California based Family Health Centers Addresses Social Determinants of Health using Care Interface
The Challenge
Social Factors Affecting health
Like demographics: race, ethnicity, etc
Social factors like - food,
neighbourhood, light exposure, safety etc
Missed revenue due to episodic visits, poor care plan adherence impacting HEDIS metrics
Staff constraints for the
Risk assessment, intervention, navigation and constant care management
The Family Health Center in Southern California provides high-quality primary and preventive outpatient care to adults and children regardless of their ability to pay or health insurance status. Standing out as a fascinating example of how the group of Family Health Centers is using Conversational Care Management platform to achieve organizational goals and support the dynamic needs of their patient population.
Headed up by the Clinical Director and the Quality of Care Improvement Director, the team was driven to strategically drive care outcomes by addressing SDoH for their diverse population base. Tasked with streamlining and scaling up the SDoH and Care management as well as their patient outcomes strategy they faced a mix of challenges.
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.
IMPLEMENTATION
The program was implemented in a stepwise approach to scaling to all sites.
Multidisciplinary teams consisting of Case Managers, Physician Champions and practice administrators were formed to develop site-specific workflows, taking into consideration each site’s culture, workforce, and other competing priorities. Standardized PRAPARE SDoH and clinical risk assessment interviews were incorporated into pre appointment workflows, and results were integrated into the electronic medical record.
Once SDoH screening and clinical risk assessment was implemented prior to the visit and in the waiting room without jeopardizing clinical workflow, the program expanded to all the sites and disciplines over a period of twelve months.
To prepare practices, a series of webinar lectures were given to Case Managers, CHWs, physicians and practice staff that focused on the role of SDoH in health care and the importance of identifying and addressing social needs.
The platform was set to use by configuring the SDoH conversational screening, customizing the addition of CBOs resources, access keys for Case Managers and Outreach Administrative head.
Volunteers helped patients with educating during the consent process, on why the program was being run and how they could chat about their needs and answer the questions.
For Patients with computer and health literacy issues, volunteers provided community resources to patients based on identified needs using Care Interface screening platform, and collected data to support ongoing performance improvement.
Program and site leadership met regularly to discuss implementation challenges such as information technology and clinical workflow. Run charts were developed to track progress at each site, and screening data were shared regularly with site leadership to monitor and improve performance. Each discipline was supported with their unique challenges with adoption.
After autonomous interviews on the patient devices and during the visit via tablets, Care Interface automatically indexed each patients’ need according to risk level and produced customized referrals to social services within the patient’s community for identified needs.
High-risk patients (defined as having two or more emergency room visits in the last 12 months and at least one social need identified through the AHC Screening Tool) who consented to social services navigation received autonomous closed-loop referrals to collaborating community service providers through information technology connectivity established by Care Interface.
Care Interface TECHNOLOGY:
Conversational AI
Identifying Care Gaps
Standard SDoH and clinical risk assessment 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
