Real-time prediction of mortality, readmission, and length of stay using electronic health record data

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs).

Related collections

Most cited references 12

Record: found
Abstract: found
Article: found

Is Open Access

A review of approaches to identifying patient phenotype cohorts using electronic health records

Chaitanya Shivade, Preethi Raghavan, Eric Fosler-Lussier … (2013)

Objective To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. Materials and methods We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. Results Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. Discussion We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. Conclusions There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

0 comments Cited 136 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

A Review of Methods for Missing Data

Therese D. Pigott (2001)

0 comments Cited 107 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Is Open Access

Development and validation of a continuous measure of patient condition using the Electronic Medical Record.

Michael Rothman, Steven I Rothman, Joseph Beals (2013)

Patient condition is a key element in communication between clinicians. However, there is no generally accepted definition of patient condition that is independent of diagnosis and that spans acuity levels. We report the development and validation of a continuous measure of general patient condition that is independent of diagnosis, and that can be used for medical-surgical as well as critical care patients. A survey of Electronic Medical Record data identified common, frequently collected non-static candidate variables as the basis for a general, continuously updated patient condition score. We used a new methodology to estimate in-hospital risk associated with each of these variables. A risk function for each candidate input was computed by comparing the final pre-discharge measurements with 1-year post-discharge mortality. Step-wise logistic regression of the variables against 1-year mortality was used to determine the importance of each variable. The final set of selected variables consisted of 26 clinical measurements from four categories: nursing assessments, vital signs, laboratory results and cardiac rhythms. We then constructed a heuristic model quantifying patient condition (overall risk) by summing the single-variable risks. The model's validity was assessed against outcomes from 170,000 medical-surgical and critical care patients, using data from three US hospitals. Outcome validation across hospitals yields an area under the receiver operating characteristic curve(AUC) of ≥0.92 when separating hospice/deceased from all other discharge categories, an AUC of ≥0.93 when predicting 24-h mortality and an AUC of 0.62 when predicting 30-day readmissions. Correspondence with outcomes reflective of patient condition across the acuity spectrum indicates utility in both medical-surgical units and critical care units. The model output, which we call the Rothman Index, may provide clinicians with a longitudinal view of patient condition to help address known challenges in caregiver communication, continuity of care, and earlier detection of acuity trends.

0 comments Cited 75 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Title: Journal of the American Medical Informatics Association

Abbreviated Title: J Am Med Inform Assoc

Publisher: Oxford University Press (OUP)

ISSN (Print): 1067-5027

ISSN (Electronic): 1527-974X

Publication date Created: June 10 2016

Publication date (Print): May 15 2016

Volume: 23

Issue: 3

Pages: 553-561

Article

DOI: 10.1093/jamia/ocv110

PMC ID: 7839923

PubMed ID: 26374704

SO-VID: c5323fce-0cbf-4978-a4cf-31dc15aa8f3f

History

Data availability:

Comments

Comment on this article

scite_

Cited by 22

See all cited by

- Version 1
- Version 1

Real-time prediction of mortality, readmission, and length of stay using electronic health record data

Read this article at

Abstract

Related collections

Genome Engineering using CRISPR

Most cited references 12

A review of approaches to identifying patient phenotype cohorts using electronic health records

A Review of Methods for Missing Data

Development and validation of a continuous measure of patient condition using the Electronic Medical Record.

Author and article information

Journal

Article

History

Comments

Comment on this article

Similar content 1,540

Cited by 22