Medical error, estimated as the third leading cause of death in the United States, occurs when an unintended act related to medical care is committed or omitted resulting in unintentional medical consequences.1 Even conservative estimates signal a serious issue.1,2 An estimated 123 603 medical event deaths occurred between 1990 and 2016.2 More than 22 000 US deaths occur annually due to adverse medical events.3 These numbers, though striking, are a likely underestimation since error is often missed on death certifications or misreported.
Medication error is the most frequent acute care medical error and occurs when a medication-related preventable event causes inappropriate use of medications or patient harm.4,5 Medication errors include prescribing, monitoring, or protocol errors; medication omission; administration of unauthorized medication; or medication administered with an incorrect dosage, preparation, timing, route, or patient.6 Annually, more than 7000 US deaths and $26 billion are attributed to medication error.7,8 Medication error is also a global issue. In the United Kingdom, medication error is associated with 20% of preventable hospital deaths.8 Eleven percent of medication error in Australia was associated with severe patient harm.8
Medication administration timing error (MATE), a specific type of medication error, occurs when a medication is administered before or after a prescribed time. When a medication is not administered on the correct schedule, poor medication efficacy or patient harm can occur.9 MATE has been linked to severe harm and death, especially in high alert medications such as antithrombotics, insulins, and opioids.10
Nurses spend about 40% of working hours administering medications.11 Nurses may lack understanding of the importance of correct medication administration timing or may be unwilling to participate in medication safety initiatives.12 Organizations may be hesitant to acknowledge the extent of the problem, lack initiatives to improve, and provide inadequate education and training to prevent medication error. To improve, proper reporting of errors is necessary. Underreporting of nursing error is common in the medical-surgical setting.13 Nurses often use judgment rather than policy to identify whether a medication error has occurred.14,15
Medication administration timing error is most frequently defined as medications administered outside of an allotted time frame. This time frame is most commonly identified as more than 60 minutes before or after scheduled or prescribed time.16–18 Other definitions of MATE include administration 2 hours before or after a scheduled dose of medication that is scheduled daily or less frequently than daily,8 or as an administration of a subsequent dose too close to the initial dose of a new medication.19 Timing is frequently discussed as one of the necessary rights for correct administration of medications.4,5,8,15,20 Assurance that medications are being administered at the right time is an essential function of medication administration.
This systematic review addresses the gap that exists surrounding the frequency of MATE. The purpose of this systematic review is to answer the following questions: (1) What is the frequency of MATE? (2) What are the characteristics of studies that identify MATE?
METHODS
This review follows guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 checklist and guidelines.21 The databases Cumulative Index of Nursing and Allied Health Literature, ProQuest, and PubMed were searched utilizing the following search terms: inpatient and medication administration timing error, wrong time medication, medication timing, late medication, and “wrong time” medication. Inclusion dates were January 1999 through February 2021; these dates were included as To Err Is Human was published in 1999, which punctuated the state of medical care error and its risk to patients.22 Inclusion criteria were full-text peer-reviewed journal articles of primary research, completed in an inpatient setting that included a calculation of medication timing error, and published in English. Additional publications meeting inclusion criteria were found through searching article references.
The first author (T.P.) screened all records. The Figure provides a flow diagram of record review yielding 23 articles included in this review. Abstracted data included year, country, design, sample (including sex, age, and race/ethnicity), definitions, measures, procedures, and prevalence rate (see Supplemental Digital Content Table 1, available at: https://links.lww.com/JNCQ/B45).
;)
Figure.: Flow diagram of search. CINAHL indicates Cumulative Index of Nursing and Allied Health Literature; MATE, medication administration timing error.
