Overview
Initiative type
Service Improvement
Status
Deliver
Published
June 2025
Summary
The nation's first co-designed healthcare associated infection surveillance dataset has been developed and implemented in Queensland, improving data quality, easing reporting, and enabling consistent surveillance practices.
Implementation sites: Queensland statewide
Partnerships: University of Queensland
This project was presented as a Poster at CEQ Showcase 2025 (PDF 348KB).
Aim
To co-design and implement a statewide, clinician-endorsed HAI (Healthcare Associated Infections) surveillance dataset to reduce variation, improve data quality, and inform infection prevention strategies across Queensland.
Outcomes
- 13-item HAI minimum dataset developed and endorsed by clinicians, ID physicians, and consumers (policy QH-GDL-321-7-1; Queensland Health’s updated Healthcare-Associated Infection (HAI) Surveillance Definitions, 2024; manuscript[1])
- Implementation endorsed by the Department of Health and all HHSs from 1 January 2025
- 180+ IPC professionals trained via Quick Bites education sessions
- Definitions and tools integrated into existing Multiprac surveillance software
- Evaluation demonstrates increased acceptability, feasibility, and consistency of surveillance practices
- This model could be adapted to other priority conditions requiring state-wide surveillance standardisation (e.g., antimicrobial resistance, sepsis, or device-related complications). It offers a scalable framework.
Background
Healthcare-associated infections (HAIs) are a persistent challenge, contributing to preventable harm, prolonged hospital stays, and increased costs[2, 3]. Until recently, Queensland lacked a consistent state-wide HAI surveillance system, resulting in variation across Hospital and Health Services (HHSs) and an inability to benchmark performance[4].
Drawing on a modified Delphi approach and in partnership with the Queensland Infection Prevention and Control Unit (QIPCU), we sought to co-design a standardised HAI surveillance minimum dataset with associated case definitions to support state-wide HAI surveillance activities.
Methods
Delphi Phase
A two-round Delphi study was conducted with Queensland-based experts in infection prevention and control (IPC), infectious diseases, and microbiology[5-8]. In Round One, 49 healthcare professionals participated in an online survey to rate the importance, feasibility, and acceptability of 36 proposed HAI surveillance measures derived from national and international literature and jurisdictional guidelines. Measures were grouped under bloodstream infection, pneumonia, urinary tract infection, surgical site infection, and significant organisms. Definitions for each measure were also assessed for clinical acceptability. Items that met predefined thresholds for importance (median ≥7) and feasibility (median score of 3–4) progressed to Round 2. Qualitative free-text responses were analysed to capture contextual insights.
In Round Two, 14 experts convened in a hybrid (in-person and virtual) panel meeting. This expert group—comprising senior clinicians from all Hospital and Health Services (HHSs)—reviewed Round 1 findings and iteratively refined the MDS through structured discussion and consensus (defined as ≥70% agreement) [6, 9]. Final definitions were aligned with national (ACSQHC) and international standards (e.g., NHSN), incorporating expert suggestions and consensus feedback.
Implementation Framework
Post-consensus, implementation activities were guided by CFIR[10, 11] to ensure systematic consideration of context and sustainability. Implementation constructs addressed included:
- Intervention Characteristics: The 13-item MDS and definitions were designed to be pragmatic, relevant, and compatible with existing surveillance systems (e.g., Multiprac).
- Outer Setting: System-wide rollout was supported by a Director-General-endorsed memorandum and alignment with Queensland Pathology data infrastructure.
- Inner Setting: Recognising the variable IPC capacity across HHSs, a suite of tools and supports was developed to address readiness for implementation.
- Characteristics of Individuals: End-user capacity was strengthened through Quick Bites education sessions (n=14 sessions; >180 attendees), focused on practical application of the new MDS.
- Process: The Queensland Infection Prevention and Control Unit (QIPCU) led the structured rollout, including development of Multiprac-specific implementation guides and stakeholder consultation.
Evaluation Strategy
An implementation evaluation is underway using validated Likert scale measures of acceptability, appropriateness, and feasibility[12]. These are being assessed via surveys and targeted feedback from end users at Queensland HHS sites, with findings to inform iterative refinements and support long-term fidelity.
This multi-phase approach—consensus-building followed by framework-informed implementation—ensures the MDS reflects clinician priorities, is operationally feasible, and aligns with national ambitions for improved HAI surveillance. It also provides a reproducible model for other jurisdictions aiming to strengthen surveillance through co-design, rigorous implementation science, and state-wide coordination.
