The information below will answer your questions and help you see why data analytics and MS-DRG validation are the perfect fit.
What is data analytics?
With data analytics, you can perform a sweep of your MS-DRG and claims data to discover patterns and potential outliers representative of erroneous claims that may result in improper payments (overpayments or underpayments). This can be performed manually, but it is time-consuming and limited. To expedite the process, use a coding and medical necessity claims analyzer that includes pre-programmed logic and reporting.
Why perform data analytics?
There are many reasons, including the following: to be proactive, to identify aberrant patterns in your data that purport improper payments, to quickly correct issues and to monitor and prevent future improper payment. The medical record documentation begins as what ends up in the form of publicly reported data through the conversion to codes, information and public reports.
Healthcare leaders in support of reform for quality, cost efficiency and transparency must begin seeing the "health" of hospitals through the transformation of data to meaningful and measureable reports, plus the use of sophisticated data-analytic engines. It's similar to a telemetry machine that monitors and reports the health of your heart rhythms through sophisticated electronic and programmable means. By determining the type of rhythm abnormality, this machine helps to identify the appropriate treatment option. It also helps to determine whether long-term medication management may be needed to prevent future exacerbations.
Why should data analytics be performed before a MS-DRG validation audit?
Hospital clinical care teams establish safeguards and controls all the time. There are many, like infection control, medication error management and even fall prevention. To better understand how this works, let's use fall prevention as an example that will clarify why it's important to build concurrent safeguards into the documentation, coding and claims process.
First we need to acknowledge that an improper payment may occur, just as a fall may occur, unless we analyze the situation and build safeguards to prevent it. Just as not every person will fall in a hospital, not every claim will be submitted incorrectly. We need to determine the type of claims that are more likely to be incorrect just as we need to determine the characteristics of a patient who's more likely to fall. After doing that we can develop and initiate prevention strategies, and data allows us to accomplish this.
Consider a case study: a large hospital system identified the need to implement a fall-prevention program and applied four key strategies:
- Continued assessment of fall risk factors throughout the patient's stay, considering changes in caregiver and patient status;
- Continuous observation for visible cues of the patient's risk for falling;
- Increased communication about fall risks, handoffs and the need for safe actions with staff as well as patients; and
- Increased and repeated communication with family and visitors regarding the need for safe action.
After four years of data collection, this hospital system proudly reported a decrease in falls that resulted in a rate 10 percent below the national average.
Similarly, before we audit a chart to survey characteristics that contribute to potential improper payment, we must take steps to obtain the most useful data from the sample size and make decisions against the entire population.
When an elderly, frail nursing home patient who uses a walker and suffers from hemiplegia residual from a recent stroke is admitted to your hospital for weakness, the nursing staff automatically enacts concurrent controls to ensure that no falls occur. This is a proactive approach. Similarly, a data-analytics engine programmed with those "likely characteristics" that contribute to improper payments targeting your claims data (preferably concurrently or retrospectively) will identify your high-risk MS-DRGs and claims for review - particularly those MS-DRGs with high volume, high reimbursement and those deemed "at risk" by external regulatory agencies.
There are both internal and external triggers to determine or identify high-risk MS-DRGs, coding and medical necessity. Internal triggers may be trends identified during internal or corporate MS-DRG, coding or medical necessity audits; new-hire education and training; or performance review audits. External triggers may be identified by examining Recovery Audit Contractor (RAC)-approved issues for MS-DRG and coding audits, RAC contractor demonstration results, Comprehensive Error Rate Testing (CERT) reports, Program for Evaluating Payment Patterns Electronic Report (PEPPER) Reports, Department of Health & Human Services' Office of Inspector General (OIG) work plans and reports, private payer (such as Blue Cross and Blue Shield) audit findings and insurance denial reports.
Understandably, hospital clinical staff can identify visual evidence of a risk of falling and promptly provide excellent service to avoid it. The clinical, Health Information Management (HIM), financial and information technology teams need to be just as acutely aware of the "visible" clues of improper payments through the analysis of claims data. As in step two above, this requires increased communication among the healthcare teams and an understanding of risk areas during "handoffs" to ensure that improper claims are not submitted.
Consider another case study: a patient admitted to the emergency department for abdominal pain is registered as an observation patient and then switched (handed off) to inpatient status the next day, as increased severity, illness or intensity of service were not present to justify an inpatient level of care. This two-day Length-Of-Stay (LOS) abdominal pain case (at-risk MS-DRG 392) is now handed off to a HIM staff member who codes the case and drops the inpatient claim for full MS-DRG reimbursement. The billing department then processes the claim to the payer with the potential for improper payment.
Ed. Note: This concludes Part 1 of Carol Spencer's article on MS-DRG Validation. Part 2 continues next week.
About the Author
Carol Spencer, RHIA, CCS, CHDA is a senior healthcare consultant with Medical Learning, Inc. (MedLearn®) in St. Paul, Minn. MedLearn is a nationally recognized expert in healthcare compliance and reimbursement. Founded in 1991, MedLearn delivers actionable answers that equip healthcare organizations with coding, chargemaster, reimbursement management and RAC solutions.
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