Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv cs.CL 05/29/26, 04:00 AM Papers
Summary
Introduces HDSR and HDSR-PL, methods that use hallucination detectors to guide iterative self-refinement and preference learning, achieving up to 48% reduction in hallucinations for clinical summarization using Llama and Gemma models on MIMIC-IV-Note.
arXiv:2605.28910v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce \itermodelfull (\itermodel), an inference-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections. Building on this, we propose \itermodel for Preference Learning (\model), which converts detector-guided refinement trajectories into preference pairs for model finetuning. Extensive experiments show that our methods substantially reduce hallucinations for Llama and Gemma models in summarizing real-world clinical notes from \MimicIV. For example, \itermodel reduces 24\% and \model reduces 48\% hallucinations in Llama-3.1-8B-Instruct. Importantly, both methods preserve summary fluency, coherence, and relevance according to human expert and LLM-Jury evaluations. Together, these results demonstrate that detection-informed refinement and preference learning offer an automated solution for improving factual faithfulness in clinical summarization.
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# Hallucination Detection-Guided Preference Optimization for Clinical Summarization
Source: [https://arxiv.org/html/2605.28910](https://arxiv.org/html/2605.28910)
Shamanth Kuthpadi Seethakantha1∗Dung Ngoc Thai2∗Vara Prasad Gudi1∗ Simran Tiwari2Rami Matar3Avijit Mitra1 Wenlong Zhao1Wael Salloum2Andrew McCallum4 1,4Manning College of Information and Computer Sciences2Ensemble HP3Columbia College 1\{skuthpadi,vgudi,avijit,wenlongzhao\}@umass\.edu,2\{simran\.tiwari,june\.thai,wael\.salloum\}@ensemblehp\.com 3\{rhm2142\}@columbia\.edu,4\{mccallum\}@cs\.umass\.edu ∗Equal contribution

###### Abstract

Large language models \(LLMs\) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications\. We introduceHallucination Detection guided Self\-Refinement\(HDSR\), an inference\-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections\. Building on this, we proposeHDSRfor Preference Learning \(HDSR\-PL\), which converts detector\-guided refinement trajectories into preference pairs for model finetuning\. Extensive experiments show that our methods substantially reduce hallucinations for Llama and Gemma models in summarizing real\-world clinical notes fromMIMIC\-IV\-Note v2\.2\. For example,HDSRreduces 24% andHDSR\-PLreduces 48% hallucinations in Llama\-3\.1\-8B\-Instruct\. Importantly, both methods preserve summary fluency, coherence, and relevance according to human expert and LLM\-Jury evaluations\. Together, these results demonstrate that detection\-informed refinement and preference learning offer an automated solution for improving factual faithfulness in clinical summarization\.

Hallucination Detection\-Guided Preference Optimization for Clinical Summarization

Shamanth Kuthpadi Seethakantha1∗Dung Ngoc Thai2∗Vara Prasad Gudi1∗Simran Tiwari2Rami Matar3Avijit Mitra1Wenlong Zhao1Wael Salloum2Andrew McCallum41,4Manning College of Information and Computer Sciences2Ensemble HP3Columbia College1\{skuthpadi,vgudi,avijit,wenlongzhao\}@umass\.edu,2\{simran\.tiwari,june\.thai,wael\.salloum\}@ensemblehp\.com3\{rhm2142\}@columbia\.edu,4\{mccallum\}@cs\.umass\.edu∗Equal contribution

## 1Introduction

![Refer to caption](https://arxiv.org/html/2605.28910v1/x1.png)Figure 1:Overview of hallucination mitigation via detection\-informed self\-refinement\.Given an input clinical note, a language model generates an initial summary that may contain unsupported or hallucinated medical content\. A hallucination detector identifies unsupported content, which is used to guide iterative self\-refinement toward removing factual errors rather than stylistic changes \(top;HDSR\)\. The intermediate outputs fromHDSRprocess are converted into preference pairs and used for preference learning \(e\.g\., DPO\), amortizing faithful behavior and yielding hallucination\-mitigated summaries at inference time \(bottom;HDSR\-PL\)\.Large language models achieve strong summarization performance but often generate hallucinations, defined as content not supported by the source or inconsistent with world knowledge\(Maynezet al\.,[2020](https://arxiv.org/html/2605.28910#bib.bib21); Jiet al\.,[2023a](https://arxiv.org/html/2605.28910#bib.bib15); Tanget al\.,[2023](https://arxiv.org/html/2605.28910#bib.bib26)\)\. Hallucinations remain prevalent even in recent LLMs, with rates of roughly 40\-50% on benchmarks such asFaithBench\(Baoet al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib2)\)indicating persistent factual unreliability\. This issue is critical in clinical summarization, where LLMs condense long patient records to support care delivery\. Despite comparable performance with clinician‑written summaries\(Veenet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib28)\), hallucinations manifest as fabricated or distorted clinical statements that are hard to detect\(Asgariet al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib1); Kimet al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib16)\)\. Even domain‑adapted models frequently introduce hallucinations expressed with medically plausible terminology, requiring expert review\(Hegselmannet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib12); Williamset al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib29); Fanget al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib10); Daset al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib6)\), and underscoring the need for stricter factual reliability in clinical summarization than in general‑domain tasks\.

Existing hallucination mitigation strategies are broadly divided into training\-time and inference\-time approaches\. Training\-time methods include domain pretraining, continual pretraining, supervised fine\-tuning, parameter\-efficient adapters\(Veenet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib28); Zaretskyet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib30)\), and specialized loss functions or reinforcement learning objectives\(Fabbriet al\.,[2021](https://arxiv.org/html/2605.28910#bib.bib8); Baoet al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib2); Asgariet al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib1)\)\. These methods require large volumes of high\-quality domain data\(Hegselmannet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib12)\)and depend on factuality metrics that correlate weakly with clinical correctness, limiting their practical impact\. Reinforcement learning from human or AI feedback offers greater flexibility, but requires carefully engineered preference data that is difficult to scale in medical settings\(Leeet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib48)\)\. Inference‑time techniques avoid altering model parameters: retrieval‑augmented generation grounds outputs in external documents yet depends on retrieval quality\(Koopman and Zuccon,[2023](https://arxiv.org/html/2605.28910#bib.bib44); Wanet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib63); Kimet al\.,[2025](https://arxiv.org/html/2605.28910#bib.bib16)\), while self‑refinement\(Madaanet al\.,[2023](https://arxiv.org/html/2605.28910#bib.bib52)\)and verification loops\(Tanget al\.,[2023](https://arxiv.org/html/2605.28910#bib.bib26)\)iteratively critique and revise generations, improving factuality at the cost of higher inference overhead and a risk of over‑editing\. Recent work combining iterative refinement with alignment shows such approaches can strip salient content or introduce new hallucinations even when fluency is preserved\(Jiet al\.,[2023b](https://arxiv.org/html/2605.28910#bib.bib42)\)\. A closely related approach isSynFac\-Edit\(Mishraet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib54)\), which generates synthetic edit feedback for preference optimization but relies on predefined error types and external edit models\.

Our pipeline combines hallucination detection with iterative self refinement, using detector feedback to guide self refinement and to form preference pairs from the original and revised summaries\.

We then train LLMs using direct preference optimization on these preference pairs, producing models that generate more faithful summaries with fewer hallucinations\. On the MIMIC IV clinical summarization hallucination datasets\(Hegselmannet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib12)\), our method reduces hallucinations by approximately 24% through self refinement and by up to 48% after fine tuning, while preserving summary quality and fluency according to both human and LLM\-as\-judge evaluations, and without incurring additional inference time cost\.

## 2Method

Figure[1](https://arxiv.org/html/2605.28910#S1.F1)illustrates our pipeline, which uses hallucination detection feedback to guide summary revision and train models to prefer faithful summaries\.

### 2\.1Problem Statement

Given a source document and a generated summary, the goal of hallucination mitigation is to produce summaries that are informative while remaining faithful to the source\. We view a source document and its summary as describing clinical facts documented during a patient’s hospital course\. A summary is faithful if these facts are supported by the source document\. Hallucinations arise when a summary introduces unsupported facts or adds procedures or findings that were not documented, for example when “leg ultrasound negative” appears in the summary without support from the source\.

### 2\.2Hallucination Detection Guided Self\-Refinement

During self\-refinement, a hallucination detector is applied to the generated summary to identify content that is unsupported or inconsistent with the source document\. Detector feedback highlights unsupported spans or statements, and the model is prompted to revise these parts while preserving supported content \(see Appendix[A\.2](https://arxiv.org/html/2605.28910#A1.SS2)\)\. This process focuses revisions on factual corrections rather than stylistic changes\.

Detection and revision are applied iteratively\. After each revision, the updated summary is re\-evaluated by the detector, and the resulting feedback guides subsequent revisions until a fixed iteration limit is reached or no further hallucinations are detected\. This detector guided refinement improves factual alignment while maintaining overall fluency\.

