Abstract
Pathology laboratory playing major role in healthcare diagnostics and with development in automated testing methods machine or the analyzer taking place of manual testing process. These machines perform accurate and consistent testing. The result generated or populated by the analyzers are used for final report generation using manual method or through report generation software like laboratory information management system (LIMS). Different advance analyzers support direct data exchange with computer or supporting connected system for automated data exchange between analyzer and system software to avoid the manual human errors in reporting. Development of these types of integration software needs different levels of skillsets and understanding of analyzer integration methodologies apart from programming skill. Different machine works on different physical connection modes and the different communication protocols. Different subcomponent or the software tools required for successful implementation of analyzer integration. This paper deals with the methodology study and covering the skillsets required for LIS integration, and it also covers the different protocols may be used for data exchange.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baghal A, Al-Shukri S, Kumari A (2019) Agile natural language processing model for pathology knowledge extraction and integration with clinical enterprise data warehouse. In: 2019 Sixth international conference on social networks analysis, management and security (SNAMS). https://doi.org/10.1109/snams.2019.8931828
Bender D, Sartipi K (2013) HL7 FHIR: An Agile and RESTful approach to healthcare information exchange. In: Proceedings of the 26th IEEE international symposium on computer-based medical systems. https://doi.org/10.1109/cbms.2013.6627810
Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5(1):8869–8879
Qian B, Wang X, Cao N, Li H, Jiang Y-G (2015) A relative similarity-based method for interactive patient risk prediction. Data Mining Knowl Discov 29(4):1070–1093
Saripalle R, Runyan C, Russell M (2019) Using HL7 FHIR to achieve interoperability in patient health record. J Biomed Inform 94:103188. https://doi.org/10.1016/j.jbi.2019.103188
Kilintzis V, Kosvyra A, Beredimas N, Natsiavas P, Maglaveras N, Chouvarda I (2019) A sustainable HL7 FHIR based ontology for PHR data*. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). https://doi.org/10.1109/embc.2019.8856415
Houta S, Ameler T, Surges R (2019) Use of HL7 FHIR to structure data in epilepsy self-management applications. In: 2019 International conference on wireless and mobile computing, networking and communications (WiMob). https://doi.org/10.1109/wimob.2019.8923179
Gomes F, Freitas R, Ribeiro M, Moura C, Andrade O, Oliveira M (2019) GIRLS, a Gateway for Interoperability of electronic health record in low-cost system: * interoperability beetween FHIR and OpenEHR standards. In: 2019 IEEE international conference on e-health networking, application & services (HealthCom). https://doi.org/10.1109/healthcom46333.2019.9009602
Mukhiya SK, Rabbi F, Pun KI, Lamo Y (2019) An architectural design for self-reporting e-health systems. In: 2019 IEEE/ACM 1st international workshop on software engineering for healthcare (SEH). https://doi.org/10.1109/seh.2019.00008
Corici AA, Olaf R, Kraufmann B, Billig A, Caumanns J, Deglmann M, Walter V, Rexin J, Nolte G (2020) Interoperable and discrete eHealth data exchange between hospital and patient. In: 2020 23rd Conference on innovation in clouds, internet and networks and workshops (ICIN). https://doi.org/10.1109/icin48450.2020.9059335
Khalique F, Khan SA, Nosheen I (2019) PHF-A framework for public health monitorin, analytics and research. IEEE Access, pp 1–1. https://doi.org/10.1109/access.2019.2930730
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Raich, D., Singh, Y., Ambhaikar, A. (2022). Implementation of Laboratory Information Management to Medical Analyzer Data Integration. In: Sharma, H., Shrivastava, V., Kumari Bharti, K., Wang, L. (eds) Communication and Intelligent Systems . Lecture Notes in Networks and Systems, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-19-2130-8_33
Download citation
DOI: https://doi.org/10.1007/978-981-19-2130-8_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2129-2
Online ISBN: 978-981-19-2130-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)