Skip to main content
SpringerLink
Log in

The Pharmacist’s View: Patient-Centered Care Through the Lens of a Pharmacist

  • Chapter
  • First Online:
Nursing Informatics

Part of the book series: Health Informatics ((HI))

Abstract

This chapter discusses the role of informatics pharmacists in health care and how the profession can spearhead efforts to tackle future challenges in the medication use process. With the emergence of artificial intelligence (AI) and its potential impact on the healthcare setting, both the risks and its benefits are examined as it pertains to the pharmacy profession. As our healthcare ecosystem becomes more dependent on data, especially for AI, the priority for the pharmacy profession is clear: adoption and implementation of medication-related standards as well as closing the gaps in pharmacy informatics education. Lastly, the chapter provides examples of collaborations between informatics pharmacists and other healthcare professionals to emphasize the complexity and nuances of implementing health information technology.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 89.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping 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. Pitre M, Thickson N. Should pharmacy informatics officer positions be based in, and report to, the pharmacy department, rather than the health information technology department? Can J Hosp Pharm. 2011;64(6):459–61.

    Google Scholar 

  2. Fox BI, Flynn AJ, Fortier CR, Clauson KA. Knowledge, skills, and resources for pharmacy informatics education. Am J Pharm Educ. 2011;75(5):93.

    Article  Google Scholar 

  3. ASHP. Statement on the pharmacist’s role in clinical informatics. Am J Health Syst Pharm. 2016;73(6):410–3.

    Article  Google Scholar 

  4. Dustin Waters C. Pharmacist-driven antimicrobial stewardship program in an institution without infectious diseases physician support. Am J Health Syst Pharm. 2015;72(6):466–8.

    Article  Google Scholar 

  5. Fox BI, Pedersen CA, Gumpper KF. ASHP national survey on informatics: assessment of the adoption and use of pharmacy informatics in US hospitals—2013. Am J Health Syst Pharm. 2015;72(8):636–55.

    Article  Google Scholar 

  6. Woodhead V. Why pharmacists should consider clinical data management as a career [Internet]. The Pharmaceutical Journal. 2003. https://www.pharmaceutical-journal.com/news-and-analysis/features/why-pharmacists-should-consider-clinical-data-management-as-a-career/20008578.article?firstPass=false. Accessed 11 Apr 2020.

  7. Vest TA, Gazda NP, Schenkat DH, Eckel SF. Practice-enhancing publications about the medication use process in 2017. Am J Health Syst Pharm. 2019;76(10):667–76.

    Article  Google Scholar 

  8. Executive summary of the 2019 ASHP Commission on Goals: impact of artificial intelligence on healthcare and pharmacy practice. Am J Health Syst Pharm. 2019;76(24):2087–92.

    Google Scholar 

  9. Vermeulen LC, Swarthout MD, Alexander GC, Ginsburg DB, Pritchett KO, White SJ, et al. ASHP Foundation pharmacy forecast 2020: strategic planning advice for pharmacy departments in hospitals and health systems. Vermeulen LC, Zellmer WA, editors. Am J Health Syst Pharm. 2020;77(2):84–112.

    Article  Google Scholar 

  10. Matheny M, Israni ST, Whicher D. Artificial intelligence in health care: the hope, the hype, the promise, the peril [Internet]. National Academy of Medicine. 2019; p. 269 (The Learning Health System). https://nam.edu/wp-content/uploads/2019/12/AI-in-Health-Care-PREPUB-FINAL.pdf. Accessed 5 Mar 2020.

  11. Flynn A. Using artificial intelligence in health-system pharmacy practice: finding new patterns that matter. Am J Health Syst Pharm. 2019;76(9):622–7.

    Article  Google Scholar 

  12. Segal G, Segev A, Brom A, Lifshitz Y, Wasserstrum Y, Zimlichman E. Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting. J Am Med Inform Assoc. 2019;26(12):1560–5.

    Article  CAS  Google Scholar 

  13. Wong A, Plasek JM, Montecalvo SP, Zhou L. Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacotherapy. 2018;38(8):822–41.

    Article  Google Scholar 

  14. Danysz K, Cicirello S, Mingle E, Assuncao B, Tetarenko N, Mockute R, et al. Artificial intelligence and the future of the drug safety professional. Drug Saf. 2019;42(4):491–7.

    Article  Google Scholar 

  15. Maharao N, Antontsev V, Wright M, Varshney J. Entering the era of computationally driven drug development. Drug Metab Rev. 2020;21:1–16.

    Article  Google Scholar 

  16. Galozy A, Nowaczyk S, Sant’Anna A, Ohlsson M, Lingman M. Pitfalls of medication adherence approximation through EHR and pharmacy records: definitions, data and computation. Int J Med Inf. 2020;136:104092.

    Article  Google Scholar 

  17. Matheny ME, Whicher D, Israni ST. Artificial intelligence in health care: a report from the National Academy of Medicine. JAMA. 2020;323(6):509–10.

    Article  Google Scholar 

  18. Maddox TM, Rumsfeld JS, Payne PRO. Questions for artificial intelligence in health care. JAMA. 2019;321(1):31–2.

    Article  Google Scholar 

  19. Bodenreider O, Cornet R, Vreeman DJ. Recent developments in clinical terminologies—SNOMED CT, LOINC, and RxNorm. Yearb Med Inform. 2018;27(1):129–39.

    Article  Google Scholar 

  20. Integrated Health Information Systems (IHiS). Integrated Health Information Systems (IHiS) Standards [Internet]. 2018. https://cms.ihis.com.sg/Standards/Pages/Default.aspx. Accessed 29 Apr 2020.

