SpringerLink
Log in

Cohort Identification for Translational Bioinformatics Studies

  • Protocol
  • First Online:
Translational Bioinformatics for Therapeutic Development

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2194))

  • 1625 Accesses

Abstract

Translational studies for therapeutic development require cohort identification to identify appropriate biological materials from patients that can be utilized to test a specific hypothesis. Robust health information systems exist, but there are numerous challenges in accessing the information to select appropriate biological specimens needed for translational experiments. This chapter on methods describes the current standard process for cohort identification utilized by the Cutaneous Oncology Program and the Collaborative Data Services Core (CDSC) at Moffitt Cancer Center. The methods include utilization of graphical user interfaces coupled with database querying. As such, this chapter outlines the regulatory and procedural processes needed to utilize a health information management system to filter patients for cohort identification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lau EC, Mowat FS, Kelsh MA et al (2011) Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data. Clin Epidemiol 3:259–272

    PubMed  PubMed Central  Google Scholar 

  2. Embi PJ, Payne PR (2009) Clinical research informatics: challenges, opportunities and definition for an emerging domain. J Am Med Inform Assoc 16(3):316–327

    Article  PubMed  PubMed Central  Google Scholar 

  3. Berg M (2006) Health information management: integrating information technology in health care work. Routledge, New York

    Google Scholar 

  4. Hammond WE, Jaffe C, Cimino JJ, Huff SM (2014) Standards in biomedical informatics. In: Shortliffe EH, Cimino JJ (eds) Biomedical informatics: computer applications in health care and biomedicine. Springer London, London, pp 211–253

    Chapter  Google Scholar 

  5. Vogel LH (2014) Management of information in health care organizations. In: Shortliffe EH, Cimino JJ (eds) Biomedical informatics: computer applications in health care and biomedicine. Springer London, London, pp 443–474

    Chapter  Google Scholar 

  6. Yasnoff WA (2014) Health information infrastructure. In: Shortliffe EH, Cimino JJ (eds) Biomedical informatics: computer applications in health care and biomedicine. Springer London, London, pp 423–441

    Chapter  Google Scholar 

  7. Chen ET (2013) An observation of healthcare knowledge management. Commun IIMA 13(3):7

    Google Scholar 

  8. Payne TH, Graham G (2006) Managing the life cycle of electronic clinical documents. J Am Med Inform Assoc 13(4):438–445

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bankert EA, Amdur RJ, Amdur RJ (2006) Institutional review board: management and function. Jones and Bartlett, Sudbury, MA

    Google Scholar 

  10. Parker GE (2016) A framework for navigating Institutional Review Board (IRB) oversight in the complicated zone of research. Cureus 8(10):e844

    PubMed  PubMed Central  Google Scholar 

  11. Li B, Eschrich SA, Berglund A et al (2017) Use of the total cancer care system to enrich screening for CD30-positive solid tumors for patient enrollment into a brentuximab vedotin clinical trial: a pilot study to evaluate feasibility. JMIR Res Protoc 6(3):e45

    Article  PubMed  PubMed Central  Google Scholar 

  12. Fenstermacher DA, Wenham RM, Rollison DE, Dalton WS (2011) Implementing personalized medicine in a cancer center. Cancer J 17(6):528–536

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lindsey H (2017) ORIEN uses big data to improve care for high-risk patients. Oncol Times 39(11):31, 40

    Article  Google Scholar 

  14. Dalton WS, Sullivan D, Ecsedy J, Caligiuri MA (2018) Patient enrichment for precision-based cancer clinical trials: using prospective cohort surveillance as an approach to improve clinical trials. Clin Pharmacol Ther 104(1):23–26

    Article  PubMed  PubMed Central  Google Scholar 

  15. Silver M, Sakata T, Su HC, Herman C, Dolins SB, O'Shea MJ (2001) Case study: how to apply data mining techniques in a healthcare data warehouse. J Healthc Inf Manag 15(2):155–164

    CAS  PubMed  Google Scholar 

  16. Coronel C, Morris S (2019) Database systems: design, implementation, and management. Cengage Learning, Inc. 13th edition, Boston, MA, USA

    Google Scholar 

  17. Murphy SN, Morgan MM, Barnett GO, Chueh HC (1999) Optimizing healthcare research data warehouse design through past COSTAR query analysis. Proc AMIA Symp:892–896

    Google Scholar 

  18. Levy C, Beauchamp C, Hammond JE (1995) A managed care workstation for support of ambulatory care in Veterans Health Administration medical centers. J Med Syst 19(5):387–396

