K- QA : A Real-World Medical Q & A Benchmark (2024)

Abstract: Ensuring the accuracy of responses provided by large language models (LLMs) is crucial, particularly in clinical settings where incorrect information may directly impact patient health. To address this challenge, we construct K-QA, a dataset containing 1,212 patient questions originating from real-world conversations held on a popular clinical online platform. We employ a panel of in-house physicians to answer and manually decompose a subset of K-QA into self-contained statements. Additionally, we formulate two NLI-based evaluation metrics approximating recall and precision: (1) comprehensiveness, measuring the percentage of essential clinical information in the generated answer and (2) hallucination rate, measuring the number of statements from the physician-curated response contradicted by the LLM answer. Finally, we use K-QA along with these metrics to evaluate several state-of-the-art models, as well as the effect of in-context learning and medically-oriented augmented retrieval schemes developed by the authors. Our findings indicate that in-context learning improves the comprehensiveness of the models, and augmented retrieval is effective in reducing hallucinations. We will make K-QA available to to the community to spur research into medically accurate NLP applications.

## introduction recent advancements in large language models (llms) have led to a growing interest in their use in the medical domain in patient-facing applications, where llms hold the promise of providing laypersons with high-quality advice at a relatively low cost @xcite . for instance, in response to the question "what's good for muscular pain?", a good patient-facing response may include, in addition to medical information (the name of a muscle relaxant), also the advice "seek medical attention if you have numbness or tingling in limbs". however, there is a lack of benchmarks reflecting user needs and corresponding medicallyaccurate answers to test these models under realworld conditions. most existing benchmarks assume textbook questions with multiple-choice or span-based answers @xcite @xcite . in contrast, real-world questions, like "is there any way i can get some medicine for cold sore and ulcer that is killing me?", often include various interacting medical conditions ("cold sore", "ulcer"), use ambiguous, non-medical jargon ("that is killing me"), and require long-form, nuanced answers. in this work, we present k-qa, a medical qa benchmark containing 1,212 deidentified questions asked by real users on k health, foot_1 an ai-driven clinical platform with over 8 million unique users. the questions in k-qa were curated from k health's vast database of patient-physician interactions, aiming to capture stand-alone medical questions. these can be answered solely based on the information provided in the question, and do not require any prior knowledge about the patient's history or demographics. the resulting corpus is diverse and challenging, spanning over 100 different medical conditions (see examples in figure 1 ). to evaluate state-of-the-art models against k-qa, a team of 12 in-house medical doctors invested more than 400 person hours rigorously answering 201 questions from the dataset in a free-text format. doctors consulted credible medical sources, such as uptodate 3 and pubmed foot_3 to provide accurate and scientifically-backed answers. their answers were further reviewed by an experienced overseeing physician. the remaining portion of roughly 1k questions present opportunities for expanding the benchmark through various augmentation techniques, as already demonstrated by @xcite . to allow fine-grained evaluation, doctors decomposed each answer into an average of roughly 8 minimal semantic content units @xcite , resulting in over 1.5k individual statements. in addition, the importance of each statement was manually marked as either (1) must have, indicating that a model must include this statement in order to be medically accurate (e.g., providing all contraindications for a drug) , or (2) nice to have, indicating the statement is supplemental in nature (e.g., providing additional conditions where this drug may be helpful). following recent work on evaluation of text generation, we use the decomposed ground-truth answers in a natural language inference (nli)-based evaluation of predicted answers @xcite @xcite . concretely, we define two complementing evaluation metrics. first, comprehensiveness, which is similar to recall, measures the percentage of ground-truth statements conveyed in the predicted answer. in order to excel in this metric, a model must cover all of the must have statements annotated by doctors. second, hallucination rate, which is similar to precision, measures how many of all ground-truth statements (either must have or nice to have) contradict the predicted answer. to excel in this metric models must not produce any medically-inaccurate statements. we find that recent llms, like gpt-4, are able to approxi-mate both comprehensiveness and hallucination rate, nearing human assessment of both metrics. finally, we evaluate various state-of-the-art llm-based architectures on k-qa, spanning a wide range of families, including open-and closedsource models, zero-shot vs. in-context learning, and retrieval-augmented generation. we find that