FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models Article Swipe

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Merlin Engelke , Giulia Baldini , Jens Kleesiek , Felix Nensa , Amin Dada ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1093/jamia/ocaf165

Objective To address the challenges of data heterogeneity and manual feature engineering in clinical predictive modeling, we introduce FHIR-Former, an open-source framework integrating Fast Healthcare Interoperability Resources (FHIR) with large language models (LLMs) to automate and standardize clinical prediction tasks. Materials and Methods FHIR-Former dynamically processes structured (eg, lab results, medications) and unstructured (eg, clinical notes) data from FHIR resources. The pipeline supports multiple classification tasks, including 30-day readmission, imaging study prediction, and ICD code classification. Leveraging open-source LLMs (GeBERTa), we trained models on 1.1 million data points across ten FHIR resources using retrospective inpatient data (2018-2024). Hyperparameters were optimized via Bayesian methods, and outputs were mapped to FHIR RiskAssessment resources for interoperability. Results FHIR-Former achieved an F1-score of 70.7% and accuracy of 72.9% for 30-day readmission, 51.8% F1-score (88.1% accuracy) for mortality prediction, and 61% macro F1-score for imaging study classification. The ICD code prediction model attained 94% accuracy. Performance demonstrated promising performance for readmission and showed scalability across tasks without manual feature engineering. Discussion FHIR-Former eliminates institution-specific preprocessing by adapting to diverse FHIR implementations, enabling seamless integration of multimodal data. Its configurable architecture outperformed prior frameworks reliant on static inputs or limited to unstructured text. Real-time risk scores embedded in FHIR servers enhance clinical workflows without disrupting existing practices. Conclusion By harmonizing FHIR standardization with LLM flexibility, FHIR-Former advances scalable, interoperable predictive modeling in healthcare. The open-source framework facilitates automation, improves resource allocation, and supports personalized decision-making, bridging gaps between AI innovation and clinical practice.

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Language: en
Landing Page: https://doi.org/10.1093/jamia/ocaf165
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DOI: https://doi.org/10.1093/jamia/ocaf165

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Title: FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models

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Publication year: 2025

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Publication date: 2025-09-16

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Authors: Merlin Engelke, Giulia Baldini, Jens Kleesiek, Felix Nensa, Amin Dada

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Landing page: https://doi.org/10.1093/jamia/ocaf165

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abstract_inverted_index.pipeline	62
abstract_inverted_index.resource	234
abstract_inverted_index.results,	50
abstract_inverted_index.seamless	178
abstract_inverted_index.supports	63, 237
abstract_inverted_index.Materials	41
abstract_inverted_index.Objective	1
abstract_inverted_index.Real-time	198
abstract_inverted_index.Resources	27
abstract_inverted_index.accuracy)	131
abstract_inverted_index.accuracy.	150
abstract_inverted_index.framework	22, 230
abstract_inverted_index.including	67
abstract_inverted_index.inpatient	95
abstract_inverted_index.introduce	18
abstract_inverted_index.modeling,	16
abstract_inverted_index.mortality	133
abstract_inverted_index.optimized	100
abstract_inverted_index.practice.	247
abstract_inverted_index.processes	46
abstract_inverted_index.promising	153
abstract_inverted_index.resources	92, 111
abstract_inverted_index.scalable,	222
abstract_inverted_index.workflows	207
abstract_inverted_index.(GeBERTa),	80
abstract_inverted_index.Conclusion	212
abstract_inverted_index.Discussion	166
abstract_inverted_index.Healthcare	25
abstract_inverted_index.Leveraging	77
abstract_inverted_index.challenges	5
abstract_inverted_index.disrupting	209
abstract_inverted_index.eliminates	168
abstract_inverted_index.frameworks	188
abstract_inverted_index.innovation	244
abstract_inverted_index.multimodal	181
abstract_inverted_index.practices.	211
abstract_inverted_index.prediction	39, 146
abstract_inverted_index.predictive	15, 224
abstract_inverted_index.resources.	60
abstract_inverted_index.structured	47
abstract_inverted_index.FHIR-Former	44, 115, 167, 220
abstract_inverted_index.Performance	151
abstract_inverted_index.allocation,	235
abstract_inverted_index.automation,	232
abstract_inverted_index.dynamically	45
abstract_inverted_index.engineering	12
abstract_inverted_index.facilitates	231
abstract_inverted_index.harmonizing	214
abstract_inverted_index.healthcare.	227
abstract_inverted_index.integrating	23
abstract_inverted_index.integration	179
abstract_inverted_index.open-source	21, 78, 229
abstract_inverted_index.performance	154
abstract_inverted_index.prediction,	72, 134
abstract_inverted_index.readmission	156
abstract_inverted_index.scalability	159
abstract_inverted_index.standardize	37
abstract_inverted_index.(2018-2024).	97
abstract_inverted_index.FHIR-Former,	19
abstract_inverted_index.architecture	185
abstract_inverted_index.configurable	184
abstract_inverted_index.demonstrated	152
abstract_inverted_index.engineering.	165
abstract_inverted_index.flexibility,	219
abstract_inverted_index.medications)	51
abstract_inverted_index.outperformed	186
abstract_inverted_index.personalized	238
abstract_inverted_index.readmission,	69, 127
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abstract_inverted_index.heterogeneity	8
abstract_inverted_index.interoperable	223
abstract_inverted_index.preprocessing	170
abstract_inverted_index.retrospective	94
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abstract_inverted_index.Hyperparameters	98
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abstract_inverted_index.standardization	216
abstract_inverted_index.Interoperability	26
abstract_inverted_index.decision-making,	239
abstract_inverted_index.implementations,	176
abstract_inverted_index.interoperability.	113
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