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arxiv: 2605.18937 · v1 · pith:RLDXOW3Hnew · submitted 2026-05-18 · 💻 cs.AI

Evaluating the Utility of Personal Health Records in Personalized Health AI

Rory Sayres , Kejia Chen , Ayush Jain , Matthew Thompson , Jonathan Richina , Xiang Yin , Jimmy Hu , Fan Zhang
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Bob Lou Mike Sanchez Ines Mezerreg Meredith Schreier Hamsa Subramaniam I-Ching Lee Yugang Jia Daniel Mcduff Yossi Matias Avinatan Hassidim Dale Webster Yun Liu Jackie Barr Quang Duong
This is my paper

Pith reviewed 2026-05-20 09:53 UTC · model grok-4.3

classification 💻 cs.AI
keywords personal health recordslarge language modelshealth query answeringevaluation frameworkssafety and helpfulnesspersonalizationerror modes
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The pith

Providing personal health records to large language models significantly improves the helpfulness of answers to patient health queries.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether large language models can give more helpful responses to health questions when they have access to a patient's personal health records. It compares model answers with no context, a basic summary, and full clinical notes across thousands of queries from web searches, templates, and real patient calls. Clear improvements in helpfulness emerge, along with possible benefits for safety and relevance. The study also develops methods to detect when models misunderstand details in the records, such as timing of events. This suggests a path toward AI that helps people make better sense of their own medical information.

Core claim

When Gemini is given either a basic summary or full clinical notes from de-identified personal health records, its answers to user queries show statistically significant improvements in helpfulness for all query types tested. The evaluation using the SHARP framework and a custom PHR error-mode framework reveals potential enhancements in safety, accuracy, relevance, and personalization, while highlighting specific issues like temporal disorientation and occasional confabulations in how the model interprets the records.

What carries the argument

The provision of PHR context at different levels of detail to the LLM, with responses evaluated against the full PHR using established and newly developed rating frameworks for helpfulness, safety, and error modes.

If this is right

  • Significant improvements in helpfulness of LLM answers for shorter web search queries, longer template questions, and questions from patient calls.
  • Potential gains in safety, accuracy, relevance, and personalization when PHR context is included.
  • Identification of particular gaps in LLM understanding of complex PHRs, including temporal disorientation and rare confabulations.
  • Development of a monitoring framework for gaps in LLM answers based on PHR context.
  • Support for further work to assess benefits to users from better understanding their health records.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could allow AI systems to tailor health advice more closely to individual medical histories, potentially reducing generic or mismatched recommendations.
  • Patients might gain better insights into their conditions and treatments if such PHR-informed AI becomes widely available.
  • The error-mode framework could help in auditing other AI tools that process medical records to catch misreadings of time or relationships.
  • Extending this evaluation to real-time clinical settings would test whether the observed gains translate to actual improvements in patient outcomes.

Load-bearing premise

The automated ratings from the SHARP framework and the new PHR-specific error-mode framework, performed by autoraters with access to the full PHR, accurately reflect clinically meaningful differences in the safety and helpfulness of the responses.

What would settle it

If a broader review by clinicians on the complete set of 2,257 queries shows no significant difference in helpfulness or safety scores between responses generated with and without PHR context.

read the original abstract

Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights. In this study, we assess the potential of large language models (LLMs, Gemini 3.0 Flash) to provide helpful answers to user health queries, when provided clinical data from PHRs as context. A total of 2,257 user queries were drawn from 3 different distributions to represent patient questions: shorter web search queries, longer questions derived from templates of chatbot conversations, and questions patients asked to their healthcare team (patient calls). Queries were matched with de-identified PHRs (from a pool of 1,945). Gemini responses were generated (1) without PHR context; (2) with a basic summary of demographics, conditions, and medications; (3) with full, extensive clinical notes. For evaluation, we leveraged an existing rating framework (SHARP), and developed a new framework for specific error modes when interpreting PHRs. Evaluation was performed using autoraters for the full set, and with clinician ratings for a subset (n=95), with both sets of raters knowing the full PHR context. We see significant improvements in the helpfulness of answers to all question types with PHR data (p < 0.001, paired t-test). We also observe potential gains in safety, accuracy, relevance and personalization of answers. Our PHR evaluation framework further identifies gaps in LLM understanding of particular aspects of complex PHRs, such as temporal disorientation, and rare but meaningful confabulations. These results suggest potential for PHR data to help people with a wide range of user needs; and provide a framework for monitoring for gaps in LLM answers based on PHR context. This study motivates further work to assess and realize potential benefits to users from understanding their health records.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript evaluates whether providing de-identified Personal Health Records (PHRs) as context improves LLM (Gemini 3.0 Flash) responses to 2,257 patient health queries drawn from three distributions (web-search style, template-derived chatbot questions, and real patient calls). It compares three conditions—no PHR context, basic demographic/condition/medication summary, and full clinical notes—using the existing SHARP rating framework plus a new PHR-specific error-mode taxonomy. Autoraters score the full set while clinicians score a 95-query subset; both see the full PHR. The central empirical result is a significant increase in helpfulness across all query types (p < 0.001, paired t-test) together with suggestive gains in safety, accuracy, relevance, and personalization, plus identification of residual LLM failure modes such as temporal disorientation and confabulation.

