Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data

Matthew J. Swain, Hadi H K Kharrazi

Research output: Contribution to journalArticle

Abstract

Introduction: Unplanned 30-day hospital readmission account for roughly 17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most researchers have used insurance claims or administrative data to train and operationalize their Readmission Risk Prediction Models (RRPMs). Some RRPM developers have also used electronic health records data; however, using health informatics exchange data has been uncommon among such predictive models and can be beneficial in its ability to provide real-time alerts to providers at the point of care. Methods: We conducted a semi-systematic review of readmission predictive factors published prior to March 2013. Then, we extracted and merged all significant variables listed in those articles for RRPMs. Finally, we matched these variables with common HL7 messages transmitted by a sample of health information exchange organizations (HIO). Results: The semi-systematic review resulted in identification of 32 articles and 297 predictive variables. The mapping of these variables with common HL7 segments resulted in an 89.2% total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4%. The PID (patient identification) and OBX (observation results) segments cover 13.9% and 9.1% of the variables. Evaluating the same coverage in three sample HIOs showed data incompleteness. Discussion: HIOs can utilize HL7 messages to develop unique RRPMs for their stakeholders; however, data completeness of exchanged messages should meet certain thresholds. If data quality standards are met by stakeholders, HIOs would be able to provide real-time RRPMs that not only predict intra-hospital readmissions but also inter-hospital cases. Conclusion: A RRPM derived using HIO data exchanged through may prove to be a useful method to prevent unplanned hospital readmissions. In order for the RRPM derived from HIO data to be effective, hospitals must actively exchange clinical information through the HIO and develop actionable methods that integrate into the workflow of providers to ensure that patients at high-risk for readmission receive the care they need.

Original languageEnglish (US)
Pages (from-to)1048-1056
Number of pages9
JournalInternational Journal of Medical Informatics
Volume84
Issue number12
DOIs
StatePublished - Dec 1 2015

Fingerprint

Patient Readmission
Point-of-Care Systems
Organizations
Informatics
Workflow
Health Information Exchange
Electronic Health Records
Medicare
Insurance
Research Personnel
Observation
Health

Keywords

  • Health information exchange
  • Health information organization
  • Health information technology
  • Hospital readmissions
  • Risk prediction model

ASJC Scopus subject areas

  • Health Informatics

Cite this

Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data. / Swain, Matthew J.; Kharrazi, Hadi H K.

In: International Journal of Medical Informatics, Vol. 84, No. 12, 01.12.2015, p. 1048-1056.

Research output: Contribution to journalArticle

@article{f685963f154f44bdb0b07986959f0206,
title = "Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data",
abstract = "Introduction: Unplanned 30-day hospital readmission account for roughly 17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most researchers have used insurance claims or administrative data to train and operationalize their Readmission Risk Prediction Models (RRPMs). Some RRPM developers have also used electronic health records data; however, using health informatics exchange data has been uncommon among such predictive models and can be beneficial in its ability to provide real-time alerts to providers at the point of care. Methods: We conducted a semi-systematic review of readmission predictive factors published prior to March 2013. Then, we extracted and merged all significant variables listed in those articles for RRPMs. Finally, we matched these variables with common HL7 messages transmitted by a sample of health information exchange organizations (HIO). Results: The semi-systematic review resulted in identification of 32 articles and 297 predictive variables. The mapping of these variables with common HL7 segments resulted in an 89.2{\%} total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4{\%}. The PID (patient identification) and OBX (observation results) segments cover 13.9{\%} and 9.1{\%} of the variables. Evaluating the same coverage in three sample HIOs showed data incompleteness. Discussion: HIOs can utilize HL7 messages to develop unique RRPMs for their stakeholders; however, data completeness of exchanged messages should meet certain thresholds. If data quality standards are met by stakeholders, HIOs would be able to provide real-time RRPMs that not only predict intra-hospital readmissions but also inter-hospital cases. Conclusion: A RRPM derived using HIO data exchanged through may prove to be a useful method to prevent unplanned hospital readmissions. In order for the RRPM derived from HIO data to be effective, hospitals must actively exchange clinical information through the HIO and develop actionable methods that integrate into the workflow of providers to ensure that patients at high-risk for readmission receive the care they need.",
keywords = "Health information exchange, Health information organization, Health information technology, Hospital readmissions, Risk prediction model",
author = "Swain, {Matthew J.} and Kharrazi, {Hadi H K}",
year = "2015",
month = "12",
day = "1",
doi = "10.1016/j.ijmedinf.2015.09.003",
language = "English (US)",
volume = "84",
pages = "1048--1056",
journal = "International Journal of Medical Informatics",
issn = "1386-5056",
publisher = "Elsevier Ireland Ltd",
number = "12",

}

TY - JOUR

T1 - Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data

AU - Swain, Matthew J.

