Case Studies
Learn how a Medicaid Plan improved member outcomes and addressed SDoH using Care Interface
Improving HEDIS Measures of a Medicaid population
Home State Health - Improving HEDIS Measures of a Medicaid population
Goal
Orchestrate delivery of tailored conversations to engage members and activate them to complete key health tasks
Drive increased completion of key health checks and preventive screening
Improve utilization of health services
CARE INTERFACE COMPANY:
Care interface provides technology infrastructure which helps provider networks and payers to address the factors which affect the outcomes outside the clinical setting, address SDoH and improve health equity.
Boston Based Company with teams in New York, Wisconsin and California. Sarah Miller the Director of IT and Project management headed the implementation along with Dr. Akhila Adabala the Chief of Medical.
Execution
CI orchestrated automated text message conversations with members across 40 topics to help improve key HEDIS measures
19,208 members received the messages, with topics prioritized by a dynamic profile of each member's engagement rate, communication preferences, previous responses and plan data
81 unique dialogue types were sent to members to ensure maximum engagement
Over 444,700 automated conversations were completed during a 1-year period of ongoing engagement
Care Interface TECHNOLOGY:
Conversational AI
Standard SDoH Screener Prebuilt
Understanding NLP
Indexing Social needs in priority
Autonomous Referrals
Autonomous Nudges to the CBOs and Case Managers
Referral Tracking and Loop Closure
Analytics
RESULTS and OUTCOMES
Objectives:
Identifying unmet Social needs in the patient population
Reaching more MCO assigned members within the 30 day period
Address these Social needs and provide visibility to providers at the point of care
Improve outcomes by tracking the visits
Preliminary data revealed that, 13 273 patients were screened across four sites and three disciplines:
~2/3rd of patients identified with social needs had previously undetected needs, and 60% of these are enrolled in autonomous navigation to address social service needs.
Most used methods of communication were Text message 54% followed by the Online Chat used by the patients. The completion rate of the screenings was 72% and there was a 23% followup completion rate for the SDoH Screenings.
Of the population, 27% screened positive for food insecurity, 25% screened positive for housing insecurity, 12% screened positive for transportation needs, 8% screened positive for utility needs, and 1% screened positive for safety needs.
The Food security and NEMT transport referrals were configured to be autonomously served.
Hence 48% of the needs were autonomously served using the platform while for the rest the loop closure was 3X faster than any methods before.
Of the population screened, 82% identified as Hispanic, 14% identified as Black/African American, and 68% identified as female. The average household size was 3.6, with an average household income of $24 000
