OpenLB
Open Library of Bioscience
Advanced Search
Journal of medical systems
Apr 17, 2024;48 (1): 43.

Comparing the Effectiveness of a Clinical Decision Support Tool in Reducing Pediatric Opioid Dose Calculation Errors: PediPain App vs. Traditional Calculators - A Simulation-Based Randomised Controlled Study.

Clyde T Matava, Martina Bordini, Amanda Jasudavisius, Carmina Santos, Monica Caldeira-Kulbakas
Author Information
  1. Clyde T Matava: Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. clyde.matava@sickkids.ca.
  2. Martina Bordini: Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
  3. Amanda Jasudavisius: Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
  4. Carmina Santos: Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
  5. Monica Caldeira-Kulbakas: Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
PMID: 38630157 DOI: 10.1007/s10916-024-02060-4

Abstract

Wrong dose calculation medication errors are widespread in pediatric patients mainly due to weight-based dosing. PediPain app is a clinical decision support tool that provides weight- and age- based dosages for various analgesics. We hypothesized that the use of a clinical decision support tool, the PediPain app versus pocket calculators for calculating pain medication dosages in children reduces the incidence of wrong dosage calculations and shortens the time taken for calculations. The study was a randomised controlled trial comparing the PediPain app vs. pocket calculator for performing eight weight-based calculations for opioids and other analgesics. Participants were healthcare providers routinely administering opioids and other analgesics in their practice. The primary outcome was the incidence of wrong dose calculations. Secondary outcomes were the incidence of wrong dose calculations in simple versus complex calculations; time taken to complete calculations; the occurrence of tenfold; hundredfold errors; and wrong-key presses. A total of 140 residents, fellows and nurses were recruited between June 2018 and November 2019; 70 participants were randomized to control group (pocket calculator) and 70 to the intervention group (PediPain App). After randomization two participants assigned to PediPain group completed the simulation in the control group by mistake. Analysis was by intention-to-treat (PediPain app = 68 participants, pocket calculator = 72 participants). The overall incidence of wrong dose calculation was 178/576 (30.9%) for the control and 23/544 (4.23%) for PediPain App, P < 0·001. The risk difference was - 32.8% [-38.7%, -26.9%] for complex and - 20.5% [-26.3%, -14.8%] for simple calculations. Calculations took longer within control group (median of 69 Sects. [50, 96]) compared to PediPain app group, (median 48 Sects. [38, 63]), P < 0.001. There were no differences in other secondary outcomes. A weight-based clinical decision support tool, the PediPain app reduced the incidence of wrong doses calculation. Clinical decision support tools calculating medications may be valuable instruments for reducing medication errors, especially in the pediatric population.

