DOI QR코드

DOI QR Code

The Intelligent Clinical Laboratory as a Tool to Increase Cancer Care Management Productivity

  • Mohammadzadeh, Niloofar (Department of Health Information Management, Tehran University of Medical Sciences) ;
  • Safdari, Reza (Department of Health Information Management, Tehran University of Medical Sciences)
  • 발행 : 2014.03.30

초록

Studies of the causes of cancer, early detection, prevention or treatment need accurate, comprehensive, and timely cancer data. The clinical laboratory provides important cancer information needed for physicians which influence clinical decisions regarding treatment, diagnosis and patient monitoring. Poor communication between health care providers and clinical laboratory personnel can lead to medical errors and wrong decisions in providing cancer care. Because of the key impact of laboratory information on cancer diagnosis and treatment the quality of the tests, lab reports, and appropriate lab management are very important. A laboratory information management system (LIMS) can have an important role in diagnosis, fast and effective access to cancer data, decrease redundancy and costs, and facilitate the integration and collection of data from different types of instruments and systems. In spite of significant advantages LIMS is limited by factors such as problems in adaption to new instruments that may change existing work processes. Applications of intelligent software simultaneously with existing information systems, in addition to remove these restrictions, have important benefits including adding additional non-laboratory-generated information to the reports, facilitating decision making, and improving quality and productivity of cancer care services. Laboratory systems must have flexibility to change and have the capability to develop and benefit from intelligent devices. Intelligent laboratory information management systems need to benefit from informatics tools and latest technologies like open sources. The aim of this commentary is to survey application, opportunities and necessity of intelligent clinical laboratory as a tool to increase cancer care management productivity.

키워드

참고문헌

  1. American Society for Testing and Materials (2006). ASTM E1578-06: Standard Guide for Laboratory Information Management Systems (LIMS).
  2. Bonini P, Plebani M, Ceriotti F, Rubboli F (2002). Errors in Laboratory Medicine. Clin Chem, 48, 691-8.
  3. Bria FW, Finn BN (2009). Digital communication in medical practice. Springer-Verlag: 8-9.
  4. Bichindaritz I, Vaidya S, Jain A, Jain L C (2010). Computational intelligence in healthcare 4. Springer-Verlag. 25-48.
  5. Citak E, Toruner E, Gunes N (2013). Exploring communication difficulties in pediatric hematology: oncology nurses. Asian Pac J Cancer Prev, 14, 5477-82. https://doi.org/10.7314/APJCP.2013.14.9.5477
  6. Gibbon AG (1996). A brief history of LIMS. Laboratory automation and information management, 32, 1-5. https://doi.org/10.1016/1381-141X(95)00024-K
  7. Hackl H, Stocker G, Charoentong P, et al (2010). Information technology solutions for integration of bio molecular and clinical data in the identification of new cancer biomarkers and targets for therapy. Pharmacol Ther, 128, 488-98. https://doi.org/10.1016/j.pharmthera.2010.08.012
  8. Isern D (2008). Agent-based management of clinical guidelines [PhD Thesis]. Barcelona, Spain: Universitat Politecnica de Catalunya.
  9. Kalra J (2004). Medical errors: impact on clinical laboratories and other critical areas. Clin Biochem, 37, 1052-62. https://doi.org/10.1016/j.clinbiochem.2004.08.009
  10. Khoumbati K, Dwivedi Y, Srivastava A, Lal B (2010). Handbook of Research on Advances in Health Informatics and Electronic Healthcare Applications: Global Adoption and Impact of Information Communication Technologies. Medical information science reference: Hershey New York, 146.
  11. Mohammadzadeh N, Safdari R, Rahimi A (2013). Positive and Negative Effects of IT on Cancer Registries. Asian Pac J Cancer Prev, 14, 4455-7. https://doi.org/10.7314/APJCP.2013.14.7.4455
  12. McClatchy D K (2002). Clinical Laboratory Medicine. 2nd ed. Lippincott Williams and Wilkins: Philadelphia, 122.
  13. Mohammadzadeh N (2011). Health information security in mobile devices. J AHIMA, 7, 31-7.
  14. Plebani M, Carraro P (1997). Mistakes in a stat laboratory: types and frequency. Clin Chem, 43, 1348-51.
  15. Safdari R, Dargahi H, Mahmoodi M, et al (2006). Assessing the viewpoint of faculty members of medical record departments in Iran about the impact of Information Technology on health system 2004. Iran J Med Sci, 9, 93-101.
  16. Safdari R, Mohammadzadeh N (2012). Intelligent clinical laboratory: the most important strategy for improving the laboratory management. Lecture in 3nd eHospital and Telemedicine Conference. Tehran University of Medical Sciences 2012, Tehran, Iran.
  17. Singh V, Chandra S, Kumar S, et al (2009). A common medical error: lung cancer misdiagnosed as sputum negative tuberculosis. Asian Pac J Cancer Prev, 10, 335-8.
  18. Tagger B (2011). An introduction and guide to successfully implementing a laboratory information management system (LIMS).
  19. Troshin VP, Postis LG V, Ashworth D, et al (2011). PIMS sequencing extension: a laboratory information management system for DNA sequencing facilities. BMC Research Notes, 4, 48. https://doi.org/10.1186/1756-0500-4-48
  20. Triestram, Partner GmbH (2013). LISA, Laboratory Information and Management System.
  21. Whelan EK, King DR (2004). Intelligent software for laboratory automation. Trends Biotechnol, 22, 440-5. https://doi.org/10.1016/j.tibtech.2004.07.010
  22. Wooldridge M (2009). An Introduction to Multi-agent Systems. UK: John Wiley and Sons.p 9.

피인용 문헌

  1. Prevention and Early Detection of Occupational Cancers - a View of Information Technology Solutions vol.16, pp.14, 2015, https://doi.org/10.7314/APJCP.2015.16.14.5607
  2. Construction and evaluation of PHP-based management and training system for electrical power laboratory vol.24, pp.3, 2016, https://doi.org/10.1002/cae.21715