We use existing hallucination detectors from prior work, specifically MedCat\(Kraljevicet al\.,[2021](https://arxiv.org/html/2605.28910#bib.bib70)\)and prompt based detectors following the MedAlign annotation guidelines\(Fleminget al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib71)\)\. MedCat links clinical concepts in the source document and the summary to biomedical ontologies in order to flag unsupported or missing content\. MedAlign defines a clinically motivated taxonomy of hallucination categories, such as unsupported procedures or medications, which we implement as a prompted detector\.

### 2\.3Preference Learning from Detection Guided Self\-Refinement

Detection\-guided self\-refinement produces pairs of summaries for each source document, consisting of an initial summary and a revised summary with fewer hallucinations\. We treat the revised summary as the preferred output and the initial summary as the non\-preferred output, thereby forming preference pairs without human annotation\. We train the summarization model using direct preference optimization on these pairs\. This objective encourages the model to internalize detector guided factual corrections, amortizing improvements from self refinement into the model parameters and enabling faithful generation at inference time without additional refinement steps\.

## 3Experiments

### 3\.1Experimental Setup

We study clinical summarization from Brief Hospital Course \(BHC\) sections to Discharge Instructions \(DI\) using datasets derived fromMIMIC\-IV\-Note v2\.2\(Hegselmannet al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib12)\)\. Experiments are evaluated on the hallucination\-annotated subsetHallucination\-Generated\-DI, following the task formulation and annotation guidelines ofHegselmannet al\.\([2024](https://arxiv.org/html/2605.28910#bib.bib12)\)\.

We include GPT\-5\(OpenAI,[2025](https://arxiv.org/html/2605.28910#bib.bib73)\)zero\-shot as a closed\-source reference and primarily focus on LLaMA\-3\.1\-8B\-Instruct\(Meta,[2024b](https://arxiv.org/html/2605.28910#bib.bib67)\)as the open\-source base model\. In addition, we report results for two smaller open\-source models, LLaMA\-3\.2\-3B\-Instruct\(Meta,[2024a](https://arxiv.org/html/2605.28910#bib.bib68)\)and Gemma\-3\-4B\-IT\(Google,[2025](https://arxiv.org/html/2605.28910#bib.bib69)\), evaluated under a limited setting\. For LLaMA\-3\.1\-8B\-Instruct, we evaluate zero\-shot generation, supervised fine\-tuning \(SFT\), detection\-informed self\-refinement at inference time \(HDSR\), and detection\-informed preference learning at training time \(HDSR\-PL\)\. Detection\-informed variants use MedCat\(Kraljevicet al\.,[2021](https://arxiv.org/html/2605.28910#bib.bib70)\)and MedAlign\(Fleminget al\.,[2024](https://arxiv.org/html/2605.28910#bib.bib71)\)as hallucination detectors\.

#### Evaluation\.

We report entity\-level hallucination counts and clinician\-based human evaluation on summary quality across four main metrics: Consistency, Coherence, Fluency, and Relevance\. We follow the protocol ofHegselmannet al\.\([2024](https://arxiv.org/html/2605.28910#bib.bib12)\), which includes quantitative fine\-grained hallucination annotations identifying unsupported and contradicted content, as well as qualitative analysis of summary quality across the same dimensions\. Annotations are performed by a team of clinicians blinded to model identity: for the quantitative task, each summary is annotated independently by two annotators \(H1 and H2\) with adjudication of disagreements, while for the qualitative analysis, two annotators score summaries independently without adjudication\.

For LLaMA\-3\.1\-8B\-Instruct, human evaluation is conducted in a blind setting with double annotation and adjudication for hallucination labels, forming the basis of the main results in Table[1](https://arxiv.org/html/2605.28910#S3.T1)\. For LLaMA\-3\.2\-3B\-Instruct and Gemma\-3\-4B\-IT, due to resource constraints, we report clinician\-provided hallucination counts annotated by a single clinician \(H1\) and replace human qualitative evaluation with automatic LLM\-as\-judge scores\.

### 3\.2Main Results

Model / MethodHallucinationCount↓\\downarrowSummary Quality MetricsConsistency↑\\uparrowCoherence↑\\uparrowFluency↑\\uparrowRelevance↑\\uparrowAverage↑\\uparrowGPT\-5Prompting363\.554\.734\.734\.084\.27LLaMA\-3\.1\-8B\-InstructPrompting294\.083\.834\.053\.233\.79SFT573\.034\.434\.533\.033\.75HDSR\(best; MedAlign\)224\.134\.484\.533\.954\.27HDSR\-PL\(best; MedCat\)154\.404\.284\.053\.904\.16LLaMA\-3\.2\-3B\-InstructPrompting264\.134\.584\.684\.204\.39HDSR\-PL\(best; MedCat\)134\.234\.584\.684\.304\.44Gemma\-3\-4b\-itPrompting154\.234\.654\.684\.484\.51HDSR\-PL\(best; MedCat\)134\.334\.654\.704\.634\.58Table 1:Results onHallucination\-Generated\-DI\.Both variants of our detector\-guided approach outperform the zero\-shot and SFT baselines\. Note that for LLama\-3\.2\-3B\-Instruct and Gemma\-3\-4b\-it, we use automatic LLM\-as\-Judge for summary quality metrics\.Table 2:Distribution of hallucination error types across models\.ModelUnsup\. Condition Unsup\. Procedure Unsup\. Medication Unsup\. Time Unsup\. Location Unsup\. Number Unsup\. Name Unsup\. Word Unsup\. Other Contradicted Fact Incorrect Fact *LLaMA\-3\.1\-8B\-Instruct*Prompting82134012080SFT1511836033170Self\-Refine \(No Det\.\)102623311120HDSR\(w/MedCat\)71343222010HDSR\(w/MedAlign\)42133110250HDSR\-PL\(best; MedCat\)31012000080

Table 3:Impact of detection signal for hallucination mitigation\.Comparison between self\-refinement with and without detectors on LLaMA\-3\.1\-8B\.Model / MethodHallucinationCount↓\\downarrowSummary Quality MetricsConsistency↑\\uparrowCoherence↑\\uparrowFluency↑\\uparrowRelevance↑\\uparrowSelf\-Refine \(No Det\.\)313\.854\.254\.584\.20HDSR\(w/MedCat\)254\.084\.554\.584\.13HDSR\(w/MedAlign\)224\.134\.484\.533\.95

Table[1](https://arxiv.org/html/2605.28910#S3.T1)presents the main results on theHallucination\-Generated\-DIbenchmark, comparing zero\-shot generation, supervised fine\-tuning \(SFT\),HDSR, andHDSR\-PLusing LLaMA\-3\.1\-8B\-Instruct\. We also report limited results for LLaMA\-3\.2\-3B\-Instruct and Gemma\-3\-4B\-IT\. The following analysis focuses on LLaMA\-3\.1\.

#### SFT amplifies hallucinations\.

Supervised fine\-tuning on clinician\-written references substantially worsens factual alignment and increases hallucinations \(57\) to nearly twice the zero\-shot baseline\. Although supervised fine\-tuning improves fluency \(4\.53\) and coherence \(4\.43\), consistency drops sharply \(3\.03\)\.

#### HDSRimproves summary quality and mitigates hallucinations\.

OurHDSRyields a substantial improvement over both baselines\. Using MedAlign as the detector,HDSRreduces hallucinations to 22 while simultaneously achieving the best overall human\-evaluated summary quality across all dimensions\.

#### HDSR\-PLfurther mitigates hallucinations\.

HDSR\-PL, using MedCat\-derived preference pairs, achieves the lowest hallucination count overall \(15\), corresponding to an approximate 48% reduction relative to the zero\-shot baseline and a 74% reduction relative to SFT\.HDSR\-PLslightly underperformsHDSRon fluency and coherence\. This trade\-off reflects the difference between the two variants of our pipeline:HDSRbenefits from direct, entity\-specific detector feedback, whereasHDSR\-PLlearns a more general preference for factual alignment that may smooth over fine\-grained stylistic details but yields factually aligned outputs\.

#### Analyzing the types of hallucinations\.

Table[3](https://arxiv.org/html/2605.28910#S3.T3)breaks down hallucinations by error type for LLaMA\-3\.1\-8B, revealing that unsupported conditions, procedures, and medications account for the majority of errors in zero\-shot and supervised fine\-tuned models\. SFT amplifies these clinically critical error types, increasing unsupported conditions from 8 to 15 and procedures from 2 to 11\. Self\-refinement without detection reduces some surface\-level errors but leaves substantial unsupported content uncorrected\. In contrast,HDSRvariants consistently reduce errors across multiple categories, particularly unsupported conditions and procedures, indicating that the detector guides corrections toward factually grounded revisions\.

#### Impact of detector on hallucination mitigation\.

Table[3](https://arxiv.org/html/2605.28910#S3.T3)shows that incorporating an explicit detection signal reduces hallucination count forHDSRby 29% when using MedAlign as the hallucination detector and 19% when using MedCat\.

