🔍 An estimated 22% of service capacity was lost due to cancellations and non-attendance, highlighting significant utilisation gaps. Analysis of these gaps identified the primary drivers as client demographics (age, gender, equity indicators, and risk factors), referral sources, and funding streams.
Community Child & Family Health Services, a fictional Victorian not-for-profit organisation supporting families with children aged 0–5, is modernising its operations through the implementation of a new Client Information Management System (CIMS) to improve data quality, service reporting, and organisational performance. Operating within the community health and social services sector, CCFHS delivers residential programs, telehealth support, group sessions, and community outreach, all of which are governed by strict state and federal reporting obligations.
This project analyses program utilisation across multiple datasets to support alignment of services and operational planning within healthcare teams, ensuring more responsive and effective support for patient needs.
Executive-driven questions:
- What proportion of service capacity was utilised versus lost?
- During which periods do attended services peak, and when do cancellations and DNAs spike?
- Which clients (by demographics, equity indicators, and risk factors) receive the most services, and what are the service types?
- Which clients (by demographics, equity indicators, and risk factors, referral sources, and funding streams) account for capacity loss during that period, and which service types are most affected?
Insights and recommendations are provided on the following key areas:
- Program service growth (Year-over-Year: 2023→2024): High-level analysis of service delivery trends to inform workforce planning, resource allocation, and operational decision-making.
- Service type importance (RPP, Day Stay, Telehealth, In-Home, Workshop, Mental Health Support): Assessment of the relative importance and utilisation of each service type to support data-driven prioritisation and service design decisions.
- Client Demographic Contribution (Age and Gender): Analysis of key demographic segments driving program utilisation to guide targeted service planning, engagement strategies, and capacity planning.
- Equity Indicators and Risk Factors: Evaluation of utilisation patterns across equity and risk groups (e.g. CALD, NDIS, Indigenous status, DV risk, SEIFA) to identify potential access gaps and opportunities for targeted outreach and equitable service delivery.
- Average Wait Time (Days): Review of wait time trends across services to assess access, operational efficiency, and consistency of service delivery over time.
- Data Quality Check: Data quality checks were conducted, identified issues were documented, and the data was cleaned prior to analysis. Details can be found here. [link].
- Program Utilisation Report: Explore program utilisation through this interactive Power BI report, currently in early development and not yet finalised.
The dataset (2023-2025) is designed as a relational model to replicate a real-world Client Information Management System (CIMS) environment, supporting operational, compliance, and outcome reporting. It contains five interconnected tables:
clients(12,000 rows): Demographics, risk factors, and equity indicators for each unique client.services(50,000 rows): Each service episode delivered, linked to a client viaclient_id. Includes program type, clinician, referral source, wait times, and attendance status.outcomes(~35,000 rows): Pre- and post-program outcome measures for attended services, including K10, EPDS, and parenting confidence scores. Linked toservicesviaservice_id.data_quality(50,000 rows): Simulated validation checks and completeness flags for each service record, supporting governance and CIMS migration analysis.sla_performance(50,000 rows): Service-level agreement and compliance metrics, including wait-time compliance, attendance compliance, and timeliness of data entry.
- This analysis focuses on the 2023–2024 period to ensure a balanced year-over-year comparison. A star schema was used to design the data model, with the
servicestable serving as the fact table and the remaining five tables structured as dimension tables. - This analysis leverages the
clientsandservicesdatasets to generate actionable insights and data-driven visualisations. Additional datasets will be integrated in future analyses to deepen insights and support more comprehensive program utilisation decision-making.
Prior to beginning the analysis, a variety of checks were conducted for quality control and familiarization with the datasets can be found here. It also includes data dictionary.
Service attended volumes grew modestly by +1.1% from 2023 to 2024, reflecting stable demand. While monthly results follow an expected seasonal pattern, Nov 2024 stands out with a 5.8% decline compared to Nov 2023. This warrants further investigation to identify underlying drivers.
- Majority of the services were attended, with attendance rate ranging between ~76% to 78%. However, Did Not Attend (DNA) and Cancelled appointment together accounts for nearly one quarter of total service utilisation, highlighting opportunities to improve utilisation.