Keywords

Clinical decision support tool Medication errors Safety mHealth

References

  1. Neuspiel DR, Taylor MM. Reducing the Risk of Harm from Medication Errors in Children. Health Services Insights. 2013;6:HSI.S10454.
  2. Kunac DL, Kennedy J, Austin N, Reith D. Incidence, preventability, and impact of Adverse Drug Events (ADEs) and potential ADEs in hospitalized children in New Zealand: a prospective observational cohort study. Paediatr Drugs. 2009;11(2):153–60. [DOI: 10.2165/00148581-200911020-00005]
  3. Wong IC, Ghaleb MA, Franklin BD, Barber N. Incidence and nature of dosing errors in paediatric medications: a systematic review. Drug Saf. 2004;27(9):661–70. [DOI: 10.2165/00002018-200427090-00004]
  4. Merry AF, Webster CS, Hannam J, Mitchell SJ, Henderson R, Reid P, et al. Multimodal system designed to reduce errors in recording and administration of drugs in anaesthesia: prospective randomised clinical evaluation. BMJ. 2011;343:d5543. [DOI: 10.1136/bmj.d5543]
  5. Ghaleb MA, Barber N, Franklin BD, Wong IC. The incidence and nature of prescribing and medication administration errors in paediatric inpatients. Arch Dis Child. 2010;95(2):113–8. [DOI: 10.1136/adc.2009.158485]
  6. Mc Donnell C. Opioid medication errors in pediatric practice: four years’ experience of voluntary safety reporting. Pain Res Manag. 2011;16(2):93–8. [DOI: 10.1155/2011/739359]
  7. Doherty C, Mc Donnell C. Tenfold medication errors: 5 years’ experience at a university-affiliated pediatric hospital. Pediatrics. 2012;129(5):916–24. [DOI: 10.1542/peds.2011-2526]
  8. Black S, Lerman J, Banks SE, Noghrehkar D, Curia L, Mai CL, et al. Drug Calculation Errors in Anesthesiology Residents and Faculty: An Analysis of Contributing Factors. Anesthesia & Analgesia. 2019;128(6).
  9. Jensen LS, Merry AF, Webster CS, Weller J, Larsson L. Evidence-based strategies for preventing drug administration errors during anaesthesia. Anaesthesia. 2004;59(5):493–504. [DOI: 10.1111/j.1365-2044.2004.03670.x]
  10. Merry AF, Anderson BJ. Medication errors–new approaches to prevention. Paediatr Anaesth. 2011;21(7):743–53. [DOI: 10.1111/j.1460-9592.2011.03589.x]
  11. Jamal A, Temsah MH, Khan SA, Al-Eyadhy A, Koppel C, Chiang MF. Mobile Phone Use Among Medical Residents: A Cross-Sectional Multicenter Survey in Saudi Arabia. JMIR Mhealth Uhealth. 2016;4(2):e61. [DOI: 10.2196/mhealth.4904]
  12. Simpao AF, Tan JM, Lingappan AM, Gálvez JA, Morgan SE, Krall MA. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems. J Clin Monit Comput. 2017;31(5):885–94. [DOI: 10.1007/s10877-016-9921-x]
  13. Wang E, Brenn BR, Matava CT. State of the art in clinical decision support applications in pediatric perioperative medicine. Curr Opin Anaesthesiol. 2020;33(3):388–94. [DOI: 10.1097/ACO.0000000000000850]
  14. Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. BMJ. 2016;355:i5239. [DOI: 10.1136/bmj.i5239]
  15. Lukas H, Raffael S, Karin K, Monika W, Michael G, Priska V. Impact of a clinical decision support system on paediatric drug dose prescribing: a randomised within-subject simulation trial. BMJ Paediatrics Open. 2023;7(1):e001726. [DOI: 10.1136/bmjpo-2022-001726]
  16. Fleming S, Brady A-M, Malone A-M. An evaluation of the drug calculation skills of registered nurses. Nurse Education in Practice. 2014;14(1):55–61. [DOI: 10.1016/j.nepr.2013.06.002]
  17. Glover ML, Sussmane JB. Assessing pediatrics residents’ mathematical skills for prescribing medication: a need for improved training. Acad Med. 2002;77(10):1007–10. [DOI: 10.1097/00001888-200210000-00013]
  18. Whitfield S, Veronese JP, Baran C, Boyle M, Eastwood K. Determining the Ability of Paramedic Students to Do Drug Calculations. Australasian Journal of Paramedicine. 2020;17:1–6. [DOI: 10.33151/ajp.17.819]
  19. Boyle MJ, Eastwood K. Drug calculation ability of qualified paramedics: A pilot study. World J Emerg Med. 2018;9(1):41–5. [DOI: 10.5847/wjem.j.1920-8642.2018.01.006]
  20. Lesar TS. Errors in the Use of Medication Dosage Equations. Archives of Pediatrics & Adolescent Medicine. 1998;152(4):340–4. [DOI: 10.1001/archpedi.152.4.340]
  21. Merry AF, Webster CS, Connell H. A new infusion syringe label system designed to reduce task complexity during drug preparation. Anaesthesia. 2007;62(5):486–91. [DOI: 10.1111/j.1365-2044.2007.04993.x]
  22. Siebert JN, Ehrler F, Combescure C, Lovis C, Haddad K, Hugon F, et al. A mobile device application to reduce medication errors and time to drug delivery during simulated paediatric cardiopulmonary resuscitation: a multicentre, randomised, controlled, crossover trial. Lancet Child Adolesc Health. 2019;3(5):303–11. [DOI: 10.1016/S2352-4642(19)30003-3]
  23. Evans J, Morrison Z, Thomas-Turner R, Bouamra O, Mullen S, Morgan J. Smartphone use for Paediatric Calculations in Emergencies (SPaCE). Arch Dis Child. 2023.
  24. Oommen T, Thommandram A, Palanica A, Fossat Y. A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study. JMIR Form Res. 2022;6(3):e30577. [DOI: 10.2196/30577]
  25. Koldeweij C, Clarke J, Nijman J, Feather C, de Wildt SN, Appelbaum N. CE Accreditation and Barriers to CE Marking of Pediatric Drug Calculators for Mobile Devices: Scoping Review and Qualitative Analysis. J Med Internet Res. 2021;23(12):e31333. [DOI: 10.2196/31333]
  26. Suppan M, Beckmann TS, Gercekci C, Sigrist T, Savoldelli GL, Fournier R, et al. Development and Preliminary Validation of LoAD Calc, a Mobile App for Calculating the Maximum Safe Single Dose of Local Anesthetics. Healthcare (Basel). 2021;9(7).
  27. Shen C, Jiang B, Yang Q, Wang C, Lu KZ, Gu M, et al. Mobile Apps for Drug-Drug Interaction Checks in Chinese App Stores: Systematic Review and Content Analysis. JMIR Mhealth Uhealth. 2021;9(6):e26262. [DOI: 10.2196/26262]
  28. Morse SS, Murugiah MK, Soh YC, Wong TW, Ming LC. Mobile Health Applications for Pediatric Care: Review and Comparison. Ther Innov Regul Sci. 2018;52(3):383–91. [DOI: 10.1177/2168479017725557]
  29. Green MS, Mathew JJ, Gundigi Venkatesh A, Green P, Tariq R. Utilization of Smartphone Applications by Anesthesia Providers. Anesthesiol Res Pract. 2018;2018:8694357. [PMID: 29593787]
  30. Li C, Salman M, Esmail T, Matava C. Use of Peer-Led Web-Based Platforms for Peer-Assisted Learning Among Canadian Anesthesia Residents and Fellows: Cross-Sectional Study. JMIR Form Res. 2023;7:e47977. [DOI: 10.2196/47977]
  31. Kazemi P, Lau F, Matava C, Simpao AF. An Environmental Scan of Anesthesia Information Management Systems in the American and Canadian Marketplace. J Med Syst. 2021;45(12):101. [DOI: 10.1007/s10916-021-01781-0]
  32. Kazemi P, Lau F, Simpao AF, Williams RJ, Matava C. The state of adoption of anesthesia information management systems in Canadian academic anesthesia departments: a survey. Can J Anaesth. 2021;68(5):693–705. [DOI: 10.1007/s12630-021-01924-4]
  33. Kruger J, Dunning D. Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J Pers Soc Psychol. 1999;77(6):1121–34. [DOI: 10.1037/0022-3514.77.6.1121]

