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Related Concept Videos

Data Collection by Survey01:07

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Automated survey collection with LLM-based conversational agents.

Kurmanbek Kaiyrbekov1, Nicholas J Dobbins2, Sean D Mooney1

  • 1Cyberinfrastructure and Artificial Intelligence Platforms Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, United States.

JAMIA Open
|November 3, 2025
PubMed
Summary
This summary is machine-generated.

Conversational Large Language Models (LLMs) can power scalable AI phone surveys. This approach reduces human effort in healthcare data collection, achieving 98% accuracy in response extraction.

Keywords:
large language modelsmachine learningnatural language processingsurveys

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Area of Science:

  • Artificial Intelligence
  • Health Informatics
  • Natural Language Processing

Background:

  • Traditional phone surveys for health data are costly and difficult to scale.
  • Limitations include expense, time consumption, and scalability challenges.

Purpose of the Study:

  • To propose and evaluate a novel survey collection method using conversational Large Language Models (LLMs).
  • To overcome the limitations of traditional phone surveys through AI-powered automation.

Main Methods:

  • Developed a framework using an LLM conversational agent for survey conduction and transcription.
  • Employed GPT-4o for extracting survey responses from transcripts.
  • Evaluated transcription errors, response accuracy, and user experience with 40 responses from 8 participants using LLM personas.

Main Results:

  • GPT-4o achieved 98% average accuracy in extracting survey responses.
  • Transcription word error rate averaged 7.7%.
  • Participants noted occasional conversational agent errors but appreciated its comprehension and engagement.

Conclusions:

  • LLM agents demonstrate significant potential for scalable, AI-powered phone surveys.
  • This technology can reduce human effort and enhance healthcare data collection efficiency.