CareMatch Intelligence Matching methodology
HOW CAREMATCH WORKS

Safety gates first. Comparison second.

CareMatch structures the evidence needed to decide whether a home can safely meet this child’s assessed needs. Only eligible homes receive a comparative fit score. It does not make the placement decision.

01 · STRUCTURED EVIDENCE

Compare like with like

A referral records age, placement type, needs, current risks and safeguards, education, therapeutic and cultural needs, family contact, location, accessibility and required staffing. A verified home profile records its registration, vacancies, care model, environment, workforce, transport, location and pseudonymised existing-child compatibility information.

Referral evidence

Current, proportionate information approved by the responsible authority.

Provider evidence

Timestamped capability and vacancy information supplied by an authorised provider.

Existing-child compatibility

Only necessary pseudonymised risks, vulnerabilities, triggers and safeguards are shown. Identities are never shared.

02 · MANDATORY ADMISSION GATES

A vacancy never equals suitability

Before ranking, CareMatch asks whether this home can safely meet this child’s needs in everyday care and foreseeable crises without creating an unacceptable risk to the child or the existing group.

Registration and Statement of PurposeCritical referral informationSafeguarding and missing/exploitation planExisting-child compatibilityStaffing and supervision capacityChild-specific location and environmentHealth, education and specialist supportWorkable care or placement planChild’s views and safety needs
Four explicit outcomes

Suitable, Suitable with conditions, No-go, or Insufficient information. Missing evidence pauses the decision; it is not treated as proof that a home is unsafe or safe.

This design follows the official principle that a registered person should accept a placement only when the home can respond effectively to assessed needs after considering the impact on children already living there. See the Department for Education guide to the Children’s Homes Regulations and Ofsted’s children’s homes inspection framework.

03 · WEIGHTED COMPARISON

Only eligible homes are compared using configurable weights

A score cannot compensate for a failed gate. Homes marked No-go or Insufficient information are not ranked. The starting weights below are a pilot configuration and must be governed, versioned and tested.

Safeguarding / risk compatibility30%
Existing-child compatibility20%
Staff competency match20%
Therapeutic / environment fit15%
Location, education & family contact10%
Operational availability5%
Overall indicative score

For each eligible home, CareMatch adds each factor score multiplied by its active weight, then divides by the total weight. The result is a comparative 0–100 score. It is not a probability of placement success.

04 · EXPLANATION

Professionals see more than a percentage

Strengths supported by current evidenceConcerns, uncertainty and missing informationAny hard exclusions triggeredRisk and safeguarding compatibilityRequired and evidenced staff skillsAssessment against every child currently living in the homeTherapeutic, environmental and practical fitWhy each home ranks above or below alternatives
05 · HUMAN DECISION & AUDIT

The recommendation can be challenged or overridden

1ReviewCheck source evidence and explanation
→
2ConsultChild, provider and relevant professionals
→
3DecideAuthorised professional records decision
→
4AuditOverride requires a written reason
Immutable decision record

The referral and home snapshots, rule version, factor scores, ranking, exclusions, user, timestamp, final decision and override reason are retained as an append-only history.

06 · OUTCOMES, NOT SELF-LEARNING

Outcome data supports evaluation separately from live scoring

Acceptance, start date, duration, stability, incidents, breakdowns, reasons, successful move-on and commissioner/provider feedback are stored in a structured outcome record. The current MVP does not automatically learn from or change its rules using that data.

Future development boundary

Any later machine-learning model would require a separate lawful basis and DPIA, representative data, outcome definitions, bias and performance testing, independent validation, change control and ongoing human oversight.