Subscribe to RSS
DOI: 10.1055/a-2216-5775
Using Clinical Decision Support Systems to Decrease Intravenous Acetaminophen Use: Implementation and Lessons Learned
Abstract
Background Clinical decision support systems (CDSS) can enhance medical decision-making by providing targeted information to providers. While they have the potential to improve quality of care and reduce costs, they are not universally effective and can lead to unintended harm.
Objectives To describe the implementation of an unsuccessful interruptive CDSS that aimed to promote appropriate use of intravenous (IV) acetaminophen at an academic pediatric hospital, with an emphasis on lessons learned.
Methods Quality improvement methodology was used to study the effect of an interruptive CDSS, which set a mandatory expiry time of 24 hours for all IV acetaminophen orders. This CDSS was implemented on April 5, 2021. The primary outcome measure was number of IV acetaminophen administrations per 1,000 patient days, measured pre- and postimplementation. Process measures were the number of IV acetaminophen orders placed per 1,000 patient days. Balancing measures were collected via survey data and included provider and nursing acceptability and unintended consequences of the CDSS.
Results There was no special cause variation in hospital-wide IV acetaminophen administrations and orders after CDSS implementation, nor when the CDSS was removed. A total of 88 participants completed the survey. Nearly half (40/88) of respondents reported negative issues with the CDSS, with the majority stating that this affected patient care (39/40). Respondents cited delays in patient care and reduced efficiency as the most common negative effects.
Conclusion This study underscores the significance of monitoring CDSS implementations and including end user acceptability as an outcome measure. Teams should be prepared to modify or remove CDSS that do not achieve their intended goal or are associated with low end user acceptability. CDSS holds promise for improving clinical practice, but careful implementation and ongoing evaluation are crucial for maximizing their benefits and minimizing potential harm.
Keywords
clinical decision support system - quality improvement - electronic health record - pediatricsProtection of Human and Animal Subjects
The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by Stanford University's Institutional Review Board.
Publication History
Received: 20 July 2023
Accepted: 22 November 2023
Accepted Manuscript online:
23 November 2023
Article published online:
24 January 2024
© 2024. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Das M, Eichner J. . Challenges and Barriers to Clinical Decision Support (CDS) Design and Implementation Experienced in the Agency for Healthcare Research and Quality CDS Demonstrations (Prepared for the AHRQ National Resource Center for Health Information Technology under Contract No. 290-04-0016.) AHRQ Publication No. 10-0064-EF. Rockville, MD: Agency for Healthcare Research and Quality. March 2010.
- 2 Kharbanda EO, Asche SE, Sinaiko AR. et al. Clinical decision support for recognition and management of hypertension: a randomized trial. Pediatrics 2018; 141 (02) e20172954
- 3 Prgomet M, Li L, Niazkhani Z, Georgiou A, Westbrook JI. Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis. J Am Med Inform Assoc 2017; 24 (02) 413-422
- 4 Hayatghaibi SE, Sammer MBK, Varghese V, Seghers VJ, Sher AC. Prospective cost implications with a clinical decision support system for pediatric emergency head computed tomography. Pediatr Radiol 2021; 51 (13) 2561-2567
- 5 Lewkowicz D, Wohlbrandt A, Boettinger E. Economic impact of clinical decision support interventions based on electronic health records. BMC Health Serv Res 2020; 20 (01) 871
- 6 MacMillan TE, Gudgeon P, Yip PM, Cavalcanti RB. Reduction in unnecessary red blood cell folate testing by restricting computerized physician order entry in the electronic health record. Am J Med 2018; 131 (08) 939-944
- 7 Chin KK, Hom J, Tan M. et al. Effect of electronic clinical decision support on 25(OH) vitamin D testing. J Gen Intern Med 2019; 34 (09) 1697-1699
- 8 Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3 (01) 17
- 9 Powers EM, Shiffman RN, Melnick ER, Hickner A, Sharifi M. Efficacy and unintended consequences of hard-stop alerts in electronic health record systems: a systematic review. J Am Med Inform Assoc 2018; 25 (11) 1556-1566
- 10 Sweeney J. IV vs. oral acetaminophen for children: weighing cost against need. Pharmacy Today. 2019; 25 (06) 4
- 11 Bourgeois FT, Graham DA, Kesselheim AS, Randolph AG. Cost implications of escalating intravenous acetaminophen use in children. JAMA Pediatr 2019; 173 (05) 489-491
- 12 Nguyen LP, Nguyen L, Austin JP. A quality improvement initiative to decrease inappropriate intravenous acetaminophen use at an academic medical center. Hosp Pharm 2020; 55 (04) 253-260
- 13 Bertolini A, Ferrari A, Ottani A, Guerzoni S, Tacchi R, Leone S. Paracetamol: new vistas of an old drug. CNS Drug Rev 2006; 12 (3-4): 250-275
- 14 Jahr JS, Lee VK. Intravenous acetaminophen. Anesthesiol Clin 2010; 28 (04) 619-645
- 15 Tompkins DM, DiPasquale A, Segovia M, Cohn SM. Review of intravenous acetaminophen for analgesia in the postoperative setting. Am Surg 2021; 87 (11) 1809-1822
- 16 Wasserman I, Poeran J, Zubizarreta N. et al. Impact of intravenous acetaminophen on perioperative opioid utilization and outcomes in open colectomies: a claims database analysis. Anesthesiology 2018; 129 (01) 77-88
- 17 Ogrinc G, Davies L, Goodman D, Batalden P, Davidoff F, Stevens D. SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process. BMJ Qual Saf 2016; 25 (12) 986-992
- 18 Benneyan JC, Lloyd RC, Plsek PE. Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care 2003; 12 (06) 458-464
- 19 Langley G, Moen R, Nolan K, Nolan T, Norman C, Provost L. The Improvement Guide. 2nd ed. Jossey-Bass; 2009
- 20 Carroll AR, Johnson DP. Know it when you see it: identifying and using special cause variation for quality improvement. Hosp Pediatr 2020; 10 (11) e8-e10
- 21 Strom BL, Schinnar R, Aberra F. et al. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. Arch Intern Med 2010; 170 (17) 1578-1583
- 22 Jankovic I, Chen JH. Clinical decision support and implications for the clinician burnout crisis. Yearb Med Inform 2020; 29 (01) 145-154
- 23 Bates DW, Kuperman GJ, Wang S. et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
- 24 Van de Velde S, Kunnamo I, Roshanov P. et al; GUIDES expert panel. The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support. Implement Sci 2018; 13 (01) 86
- 25 Jacob V, Thota AB, Chattopadhyay SK. et al. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review. J Am Med Inform Assoc 2017; 24 (03) 669-676
- 26 Bright TJ, Wong A, Dhurjati R. et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012; 157 (01) 29-43
- 27 Mollon B, Chong Jr J, Holbrook AM, Sung M, Thabane L, Foster G. Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials. BMC Med Inform Decis Mak 2009; 9 (01) 11
- 28 Kwan JL, Lo L, Ferguson J. et al. Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials. BMJ 2020; 370: m3216