Posts for tag: Patient Risk Score
June 19, 2020 | Mia Scioscia
IntegriChain is utilizing AI and machine learning to develop risk score models that will allow Field Reimbursement Managers (FRMs) and similar functions to proactively intervene on behalf of patients who are likely to experience roadblocks in their journey -- before the roadblocks occur. Predictive patient journey analytics look at analog patient cases and outcomes to understand the likelihood of a new referral experiencing an issue such as lengthy time stalled in prior authorization and appeal. These actionable insights allow an FRM to prioritize potentially challenging patient cases and work to resolve the anticipated roadblock.
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Tags: patient journey, Patient Journey Analytics, Patient Risk Score, Patient Services, Predictive Analytics, Specialty Patient Status Data
June 10, 2020 | integrichain
IntegriChain is utilizing AI and machine learning to develop risk score models that will allow Field Reimbursement Managers (FRMs) and similar functions to proactively intervene on behalf of patients who are likely to experience roadblocks in their journey -- before the roadblocks occur. Predictive patient journey analytics look at analog patient cases and outcomes to understand the likelihood of a new referral experiencing an issue such as lengthy time stalled in prior authorization and appeal. These actionable insights allow an FRM to prioritize potentially challenging patient cases and work to resolve the anticipated roadblock.
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Tags: patient journey, Patient Journey Analytics, Patient Risk Score, Patient Services, Predictive Analytics, Specialty Patient Status Data
August 15, 2019 | Lucas Dan
IntegriChain is utilizing AI and machine learning to develop risk score models that will allow Field Reimbursement Managers (FRMs) and similar functions to proactively intervene on behalf of patients who are likely to experience roadblocks in their journey -- before the roadblocks occur. Predictive patient journey analytics look at analog patient cases and outcomes to understand the likelihood of a new referral experiencing an issue such as lengthy time stalled in prior authorization and appeal. These actionable insights allow an FRM to prioritize potentially challenging patient cases and work to resolve the anticipated roadblock.
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Tags: Field Reimbursement, Fill Rate, Machine learning, patient journey, Patient Journey Analytics, Patient Risk Score, Patient Services, Predictive Analytics, Specialty Patient Status Data, Time to First Fill