- Reductions in Did Not Attend (DNA) and Cancelled appointments (-0.14 and -0.11 p.p. respectively) suggest improved attendance outcomes in 2024.
- Monthly trend analysis shows that Did Not Attend (DNA) appointments increased year-over-year from Jun to Dec, with the exception of November. Cancelled appointments also showed year-over-year increases in Jan, June, Aug - Sep, and December. This pattern highlights the second half of the year as a key period of elevated non-attendance risk.
- Average wait days remained stable at ~12 days across all services from 2023 to 2024, indicating consistent access and operational efficiency.
- The 25–34 age group represented the highest utilisation of services, highlighting them as the primary user base across offerings. Similarly, females accounted for the largest share of service utilisation overall.
- Analysis of service utilisation by Equity Indicators and Risk Factors in 2024 shows the following contributions from each group:
- CALD: Non-CALD background accounted for ~83% of utilisation.
- NDIS: Non-NDIS category represented ~97% of utilisation.
- Domestic Violence (DV) Risk: Non-DV risk category contributed ~90%.
- Indigenous Status: Non-Indigenous background accounted for ~96%.
- SEIFA: Mid socioeconomic group represented ~50% of utilisation.
- The November 2024 decline in attended services was largely attributable to a 17.5% drop in In-Home services, with additional declines observed in Workshops and Telehealth. Seasonal factors and evolving service delivery preferences may have contributed to this trend.
💡Recommendations
- Reassess the allocation of resources between in-home, telehealth, and workshop services to match post-pandemic patient preferences.
- Explore opportunities to promote hybrid models that integrate in-person, telehealth, and targeted in-home care for high-need clients.
- Implement seasonal planning to anticipate and mitigate end-of-year slowdowns.
- Telehealth accounted for the largest attended service volume, ranging from ~21% to 25%, but experienced a notable year-end decline of 13.4% in 2024 compared to the previous period. Overall, Telehealth dropped by 15% for the year, despite ongoing demand. These trends likely reflect post-pandemic normalization, with patients increasingly returning to in-person or hybrid care, combined with typical seasonal slowdown.
💡Recommendations
- Refocusing Telehealth on high-value use cases (e.g. follow-ups, low-complexity care, rural access, after-hours services) where it delivers the strongest access and efficiency gains.
- Aligning workforce and capacity planning to seasonal demand patterns, reducing over-allocation during predictable year-end slowdowns.
- Strengthening hybrid care pathways, enabling flexible transitions between Telehealth and in-person care based on clinical appropriateness and patient preference.
- Monitoring patient outcomes and utilisation by modality, ensuring Telehealth continues to deliver comparable or superior outcomes rather than purely service volume.
- Mental Health Support services saw the largest growth among all service types, increasing by 7.8% in 2024. This increase may indicate growing demand for mental health support, potentially reflecting higher prevalence or recognition of mental health issues in the community. It could also reflect improved access or awareness of mental health services, or expanded program capacity.
💡Recommendations
- Consider expanding capacity for mental health programs to meet increasing demand.
- Invest in workforce training and recruitment for mental health professionals.
- Strengthen outreach and awareness campaigns to ensure communities can access mental health support efficiently.
- Telehealth attended service accounted for approximately 17% of service utilization among the 25–34 age group and was the leading channel for accessing Mental Health Support, representing ~3% of total services.
💡Recommendations This reflects the 25–34 age group’s comfort and familiarity with technology-enabled healthcare, alongside a higher prevalence or awareness of mental health issues within this demographic.
- Prioritise Telehealth for youth-focused mental health pathways, particularly for early intervention, follow-ups, and continuity of care.
- Tailor service design and communication to digitally native cohorts to sustain engagement and reduce barriers to access.
- Protect and allocate Telehealth capacity for mental health services where demand, accessibility, and patient preference clearly align.
- Track outcomes and engagement by age and modality, ensuring Telehealth delivers equitable outcomes while supporting service efficiency.
- The 45+ age group experienced the largest increase by ~49% in Mental Health Support utilisation in 2024.
