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Denial Prevention: Addressing Root Causes through Data Analytics and a Team-based Culture

Denial Prevention: Addressing Root Causes through Data Analytics and a Team-based Culture. Tracey Tomak, RHIA, PMP Senior Director, Project Management and Client Engagement Intersect Healthcare, Inc. Towson, MD. Learning Objectives. Title Version C.

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Denial Prevention: Addressing Root Causes through Data Analytics and a Team-based Culture

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  1. Denial Prevention: Addressing Root Causes through Data Analytics and a Team-based Culture

  2. Tracey Tomak, RHIA, PMP Senior Director, Project Management and Client Engagement Intersect Healthcare, Inc. Towson, MD

  3. Learning Objectives Title Version C At the completion of this educational activity, the learner will be able to: Define data, data analysis, information and knowledge management Identify key contributors of data in various healthcare settings Clearly distinguish the difference between Reason Code, Issue and Root Cause Determine what data elements are relevant for analysis Establish an action plan based on the information obtained from data analysis (including reports and key stakeholders)

  4. Defining Data Analytics

  5. Data - Defined facts and statistics collected together for reference or analysis the quantities, characters, or symbols on which operations are performed by a computer, being stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media. Webster’s Dictionary

  6. Data – Examples of Revenue Cycle Data Primary Diagnosis Codes Secondary Diagnosis Codes Procedure codes Revenue Codes Hospital Service Codes Attending Physician Ordering Physician Operating Physician Admit Source

  7. Data Analysis - Defined 1.A resolution of anything, whether an object of the senses or of the intellect, into its constituent or original elements; an examination of the component parts of a subject, each separately, as the words which compose a sentence, the tones of a tune, or the simple propositions which enter into an argument. It is opposed to synthesis. 3.(Logic) The tracing of things to their source, and the resolving of knowledge into its original principles Webster’s Dictionary

  8. Data Analysis – Within the Revenue Cycle Diagnosis and Procedure Codes – Are they supported by the documentation? Volume by physician, by hospital service? Revenue Codes – Are they appropriate for the setting? Are the correct units reported? Hospital Service Codes – Assists when drilling down to where certain patterns are appearing in the organization. Type of physician – Just because the Attending MD is reported on a case with an error, do not be quick to place fault on the attending MD. Consider also the type of service (consulting specialist or surgeon for example) Admit Source – Was the admit source captured appropriately? (example – urgent, direct admit, emergent)

  9. Information - Defined 1.The act of informing, or communicating knowledge or intelligence. 2.Any fact or set of facts, knowledge, news, or advice, whether communicated by others or obtained by personal study and investigation; any datum that reduces uncertainty about the state of any part of the world; intelligence; knowledge derived from reading, observation, or instruction. Webster’s Dictionary

  10. Information – Revenue Cycle Information Diagnosis and Procedure Codes – insufficient documentation is present, Dr. X’s patients have the highest percentage of sepsis compared to other specialists in the same service line. Revenue Codes – There are multiple units of Revenue Code 360 being reported on a single day out of ambulatory surgery in Pavilion B only. Hospital Service Codes – Service code CAR had 50K written off last month due to Reason Code 197 (authorization issue). Type of physician – Dr. Z is an attending in CAR. Admit Source – 100 cases were denied by Anthem last week and all were billed with Admit Source = Urgent.

  11. Knowledge Management -Defined Efficient handling of information and resources within a commercial organization. Oxford English Dictionary

  12. Knowledge Management – Revenue Cycle Information: Hospital Service Codes – Service code CAR had 50K written off last month due to Reason Code 197 (authorization issue) Knowledge Management (transforming the information into relevant context and determining next steps): For Example: 95% of the write-offs for lack of auth were for one test, one insurance payer and came out of CAR. Need to follow-up with staff to determine how they are obtaining or NOT obtaining authorizations for this test and establish a new process OR discuss with the payer if this is not a requirement found in the payer manual or contract.

  13. Revenue Cycle Data Sources in the Healthcare Setting

  14. Common Data Sources - Admitting • Patient demographics • Payer and payer plan information • Financial Class • Admitting/ordering physician • Authorization information • Admission Date and time • Service Site • Service Type • Test ordered • Discharge Date, disposition and time

  15. Common Data Sources - Charging • CPT service/procedure codes • Revenue codes • Charged units • Line item Description • Unique charge code • Modifiers • Line item charge amount

  16. Common Data Sources –Coding/Abstracting • Diagnosis codes • Procedure codes • DRGs • APCs • HACs • Physicians – Attending, consulting, operating • Admit Type • Discharge disposition • Modifiers

  17. Common Data Sources - Billing • Condition codes • Modifiers • Occurrence codes • Billed units • Non-covered charges • Bill type

  18. Common Data Sources – EOB/remittance and Follow-up • Reason Codes • Issues • Write-off codes • Denial and variance volumes • Audit volumes • Audit outcomes • Root Causes • Medical Record Requests (Pre-pay and post-pay : RFI, ADR, etc.) • Contractual Adjustments • Line level vs. claim level adjustments/denials

  19. Distinguishing between Reason Code, Issue and Root Cause

  20. Reason Code 197 versus Denial “Issue” Reason Code Issue No Auth at all Wrong Auth Wrong date of service Wrong CPT code Wrong Setting Missed recertification – Length of stay, Level of care, number of visits • 197- Payment adjusted for absence of recertification/ authorization.

