Sole Source Letter - Social Data Analytics.pdf
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- Attached to
- SSA - Risk Stratification & Supervision Tools
- Solicitation number
- 942
- Issued by
- Idaho
About this file
This is a Sole Source Letter from Social Data Analytics LLC to the Idaho Department of Health and Welfare justifying proprietary authorization for Risk Stratification & Supervision Tools. Social Data Analytics LLC proposes to provide a suite of supervision tools featuring a proprietary risk stratification model that will be customized specifically for the State of Idaho using locally held child welfare data. The tools stratify child welfare cases into risk tiers from very high to very low using predictive analytics methodology developed through over a decade of academic research by principal researchers Dr. Rhema Vaithianathan and Dr. Emily Putnam-Hornstein, with findings published in peer-reviewed journals. The letter was submitted on April 22, 2025, to Monty Prow at the Idaho Department of Health and Welfare. No specific contract term, renewal options, response deadline, or award date is explicitly stated in the sole source justification letter itself.
Social Data Analytics LLC asserts that its tools constitute a unique, sole-source solution not commercially available through any other vendor, distinguishing itself through three key capabilities: an operationally-validated risk stratification model tailored to child welfare investigations, on-premises deployment ensuring all data remains securely on local Idaho servers with no external data transfer or cloud storage, and modular architecture enabling direct integration with existing CWIS/SACWIS and IT systems for seamless frontline access. The company indicates its tools are currently in active use by multiple agencies and jurisdictions across the United States supporting critical decision-making for child safety and service prioritization. No specific pricing, budget information, set-asides, incumbents, or funding sources are detailed in this justification letter. According to the related contract opportunity notice, interested vendors may file a challenge to this sole source procurement within five business days after the closing date by contacting purchasing@adm.idaho.gov.
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sdanalyticsllc@gmail.com www.socialdataanalytics.com
Social Data Analytics LLC 3 The Glen Chapel Hill
NC 27514
sdanalyticsllc@gmail.com
22 April 2025
Idaho Dept of Health & Welfare Monty Prow monty.prow@dhw.idaho.gov 208-334-5500
Subject: Sole Source Justification - Social Data Analytics LLC (SDA)
To Whom It May Concern, This letter serves as formal confirmation that the suite of Supervision Tools developed and offered by Social Data Analytics LLC (SDA) constitutes a proprietary, sole-source solution that is exclusively designed, implemented, and supported by SDA. These tools are not commercially available through any other vendor or distributor.
The SDA Tools include a risk stratification model built on proprietary software that will be specifically customized to support the State of Idaho using locally held child welfare data.
The model stratifies child welfare cases (calls, investigations and open cases) into risk tiers ranging from very high to very low based on a predictive analytics approach developed through over a decade of academic research and field implementation.
The methodology is grounded in peer-reviewed studies by SDA's principal researchers, Dr.
Rhema Vaithianathan and Dr. Emily Putnam-Hornstein, including:
• Vaithianathan, R., Maloney, T., Putnam-Hornstein, E., & Jiang, N. (2013). Children in the public benefit system at risk of maltreatment: A retrospective cohort study using linked administrative data. American Journal of Public Health, 103(9), e85–e91.
https://doi.org/10.2105/AJPH.2013.301316
• Vaithianathan, R., Rouland, B., & Putnam-Hornstein, E. (2018). Injury and mortality among children identified as at high risk of maltreatment. Pediatrics, 141(5), e20172882. https://doi.org/10.1542/peds.2017-2882
• Putnam-Hornstein, E., Cuccaro-Alamin, S., Vaithianathan, R., & Foust, R. (2017).
Risk assessment and decision making in child protective services: Predictive risk modeling in context. Children and Youth Services Review, 79, 291–298.
https://doi.org/10.1016/j.childyouth.2017.06.027 mailto:sdanalyticsllc@gmail.com http://www.socialdataanalytics.com/
• Vaithianathan, R., Cuccaro-Alamin, S., & Putnam-Hornstein, E. (2023).
Improving child welfare practice through predictive risk modeling: Lessons from the field. In C. M. Connell & D. M. Crowley (Eds.), Strengthening child safety and well-being through integrated data solutions (pp. 115–126). Springer.
https://doi.org/10.1007/978-3-031-36608-6_8
• Vaithianathan, R., Benavides-Prado, D., Dalton, E., Chouldechova, A., & Putnam- Hornstein, E. (2021). Using a machine learning tool to support high-stakes decisions in child protection. AI Magazine, 42(1), 53–60. https://doi.org/10.1002/aaai.12071
SDA’s solution is unique in several key respects:
• It is the only known tool that provides an operational, empirically-validated risk stratification model tailored to the child welfare investigation process.
• It is delivered as an on-premises deployment, ensuring that all data remains securely housed on local Idaho servers with no external data transfer or cloud storage.
• The tool is built with modular architecture, enabling straightforward integration with existing CWIS/SACWIS and IT systems. This ensures that frontline supervisors and caseworkers can access decision-support features directly within their existing workflows.
SDA’s tools are currently in active use by multiple agencies and jurisdictions across the U.S., where they support critical decision-making for child safety and service prioritization. To our knowledge, SDA is the only provider offering this specific toolset in the child welfare domain, with daily operational use and evidence of improved outcomes.
Should you require further information or clarification, please do not hesitate to contact our team.
Warm regards, Larissa Lorimer Contract Manager
File details come from the government source that posted it. Updated .