How AI Search Is Recommending Eating Disorder Treatment Centers: Monthly Trends

Executive Summary

August 2026 was a quiet month for AI search recommendations in the eating disorder treatment center category: no brand's valid recommendation coverage moved beyond normal month-to-month variation, and every tracked brand is classified as stable against its July baseline. Alsana and The Emily Program now share the recommendation lead at 7.0% valid recommendation coverage each, a shift from July 2026, when The Emily Program held the lead alone at 7.7% and Alsana trailed at 4.4%.

Beneath that quiet headline, two sub-metric changes stand out. Alsana's top-three placement rate rose from 1.1% in July to 7.0% in August, while ERC Pathlight's rank-one rate fell from 4.4% in July to 0.0% in August. Across the same two months, the gap between Alsana and ERC Pathlight moved from a 2.2-point ERC Pathlight lead to a 4.2-point Alsana lead — a 6.4-point change that the benchmark has recorded between these two tracked months.

Against the July baseline, all nine tracked brands remain within normal month-to-month variation on their primary coverage metric. Veritas Collaborative, Walden Behavioral Care, and Center for Discovery continued to record no valid recommendation coverage in either month.

Each monthly run begins with 270 prompt-surface observations (243 unique questions) in August 2026, up from 213 prompt-surface observations (209 unique questions) in July 2026, across the benchmark's defined AI/search surface universe. Of those, 270 mentioned a tracked brand or competitor in August and 213 did in July; 184 were relevant and 86 were irrelevant in August, versus 138 relevant and 75 irrelevant in July. The public metrics use the 71 qualified observations in August and 91 in July that survive both qualification stages.

Key Findings

Benchmark Context

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

In July 2026 the benchmark captured observations across two AI surface families: AI Overviews and AI Mode. August 2026 added Gemini as a third surface, expanding qualified surface breadth from 2 to 3 while the qualified observation count fell from 91 to 71. The raw prompt collection grew from 213 to 270, but a larger share was reserved or filtered, leaving a smaller qualified set for brand-level measurement.

Benchmark-Level Metrics

AI Recommendation Trend

The category is tightly clustered with no dominant leader. In August 2026 Alsana and The Emily Program share the top position at 7.0%, while ERC Pathlight, Rogers Behavioral Health, and Monte Nido occupy a mid-tier between 1.4% and 2.8%, and three brands hold no valid recommendation coverage at all.

Valid Recommendation Coverage by Brand

No brand moved beyond normal month-to-month variation in its overall valid recommendation coverage in August 2026. The category-level shift came from the combination of several smaller movements, with three brands moving up and three moving down by less than four points each, plus three brands holding flat at zero.

What Changed This Month

Alsana: Rising Toward a Shared Lead

Alsana's valid recommendation coverage rose from 4.4% in July to 7.0% in August, a 2.6-point gain that places it in a tie with The Emily Program for the category lead. The brand was present in 13.2% of July observations and 16.9% in August.

The change was concentrated in stronger placement within recommendation shortlists. Alsana's top-three rate jumped from 1.1% in July to 7.0% in August, and its valid recommendation count rose from 4 to 5. It captured no rank-one recommendations in either month.

Alsana's gain came from being more frequently included among the top three options AI systems present, not from being the first recommendation. Its average recommended rank improved from 3 to 2.6, but it still has not secured a single top placement.

Highest-priority diagnostic: Which prompt types are driving Alsana's new top-three placements, and which brand is taking the rank-one slot in those same answers?

ERC Pathlight: The Sharpest Decline in Valid Recommendation Coverage

ERC Pathlight's valid recommendation coverage fell from 6.6% in July to 2.8% in August, a 3.8-point decline. Its presence also narrowed, from 65.9% of observations in July to 59.2% in August.

The more notable sub-metric change is at the top of the list. ERC Pathlight's rank-one rate dropped from 4.4% in July to 0.0% in August — the largest sub-metric change recorded for the brand this month. Its valid recommendation count fell from 6 to 2, and its top-three rate declined from 4.4% to 2.8%.

ERC Pathlight is still widely surfaced in AI answers, but it has largely stopped being the first or primary recommendation. The brand remains present in most conversations while losing its position as the lead option.