## 4Conclusion

We presented two complementary approaches for mitigating hallucinations in clinical summarization using automatic hallucination detection as supervision\.HDSRis an inference\-time method that integrates detector feedback into iterative self\-refinement, focusing revisions on unsupported or contradicted medical content while preserving grounded information\.HDSR\-PLextends this framework to training time by transforming refinement trajectories into preference pairs, enabling direct preference optimization that amortizes factual alignment\.

## Limitations

Our work highlights several areas for further investigation\. First, bothHDSRandHDSR\-PLrely on the quality of the hallucination detector used to provide supervision\. While we treat detectors as black\-box assessors, their errors–such as false positives that signal supported content or false negatives that miss subtle inconsistencies–can limit the effectiveness of refinement and downstream preference learning\. Improving detector accuracy or combining multiple complementary detectors may further enhance performance\.

In addition, our evaluation focuses on clinical summarization tasks derived fromMIMIC\-IV\-Note v2\.2, specifically BHC to Discharge Instructions generation\. Although this is a high\-impact and clinically relevant setting, the transferability of our approach to other medical note types or non\-clinical summarization domains remains to be explored\.

Finally, inference\-time self\-refinement \(HDSR\) incurs additional computational cost due to multiple refinement iterations and detector calls, which may limit its applicability in latency\-sensitive settings\. WhileHDSR\-PLaddresses this by amortizing refinement into training, it requires additional training resources and preference optimization, which may not be feasible in all deployment scenarios\.

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- A\. Madaan, N\. Tandon, P\. Gupta, S\. Hallinan, L\. Gao, S\. Wiegreffe, U\. Alon, N\. Dziri, S\. Prabhumoye, Y\. Yang, S\. Gupta, B\. P\. Majumder, K\. Hermann, S\. Welleck, A\. Yazdanbakhsh, and P\. Clark \(2023\)Self\-Refine: Iterative Refinement with Self\-Feedback\.arXiv\.External Links:2303\.17651,[Document](https://dx.doi.org/10.48550/arXiv.2303.17651)Cited by:[§1](https://arxiv.org/html/2605.28910#S1.p2.1)\.
- J\. Maynez, S\. Narayan, B\. Bohnet, and R\. McDonald \(2020\)On Faithfulness and Factuality in Abstractive Summarization\.InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics,D\. Jurafsky, J\. Chai, N\. Schluter, and J\. Tetreault \(Eds\.\),Online,pp\. 1906–1919\.External Links:[Document](https://dx.doi.org/10.18653/v1/2020.acl-main.173)Cited by:[§1](https://arxiv.org/html/2605.28910#S1.p1.1)\.
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- Meta \(2024b\)Meta‑llama/llama‑3\.1‑8b‑instruct\.Note:Online; accessed 2025‑02‑21External Links:[Link](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)Cited by:[§3\.1](https://arxiv.org/html/2605.28910#S3.SS1.p2.1)\.
- P\. Mishra, Z\. Yao, P\. Vashisht, F\. Ouyang, B\. Wang, V\. D\. Mody, and H\. Yu \(2024\)SYNFAC\-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization\.arXiv\.External Links:2402\.13919,[Document](https://dx.doi.org/10.48550/arXiv.2402.13919)Cited by:[§1](https://arxiv.org/html/2605.28910#S1.p2.1)\.
- OpenAI \(2025\)GPT\-5 system card\.Note:[https://cdn\.openai\.com/gpt\-5\-system\-card\.pdf](https://cdn.openai.com/gpt-5-system-card.pdf)Official technical report describing the GPT\-5 architecture and capabilitiesCited by:[§3\.1](https://arxiv.org/html/2605.28910#S3.SS1.p2.1)\.
- L\. Tang, Z\. Sun, B\. Idnay, J\. G\. Nestor, A\. Soroush, P\. A\. Elias, Z\. Xu, Y\. Ding, G\. Durrett, J\. F\. Rousseau, C\. Weng, and Y\. Peng \(2023\)Evaluating large language models on medical evidence summarization\.npj Digital Medicine6\(1\),pp\. 158\.External Links:ISSN 2398\-6352,[Document](https://dx.doi.org/10.1038/s41746-023-00896-7)Cited by:[§1](https://arxiv.org/html/2605.28910#S1.p1.1),[§1](https://arxiv.org/html/2605.28910#S1.p2.1)\.
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- A\. Wan, E\. Wallace, and D\. Klein \(2024\)What Evidence Do Language Models Find Convincing?\.InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics \(Volume 1: Long Papers\),L\. Ku, A\. Martins, and V\. Srikumar \(Eds\.\),Bangkok, Thailand,pp\. 7468–7484\.External Links:[Document](https://dx.doi.org/10.18653/v1/2024.acl-long.403)Cited by:[§1](https://arxiv.org/html/2605.28910#S1.p2.1)\.
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## Appendix AAdditional Details

### A\.1Summarization Prompt

Summarization Prompt[⬇](data:text/plain;base64,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)<\|system\|\>Youareahelpfulassistantthathelpspatientsunderstandtheirmedicalrecords\.<\|end\|\><\|user\|\>Youwillbegivensomedoctor’snotesandyouwillneedtosummarizethepatient’sbriefhospitalcourseinoneparagraph\.Pleaseonlyincludekeyeventsandfindingsandavoidusingmedicaljargons,andyouMUSTstartthesummarywith"Youwereadmitted"\.\{%ifdemonstrations%\}Herearesomeexamples:\{%forexampleindemonstrations%\}DOCUMENT:\{\{example\.text\}\}SUMMARY:\{\{example\.summary\}\}\{%endfor%\}\{%endif%\}DOCUMENT:\{\{context\}\}<\|end\|\>

### A\.2Self\-refinement Prompts

Self\-refinement revision without detectors Prompt[⬇](data:text/plain;base64,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)<\|system\|\>Youareaclinicalsummarizationassistant\.<\|end\|\><\|user\|\>Youwillbegiven:\-Abriefhospitalcourse\(SOURCEFACTS\)\-ADRAFTSUMMARYthatcontainspotentialissues\.YourtaskistoREVISEtheDRAFTSUMMARYsothatitisfullyconsistentwiththeSOURCEFACTSandsuitableasanaccurateafter\-visitsummary\.Instructions:1\.TreattheSOURCEFACTSasauthoritative\.2\.CheckformissingorincompleteinformationintheDRAFTSUMMARYcomparedtotheSOURCEFACTS,especially:\-Chiefcomplaint/reasonforvisit\-Presentingsymptoms\-Proceduresperformed\-Medications\(new,changed,ordiscontinued\)\-Vitalsigns\-Keylaboratoryorimagingfindings4\.Do\*\*not\*\*inventorinfernewdiagnoses,medications,procedures,ordates\.5\.Keepprofessionaltoneandstructuresuitableforadischargeorafter\-visitsummary\.6\.PrefertermsandvaluesexactlyasstatedintheSOURCEFACTS\.7\.IftheDRAFTSUMMARYismissingclinicallyimportantinfothatISpresentintheSOURCEFACTS\(e\.g\.akeyprocedureordischargemedication\),ADDit\.8\.Alwaysstartthesummarywith"Youwereadmitted"andrefertothepatientasyou/your\.9\.Returnonlytherevisedsummarytext\(noexplanationsormarkup\)\.\-\-\-\[SOURCEFACTS/BRIEFHOSPITALCOURSE\]\{\{context\}\}\[DRAFTSUMMARYWITHFEEDBACKANNOTATIONS\]\{\{summary\}\}<\|end\|\>

Self\-refinement revision with detectors Prompt[⬇](data:text/plain;base64,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)<\|system\|\>Youareaclinicalsummarizationassistant\.<\|end\|\><\|user\|\>Youwillbegiven:\-Abriefhospitalcourse\(SOURCEFACTS\)\-ADRAFTSUMMARYthatcontainsfeedbackannotationsmarkingpotentialissues\.Potentiallyincorrectorunsupportedtextisenclosedin<error\>\.\.\.</error\>tags\.YourtaskistoREVISEtheDRAFTSUMMARYsothatitisfullyconsistentwiththeSOURCEFACTSandsuitableasanaccurateafter\-visitsummary\.Instructions:1\.TreattheSOURCEFACTSasauthoritative\.2\.Foreachsegmentwrappedin<error\>\.\.\.</error\>:\-IfthecontentisunsupportedorcontradictedbytheSOURCEFACTS\-\>removeorcorrectit\.\-Ifthecontentispartiallycorrect\-\>rewriteitusinginformationfromtheSOURCEFACTS\.\-Ifthecontentisaccurate\-\>keepit,butremovethe<error\>tags\.3\.CheckformissingorincompleteinformationintheDRAFTSUMMARYcomparedtotheSOURCEFACTS,especially:\-Chiefcomplaint/reasonforvisit\-Presentingsymptoms\-Proceduresperformed\-Medications\(new,changed,ordiscontinued\)\-Vitalsigns\-Keylaboratoryorimagingfindings4\.Do\*\*not\*\*inventorinfernewdiagnoses,medications,procedures,ordates\.5\.Keepprofessionaltoneandstructuresuitableforadischargeorafter\-visitsummary\.6\.PrefertermsandvaluesexactlyasstatedintheSOURCEFACTS\.7\.IftheDRAFTSUMMARYismissingclinicallyimportantinfothatISpresentintheSOURCEFACTS\(e\.g\.akeyprocedureordischargemedication\),ADDit\.8\.Alwaysstartthesummarywith"Youwereadmitted"andrefertothepatientasyou/your\.9\.Returnonlytherevisedsummarytext\(noexplanationsormarkup\)\.\-\-\-\[SOURCEFACTS/BRIEFHOSPITALCOURSE\]\{\{context\}\}\[DRAFTSUMMARYWITHFEEDBACKANNOTATIONS\]\{\{summary\_with\_errors\}\}<\|end\|\>