    Article  CAS  PubMed  Google Scholar 

  19. O'Kane KC, McColligan EE (1997) A case study of a MUMPS intranet patient record. Healthc Inf Manage 11(3):81–95

    CAS  PubMed  Google Scholar 

  20. Garcia-Molina H, Ullman JD, Widom J (2014) Database systems the complete book. Pearson Prentice Hall, 2nd editionUpper Saddle River, NJ, USA

    Google Scholar 

  21. Lianas L, Frexia F, Delussu G, Anedda P, Zanetti G (2009) pyEHR: A scalable clinical data management toolkit for biomedical research projects. Paper presented at: 2014 IEEE 16th international conference on e-health networking, applications and services (Healthcom); 15–18 Oct 2014

    Google Scholar 

  22. Kline K (2017) SQL in a nutshell: a desktop quick reference guide. O’Reilly Media

    Google Scholar 

  23. Miller ML, Ruprecht J, Wang D et al (2011) Physician assessment of disease activity in JIA subtypes. Analysis of data extracted from electronic medical records. Pediatr Rheumatol Online J 9(1):9

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dougoud-Chauvin V, Lee JJ, Santos E et al (2018) Using Big Data in oncology to prospectively impact clinical patient care: a proof of concept study. J Geriatric Oncol 9(6):665–672

    Article  Google Scholar 

  25. Stallings W (1987) Handbook of computer-communications standards: the open systems interconnection (OSI) model and OSI-related standards. Macmillan

    Google Scholar 

  26. Marsan J, Pare G (2013) Antecedents of open source software adoption in health care organizations: a qualitative survey of experts in Canada. Int J Med Inform 82(8):731–741

    Article  PubMed  Google Scholar 

  27. Gopinath MP, Tamilzharasi GS, Aarth SL, Mohanasundram (2017) An analysis and performance evaluation of NoSQL databases for efficient data management in E-health clouds. Int J Pure Appl Math 117(21):177–197

    Google Scholar 

  28. Ercan MZ, Lane M (2014) Evaluation of NoSQL databases for EHR systems. 25th Australasian conference on information systems, 2014, Auckland, New Zealand

    Google Scholar 

  29. Wang X, Williams C, Liu ZH, Croghan J (2019) Big data management challenges in health research-a literature review. Brief Bioinform 20(1):156–167

    Article  PubMed  Google Scholar 

  30. Dyrbye LN, Thomas MR, Mechaber AJ et al (2007) Medical education research and IRB review: an analysis and comparison of the IRB review process at six institutions. Acad Med 82(7):654–660

    Article  PubMed  Google Scholar 

  31. Lyman JA, Scully K, Harrison JH (2008) The development of health care data warehouses to support data mining. Clin Lab Med 28(1):55–71

    Article  PubMed  Google Scholar 

  32. Farooqui NA, Mehra R (2018) Design of a data warehouse for medical information system using data mining techniques. Paper presented at: 2018 fifth international conference on parallel, distributed and grid computing (PDGC), 20–22 Dec 2018

    Google Scholar 

  33. Rstudio web page. https://rstudio.com. Accessed 9 Jan 2020

  34. The R project for statistical computing. https://www.r-project.org/. Accessed 9 Jan 2020

  35. Chan BKC (2018) Data analysis using R programming. Adv Exp Med Biol 1082:47–122

    Article  PubMed  Google Scholar 

  36. Gabbrielli M, Martini S (2010) Programming languages: principles and paradigms. Springer, London/New York

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Collaborative Data Services Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Tiffany A. Lin

  2. Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Tiffany A. Lin, Zeynep Eroglu & Joseph Markowitz

  3. Department of Oncologic Sciences, University of South Florida, Morsani School of Medicine, Tampa, FL, USA

    Zeynep Eroglu & Joseph Markowitz

  4. Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Rodrigo Carvajal

Authors
  1. Tiffany A. Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeynep Eroglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rodrigo Carvajal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph Markowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joseph Markowitz .

Editor information

Editors and Affiliations

  1. Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Joseph Markowitz

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Lin, T.A., Eroglu, Z., Carvajal, R., Markowitz, J. (2021). Cohort Identification for Translational Bioinformatics Studies. In: Markowitz, J. (eds) Translational Bioinformatics for Therapeutic Development. Methods in Molecular Biology, vol 2194. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0849-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0849-4_3

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0848-7

  • Online ISBN: 978-1-0716-0849-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation