ICDA: a Platform for Intelligent Care Delivery Analytics

Overview

Journal AMIA Annu Symp Proc

Specialty Medical Informatics

Date 2013 Jan 11

PMID 23304296

Citations 6

Authors

David Gotz

Harry Stavropoulos

Jimeng Sun

Fei Wang

Affiliations

Soon will be listed here.

Abstract

The identification of high-risk patients is a critical component in improving patient outcomes and managing costs. This paper describes the Intelligent Care Delivery Analytics platform (ICDA), a system which enables risk assessment analytics that process large collections of dynamic electronic medical data to identify at-risk patients. ICDA works by ingesting large volumes of data into a common data model, then orchestrating a collection of analytics that identify at-risk patients. It also provides an interactive environment through which users can access and review the analytics results. In addition, ICDA provides APIs via which analytics results can be retrieved to surface in external applications. A detailed review of ICDA's architecture is provided. Descriptions of four use cases are included to illustrate ICDA's application within two different data environments. These use cases showcase the system's flexibility and exemplify the types of analytics it enables.

Citing Articles

Sharafoddini A, Dubin J, Lee J JMIR Med Inform. 2017; 5(1):e7.

PMID: 28258046 PMC: 5357318. DOI: 10.2196/medinform.6730.

Brown S Front Physiol. 2016; 7:561.

PMID: 27932992 PMC: 5121278. DOI: 10.3389/fphys.2016.00561.

"Big data" and the electronic health record.

Ross M, Wei W, Ohno-Machado L Yearb Med Inform. 2014; 9:97-104.

PMID: 25123728 PMC: 4287068. DOI: 10.15265/IY-2014-0003.

A review of analytics and clinical informatics in health care.

Simpao A, Ahumada L, Galvez J, Rehman M J Med Syst. 2014; 38(4):45.

PMID: 24696396 DOI: 10.1007/s10916-014-0045-x.

PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records.

Ng K, Ghoting A, Steinhubl S, Stewart W, Malin B, Sun J J Biomed Inform. 2013; 48:160-70.

PMID: 24370496 PMC: 4075460. DOI: 10.1016/j.jbi.2013.12.012.

References

. STREPTOMYCIN treatment of pulmonary tuberculosis. Br Med J. 1948; 2(4582):769-82. PMC: 2091872. View

Ohno-Machado L, Bafna V, Boxwala A, Chapman B, Chapman W, Chaudhuri K . iDASH: integrating data for analysis, anonymization, and sharing. J Am Med Inform Assoc. 2011; 19(2):196-201. PMC: 3277627. DOI: 10.1136/amiajnl-2011-000538. View

Murphy S, Weber G, Mendis M, Gainer V, Chueh H, Churchill S . Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 2010; 17(2):124-30. PMC: 3000779. DOI: 10.1136/jamia.2009.000893. View

Kurosaki M, Hiramatsu N, Sakamoto M, Suzuki Y, Iwasaki M, Tamori A . Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C. J Hepatol. 2011; 56(3):602-8. DOI: 10.1016/j.jhep.2011.09.011. View

Campbell-Scherer D . Multimorbidity: a challenge for evidence-based medicine. Evid Based Med. 2010; 15(6):165-6. DOI: 10.1136/ebm1154. View

Rea S, Pathak J, Savova G, Oniki T, Westberg L, Beebe C . Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project. J Biomed Inform. 2012; 45(4):763-71. PMC: 4905766. DOI: 10.1016/j.jbi.2012.01.009. View

McKee P, CASTELLI W, MCNAMARA P, Kannel W . The natural history of congestive heart failure: the Framingham study. N Engl J Med. 1971; 285(26):1441-6. DOI: 10.1056/NEJM197112232852601. View

Ebadollahi S, Sun J, Gotz D, Hu J, Sow D, Neti C . Predicting Patient's Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics. AMIA Annu Symp Proc. 2011; 2010:192-6. PMC: 3041306. View

Tang H, Poynton M, Hurdle J, Baird B, Koford J, Goldfarb-Rumyantzev A . Predicting three-year kidney graft survival in recipients with systemic lupus erythematosus. ASAIO J. 2011; 57(4):300-9. DOI: 10.1097/MAT.0b013e318222db30. View

10.

Hu J, Wang F, Sun J, Sorrentino R, Ebadollahi S . A healthcare utilization analysis framework for hot spotting and contextual anomaly detection. AMIA Annu Symp Proc. 2013; 2012:360-9. PMC: 3540544. View

11.

Jha A, DesRoches C, Kralovec P, Joshi M . A progress report on electronic health records in U.S. hospitals. Health Aff (Millwood). 2010; 29(10):1951-7. DOI: 10.1377/hlthaff.2010.0502. View

12.

Schoen C, Osborn R, How S, Doty M, Peugh J . In chronic condition: experiences of patients with complex health care needs, in eight countries, 2008. Health Aff (Millwood). 2008; 28(1):w1-16. DOI: 10.1377/hlthaff.28.1.w1. View

13.

Frankovich J, Longhurst C, Sutherland S . Evidence-based medicine in the EMR era. N Engl J Med. 2011; 365(19):1758-9. DOI: 10.1056/NEJMp1108726. View