### A\.3Hallucination Detection

Medalign zero\-shot Prompt[⬇](data:text/plain;base64,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)<\|system\|\>Youareahelpfulassistantthathelpspatientsunderstandtheirmedicalrecords\.<\|end\|\><\|user\|\>Youwillbegivenadoctor’snotesandasummarywithpotentiallyincorrectness\.Yourtaskistoidentifyspanswitherroneous,contradictory,orunsupportedfactsinthesummary,andlabelthemusingthe<error\>tag\(e\.g\.<error\>incorrectfact</error\>\)\.Therecouldbemorethanoneerrorinthesummary\.\{%ifdemonstrations%\}Herearesomeexamples:\{%forexampleindemonstrations%\}DOCUMENT:\{\{example\.context\}\}ORIGINALSUMMARY:\{\{example\.summary\}\}SUMMARYWITHLABELEDERRORS:\{\{example\.summary\_with\_errors\}\}\{%endfor%\}\{%endif%\}Canyouidentifytheerrorsforthefollowingdocumentandsummary?DOCUMENT:\{\{context\}\}ORIGINALSUMMARY:\{\{summary\}\}SUMMARYWITHLABELEDERRORS:<\|end\|\>

Medalign Chain\-of\-thought k\-shot Prompt[⬇](data:text/plain;base64,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)<\|system\|\>Youareahelpfulassistantthathelpspatientsunderstandtheirmedicalrecords\.<\|end\|\><\|user\|\>Wewillpresentyouwithapairofabriefhospitalcourse\(BHC\)andapatientaftervisitsummary\(AVS\)\.TheAVSisalsoreferredtoasdischargesummary\.TheBHCcontainsadetailedsummaryofthehospitalstaywrittenbymedicalservice\.Itusuallycontainsmedicaljargon,anditcanfollowdifferentstructuresbasedonthehospitalcourseandresponsiblemedicalspecialty\.TheAVSsummarizesthehospitalstayforthepatientinplainlanguage\.Inpractice,theBHCisnottheonlysourceofinformationtowritetheAVS\.However,inoursettingwetreattheBHCastheonlycontextforthesummary\.Forthislabelingtask,weareinterestedinerrorsintheAVSthatareeitherunsupportedbytheBHC,contradictcontentintheBHC,orarewrongmedicalfacts\.Weallowstatementsthatcontaingeneralmedicalknowledgeoradvicethatareoftenusedinpatientsummaries\.Mosterrorsareduetounsupportedfacts,sowefurtherdistinguishthosebasedontheirspecificcontent\.Thisleadstothefollowingerrortypesorlabels:1\.Unsupportedfacts,includingcondition/procedure/medication/time/location/number/name/word/other2\.Contradictedfact3\.IncorrectfactAndbelowisthedetailedguideline,andwelabelerrorspanswiththe<error\>tag\(e\.g\.<error\>incorrectfact</error\>\)\.\#\#\#AllowedGeneralMedicalKnowledgeandMedicalAdviceWeallowgeneralmedicalknowledgeandadvicethatisoftenpartoftheAVS\.Usually,theseareinformationthatarenotspecificforthehospitalcoursegivenintheBHC\.Forexample\-"Pleasetakeyourmedicationsasprescribed"containsnoerroreventhoughtheBHCdoesnotcontainthisinstructionbecausethisisgeneralmedicaladvice\.\-"Ifthesymptomsgetworse,pleasecontactyourdoctor"containsnoerrorevenwhentheBHCdoesnotcontainthisfact,sinceitisgeneralmedicalknowledgethatadoctorshouldbeseenforworseningsymptoms\.\#\#\#DeterminingSpanofErrorsWelabelthesmallestpossibleconsecutivespanthatspecifiestheerrorgiventheBHCasacontext\.Removingfurtherpartsfromthespanwouldremoveimportantinformation\.Ausefulheuristicistoidentifytheminimalspanthatmustbereplacedtoobtainacorrectstatementthatisgrammaticallycorrect\.Forexample\-"Weperformedan<error\>esophageal\-gastro\-duodenoscopy\(EGD\)\.<error\>"whennosuchprocedureisreportedintheBHC\.Thearticle"an"isnotlabeledasanerror\.Whennoprocedureatallwasperformed"performedanesophageal\-gastro\-duodenoscopy\(EGD\)"shouldbelabeledaserrorbecausethereisnosuitablesubstitutefor"esophageal\-gastro\-duodenoscopy\(EGD\)"\.\-"Afterthesurgery,we<error\>transitionedyoutooraloxycodone</error\>\."whentheBHCcontainsnoinformationforsuchatransition\.IfanothermedicationtransitionismentionedintheBHCandmakessenseinthissentenceonly"oraloxycodone"shouldbelabeled\.IfanotheroralmedicationtransitionismentionedintheBHConly"oxycodone"shouldbelabeled\.\-"<error\>Yoursymptomsrespondedwell</error\>\."whennopartofthesentencemakessenseinthegivencontextoftheAVS\.\#\#\#DealingwithDeidentifiedInformationThedatacontainsdeidentifiedinformationshownwith"\_\_\_"inthetext\.Wealwaystreatthisasnon\-existentinformation\.So,theannotatorsshouldnotinferwhatthedeidentifiedinformationcouldbe\.Ingeneral,deidentifiedfieldsintheAVSshouldnotbelabeledaserrors\.However,sometimestheybelongtoawrongstatementorclearlycontainunsupportedinformation\(e\.g\.,adoctor’snameorphonenumbers\)thatarenotgivenintheBHC\.Inthesecases,deidentifiedfieldsshouldbeincludedintheerrorspan\.Forexample\-"Take\_\_\_<error\>200mgdaily</error\>andtrytorest"whennosuchdosageinformationisprovidedintheBHC,butthestatementtorest\.Thedeidentifiedmedicationnameisexcludedfromtheerrorspan\.\-"Pleaseavoidgoingup<error\>morethan\_\_\_stairs</error\>atatime"whenrestrictionsforthenumberofstairstakenatatimearenotementionedintheBHC\.\-"<error\>Dr\.\_\_\_willfollowupwithyou</error\>"whennofollow\-upismentionedintheBHC\.\-"PleasestoptakingAspirin<error\>on\_\_\_</error\>"whennostoppingdateisgivenintheBHC\.\-"YourRBCpeaked<error\>at\_\_\_million</error\>"ifthereisnohintofaspecificredbloodcellcountgivenintheBHC\.\#\#\#OneErrorperSpanTogetreliableerrorcountsaspanshouldonlycontainasingleerror\.\-"Youreceived<error\>Tylenol</error\>and<error\>Ciprofloxacin</error\>"whenthereisnoevidenceintheBHCthatthetwomedicationswereadministeredtothepatient\.\-"Youhavea<error\>follow\-upappointmentwithyourPCP</error\>and<error\>yourcardiologist</error\>"whennosuchfollowupismentionedintheBHC\.Botherrorsarelabeledseparately\.\{%forexampleindemonstrations%\}\#\#\#Example\{\{loop\.index\}\}BHC:\{\{example\.context\}\}AVS:\{\{example\.summary\}\}\{%ifexample\.error\_descriptions%\}ERRORS:\{\{example\.error\_descriptions\}\}\{%endif%\}AVSWITHERRORSLABELED:\{\{example\.summary\_with\_errors\}\}\{%endfor%\}\{%ifdemonstrations%\}\#\#\#Example\{\{len\(demonstrations\)\+1\}\}\{%endif%\}BHC:\{\{context\}\}AVS:\{\{summary\}\}ERROR:<\|end\|\>

Medalign Chain\-of\-thought K\-Shot with Class Explanation Prompt[⬇](data:text/plain;base64,<|system|>
You are a helpful assistant that helps patients understand their medical records.
<|end|>

<|user|>
We will present you with a pair of a brief hospital course (BHC) and a patient after visit summary (AVS). The AVS is also referred to as discharge summary. The BHC contains a detailed summary of the hospital stay written by medical service. It usually contains medical jargon, and it can follow different structures based on the hospital course and responsible medical specialty. The AVS summarizes the hospital stay for the patient in plain language. In practice, the BHC is not the only source of information to write the AVS. However, in our setting we treat the BHC as the only context for the summary.