Each study was examined for quality of reporting utilizing the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) combined checklist and guidelines (see Supplemental Digital Content Table 2, available at: https://links.lww.com/JNCQ/B46).23 Each article was scored using the STROBE guidelines. To provide a quality score, each criterion received 1 point if present and 0 points if absent for a possible range of 0 to 34 points for each article. Since the combined STROBE checklist includes cohort, case-control, and cross-sectional criteria, some criteria did not apply and were excluded. Articles were considered low quality if they received a score of 16 or less (meeting <50% of criteria), moderate quality for a score of 16 to 25 (meeting 50%-75% of criteria), and high quality for scores 26 and higher (meeting 76%-100% of criteria). Each study report was individually scored by 2 authors (T.P. and S.W.L.). A third author (C.L.R.) resolved discrepancies.
RESULTS
Twenty-three articles met criteria and were included in this review. Articles were published between 1999 and 2020. Of the 23 articles, 1 (4%) was published in 1999,24 8 (35%) were published between 2000 and 2009,16,17,25–30 12 (52%) were published between 2010 and 2019,31–42 and 2 (9%) were published in 2020.4,43 Seven (30%) of the articles were from the United States,25,27–29,36,38,41 3 (13%) from France,24,32,35 3 (13%) from Ethiopia,4,34,43 2 (9%) from Malaysia,16,17 with 1 (4%) each from Pakistan,40 Japan,37 Brazil,26 India,39 Iran,31 Australia,42 Indonesia,33 and Denmark.30 The 23 studies included varied quantitative designs, including 6 (26%) descriptive,16,27,28,31,40,42 5 (22%) prospective,17,24,25,33,37 4 (17%) cross-sectional,4,30,34,43 2 (13%) retrospective,36,41 2 (9%) descriptive/exploratory,32,39 2 (9%) quasi-experimental,35,38 1 (4%) exploratory,26 and 1 (4%) a simple data summary.29
Samples consisted of nurses, patients, patient records, medication orders, medication administration, and error records. Of the 23 studies, 9 (39%) samples consisted of patient records, medication administrations, orders, or opportunities, or error records to obtain data on medication administrations.* Six (26%) studies included samples of patients and/or medication orders, medication doses, or opportunities for error during administration to identify medication administration errors.24,26,27,30,33,37 Four (17%) studies included nurses as the sample population, either alone or in addition to a sample of medication doses.4,31,39,43 Four (17%) articles sampled nurses and patients as medication opportunities occurred, alone or in addition to medication orders, doses, or administration.32,34,41,42 One (25%) of those 4 articles provided no details on sample patient age, sex, race, or ethnicity.32
Regarding demographics, 8 articles (35%) reported patient sex ranging from 44.2% to 64.6% male.30,31,33–35,37,38,41 Eight articles (35%) reported patient ages ranging from 39 to 72.6 years.28,30,31,33,35,38,41,42 One article (4%) provided percentage of patients older than 65 years (62%) but provided no mean age.37 One (4%) article provided information on race and ethnicity, with White (40.6%), African American (14%), and Hispanic/Latino (42.1%) the most commonly reported race or ethnicity.41 Six (26%) articles reported nurses' sex, with a range of 0% to 45.7% male.4,31,32,34,39,43 Nursing mean age was reported by 3 (13%) articles, with a range of 29 to 33.96 years.4,34,43
The Table presents MATE definitions. Administration more than 60 minutes before or after a scheduled dose was the definition in 13 (57%) studies.† Among those 13 studies, 2 (9%) also included administration more than 30 minutes before or after scheduled time if scheduled with a meal.25,42 One (4%) article defined MATE as administration beyond 15 minutes before or after scheduled time if ordered every 4, 6, 8, or 12 hours or more than 30 minutes before or after scheduled time if ordered daily.26 One (4%) article defined MATE as medication not administered at scheduled time.43 Seven (30%) articles did not include a clear definition of MATE.29–31,33,36,37,40
Table. -
Included Articles' Definitions of MATE
| Author (Year) |
Definition of MATE |
