JMIR Publications

ABSTRACT

Background:

Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium.

Objective:

The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data.

Methods:

This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record (EHR) data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement.

Results:

This study was funded by the National Institutes of Health, National Institute on Aging; work will take place from 2021-2024. We will use data from over 300,000 patient encounters that occurred between 2012-2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals.

Conclusions:

Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real-time. This model has the potential to be integrated into the EHR and provide point-of-care decision support to prevent harm and improve quality of care.