Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide

Bas C T van Bussel; Ben Van Calster; Elena Albu; Frank E. Rademakers; Laure Wynants; Pieter Stijnen; Shan Gao; Taya Collyer; Tina Hernandez-Boussard

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide

T0 review · 0 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper compiles more than forty named challenges in extracting and preparing electronic health record data for dynamic prediction modeling, pairs each with actionable recommendations, and argues that following them improves data…

desk verdict A genuinely useful, well-organized practical guide to EHR data extraction; the 'comprehensive' claim is stronger than the selective review can support, but the paper's own caveats keep that from being disqualifying. read the letter →

arxiv 2501.10240 v2 pith:SZAAY4HM submitted 2025-01-17 cs.LG cs.AI

Elena Albu , Shan Gao , Pieter Stijnen , Frank E. Rademakers , Bas C T van Bussel , Taya Collyer , Tina Hernandez-Boussard , Laure Wynants

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Ben Van Calster

This is my paper · ORCID

classification cs.LGcs.AI

keywords electronichealthrecordsdynamicpredictionmodelsdataextractionpreparationqualitytemporalleakagefeatureengineeringcohortdefinition

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that the quality and clinical usefulness of dynamic prediction models built from electronic health records are decided before any model is trained, in the data extraction and preparation stages. It assembles more than forty concrete problems observed in those stages and organizes them into four categories—cohort definition, outcome definition, feature engineering, and data cleaning—each paired with practical recommendations. The intended contribution is a hands-on checklist that data engineers and researchers can consult during a project, so that data quality issues are anticipated and handled in a way that can be reproduced at the moment the model runs in the hospital. A sympathetic reader would care because model performance reported in training can be misleading if extraction and preparation introduce temporal leaks, selection bias, or cleaning steps that cannot be rerun at prediction time.

What carries the argument

The carrying object is the four-category challenge-and-recommendation taxonomy—cohort definition, outcome definition, feature engineering, and data cleaning—with each item mapped to established data-quality dimensions. The mechanism that connects the taxonomy to model performance is the staged data flow from collection to extraction to preparation to implementation, together with a point-in-time discipline: feature construction must use the time a predictor actually becomes available in the system, while outcome labels should use the clinically relevant time of the event; mismatches between these two timestamps are the main generator of temporal leaks.

What would settle it

A project-level audit could settle the generality claim: take the documented data-preparation incident logs from several completed EHR prediction projects at hospitals that did not contribute to this paper, and check whether every incident falls into one of the four categories and is addressed by a listed recommendation; if more than a small fraction, say 20 percent, of incidents fall outside the taxonomy or lack a usable recommendation, the claim that the list is comprehensive for single-hospital structured extractions would be disproved.

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Extended reading notes

Core claim

The paper's central claim is that the recurring failures of EHR-based dynamic prediction models can be traced to a finite, nameable set of problems in the stages before modelling, and that these problems can be managed with preparation-stage practices rather than by changing EHR software or clinical workflows. It claims that cohort definitions must be checked against what is available at the first prediction trigger; that outcomes should prefer timestamped clinical or surveillance definitions over billing codes; that feature engineering must respect the difference between the clinically relevant time of an event and the time the data item becomes available in the system; and that data cleaning should be programmatic, reproducible, and 'leave-as-is' when a correction cannot be rerun at implementation. If the paper is right, the practical payoff is a shorter path from raw hospital databases to models whose evaluation reflects what the model will encounter in live use.

Load-bearing premise

The list's coverage rests on the assumption that the authors' own extraction projects and the selectively reviewed literature are representative enough that the named challenges and recommendations generalize to other hospitals, EHR vendors, and extraction platforms, a limitation the paper itself acknowledges.

Editorial extensions

If this is right

Teams can use the four-category checklist during project planning to spot likely data problems before extraction, for example whether key features have usable timestamps or enough historical coverage.
Dynamic prediction models should be built and evaluated under the same extraction and preparation logic that will run at implementation, so that training-time performance is not inflated by temporal leaks.
Acute-event outcomes should be derived from timestamped clinical or surveillance definitions rather than billing codes, which are typically assigned after discharge and are not timestamped.
Data cleaning should be programmatic and reproducible at prediction time; manual one-off corrections to training data widen the gap between development and live data.
Extracting a broader patient context than the strict inclusion criteria, such as full admissions and a larger set of clinical items, gives preparation and imputation more room and reduces selection bias.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the four-category taxonomy could be turned into a standardised pre-modelling audit questionnaire, letting hospitals report which pitfalls they actually encountered and generating evidence about which items matter most for model performance.
Editorial inference: the paper's timestamp principle suggests a concrete stress test for any dynamic model: replay each admission incrementally up to each prediction trigger and compare the features with the batch-computed version; the size of any difference quantifies the temporal-leak risk the paper warns about.
Editorial inference: the reproducibility principle implies that post-deployment monitoring should watch for changes in data recording and extraction logic, not only drift in model inputs, because the paper notes that EHR software updates introduce new bugs over time.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

