KSMModel.ai Reports 50-Point Gain in Healthcare AI Visibility Study

KSM™ Healthcare AI Visibility Study: PeptideDoc's readiness score increased from 35/100 before launch to 62/100 on Day 1 and 85/100 at Day 30, a 50-point improvement across website clarity, brand recognition, and citation authority.

KSM™ AI Visibility Day 30 results show how digital structure, entity recognition and external corroboration can strengthen AI visibility.

NEW YORK, NY, UNITED STATES, November 3, 2026 /EINPresswire.com/ -- NEW YORK, NY — November 3, 2026 — KSMModel.ai today released the Day 30 findings from its ongoing 90-day healthcare AI Visibility study, continuing the research initiative announced in October involving PeptideDoc, a physician-led medical practice in Bayside, Queens, New York.

The study uses the Knowledge Structuring Model™ (KSM™) to examine how effectively search engines and artificial intelligence systems can understand an organization, recognize its core entities, and corroborate those relationships through independent external sources.

PeptideDoc began the study with a 35/100 pre-launch KSM™ score. Following the initial implementation phase, its score increased to 62/100 on Day 1. At the Day 30 checkpoint, the organization reached 85/100, representing a 50-point improvement from the original baseline and a 23-point increase from Day 1.

“This is a continuation of the same study we launched in October, not a separate assessment,” said Dr. Anthony Q. Bowen, Founder of KSMModel.ai. “The purpose of the 90-day case study is to observe how the constraints on AI visibility change as an organization becomes more structured, more recognizable, and more externally corroborated.”

KSM™ evaluates AI Visibility across three weighted dimensions.

Structured Extractability, weighted at 40 percent, measures whether machines can reliably retrieve and interpret key organizational information.

Entity Salience, also weighted at 40 percent, measures whether search and AI systems can correctly connect an organization with its leadership, location, services, credentials, and authoritative profiles.

Citation Authority, weighted at 20 percent, measures whether independent external sources provide corroborating evidence that reinforces those relationships.

At Day 30, PeptideDoc scored 36/40 for Structured Extractability, 34/40 for Entity Salience, and 15/20 for Citation Authority, for a total score of 85/100.

Those results compare with 16/40, 14/40, and 5/20 at pre-launch, and 30/40, 24/40, and 8/20 on Day 1.

The Day 1 score remains frozen at 62/100. Changes completed after the Day 1 assessment were measured only as part of the Day 30 checkpoint rather than being used to revise the earlier score. This preserves the longitudinal methodology of the study.

The first phase of the PeptideDoc implementation focused primarily on digital structure and entity clarity. Work included improvements to canonical relationships, indexing signals, structured information, organizational identifiers, physician identity, location data, service relationships, and machine-readable markup.

By Day 1, those changes had substantially improved how the organization could be interpreted across its owned digital properties.

By Day 30, the research focus had shifted.

As PeptideDoc’s digital information became more structured, the study increasingly examined whether external sources could independently reinforce the same organizational relationships. Those signals included business profiles, physician directories, professional platforms, credential references, third-party profiles, and media coverage.

Citation Authority increased from 5/20 at pre-launch to 15/20 at Day 30, indicating that external corroboration had become a more meaningful component of the organization’s overall AI Visibility profile.

“The most important finding is not simply that the score increased,” Bowen said. “The constraint changed. At the beginning, the question was whether AI systems could understand the organization. After the first implementation phase, the question became whether they could correctly recognize and connect the core entities. By Day 30, the research increasingly focused on whether independent external sources could corroborate those relationships.”

That progression can be summarized as:

Understand → Recognize → Corroborate

Within the formal KSM™ framework:

Structured Extractability → Entity Salience → Citation Authority

Healthcare provides a useful environment for this research because organizational identity may depend on multiple interconnected signals, including the practice, physician, physical location, professional credentials, services, and third-party references.

PeptideDoc serves as the real-world healthcare implementation for testing whether those relationships become progressively easier for search engines and AI systems to interpret.

The study does not evaluate medical treatments or clinical outcomes. It evaluates digital structure, entity recognition, and external authority as components of AI Visibility.

The full case-study progression is available at:

ksmmodel.ai/clients/peptidedoc
About KSMModel.ai
KSMModel.ai develops research, assessment, and advisory tools focused on AI Visibility, Answer Engine Optimization, Generative Engine Optimization, and organizational knowledge structure. Its Knowledge Structuring Model™ (KSM™) evaluates AI Visibility across Structured Extractability, Entity Salience, and Citation Authority.
About PeptideDoc
PeptideDoc is a physician-led medical practice based in Bayside, Queens, New York. The organization is participating as the healthcare implementation in the KSM™ 90-Day AI Visibility Case Study.

Dr Elinor Garely
KSMModel.ai
+1 212-682-4499
contact@ksmmodel.ai
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