Highest-priority diagnostic: What changed about the evidence sources or prompt contexts where ERC Pathlight previously earned rank-one recommendations, and which brand now occupies that position?

The Emily Program: Sharing the Lead After a Slight Ease

The Emily Program eased slightly from 7.7% in July to 7.0% in August, yet it now shares the category lead with Alsana. Its valid recommendation count declined from 7 to 5, but its quality improved at the top.

The Emily Program's top-three rate rose from 5.5% in July to 7.0% in August, and its rank-one rate climbed from 2.2% to 7.0%. Its presence expanded from 51.6% of observations in July to 56.3% in August.

The Emily Program lost one recommendation slot overall, but every recommendation it retained was in the top three, and its first-position placements rose. Its average recommended rank improved from 1.6 to 1.

Highest-priority diagnostic: Which evidence sources are supporting The Emily Program's new rank-one placements, and are those sources durable across prompt variations?

Monte Nido: Widely Present but Rarely Recommended

Monte Nido remains the most visible brand in the category, appearing in 73.2% of August qualified observations, up slightly from 72.5% in July, but its valid recommendation coverage fell from 2.2% to 1.4%. Its valid recommendation count declined from 2 to 1.

The brand's top-three rate moved slightly from 1.1% in July to 1.4% in August, and it recorded no rank-one recommendations in either month. Its average recommended rank held at 3.

Monte Nido's category-wide visibility is exceptionally high, yet that presence converts to a valid recommendation in only 1 of 71 August observations. High visibility is not translating into recommendation credit.

Highest-priority diagnostic: Why is Monte Nido's extensive presence not converting into top-of-list recommendations, and what attributes do AI systems associate with the brand when they mention it?

The Gap Between Alsana and ERC Pathlight

Alsana and ERC Pathlight moved in opposite directions across the two tracked months. In July, ERC Pathlight held a 2.2-point lead over Alsana; by August, Alsana led by 4.2 points — a 6.4-point swing, the most notable competitive gap change the benchmark has recorded between these two months.

The distinction to notice: this is a category-level observation about the current output distribution, not evidence about why either brand moved. The benchmark identifies where attention is warranted, not the cause.

Highest-priority diagnostic: Which specific prompts and surface combinations now favor Alsana over ERC Pathlight, and are the evidence sources behind those answers stable?

Buyer-Intent Interpretation

In August 2026 all 71 qualified observations fell into the Brand Recommendation cluster, the same concentration seen across all 91 qualified observations in July 2026. No qualified observations fell into the Pricing & Value or Multi-Brand Comparison clusters in either month, meaning the public benchmark cannot yet answer questions about how AI systems frame cost, insurance, or direct treatment center comparisons. For an eating disorder treatment category where financial accessibility and fit are decisive factors, that is a meaningful gap in what the public data can reveal.

Brand Opportunity Summary

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations capturing the query, surface, recommendation outcome, rank, sentiment, and citations where exposed. Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of the LLM Authority Index AI Market Discovery research program.

• AI Industry Market Discovery Methodology
• AI Industry Market Discovery Metrics
• AI Industry Market Discovery Standards

Report-Specific Interpretation Notes

• Small observation counts are valid for this niche vertical: the August 2026 qualified set is 71 observations, down from 91 in July. Brand-level percentages rest on small absolute counts (for example, 5 valid recommendations for Alsana and 1 for Monte Nido), so single-prompt shifts can move percentages by several points.
• Movement between months identifies changes worth investigating; it does not establish cause. ERC Pathlight's rank-one decline flags a question, not an explanation.
• The qualified denominator differs from the raw collection: 71 of 270 August prompts and 91 of 213 July prompts survived qualification stages, so public percentages reflect the qualified set only.
• The data describes the output distribution, not its cause. Attribution requires company-level audit.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

Beneath the aggregate percentages sit the questions that determine real-world impact: which high-intent prompts does a brand win or lose, which competitor takes the recommendation when a brand loses, what attributes AI systems associate with each treatment center, and which external sources shape those answers. For a prospective patient or referring clinician, the difference between a rank-one and a rank-four recommendation can be the difference between being chosen and being overlooked.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. It can help explain why ERC Pathlight's rank-one placements dropped, why Monte Nido's presence does not convert, and why Alsana's top-three rate increased, then turn those answers into concrete action.

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