| Bagheri-Nesami et al (2015)31
|
Unclear |
| Barker et al (2002)25
|
>60 min before/after scheduled time or 30 min before after dosing with meal |
| Berdot et al (2012)32
|
>60 min before/after scheduled time |
| Bohomol et al (2009)26
|
>15 min after scheduled time if scheduled every 4, 6, 8, or 12 h, or >30 min for daily doses |
| Calabrese et al (2001)27
|
>60 min before/after scheduled time |
| Chua et al (2009)16
|
>60 min before/after scheduled time |
| Chua et al (2009)17
|
>60 min before/after scheduled time |
| Ernawati et al (2014)33
|
Unclear |
| Feleke et al (2015)34
|
>30 min before/after scheduled time |
| FitzHenry et al (2007)28
|
>60 min before/after scheduled time |
| Hernandez et al (2015)35
|
>60 min before/after scheduled time |
| Hicks et al (2004)29
|
Not defined—Reported from facilities |
| Lisby et al (2005)30
|
Unclear—Utilized guideline |
| Morelock and Kirk (2019)36
|
Unclear |
| Noguchi et al (2016)37
|
Unclear |
| Poon et al (2010)38
|
60 min before/after scheduled time |
| Ramya and Vineetha (2014)39
|
>60 min before/after scheduled time |
| Taufiq (2015)40
|
Unclear |
| Tissot et al (1999)24
|
>60 min before/after scheduled time |
| Tsegaye et al (2020)43
|
Defined as not administered at scheduled time |
| Welton et al (2018)41
|
>60 min before/after scheduled time |
| Westbrook (2010)42
|
>60 min before/after scheduled time, or >30 min before/after dosing with meal |
| Wondmieneh et al (2020)4
|
>60 min before/after scheduled time |
Abbreviation: MATE, medication administration timing error.
Direct observation was conducted by 11 (48%) studies.‡ Data extraction from medical records or incident report systems of medication error was used in 6 (26%) studies.28,29,36,37,40,41 Self-report alone was utilized by 3 (13%) studies.26,31,39 Three (13%) studies used 2 techniques, observation plus an additional technique of self-report or medical record review.4,30,43
Eight (35%) studies calculated MATE frequency as a percentage of administration errors that occurred.16,17,27,30,32,33,36,38 Five (22%) studies calculated MATE frequency as the percentage of all errors that occur throughout the medication process (including orders and administrations) for error.26,29,31,37,39 Eight studies (35%) calculated MATE frequency as the percentage of occurrences out of total medication doses or administrations.4,24,25,34,35,40–42 One study calculated MATE as a percentage of orders and also as a percentage of doses.28 Finally, 1 (4%) study calculated the rate as number of nurses who had been involved in MATE, with a numerator of all nurses reporting MATE and a denominator of all nurses surveyed.43
The lowest rate of MATE was reported as 0.7% from retrospective medical record review,35 while the highest rate was 72.6% of administration error from observation.32 Six (26%) articles identified MATE as the most common medication error.16,17,25,32,42,43 Frequency of medication timing error varied depending on the definition and calculation of MATE. To identify potential patterns, studies utilizing similar definitions and measures were compared. Reported frequency of MATE ranged from 0.7% to 72.6% among the 7 (30%) studies that defined MATE as a medication administered more than 60 minutes before or after scheduled dose and as total administration errors.16,27,32,35,39,41,42 The frequency of MATE ranged from 3.7% to 57.8% in 4 (17%) studies that defined MATE as a medication administered more than 60 minutes before and after scheduled dose and total of medication errors throughout the entire medication process.4,24,28,38 Three (13%) studies did not clearly define MATE, measuring MATE as a percentage of all medication administrations, with a range of 3.1% to 39.3%.29,31,36 Three (13%) studies did not have a clear definition of MATE measuring MATE as a percentage of administration errors resulting in a range of 1% to 10.8%.30,33,37 To identify potential patterns in calculated frequency between studies, 4 (17%) studies that utilized observation to collect measurement of MATE and shared similar definition and measures (>60 minutes; percentage of administration errors) were compared.16,27,32,35,42 The frequency of MATE ranged from 0.7% to 72.6%.
