Beyond the Fusion Rate

By Your Health Magazine Contributor

Your Health Magazine Contributor

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Beyond the Fusion Rate

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Collecting patient-reported data at scale and using it to improve care are not interchangeable achievements – in Australian health care, the gap between them is now documented rather than merely assumed. By April 2026, the Cancer Institute NSW had embedded an electronic patient-reported measures system across 13 local health districts, accumulating more than 40,000 surveys from over 20,000 patients. That’s a genuine infrastructure commitment. But key challenges remain before patient experience data can translate into improved care quality, as the Australian Commission on Safety and Quality in Health Care concluded in its national rapid review of patient experience measurement.

In registry-enabled specialist surgery, the stakes are direct. The Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) PROMs program listed 217 participating hospitals in June 2022; by June 2025, the Australian Orthopaedic Association (AOA) Annual Report 2024–2025 records participation at 244 hospitals and 841 surgeons. The Australian Spine Registry collects PROMs before surgery and at six, 12, and 24 months post-surgery. Expanded participation is real. Whether it changes how care is evaluated and delivered depends on conditions that participation counts alone don’t establish.

Two Measurements, Two Different Questions

Revision risk and patient-reported recovery appear, intuitively, to track the same outcome: whether a procedure worked. Wayne Hoskins, an orthopaedic surgeon and registry researcher with the Australian Orthopaedic Association National Joint Replacement Registry and the University of Melbourne, identified why that assumption fails. Reporting on an AOANJRR-linked analysis of primary hip and knee arthroplasty, Hoskins concluded that “PROMs and revision rates may be analyzing two different facets of function after arthroplasty.” The analysis compared individual surgeons’ early revision risk with their patients’ six-month patient-reported outcome scores and found weak, near-zero correlations at the surgeon level. Technical durability and patient-reported recovery need not move together – the measures describe outcomes from different vantage points.

Dr Timothy Steel, a Sydney-based neurosurgeon and minimally invasive spine surgeon at St Vincent’s Private and Public Hospitals, has documented both measures in parallel within a standardised cervical reconstruction pathway for atlantoaxial osteoarthritis. A 95.5% radiographic fusion rate confirms that posterior C1–C2 fixation achieved its structural objective. The patient-reported data answers a different question: Visual Analogue Scale pain scores fell from 9.4 to 2.9, Neck Disability Index dropped from 72.2 to 18.9, and 91% of patients indicated willingness to undergo the procedure again. A fusion rate and a pain score measure different aspects of outcome. One provides information about structural stability, while the other reflects the patient’s reported experience.

For a patient whose procedure succeeds structurally while their pain continues, whether that experience enters the clinical record at all depends on whether the programme was designed to capture it. Dr Steel’s paired documentation makes the categorical distinction concrete – and raises the question that follows: if both measures matter, what does it take to treat patient-reported outcomes as a meaningful part of routine outcome assessment rather than supplementary data?

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The Infrastructure the Reorientation Actually Requires

A mixed-methods study of electronic patient-reported measures implementation in NSW public oncology care frames the challenge directly: ePRM is a workflow-and-systems integration task, not a survey deployment task. The study identifies variable IT infrastructure and clinician workload pressure as conditions under which PROMs can be collected while remaining non-actionable in routine clinical decisions. Implementation and collection are different engineering problems – and the gap between them is where programmes stall.

Professor Ilana Ackerman, a musculoskeletal epidemiologist at Monash University and PROMs Advisor to the AOANJRR, has quantified those conditions at hospital scale. The ICHOM Standard Set feasibility study she led – implementing PROMs across two Australian hospital settings over 17 months – cost approximately AUD 94,955 and required 0.2–0.4 FTE of dedicated coordinator time plus additional staff support to maintain data completeness. Patient preference data from the same study showed most participants preferred paper-based questionnaire completion, establishing that mode flexibility is a logistical requirement built into programme design rather than a later refinement. Explicit IT funding for database development, reminder systems, and data extraction was identified as essential, not optional.

At registry scale, an AOANJRR national PROMs pilot that Ackerman co-authored – collecting preoperative and six-month postoperative data from 44 Australian hospitals – confirmed that sustaining collection at that volume required a project manager, software developers, data managers, statisticians, and dedicated coordinators. Together, the hospital-level feasibility study and registry-scale pilot suggest that effective PROM implementation depends on staffing, IT infrastructure, and patient-engagement strategies being planned and funded before collection begins.

Scale Proves Achievable; Use Proves Harder

What Australian specialist programmes have mostly demonstrated so far is that patient-reported data can be gathered at volume. What happens after collection – whether data reaches clinicians, shapes decisions, or feeds back into service design – is where the evidence runs considerably thinner.

That NSW cancer PRMs rollout illustrates what committed infrastructure can produce – surveys gathered at volume, systematically, across most of the state’s local health districts. At that point the easy question ends.

Collection volumes get reported as programme progress. The feedback loops that would turn those volumes into changed clinical practice are harder to quantify – and considerably less often present. The AOANJRR national PROMs pilot documented this directly: even within a well-resourced registry, patient and surgeon engagement with PROM feedback dashboards was variable, meaning collected data did not automatically reach those positioned to act on it. The Commission, whose rapid review examined patient experience measurement nationally, was direct: “However, there is limited published information on how patient experience data are used to improve the quality and safety of health care.” That finding doesn’t describe a field in transition. It describes a structural feature of current practice. Ruseckaite et al. (2025), reviewing 114 Australian registries, put numbers to the drop-off: 60% were capturing or planning to collect PRMs, but only 66% of those were reporting them, and among PRM-reporting registries, just 10 (22.2%) produced site reports – the feedback mechanism most directly positioned to translate collected data into changed service behaviour.

What a Primary Signal Actually Demands

The conditions required to treat patient-reported outcomes as a primary signal in surgical care are no longer unknown. Australian research has specified them: coordination staffing, funded IT infrastructure, mode flexibility, and feedback loops that reach clinicians and services before patients are discharged from follow-up. The available evidence suggests, however, that implementing those conditions as a programme standard – rather than as a feature of individual pilots – has not kept pace with collection growth.

The gap between collecting patient-reported data and improving care through it is not solely a measurement problem. Programme design, resources, clinical workflows, and organisational commitment also influence whether the data are used. Dr Timothy Steel’s cervical reconstruction pathway tracks radiographic fusion alongside patient-reported pain, disability, and willingness data as parallel outcome measures. That paired documentation provides one example of structural and patient-reported outcomes being captured alongside one another, each answering its own question about what happened to the patient. Professor Ackerman’s work at hospital and registry scale names what it takes to make that practice systematic: funded staffing, engineered IT, and feedback loops built before collection begins rather than bolted on after. In Australian specialist surgery, the distance between gathering the data and acting on it remains open – and collecting more surveys won’t close it.