  21. RxNorm overview [Internet]. U.S. National Library of Medicine. https://www.nlm.nih.gov/research/umls/rxnorm/overview.html. Accessed 30 Apr 2020.

  22. Khairat S, Sandefer R, Marc D, Pyles L. A review of biomedical and health informatics education: a workforce training framework. J Hosp Adm. 2016;5(5):10.

    Google Scholar 

  23. Weiner JP, Kfuri T, Chan K, Fowles JB. “e-Iatrogenesis”: the most critical unintended consequence of CPOE and other HIT. J Am Med Inform Assoc. 2007;14(3):387–8.

    Article  Google Scholar 

  24. Dumitru D. The pharmacy informatics primer. 1st ed. Bethesda, MD: American Society of Health-System Pharmacists (ASHP); 2008. p. 264.

    Google Scholar 

  25. Quist AJL, Hickman T-TT, Amato MG, Volk LA, Salazar A, Robertson A, et al. Analysis of variations in the display of drug names in computerized prescriber-order-entry systems. Am J Health Syst Pharm. 2017;74(7):499–509.

    Article  Google Scholar 

  26. ISMP guidelines for safe electronic communication of medication information [Internet]. Institute for Safe Medication. Practices (ISMP). 2019. https://www.ismp.org/node/1322. Accessed 17 Apr 2020.

  27. Cramb A. Giving up smoking? Take a Viagra. The Telegraph [Internet]. 2006. https://www.telegraph.co.uk/news/uknews/1536890/Giving-up-smoking-Take-a-Viagra.html. Accessed 17 Apr 2020.

  28. ASHP Section of Pharmacy Informatics and Technology. ASHP guidelines on pharmacy planning for implementation of computerized provider-order-entry systems in hospitals and health systems. Am J Health Syst Pharm. 2011;68:e9–31.

    Article  Google Scholar 

  29. Biltoft J, Finneman L. Clinical and financial effects of smart pump–electronic medical record interoperability at a hospital in a regional health system. Bull Am Soc Hosp Pharm. 2018;75(14):1064–8.

    Google Scholar 

  30. Dentzer S. Creating the future of artificial intelligence in health-system pharmacy. Am J Health Syst Pharm. 2019;76(24):1995–6.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Mental Health, Singapore, Singapore

    Franky

  2. Mayo Clinic, Rochester, MN, USA

    Brian K. Fung

Authors
  1. Franky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian K. Fung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Franky .

Editor information

Editors and Affiliations

  1. University of Applied Sciences (UAS) Osnabrück, Osnabrück, Niedersachsen, Germany

    Ursula H. Hübner

  2. The University of Texas at Arlington (UTA), Arlington, TX, USA

    Gabriela Mustata Wilson

  3. Healthcare Information and Management Systems Society (HIMSS), Chicago, IL, USA

    Toria Shaw Morawski

  4. The University of Texas at Arlington (UTA), Arlington, TX, USA

    Marion J. Ball

Appendix: Answers to Review Questions

Appendix: Answers to Review Questions

  1. 1.

    Which of the following scenarios has AI been studied as an application within the practice of pharmacy?

  1. (a)

    Detection of adverse drug events

  2. (b)

    Drug discovery

  3. (c)

    Drug selection

  4. (d)

    Inventory management

  5. (e)

    All of the above

Explanation: All of the following have been studied as an application of AI in pharmacy.

  1. 2.

    Which of the following are important considerations in the development and deployment of AI?

  1. (a)

    Training AI models with limited data sets to avoid complexity.

  2. (b)

    Trusting the prediction outcomes, as algorithms have proven to outperform humans.

  3. (c)

    Data provenance and validation of data used to train AI models.

  4. (d)

    Mismatch of demand and supply in AI deployment due to a surplus of trained individuals.

Explanation: (a) is incorrect as training models require large data sets that can be upwards of tens of thousands of records; (b) is incorrect as data quality issues can lead to incorrect assumptions and should be appropriately scrutinized; (d) is incorrect as there is a shortage of trained individuals to effectively deploy AI.

  1. 3.

    Which of the following clinical standards is NOT a medication standard?

  1. (a)

    AMT

  2. (b)

    LOINC

  3. (c)

    RxNorm

  4. (d)

    dm+d

  5. (e)

    SDD

Explanation: LOINC is a terminology standard for health measurements, observations, and documents. It is not a medication standard.

  1. 4.

    True or False: Existing standards coupled with EHRs are sufficient for the widespread deployment of AI.

  1. (a)

    True

  2. (b)

    False

Explanation: Further maturation of existing infrastructure, especially as it pertains to enriching data quality and representation, is needed before we can achieve widespread deployment of AI. Moreover, near-term solutions should be focused on augmented intelligence rather than full automation. See the article from the National Academy of Medicine for further reading.

  1. 5.

    Which of the following will unlikely lead to e-iatrogenesis?

  1. (a)

    Designing poor user interfaces for CPOE.

  2. (b)

    Configuring medication records with different display names to ensure variability within the EHR.

  3. (c)

    Coordinating with stakeholders to harmonize data sets between smart pumps and EHR medication formularies.

  4. (d)

    Designing workflows and tools to be fully automated to avoid human-computer interaction altogether.

Explanation: all of the choices except (c) were listed as examples that could lead to e-iatrogenesis. Only (c) was an example of how to avoid e-iatrogenesis.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Franky, Fung, B.K. (2022). The Pharmacist’s View: Patient-Centered Care Through the Lens of a Pharmacist. In: Hübner, U.H., Mustata Wilson, G., Morawski, T.S., Ball, M.J. (eds) Nursing Informatics . Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-91237-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91237-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91236-9

  • Online ISBN: 978-3-030-91237-6

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation