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I

Detection and Evaluation of Medication errors at

Jordan University Hospital

by

Zena Hilal Sulaiman

A Thesis Submitted in

Partial Fulfilment of the

Requirements for the Degree of

Master of Science

in Pharmaceutical Sciences

at

University of Petra,

Faculty of Pharmacy and Medical Sciences

Amman-Jordan

June 2014

II

Detection and Evaluation of Medication errors at Jordan

University Hospital

by

Zena Hilal Sulaiman

A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of

Master of Science

in Pharmaceutical Sciences

at

University of Petra,

Faculty of Pharmacy and Medical Sciences

Amman-Jordan

June 2014

Supervisor Name:

Prof. Salim Hamadi

Co-supervisor Name:

Dr. Iman Basheti

Examination Committee

1. Prof. Salim Hamadi

2. Dr. Iman Basheti

3. Prof. Tawfiq Alhussainy

4. Dr. Feras Darwish Elhajji

Signature:

--------------------------

--------------------------

Signature:

--------------------------

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III

ABSTRACT

Detection and Evaluation of Medication errors at Jordan University

Hospital

by

Zena Hilal Sulaiman

University of Petra, 2014

Under the supervision of Prof. Salim Hamadi and Dr. Iman Basheti

Aim: In view of the fact that medication errors in Jordan are under estimated, this study aims

to detect and evaluate medication errors. Main objectives of this study are to evaluate the

rate, frequency, and severity of detected medication errors, and to determine risk factors

associated with the occurrence of these errors.

Methodology: This cross-sectional prospective study of medication errors used two methods:

disguised direct observation and chart review methods. The study was conducted over 6

months (from June to December 2013) at the internal medicine ward (sixth floor) of Jordan

University Hospital. Up to 10 inpatients were selected for observation during medication

administration session on daily basis. The observation included only the nurse who

prepared/administered the medications. The chart reviewing verified if all prescriptions in the

medication chart were identical to the prescriptions in the transcribed labels.

Results: This study detected a total of 803 medication errors within 6396 opportunities for

errors (12.60%). During the 3667 observed administrations to 283 patients by 15 nurses, 739

administration errors were detected (20.20%), involving wrong time errors (18.20%),

omission errors (1.50%), wrong administration technique errors (0.20%), extra dose errors

(0.20%), unauthorized dose errors (0.10%), and wrong route errors (0.01%). Transcription

IV

errors were the second errors detected (1.50%) among total 2729 screened prescriptions.

Errors in dispensing (0.80%) and prescribing stages (0.10%) were also identified in this

study. The majority of detected errors (92.50%) were categorized as 'C' (error reached the

patient with no harm). Risk factors associated with the total number of detected errors in this

study included: shorter nurse's experience in the ward (R2=0.456, p<0.042), higher no. of

doses given to the patient (R2=0.451, p<0.025), higher patient to nurse ratio (R

2=0.409,

p<0.010), longer length of hospitalization (R2=0.399, p<0.049).

Conclusion: Medication errors are of concern in the Jordan University Hospital. This study

revealed that medication errors occurred mainly during the administration and transcription

stages of medication use process. Longer nurse experience and lower job pressure can lead to

lower rate of medication errors.

Major Supervisor Signature

Prof. Salim Hamadi

---------------------------

Co-supervisor Signature

Dr. Iman Basheti

-------------------------

V

ACKNOWLEDGEMENTS

In The Name of Allah, the most Gracious, the most Merciful

Foremost, my thanks and gratitude for ALMIGHITY ALLAH, the Omnipotent, the

Omnipresent, the Compassionate, the Beneficent and the source of all knowledge and

wisdom, who gave the strength and courage to complete this work.

Thanks, gratitude and appreciation to my supervisor Prof. Salim Hamadi for his continuous

support during my master research, for his patience, motivation, enthusiasm, and immense

knowledge.

Wholehearted thanks and gratitude to my co-supervisor, Dr. Iman Basheti, for her support,

encouragement, and immense knowledge to help making the work enlightening.

For my advisors, actually words are not enough to express my feelings towards both of you,

your influence and recommendations will continue through my life and career as being my

role models.

To my family, no words can describe the love, affection, amiable attitude, advices, unceasing

prayers, support, and inspiration that showed me during my whole life. I would always be in

dept and grateful for my father, without him and his encouragement, trust, support, advices

and willingness to help me made this research in the best manner. My mother who stood

beside me step by step and always believed in me and pushed me to the limits so I can

achieve my goals. To my brothers and sister in low no words can describe my thanks and

love. Many thanks also to my extended family for their prayers.

