CARERS NSW

Q: Tell us about a time you used data analysis to identify and address a service gap.

S: After the 2024 National Carer Survey, Carers NSW noticed carers aged 25–44 had high distress but low use of Gateway services.
T: I was asked to determine where referrals broke down and how to improve engagement.
A: I merged Carer Gateway CRM, Survey, and NSW Health datasets using Power Query and SPSS regression. Analysis showed part-time regional carers were 1.8× more likely to disengage. I visualised this in Power BI dashboards and led co-design sessions with PHN and LHDs to create a “Warm Transition Protocol” with 14- and 30-day digital check-ins and alerts for declining wellbeing.
R: Follow-up completion rose 23 %, distress scores fell 17 %, and the model was embedded in the 2025–26 Quality Improvement Plan and state strategy submission

Q: Give an example of redesigning a process or pathway to improve outcomes.

S: Fragmented follow-ups left many carers without post-referral support.
T: I was to design a more consistent follow-up system.
A: Using referral data and qualitative feedback, I co-developed digital check-ins at 14 and 30 days plus automated alerts for high distress. This was integrated into CRM workflows and tested with regional partners.
R: Within six months, follow-up completion rose 23 % and average distress scores dropped 17 %, informing the Carers NSW Quality Improvement Plan and future funding cycles

Q: Tell us about working with multiple agencies to validate findings or co-design solutions.

S: Statistical findings needed real-world validation before adoption 
T: I had to engage PHN and two LHDs to test assumptions and refine the model.
A: I ran joint workshops, mapped workflow bottlenecks, and coded feedback against the AIHW Carer Indicators and NSW Integrated Care domains. This turned the project into a shared improvement effort rather than an external audit.
R: Partner buy-in ensured smooth implementation across regions and the protocol was adopted into regular referral practice

Q. How do you design data and reporting systems that drive quality improvement and policy influence?

S: Carers NSW’s referral and wellbeing data were fragmented across several systems, limiting our ability to link service outcomes to policy goals.
T: I was asked to build an integrated analytics and reporting approach that could both guide operational improvements and inform external reporting.
A: I established a unified data model linking CRM, survey, and health-service inputs; developed dashboards showing referral flows and wellbeing trends; and aligned indicators with the AIHW Carer Indicators and NSW Integrated Care Framework to ensure policy relevance. I also embedded automated data-quality checks and privacy safeguards.
R: The new reporting suite enabled executives to track performance in real time, underpinned the 2025–26 Quality Improvement Plan, and strengthened Carers NSW’s evidence base for its state-strategy submission

Q: How did you measure success and impact?

S: We needed proof the new follow-up system worked.
T: Define and track quantitative KPIs and qualitative feedback.
A: Established indicators for completion rates and distress trend changes; benchmarked against AIHW Carer Wellbeing Measures in Power BI.
R: 23 % increase in follow-ups, 17 % drop in distress, and model integrated into Carers NSW reporting suite

Q: Describe how you align your work with broader policy.

S: Carers NSW wanted reforms consistent with state policy.
T: Ensure our model fit within NSW Health Integrated Care and AIHW Indicators.
A: Framed metrics and outcomes using those domains and linked findings to the Carers Strategy 2024–26 mid-term review.
R: The project strengthened Carers NSW’s policy credibility and was referenced in its official submission

Q: How do you communicate complex findings to executives?

S: Executive team needed clear insights from dense data.
T: Translate technical outputs into decisions.
A: Created dashboards and briefings with storyline flow (Problem → Insight → Solution); used heatmaps to visualise risk cohorts.
R: Leadership approved protocol roll-out and cited the approach in state review papers

Q: Describe taking ownership of an ambiguous problem.

S: Engagement gap was flagged without clear method to address it.
T: Define scope and design solution independently.
A: Built project from zero — data integration plan, analysis, and co-design of protocol with external partners.
R: Delivered an adopted model improving carer outcomes and internal reporting capacity

Q: Tell us about improving outcomes for an under-served group.

S: Younger regional carers were least engaged and most distressed.
T: Create accessible and equitable follow-up options.
A: Co-designed digital check-ins suited to work-life constraints and validated content through carer feedback sessions.
R: Re-engaged under-served cohort and reduced distress rates by 17 %

Q: Walk us through designing a new service pathway.

S: Data showed many carers disengaged soon after referral—that is, once their initial Carer Gateway intake was complete but before any follow-up wellbeing contact occurred.
T: I was tasked with designing a consistent post-referral follow-up system to sustain engagement and monitor wellbeing.
A: Drawing on regression findings and partner input, I co-designed the Warm Transition Protocol: digital wellbeing check-ins at 14 and 30 days after referral, combined with automated alerts for carers showing deteriorating scores. Regional PHN and LHD staff were trained to respond directly to alerts through existing CRM workflows.
R: Within six months, follow-up completion increased 23 %, average distress scores fell 17 %, and the model was embedded in Carers NSW’s reporting suite and Quality Improvement Plan 2025–26

Q: How did you drive adoption of changes?

S: The new Warm Transition Protocol required case coordinators and regional partners to adopt unfamiliar digital dashboards and automated alerts.
T: I needed to build readiness and secure consistent practice change across all regions.
A: I developed concise training modules, ran live demonstrations with frontline teams, and created a short visual guide linking each alert to follow-up actions. Usage was monitored through Power BI adoption metrics, and I shared monthly feedback reports highlighting time saved and reduced missed contacts. I also presented early success data to executives to reinforce sponsorship.
R: Staff confidence rose markedly; adoption reached 90 % within the pilot regions, and the system was incorporated into standard operating procedures and regional performance reviews

Q: How do you handle disagreement between stakeholders?

S: During validation, North Coast PHN, Northern NSW LHD, and Mid North Coast LHD differed on who owned wellbeing follow-ups after the initial Gateway intake.
T: Maintain collaboration while clarifying accountabilities so the reform could proceed.
A: I facilitated workshops anchored in the AIHW Carer Indicators and NSW Integrated Care Framework, mapped touchpoints, and co-drafted a single post-referral workflow (the Warm Transition Protocol) that all parties could operationalise.
R — Specific outcome: Partners agreed to and implemented the Warm Transition Protocol with defined responsibilities (LHD care coordinators for psychosocial follow up, PHNs for data monitoring and system integration) for 14- and 30-day digital check-ins and alert responses; this alignment enabled rollout across regions and contributed to the 23% rise in follow-up completion and 17% drop in distress recorded in the pilot, which was then embedded into the Quality Improvement Plan 2025–26.

Q: What did you learn, and what would you do differently?

S: The project achieved strong outcomes, but it also revealed challenges around data handling and communication between partners.
T: I wanted to reflect on how to strengthen engagement and efficiency if I were managing the same initiative again.
A: I realised that early co-design with frontline staff was crucial — some referral teams initially found the data inputs unclear. I also learned to build in more time for data validation and user testing before rollout, to prevent manual errors and ensure everyone understood the indicators being tracked.
R: These reflections deepened my approach to balancing analytical rigour with practical usability. As a result, the final dashboard and reporting model were clearer, more reliable, and widely adopted across regional teams