Using Advanced Technology to Simplify Revenue Cycle Management

Satish MalnaikWhy do we use iPhones routinely to navigate through traffic but not use them to track patient conditions? We have access to more data on our uploaded photos than we do on our insurance claims. While technological advances seem to have permeated into our day-to-day lives, they seem to enter slowly into our work lives as healthcare professionals. This double-life comes at the cost of our growth as individual organizations and collectively as an industry.

It's far easier to implement technology ideas today than it has ever been in the past. However, several medical organizations continue to operate with archaic and expensive technology, untrained staff and broken processes. Consider basic processes such as billing claims, posting payments, following up on unpaid claims — are these done any differently than they were five or even 10 years ago?

This paper focuses on applying new technology ideas to fundamental processes such as revenue cycle management to extract greater profitability from day-to-day operations of ambulatory surgery centers.

Applying Analytics to Revenue Cycle Management
Which zip codes have the timeliest patient payments? Which insurance plans of BlueCrossBlueShield take the longest time to pay? What would be the downstream revenue of new patients seen this week? These types of questions are difficult to answer intuitively and by doing simple computations over Excel. These require a dynamic system that automatically tracks and analyzes various aspects of the process — from entering charges to posting payments to tracking denials. Applying analytics to revenue cycle management will help centers make informed business decisions.

Building a Dashboard
The basic premise of using analytics in operations is to detect changes before they become obvious. Before reimbursements decline, the insurance mix changes or new patients reduce or basic denials occur. It may take several months before smaller changes add up to tangibly show that profitability has changed. Here are a few sample indicators that must be on ever center's dashboard:

•    60+ insurance accounts receivables
•    Days in AR
•    Collection percentage
•    Mix of insurances
•    Physician contributions
•    Denials due to front desk
•    Denials due to billing
•    New patients vs. existing patient ratio
•    Contract timelines
•    Medical supplies cost and utilization
•    Room utilization
•    Staff utilization
•    Case volume and costs
•    Procedure volume and costs

Traditionally, these indicators are compared across previous months, quarters, years. The dashboard must allow the administrator to drill deeper into specific indicators. For example, if there is an increase in denials due to the front desk in a given month, it could be because of a certain staff member. Exploring further, the administrator may discover that the denial was due to lack of timely authorization from a particular insurance company. When data is available, comparisons must be made with other centers and state or national averages.

When a Dashboard Becomes an MIS
The basic dashboard described above can be extended to evolve into a Management Information System that integrates dynamically with the practice management and electronic health record system to assist, track and provide timely insights during everyday operations. Let's consider a couple of examples on how this may be done.

Example 1: Consider an insurance plan X that pays on an average of 21 days after submission and an insurance plan Y that pays on an average of 40 days after submission. When a patient with insurance X arrives, how the revenue cycle behaves has to be substantially different from when a patient with insurance Y arrives. Insurance Y is riskier than insurance X — in terms of the time it takes to get paid — and this risk must be built into the billing process. The MIS must know and allocate work accordingly — greater follow-ups must be built in for Y. Similarly, the MIS must know overall payment trends associated with both insurances X and Y (by say a weighted average grading system) and make decisions using that information (for example, Y may require more follow-up calls vs. X and therefore needs more resources than X does).

Example 2: Consider patient data from an EHR. Could patients with higher risk of colon cancer (identified from the EHR) get further follow-up calls for screening colonoscopies? Can the MIS identify such a patient when she shows up at the door? Can the process be built around such patients — tracking medication compliance, billing and payment trends together? What impact would this have on AR and overall collections? Would these patients sign up for virtual visits over the internet and be willing to pay in cash?

These examples show that there has to be a new way of executing the revenue cycle.

When an MIS Becomes a Predictive Tool
An MIS can extend further into a predictive tool providing vital clues to the future of the center. This could vary from calculating how much the center will earn in 2014 to considering the impact of the new healthcare laws on the center's profitability in two years. Predictive systems are often learning systems that base decisions on not just historical data but also incorporating current data and external data.

There are many commercially available predictive algorithms but the right systems provide precise insight at the time of need. For example, IBM Watson, an artificially intelligent computer system capable of understanding natural language, has recently announced an application program interface that can be integrated and used by other systems. Developments such as these would allow future center administrators to leverage computational power of complex predictive system to make operational decisions more analytically. Future center administrators will see themselves as custodians of data who can direct systems and extract useful insights.

Artificial intelligence systems are used every time we swipe our credit card, shop online or buy a book. It's time to apply them to the revenue cycle.

Automating Revenue Cycle Management
There are several aspects of the revenue cycle that are repeatable and predictable. The functions and tasks are similar. From optical character recognition software to software that can integrate in the backend with clearinghouse databases, there are several avenues to minimize manual work of the revenue cycle. Here are a few examples where intelligent software scripts can save time, resources and also increase quality.

Scheduling. By using online scheduling tools to integrate patient portals where patients can schedule themselves in, surgery centers can minimize phone calls associated with scheduling function. Extending access to physicians' phones/tablet devices can allow physicians to independently monitor schedules without assistance from staff.

Eligibility, benefits verification and authorizations. Software programs can automatically verify eligibility of patients and benefits for most major insurances. Some of these programs are commercially available or can be developed in-house. Similarly, there are automated programs that can be used to obtain insurance authorizations well before the procedure date.

Claims submission. With the implementation of EHR systems, claims can be automatically entered, scrubbed and reviewed against Local Coverage Determination guidelines and clean claims can be submitted directly to clearinghouses.

Automated quality. Prior submitting claims, automated quality scripts can verify claim data against all possible insurance rules to submit errorless claims. Examples of such rules can be from checking compatibility of the reported ICD code with the CPT code to verifying for appropriate modifier usage. AR activity can be controlled by ensuring clean and timely claim submissions.

Claim status. Custom or commercially available software can be deployed to verify payment status of claims. Using software allows this process to be conducted daily and therefore optimize calling insurance companies for AR follow-up.

AR tracker. Instead of the traditional 0-30, 30-60, 60-90 and 90-120 day bucket system to track claims cycle, AR tracker software can allocate follow-up calls based on average payment days of different insurance companies. A predictive system could even provide suggestions on a future follow-up date based on a call made to an insurance company.

Patient collectables. Centers can use software that can track a particular patients account since the date of service. It can be pre-programmed with a fixed time period, after which the software automatically generates patient statements and reminders.

State/federal compliance. Many states require ASCs to adhere to the process of submitting service quality data to state registries. For example, in New York State, it is obligatory for outpatient surgery centers to submit quality data to SPARCS and HCRA reporting systems. Software can be developed to automate data submission.

Future of Revenue Cycle Management
When technology is actively applied to fundamental processes such as revenue cycle management, the natural evolution would be for complexity in billing to disappear. There would be no more billing errors, delayed payments, denials from insurance companies, mounting patient payments or AR days. Physicians would submit claims correctly and get paid instantly. Patients would have clarity on the type of service that will be provided and the associated costs with it. Insurances wouldn't build business models around staggered reimbursements. However, until such a Utopian future happens, using advanced technology will help center administrators and owners utilize their time toward meaningful challenges.

More Articles on Surgery Centers:
2014 CPT Code & Medicare List Updates: What Do ASC Leaders Need to Know?

ASC QI Projects: Best Ideas for Biggest Impact

Cost Cutting at ASCs: Best Initiatives From ASC Leaders

Copyright © 2022 Becker's Healthcare. All Rights Reserved. Privacy Policy. Cookie Policy. Linking and Reprinting Policy.


Featured Webinars

Featured Whitepapers

Featured Podcast