Artificial intelligence in the clinical pharmacy service in a public hospital in Belo Horizonte/MG

Authors

DOI:

https://doi.org/10.30968/rbfhss.2023.143.0991

Abstract

Objective: to evaluate aspects related to the analysis of prescriptions by clinical pharmacists and the rate of medication-related errors after the implementation of an AI tool for the analysis of medical prescriptions in a large public teaching hospital in the city of Belo Horizonte/MG. Method: This is an observational study in which the results of the analysis of medical prescriptions performed in two periods were verified: the first (denoted BEFORE), period previously to the use of the AI tool (NoHarm.ai), in the months of March to September 2021; the second (named AFTER), comprises the same period in 2022, already in use of the AI tool. Results: In the BEFORE period, it was found that the rate of prescriptions evaluated was 0.6%, with an error rate of 13% and an average of 85 pharmaceutical interventions/month, which resulted in average savings of direct medication costs of R$1020.76/month. In the AFTER period, there was a 49% evaluated prescription rate and a 0.3% error rate and an average of 239 pharmaceutical interventions/month, with an estimated savings of R$ 7848.39/month. Conclusion: The use of an AI tool contributed substantially to the pharmaceutical analysis of medical prescriptions with an average increase of 50% in the prescriptions evaluated, a 43-fold reduction in the number of errors and generated almost triple the number of pharmaceutical interventions after the implementation of the tool, in addition to the direct savings obtained with these interventions that increased sevenfold. The results of this study show that the use of an AI tool probable save of financial resources, increased productivity of the Clinical Pharmacy Service, and increased safety related to the medication use.

Downloads

Download data is not yet available.

References

Mendes W, Martins M, Rozenfeld S, Travassos C. The assessment of adverse events in hospitals in Brazil. Int J Qual Health Care. 2009 Aug;21(4):279-84. DOI: 10.1093/intqhc/mzp022.

National Coordinating Council for Medication Error Reporting and Prevention. What is a Medication Error? Available in: https://www.nccmerp.org/about-medication-errors. Accessed on 16th January 2023.

World Health Organization. Medication Without Harm. Available in: https://www.who.int/initiatives/medication-without-harm. Accessed on 13th May 2022.

Luxford K. ‘First, do no harm’: shifting the paradigm towards a culture of health. Patient Exp J 2016;3:5–8. DOI: 10.35680/2372-0247.1189.

Instituto para práticas seguras no uso de medicamentos. Prevenção de erros de prescrição. Boletim ISMP 2021;10. Available in: https://www.ismp-brasil.org/site/wp-content/uploads/2021/03/Boletim-ISMP-Prevencao-Erros-Prescricao.pdf. Accessed on 14th January 2023.

Schiff GD, Volk LA, Volodarskaya M, et al. Screening for medication errors using an outlier detection system. Journal of the American Medical Informatics Association: JAMIA. 2017 Mar;24(2):281-287. DOI: 10.1093/jamia/ocw171.

Corny J, Rajkumar A, Martin O, Dode X, Lajonchère JP, Billuart O, Bézie Y, Buronfosse A. A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. J Am Med Inform Assoc. 2020 Nov 1;27(11):1688-1694. DOI: 10.1093/jamia/ocaa154.

Botelho SF, Neiva Pantuzza LL, Marinho CP, Moreira Reis AM. Prognostic prediction models and clinical tools based on consensus to support patient prioritization for clinical pharmacy services in hospitals: A scoping review. Res Social Adm Pharm. 2021 Apr;17(4):653-663. DOI: 10.1016/j. sapharm.2020.08.002.

Alshakrah MA, Steinke DT, Lewis PJ. Patient prioritization for pharmaceutical care in hospital: A systematic review of assessment tools. Res Social Adm Pharm. 2019 Jun;15(6):767- 779. DOI: 10.1016/j.sapharm.2018.09.009.

Santos HDP. Applying machine learning to electronic health records: a study on two adverse events. Programa de Pós-Graduação em Ciência da Computação - Doutorado em Ciência da Computação. Pontifícia Universidade Católica do Rio Grande do Sul, 2021.Available in: https://repositorio.pucrs.br/dspace/handle/10923/17324. Accessed on 14th January 2023.

NoHarm.ai. Como a IA da NoHarm calcula os escores dos medicamentos? n.d. https://noharm.octadesk.com/kb/article/escore-e-tags-clicaveis. Accessed on 16th January 2023.

Ministério da Saúde. Portaria no 2.095. Aprova os Protocolos Básicos de Segurança do Paciente; 2013. Available in https://bvsms.saude.gov.br/bvs/saudelegis/gm/2013/prt2095_24_09_2013.html. Accessed on 13th May 2022.

Conselho Federal de Farmácia. Resolução no 585 de 29 de agosto de 2013. Regulamenta as atribuições clínicas do farmacêutico e dá outras providências. Available in https://www.cff.org.br/userfiles/file/resolucoes/585.pdf. Accessed on 13h May 2022.

Rotta I, Salgado TM, Felix DC, Souza TT, Correr CJ, Fernandez-Llimos F. Ensuring consistent reporting of clinical pharmacy services to enhance reproducibility in practice: an improved version of DEPICT. J Eval Clin Pract. 2015 Aug;21(4):584-90. DOI: 10.1111/jep.12339.