AU - Kharrazi, Hadi H K

PY - 2015/12/1

Y1 - 2015/12/1

N2 - Introduction: Unplanned 30-day hospital readmission account for roughly 17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most researchers have used insurance claims or administrative data to train and operationalize their Readmission Risk Prediction Models (RRPMs). Some RRPM developers have also used electronic health records data; however, using health informatics exchange data has been uncommon among such predictive models and can be beneficial in its ability to provide real-time alerts to providers at the point of care. Methods: We conducted a semi-systematic review of readmission predictive factors published prior to March 2013. Then, we extracted and merged all significant variables listed in those articles for RRPMs. Finally, we matched these variables with common HL7 messages transmitted by a sample of health information exchange organizations (HIO). Results: The semi-systematic review resulted in identification of 32 articles and 297 predictive variables. The mapping of these variables with common HL7 segments resulted in an 89.2% total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4%. The PID (patient identification) and OBX (observation results) segments cover 13.9% and 9.1% of the variables. Evaluating the same coverage in three sample HIOs showed data incompleteness. Discussion: HIOs can utilize HL7 messages to develop unique RRPMs for their stakeholders; however, data completeness of exchanged messages should meet certain thresholds. If data quality standards are met by stakeholders, HIOs would be able to provide real-time RRPMs that not only predict intra-hospital readmissions but also inter-hospital cases. Conclusion: A RRPM derived using HIO data exchanged through may prove to be a useful method to prevent unplanned hospital readmissions. In order for the RRPM derived from HIO data to be effective, hospitals must actively exchange clinical information through the HIO and develop actionable methods that integrate into the workflow of providers to ensure that patients at high-risk for readmission receive the care they need.

AB - Introduction: Unplanned 30-day hospital readmission account for roughly 17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most researchers have used insurance claims or administrative data to train and operationalize their Readmission Risk Prediction Models (RRPMs). Some RRPM developers have also used electronic health records data; however, using health informatics exchange data has been uncommon among such predictive models and can be beneficial in its ability to provide real-time alerts to providers at the point of care. Methods: We conducted a semi-systematic review of readmission predictive factors published prior to March 2013. Then, we extracted and merged all significant variables listed in those articles for RRPMs. Finally, we matched these variables with common HL7 messages transmitted by a sample of health information exchange organizations (HIO). Results: The semi-systematic review resulted in identification of 32 articles and 297 predictive variables. The mapping of these variables with common HL7 segments resulted in an 89.2% total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4%. The PID (patient identification) and OBX (observation results) segments cover 13.9% and 9.1% of the variables. Evaluating the same coverage in three sample HIOs showed data incompleteness. Discussion: HIOs can utilize HL7 messages to develop unique RRPMs for their stakeholders; however, data completeness of exchanged messages should meet certain thresholds. If data quality standards are met by stakeholders, HIOs would be able to provide real-time RRPMs that not only predict intra-hospital readmissions but also inter-hospital cases. Conclusion: A RRPM derived using HIO data exchanged through may prove to be a useful method to prevent unplanned hospital readmissions. In order for the RRPM derived from HIO data to be effective, hospitals must actively exchange clinical information through the HIO and develop actionable methods that integrate into the workflow of providers to ensure that patients at high-risk for readmission receive the care they need.

KW - Health information exchange

KW - Health information organization

KW - Health information technology

KW - Hospital readmissions

KW - Risk prediction model

UR - http://www.scopus.com/inward/record.url?scp=84945584840&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84945584840&partnerID=8YFLogxK

U2 - 10.1016/j.ijmedinf.2015.09.003

DO - 10.1016/j.ijmedinf.2015.09.003

M3 - Article

VL - 84

SP - 1048

EP - 1056

JO - International Journal of Medical Informatics

JF - International Journal of Medical Informatics

SN - 1386-5056

IS - 12

ER -