June 10, 2020 | integrichain The Four Challenge Cases in Patient Data Mastering 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. Read More Tags: patient journey, Patient Journey Analytics, Patient Risk Score, Patient Services, Predictive Analytics, Specialty Patient Status Data
August 15, 2019 | Lucas Dan Utilizing AI and Machine Learning to Predict Challenging Patient Cases 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. Read More 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
July 12, 2019 | Brandon Underwood Cancellation Reasons Highlight the Opportunity to Proactively Support Stalled Patient Cases The quality of status and sub-status data reported by SPs is an ongoing challenge that limits the actionability of data specialty brands have contracted to receive. Clearly, poor data quality limits the level of insights that analytics can surface for key stakeholders. Simply cleansing and stewarding Cancellation reasons unlocks tremendous value in understanding why patients are abandoning scripts. Read More Tags: Canellations, Field Reimbursement, Fill Rate, patient journey, Patient Journey Analytics, Patient Services, Specialty Patient Status Data, Time to First Fill
February 7, 2019 | Brandon Underwood Specialty Pharmacy Patient Status Data Quality Challenges and Solutions Without visibility into each individual patient journey, your specialty brand cannot consistently intervene to help patients overcome barriers to therapy initiation and adherence. Today, the poor quality of specialty patient status data is making it impossible to calculate accurate KPI metrics and uncover true patient journey insights. This blog post examines in detail three critical patient status data quality challenges and recommends solutions your specialty brand can implement to overcome these challenges, and ultimately, help patients start therapy faster and stay on therapy longer. Read More Tags: patient journey, Patient Journey Analytics, Patient Journey Modeling, Specialty Data Aggregation, Specialty Patient Status Data
December 10, 2018 | Brandon Underwood Five Steps to a Better Specialty Pharmacy Data Contracting Strategy An effective specialty data contracting strategy starts at the finish line and works backward. The decisions made in terms of data fields, sub-status granularity, reporting frequency, and contact language, determine the metric outputs that are possible. This post looks at how the right specialty data contracting decisions enable diagnostic patient journey insights that help specialty brands save patient days of therapy. Read More Tags: patient journey, Patient Journey Analytics, Specialty Data Aggregation, Specialty Data Contracting, Specialty Patient Status Data