Quality scoring of the 23 articles resulted in a mean score of 22.09, a median of 23, and a range of 15 to 27. After final scoring, 1 (4%) article was considered low quality, with a quality score of 15.40 Eighteen (78%) articles were considered moderate quality, with a mean score of 21.44 and a median of 21.§ Four (17%) articles were considered high quality, with a mean score of 26.75 and a median of 27.34,35,42,43
DISCUSSION
Diversity in design was apparent between studies. Definitions of samples and inclusion/exclusion criteria were not consistent across studies. Samples ranged from medication orders to individual patients or nurses. The goal of quantitative research is to draw conclusions that can be generalized to larger populations by choosing a representative sample that meets logistical requirements and is appropriate for study objectives.44 Thus, the inconsistencies between the included study samples hinder the capability to provide that generalizability. Study aims, available resources, and differing objectives and outcomes required different approaches; however, variation in methods and potential flaws in study design choice impede our ability to provide a comprehensive interpretation of results.45 Finally, the frequency of MATE varied widely due to inconsistencies in definition, measurement procedures, and calculation techniques.
Currently, scientific literature on MATE measurement is in the descriptive stage. With lack of consistency between study design, procedures, and definitions, movement to the next stage would be premature, as extent of the issue is still not well described.
Lack of consistency across research can lead to unknown or unaccounted for random variation.46 Lack of representative samples may decrease external validity. Research with a larger and more representative sample could provide more relevant scientific evidence on the extent of MATE in acute care.47 The ideal sample for measuring MATE is the total number of opportunities for error to occur, which could be measured through sampling all medication orders. This would provide consistency across studies, allowing more accurate synthesis of the literature. Utilizing a planned number of clients, medical records, or days does not offer consistent measurement or sources.
What constituted a MATE was inconsistent between studies, and no standard definition of MATE was identified. While the majority of studies identified a MATE as administration of medication more than 60 minutes earlier or later than scheduled time, this definition was one of several, including alternate time frames of more than 15 or 30 minutes before scheduled doses. In 7 studies, a clear definition was not provided, leading to a lack of measurement clarity.29–31,33,36,37,40 This ambiguity in definition can lead to false assumptions or to missing important findings.48 Because of the need to make assumptions about missing information, uncertainty in reporting reduces ability to critically evaluate this body of research, thus highlighting a continued lack of understanding of the issue.
Utilization of a guideline for measurement of MATE would provide consistent definition across research and decrease ambiguity. The Institute for Safe Medication Practices (ISMP) developed Acute Care Guidelines for Timely Administration of Scheduled Medications, which provide guidelines for acute care nurses administering scheduled medication.49 Utilizing this guideline consistently would provide a standardized approach as it offers a detailed timeline for administering medications in acute care for both time-critical and non–time-critical medications.
The techniques used to measure MATE varied between studies. Procedures to measure MATE included data abstraction, self-report, and observation. Observation provides the most accurate technique to capture active errors and can offer good-quality data about administration errors.50 This technique, however, can often be more time-consuming and requires specific training. Studies that utilized alternate techniques did not give clear reasoning behind the choices of alternate measurement procedures.26,28,31,35–37,39–41
Techniques used to calculate the frequency of MATE were not consistent. While the numerator for calculation was consistently calculated as the number of MATEs, the denominator differed among studies. Some articles identified how frequently MATE occurred in opportunities, while others identified the percentage of all MATEs. Frequently, the focus of calculation was the percentage of overall errors, which was not specific to the issue at hand.
To improve the rigor of future MATE studies and move the body of science forward, process standardization should occur. Partnership with an organization, such as the Agency for Healthcare Research and Quality (AHRQ), for development of standard practice in measurement of MATE would facilitate MATE research. An organization such as AHRQ, the lead agency in the United States whose main purpose is to improve safety and quality in American health care, is considered an authority in health care improvement strategies. In addition, adoption of a standard guideline, such as the one by ISMP, discussed earlier, for recognizing and defining MATE could provide a consensus among contemporary research.
