Simplify Excel-Based Revenue Cycle Operations To Improve Collection Rates And Accelerate Reimbursement

By Frank Moreno, vice president of product marketing, Datawatch

Healthcare offices are often overwhelmed when attempting to manage data for efficient operations, especially in the finance/billing department. Challenges escalate when a team depends on spreadsheets such as Microsoft Excel for managing insurance claims processing, payment, and revenue operations. Arming revenue managers and finance teams with self-service data preparation tools will immediately improve patient billing, provide more timely reimbursements, and enable robust insight into the revenue cycle.

Identifying And Overcoming Revenue Cycle Management Challenges

Healthcare revenue cycle managers face countless data hurdles while managing reimbursements, including:

• Resolution of Denied Claims — whether measuring denials by payer, diagnosis-related group (DRG) procedure code, adjustment reason code or other causes, the associated data needs to be extracted from various documents, reports, and files, transformed into a human-readable format and analyzed to quickly determine the real drivers for denied claims.
• Reconciliation of Outbound Claims with Inbound Remittances — tracking and correlating 837 claims submitted with paid or denied 835 claims.
• Crossover/Coordination-of-Benefit Claims — identifying complex claims with multiple payers.
• HIPPA 5010 Compliance — the 5010 form has different formats, with new data elements to accommodate ICD-10 coding structure. The need to interpret and report on 835s, whether in 4010 or 5010 format, is more critical than ever.
• Provider Level Adjustments — adjustments that are neither at a claim level or service line level still impact the actual payment to a provider and need to be considered.

It is extremely critical that a finance team integrate static report data with vital financial, clinical and operational metrics, dashboards, and information from a variety of disparate systems — including the enterprise EMR solution — to have a holistic view of the revenue cycle. Yet, when the finance group depends on manual data entry and spreadsheets, these challenges are amplified exponentially in five main categories.

1. Repeatability — data analysis is often based on reviewing the same data sets, sometimes daily. Spreadsheets require manual updates and manipulation when data is refreshed. And while Pivot Tables provide basic transformations and filtering, new data must be manually cut and pasted into a worksheet.

   Data preparation and analytics tools allows individuals to make one workspace where new data paths can be added and all calculations are done automatically. One community-based physician-owned practice in the South relies heavily on its platform to extract data from daily change files when they violate present business rules. This gives the revenue team a clear look at the daily payments and any outstanding charges that need to be billed to insurance companies.

2. Sharability — sharing spreadsheets internally and externally requires constant, manual input, file specific edits, and manual removal of sensitive data, risking compliance and data integrity.

   Finance team members can share their data sets and data preparation work without having to share sensitive data. As an example, an executive at a non-profit pediatric health system in Texas can take data from different systems and combine it for a full picture of the revenue cycle, including reimbursement rates and individual facility performance. This information can be distributed via revenue cycle PowerPoint reports to another executives and managers.

3. Templating — many systems require Excel data to be in a specific format, meaning the financial data analyst must move columns around, eliminate page headers, etc.

   Data analytics solutions can take an input format from Excel or other files and repeatedly transform that into the format needed with a one-time creation of a workspace. For instance, Pam Klein, manager of support systems at Financial Recoveries, a medical billing and collections firm for hospitals and medical practices, relies on the automation capabilities in her data preparation platform to manipulate the healthcare data flowing in and out of her office. “For years, I have used the solution to convert our clients’ data files into our system, or to manipulate our own data to our clients' exact import specifications.”

4. Appending — joining multiple data sets together is a painstaking process in Excel. Datasets are rarely in the same format, meaning manual cut and paste steps every time. With Append and/or Join utility functions in the data analytics platform, new data in any order is easily added in the common format output.

5. Calculations — when a template changes, adding a new column in the middle of a complex Excel file with calculations, individuals must manually and carefully insert that data and make sure it doesn’t screw up any calculations.

Many data analytics solutions don’t have column and row order dependency. Calculations are based on the column name, not that fact it is a specific column in the spreadsheet. The finance team at a Massachusetts community hospital uses data preparation to save time and streamline data extraction into Excel for greater manipulation and to focus more attention on creating actionable insights from accounting reports.

Self-Service Data Preparation Improves Revenue Cycle Operations
Self-service data preparation for healthcare providers allows the finance team to extract data from static reports, files, and databases to reconcile data or prepare it for operational and analytic use. Being able to access this semi-structured data and mine reports is of great value in revenue cycle management.

Individual end-users can access the data and customize the reporting, without requiring the IT department to provide countless customized financial data reports. A self-service data preparation tool accesses any documents without the time and expense of integrating with the provider’s existing systems. This means financial data can be combined with clinical data, enabling information to be shared or acted upon for better decision-making across the organization.

Finance cannot spend countless hours manually pulling data from EMRs, 835 and 837 remittances, and other files. By using a self-service data preparation and analytics solution, finance teams can easily unlock hidden data and avoid manual, risk-laden spreadsheet processes, to better manage cash flow, see revenue across service lines, drill down into claims, review aging claims, and identify gaps in the revenue cycle process.

About The Author
With more than 20 years of marketing experience, Frank Moreno is VP of product marketing at Datawatch, a data preparation software provider. Connect with him on Twitter: @fmoreno44 or LinkedIn: https://www.linkedin.com/in/fmoreno44.