Dataset Information

Classification of hospital acquired complications using temporal clinical information from a large electronic health record.

ABSTRACT: Hospital acquired complications (HACs) are serious problems affecting modern day healthcare institutions. It is estimated that HACs result in an approximately 10% increase in total inpatient hospital costs across US hospitals. With US hospital spending totaling nearly $900 billion per annum, the damages caused by HACs are no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve patient morbidity & mortality rates. Here, we describe a machine-learning model for predicting the occurrence of HACs within five distinct categories using temporal clinical data. Using our approach, we find that at least $10 billion of excessive hospital costs could be saved in the US alone, with the institution of effective preventive measures. In addition, we also identify several keystone features that demonstrate high predictive power for HACs over different time periods following patient admission. The classifiers and features analyzed in this study show high promise of being able to be used for accurate prediction of HACs in clinical settings, and furthermore provide novel insights into the contribution of various clinical factors to the risk of developing HACs as a function of healthcare system exposure.

SUBMITTER: Warner JL

PROVIDER: S-EPMC4792687 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Classification of hospital acquired complications using temporal clinical information from a large electronic health record.

Warner Jeremy L JL Zhang Peijin P Liu Jenny J Alterovitz Gil G

Journal of biomedical informatics 20151217

Hospital acquired complications (HACs) are serious problems affecting modern day healthcare institutions. It is estimated that HACs result in an approximately 10% increase in total inpatient hospital costs across US hospitals. With US hospital spending totaling nearly $900 billion per annum, the damages caused by HACs are no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve patient morbidity & mortality rates. Here, we descr ...[more]

PMID: 26707449

Similar Datasets

Project description:BackgroundPatients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI.ObjectiveThe objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition.MethodsWe assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers.ResultsWe illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label.ConclusionsStandardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.

Project description:Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital's data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one's own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls.

Dataset Information

Classification of hospital acquired complications using temporal clinical information from a large electronic health record.

Publications

Classification of hospital acquired complications using temporal clinical information from a large electronic health record.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets