cs.AIOct 20, 2026

Temporal Evidence Allocation For Longitudinal Clinical Retrieval: A Position And Technical Report

Authors: Soumya Ranjan Sahoo, Divya Bharti, Sanand Sasidharan

Organizations: GE HealthCare Technology and Innovation Center, India

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Abstract

Electronic health records are written to support care, not retrieval. Across years of oncology notes, the primary diagnosis is restated in report after report, while the events that shape a patient's course, such as a recurrence or a change in therapy, may be recorded only once. A retriever that ranks passages by similarity to the query keeps returning the repeated material and can miss these transitions, that is, it exhibits temporal redundancy across historical narratives. We argue that longitudinal clinical retrieval should be treated as allocating a limited evidence budget across time, not just ranking by relevance. We separate the task into three objectives: temporal evidence allocation, candidate reconstruction, and clinical validation. In Study A (13 clinical queries over 20 patient records, with two-stage reconstruction), the Temporal-15 configuration reaches a milestone slot recall of 0.538, compared with 0.388 for the Global-50 baseline. However, it also retrieves far more evidence (496 versus 50 indexed units per query), so the gain cannot be credited to allocation alone. When evidence is held fixed, removing prompted precedence rules raises schema-order reversals from 0.010 to 0.080. A five-patient, single-query pilot with fixed budgets finds that wider temporal coverage does not reliably yield more dated milestones. Together, these results suggest that how much a record says about a period is a poor guide to how useful that period is as evidence. Because no expert reference timelines are available, our measurements describe retrieval behavior rather than clinical accuracy. This position and technical report presents the retrieval analysis behind the Onco-CONSTRUE framework and sets out a research agenda for budget-constrained longitudinal clinical retrieval.

Figures & tables

Figure 1: From irregular records to candidate milestones. (A) Indexed-unit counts in three-month periods for five de-identified patients, aligned to their first indexed event. Gray cells follow the last indexed unit; color shows log⁡10(1+units)\log_{10}(1+\mathrm{units}) . (B) Saved selections for one chemotherapy query in Study A, and counts differ across policies. (C) An author-constructed example links dated evidence views to prompted chronology checks and candidate milestones. E1–E5 are illustrative source links. 1L is the first systemic treatment line.
Figure 2: The data-to-timeline framework. Narrative facts and structured OMOP observations form a dated, source-linked index. Study A retrieves evidence for 13 clinical queries, generates their evidence views, and reconstructs 11 candidate milestones. The dashed bypass shows Study B’s direct reconstruction from one whole-course retrieval request. The downstream box describes intended real-world-evidence research, not an evaluated endpoint pipeline.
PolicyFillReversalsSupportAbstainStability
Global-50.388.010.959.612.586
No-constraints.405.080.981.595.600
Temporal-15.538.000.951.462.801
Density-Adaptive.553.011.936.447.821
Table 1: Study A: descriptive means over 20 patients and three runs. Higher values indicate more fill, date-near cited support, and citation-removal stability, lower schema-order reversal rates are preferable. Fill and abstention are complementary in these outputs, not two accuracy measures. Support means a cited unit lies within 30 days of the assigned date, not semantic entailment. Stability means another date-near cited unit remains after one citation is removed.
Figure 3: Study B: selecting evidence under fixed indexed-unit limits in five patients. Each method selects 50, 100, or 200 indexed units, and the graphic’s “Record budget K” counts units, not full notes. (A) Percentage of three-month periods containing units from which at least one unit is selected. (B) The percentage of the 11 milestones assigned a date reflects output completeness rather than correctness. (C) Model input length in tokens. Bars represent the average of the five policy means, while whiskers indicate the minimum and maximum policy means rather than statistical uncertainty. “Query adaptive” denotes relevance-weighted temporal allocation. All methods use one whole-course query and reconstruct milestones directly. Selecting units from more periods does not consistently produce more dated milestones.
Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

PolicyUnits/queryDistinctTokens
Global-505020036k
Temporal-153441,006164k
Density-Adaptive7041,883313k
Appendix
Table 2: Study A’s five-patient sensitivity sample, not Study B. Tokens are character-based input proxies.
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