My sincere thanks and gratitude goes to Mr. Haidar Rasheed for his support, advices and

willingness to help make my study analysis in the best manner.

My sincere thanks also go to Prof. Tawfiq Alhussainy for his helpful attitude, and support

throughout the research.

VI

I would like to thank Dr. Nathir Obeidat for his endorsement to get the IRB permission of

JUH.

Many sincere thanks to all the JUH internal medicine (sixth floor) ward staff (doctors,

pharmacists, and nurses) for their help during my data collection period.

To my best friend and soul mate Zahraa' Roudhan, thank you for standing by me in joy and

sorrow. I wanted to make sure you know that I appreciate the varieties of support you have

given me, whether emotional, informational, or tangible. I love you forever.

To my dearest friends (Zainab Alobaidy, Sarah Shubbar, Zahraa' Hassan, Sara Alani,

Aya Salah), deep sincere thanks for your support, encouragement and prayers, love you all.

VII

Dedication

I would love to dedicate this work to my father Eng. Hilal Sulaiman and my mother Dr.

Wasmaa Al Dabbagh

(I love you with all my heart)

VIII

TABLE OF CONTENTS

Contents Page

TITLE PAGE I

ABSTRACT (IN ENGLISH) III

ACKNOWLEDGEMNETS V

DEDICATION VII

TABLE OF CONTENTS VIII

LIST OF TABLES XI

LIST OF FIGURES XI

LIST OF ABBREVIATIONS XII

Chapter One: Introduction

No Page

1. Medication error 2

1.1 Definition 2

1.2 Classification of medication errors 2

1.2.1 Prescribing errors 3

1.2.2 Transcription errors 4

1.2.3 Dispensing errors 4

1.2.4 Administration errors 6

1.3 Global incidence of medication errors 7

1.4 The significance of medication errors 10

1.5 Categorization of medication errors 11

1.6 Contributing factors to the occurrence of medication errors 13

1.6.1 Contributing factors by the health care system 13

1.6.2 Contributing factors by health care professionals 16

1.7 Detection methods of medication errors 20

1.7.1 Direct observation 20

1.7.2 Chart review 21

1.7.3 Incident reports 22

1.7.4 Anonymous self reports (questionnaires) 23

1.7.5 The critical incident technique 23

1.7.6 Computerized monitoring 24

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1.8 Prevention of medication errors 24

1.8.1 Policies and procedures for prevention of medication errors 25

1.8.2 Using Information Technology to prevent medication errors 27

2. Aim and objectives of the study 29

Chapter Two: Methodology

1. Approval of the study 31

2. Description of the drug distribution system at JUH 31

3. Study population 33

3.1 Patient selection and recruitment 33

3.2 Inclusion criteria 33

3.3 Exclusion criteria 33

4. Study design 33

4.1 Direct observation 33

4.2 Chart review 34

5. Data collection 34

5.1 Characteristics of the ward and nurses 34

5.2 Characteristics of the nurse observed 35

5.3 Demographic characteristics of patients and their medical profile 35

5.3.1 Interviews (patients were asked about) 35

5.3.2 Medical files 35

5.4 Medication error detection 35

6. Data analysis 38

6.1 Descriptive analysis 38

6.2 Medication error rate (MER) and frequency of detected errors 38

6.3 Severity assessment of medication errors 39

6.4 Regression analysis 39

6.4.1 Assumptions for the Univariate regression 39

6.4.2 ''Goodness of fit'' of regression model 40

6.4.3 Interpreting the results from the regression model 40

Chapter Three: Results

1. Characteristics of the ward and nurses 42

2. Characteristics of the nurses observed 43

3. Demographic characteristics of patients and their medical profile 44

X

3.1 Demographic characteristics of patients 44

3.2 Patients' chief compliant for hospitalization 45

3.3 Patients' comorbidities 46

3.4 Prescribed medications 47

4. Medication error rate (MER) and frequency of detected errors

49

5. Severity of medication errors 53

6.

Multiple Univariate regression analysis identifying risk factors for

the identified medication errors

54

7. Examples of detected medication errors 55

7.1 Examples of medication administration errors 55

7.2 Examples of transcription errors 57

7.3 Examples of dispensing errors 58

7.4 Examples of prescribing errors 60

Chapter Four: Discussion 63

Chapter Five: Conclusion and Future Implications 74

Bibliography 78

Appendices

Appendix

No.