Limitations
A limitation of this review includes the potential to have overlooked studies that would have met inclusion criteria. An exhaustive review of databases as well as reference search was used to make every attempt to locate appropriate articles. Another limitation was exclusion of non-English studies due to authors' language limitations. A strength of this study was including quality scoring of research methods, which had not been completed before on MATE research studies.
CONCLUSION
Research literature on MATE is characterized by inconsistent definitions, measurement procedures, and calculation techniques. Despite a long-term emphasis on patient safety and efficiency, MATE continues in the inpatient setting, there is a lack of high-quality studies on MATE, and its measurement is inconsistent. Identification and implementations of best practice techniques for identifying MATE are needed to fully understand the extent of the issue. Opportunities to improve the rigor of the body of literature abound and further research to identify the extent of MATE is necessary.
REFERENCES
1. Makary MA, Daniel M.
Medical error—the third leading cause of death in the US. BMJ. 2016;353:i2139. doi:10.1136/bmj.i2139
2. Sunshine JE, Kassebaum NJ, Collison ML, Mokdad AH, Naghavi M, Meo N. Association of adverse effects of medical treatment with mortality in the United States: a secondary analysis of the Global Burden of Diseases, Injuries, and Risk Factors Study. JAMA Netw Open. 2019;2(1):e187041. doi:10.1001/jamanetworkopen.2018.7041
3. Rodwin BA, Bilan VP, Merchant NB, et al. Rate of preventable mortality in hospitalized patients: a systematic review and meta-analysis. J Gen Interm Med. 2020;35(7):2099–2106. doi:10.1007/s11606-019-05592-5
4. Wondmieneh A, Alemu W, Tadele N, Demis A. Medication administration errors and contributing factors among
nurses: a cross sectional study in tertiary hospitals, Addis Ababa, Ethiopia. BMC Nurs. 2020;19(1):1–9. doi:10.1186/s12912-020-0397-0
5. Billstein-Leber M, Carrillo COLJ, Cassano AT, Moline K, Robertson JJ. ASHP guidelines on preventing medication errors in hospitals. Am J Health Syst Pharm. 2018;75(19):1493–1517. doi:10.2146/ajhp170811
6. Medication Dispensing Errors and Prevention. StatPearls; 2021. Accessed March 16, 2021.
https://www.ncbi.nlm.nih.gov/books/NBK519065
7. Choi I, Lee S-M, Flynn L, et al. Incidence and treatment costs attributable to medication errors in hospitalized patients. Res Social Adm Pharm. 2016;12(3):428–437. doi:10.1016/j.sapharm.2015.08.006
8. Blignaut AJ, Coetzee SK, Klopper HC, Ellis SM. Medication administration errors and related deviations from safe practice: an observational study. J Clin Nurs. 2017;26(21/22):3610–3623. doi:10.1111/jocn.13732
9. Gunningberg L, Pöder U, Donaldson N, Leo Swenne C. Medication administration accuracy: using clinical observation and review of patient records to assess safety and guide performance improvement. J Eval Clin Pract. 2014;20(4):411–416. doi:10.1111/jep.12150
10. van den Bemt PM, Fijn R, van der Voort PH, Gossen AA, Egberts TC, Brouwers JR. Frequency and determinants of drug administration errors in the intensive care unit. Crit Care Med. 2002;30(4):846–850. doi:10.1097/00003246-200204000-00022
11. Armitage G, Knapman H. Adverse events in drug administration: a literature review. J Nurs Manag. 2003;11(2):130–140. doi:10.1046/j.1365-2834.2003.00359.x
12. Leufer T, Cleary-Holdforth J. Let's do no harm: medication errors in nursing: part 1. Nurse Educ Pract. 2013;13(3):213–216. doi:10.1016/j.nepr.2013.01.013
13. Scott SS, Henneman E. Underreporting of medical errors. MedSurg Nurs. 2017;26(3):211.
14. Bifftu BB, Dachew BA, Tiruneh BT, Beshah DT. Medication administration error reporting and associated factors among
nurses working at the University of Gondar Referral Hospital, Northwest Ethiopia, 2015. BMC Nurs. 2016;15(1):1–7. doi:10.1186/s12912-016-0165-3
15. Stetina P, Groves M, Pafford L. Managing
medication error—a qualitative study. Medsurg Nurs. 2005;14(3):174–178.