💡Recommendations
- Highlights an opportunity to expand targeted mental health programs and outreach for the 45+ demographic.
- Among the gender demographic, Telehealth accounted for approximately 27% of overall service utilisation and around 4% specifically for Mental Health Support services in females.
- Despite males accounting for a relatively small share of total service utilisation (~11%) in 2024, demand for Mental Health Support among males increased by ~11%. Growth in the ‘Other’ category (~43%) is subject to confirmation pending data validation.
💡Recommendations
- Expand targeted outreach and engagement for males to ensure Mental Health Support services continue to meet growing demand.
- Develop tailored communication campaigns that address stigma and promote awareness of available mental health services for men.
- Monitor utilisation trends in both male and female demographics to inform resource allocation and program planning.
- The majority of service utilisation comes from the general population rather than high-risk or priority equity groups.
💡Recommendations
- This highlights potential gaps in engagement or access for priority populations (CALD, NDIS, Indigenous, high DV risk), signaling an opportunity for targeted outreach and support programs.
- The increase in cancellations and non-attendance during this period was primarily driven by Telehealth services.
- Cancellations and non-attendance are highest among clients aged 25-34 and 35-44, with females accounting for the majority of these instances.
- The spike in cancellations and non-attendance during this period was primarily driven by the general population, as expected.
- Cancellations and non-attendance by referral source were primarily driven by Self, Maternal & Child Health, and General Practitioner (GP) referrals. As these pathways originate from community and primary care settings, this suggests greater variability in client readiness, availability, or perceived urgency compared with referrals from non-profit and hospital-based sources.
- Services funded by the Victorian Department of Health (VIC DOH) accounted for the majority of cancellations and non-attendance during the observed spike period. .
- The majority of missed appointments were for services funded by the Victorian Department of Health.
- This suggests that state-funded services, often delivered in community and primary care settings, may experience higher variability in client attendance.
- Possible contributing factors include client readiness, perceived urgency of the service, or barriers such as transport, scheduling, or awareness.
- It may highlight a need for targeted interventions (e.g., reminder systems, engagement strategies) for state-funded programs to reduce lost capacity.
💡Recommendations
- Targeted Client Engagement for High-Risk Groups
- Focus outreach on clients aged 25–44, particularly females, who account for the majority of missed appointments.
- Implement tailored reminders (SMS, email, or phone) highlighting the importance of the service and offering flexible scheduling options.
- Telehealth Service Optimization
- Investigate barriers to Telehealth attendance (e.g., technology access, digital literacy, competing commitments).
- Provide clear guidance and support for clients before Telehealth appointments, such as pre-session checks or tutorials.
- Referral Source Management
- Collaborate with Self, Maternal & Child Health, and GP referral sources to improve client preparedness and engagement prior to the appointment.
- Consider pre-appointment check-ins or education about the service’s purpose to enhance perceived urgency and readiness.
- State-Funded Program Enhancements
- For VIC DOH-funded services, introduce targeted interventions such as:
- Appointment reminder systems with multiple touchpoints.
- Flexible booking and rescheduling options.
- Transport or access support where needed.
- Monitor attendance patterns regularly to identify periods or services with recurring spikes in non-attendance.
- For VIC DOH-funded services, introduce targeted interventions such as:
- Data-Driven Monitoring and Continuous Improvement
- Establish a dashboard to track cancellations and non-attendance by service type, age group, gender, referral source, and funding stream.
- Use insights to iteratively refine engagement strategies and resource allocation.
Throughout the analysis, multiple assumptions were made to manage challenges with the data. These assumptions and caveats are noted below:
- Trends identified in this analysis are based on data recorded in CIMS and reflect documented service activity only.
- The analysis is limited to the 2023–2024 reporting period
- The analysis utilised
servicesandclienttables exclusively. - Further insights could be generated by incorporating additional tables, which is recommended for future reporting sprint.
- Explore additional dataset tables:
outcome,sla_performance,data_quality. - Develop executive-focused questions for key stakeholders, including Executive Leadership, Service Delivery Managers, and Governance and Performance teams.
- Define key metrics, dimensions, and KPIs to guide the next phase of analysis.