How System C Uses AI in Its Health and Social Care Software

System C applies AI in two distinct ways: Liquidlogic AI is embedded inside its social care case management system to support practitioners across the end-to-end care journey, while CareFlow Ambient AI – Outpatients sits inside its CareFlow EPR to capture outpatient consultations in real time, generate documentation and automate RTT (referral-to-treatment) activity capture. Both are presented as modules within existing products rather than standalone tools, so the AI is only available to organisations already using — or adopting — the relevant System C platform.

Liquidlogic AI: AI inside social care case management

Liquidlogic AI is described as a unified AI module embedded within Liquidlogic Social Care Case Management. The design intent is to support practitioners throughout the whole care journey rather than at a single step.

What the source material establishes:

• Placement: it is part of the case management system, not a separate application.
• Scope: support runs across the end-to-end care journey.
• Users: social care practitioners working in the Liquidlogic environment.

What it does not specify: which specific tasks the module automates, what data it draws on, or how outputs are reviewed. If you are evaluating it, those are the questions to put to System C directly — ask for a task-level breakdown (e.g. what is drafted, summarised or flagged) and how practitioner oversight works.

CareFlow Ambient AI – Outpatients: consultation capture inside the EPR

This module is embedded within CareFlow EPR, System C's cloud-based, mobile-enabled Electronic Patient Record platform for hospitals. Its stated functions are:

1. Real-time consultation capture during outpatient appointments.
2. Documentation generated from that capture.
3. Automated RTT — referral-to-treatment activity capture.

The claimed outcomes are freeing clinicians from administrative burden, reducing burnout, improving continuity of care, and ensuring complete activity capture. Note that these are the vendor's stated benefits, not independently verified results.

Why the RTT element matters

RTT is a UK NHS waiting-time measure, and incomplete activity capture is a familiar operational problem: if activity isn't recorded, it can't be counted. Automating capture at the point of consultation targets that gap directly. The practical question for a trust is how the automated RTT entries are validated before they reach reporting — ask for the review and correction workflow.

How the two strands differ

What this signals about System C's approach

System C describes itself as the only British provider of software dedicated solely to healthcare, social care and education, and frames its development as "future focused AI-powered." The pattern across both products is consistent: AI is delivered as a layer inside an established record system, where the clinical or case data already lives, rather than as a bolt-on.

A concrete example of where that matters: in an outpatient clinic, ambient capture only produces usable documentation and RTT entries if it is wired into the same record the clinician signs off. Embedding it in the EPR is what makes that loop possible — and it is also why the AI is not separable from the underlying platform.

Practical checks before deciding

• Confirm eligibility: both modules are tied to their host products; establish whether you must be a Liquidlogic or CareFlow customer first.
• Ask for task-level detail: the public descriptions are outcome-oriented. Request a demonstration of exactly what is captured, drafted and filed.
• Clarify oversight: ask how clinicians and practitioners review and amend AI-generated content before it becomes part of the record.
• Check RTT validation: for CareFlow, ask how automated RTT entries are checked against reporting requirements.
• Note the evidence gap: pricing, deployment timelines and independent evaluations are not covered in the available material, so treat the stated benefits as vendor claims until you see them in a reference site.

For organisations already running these platforms, the relevant question is which module addresses your biggest administrative bottleneck — documentation in outpatients, or practitioner support across social care cases. For everyone else, the AI capability is a reason to evaluate the underlying platform, not a standalone purchase.