This is a genuinely useful practical tutorial, not a research result. If you work with single-site structured EHR data for prediction, it is the kind of document you wish someone had handed you at the start of the project. It synthesizes over forty challenges into four categories—cohort definition, outcome definition, feature engineering, and data cleaning—and pairs each with concrete, actionable recommendations. The dynamic-prediction focus is what sets it apart from generic data-cleaning checklists: the authors consistently ask whether a correction made at training time can be reproduced when the model runs live in the EHR. That question runs through the entries on timestamps, nursing documentation, coded procedures, and aggregate features, and it is the most valuable thread in the paper. The advice is also refreshingly pragmatic; leaving things as-is is treated as a legitimate choice, not a failure.

What the paper does well is organize existing lessons-learned literature into a usable reference and map it to the Weiskopf–Weng and METRIC quality frameworks. The writing is clear, the tables are usable, and the authors are transparent that their review was selective and their scope single-site structured data. They also state plainly that they do not assess the impact of each challenge on final model performance, so the recommendations are expert opinion supported by plausibility and experience, not measured effect sizes. For a tutorial, that is acceptable; the paper should not be read as evidence that following the advice improves model performance.

The soft spots are real but mostly self-acknowledged. The abstract's “comprehensive list” overstates what a selective review plus one group's experience can support. There is no systematic search, no coverage check across vendors or extraction platforms, and the authors note in Section 5 that other groups will likely identify additional issues. That caveat substantially weakens the concern. A second weakness is that the framework mappings feel somewhat decorative; they do not drive the entries, and some mappings are clear while others read as assignments after the fact. Minor.

Citations are fine. The authors draw on the prior lessons-learned papers you would expect, and their self-citations are relevant examples of competing-risk and auxiliary-outcome modeling, not circular justification. The central content is an independent synthesis.

Verdict: send it to peer review. A serious referee will probably ask for a more carefully qualified scope statement or a small empirical check of coverage, but this paper deserves referee time. I would not bring it to a reading group for debate; it is a reference, not a provocation. I would cite it in my own work when doing EHR prediction.

Referee Report

0 major / 5 minor

Summary. This paper is a tutorial-style practical guide to the data extraction and preparation stages that precede dynamic prediction modeling with single-hospital structured EHR data. The authors describe the data flow from collection through extraction (ETL) and preparation to model building and clinical implementation, and then present a categorized list of over 40 challenges in cohort definition, outcome definition, feature engineering, and data cleaning. Each challenge is accompanied by a description, a recommendation, and a mapping to the Weiskopf–Weng and METRIC data quality frameworks. The central contribution is the checklist itself, with particular emphasis on implementation-time reproducibility: correct interpretation of timestamps, avoidance of temporal leaks, distinction between clinically relevant and recording timestamps, and reproducible programmatic data cleaning. The paper explicitly declares its scope (single-site structured data, excluding multi-center and unstructured text) and its limitations (selective literature review, no empirical assessment of the impact of each challenge on model performance).

Significance. If judged as a consolidation of practical expertise rather than as an empirical evaluation, the paper is valuable. It provides a well-organized inventory that maps to established data-quality frameworks and draws attention to implementation-time reproducibility issues, such as timestamp semantics (Table 8), temporal leaks (Table 9), and aggregate features (Table 7), that are often underemphasized in machine-learning guides. The authors are transparent about their methods and limitations: Section 2 states that the review was selective, and Section 5 explicitly disclaims exhaustiveness and notes that the impact of individual challenges on model performance is not assessed. The recommendations are appropriately hedged as context-dependent, and the fitness-for-use principle is invoked throughout. The paper does not overclaim empirical support; its value lies in providing a structured, expert-derived checklist for practitioners. The main limitations, which are disclosed, mean the work should be read as a practical guide rather than a systematic review or a quantitative study.

minor comments (5)

