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.

In the above example, how many handoffs occurred? How many times per day do handoffs go unchecked, thus potentially contributing to large sums in improper payments? Data analytics assist in controlling this process and preventing improper claims, which, in turn, result in increased data integrity, revenue integrity and quality data reporting.

After identifying a list of at-risk MS-DRGs, data mining analyzes the code assignments on the claims and assists in the selection of the highest-risk claims within the highest-risk MS-DRGs. Each MS-DRG has its own programmed trigger that flags potentially erroneous claims. It is at this level that cases are flagged for review. Cases may hit an edit for sequencing, such as sepsis as a secondary diagnosis when the condition was present on admission with a principal respiratory diagnosis including a ventilator procedure code. If this is the case, flag the case for review by a preferably concurrent “pre-bill auditor” or a retrospective auditor to evaluate the documentation, medical necessity and coding to ensure accuracy and the correct assignment of patient type.

Another example may be a symptom code as the principal diagnosis with a two-day LOS and a discharge home with low charges. This may be a medical necessity or coding issue. Edits flag cases with one major complication and co-morbidity (MCC) or one complication and co-morbidity (CC) for validation of documentation to code assignment. Flags also can be established for at-risk procedure codes such as excisional debridement or mechanical ventilation codes, and even may be established for newly hired coders or case managers “in training” to ensure all at-risk cases are being reviewed carefully until acceptable levels of accuracy are achieved. Flags for transfer MS-DRGs and discharge disposition codes at high risk, such as discharge disposition code 03 (skilled nursing facility) also would be useful.

The advantages of performing data analytics before an audit include fewer denials, external audits and duplication of work efforts. Performing analytics also produces reliable results when it comes to decision-making and quality data in addition to upholding revenue integrity, increasing patient satisfaction, safeguarding your hospital’s reputation, building knowledgeable workers and erasing departmental boundaries. It also helps build a commitment to ethics and compliance, and assists clinical, HIM and financial operations by adopting enabling technology.

Disadvantages may include requiring more advance preparation time and the need to maintain resources to purchase a product. Most people carry a cell phone (or two) and don’t mind paying for services and technology that are meaningful to them. Soon there will be a time when you wonder how you lived without data analytics to assist in identifying erroneous claims for correction prior to billing.