all models struggle on comprehensiveness, with the best performing model covering only 67.7% of medically-important statements, and while hallucinations seem to decrease with model size and augmented generation, all models still provide medically-dangerous advice in subtle ways which are especially risky for lay users. we hope that future work adopts k-qa and accompanying metrics as a valuable benchmark to produce medically-accurate nlp applications which can be safely deployed in real-world scenarios. ## background we review existing medical nlp datasets and other recent challenging benchmarks. we highlight key comparisons with k-qa in medical qa benchmarks. several diverse health-related question-answering datasets have been compiled, including over biomedical scientific literature @xcite and medical examinations @xcite . the majority of these datasets rely on multiple-choice or span extraction @xcite , which simplify the evaluation process but do not reflect complexity of free-form responses which are often needed in real-world situations @xcite . in the context of consumer health questions, our dataset is different from existing benchmarks like mediqa-ans @xcite , livemedqa @xcite and medicationqa @xcite in several additional key ways. while these datasets source their questions from users searching healthcare websites via the chiqa system @xcite and retrieve answers through keyword matching, ours originates from authentic patient-physician interactions, ensuring genuine medical inquiries. furthermore, our dataset includes free-form open-domain responses carefully curated by medical professionals. in addition, the answers in k-qa are segmented into finer atomic statements, enabling fine-grained evaluation. ## the k-qa benchmark in this section, we describe curation and annotation the k-qa dataset, depicted in figure 2 . k-qa consists of two portions -a medium-scale corpus of diverse real-world medical inquiries written by patients on an online platform (section 3.1) and a subset of rigorous and granular answers, annotated by a team of in-house medical experts (section 3.2). in section 3.3, we present an analysis of the dataset, illustrating its medical and linguistic diversity. ## evaluation metrics for k-qa to evaluate models against k-qa, we propose a natural language inference (nli; @xcite framework, following recent text generation evaluation @xcite @xcite . our evaluation framework is inspired by factscore @xcite , a metric that measures factual precision by computing the percentage of atomic facts in a generated answer supported by a reliable external source. unlike factscore, which automatically generates statements and as-signs them equal importance, our approach involves predefined medical statements with varying clinical significance. this modification enables us to extend the framework and establish a proxy metric for factual recall as well. we consider a predicted answer as a premise and each ground-truth statement derived from an annotated answer as an hypothesis. intuitively, a correctly predicted answer should entail every groundtruth statement. this formulation aims to quantify the extent to which the model's answer captures the meaning of the gold answer, abstracting over the wording chosen by a particular expert annotator. as formulated below, we devise two nli-based metrics: comprehensiveness and hallucination rate. these adapt the evaluation of text generation to the medical domain by taking into account k-qa's annotation of must have, i.e., clinically crucial facets of information, and nice to have statements, which are supplemental in nature. both metrics were aggregated across all assessed questions, where higher values of the comprehensiveness metric and lower values of hallucination rates indicate better performance. figure 5 provides an example illustrating the complete process of evaluating a generated answer and deriving these metrics. formally, let p denote the model's predicted answer, must have represents the set of ground-truth statements marked as crucial, nice to have represents the set of ground-truth statements marked as supplemental, and @xmath0. comprehensiveness metric. this metric measures how many of the clinically crucial claims are figure 5 : an example of the evaluation procedure, starting with a patient's question and a generated answer from a language model. each statement is then automatically tested in an nli framework to determine its relationship to the generated answer. finally, our metrics are computed, where hall( p ) counts the number of contraditions (1), and comp( p ) is equal to 0, because none of the must have statements were entailed. different robot symbols signify different models, and the example was simplified for presentation. ## evaluating state-of-the art models following the creation of k-qa and the formulation of evaluation metrics, we turn to evaluate the current state of the art in this challenging task. ## conclusion we introduce k-qa, a question-answering benchmark with real-world patients' questions and carefully curated physician answers. we formulate metrics that quantify how well a predicted answer covers important information and to what extent it contradicts gold answers. llms improve with size and augmented generation, but there is still a lot of room for improvement in both comprehensiveness and hallucination rate.