## Instructions

For this labeling task, we are interested in errors in the AVS that are either unsupported by the BHC, contradict content in the BHC, or are wrong medical facts. We allow statements that contain general medical knowledge or advice that are often used in patient summaries. Most errors are due to unsupported facts, so we further distinguish those based on their specific content. This leads to the following error types or labels:
1. Unsupported facts, including condition/procedure/medication/time/location/number/name/word/other
2. Contradicted fact
3. Incorrect fact
And below is the detailed guideline, and we label error spans with the <error> tag (e.g. <error>incorrect fact</error>).

### Determining Span of Errors
We label the smallest possible consecutive span that specifies the error given the BHC as a context. Removing further parts from the span would remove important information. A useful heuristic is to identify the minimal span that must be replaced to obtain a correct statement that is grammatically correct. For example
- "We performed an <error>esophageal-gastro-duodenoscopy (EGD).<error>" when no such procedure is reported in the BHC. The article "an" is not labeled as an error. When no procedure at all was performed "performed an esophageal-gastro-duodenoscopy (EGD)" should be labeled as error because there is no suitable substitute for "esophageal-gastro-duodenoscopy (EGD)".
- "After the surgery, we <error>transitioned you to oral oxycodone</error>." when the BHC contains no information for such a transition. If another medication transition is mentioned in the BHC and makes sense in this sentence only "oral oxycodone" should be labeled. If another oral medication transition is mentioned in the BHC only "oxycodone" should be labeled.
- "<error>Your symptoms responded well</error>." when no part of the sentence makes sense in the given context of the AVS.

We allow general medical knowledge and advice that is often part of the AVS. Usually, these are information that are not specific for the hospital course given in the BHC. For example
- "Please take your medications as prescribed" contains no error even though the BHC does not contain this instruction because this is general medical advice.
- "If the symptoms get worse, please contact your doctor" contains no error even when the BHC does not contain this fact, since it is general medical knowledge that a doctor should be seen for worsening symptoms. 

We try to ignore grammatical errors in the BHC and AVS. If the original meaning can still be inferred (e.g. "medictaions" instead of "medications"), the most likely corrected form can be used. If the meaning cannot be inferred, they can be ignored in the BHC or labeled as Unsupported Other in the AVS.

If a sentence or phrase is repeated, then please treat it as you would any other sentence and highlight all errors (even if you did so in a previous sentence). For example
- "Please take Tylenol. Please take Tylenol" when Tylenol was prescribed in the BHC.
- "Limit your <error>use of stairs</error>. Please limit <error>use of stairs</error>" when movement was encouraged.

To get reliable error counts a span should only contain a single error.
- "You received <error>Tylenol</error> and <error>Ciprofloxacin</error>" when there is no evidence in the BHC that the two medications were administered to the patient.
- "You have a <error>follow-up appointment with your PCP</error> and <error>your cardiologist</error>" when no such follow up is mentioned in the BHC. Both errors are labeled separately.

### Dealing with Deidentified Information
The data contains deidentified information shown with "___" in the text. We always treat this as non-existent information. So, the annotators should not infer what the deidentified information could be. In general, deidentified fields in the AVS should not be labeled as errors. However, sometimes they belong to a wrong statement or clearly contain unsupported information (e.g., a doctor's name or phone numbers) that are not given in the BHC. In these cases, deidentified fields should be included in the error span. For example
- "Take ___ <error>200mg daily</error> and try to rest" when no such dosage information is provided in the BHC, but the statement to rest. The deidentified medication name is excluded from the error span.
- "Please avoid going up <error>more than ___ stairs</error> at a time" when restrictions for the number of stairs taken at a time are note mentioned in the BHC.
- "<error>Dr. ___ will follow up with you</error>" when no follow-up is mentioned in the BHC.
- "Please stop taking Aspirin <error>on ___</error>" when no stopping date is given in the BHC. 
- "Your RBC peaked <error>at ___ million</error>" if there is no hint of a specific red blood cell count given in the BHC.

### Error Types
In general, we ask for the most specific error that is applicable. If there is uncertainty which type applies, prefer the one mentioned first in the enumeration of all error types shown earlier. For instance, if the error contains an unsupported medication name, the Unsupported medication type should be used instead of the Unsupported name type. Here is a detailed description of the error types:
- `Unsupported Condition`: includes unsupported symptoms, diseases, or findings of the patient. For example
    - "You were found to have a <error>left clavicle fracture</error>" when no information was given for this condition in the BHC.
- `Unsupported Procedure`: includes any unsupported medical procedures. For example
    - "You had a <error>filter placed in your vein</error>" when no intervention with a filter was mentioned.
- `Unsupported Medication`: contains all errors related to unsupported medications. This includes medication classes, substances, routes, frequencies, and dosages. For example
    - "You were placed on <error>antibiotics</error>" when only blood thinners were prescribed.
- `Unsupported Time`: includes all errors for unsupported time or interval statements. For example
    - "Keep your arm in a sling for the <error>next 6 weeks</error>" when no specific duration is given.
- `Unsupported Location`: Locations include both unsupported physical places as well as regions of the patient. For example
    - "The patient was admitted to the <error>Acute Surgery Service</error>" when no admission location was provided in the BHC.
- `Unsupported Number`: any number either as digits or written that are unsupported. This also includes words such as "a" and "an". For example
    - "Your pacemaker rate was increased to <error>50</error>" when the rate of 50 is not given in the BHC.
- `Unsupported Name`: named entities that are not supported by the BHC. For example
    - "You were seen by the <error>interventional pulmonary service</error>" when no consult with this service was mentioned in the BHC.
- `Unsupported Word`: incorrect or inappropriate words or phrases which do not fit in any of the above types. For example
    - "We will send you home with a <error>drain</error> in place" when drain not mentioned in the BHC.
- `Unsupported Other`: If there is a mistake which clearly does not belong to any of the above categories, you may use this category as a last resort. We cannot give precise instructions because the "other" category is very broad.
- `Contradicted Fact`: This error type is independent of the content and contains all facts that clearly contradict information provided in the BHC. For example
    - "Your pacemaker rate was increased to <error>50</error>" when the context state a pacemaker rate of 40.
- `Incorrect Fact`: This error type is independent of the content and contains all facts that clearly contradict general medical knowledge or advice. For example
    - "We diagnosed a seizure, and you <error>can continue driving your car</error>" when no reason for allowing driving after a seizure is provided this contradict common medical knowledge.

