Every year, providers face increasing volumes of recovery, government and commercial audits. Many expect 2016 will be different. From the return of recovery audit contractors (RACs) to the surge in medical necessity reviews, a deluge of information requests has hospitals shouldering greater administrative burdens related to audit management.

In addition to audits that carry financial impact, health plans are requesting more records to compile quality data, assess new member populations and maximize reimbursement from CMS. While voluminous health plan audits pose no direct financial risk to providers, they further drain organizational resources.

Consider this list of currently active audits faced by providers thus far in 2016:

Commercial Plan Audits

  • Medicare Risk Adjustment
  • Commercial Risk Adjustment

Government Audits

  • Medicare Administrator Contractor program
  • Recovery Audit Contractor program
  • Comprehensive Error Rate Testing program
  • Office of the Inspector General
  • Quality Improvement Organization

With so much effort expended on audits, how can providers reap a return on their human capital investment? The answer lies in data. It’s important for organizations to collate and incorporate audit data within an overall enterprise data warehouse.

Savvy providers capture audit data and add it to their enterprise data warehouses to achieve a complete picture of their revenue cycle programs. Audit data pieces together the entire revenue cycle: from registration to potential claims denial and revenue takeback.

Audit data collected and managed through a centralized audit software application is recognized as best practice. Also, it should meet these three data guidelines—it should be effectively gathered; meet data integrity requirements; and establish a presence within the organization’s overall data warehouse strategy

Data warehouse strategies typically focus on clinical data being incorporated from all the various electronic health records (EHRs) and clinical systems. But data analytics only achieves its full potential when both clinical data and revenue cycle data are considered in the analyses. Here are two examples of how financial data included in your overall enterprise data strategy drives a faster return on investment.

Costing information is always a strong component of data warehouse strategies. Whether for pharmaceuticals or surgical supplies, an organization’s ability to connect cost to patient outcomes is parmount—especially under value-based reimbursement.

Consider the common scenario of hip replacement procedures. Does a high-cost prosthesis deliver better patient outcomes in length of stay, readmission rates or patient mobility? Or can a lower cost device be used by orthopedic surgeons to yield the same or even better clinical outcomes?

Only by connecting financial and clinical information together can executives provide valuable guideance back to the medical staff and potentially shift physician behavior.

Chemotherapy provides another example of how financial information alongside clinical outcomes shifts utilization. Less expensive therapies, including anti-nausea drugs, can often be provided at less cost with same or better clinical results. Data analysis demonstrates these points.But what about all of the other revenue cycle information? Let’s look at an example of audit data coupled with clinical documentation improvement (CDI) and denials.

A large, Level 1 trauma center recently completed data analysis on three pieces of information: MAC probe and educate audit findings, clinical documentation and denials. Initially, the audit and compliance teams didn’t see a problem with the MAC audit results, but upon further investigation and comparison to denials data, huge opportunities for clinical documentation improvement (CDI) were uncovered.

By pulling together all three data points, the organization established a data-driven improvement strategy including:

  • Physician education was conducted using an oline learning system.
  • EHR documentation workflow was changed to include hard stops within the EHR prior to patient discharge.
  • IT-savvy physician champions were added as advisors to the audit, compliance and CDI teams.

This organization conducts more than 40 audits every year. Each audit performed is another valuable data source. Insights gleaned drive compliance efforts by identifying areas of concern so that they may be addressed proactively rather than reactively.

Finally, the organization also leans on audit data to improve clinical coding outcomes. Clinical coding is another area for audit data analytics and applications—especially as organizations begin to analyze performance in ICD-10, and receive preliminary rounds of coding denials. Audit data identifies the most commonly audited diagnosis and procedures, and find coding errors and gaps. These efforts can serve to inform the direction of ongoing coding educational programs.

The integrity of audit data is critical to implement effective process improvement and recognize prompt return on investment. The old adage, “garbage in, garbage out,” remains true. You must have clean audit data going in to expect value coming out. And in enterprise data warehousing, the integrity of financial data and revenue cycle data are of equal importance.