Donabedian A. Evaluating the quality of medical care. 1966. Milbank Q. 2005;83(4):691-729. DOI: 10.1111/j.1468-0009.2005.00397.x.

Ooi PL, Zainal H, Lean QY, Ming LC, Ibrahim B. Pharmacists’ Interventions on Electronic Prescriptions from Various Specialty Wards in a Malaysian Public Hospital: A Cross-Sectional Study. Pharmacy (Basel). 2021 Oct 1;9(4):161. DOI: 10.3390/pharmacy9040161.

Klopotowska JE, Kuiper R, van Kan HJ, de Pont AC, Dijkgraaf MG, Lie-A-Huen L, Vroom MB, Smorenburg SM. On-ward participation of a hospital pharmacist in a Dutch intensive care unit reduces prescribing errors and related patient harm: an intervention study. Crit Care. 2010;14(5):R174. DOI: 10.1186/cc9278.

Moyen E, Camiré E, Stelfox HT. Clinical review: medication errors in critical care. Crit Care. 2008;12(2):208. DOI: 10.1186/cc6813.

Conselho Federal de Farmácia. Resolução no 675, de 31 de outubro de 2019. Regulamenta as atribuições do farmacêutico clínico em unidades de terapia intensiva, e dá outras providências; 2019. Available in: https://pesquisa.in.gov.br/imprensa/jsp/visualiza/index.jsp?data=21/11/2019&jornal=515&pagina=128&totalArquivos=133https://pesquisa.in.gov.br/imprensa/jsp/visualiza/index.jsp?data=21/11/2019&jornal=515&pagina=128&totalArquivos=133. Accessed on 22th January 2023.

Levivien C, Cavagna P, Grah A, Buronfosse A, Courseau R, Bézie Y, Corny J. Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice. Int J Clin Pharm. 2022 Apr;44(2):459-465. DOI: 10.1007/s11096-021-01366-4.

Reis WC, Scopel CT, Correr CJ, Andrzejevski VM. Analysis of clinical pharmacist interventions in a tertiary teaching hospital in Brazil. Einstein (Sao Paulo). 2013 Apr-Jun;11(2):190-6. DOI: 10.1590/s1679-45082013000200010.

Lewis PJ, Dornan T, Taylor D, Tully MP, Wass V, Ashcroft DM. Prevalence, incidence and nature of prescribing errors in hospital inpatients: a systematic review. Drug Saf. 2009;32(5):379- 89. DOI: 10.2165/00002018-200932050-00002.

Korb-Savoldelli V, Boussadi A, Durieux P, Sabatier B. Prevalence of computerized physician order entry systems-related medication prescription errors: A systematic review. Int J Med Inform. 2018 Mar;111:112-122. DOI: 10.1016/j.ijmedinf.2017.12.022.

Naseralallah LM, Hussain TA, Jaam M, Pawluk SA. Impact of pharmacist interventions on medication errors in hospitalized pediatric patients: a systematic review and meta-analysis. Int J Clin Pharm. 2020 Aug;42(4):979-994. DOI: 10.1007/s11096-020-01034-z.

Tasaka Y, Tanaka A, Yasunaga D, Asakawa T, Araki H, Tanaka M. Potential drug-related problems detected by routine pharmaceutical interventions: safety and economic contributions made by hospital pharmacists in Japan. J Pharm Health Care Sci. 2018 Dec 13;4:33. DOI: 10.1186/s40780-018-0125-z.

Barros ME, Araújo IG. Evaluation of pharmaceutical interventions in an intensive care unit of a teaching hospital. Rev Bras Farm Hosp Serv Saude. 2021;12(3):0561. DOI: 10.30968/rbfhss.2021.123.0561.

Blum MR, Sallevelt BTGM, Spinewine A, O’Mahony D, Moutzouri E, Feller M, et al. Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): Cluster randomised controlled trial. BMJ 2021;374. DOI: https://doi.org/10.1136/bmj.n1585.

Dias D, Wiese LPL, Pereira EM, Fernandes FM. Evaluation of pharmaceutical clinical interventions in the icu of a public hospital of Santa Catarina. Rev Bras Farm Hosp Serv Saude, 9(3): 1-5, 2019. DOI: https://doi.org/10.30968/rbfhss.2018.093.005.

Arantes T, Durval C, Pinto V. Avaliação da economia gerada por meio das intervenções farmacêuticas realizadas em um hospital universitário terciário de grande porte. Clin Biomed Res 2020;40:96–104. DOI: https://doi.org/10.22491/2357-9730.95646.

Published

2023-09-27

How to Cite

1.
LEITÃO CL, MEDEIROS AF, DIAS EF, SOUZA RP, MARTINS MA. Artificial intelligence in the clinical pharmacy service in a public hospital in Belo Horizonte/MG. Rev Bras Farm Hosp Serv Saude [Internet]. 2023Sep.27 [cited 2024Jul.16];14(3):991. Available from: https://rbfhss.org.br/sbrafh/article/view/991

Issue

Section

ORIGINAL ARTICLES

Most read articles by the same author(s)