{% for example in demonstrations %}
## Examples
### Example {{ loop.index }}

BHC:
{{ example.context }}

AVS:
{{ example.summary }}
{% if example.error_descriptions %}

ERRORS:
{{ example.error_descriptions }}

{% endif %}
AVS WITH ERRORS LABELED:
{{ example.summary_with_errors }}

{% endfor %}
{% if demonstrations %}

### Example {{ len(demonstrations) + 1 }}
{% endif %}
BHC:
{{ context }}

AVS:
{{ summary }}
<|end|>)<\|system\|\>Youareahelpfulassistantthathelpspatientsunderstandtheirmedicalrecords\.<\|end\|\><\|user\|\>Wewillpresentyouwithapairofabriefhospitalcourse\(BHC\)andapatientaftervisitsummary\(AVS\)\.TheAVSisalsoreferredtoasdischargesummary\.TheBHCcontainsadetailedsummaryofthehospitalstaywrittenbymedicalservice\.Itusuallycontainsmedicaljargon,anditcanfollowdifferentstructuresbasedonthehospitalcourseandresponsiblemedicalspecialty\.TheAVSsummarizesthehospitalstayforthepatientinplainlanguage\.Inpractice,theBHCisnottheonlysourceofinformationtowritetheAVS\.However,inoursettingwetreattheBHCastheonlycontextforthesummary\.\#\#InstructionsForthislabelingtask,weareinterestedinerrorsintheAVSthatareeitherunsupportedbytheBHC,contradictcontentintheBHC,orarewrongmedicalfacts\.Weallowstatementsthatcontaingeneralmedicalknowledgeoradvicethatareoftenusedinpatientsummaries\.Mosterrorsareduetounsupportedfacts,sowefurtherdistinguishthosebasedontheirspecificcontent\.Thisleadstothefollowingerrortypesorlabels:1\.Unsupportedfacts,includingcondition/procedure/medication/time/location/number/name/word/other2\.Contradictedfact3\.IncorrectfactAndbelowisthedetailedguideline,andwelabelerrorspanswiththe<error\>tag\(e\.g\.<error\>incorrectfact</error\>\)\.\#\#\#DeterminingSpanofErrorsWelabelthesmallestpossibleconsecutivespanthatspecifiestheerrorgiventheBHCasacontext\.Removingfurtherpartsfromthespanwouldremoveimportantinformation\.Ausefulheuristicistoidentifytheminimalspanthatmustbereplacedtoobtainacorrectstatementthatisgrammaticallycorrect\.Forexample\-"Weperformedan<error\>esophageal\-gastro\-duodenoscopy\(EGD\)\.<error\>"whennosuchprocedureisreportedintheBHC\.Thearticle"an"isnotlabeledasanerror\.Whennoprocedureatallwasperformed"performedanesophageal\-gastro\-duodenoscopy\(EGD\)"shouldbelabeledaserrorbecausethereisnosuitablesubstitutefor"esophageal\-gastro\-duodenoscopy\(EGD\)"\.\-"Afterthesurgery,we<error\>transitionedyoutooraloxycodone</error\>\."whentheBHCcontainsnoinformationforsuchatransition\.IfanothermedicationtransitionismentionedintheBHCandmakessenseinthissentenceonly"oraloxycodone"shouldbelabeled\.IfanotheroralmedicationtransitionismentionedintheBHConly"oxycodone"shouldbelabeled\.\-"<error\>Yoursymptomsrespondedwell</error\>\."whennopartofthesentencemakessenseinthegivencontextoftheAVS\.WeallowgeneralmedicalknowledgeandadvicethatisoftenpartoftheAVS\.Usually,theseareinformationthatarenotspecificforthehospitalcoursegivenintheBHC\.Forexample\-"Pleasetakeyourmedicationsasprescribed"containsnoerroreventhoughtheBHCdoesnotcontainthisinstructionbecausethisisgeneralmedicaladvice\.\-"Ifthesymptomsgetworse,pleasecontactyourdoctor"containsnoerrorevenwhentheBHCdoesnotcontainthisfact,sinceitisgeneralmedicalknowledgethatadoctorshouldbeseenforworseningsymptoms\.WetrytoignoregrammaticalerrorsintheBHCandAVS\.Iftheoriginalmeaningcanstillbeinferred\(e\.g\."medictaions"insteadof"medications"\),themostlikelycorrectedformcanbeused\.Ifthemeaningcannotbeinferred,theycanbeignoredintheBHCorlabeledasUnsupportedOtherintheAVS\.Ifasentenceorphraseisrepeated,thenpleasetreatitasyouwouldanyothersentenceandhighlightallerrors\(evenifyoudidsoinaprevioussentence\)\.Forexample\-"PleasetakeTylenol\.PleasetakeTylenol"whenTylenolwasprescribedintheBHC\.\-"Limityour<error\>useofstairs</error\>\.Pleaselimit<error\>useofstairs</error\>"whenmovementwasencouraged\.Togetreliableerrorcountsaspanshouldonlycontainasingleerror\.\-"Youreceived<error\>Tylenol</error\>and<error\>Ciprofloxacin</error\>"whenthereisnoevidenceintheBHCthatthetwomedicationswereadministeredtothepatient\.\-"Youhavea<error\>follow\-upappointmentwithyourPCP</error\>and<error\>yourcardiologist</error\>"whennosuchfollowupismentionedintheBHC\.Botherrorsarelabeledseparately\.\#\#\#DealingwithDeidentifiedInformationThedatacontainsdeidentifiedinformationshownwith"\_\_\_"inthetext\.Wealwaystreatthisasnon\-existentinformation\.So,theannotatorsshouldnotinferwhatthedeidentifiedinformationcouldbe\.Ingeneral,deidentifiedfieldsintheAVSshouldnotbelabeledaserrors\.However,sometimestheybelongtoawrongstatementorclearlycontainunsupportedinformation\(e\.g\.,adoctor’snameorphonenumbers\)thatarenotgivenintheBHC\.Inthesecases,deidentifiedfieldsshouldbeincludedintheerrorspan\.Forexample\-"Take\_\_\_<error\>200mgdaily</error\>andtrytorest"whennosuchdosageinformationisprovidedintheBHC,butthestatementtorest\.Thedeidentifiedmedicationnameisexcludedfromtheerrorspan\.\-"Pleaseavoidgoingup<error\>morethan\_\_\_stairs</error\>atatime"whenrestrictionsforthenumberofstairstakenatatimearenotementionedintheBHC\.\-"<error\>Dr\.\_\_\_willfollowupwithyou</error\>"whennofollow\-upismentionedintheBHC\.\-"PleasestoptakingAspirin<error\>on\_\_\_</error\>"whennostoppingdateisgivenintheBHC\.\-"YourRBCpeaked<error\>at\_\_\_million</error\>"ifthereisnohintofaspecificredbloodcellcountgivenintheBHC\.\#\#\#ErrorTypesIngeneral,weaskforthemostspecificerrorthatisapplicable\.Ifthereisuncertaintywhichtypeapplies,prefertheonementionedfirstintheenumerationofallerrortypesshownearlier\.Forinstance,iftheerrorcontainsanunsupportedmedicationname,theUnsupportedmedicationtypeshouldbeusedinsteadoftheUnsupportednametype\.Hereisadetaileddescriptionoftheerrortypes:\-‘UnsupportedCondition‘:includesunsupportedsymptoms,diseases,orfindingsofthepatient\.Forexample\-"Youwerefoundtohavea<error\>leftclaviclefracture</error\>"whennoinformationwasgivenforthisconditionintheBHC\.\-‘UnsupportedProcedure‘:includesanyunsupportedmedicalprocedures\.Forexample\-"Youhada<error\>filterplacedinyourvein</error\>"whennointerventionwithafilterwasmentioned\.\-‘UnsupportedMedication‘:containsallerrorsrelatedtounsupportedmedications\.Thisincludesmedicationclasses,substances,routes,frequencies,anddosages\.Forexample\-"Youwereplacedon<error\>antibiotics</error\>"whenonlybloodthinnerswereprescribed\.\-‘UnsupportedTime‘:includesallerrorsforunsupportedtimeorintervalstatements\.Forexample\-"Keepyourarminaslingforthe<error\>next6weeks</error\>"whennospecificdurationisgiven\.\-‘UnsupportedLocation‘:Locationsincludebothunsupportedphysicalplacesaswellasregionsofthepatient\.Forexample\-"Thepatientwasadmittedtothe<error\>AcuteSurgeryService</error\>"whennoadmissionlocationwasprovidedintheBHC\.\-‘UnsupportedNumber‘:anynumbereitherasdigitsorwrittenthatareunsupported\.Thisalsoincludeswordssuchas"a"and"an"\.Forexample\-"Yourpacemakerratewasincreasedto<error\>50</error\>"whentherateof50isnotgivenintheBHC\.\-‘UnsupportedName‘:namedentitiesthatarenotsupportedbytheBHC\.Forexample\-"Youwereseenbythe<error\>interventionalpulmonaryservice</error\>"whennoconsultwiththisservicewasmentionedintheBHC\.\-‘UnsupportedWord‘:incorrectorinappropriatewordsorphraseswhichdonotfitinanyoftheabovetypes\.Forexample\-"Wewillsendyouhomewitha<error\>drain</error\>inplace"whendrainnotmentionedintheBHC\.\-‘UnsupportedOther‘:Ifthereisamistakewhichclearlydoesnotbelongtoanyoftheabovecategories,youmayusethiscategoryasalastresort\.Wecannotgivepreciseinstructionsbecausethe"other"categoryisverybroad\.\-‘ContradictedFact‘:ThiserrortypeisindependentofthecontentandcontainsallfactsthatclearlycontradictinformationprovidedintheBHC\.Forexample\-"Yourpacemakerratewasincreasedto<error\>50</error\>"whenthecontextstateapacemakerrateof40\.\-‘IncorrectFact‘:Thiserrortypeisindependentofthecontentandcontainsallfactsthatclearlycontradictgeneralmedicalknowledgeoradvice\.Forexample\-"Wediagnosedaseizure,andyou<error\>cancontinuedrivingyourcar</error\>"whennoreasonforallowingdrivingafteraseizureisprovidedthiscontradictcommonmedicalknowledge\.\{%forexampleindemonstrations%\}\#\#Examples\#\#\#Example\{\{loop\.index\}\}BHC:\{\{example\.context\}\}AVS:\{\{example\.summary\}\}\{%ifexample\.error\_descriptions%\}ERRORS:\{\{example\.error\_descriptions\}\}\{%endif%\}AVSWITHERRORSLABELED:\{\{example\.summary\_with\_errors\}\}\{%endfor%\}\{%ifdemonstrations%\}\#\#\#Example\{\{len\(demonstrations\)\+1\}\}\{%endif%\}BHC:\{\{context\}\}AVS:\{\{summary\}\}<\|end\|\>

Medalign Chain\-of\-thought K\-Shot with Class Explanation and Class Aware Prediction Prompt[⬇](data:text/plain;base64,<|system|>
You are a helpful assistant that helps patients understand their medical records.
<|end|>

<|user|>
We will present you with a pair of a brief hospital course (BHC) and a patient after visit summary (AVS). The AVS is also referred to as discharge summary. The BHC contains a detailed summary of the hospital stay written by medical service. It usually contains medical jargon, and it can follow different structures based on the hospital course and responsible medical specialty. The AVS summarizes the hospital stay for the patient in plain language. In practice, the BHC is not the only source of information to write the AVS. However, in our setting we treat the BHC as the only context for the summary.