Organizations must also be intentional about business intelligence (BI) and enterprise analytics strategies. BI conducted in different departments is not as effective as a centralized and more cohesive effort. Here are three steps to take towards stronger audit data integrity and analytics.

Information governance. What are the most important things to consider in order to maintain the integrity of audit data? At the top of the list would have to be adherence to the eight AHIMA's Information Governance Principles for Healthcare (IGPHC). Paraphrasing from the AHIMA document, they are as follows:

Accountability: An accountable member of senior leadership shall oversee IG.

Transparency: IG shall be documented in an open and verifiable manner.

Integrity: Information has a reasonable and suitable guarantee of authenticity and reliability.

Protection: IG program must ensure the appropriate levels of protection from breach, corruption and loss.

Compliance: IG program shall be constructed to comply with applicable laws, regulations, standards and organizational policies.

Availability: Organization shall maintain information in a manner that ensures timely, accurate and efficient retrieval.

Retention: Organization shall maintain its information for an appropriate time, taking into account its legal, regulatory, fiscal, operational, risk and historical requirements.

Disposition: Organization shall provide secure and appropriate disposition for information no longer required to be maintained by applicable laws and the organization’s policies.

Secondly, employment and development of a dedicated audit team is essential as more audits emerge and workload demands increase.

Audit specialists should be well-versed in best practices in effective audit data cleanup; able to define national statistics for audit volumes, types and results; and capable of determining the level of involvement for each specific case.

These experts must be able to uncover audit data integrity issues, which are exacerbated when multiple audit-data sources are used.They should also be knowledgeable about national audit data, the most common audits currently being conducted, and additional documentation request (ADR) volumes and trends.

The dozen-item checklist that follows is specifically for audit team members, but may be extrapolated for use by members of the finance and revenue cycle teams. In order to be effective, team members must understand:

  • The need for a strong audit database, absolutely essential to track all sorts of audit data
  • The latest volumes, trends and rules for most common governmental and payer audit programs
  • The importance of utilizing a standardized tool capable of drilling down data to determine where improvements can be made
  • Data integrity issues, which are exacerbated when multiple audit-data sources are used, including home-grown systems
  • How to effectively clean-up audit program databases
  • Tracking and decreasing the number of high-volume denials
  • Which data is most important and to whom
  • What specific insights can be gleaned from audit data in support of HIM functions
  • How to fix reporting issues
  • What can be gleaned from audit data
  • How combining release-of-information (ROI) and audit-tracking databases improves practices and maximizes information
  • How integrating audit management with HIM improves practices and mitigates financial risk.

With clean data ensured and a dedicated team in place, the final step is to establish formal data ownership, policies and procedures. Here are three questions to ask.

Who should be the owner of audit data integrity in the warehouse? Probably not the IT department. Also probably not the HIM department; not all HIM pros are data savvy when it comes to the financial side of the equation. The leader of the audit team is best suited to be responsible for audit data integrity.

Who can understand all the multiple sources of data (financial; audit; revenue cycle, etc.) input into the warehouse of data? Instead of just the IT department, this oversight requires a multi-departmental effort. Revenue cycle, HIM, audit, compliance and clinical docementation improvement (CDI) should all contribute. Oversight is best kept with someone that understands all the myriad data elements. These team specialists must be aware that information increases greatly in value when clinical and revenue cycle data are brought together in some meaningful way.

Who are all the various downstream uses of data intelligence reports from a financial data warehouse? This changes depending on what specifically is being discussed; but it behooves all organizations to determine the answer to this question for all possible combinations of data flow. For example, the first possible data error in financial data typically occurs during the patient’s initial registration.

The benefits of incorporating financial data, including audit data, are numerous. This will continue to be true as audit volumes rise, audit data grows and the healthcare industry moves toward value-based reimbursement models.

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