alike.