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Published on 27.10.15 in Vol 1, No 1 (2015): October

This paper is in the following e-collection/theme issue:

    Abstract

    Predictive Modeling of Emergency Hospital Transport Using Medical Alert Pattern Data: Retrospective Cohort Study

    1Philips Research, Eindhoven, Netherlands

    2Philips Home Monitoring, Framingham, MA, United States

    Corresponding Author:

    Jorn op den Buijs, Ph. D

    Philips Research

    HTC 34.5.038

    High Tech Campus 34

    Eindhoven, 5656AE

    Netherlands

    Phone: 31 631926890

    Fax:31 000000000

    Email: jorn.op.den.buijs@philips.com


    ABSTRACT

    Background: In the transition from a fee-for-service to a fee-for-value system, health care organizations (HCOs) are under pressure to keep patients healthy through preventive services and population health management. Predictive analytics based on the past health behavior of the patient population can be used to predict future risk of decline.

    Objective: The objective of this study was to develop robust predictive models of impending emergency transports to the hospital based on enrollment and medical alert pattern data from subscribers of a Personal Emergency Response System (PERS) service. This enables targeting of clinical programs to members that need it the most.

    Methods: De-identified medical alert pattern data of 551,127 subscribers to a PERS service were used. Multivariate logistic regression was performed on subscriber demographics, self-reported medical conditions, variables related to the care giver network and variables derived from up to one year of retrospective medical alert data. A 10-fold cross-validation scheme was used to predict transport to the hospital by emergency medical services in the next 30 days. Furthermore, the model performance was evaluated after retraining using up to 90 days of medical alert data, and using enrollment data only.

    Results: Emergency hospital transport in the 30-day window was experienced by 2.4% of all subscribers. The area under the receiver operator characteristic curve (auROC) was 0.75 ± 0.01 in the validation cohorts. The model using up to 90 days of data resulted in auROC = 0.71 ± 0.01 and the model using enrollment data only resulted in auROC = 0.62 ± 0.01.

    Conclusions: Our model for emergency hospital transport in subscribers of a medical alert service showed good discriminatory accuracy on retrospective validation data. While the model yields good discriminatory accuracy with up to 90 days of data, best performance is achieved using up to one year of medical alert data. The model using enrollment data only, without medical alert pattern data, does not perform as well. We are planning a prospective validation of the algorithm to determine the value of the predictive model in assisting HCOs with planning early interventions to avoid emergency department visits and hospitalizations.

    iProc 2015;1(1):e19

    doi:10.2196/iproc.4772

    KEYWORDS


    (This is a conference paper presented at the Connected Health Symposium, Boston, 2015, which was not edited and is only lightly peer-reviewed).

    Multimedia Appendix 1

    Extended abstract.

    PDF File (Adobe PDF File), 739KB

    References

      Edited by G Eysenbach; submitted 29.05.15; peer-reviewed by K Chen, R Gouripeddi; comments to author 25.06.15; accepted 20.07.15; published 27.10.15

      ©Jorn op den Buijs, Tine Smits, Marten Pijl, Mariana Simons, Linda Schertzer. Originally published in JMIR Mhealth and Uhealth (http://www.iproc.org), 27.10.2015.

      This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.