16. Chua SS, Chua HM, Omar A. Drug administration errors in paediatric wards: a direct observation approach. Eur J Pediatr. 2009;169(5):603–611. doi:10.1007/s00431-009-1084-z
17. Chua SS, Tea MH, Rahman MH. An observational study of drug administration errors in a Malaysian hospital. J Clin Pharm Ther. 2009;34(2):215–223. doi:10.1111/j.1365-2710.2008.00997.x
18. Feleke Y, Girma B. Medication administration errors involving paediatric in-patients in a hospital in Ethiopia. Trop J Pharm Res. 2010;9(4):401–407. doi:10.4314/tjpr.v9i4.58942
19. Idemoto LM, Williams BL, Ching JM, Blackmore CC. Implementation of a custom alert to prevent medication-timing errors associated with computerized prescriber order entry. Am J Health Syst Pharm. 2015;72(17):1481–1488. doi:10.2146/ajhp140790
20. Hammoudi BM, Ismaile S, Abu Yahya O. Factors associated with medication administration errors and why
nurses fail to report them. Scand J Caring Sci. 2017;32(3):1038–1046. doi:10.1111/scs.12546
21. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;134:178–189. doi:10.1016/j.jclinepi.2021.03.001
22. Institute of Medicine (US) Committee on Quality of Health Care in America; Kohn LT, Corrigan JM, Donaldson MS, eds. To Err Is Human: Building a Safer Health System. National Academies Press; 2000. doi:10.17226/9728
23. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. Strengthening the Reporting of Observational Studies in Epidemiology (strobe) statement: guidelines for reporting observational studies. BMJ. 2007;335(7624):806–808. doi:10.1136/bmj.39335.541782.ad
24. Tissot E, Cornette C, Demoly P, Jacquet M, Barale F, Capellier G. Medication errors at the administration stage in an intensive care unit. Intensive Care Med. 1999;25(4):353–359. doi:10.1007/s001340050857
25. Barker KN, Flynn EA, Pepper GA, Bates DW, Mikeal RL. Medication errors observed in 36 health care facilities. Arch Intern Med. 2002;162(16):1897. doi:10.1001/archinte.162.16.1897
26. Bohomol E, Ramos LH, D'Innocenzo M. Medication errors in an intensive care unit. J Adv Nurs. 2009;65(6):1259–1267. doi:10.1111/j.1365-2648.2009.04979.x
27. Calabrese AD, Erstad BL, Brandl K, Barletta JF, Kane SL, Sherman DS. Medication administration errors in adult patients in the ICU. Intensive Care Med. 2001;27(10):1592–1598. doi:10.1007/s001340101065
28. FitzHenry F, Peterson JF, Arrieta M, Waitman LR, Schildcrout JS, Miller RA. Medication administration discrepancies persist despite electronic ordering. J Am Med Inform Assoc. 2007;14(6):756–764. doi:10.1197/jamia.m2359
29. Hicks RW, Cousins DD, Williams RL. Selected medication-error data from USP's MEDMARX program for 2002. Am J Health Syst Pharm. 2004;61(10):993–1000. doi:10.1093/ajhp/61.10.993
30. Lisby M. Errors in the medication process: frequency, type, and potential clinical consequences. Int J Qual Health Care. 2005;17(1):15–22. doi:10.1093/intqhc/mzi015
31. Bagheri-Nesami M, Esmaeili R, Tajari M. Intravenous medication administration errors and their causes in cardiac critical care units in Iran. Mater Sociomed. 2015;27(6):442. doi:10.5455/msm.2015.27.442-446
32. Berdot S, Sabatier B, Gillaizeau F, Caruba T, Prognon P, Durieux P. Evaluation of drug administration errors in a teaching hospital. BMC Health Serv Res. 2012;12(1):60. doi:10.1186/1472-6963-12-60