Page

No.

Appendix 1 Official approval form of the study 101

Appendix 2 Examples of physician handwritten prescriptions 102

Appendix 3 Examples of transcribed labels and dispensed medication 104

Appendix 4 Informed consent 106

Appendix 5 Direct observation form 107

Appendix 6 Criteria for medication errors 091

Appendix 7 Chart review form 112

Appendix 8 Characteristics form of each nurse observed 113

Appendix 9 Demographic characteristics and medical profile form of each

patient observed 114

Abstract (in Arabic) 111

XI

List of Table


Table No. Page No.

1 Classification of dispensing errors 5


2 Characteristics of the ward and nurses 42

3 Characteristics of the nurses observed 43

4 Demographic characteristics of the patients 44

5 Characteristics of prescribed medications 48

6 Medication error rate (MER) and frequency of detected errors 49

7 Severity categories of detected medication errors 53

8 Multiple Univariate regression analysis 54

9 Examples of medication administration errors 55

10 Examples of transcription errors 57

11 Examples of dispensing errors 58

12 Examples of prescribing errors 60

List of Figures


Table No. Page No.

1 NCC MERP Index for categorizing medication errors 12

2 The role of IT by stage in the medication use process 28


3 Mechanism of drug distribution system at JUH 32

4 Example of a regular physician order 32

5 Medication errors detection mechanism using disguised direct

observation and chart review 37


XII

6 Patients chief compliant for hospitalization 46

7 Patients comorbidities 47

8 Types of detected medication errors 50

9 Types of medication administration errors 51

10 Types of transcription errors 51

11 Types of dispensing errors 52

12 Types of prescribing errors 52

13 Prescribing error (1) 60

14 Prescribing error (2) 61

List of Abbreviations

NCCMERP National Coordinating Council of Medication Error Reporting and Prevention

ASHP American Society of Hospital Pharmacists

ICU Intensive Care Unit

NPSA National Patient Safety Agency

I.V Intravenous

IOM Institute of Medicine

c.h.f.g clinical human factor group

CMR Central Medication Incidents Registration

IT Information Technology

JUH Jordan University Hospital

MAR Medication Administration Record

MER Medication Error Rate

OE Opportunity for Error

1

Chapter One

Introduction

2


1. Medication Errors

1.1. Definition

Over the past 45 years, various terms and definitions have been used to describe medication

errors. In the past, the term (medication error) referred to administration errors only. However

today, it refers to errors at any stage of the medication use process (Barker et al., 1966)

The National Coordinating Council for Medication Error Reporting and Prevention (NCC

MERP) approves the following definition of a medication error: "Any preventable event that

may cause or lead to inappropriate medication use or patient harm, while the medication is in

the control of the health care professional, patient, or consumer. Such events may be related

to professional practice, health care products, procedures, and systems including prescribing,

order communication, product labeling, packaging, and nomenclature, compounding,

dispensing, distribution, administration, education; monitoring, and use."(NCCMERP, 2005).

A similar definition of medication errors was also reported in previous studies (Wilson et al.,

1995, Kohn et al., 2000, Helper and Segal, 2003, Meadows, 2003, Cohen, 2007, Williams,

2007).

American Society of Health System Pharmacy (ASHP) defines a medication error as ''a dose

of medication that deviates from the physician’s order as written in the patient’s chart or from

standard hospital policy and procedures'' (ASHP, 1982).

1.2. Classification of medication errors

It is important to classify medication errors. It helps the health care system to determine the

occurrence and severity of errors, and to develop measures that improve the medication use

process and minimize the incidence of medication errors. The classification of medication

errors varied, some reporting systems classifying medication errors according to the

3

medication use process stages, while other systems focus on the outcome of error and its

severity. Furthermore medication errors could be sub-classified based on the profession

committing the error or the particular drug involved in the error (Appleton and Lange, 1996,

Runciman et al., 2003, Parshuram et al., 2008, Brady et al., 2009).

Medication errors are classified into five stages according to where they occur in the

medication use process: prescribing, transcription, dispensing, administration, and monitoring

(Anderson, 1971, ASHP, 1980, Davis et al., 1981, ASHP, 1982, Ingrim et al., 1983, Betz and

Levy, 1985, JW., 1986, Fuqua and Stevens, 1988, ASHP, 1989, Lesar et al., 1990, Allan and

Barker, 1990, Leape et al., 1991, Bedell et al., 1991).

