Most operators treat medication exceptions like any other compliance metric, something to drive toward zero. A refusal, a missed sleep-time dose, a medication that wasn't available at pass time: each gets logged, reviewed, and filed under "needs improvement," then mostly forgotten until the next audit.
When exceptions are treated like a compliance number, that's all they become, a metric to drive toward zero. However, when treated as a real-time piece of information about how care is unfolding in your community, they can reveal clinical blind spots before they grow into something bigger.
This is what Molly Hawker, Director of Customer Impact at August Health, explored in her recent video, Understanding medication exception patterns using August Health. Here’s what she uncovered:
What counts as a meaningful exception
Not every exception carries the same signal. Four patterns are worth watching closely:
- Refusals: Could point to resident's evolving preferences, a care approach that needs adjusting, or a conversation the clinical team hasn't had yet.
- Sleep-related misses: Can reflect a resident's changing routine or a dosing schedule that no longer fits their day.
- Medication availability issues: Often trace back to a workflow gap between ordering, pharmacy, and the community rather than a one-off mistake.
- Order changes: Show where a resident's care plan is actively shifting, and how quickly the team is adapting to it.
Individually, each of these is just a data point. Track them together over time, though, and they start to describe how care is being delivered.
The data behind the pattern
To understand what these patterns look like at scale, August Health's Customer Insights team analyzed medication exception data across 17 operators over a 12-month period, covering more than 75 million charting episodes. Three consistent trends emerged.
Exception rates stabilize quickly after implementation
Across the operators studied, exception rates settled into a consistent baseline shortly after implementing August Health eMAR. This gives teams a reliable starting point to measure against, instead of a constantly shifting target.
Self-administration charting varies by community
Some communities chart self-administered medications with a high degree of consistency, while others show noticeable gaps. That doesn't necessarily point to a resident-care issue. More often it reflects how differently teams interpret the same workflow, which gives clinical leaders a concrete starting point for aligning charting practices across communities.
The "other" category gets used even when a more specific reason exists
In a meaningful share of cases, staff selected a general "other" reason for an exception when a more structured, specific reason was already available in the system. Taken alone, that looks like a minor data-quality footnote. Across communities, though, the pattern says something real about charting habits, and where additional training or workflow clarity could help.
What this means for clinical leaders
None of this is about tightening compliance for its own sake so much as giving clinical leaders a clearer view into how their own teams work and how their residents' needs are shifting over time. A stabilized baseline tells you what "normal" looks like for your community, so a deviation is easier to catch early. Charting variation points to where teams may need alignment, not correction. And leaning on "other" flags where documentation habits, not resident care, need attention.
Treated this way, exception data functions less like a report card and more like a diagnostic tool, one that flags where to look closer before a small gap becomes a resident-care issue.
The takeaway
Analyzing medication exception patterns means more than reviewing data. It means seeing how care is delivered, how resident needs evolve, and where your teams could use more clarity or support. That's the real payoff: not fewer exceptions, but clearer insight into the care already happening in your communities.
