The concept of a revenue cycle exists in every business that delivers goods and services in exchange for value. However the term Revenue Cycle is most commonly associated with Healthcare Finance. In this blog post I wanted to highlight some of the similar Data Preparation challenges that both Financial Services organizations and Healthcare Providers face in their respective revenue cycles.

Please note: Most of my professional experience has been in Financial Services, and my knowledge of Healthcare Finance is largely from the HFMA Certified Revenue Cycle certification, and great content by produced by Becker's, Besler, Ensemble Health Partners, and Huron.

Data plays an important role in all revenue cycles, at the beginning, middle, and end. Let’s walk through these three stages and some of the challenges in them. 

Stage 1: Pre-Service (Healthcare)/Loan Application (Financial Services)

In Healthcare the cycle begins when a patient is referred by a physician to pre-register for a service. The service needs to be scheduled at this stage which requires critical data capture to facilitate insurance verification, prior-authorization, per-certification, price estimation and financial counseling. Data such as patient name, address, demographics, insurance information, procedure, medical necessity etc. As much as 40% of billing information is collected during this stage.

Similarly in Financial Services the revenue cycle begins when a customer applies for a product, we’ll use a Loan as an example here. The customer has to supply information that will help the organization determine the 4 Cs of Credit: Capacity to pay (employment history, income, etc ), Capital (account balances), Collateral (property, possession that could be pledged against the loan), and Credit (Credit Score and history of paying bills).

In both scenarios, data quality issues with inaccurate or incomplete information can have a significant impact on the rest of the revenue cycle. For example, with Healthcare, not validating medical necessity or having the patient name as exactly as it is with the insurance of the patient could lead to a denial of payment or a delay, leading to unnecessary administrative expenses. Similarly, In Financial Services, not validating the data for all of the 4Cs could put the organization at higher risk of issuing a loan that might default.

Data Preparation can help in this stage because it often requires the need to acquire, clean, transform, and combine data from many sources like the Master Patient Index (MPI), and Scheduling Database, in an agile and iterative manner to validate patient data, insurance data, avoid duplicates, all to help ultimately generate an accurate bill and submit a clean claim in Healthcare, and to minimize default risk on Loans in Financial Services.

Stage 2: Time-of-Service(Healthcare)/Loan Approval and Funding (Financial Services)

In Healthcare, at stage 2 the patient arrives for service. Many providers (Hospitals as a primary example) will once again capture and validate data to confirm the identity of the patient, and also insurance information. In many cases payment in the form of a co-pay or deductible might be due at this time before the patient then goes in for the procedure. By the way, there was also a lot of behind the scenes work along the lines of supply chain and service readiness to prepare for the procedure; ensuring the operating room is ready with all the right supplies, and coordination with all the resources that will have a role to play in the service. All of this work would have involved data as well. 

A critical part of this stage is what is referred to as ‘Charge Capture’. In short, the service and all of the resources, and medical supplies associated with it have an associated code, that code has an associated dollar cost, which in turn will be tied to a financial cost center or general ledger, which will finally help to generate the bill and what portion will be paid by insurance and the patient. This is another vulnerable part of the revenue cycle from a data quality and data preparation perspective. An inaccurate or incomplete Charge Capture process can lead to billing errors, denials, and minimized reimbursement. A ‘Charge Master’ helps to facilitate the charge capture process because it contains a list of all charges, but keeping the Charge Master up to date itself is no small feat.

In Financial Services, Stage 2 consists of all the steps required to finally approve and fund the loan after the application process. Steps such as Underwriting, Appraisal and Title, closing disclosures. These steps are necessary to further validate the 4Cs of Credit, and to abide by a host of laws and regulations (another similarity with Healthcare). Analyzing payment history, credit history, loan to value ratio, analyzing loan documents, title insurance etc. I recall one example related to Auto loans where a financial institution partnered with a digital title issuer for automobiles. Data preparation, as a strategy, was helpful in performing an exception analysis to identify which new Auto loans had not been issued a digital title yet by the vendor, thus slowing down the loan approval process. 

Both Healthcare and Financial services both benefit from Data Preparation here with regards to Charge Integrity to help maintain an accurate Charge Master and to minimize risk in the Loan Origination process, respectively.

Stage 3: Post-Service(Healthcare)/Loan Servicing (Financial Services)

Finally, stage 3 is where the lions share of many Data Preparation tasks are performed in both Healthcare and Financial Services to manage Post-Service activities and Loan Servicing respectively.

In Healthcare, after the procedure is completed and the patient has been discharged, all associated Charges need to be finalized so that the bill to the patient can be generated as well as a clean claim submission to insurance. One of the key metrics to monitor here is the Discharged-Not-Final-Billed (DNFB), which is the list of all completed services but for some reason have not been billed yet. This in itself can become a ‘Aged Trial Balance’ that can consume administrative resources to manage. Other activities in Post-Service include processing claims, remittances, denials, posting payments, resolving rejected payments, and collections. These same activities have prevention initiatives such as Denials Management; researching top denial reasons, their root causes and how to address them. Collections management; managing Accounts Receivables (AR) and monitoring key metrics like Net Days in AR. Payer Analysis; understanding which Approval processes for each Payers, timing of payments, and denials analysis by Payer.

As a result of these activities many Accounting General Ledger Journal entries need to be made such as postings to patient accounts, postings to resolve rejected items, and payment adjustments. Every single one of these tasks require a Data Preparation Strategy to work with the variety of data sources to clean, transform, and prepare data within business context. 

In Financial Services, Loan Servicing consists of all the operational tasks required to manage a loan that has been approved and funded. Many similar tasks as in Healthcare: Posting payments, especially rejected payments. There are quite a few rejected payments that can occur with Credit card payments made via ACH for various reasons such as wrong routing information, incorrect account information, or an account closure. Other activities in Credit Card Loan servicing involve resolving missing points balances for cardholders, sending Change In Terms Letters, Managing the Credit Card Master-file, and Daily Settlement. In addition to these kind of tasks, Mortgage Servicing requires accurate Investor reporting in order to facilitate remittance payments to respective investors, and diligent Escrow Administration to manage taxes and insurance for portfolio loans.

Summary

Whether you’re in Financial Services or Healthcare the struggle is real when it comes to working with Data. Interestingly the use cases are quite similar especially in Post Service (Healthcare) and Loan Servicing (Financial Services). 

Nevertheless in all stages of the Revenue Cycle Data Preparation helps to speed up the process of acquiring, cleaning, transforming and preparing data for for a business objective.

In part 2 of this blog I will highlight the similar trends, the top vendors, and Data Preparation best practices in both Healthcare and Financial Services.

I’d love to hear your thoughts and questions, if any, on this.