[Abstract and Section 2] The phrase 'comprehensive list' is stronger than the method and limitations support. Section 5 explicitly states that the review was selective and cannot be exhaustive. I recommend replacing 'comprehensive' with 'structured' or 'broad' in the abstract and in Section 2, and adjusting the final sentence of Section 5, so that the claim matches the disclosed limitations.
[Section 2] In the third paragraph, 'OMOP CMD' should be 'OMOP CDM'; the acronym is spelled correctly elsewhere, e.g., in Section 1 and in Table 6.
[Table 11] The recommendation for 'Missing values that can be fully recovered from other fields' suggests using a powerful missing-data imputation algorithm. To be consistent with the paper's earlier advice in Section 3(c) to separate train and test sets before preprocessing, the recommendation should explicitly state that the imputation model must be fitted on the training data only, otherwise readers may inadvertently introduce test-train leakage.
[Tables 4 and 9] Several challenges that are fundamentally about timeliness, such as 'Label leakage due to temporal leakage' and 'Temporal leaks due to extraction errors', are mapped to METRIC/WW = NA. If the mapping is intended to be complete, it would help to explain why these entries have no mapping, or to add the relevant Timeliness/Currency dimensions.
[Section 4 (Tables 1-11)] The tables are dense and would benefit from a short 'how to use this list' paragraph stating whether the order of challenges implies priority. The current text advises case-by-case assessment but does not suggest an order of operations or a triage strategy, which would increase the practical actionability of the guide.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the guide is a self-contained synthesis of external literature and experience, with explicit non-exhaustiveness caveats.

full rationale

This paper makes no quantitative derivation and contains no fitted parameters, no prediction that reduces to an input, and no uniqueness argument imported from prior work. Its central content is a categorized list of EHR data extraction and preparation challenges with recommendations, assembled from a selective literature review and the authors' own experience. The only self-citations (Albu et al. 2024 and Gao et al. 2024) are used as illustrative examples of competing-risk modeling and auxiliary outcomes; they do not carry the argument, and the recommendations do not depend on accepting those papers' results. The paper explicitly frames the categorization as 'one possible categorization' and Section 5 concedes that the review 'cannot be exhaustive' and that 'every project will face use case specific challenges.' It also states that the authors 'do not assess the impact of each challenge on the final prediction model,' which weakens the practical-utility claim but does not make the derivation circular. The acknowledged limitations concern representativeness and empirical validation, not logical self-reference. Thus no circular step meets the evidentiary standard of this review.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters, invented entities, or mathematical axioms. Its central claims rest on the representativeness of the authors' experience and selected literature, and on the stated premise that preparation logic must be reproducible at implementation time. These assumptions are disclosed in Sections 2 and 5.

assumptions (3)

domain assumption Single-hospital structured EHR data is a sufficient scope for dynamic prediction modeling guidance.
Section 2 Objective explicitly restricts scope to single-hospital structured data and states that multi-center, registry, and unstructured data are not addressed; the recommendations inherit this limitation.
domain assumption The authors' experiences and the selected literature are representative of typical EHR extraction and preparation projects.
Section 2 and Section 5 state that insights are drawn from a selective literature review and the authors' own experience. This premise underwrites the claim that the list of challenges is usefully comprehensive.
domain assumption Extraction and preparation logic should be reproducible at clinical implementation time.
Throughout Section 4 and Section 5, the authors assume that the same logic used for training data should be reused at deployment, and several recommendations depend on this normative premise, such as the recommendation to avoid manual corrections that cannot be programmatically reproduced.

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Cite this review

Pith. "Pith review of Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide." pith.science (2026). https://pith.science/paper/SZAAY4HM

@misc{pith2026250110240,
  author       = {Pith},
  title        = {Pith review of: Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SZAAY4HM}},
  note         = {Machine review of arXiv:2501.10240}
}

read the original abstract

Dynamic predictive modelling using electronic health record (EHR) data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of the underlying data, which is, in part, determined by the stages preceding the model development: data extraction from EHR systems and data preparation. In this article, we identified over forty challenges encountered during these stages and provide actionable recommendations for addressing them. These challenges are organized into four categories: cohort definition, outcome definition, feature engineering, and data cleaning. This comprehensive list serves as a practical guide for data extraction engineers and researchers, promoting best practices and improving the quality and real-world applicability of dynamic prediction models in clinical settings.

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Figures reproduced from arXiv: 2501.10240 by the authors.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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