Provider Target Model – Clinical

• CitiusTech

Using data received from BIC we get proper mappings between member, provider and other hierarchies (e.g. provider group, sub-network etc.). At each contract, measure and provider hierarchy, member level data is aggregated and scores for the elements (providers, provider groups, sub-networks etc.) belonging to that level are calculated. These scores are derived using TOPSIS algorithm. These scores range between 0 to 100 and are comparative scores. The scores are heavily influenced by the non-compliant members lying in that particular group. In this way we can track a provider (and rest of the hierarchies) with high number of non-compliant members, with high previous year compliance score, having good number of target members and having good number of members that are likely to be compliant. Based on these comparative score providers can be ranked. Payers can focus on the providers, provider groups, sub-networks etc. that are at higher ranks. In this way maximum members are covered via their provider association and compliance scores are improved. This would result in improvement in final star ratings with managed efforts. It is executed Weekly/Monthly based on the data available at clients end, Works on HEDIS and PDE data source. Contract-measure level projections METHODOLOGY: 1. From member model output, we get member level data. It has compliance state for a member, its Likelihood of engagement and a flag if it is a target member. We get previous year’s compliance state for same member in member summary. 2. From data received by BIC, a mapping between members, providers and other hierarchies supported to the contracts can be obtained. 3. For each contract, measure and provider hierarchy, TOPSIS is performed with following input variables under the respective provider hierarchy: a. Number of non-compliant members b. Previous year’s compliance score c. Number of target members d. Number of members having likelihood of engagement flag 1 (number of members that are likely to be complaint) 4. Output of the TOPSIS model here, is a score between 0 to 100 that is comparative score between providers. Better the score, higher the rank given to the provider, provider group, sub-network etc. Payers can focus on the provider hierarchies as per their rank.

Input variables : Member Target Model Output, Member Summary Data, Member Provider Attribution
Output Variables : Provider Rank

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