💡 Automated treatment efficacy scoring from paired mite counts

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Summary

Automatically calculate and visualize treatment efficacy by comparing pre-treatment and post-treatment Varroa mite counts, with per-product and per-method tracking across seasons. This extends beyond simple treatment compliance tracking into quantitative outcome measurement — feeding treatment efficacy data back into the recommendation engine to improve future treatment confidence scoring. No major beekeeping app besides VarroaVault currently offers automatic efficacy calculation from paired counts.

Market Signal

VarroaVault explicitly claims "no other major beekeeping app in 2026 automatically calculates efficacy from paired pre and post counts" — calling it "the most significant gap in the beekeeping software market." Their treatment efficacy calculator captures product, lot number, application date, and temperature at time of application, then compares pre/post mite counts to calculate percent reduction. Their multi-apiary mite dashboard provides operation-wide visibility. BroodMinder's 2026 roadmap lists Varroa counting as a major focus area with approximately 100 BeeTV units expected by mid-2026.

User Signal

Treatment decisions are the highest-stakes moment in hobbyist beekeeping — wrong timing or ineffective products directly cause colony loss. The PRD identifies "wrong advice risk" as the top technical risk and requires "confidence scoring, evidence attribution, and safe fallback actions." Treatment efficacy data is the most concrete evidence source for calibrating future treatment recommendations. Discussion #85 covers treatment compliance and PHI alerts but not automated efficacy measurement — this proposal extends that idea into quantitative outcome tracking.

Technical Opportunity

The architecture already includes inspection and observation tables (Story 3-2, 3-3), the recommendation engine, and the confidence-scoring contract. Adding paired mite-count tracking requires: (1) a mite_count observation type with sampling_method, sample_size, and count fields in the inspection schema, (2) automatic pairing logic linking pre/post counts to a treatment record via temporal proximity and hive ID, (3) efficacy calculation (percent mite reduction adjusted for sampling method reliability), and (4) feeding efficacy history into the recommendation engine's confidence scoring for future treatment suggestions. This creates a feedback loop that makes the recommendation engine measurably better over time — it's not a standalone feature but a core data input for the decision-support system.

Assessment

Dimension	Score	Rationale
Feasibility	high	Builds directly on planned inspection/observation data model. Efficacy calculation is straightforward math. Schema changes are additive. No new infrastructure required.
Impact	high	Directly improves recommendation quality for the highest-stakes user decision (treatment timing/product selection). Creates a measurable feedback loop that compounds with usage. Fills a gap that the leading niche competitor (VarroaVault) identified as the biggest market gap.
Urgency	med	VarroaVault is niche and standalone — not a direct threat to Broodly's position. But the longer this gap exists, the more treatment-focused beekeepers VarroaVault captures. Schema decisions for mite count tracking should be made during MVP data model design.

Adversarial Review

Strongest objection: Mite counting methodology varies widely (alcohol wash vs sugar roll vs natural drop) and accuracy is inconsistent. Efficacy calculation from two noisy data points may give false confidence. VarroaVault already owns this niche — Broodly would be playing catch-up on their home turf.

Rebuttal: Broodly's confidence-scoring model handles noisy data better than VarroaVault's simple percentage calculator. Broodly can weight efficacy scores by sampling method reliability (alcohol wash > sugar roll > natural drop), flag low-confidence efficacy when sample sizes are small or methods change between pre/post, and combine individual efficacy data with anonymized regional treatment outcome patterns for Bayesian updating. VarroaVault is a standalone mite-tracking tool; Broodly integrates efficacy into a full decision-support system where the value is not the calculation itself but using it to improve future treatment recommendations. The competitive advantage is the recommendation engine context, not the arithmetic.

Suggested Next Step

Add mite_count observation type (with sampling_method, sample_size, count fields) to the inspection schema design in Story 3-2. Define paired-count linking logic and efficacy calculation formula. Specify how efficacy history feeds into the recommendation engine's treatment confidence scoring.

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Generated by weekly feature ideation workflow — 2026-06-26