Challenge
The growing demand for adult social care is placing immense pressure on local authorities, threatening to overwhelm existing services. Currently, there is no efficient method for understanding a user’s situation, determining care needs, and identifying appropriate care packages for frontline social workers.
Objectives of Discovery
This discovery phase aimed to investigate:
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Adult Social Care Process: Identifying workflows that can be automated to enhance efficiency.
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Data: Assessing available data to determine its feasibility for automation.
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Technology: Evaluating current technologies and exploring opportunities for improvements or new implementations to reduce demand.
Social care triage assessment
This project, funded by the Department for Leveling Up, Housing and Communities (DLUHC), involves collaboration with six partner councils. The focus of this discovery project is on automating the triage process for social workers, specifically in the backend operations.
Key Findings + Outstanding Questions
Approach and Delivery
Through our exploration during the discovery phase, we identified critical challenges faced by frontline social workers:
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Social workers invest significant time and effort in manual processes during care needs assessments.
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The case management system's data is disorganized and lacks structure.
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Information about a user’s situation is scattered across multiple fields, with notes containing confidential details.
Based on our findings, we identified several key questions to address post-discovery, leading into the Alpha stage:
How can we better support frontline social workers to work more efficiently using technology, allowing them to focus on activities that most benefit end users?
Technical problems need to be answered in this project:
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Can a common data model be implemented across all partner councils?
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Is there a potential correlation between a user’s situation and the type of care service?
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How can data be organized to facilitate reporting and predictive modeling?
User research
Cross Councils research
To gain comprehensive insights, me and our user research conducted extensive user research at Kingston Council, shadowing 5 frontline teams within the adult social care service team.
Additionally, we engaged with other councils for broader perspectives, including West Berkshire's hospital discharge and locality hub teams, Merton's first response and hospital discharge teams, and remotely reviewing access team processes with Dorset and Southwark.
Our research revealed 63 pain points, spanning operational, technical, and behavioural issues. The most common challenges across the seven councils included:
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Staff shortages and time pressures
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Manual processes
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Lack of consistency in procedures
User research
Research told us...
User journey + storyboarding
Social Worker's Journey
I synthesised our research into a representative user journey that illustrates how social workers conduct care needs assessments daily. This user journey highlights their pain points and serves as a tool for our team to brainstorm solutions that could enable social workers to work more effectively with the support of technology.
Collaboration + Technology
Learning from Other Councils & Technology
After identifying common problem areas faced by social workers across various councils, we explored how these councils manage similar cases and what new technologies on the market could alleviate social workers' manual workload. We sought to understand their internal processes and assess the broader technological landscape to uncover solutions that address the identified issues.
Our goal was to find innovative ideas and technologies that could enhance social workers' efficiency, allowing them to concentrate on tasks that matter most. By leveraging cutting-edge technology, we aimed to empower social workers, streamline their workflows, and ultimately improve the overall work process.
Final recommendation + storyboarding
Use Cases for Alpha Phase
Based on our findings, we proposed the following use cases for the Alpha phase:
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Text Extraction and Analysis Tools: For standardisation and formatting.
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Prediction Modeling: Utilising machine learning to predict care plans for users who have completed a Care Act Assessment.
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Automated Notifications: Building a support suite to integrate with notification systems.
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AI Transcribing Tools: Assisting in the transcription of Social Worker Care Act assessment visits.
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AI Governance: Assessing the implications and impact of introducing AI tools within a council setting.
We collaborated with Beam to develop "Magic notes", an innovative tool to support workers during home visits, enabling them to complete care plans for individuals more efficiently.
Reflections and Learnings
This project provided valuable insights into the potential of technology to transform social care processes. It reinforced the importance of thorough research, cross-council collaboration, and the strategic integration of technology to enhance service delivery. As we move forward into the Alpha phase, these learnings will guide our efforts to develop solutions that truly empower social workers and improve outcomes for those they support.