Dear Dr. Yang,

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Sreeram V. Ramagopalan

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: I Don't Know

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This manuscript presents a timely and relevant investigation into the completeness of EHR data within the All of Us Research Program, through linkage with data provided by Swoop. The scale of the dataset and the analytical approach are commendable and contribute meaningfully to the field of data integration in population health research.

Suggestions for Improvement

The introduction would benefit from a clearer explanation of what Swoop is, how it obtains its data, and the scope and limitations of its claims dataset. This context is essential for readers to assess the representativeness and reliability of the claims data used in the study.

The manuscript should more explicitly explain the structural and functional differences between EHR and claims data. This includes their respective purposes, typical content, and known limitations. Such clarification would help readers understand why certain types of information are more prevalent in one source than the other.

It would be helpful to briefly discuss why EHR and claims data are not routinely integrated in the U.S. healthcare system, touching on technical, legal, and institutional barriers. This would provide important context for the significance of the authors’ linkage efforts.

The manuscript states that 472,877 participants were considered for linkage, but only 246,128 were included in the final analysis. This represents a substantial reduction that should be clearly stated.

The authors should estimate and report the volume of diagnostic, procedural, and medication data that was not utilized due to unmatched or excluded participants. These figures should be critically discussed in terms of potential data loss, bias, and the impact on the study’s conclusions.

While the manuscript includes chronic conditions as covariates in regression models, it does not explore the completeness of data for these subgroups in detail. Given the importance of chronic disease populations in health research, the authors are encouraged to provide more granular analysis or discussion on how data completeness varies by condition. This would be particularly valuable for assessing equity and data quality across clinically vulnerable groups.

The percentage values in Table 1 currently display inconsistent decimal places. For clarity and professionalism, it is recommended that all percentages be presented with a uniform number of decimal places.

Reviewer #2: This study makes a valuable contribution by providing practical insights into maximizing patient-level data availability for the research of secondary use of data. However, several critical points require clarification to strengthen the manuscript's methodological rigor and practical applicability.

Point 1.

The manuscript appears to assume that duplicate data between EHR and claims sources were identified and excluded from analysis, particularly given the use of patient-event months (PEMs) as the unit of comparison. However, the methods lack a clear definition of what constitutes a duplicate event across databases (e.g., same patient, same month, same code), nor do they detail how such duplicates were detected and managed.

How were duplicate events defined between EHR and claim data? What criteria were used to identify duplicate diagnoses, procedures, and medications across EHR and claims data? How were potential legitimate duplicates (e.g., bilateral procedures, multiple prescriptions) distinguished from true redundancies?

Point 2

While the manuscript demonstrates the quantitative gain in data elements through claims linkage, it does not sufficiently discuss the specific types of research questions or clinical scenarios where claims data integration is most impactful. The authors should elaborate on concrete use cases where the breadth and continuity of claims data provide unique advantages over EHR data alone. Articulating these scenarios would help readers understand the practical value of data expansion in the research using secondary use of data.

Point 3

The study equates an increase in the number of captured data elements with improved “completeness.” In data quality frameworks, completeness refers to the absence of missing values within captured records, not the comprehensiveness of data sources. Clearly distinguish between: (a) completeness (non-missing values in existing records), (b) coverage (proportion of all healthcare events captured), and (c) comprehensiveness (breadth of data sources/types)

Point 4

The manuscript provides insufficient detail about how heterogeneous data from EHR and claims sources were harmonized for comparison. Given the inherent differences in data structure, coding systems, temporal granularity, and semantic representations between EHR and claims data, the manuscript should clearly describe the harmonization process used to enable valid comparisons. What mapping strategies, code translations, or aggregation rules were applied to ensure that events from both sources were comparable at the PEM level? Were there limitations in mapping certain codes or event types? How were discrepancies in coding standards addressed? Transparent reporting of these harmonization steps is essential for reproducibility and for interpreting the validity of the comparative analyses.

Point 5

The manuscript clearly states that the study received approval from the relevant Institutional Review Boards and that written informed consent was obtained from participants. Furthermore, the process of privacy-preserving record linkage using Datavant software—whereby personal identifiers are tokenized and encrypted prior to linkage—appears to be well described. The authors also note that researchers did not have access to identifiable information at any stage, and that all analyses were conducted within a secure research environment. However, I respectfully suggest that the manuscript could be strengthened by providing additional detail regarding the specific content of the participant consent forms, particularly regarding the scope of data linkage and secondary data use. Additionally, could you elaborate on the legal framework supporting this data linkage?

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: António da Luz Pereira

Reviewer #2: No

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Investigation into EHR data coverage in the All of Us Research Program via linkage to health insurance claims

PONE-D-25-23520R1

Dear Dr. Yang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Sreeram V. Ramagopalan

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

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Reviewer #2: The authors have thoughtfully and thoroughly addressed the concerns raised in the initial review, and the revised version represents a clear advancement in quality. I am pleased to recommend acceptance of the manuscript.

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: No

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