Adaptive questionnaires for facilitating patient data entry in clinical
decision support systems: Methods and application to STOPP/START v2
- URL: http://arxiv.org/abs/2309.10398v1
- Date: Tue, 19 Sep 2023 07:59:13 GMT
- Title: Adaptive questionnaires for facilitating patient data entry in clinical
decision support systems: Methods and application to STOPP/START v2
- Authors: Jean-Baptiste Lamy, Abdelmalek Mouazer, Karima Sedki, Sophie Dubois,
Hector Falcoff
- Abstract summary: We propose an original solution to simplify patient data entry using an adaptive questionnaire.
Considering a rule-based decision support systems, we designed methods for translating the system's clinical rules into display rules.
We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire.
- Score: 1.8374319565577155
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Clinical decision support systems are software tools that help clinicians to
make medical decisions. However, their acceptance by clinicians is usually
rather low. A known problem is that they often require clinicians to manually
enter lots of patient data, which is long and tedious. Existing solutions, such
as the automatic data extraction from electronic health record, are not fully
satisfying, because of low data quality and availability. In practice, many
systems still include long questionnaire for data entry.
In this paper, we propose an original solution to simplify patient data
entry, using an adaptive questionnaire, i.e. a questionnaire that evolves
during user interaction, showing or hiding questions dynamically. Considering a
rule-based decision support systems, we designed methods for translating the
system's clinical rules into display rules that determine the items to show in
the questionnaire, and methods for determining the optimal order of priority
among the items in the questionnaire. We applied this approach to a decision
support system implementing STOPP/START v2, a guideline for managing
polypharmacy. We show that it permits reducing by about two thirds the number
of clinical conditions displayed in the questionnaire. Presented to clinicians
during focus group sessions, the adaptive questionnaire was found "pretty easy
to use". In the future, this approach could be applied to other guidelines, and
adapted for data entry by patients.
Related papers
- Large Language Models in the Clinic: A Comprehensive Benchmark [63.21278434331952]
We build a benchmark ClinicBench to better understand large language models (LLMs) in the clinic.
We first collect eleven existing datasets covering diverse clinical language generation, understanding, and reasoning tasks.
We then construct six novel datasets and clinical tasks that are complex but common in real-world practice.
We conduct an extensive evaluation of twenty-two LLMs under both zero-shot and few-shot settings.
arXiv Detail & Related papers (2024-04-25T15:51:06Z) - Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions [19.436999992810797]
We construct two new datasets: JAMA Clinical Challenge and Medbullets.
JAMA Clinical Challenge consists of questions based on challenging clinical cases, while Medbullets comprises simulated clinical questions.
We evaluate seven LLMs on the two datasets using various prompts.
arXiv Detail & Related papers (2024-02-28T05:44:41Z) - Prompt-based Personalized Federated Learning for Medical Visual Question
Answering [56.002377299811656]
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns.
We regard medical datasets from different organs as clients and use pFL to train personalized transformer-based VQA models for each client.
arXiv Detail & Related papers (2024-02-15T03:09:54Z) - Zero-Shot Clinical Trial Patient Matching with LLMs [40.31971412825736]
Large language models (LLMs) offer a promising solution to automated screening.
We design an LLM-based system which, given a patient's medical history as unstructured clinical text, evaluates whether that patient meets a set of inclusion criteria.
Our system achieves state-of-the-art scores on the n2c2 2018 cohort selection benchmark.
arXiv Detail & Related papers (2024-02-05T00:06:08Z) - RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19
Assessment in Primary Care [45.43645878061283]
We present a framework that performs knowledge graph construction from raw GP medical notes written during or after patient consultations.
Our knowledge graphs include information about existing patient symptoms, their duration, and their severity.
We apply our framework to consultation notes of COVID-19 patients in the UK.
arXiv Detail & Related papers (2023-06-17T23:35:51Z) - SPeC: A Soft Prompt-Based Calibration on Performance Variability of
Large Language Model in Clinical Notes Summarization [50.01382938451978]
We introduce a model-agnostic pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization.
Experimental findings indicate that our method not only bolsters performance but also effectively curbs variance for various language models.
arXiv Detail & Related papers (2023-03-23T04:47:46Z) - Machine learning-based patient selection in an emergency department [0.0]
This paper investigates the potential of an Machine Learning (ML) based patient selection method.
It incorporates a comprehensive state representation of the system and a complex non-linear selection function.
Results show that the proposed method significantly outperforms the APQ method for a majority of evaluated settings.
arXiv Detail & Related papers (2022-06-08T08:56:52Z) - Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain
Management [5.044336341666555]
We introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management.
We propose a new, rigorous framework, including a sample experimental design, to measure the potential biases present when making treatment decisions.
arXiv Detail & Related papers (2021-08-03T21:55:28Z) - Where's the Question? A Multi-channel Deep Convolutional Neural Network
for Question Identification in Textual Data [83.89578557287658]
We propose a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions.
We conducted a comprehensive performance comparison analysis of the proposed network against other deep neural networks.
The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.
arXiv Detail & Related papers (2020-10-15T15:11:22Z) - DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment
Prediction [67.91606509226132]
Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment.
DeepEnroll is a cross-modal inference learning model to jointly encode enrollment criteria (tabular data) into a shared latent space for matching inference.
arXiv Detail & Related papers (2020-01-22T17:51:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.