## Instructions

For this labeling task, we are interested in errors in the AVS that are either unsupported by the BHC, contradict content in the BHC, or are wrong medical facts. We allow statements that contain general medical knowledge or advice that are often used in patient summaries. Most errors are due to unsupported facts, so we further distinguish those based on their specific content. This leads to the following error types or labels:
1. Unsupported facts, including condition/procedure/medication/time/location/number/name/word/other
2. Contradicted fact
3. Incorrect fact
And below is the detailed guideline, and we label error spans with the <error> tag (e.g. <error class="error_type">incorrect fact</error>).

### Determining Span of Errors
We label the smallest possible consecutive span that specifies the error given the BHC as a context. Removing further parts from the span would remove important information. A useful heuristic is to identify the minimal span that must be replaced to obtain a correct statement that is grammatically correct. For example
- "We performed an <error>esophageal-gastro-duodenoscopy (EGD).<error>" when no such procedure is reported in the BHC. The article "an" is not labeled as an error. When no procedure at all was performed "performed an esophageal-gastro-duodenoscopy (EGD)" should be labeled as error because there is no suitable substitute for "esophageal-gastro-duodenoscopy (EGD)".
- "After the surgery, we <error>transitioned you to oral oxycodone</error>." when the BHC contains no information for such a transition. If another medication transition is mentioned in the BHC and makes sense in this sentence only "oral oxycodone" should be labeled. If another oral medication transition is mentioned in the BHC only "oxycodone" should be labeled.
- "<error>Your symptoms responded well</error>." when no part of the sentence makes sense in the given context of the AVS.

We allow general medical knowledge and advice that is often part of the AVS. Usually, these are information that are not specific for the hospital course given in the BHC. For example
- "Please take your medications as prescribed" contains no error even though the BHC does not contain this instruction because this is general medical advice.
- "If the symptoms get worse, please contact your doctor" contains no error even when the BHC does not contain this fact, since it is general medical knowledge that a doctor should be seen for worsening symptoms. 

We try to ignore grammatical errors in the BHC and AVS. If the original meaning can still be inferred (e.g. "medictaions" instead of "medications"), the most likely corrected form can be used. If the meaning cannot be inferred, they can be ignored in the BHC or labeled as Unsupported Other in the AVS.

If a sentence or phrase is repeated, then please treat it as you would any other sentence and highlight all errors (even if you did so in a previous sentence). For example
- "Please take Tylenol. Please take Tylenol" when Tylenol was prescribed in the BHC.
- "Limit your <error>use of stairs</error>. Please limit <error>use of stairs</error>" when movement was encouraged.

To get reliable error counts a span should only contain a single error.
- "You received <error>Tylenol</error> and <error>Ciprofloxacin</error>" when there is no evidence in the BHC that the two medications were administered to the patient.
- "You have a <error>follow-up appointment with your PCP</error> and <error>your cardiologist</error>" when no such follow up is mentioned in the BHC. Both errors are labeled separately.

### Dealing with Deidentified Information
The data contains deidentified information shown with "___" in the text. We always treat this as non-existent information. So, the annotators should not infer what the deidentified information could be. In general, deidentified fields in the AVS should not be labeled as errors. However, sometimes they belong to a wrong statement or clearly contain unsupported information (e.g., a doctor's name or phone numbers) that are not given in the BHC. In these cases, deidentified fields should be included in the error span. For example
- "Take ___ <error>200mg daily</error> and try to rest" when no such dosage information is provided in the BHC, but the statement to rest. The deidentified medication name is excluded from the error span.
- "Please avoid going up <error>more than ___ stairs</error> at a time" when restrictions for the number of stairs taken at a time are note mentioned in the BHC.
- "<error>Dr. ___ will follow up with you</error>" when no follow-up is mentioned in the BHC.
- "Please stop taking Aspirin <error>on ___</error>" when no stopping date is given in the BHC. 
- "Your RBC peaked <error>at ___ million</error>" if there is no hint of a specific red blood cell count given in the BHC.

### Error Types
In general, we ask for the most specific error that is applicable. If there is uncertainty which type applies, prefer the one mentioned first in the enumeration of all error types shown earlier. For instance, if the error contains an unsupported medication name, the Unsupported medication type should be used instead of the Unsupported name type. Here is a detailed description of the error types:
- `Unsupported Condition`: includes unsupported symptoms, diseases, or findings of the patient. For example
    - "You were found to have a <error class="unsupported_condition">left clavicle fracture</error>" when no information was given for this condition in the BHC.
- `Unsupported Procedure`: includes any unsupported medical procedures. For example
    - "You had a <error class="unsupported_procedure">filter placed in your vein</error>" when no intervention with a filter was mentioned.
- `Unsupported Medication`: contains all errors related to unsupported medications. This includes medication classes, substances, routes, frequencies, and dosages. For example
    - "You were placed on <error class="unsupported_medication">antibiotics</error>" when only blood thinners were prescribed.
- `Unsupported Time`: includes all errors for unsupported time or interval statements. For example
    - "Keep your arm in a sling for the <error class="unsupported_time">next 6 weeks</error>" when no specific duration is given.
- `Unsupported Location`: Locations include both unsupported physical places as well as regions of the patient. For example
    - "The patient was admitted to the <error class="unsupported_location">Acute Surgery Service</error>" when no admission location was provided in the BHC.
- `Unsupported Number`: any number either as digits or written that are unsupported. This also includes words such as "a" and "an". For example
    - "Your pacemaker rate was increased to <error class="unsupported_number">50</error>" when the rate of 50 is not given in the BHC.
- `Unsupported Name`: named entities that are not supported by the BHC. For example
    - "You were seen by the <error class="unsupported_name">interventional pulmonary service</error>" when no consult with this service was mentioned in the BHC.
- `Unsupported Word`: incorrect or inappropriate words or phrases which do not fit in any of the above types. For example
    - "We will send you home with a <error class="unsupported_word">drain</error> in place" when drain not mentioned in the BHC.
- `Unsupported Other`: If there is a mistake which clearly does not belong to any of the above categories, you may use this category as a last resort. We cannot give precise instructions because the "other" category is very broad.
- `Contradicted Fact`: This error type is independent of the content and contains all facts that clearly contradict information provided in the BHC. For example
    - "Your pacemaker rate was increased to <error class="contradicted_fact">50</error>" when the context state a pacemaker rate of 40.
- `Incorrect Fact`: This error type is independent of the content and contains all facts that clearly contradict general medical knowledge or advice. For example
    - "We diagnosed a seizure, and you <error class="incorrect_fact">can continue driving your car</error>" when no reason for allowing driving after a seizure is provided this contradict common medical knowledge.