33. Ernawati D, Lee YP, Hughes J. Nature and frequency of medication errors in a geriatric ward: an Indonesian experience. Ther Clin Risk Manag. 2014;10:413–421. doi:10.2147/tcrm.s61687
34. Feleke SA, Mulatu MA, Yesmaw YS. Medication administration error: magnitude and associated factors among
nurses in Ethiopia. BMC Nurs. 2015;14(1):1621–1632. doi:10.1186/s12912-015-0099-1
35. Hernandez F, Majoul E, Montes-Palacios C, et al. An observational study of the impact of a computerized physician order entry system on the rate of medication errors in an orthopaedic surgery unit. PLoS One. 2015;10(7):1–12. doi:10.1371/journal.pone.0134101
36. Morelock SG, Kirk JD. An urban medical system's exploratory study of medication errors. Nurs Open. 2019;6(3):1197–1204. doi:10.1002/nop2.319
37. Noguchi C, Sakuma M, Ohta Y, Bates DW, Morimoto T. Prevention of medication errors in hospitalized patients: the Japan adverse drug events study. Drug Saf. 2016;39(11):1129–1137. doi:10.1007/s40264-016-0458-1
38. Poon EG, Keohane CA, Yoon CS, et al. Effect of bar-code technology on the safety of medication administration. N Engl J Med. 2010;362(18):1698–1707. doi:10.1056/nejmsa0907115
40. Taufiq S. Prevalence and causes of wrong time medication administration errors: experience at a tertiary care hospital in Pakistan. Can J Nurs Inform. 2015;10(1):1–17.
https://cjni.net/journal/?p=4034
41. Welton JM, Kleiner C, Valdez C, Richardson S, Boyle K, Lucas E. Using time-referenced data to assess medication administration performance and Quality. J Nurs Adm. 2018;48(2):100–106. doi:10.1097/nna.0000000000000580
42. Westbrook JI. Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683. doi:10.1001/archinternmed.2010.65
43. Tsegaye D, Alem G, Tessema Z, Alebachew W. Medication administration errors and associated factors among
nurses. Int J Gen Med. 2020;13:1621–1632. doi:10.2147/ijgm.s289452
44. Khalid K, Abdullah HH, Dileepkumar M. Get along with quantitative research process. Int J Res Manage. 2012;2(4):15–29. Accessed March, 1, 2022.
http://rspublication.com/ijrm/march%2012/2.pdf
45. Garg AX, Hackam D, Tonelli M. Systematic review and meta-analysis: when one study is just not enough. Clin J Am Soc Nephrol. 2008;3(1):253–260. doi:10.2215/cjn.01430307
46. Carlson MDA, Morrison RS. Study design, precision, and validity in observational studies. J Palliat Med. 2009;12(1):77–82. doi:10.1089/jpm.2008.9690
47. Steckler A, McLeroy KR. The importance of external validity. Am J Public Health. 2008;98(1):9–10. doi:10.2105/AJPH.2007.126847
48. Needleman I, Moher D, Altman DG, Schulz KF, Moles DR, Worthington H. Improving the clarity and transparency of reporting health research: a shared obligation and responsibility. J Dent Res. 2008;87(10):894–895. doi:10.1177/154405910808701013
49. Guidelines for Timely Administration of Scheduled Medications (Acute). Institute for Safe Medication Practices; 2011. Accessed March 16, 2022.
https://www.ismp.org/node/361
50. Montesi G, Lechi A. Prevention of medication errors: detection and audit. Br J Clin Pharmacol. 2009;67(6):651–655. doi:10.1111/j.1365-2125.2009.03422.x
