Integration of new technology in a legacy system for collecting medical data - challenges and lessons learned

Gilchrist, Jeff; Frize, Monique; Bariciak, Erika; Townsend, Daphne

doi:10.1109/iembs.2008.4650167

Cited by 12 publications

(1 citation statement)

References 5 publications

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“…Most of clinical data are traditionally stored in legacy systems, including paper-based filing system [2, 3], and are now increasingly stored in electronic medical record system (EMR) on computers [1, 4, 5]. Accumulations of clinical data have produced “big biomedical data” (BBD).…”

Section: Introductionmentioning

confidence: 99%

A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system

Luo¹,

Li²,

Hu³

et al. 2016

BMC Med Inform Decis Mak

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BackgroundHealthcare providers generate a huge amount of biomedical data stored in either legacy system (paper-based) format or electronic medical records (EMR) around the world, which are collectively referred to as big biomedical data (BBD). To realize the promise of BBD for clinical use and research, it is an essential step to extract key data elements from unstructured medical records into patient-centered electronic health records with computable data elements. Our objective is to introduce a novel solution, known as a double-reading/entry system (DRESS), for extracting clinical data from unstructured medical records (MR) and creating a semi-structured electronic health record database, as well as to demonstrate its reproducibility empirically.MethodsUtilizing the modern cloud-based technologies, we have developed a comprehensive system that includes multiple subsystems, from capturing MRs in clinics, to securely transferring MRs, storing and managing cloud-based MRs, to facilitating both machine learning and manual reading, and to performing iterative quality control before committing the semi-structured data into the desired database. To evaluate the reproducibility of extracted medical data elements by DRESS, we conduct a blinded reproducibility study, with 100 MRs from patients who have undergone surgical treatment of lung cancer in China. The study uses Kappa statistic to measure concordance of discrete variables, and uses correlation coefficient to measure reproducibility of continuous variables.ResultsUsing the DRESS, we have demonstrated the feasibility of extracting clinical data from unstructured MRs to create semi-structured and patient-centered electronic health record database. The reproducibility study with 100 patient’s MRs has shown an overall high reproducibility of 98 %, and varies across six modules (pathology, Radio/chemo therapy, clinical examination, surgery information, medical image and general patient information).ConclusionsDRESS uses a double-reading, double-entry, and an independent adjudication, to manually curate structured data elements from unstructured clinical data. Further, through distributed computing strategies, DRESS protects data privacy by dividing MR data into de-identified modules. Finally, through internet-based computing cloud, DRESS enables many data specialists to work in a virtual environment to achieve the necessary scale of processing thousands MRs within days. This hybrid system represents probably a workable solution to solve the big medical data challenge.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0357-5) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system

Luo¹,

Li²,

Hu³

et al. 2016

BMC Med Inform Decis Mak

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Knowledge Management (KM) in a Clinical Environment: Data Acquisition, Storage, and Retrieval

Frize

2014

Health Care Engineering Part II

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Even greener IT

Fairweather

2011

Journal of Information, Communication and Ethics in Society

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Purpose -The purpose of this paper is to look at current practices and associated consumption patterns in information technology (IT), looking at how impacts of IT, for good and ill, will be evaluated by green theory. Design/methodology/approach -The paper takes an interdisciplinary approach drawing together literatures from a variety of fields, including green theory, information systems, green economics, computing, energy studies, cultural studies, waste management, and transport research. Findings -Feedback effects that cause early replacement of software and hardware form a complex, environmentally harmful, vicious circle that can appropriately be called "the upgrade treadmill". Considering wider impacts of IT suggests that imperatives to renovate, rather than replace, hardware are stronger than narrower considerations of "green IT" would suggest, and there is a responsibility on those involved in the academic disciplines associated with training future IT professionals to try to work against the "upgrade treadmill". Originality/value -This paper is novel in exposing green IT to green theory. In doing so, it seeks to move consideration of green IT onto a more rounded basis.

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Integration of new technology in a legacy system for collecting medical data - challenges and lessons learned

Cited by 12 publications

References 5 publications

A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system

A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system

Knowledge Management (KM) in a Clinical Environment: Data Acquisition, Storage, and Retrieval

Even greener IT

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