{% for example in demonstrations %}
## Examples
### Example {{ loop.index }}

BHC:
{{ example.context }}

AVS:
{{ example.summary }}
{% if example.error_descriptions %}

ERRORS:
{{ example.error_descriptions }}

{% endif %}
AVS WITH ERRORS LABELED:
{{ example.summary_with_errors }}

{% endfor %}
{% if demonstrations %}
### Example {{ len(demonstrations) + 1 }}

{% endif %}
BHC:
{{ context }}

AVS:
{{ summary }}
<|end|>)<\|system\|\>Youareahelpfulassistantthathelpspatientsunderstandtheirmedicalrecords\.<\|end\|\><\|user\|\>Wewillpresentyouwithapairofabriefhospitalcourse\(BHC\)andapatientaftervisitsummary\(AVS\)\.TheAVSisalsoreferredtoasdischargesummary\.TheBHCcontainsadetailedsummaryofthehospitalstaywrittenbymedicalservice\.Itusuallycontainsmedicaljargon,anditcanfollowdifferentstructuresbasedonthehospitalcourseandresponsiblemedicalspecialty\.TheAVSsummarizesthehospitalstayforthepatientinplainlanguage\.Inpractice,theBHCisnottheonlysourceofinformationtowritetheAVS\.However,inoursettingwetreattheBHCastheonlycontextforthesummary\.\#\#InstructionsForthislabelingtask,weareinterestedinerrorsintheAVSthatareeitherunsupportedbytheBHC,contradictcontentintheBHC,orarewrongmedicalfacts\.Weallowstatementsthatcontaingeneralmedicalknowledgeoradvicethatareoftenusedinpatientsummaries\.Mosterrorsareduetounsupportedfacts,sowefurtherdistinguishthosebasedontheirspecificcontent\.Thisleadstothefollowingerrortypesorlabels:1\.Unsupportedfacts,includingcondition/procedure/medication/time/location/number/name/word/other2\.Contradictedfact3\.IncorrectfactAndbelowisthedetailedguideline,andwelabelerrorspanswiththe<error\>tag\(e\.g\.<errorclass="error\_type"\>incorrectfact</error\>\)\.\#\#\#DeterminingSpanofErrorsWelabelthesmallestpossibleconsecutivespanthatspecifiestheerrorgiventheBHCasacontext\.Removingfurtherpartsfromthespanwouldremoveimportantinformation\.Ausefulheuristicistoidentifytheminimalspanthatmustbereplacedtoobtainacorrectstatementthatisgrammaticallycorrect\.Forexample\-"Weperformedan<error\>esophageal\-gastro\-duodenoscopy\(EGD\)\.<error\>"whennosuchprocedureisreportedintheBHC\.Thearticle"an"isnotlabeledasanerror\.Whennoprocedureatallwasperformed"performedanesophageal\-gastro\-duodenoscopy\(EGD\)"shouldbelabeledaserrorbecausethereisnosuitablesubstitutefor"esophageal\-gastro\-duodenoscopy\(EGD\)"\.\-"Afterthesurgery,we<error\>transitionedyoutooraloxycodone</error\>\."whentheBHCcontainsnoinformationforsuchatransition\.IfanothermedicationtransitionismentionedintheBHCandmakessenseinthissentenceonly"oraloxycodone"shouldbelabeled\.IfanotheroralmedicationtransitionismentionedintheBHConly"oxycodone"shouldbelabeled\.\-"<error\>Yoursymptomsrespondedwell</error\>\."whennopartofthesentencemakessenseinthegivencontextoftheAVS\.WeallowgeneralmedicalknowledgeandadvicethatisoftenpartoftheAVS\.Usually,theseareinformationthatarenotspecificforthehospitalcoursegivenintheBHC\.Forexample\-"Pleasetakeyourmedicationsasprescribed"containsnoerroreventhoughtheBHCdoesnotcontainthisinstructionbecausethisisgeneralmedicaladvice\.\-"Ifthesymptomsgetworse,pleasecontactyourdoctor"containsnoerrorevenwhentheBHCdoesnotcontainthisfact,sinceitisgeneralmedicalknowledgethatadoctorshouldbeseenforworseningsymptoms\.WetrytoignoregrammaticalerrorsintheBHCandAVS\.Iftheoriginalmeaningcanstillbeinferred\(e\.g\."medictaions"insteadof"medications"\),themostlikelycorrectedformcanbeused\.Ifthemeaningcannotbeinferred,theycanbeignoredintheBHCorlabeledasUnsupportedOtherintheAVS\.Ifasentenceorphraseisrepeated,thenpleasetreatitasyouwouldanyothersentenceandhighlightallerrors\(evenifyoudidsoinaprevioussentence\)\.Forexample\-"PleasetakeTylenol\.PleasetakeTylenol"whenTylenolwasprescribedintheBHC\.\-"Limityour<error\>useofstairs</error\>\.Pleaselimit<error\>useofstairs</error\>"whenmovementwasencouraged\.Togetreliableerrorcountsaspanshouldonlycontainasingleerror\.\-"Youreceived<error\>Tylenol</error\>and<error\>Ciprofloxacin</error\>"whenthereisnoevidenceintheBHCthatthetwomedicationswereadministeredtothepatient\.\-"Youhavea<error\>follow\-upappointmentwithyourPCP</error\>and<error\>yourcardiologist</error\>"whennosuchfollowupismentionedintheBHC\.Botherrorsarelabeledseparately\.\#\#\#DealingwithDeidentifiedInformationThedatacontainsdeidentifiedinformationshownwith"\_\_\_"inthetext\.Wealwaystreatthisasnon\-existentinformation\.So,theannotatorsshouldnotinferwhatthedeidentifiedinformationcouldbe\.Ingeneral,deidentifiedfieldsintheAVSshouldnotbelabeledaserrors\.However,sometimestheybelongtoawrongstatementorclearlycontainunsupportedinformation\(e\.g\.,adoctor’snameorphonenumbers\)thatarenotgivenintheBHC\.Inthesecases,deidentifiedfieldsshouldbeincludedintheerrorspan\.Forexample\-"Take\_\_\_<error\>200mgdaily</error\>andtrytorest"whennosuchdosageinformationisprovidedintheBHC,butthestatementtorest\.Thedeidentifiedmedicationnameisexcludedfromtheerrorspan\.\-"Pleaseavoidgoingup<error\>morethan\_\_\_stairs</error\>atatime"whenrestrictionsforthenumberofstairstakenatatimearenotementionedintheBHC\.\-"<error\>Dr\.\_\_\_willfollowupwithyou</error\>"whennofollow\-upismentionedintheBHC\.\-"PleasestoptakingAspirin<error\>on\_\_\_</error\>"whennostoppingdateisgivenintheBHC\.\-"YourRBCpeaked<error\>at\_\_\_million</error\>"ifthereisnohintofaspecificredbloodcellcountgivenintheBHC\.\#\#\#ErrorTypesIngeneral,weaskforthemostspecificerrorthatisapplicable\.Ifthereisuncertaintywhichtypeapplies,prefertheonementionedfirstintheenumerationofallerrortypesshownearlier\.Forinstance,iftheerrorcontainsanunsupportedmedicationname,theUnsupportedmedicationtypeshouldbeusedinsteadoftheUnsupportednametype\.Hereisadetaileddescriptionoftheerrortypes:\-‘UnsupportedCondition‘:includesunsupportedsymptoms,diseases,orfindingsofthepatient\.Forexample\-"Youwerefoundtohavea<errorclass="unsupported\_condition"\>leftclaviclefracture</error\>"whennoinformationwasgivenforthisconditionintheBHC\.\-‘UnsupportedProcedure‘:includesanyunsupportedmedicalprocedures\.Forexample\-"Youhada<errorclass="unsupported\_procedure"\>filterplacedinyourvein</error\>"whennointerventionwithafilterwasmentioned\.\-‘UnsupportedMedication‘:containsallerrorsrelatedtounsupportedmedications\.Thisincludesmedicationclasses,substances,routes,frequencies,anddosages\.Forexample\-"Youwereplacedon<errorclass="unsupported\_medication"\>antibiotics</error\>"whenonlybloodthinnerswereprescribed\.\-‘UnsupportedTime‘:includesallerrorsforunsupportedtimeorintervalstatements\.Forexample\-"Keepyourarminaslingforthe<errorclass="unsupported\_time"\>next6weeks</error\>"whennospecificdurationisgiven\.\-‘UnsupportedLocation‘:Locationsincludebothunsupportedphysicalplacesaswellasregionsofthepatient\.Forexample\-"Thepatientwasadmittedtothe<errorclass="unsupported\_location"\>AcuteSurgeryService</error\>"whennoadmissionlocationwasprovidedintheBHC\.\-‘UnsupportedNumber‘:anynumbereitherasdigitsorwrittenthatareunsupported\.Thisalsoincludeswordssuchas"a"and"an"\.Forexample\-"Yourpacemakerratewasincreasedto<errorclass="unsupported\_number"\>50</error\>"whentherateof50isnotgivenintheBHC\.\-‘UnsupportedName‘:namedentitiesthatarenotsupportedbytheBHC\.Forexample\-"Youwereseenbythe<errorclass="unsupported\_name"\>interventionalpulmonaryservice</error\>"whennoconsultwiththisservicewasmentionedintheBHC\.\-‘UnsupportedWord‘:incorrectorinappropriatewordsorphraseswhichdonotfitinanyoftheabovetypes\.Forexample\-"Wewillsendyouhomewitha<errorclass="unsupported\_word"\>drain</error\>inplace"whendrainnotmentionedintheBHC\.\-‘UnsupportedOther‘:Ifthereisamistakewhichclearlydoesnotbelongtoanyoftheabovecategories,youmayusethiscategoryasalastresort\.Wecannotgivepreciseinstructionsbecausethe"other"categoryisverybroad\.\-‘ContradictedFact‘:ThiserrortypeisindependentofthecontentandcontainsallfactsthatclearlycontradictinformationprovidedintheBHC\.Forexample\-"Yourpacemakerratewasincreasedto<errorclass="contradicted\_fact"\>50</error\>"whenthecontextstateapacemakerrateof40\.\-‘IncorrectFact‘:Thiserrortypeisindependentofthecontentandcontainsallfactsthatclearlycontradictgeneralmedicalknowledgeoradvice\.Forexample\-"Wediagnosedaseizure,andyou<errorclass="incorrect\_fact"\>cancontinuedrivingyourcar</error\>"whennoreasonforallowingdrivingafteraseizureisprovidedthiscontradictcommonmedicalknowledge\.\{%forexampleindemonstrations%\}\#\#Examples\#\#\#Example\{\{loop\.index\}\}BHC:\{\{example\.context\}\}AVS:\{\{example\.summary\}\}\{%ifexample\.error\_descriptions%\}ERRORS:\{\{example\.error\_descriptions\}\}\{%endif%\}AVSWITHERRORSLABELED:\{\{example\.summary\_with\_errors\}\}\{%endfor%\}\{%ifdemonstrations%\}\#\#\#Example\{\{len\(demonstrations\)\+1\}\}\{%endif%\}BHC:\{\{context\}\}AVS:\{\{summary\}\}<\|end\|\>
Hallucination Detection-Guided Preference Optimization for Clinical Summarization

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