Translating Data From an Electronic Prescribing and Medicines Administration System Into Knowledge: Application to Doctor-Nurse Time Discrepancy in Antibiotic Ordering and Administration.
Journal
Medical care
ISSN: 1537-1948
Titre abrégé: Med Care
Pays: United States
ID NLM: 0230027
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
pubmed:
5
10
2019
medline:
1
5
2020
entrez:
5
10
2019
Statut:
ppublish
Résumé
Electronic Prescribing and Medicines Administration (EPMA) systems are being widely implemented to facilitate medication safety improvement. However, translating the resulting big data into actionable knowledge has received relatively little attention. The objective of this study was to use routinely collected EPMA data in the study of exact time discrepancy between physicians' order and nurses' administration of systemic antibiotics. We evaluated first and follow-up dose administration and dose intervals and examined multifactorial determinants in ordering and administration explaining potential discrepancy. We conducted an observational study of electronic health records for all medical patient stays with antibiotic treatment from January to June 2018 (n=4392) in a large Belgian tertiary care hospital. Using an EPMA system with Barcode Medication Administration, we calculated time discrepancy between order and administration of first doses (n=6233), follow-up doses (n=87 960), and dose intervals. Multiple logistic regression analysis estimated the association between time discrepancy and various determinants in ordering and administration. Time discrepancy between physician order and nurse administration was <30 minutes for 48.7% of first doses and 61.7% of follow-up doses, with large variation across primary diagnoses. Greater dose intervals, oral versus intravenous administration, and order diversion from regular nurse administration rounds showed strongest association with less timely administration. EPMA systems show huge potential to generate actionable knowledge. Concerning antibiotic treatment, having physicians' orders coincide with regular nurse administration rounds whenever clinically appropriate, further taking contextual factors into account, could potentially improve antibiotic administration timeliness.
Sections du résumé
BACKGROUND
Electronic Prescribing and Medicines Administration (EPMA) systems are being widely implemented to facilitate medication safety improvement. However, translating the resulting big data into actionable knowledge has received relatively little attention.
OBJECTIVE
The objective of this study was to use routinely collected EPMA data in the study of exact time discrepancy between physicians' order and nurses' administration of systemic antibiotics. We evaluated first and follow-up dose administration and dose intervals and examined multifactorial determinants in ordering and administration explaining potential discrepancy.
METHODS
We conducted an observational study of electronic health records for all medical patient stays with antibiotic treatment from January to June 2018 (n=4392) in a large Belgian tertiary care hospital. Using an EPMA system with Barcode Medication Administration, we calculated time discrepancy between order and administration of first doses (n=6233), follow-up doses (n=87 960), and dose intervals. Multiple logistic regression analysis estimated the association between time discrepancy and various determinants in ordering and administration.
RESULTS
Time discrepancy between physician order and nurse administration was <30 minutes for 48.7% of first doses and 61.7% of follow-up doses, with large variation across primary diagnoses. Greater dose intervals, oral versus intravenous administration, and order diversion from regular nurse administration rounds showed strongest association with less timely administration.
CONCLUSIONS
EPMA systems show huge potential to generate actionable knowledge. Concerning antibiotic treatment, having physicians' orders coincide with regular nurse administration rounds whenever clinically appropriate, further taking contextual factors into account, could potentially improve antibiotic administration timeliness.
Identifiants
pubmed: 31584461
doi: 10.1097/MLR.0000000000001222
pii: 00005650-202001000-00014
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
83-89Références
Classen DC, Resar R, Griffin F, et al. “Global trigger tool” shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff. 2011;30:581–589.
Nguyen L, Bellucci E, Nguyen LT. Electronic health records implementation: an evaluation of information system impact and contingency factors. Int J Med Inform. 2014;83:779–796.
Jamal A, McKenzie K, Clark M. The impact of health information technology on the quality of medical and health care: a systematic review. Health Inf Manag. 2009;38:26–37.
Windle JR, Windle TA. Electronic health records and the quest to achieve the “triple aim”. J Am Coll Cardiol. 2015;65:1973–1975.
Franklin BD, O’Grady K, Donyai P, et al. The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study. Qual Saf Health Care. 2007;16:279–284.
Ammenwerth E, Schnell-Inderst P, Machan C, et al. The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J Am Med Inform Assoc. 2008;15:585–600.
Radley DC, Wasserman MR, Olsho LE, et al. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc. 2013;20:470–476.
Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety. Arch Intern Med. 2003;163:1409–1416.
Blumenthal D. Launching HITECH. N Engl J Med. 2010;362:382–385.
Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human: Building a Safer Health System. National Academy Press, Institute of Medicine; Washington DC: 1999.
Houck PM, Bratzler DW, Nsa W, et al. Timing of antibiotic administration and outcomes for medicare patients hospitalized with community-acquired pneumonia. Arch Intern Med. 2004;164:637.
Sterling SA, Miller WR, Pryor J, et al. The impact of timing of antibiotics on outcomes in severe sepsis and septic shock: a systematic review and meta-analysis. Crit Care Med. 2015;43:1907–1915.
Puskarich MA, Trzeciak S, Shapiro NI, et al. Association between timing of antibiotic administration and mortality from septic shock in patients treated with a quantitative resuscitation protocol. Crit Care Med. 2011;39:2066–2071.
Kim RY, Ng AM, Persaud AK, et al. Antibiotic timing and outcomes in sepsis. Am J Med Sci. 2018;355:524–529.
Zhang D, Micek ST, Kollef MH. Time to appropriate antibiotic therapy is an independent determinant of postinfection ICU and hospital lengths of stay in patients with sepsis. Crit Care Med. 2015;43:2133–2140.
Institute for Safe Medication Practices. CMS 30-minute rule for drug administration needs revision. ISMP Medication Safety Alert Acute Care. 2010;15:1–6.
Trossman S. 30-minute pressure cooker? ANA, nurses say multiple factors come into play with passing meds. Am Nurse. 2011;43:10–11.
Institute for Safe Medication Practices. Acute care guidelines for timely administration of scheduled medications; 2019. Available at: https://www.ismp.org/guidelines/timely-administration-scheduled-medications-acute.
Averill RF, Goldfield N, Hughes JS, et al. All patient refined diagnosis related groups (APR-DRGs): Version 20.0 methodology overview. Wallingford; 3M-Health Information Systems; 2003: p.85.
Njoku K, Iyizoba Z. Is there a ‘weekend effect’ in the door to needle time of antibiotics administration in cancer patients presenting with suspected neutropenic sepsis. Clin Oncol. 2018;30:S2.
Brent AJ. Meta-analysis of time to antimicrobial therapy in sepsis. Crit Care Med. 2017;45:e242–e243.
Cartmill RS, Walker JM, Blosky MA, et al. Impact of electronic order management on the timeliness of antibiotic administration in critical care patients. Int J Med Inform. 2012;81:782–791.
Ausserhofer D, Zander B, Busse R, et al. Prevalence, patterns and predictors of nursing care left undone in European hospitals: results from the multicountry cross-sectional RN4CAST study. BMJ Qual Saf. 2014;23:126–135.
Jun J, Kovner CT, Stimpfel AW. Barriers and facilitators of nurses’ use of clinical practice guidelines: an integrative review. Int J Nurs Stud. 2016;60:54–68.