AI Agents That Move the Needle in Home Healthcare Operations
Cera, Europe's leading digital-first home healthcare provider, is deploying close to 1,000 AI agents to support a 10,000-person workforce. The goal is simple: remove administrative drag so frontline teams can spend more time on care quality and clinical delivery.
These agents don't just surface data. They plan, reason, remember, and take action within guardrails. That translates into faster hiring, smarter scheduling, tighter quality assurance, and better retention.
Why this matters for operations
Health and social care faces sustained capacity pressure, with roughly 110,000 vacancies in adult social care and rising demand from an ageing population. That gap won't close without operational redesign supported by automation and AI.
Cera's model shows how to create headroom fast: automate the repetitive, standardize the complex, and escalate the exceptions. For context on workforce gaps, see Skills for Care's data on vacancies and demand here.
The agent stack at Cera
- Recruitment Agent (Ami): Handles initial interviews across ~500,000 carer and nurse applications per year. Outcome: doubled recruitment volumes, faster time-to-hire, lighter load on human recruiters.
- Care Coordinator Agent: Halves the time to arrange urgent staffing cover, giving coordinators hours back to focus on quality and clinical tasks.
- Field Care Supervisor Agent: Consolidates clinical data into actionable summaries, cutting care review times by up to 85% and strengthening supervisions, spot checks, and performance reviews.
- Retention Agent: Flags staff most at risk of leaving and triggers timely interventions, contributing to retention improvements of up to 22%.
Measured outcomes
- Recruitment velocity increased alongside improved candidate throughput.
- Cover scheduling time reduced by 50% in urgent scenarios.
- Care reviews up to 85% faster with sharper, consolidated insights.
- Retention improved by up to 22% through earlier interventions.
- Operational tools, including predictive analytics, contributed to a 20% reduction in fall incidents and prevented more than half of avoidable hospital admissions.
What this looks like on the ground
"Our AI Agents remove paperwork so carers can get back to caring," says Dr Ben Maruthappu MBE, Founder and CEO of Cera. That means fewer manual calls, fewer spreadsheets, and more direct time with patients.
Managers feel the shift too. "We've now got time to ring clients, find out how care is going, fix issues and chase doctors and district nurses instead of spending hours each day on admin tasks like organising cover," says Lucy Kruyer, Registered Manager at Cera Colchester.
Scaling beyond one provider
Cera is now licensing several agents to other health and care organisations. With two million adults in England living with unmet care needs, expanding access to these capabilities can help widen the talent pool and support providers under staffing pressure.
Cera is now recognised as Europe's largest HealthTech company and is using that scale to push shared infrastructure across the sector.
Operator playbook: copy what works this quarter
- Map the time sinks: Identify 3-5 workflows consuming the most hours (screening applications, scheduling cover, compiling reviews, rota changes, incident reporting).
- Pick two for automation pilots: Define clear success metrics (time-to-hire, time-to-cover, review duration, turnover rate, missed visits).
- Integrate where the work happens: Connect agents to ATS, rostering, EHR, and comms tools. Automate handoffs; keep approvals human-in-the-loop.
- Set guardrails: Audit trails, escalation rules, and data retention policies. Use role-based access and PII minimisation.
- Run a 30-60 day pilot: Small cohort, daily operational stand-ups, weekly metric reviews. Keep a simple "stop-start-continue" log.
- Operationalise the wins: Update SOPs, train leads, and move from pilot to standard practice. Then add the next workflow.
Governance and risk
Adopt clear oversight: human review for sensitive steps, audit logs for every agent action, and regular bias and error checks. Align with data protection guidance and sector regulators from day one.
For practical guidance, see the ICO's AI and data protection resources here.
The bigger picture
Cera's approach shows how to build a scalable, sustainable care model: automate the routine, centralise the data, and give clinicians time back. It protects the human touch by clearing the noise around it.
If you're building capability in-house, explore role-focused upskilling for your team with AI course pathways by job function here.
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