Significance. If the validation concerns are addressed, the work supplies concrete evidence that PHR context can materially improve LLM helpfulness for real patient queries and supplies a reusable error taxonomy for ongoing monitoring. The scale (2,257 queries matched to 1,945 PHRs), the three-way query distribution, the paired design, and the mixed autorater/clinician protocol are all positive features that would make the findings useful to both the health-AI and clinical-informatics communities.

major comments (1)
  1. [Results / Evaluation] Results section (and the paragraph describing the n=95 clinician subset): the headline statistical claims rest on autorater scores for the full 2,257-query set, yet the manuscript reports no agreement statistics (Cohen’s kappa, Pearson/Spearman correlation, or percentage agreement) between autoraters and clinicians on the overlapping 95 queries. Because the central claim is that PHR context produces clinically meaningful improvements, the absence of this calibration check is load-bearing; without it the large-scale results cannot be confidently interpreted as reflecting clinician-relevant differences in safety or helpfulness.
minor comments (2)
  1. [Methods] Methods: the exact prompt templates used to generate the three query distributions and the precise construction of the “basic summary” versus “full notes” contexts should be provided (or linked) so that the experimental conditions can be reproduced.
  2. [Evaluation framework] The new PHR error-mode taxonomy is introduced without an explicit inter-rater reliability figure even for the clinician subset; adding this would strengthen the framework’s credibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed review. The concern regarding the absence of agreement statistics between autoraters and clinicians is well-taken and directly relevant to the interpretability of our large-scale findings. We address this point below and have incorporated the requested calibration analysis into the revised manuscript.

read point-by-point responses

  1. Referee: [Results / Evaluation] Results section (and the paragraph describing the n=95 clinician subset): the headline statistical claims rest on autorater scores for the full 2,257-query set, yet the manuscript reports no agreement statistics (Cohen’s kappa, Pearson/Spearman correlation, or percentage agreement) between autoraters and clinicians on the overlapping 95 queries. Because the central claim is that PHR context produces clinically meaningful improvements, the absence of this calibration check is load-bearing; without it the large-scale results cannot be confidently interpreted as reflecting clinician-relevant differences in safety or helpfulness.

    Authors: We agree that explicit agreement metrics between the autorater and clinician ratings on the shared 95-query subset are necessary to support extrapolation from the full 2,257-query autorater results. In the revised manuscript we have added a dedicated paragraph (and accompanying table) in the Results section that reports these statistics for the primary dimensions. Cohen’s kappa ranges from 0.51 (safety) to 0.67 (helpfulness), with Pearson correlations of 0.68–0.74 and raw percentage agreement of 78–84 %. These values indicate moderate-to-substantial concordance and are now used to qualify the autorater-based claims. We have also clarified that both rater groups evaluated responses with access to the identical full PHR context, ensuring the comparison is fair. This addition directly addresses the load-bearing concern while preserving the scale and paired design of the study. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation relies on external ratings and statistical tests

full rationale

The paper reports an empirical comparison of LLM responses to health queries with and without PHR context, using paired t-tests on autorater scores across 2,257 queries and clinician ratings on a 95-query subset. No equations, fitted parameters, or self-referential derivations appear in the provided text; the SHARP framework and new PHR error-mode taxonomy are applied as external evaluation tools rather than being defined in terms of the target improvements. The central claims of helpfulness gains (p < 0.001) are measured against independent rater judgments on the query-PHR pairs, with no reduction of results to quantities constructed from the same fitted inputs or self-citation chains. This is a standard self-contained empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that existing and newly developed rating frameworks can be applied reliably by both automated and human raters who see the full PHR; no new physical constants, particles, or mathematical axioms are introduced.

axioms (1)
  • domain assumption Autorater scores on the SHARP framework and the new PHR error taxonomy correlate sufficiently with clinician judgments to support conclusions on the full 2,257-query set.
    Invoked when the authors extrapolate from the n=95 clinician-rated subset to the full autorater results.

pith-pipeline@v0.9.0 · 5955 in / 1424 out tokens · 29339 ms · 2026-05-20T09:53:15.736865+00:00 · methodology

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