MeSH Term

Humans
Child
Analgesics, Opioid
Decision Support Systems, Clinical
Mobile Applications
Research Design
Computer Simulation

Chemicals

Analgesics, Opioid
Randomized Controlled Trial Journal Article

Word Cloud

Created with Highcharts 10.0.0PediPaincalculationsgroupappdecisionsupportincidencewrongdoseerrorstoolpocketparticipantscontrolcalculationmedicationweight-basedclinicalanalgesicsAppClinicalpediatricdosagesversuscalculatingtimetakenvscalculatoropioidsoutcomessimplecomplex70medianSectsWrongwidespreadpatientsmainlyduedosingprovidesweight-age-basedvarioushypothesizedusecalculatorspainchildrenreducesdosageshortensstudyrandomisedcontrolledtrialcomparingperformingeightParticipantshealthcareprovidersroutinelyadministeringpracticeprimaryoutcomeSecondarycompleteoccurrencetenfoldhundredfoldwrong-keypressestotal140residentsfellowsnursesrecruitedJune2018November2019randomizedinterventionrandomizationtwoassignedcompletedsimulationmistakeAnalysisintention-to-treatapp = 68calculator = 72overall178/576309%23/544423%P < 0·001riskdifference- 328%[-387%-269%]- 205%[-263%-148%]Calculationstooklongerwithin69 [5096]compared48 [3863]P < 0001differencessecondaryreduceddosestoolsmedicationsmayvaluableinstrumentsreducingespeciallypopulationComparingEffectivenessDecisionSupportToolReducingPediatricOpioidDoseCalculationErrors:TraditionalCalculators-Simulation-BasedRandomisedControlledStudyMedicationSafetymHealth

Similar Articles

Cited By