  21. Reason Code 50 versus Denial “Issue” Reason Code Issue Wrong setting Lack of approved diagnosis for test/service Lack of documented prior “conservative treatments” Maximum billable units exceeded • 50- Non-covered: Not Medically Necessary

  22. Root Cause for Denial Reason Code 197 Reason Code Root Cause No attempt Different service/test ordered than performed Rescheduled to a different date of service- no notification to the payer Documentation does not support IP level of care No attempt to add days to an inpatient stay beyond initial certification Failure to request approval for additional rehab visits • 197- Payment adjusted for absence of recertification/ authorization.

  23. Root Cause for Denial Reason Code 50 Reason Code Root Cause Documentation does not support IP level of care Lack of documented specificity in reason (sign/symptom/diagnosis) for service Lack of documentation on the facility record Unaware of medical policy or documentation not supporting the need for more units. Charge or coding error • 50- Non-covered: Not Medically Necessary

  24. Determine relevant data elements for analysis

  25. Knowing the Audience – Who cares? And Why? • C-Suite– High-level volume of cases and $$ at risk or fatal • Clinical Directors – charging and documentation issues specific to their oversight • Revenue Cycle Leaders – Admitting, Registration, Scheduling, Coding, CDI, Billing, Follow-up, Chargemaster, Revenue Integrity • Managed Care/Contract Management – patterns of abuse by payers • Payers – combat misuse and disputes with data • Physicians – why does it matter to them? • Information Services – lack of proper interfaces, unable to collect supporting documents from EMR

  26. Sample Report – High Level

  27. Sample Report – Line Level from 835/EOB

  28. Sample Report (Graph) – Audit Issue (Reason Code)

  29. Sample Trending Report – Audit Issues with At Risk $$ and # of cases

  30. Reporting Data to “Drill down to the Root Cause”

  31. Sample Trending Report (Graph) – Root Cause by case created

  32. Sample Trending Report (Detail)– Root Cause by case created

  33. Establishing an action plan based on the information obtained from root cause data analysis

  34. Root Cause Analysis • In Slide 30 we can see that this organization has issues with denial issues that are both recoverable (by appeals) and fatal (not recovered even when appealed) • We can also see that there are issues recovered which account for high volume and low volume # of cases • Additionally, we can see initial at risk vs. recovered $ • With this little bit of analysis, we can begin to identify those trends that we will want to investigate and form an action plan for denial prevention

  35. Root Cause Analysis - Prioritizing

  36. Root Cause – Action plan • Issue 1 – Not Medically Necessary • Pull the detail on the 252 cases and drill further down by hospital service, service type, payer, etc. • Review detailed notes from admitting, case management, appeals, etc. • Identify any trends that may be present among the cases • Meet with key stakeholders where patterns/trends exist to develop an action plan for Process Improvement (PI) • Examples of PI: Retraining for UM/CM staff on use of clinical care guidelines such as Interqual or Millimen, use of peer-to-peer opportunity, use of MD Advisors, payer discussion, documentation improvement, etc.

  37. Root Cause – Action plan • Issue 2 – No Pre-certification/No Authorization • Pull the detail on the 125 cases and drill further down by hospital service, service type, payer, etc. • Review detailed notes from admitting, case management, appeals, etc. • Identify any trends that may be present among the cases • Meet with key stakeholders where patterns/trends exist to develop an action plan for Process Improvement (PI) • Examples of PI: Retraining for scheduling, admitting, case management, physician offices, etc.

  38. Root Cause – Action plan • Issue 3 – Level of Care • Pull the detail on the 97 cases and drill further down by hospital service, service type, payer, etc. • Review detailed notes from admitting, case management, appeals, etc. • Identify any trends that may be present among the cases • Meet with key stakeholders where patterns/trends exist to develop an action plan for Process Improvement (PI) • Examples of PI: Retraining for scheduling, admitting, case management, physicians, discussion with specific payers and contract management, etc.

  39. Root Cause – Action plan • Issue 4 – Billed principal diagnosis not present at admission • Pull the detail on the 2 cases and provide feedback to coding for review • This is a low volume issue with high potential $$ and possibly a training issue

  40. Root Cause – Action plan • Issue 5 – Other: Explained in Notes • Pull the detail on the 167 cases and drill further down by hospital service, service type, payer, etc. • Review detailed notes from admitting, case management, appeals, etc. • Identify any trends that may be present among the cases • Meet with key stakeholders where patterns/trends exist to develop an action plan for Process Improvement (PI) • This issue may require further root causes to be built if additional patterns are identified • Perform PI as needed

  41. Summary

  42. Summary • In order to prevent denials in the future, it is important to collect valid data based on actual root cause rather than simply relying on the reason codes returned by payers on 835/EOB/remittances. • This data can become valuable information when reported and analyzed for patterns and trends.

  43. Summary • When appropriately collected and analyzed, this information can be used to further define the service site, service type, payer and possibly physicians where the issues are originating and education and training can be offered to decrease denials caused by internal failures. • This information may also be used in discussion with payers to establish specific contract terms regarding payment and appeal rights. • Root Cause analysis is not a revenue cycle only matter – this effort will require breaking down silos and having honest discussions about internal processes that may require re-education, training and/or revision.

  44. Thank you. Questions? Tracey A. Tomak, RHIA, PMP ttomak@intersecthealthcare.com

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