Discussion
This project addressed a longstanding gap in Queensland’s infection surveillance system by co-designing and implementing the state’s first minimum dataset (MDS) and standardised definitions for HAI surveillance. The initiative was led through a strategic partnership between Metro North Health’s Herston Infectious Diseases Institute (HeIDI), the Queensland Infection Prevention and Control Unit (QIPCU), and infection control teams across all Hospital and Health Services (HHSs). This collaboration ensured statewide representation, strong clinical leadership, and close alignment with existing health infrastructure.
Using a modified Delphi approach, the team engaged 49 infection prevention experts, infectious disease physicians, and microbiologists to evaluate the importance, feasibility, and acceptability of 36 potential surveillance measures. The resulting 13-item MDS, with consensus-based definitions, fills a critical policy and practice gap in HAI monitoring, bringing Queensland into alignment with other jurisdictions while also accounting for local system needs. The success of this process was underpinned by an inclusive and transparent co-design model, valuing the input of clinicians who lead surveillance activities across Queensland hospitals.
The implementation strategy was informed by CIFR. Key enabling factors included leveraging existing data platforms (Multiprac), aligning the MDS with Queensland Pathology processes, and providing contextualised resources and training opportunities for health service adoption. Education sessions (n=14) were delivered by expert QIPCU clinicians, building workforce capability and awareness. Implementation guides were developed by QIPCU to support local integration.
Preliminary evaluation using validated implementation outcomes (acceptability, appropriateness, and feasibility) has shown positive results. Staff reported increased confidence in using the new definitions, greater clarity around surveillance expectations, and improved consistency in data capture across services.
While the project achieved significant progress, limitations included the exclusion of pneumonia and urinary tract infection measures from the MDS due to feasibility concerns. These areas will be revisited as digital and workforce capacity expands. Future directions include integrating the MDS into digital surveillance dashboards, supporting real-time monitoring. Additionally, further consumer involvement is planned for the next phase, including co-designing accessible notification of infection summaries and visualisation tools.
This project demonstrates the power of partnership, clinician-led innovation, and pragmatic implementation science. By drawing on the combined strengths of HeIDI’s research capacity, QIPCU’s clinical expertise and system-wide reach, and the practical insights of frontline infection prevention teams, Queensland has delivered a scalable, evidence-based solution to one of healthcare’s most persistent challenges. The approach provides a model for national adoption and highlights how collaboration can drive measurable improvements in patient safety, surveillance quality, and health system performance.
References
1. Schults, J.A., et al., Expert consensus and recommendations for health care-associated infection surveillance in Queensland, Australia: A modified Delphi study. Am J Infect Control, 2025.
2. Wozniak, T.M., A. Dyda, G. Merlo, and L. Hall, Disease burden, associated mortality and economic impact of antimicrobial resistant infections in Australia. Lancet Reg Health West Pac, 2022. 27: p. 100521.
3. Lydeamore, M.J., et al., Burden of five healthcare associated infections in Australia. Antimicrobial Resistance & Infection Control, 2022. 11(1): p. 69.
4. Schults, J., B. Henderson, L. Hall, and S. Havers, Designing for transparency and trust: Next steps for healthcare associated infection surveillance in Queensland. Infect Dis Health, 2024.
5. O'Connor, L., et al., Operationalising a modified Delphi study to progress quality care process nursing metrics for acute care. J Res Nurs, 2022. 27(7): p. 655-676.
6. Schults, J.A., et al., International recommendations for a vascular access minimum data set: A Delphi consensus-building study. BMJ Quality & Safety, 2021. 30(9): p. 722-730.
7. Schults, J.A., et al., Establishing a paediatric critical care core quality measure set using a multistakeholder, consensus-driven process. Critical Care and Resuscitation, 2024. 26(2): p. 71-79.
8. Lovegrove, J., P. Fulbrook, and S. Miles, International consensus on pressure injury preventative interventions by risk level for critically ill patients: A modified Delphi study. International Wound Journal, 2020. 17(5): p. 1112-1127.
9. Tong, A., et al., Standardised outcomes in nephrology - Haemodialysis (SONG-HD): study protocol for establishing a core outcome set in haemodialysis. Trials, 2015. 16: p. 364.
10. Damschroder, L.J., C.M. Reardon, M.A.O. Widerquist, and J. Lowery, The updated Consolidated Framework for Implementation Research based on user feedback. Implementation Science, 2022. 17(1): p. 75.
11. Damschroder, L.J., et al., Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science, 2009. 4(1): p. 50.
12. Weiner, B.J., et al., Psychometric assessment of three newly developed implementation outcome measures. Implementation Science, 2017. 12(1): p. 108.
Key contact
Belinda Henderson / Dr Jessica Schults
Chief Infection Control Nurse / Conjoint Senior Research Fellow
Queensland Infection Prevention and Control Unit