Universal Human Services Data Systems Mergers Often Fail — Here's Why Targeted Interagency Data Sharing Works

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When managing complex interagency collaborations, some pursue a vision of a universal data systems merger, a single platform that links law enforcement, justice, health, and human service data systems into one universal system. This model may appeal to those seeking to reduce or eliminate data entry and duplication, and to streamline communications. Yet, in practice, universal human services data systems integrations often fail to launch and fail to keep promises. Here’s why.

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AI Field Notes for Justice and Public Safety Community Response

Field observations recorded as contact notes provide the most predictive data for future calls for service, behavioral health crises, and public safety risks. Whether robustly collected at an initial contact or compiled across repeated interactions, field-contact notes offer critical details for harm reduction, de-escalation, diversion, referral, and case planning. 

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AI-Powered Privacy-First Data Management

   

One of the most persistent challenges facing data managers, data information officers, data stewards, and data risk or compliance officers is determining the right evidence-driven systems and strategies for collecting, managing, sharing and publishing sensitive subject information. And, doing so in a way that meets agency privacy and regulatory requirements is a chief priority. When tackling these issues, a privacy-first approach offers solutions with clear benefits for consumers and institutions.

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Private AI Sandboxes Spark Justice Innovation

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Let's take a closer look at the important role of private AI systems as a driver for innovation in public safety, justice and interagency human services collaboration. While cloud-based AI models foster innovation, transparency, and broader participation, unrestricted openness can introduce substantive risks.

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