![\"\"](\"https:\/\/risefoc.com\/wp-content\/uploads\/2022\/06\/Denials-analytics-dashboard.jpg\")
<\/p>\n<\/p>\n
By: Gina Stinson, Senior Director, Revenue Cycle<\/strong><\/p>\n Want to take your denial analytics to the next level? You can by incorporating select data elements into the mix. What are these elements, and why are they recommended? Let\u2019s dive in and learn more.<\/p>\n 1 – Group Number<\/strong><\/p>\n The group number is a systematic differentiator \u2013 it identifies employer groups and others who have purchased a health insurance policy. Think of the group number as an umbrella, and under that umbrella are all the applicable subscribers\/insured persons covered by the group policy. Incorporating the group number into your denial analytics enables providers to more quickly find and segment subsets of their denial data that are alike, enabling bulk processing whether that process is an appeal, a denial adjustment, a contractual adjustment, or underpayment. Ideally, a provider should use the group number to create \u201cif-then\u201d workflows for more efficient claim resolution handling. If a provider uses the group number for bulk appeals, the provider should incorporate the next data element into the mix – the patient\u2019s insurance identification number.<\/p>\n 2 \u2013 Insurance ID number<\/strong><\/p>\n Ask anyone in the healthcare revenue cycle who has ever talked to a claim payor. They will tell you the insurance ID number is one of those required data elements needed to obtain more information from payors, especially in the absence of a claim ID for a claim resolution. This information is especially important if a provider will use group ID for appeal segmentation and submission purposes.<\/p>\n 3 \u2013 RARC Code <\/strong><\/p>\n The RARC or Remittance Advice Remark Code is a supporting code applied during claim adjudication to clarify or supplement the applied Claim Adjustment Reason Codes (aka CARC code used on a remit to show why a claim \u2013 in whole or part – was not paid as billed). The RARC code enables more granular denial causation offering greater clarity into opportunities for denial prevention\/mitigation upstream. For example, a payor is found to routinely deny claims for group number 123456 by applying CARC code 96 (non-covered charges) due to RARC code M82 (service not covered when the patient is under age 50) for Bone Density tests. Armed with this data, the denial analytics team or designee would have the trended data necessary to see this is an issue and educate front-revenue cycle operators\/leaders that patients covered under group 123456 who are scheduled for Bone Density test must be over age 50 for the service to be covered by payor ABC. This same data could be used to optimize workstreams by firing a rule or other indicator at the time of scheduling, pre-registration, price-estimation, or registration (as the case may be) for Bone Density scheduled patients for payor ABC and group ID 123456.<\/p>\n 4 \u2013 Initial or Subsequent Denial <\/strong><\/p>\n Identifying if the denial is an initial denial (the first denial received from the payor for the claim) or a subsequent denial enables providers to identify where opportunities for denial prevention and mitigation reside. Initial denials speak to those upstream process failures occurring in the front or middle space of the revenue cycle. Subsequent denials speak to the effectiveness of denial recovery efforts. By segmenting data between these two variables, providers can better discern how effective their upstream or downstream processes are and where the most excellent opportunity for improvement exists.<\/p>\n 5 \u2013 Scheduled or Unscheduled AND Routine vs. Urgent\/Stat <\/strong><\/p>\n For claim appeal and trending purposes, it is advantageous to identify if a denied account originated as a scheduled or unscheduled encounter and if the encounter was routine or urgent\/stat. This data helps providers bulk claims for appeal purposes and ordering provider practice patterns.<\/p>\n