1.2.1. Prescribing errors

Prescribing errors have been defined as ''medication errors initiated during the prescribing

process. These include the incorrect drug selection, dose, dosage form, quantity, route, or

instructions for use of a drug product ordered by physician'' (ASHP, 1982, Lesar et al., 1997).

Based on previous studies, reported rates of prescribing errors varied greatly due to variations

in the definition of a prescribing error. They ranged from 0.6% to 48% (Lisby et al., 2005,

Pasto-Cardona et al., 2009, Patanwala et al., 2010, Vazin and Delfani, 2012, Karna et al.,

2012). Prescribing errors have been reported to be the most common observed errors in

emergency department (ED) (Patanwala et al., 2010, Gokhman et al., 2012). The percentage

of prescribing errors were estimated to up to 11% of prescriptions, with a cost of around £400

million per year in the United Kingdom (about JOD389 million) (Agency, 2007). According

to Lisby and colleagues, the most frequent prescribing errors were in: drug selection, wrong

dosage form, and route of administration, (14.3%), followed by (9.5%) for dose omission,

wrong time, and instruction errors (Lisby et al., 2005).

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1.2.2. Transcription errors

Transcribing errors are defined as any deviation in transcribing a medication order. These

include any discrepancy in drug name, drug formulation, route, dose, omission of drug, and

drugs which were not ordered (Lisby et al., 2005). Transcription errors are a specific type of

medication errors commonly made by the health care professionals (Fahimi et al., 2009).

Most errors occurred when prescriptions were transcribed into the patients’ chart (Hartel et

al., 2011).

The reported rates of transcription errors varied greatly from 0.7% to 56% (Barker et al.,

2002b, Lisby et al., 2005, Fahimi et al., 2009, Pasto-Cardona et al., 2009, Patanwala et al.,

2010, Vazin and Delfani, 2012). According to a literature review of medication errors in the

Middle East countries, they revealed that over (50%) of omission errors occurred at

transcription stage (Alsulami et al., 2013). Many studies also reported that omission in the

particular (52%) was the highest type of transcription error (Fahimi et al., 2009, Pasto-

Cardona et al., 2009, Hartel et al., 2011).

1.2.3. Dispensing errors

A dispensing error is ''the failure to dispense a medication upon physician order. It includes

the incorrect drug, dose, dosage form, quantity, incorrect labeling of medication,

inappropriate packaging or storing of medication prior to dispensing, and dispensing of

expired or chemically compromised medications'' (Monette et al., 1995). Cohen classified

dispensing errors into eight types (Cohen, 2007) as shown in Table 1.

5

Table 1. Classification of dispensing errors (Cohen, 2007).

Type of

dispensing error

Definition

Wrong-drug error Occurs when a medication different from that named in writing on a

prescription is used to fill the prescription.

Wrong-strength

error

Occurs when a dosage unit containing an amount of medication different

from what the prescriber specified is used to fill a prescription.

Wrong dosage-

form

Occurs when the form of the medication used to fill the prescription

differs from what the prescriber wrote.

Wrong-quantity

error

Occurs when the amount of medication dispensed to a patient differs

from the amount ordered without acceptable reason.

Label errors These errors are divided into two types. Wrong label-instruction errors

which occur when directions to the patient on the prescription label

deviate in one or more ways from what was prescribed. The second type

of label error is wrong prescription label information which is

determined by comparing the prescription with the label content

requirements such as; pharmacy name and address, prescription serial

number, date of the prescription, name of prescriber, patient name, drug

name, drug strength, quantity dispensed, expiration date, and

manufacture or distributer name.

Deteriorated drug

error

Occurs when a medication is beyond its expiration date or is stored in

location that is not in accordance with the manufacturer's

recommendations.

Omission Occurs when a patient fails to receive a prescribed medication.

Wrong time error Occur in ambulatory care sittings that fill blister card for long term care

or mental health facilities, in which the medication might be placed in a

location on the card that is different from what is conveyed on the

prescription.

The reported rates of dispensing errors varied greatly from 0.6% to 48% (Lisby et al., 2005,

Pasto-Cardona et al., 2009, Patanwala et al., 2010, Vazin and Delfani, 2012, Karna et al.,

2012). They occur primarily with drugs that have a similar name or appearance (Williams,

2007). The most frequent types of dispensing errors were also varied. Karna and colleagues

6

reported that 61.9% of dispensing errors were wrong medication dispensed and 38.1%

incorrect dose (Karna et al., 2012). Furthermore omission errors (60%) were the most

frequent errors of this type (Pasto-Cardona et al., 2009).

