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Over the past few months, we’ve had the opportunity to work alongside a partner health service in Victoria on a project focused on refining how National Weighted Activity Units (NWAU) are calculated for mental health services—using IHACPA’s shadow funding models and available state data extracts as our base.

What began as a task to reconcile activity data against funding calculators quickly evolved into a deep dive into one of the more intricate challenges in mental health reporting: accurately identifying and attributing Phases of Care.

Unlike traditional episode-based models where the care setting dictates structure, Phases of Care are driven by clinical assessments and care goals—not by whether someone is in a hospital bed, a residential facility, or engaged with a community team. That distinction introduces a layer of complexity that requires careful, contextual interpretation.

As we worked through the data, we uncovered a number of recurring scenarios where real-world care didn’t line up neatly with the phase-based funding logic. Below are some examples that illustrate where these mismatches appear—and how we’ve approached them.

1. Same-Day Contacts Across Teams: HONOS and Phase Attribution

In one instance, a consumer was admitted to inpatient care in the morning while a community team also logged an intake assessment later that same day. While both assessments were valid in their own right, the logic used to assign the Phase of Care defaulted to the community HONOS score.

Because Phases of Care aren't tied to setting, the attribution of that score directly affected the assigned phase—and the funding. This raised a key question: how do we prioritise assessments when multiple teams are involved on the same day?

2. Single Assessments Triggering New Phases

We came across cases where an “Assessment Only” contact—such as a triage or crisis team review—occurred mid-way through an established community episode. Although there was no change in treatment direction, the data logic interpreted this assessment as the start of a new Phase of Care.

This is a perfect example of how phase attribution based solely on timing, rather than on genuine clinical transition, can misrepresent both the care journey and the associated activity funding.

3. Deleted Episodes and Phantom Phases

Some scenarios involved deleted or closed episodes, where contacts remained visible in the data. Without a live episode to anchor them, these contacts were treated as triggering a new Phase of Care—even when no new treatment or service shift had occurred.

To address this, we introduced local business rules to suppress the generation of new phases in these cases, preserving the continuity of the original care pathway.

4. Leave Days: Timing and Phase Impact

Inpatient stays involving multiple short leave events in a single day raised another challenge. IHACPA guidance outlines specific rules for counting leave days, particularly when consumers leave and return more than once on the same day.

Because length of stay can influence the weighting of certain admitted phases, correct treatment of leave is crucial. Our review found examples where small deviations in counting left a measurable impact on total NWAUs.

5. Continuity in Phase of Care Reviews

Under the 91-day reassessment guideline, a new Phase of Care should only begin if there’s a change in clinical goal. But we observed cases where reassessments reaffirmed the same phase (e.g. Functional Gain) and yet still triggered a new phase entry.

Since phases are conceptually linked to clinical purpose—not calendar boundaries or contact frequency—this automatic reset introduced unnecessary fragmentation into what was otherwise consistent care.

Where to From Here?

In response, we’ve drafted a set of business rules that aim to align more closely with IHACPA’s guidance while also reflecting the reality of care delivery. These rules help distinguish between administrative artefacts and genuine phase transitions, ensuring that phase attribution is meaningful—not just mechanically derived.

We’re still refining our approach and working closely with stakeholders to test and validate these adjustments. But we know we’re not the only ones grappling with this.

If your team is also trying to make sense of how Phases of Care translate into activity data and funding logic, we’d love to connect.

Whether it’s questions about HONOS priority, managing contacts across service settings, or continuity in long-running episodes—we’re happy to compare notes.

Sincere thanks to Luke Garton , Bagus Permana and Abhishek Rajput, whose sharp thinking and persistence have made this work possible.

Written by Bernard Herrok, proofed by AI.

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