Studying how Medicare information moves through search and AI systems.
MedicarePlans.com conducts observational research into how Medicare information is discovered, retrieved, cited, interpreted, and represented by search engines and AI systems.
Our research uses identifiable Medicare entities and authoritative public data to measure observable system behavior over time.
The objective is straightforward: understand how Medicare facts move from authoritative data and public publishers into the search results and generated answers increasingly used by consumers, agents, carriers, and other Medicare stakeholders.
Measure What the Systems Do
Search engines and AI systems expose only part of their behavior publicly. Their internal ranking, retrieval, and generation processes are largely unavailable to outside researchers.
Our research therefore focuses on observable outcomes rather than assumptions about undisclosed algorithms.
We measure questions such as:
- Which resources are discovered?
- Which publishers appear in conventional search results?
- Which sources are retrieved by AI systems?
- Which sources are cited?
- Which factual assertions appear in generated answers?
- Which sources are associated with those assertions?
- How accurately is Medicare context preserved?
- How do these observations change over time?
Current Research
Medicare AI Visibility Study
2026–2027 · Active
A longitudinal study of how major search and AI systems discover, retrieve, cite, and represent 2,146 Medicare Plan-ID entities.
The study separately examines conventional search visibility, AI answer visibility, publisher source selection, and factual attribution across the same underlying Medicare entities.
The research follows an initial baseline established in 2026 and will continue through repeated measurement cycles.
Initial findings will be presented at Medicarians 2027.
Planned Studies
The Medicare AI Visibility Study establishes the measurement framework for a broader research program examining how specific Medicare facts and relationships are represented across search and AI systems.
Medicare Assertion Attribution Study
Coming Soon
Which publishers supply the factual assertions appearing in AI-generated Medicare answers?
This study will move beyond citation counts to examine individual Medicare facts, the sources associated with those facts, and differences in attribution among search and AI systems.
Medicare Query Language Study
Coming Soon
How does the language used to ask a Medicare question change the answer?
This study will examine whether changes in terminology, query construction, and Medicare vocabulary alter the coverage categories, entities, sources, and factual context returned by search and AI systems.
Medicare Plan-Year Integrity Study
Coming Soon
Do AI systems consistently preserve the correct Medicare plan year?
This study will examine cross-year contamination, outdated facts, effective-year interpretation, and whether generated answers correctly bind premiums, benefits, cost sharing, eligibility, and other plan facts to the appropriate coverage year.
Medicare Geographic Resolution Study
Coming Soon
Do search and AI systems correctly understand where Medicare plans are available?
This study will examine state, county, service-area, and market relationships to measure whether plans and benefits are consistently associated with the correct geographic context.
Research Foundation
The Medicare Digital Visibility & Compliance Shift: 2026–2028
Baseline Research Report · February 12, 2026
The research program builds on observations documented in The Medicare Digital Visibility & Compliance Shift: 2026–2028, a baseline research report published by the Trust Publishing Institute.
The report examined structural changes in Medicare discovery as search engines and AI systems increasingly mediated access to plan information. It proposed that conventional search metrics were becoming insufficient for measuring Medicare visibility and identified Plan-ID entities as an important surface for observing machine retrieval behavior.
The report also proposed an industry-level Medicare Plan-ID visibility measurement spanning conventional search and AI-driven retrieval systems.
The report was submitted as supplemental observational research to the federal record through public Requests for Information.
The research underway at MedicarePlans.com moves those propositions from observation toward systematic measurement.
Read the Baseline Research Report
Research Principles
Observable
We measure externally observable system behavior. We do not claim access to undisclosed search algorithms, model internals, ranking systems, training data, or retrieval processes.
Entity-Based
Wherever practical, research uses identifiable Medicare entities rather than arbitrary collections of keywords. Persistent identifiers allow the same underlying entity to be observed across publishers, platforms, and time.
Longitudinal
Individual search results, AI responses, and screenshots provide useful observations but can change. Repeated measurements across the same entities provide a stronger basis for identifying persistent patterns and changes over time.
Falsifiable
Earlier observations, hypotheses, and forecasts are propositions to test, not conclusions the research is required to confirm.
When measurements contradict an earlier expectation, that result is part of the research.
Source-Aware
Medicare facts have sources, effective periods, geographic boundaries, and entity relationships. Research should preserve those distinctions when evaluating whether a generated answer is accurate.
Research Timeline
February 2026 — Baseline Research Published
The Medicare Digital Visibility & Compliance Shift: 2026–2028 documented early observations about the transition from conventional search visibility toward AI-mediated Medicare discovery and proposed new approaches to measurement.
September 2026 — Systematic Measurement Begins
The Medicare AI Visibility Study establishes a standardized population of Medicare Plan-ID entities for repeated measurement across conventional search and AI answer systems.
2026–2027 — Longitudinal Observation
Repeated measurements will examine changes in discovery, search visibility, retrieval, citation, factual attribution, accuracy, and publisher selection.
Medicarians 2027 — Initial Findings
Initial findings from the Medicare AI Visibility Study will be presented at Medicarians 2027.
The presentation will examine what the measurements reveal about search visibility, answer visibility, publisher selection, factual attribution, and how Medicare facts move through AI-generated answers.
Ongoing — Additional Studies and Findings
Additional research will examine assertion attribution, query language, plan-year integrity, geographic resolution, and other measurable characteristics of Medicare information in search and AI environments.
Why This Research Matters
Medicare information has always required accuracy and context. AI-mediated discovery adds another layer between the organizations publishing that information and the people using it.
For a beneficiary, the fundamental question is:
Can I trust the Medicare information in the answer I am being shown?
For carriers, publishers, regulators, researchers, and technology companies, there is another:
Where did that answer get its facts?
Answering those questions requires more than measuring rankings or traffic.
It requires measuring which resources machines discover, which resources they retrieve, which sources they cite, which factual assertions they select, and whether those assertions remain attached to the correct plan, year, geography, eligibility requirements, and source.
That is the purpose of MedicarePlans.com Research.
About MedicarePlans.com Research: MedicarePlans.com conducts observational research in conjunction with the Trust Publishing Institute (TPI) using publicly available Medicare information and externally observable search and AI system behavior. Research methods may evolve as search engines, AI platforms, interfaces, and source-attribution systems change.