1.2.4. Administration errors

A medication administration error is defined as a ''discrepancy between the medication

regimen received by the patient and that intended by the prescriber or according to standard

hospital policies and procedure'' (ASHP, 1982, Dean, 1999, Greengold et al., 2003, Williams,

2007).

Medication administration is a complex process involving a large number of health care

professionals. Reports were published in the United States (US) and United Kingdom (UK)

highlighting the high number of medication administration errors in the health care systems

(O'Shea, 1999, Anderson and Webster, 2001, Fijn et al., 2002, Armitage and Knapman, 2003,

Lassetter and Warnick, 2003, Organizations., 2006, McBride-Henry and Foureur, 2006,

Williams, 2007, Fry and Dacey, 2007a, Fry and Dacey, 2007b). The National Patient Safety

Agency (NPSA) statistics reported that (59.3%) of medication errors occurred during the

administration stage (Agency, 2007). Medication administration errors were classified into

ten types, these include omission errors, unauthorized dose errors, extra dose errors, wrong

route, wrong dose, wrong administration technique, wrong rate, wrong dosage form, wrong

time and wrong preparation (Allan and Barker, 1990, ASHP, 1993, Monette et al., 1995,

Barker et al., 2002b, Cohen, 2007).

Several studies reported that medication administration errors ranked the highest rate among

detected medication errors. The rate ranged from 9.8% to 46% (Lisby et al., 2005, Prot et al.,

2005, Young et al., 2008, Font Noguera et al., 2008, Patanwala et al., 2010, Berdot et al.,

2012, Gokhman et al., 2012, Vazin and Delfani, 2012). Medication administration errors rates

7

were also varied in Middle East countries form 9.4% to 80% (Alsulami et al., 2013), as the

number and dosage forms of medications increased, the rates of medication administration

errors increased (28.8%) (Young et al., 2008). The frequency of administration error types

were varied among studies, however the most frequent type of administration errors reported

was wrong time (70.8%) (Young et al., 2008, Berdot et al., 2012). An Iranian study reported

that the most frequent medication administration errors were wrong administration technique

(19%), followed by wrong preparation (15.1%) and wrong time (11.2%) (Vazin and Delfani,

2012).

According to the first study of medication administration errors in a European Intensive Care

Unit (ICU), dose errors were the most frequent errors (31%) followed by wrong rate (22%)

(Tissot et al., 1999). However omission errors (40.3%) were the most common administration

errors reported in an Indian ICU, followed by wrong timing (18%) (Kadam et al., 2009).

The most common medication administration errors reported in elderly patients were

omission (27.1%) and unauthorized extra doses (30.1%) (Haw et al., 2007). While in

paediatric patients which are more vulnerable to medication administration errors, the most

common types of errors detected were wrong time (28.8%), wrong drug preparation (26%),

and omission (16.3%) (O'Hare et al., 1995, Chua et al., 2010); therefore based on the reported

data, the frequency of medication administration errors is relatively high (Abbasinazari et al.,

2013) and administrators need to take the initiative of developing systems that guarantee safe

medication administration (Fahimi et al., 2008).

1.3. Global incidence of medication errors

Studies on medication errors were conducted as early as 1962, where the incidence of

medication errors occurred much more frequently (16 errors per 100 doses) (Barker and Mc,

1962, Cohen, 2007). In the 1970s, the incidence of medication errors was lower than that in

the 1960s by 5% (Thornton and Koller, 1994, Scott, 2002). In the 1980s, the incident rate of

8

medication errors among pediatric patients was 12.9% (Rippe and Hurley, 1988, Sunderland

et al., 1997). While in the late 1980s and early 1990s, the incidence of medication errors rates

were found to be between 5% and 18% of drugs administered (Stewart et al., 1991).

Currently the incidence of medication errors is greatly underestimated and under-reported

(Young et al., 2008). With the aging of the population and the consequent increase in chronic

health problems that requires larger number of medications (Kohn et al., 2000, Ernst and

Grizzle, 2001, Fialova and Onder, 2009, Tobias et al., 2013) higher number of medication

errors are expected. Studies of medication errors were conducted worldwide with different

designs and definitions of medication errors resulted in differences in the reported errors (2%

to 14%) of patients admitted to hospitals (Williams, 2007).

Among many studies that were conducted in US; a study which revealed that one medication

error occurs out of every five doses in a hospital (Barker et al., 2002b). Another study

reported a 5.7% incident rate among pediatrics (Kaushal et al., 2001). In Canada, there was at

least 1 medication error occurring in 59 pediatric patients (Coffey et al., 2009). In Brazil,

there were differences in the incidence of medication errors among hospitals. The rate of

errors in one of these hospitals was considered low 2.4% (Anselmi et al., 2007). In UK, the

incidence of medication errors was 25.9% in a British old-age psychiatric hospital (Haw et

al., 2007). Another British study reported that the incidence of errors during parenteral

medication administration was 25.2% (Ferner and Upton, 1999, Bruce and Wong, 2001).

According to a study conducted in three European countries (i.e. UK, Germany and France),

the incident rates of medication errors during I.V (Intravenous) administration process were

49% in UK medical centers, 21% in the German hospitals, and 5% in the French centers

(Cousins et al., 2005).

The incidence of medication errors among other European countries, varied substantially. In

Italy the rate of errors was 1.3% (Gerber et al., 2008). However in Denmark the lowest

9

incident rate of medication errors detected was 4% at the dispensing stage and the highest

rate during patient discharge 76% (Lisby et al., 2005). In Switzerland, a high incidence of

medication errors (53.3%) was reported during transcription stage (Hartel et al., 2011) while

in Spain it has been estimated that in 1 of each 14 opportunities for error, a medication error

takes place (Climent et al., 2008) and there were 0.98% incidence of medication error per 100

patients/day (Pasto-Cardona et al., 2009).

In Australia, the incidence of medication errors in three surgical wards at one hospital was

18%, mostly during administration of I.V fluids (Han et al., 2005). In Asian countries like

India the overall incidence of medication errors was found to be 21.8% (Karna et al., 2012),

which was similar to that in Pakistan 22.6% (Shawahna et al., 2011). The incident rate of

medication errors was 29 per 100 admissions in Japan (Morimoto et al., 2011) while in

Malaysia the rate of errors was 11.4% among pediatric inpatients (Chua et al., 2010).

Studies related to medication errors in the Middle Eastern countries were relatively few in

number and of poor quality. The incident rates varied from 7.1 % to 90.5 % for prescribing

and from 9.4 % to 80 % for administration (Alsulami et al., 2013). In Iran, the rate of errors

was 7.6% (Vazin and Delfani, 2012). The incidence of medication errors was 54.2% during

prescribing in Kingdom of Saudi Arabia (KSA) (Al-Jeraisy et al., 2011). In Iraq, there was

8.7% incident rate of errors according to an evaluative study in medical and surgical units of

a teaching hospital in Dyala (Hamoudi et al., 2012).

In Jordan, medication errors are serious, escalating and require more attention in all types of

hospitals (Mrayyan and Al-Atiyyat, 2011). Few studies were conducted in the last few years

regarding the reported incidence of medication errors (Mrayyan et al., 2007, Mrayyan and Al-

Atiyyat, 2011, Al-Shara, 2011, Mrayyan, 2012). Although the picture of medication errors in

Jordan is not complete, however the average number of recalled committed medication errors

11

per nurse was 2.2, of total 42.1% reported rate of medication errors to nurse managers

(Mrayyan et al., 2007).

Across hospitals in Jordan, there were no differences found in regard to the rate of medication

errors, but the reported incidence of medication errors in private hospitals seemed to be more

than that in teaching and governmental hospitals (Hussain and Kao, 2005, Mrayyan et al.,

2007). In addition, there were significant differences in error rates between non university-

affiliated teaching hospital (NUATH), 43.1%, and 33.9% in university-affiliated teaching

hospitals in Jordan (UATH) (Mrayyan and Al-Atiyyat, 2011). While a high rate of

medication errors occurred in ICUs 36%, compared with 33.8% in wards by another

Jordanian study (Mrayyan et al., 2007). To reduce the error rate, all health care professionals

should work together to design safety systems that ensure safe medication administration and

management (Mrayyan et al., 2007, Mrayyan and Al-Atiyyat, 2011).

1.4. The Significance of medication errors

A clinically significant medication error was defined as ''a medication error with the potential

for causing a patient discomfort or jeopardizing a patient's health and safety'' (Barker et al.,

2002b). A landmark report released by the Institute of Medicine (IOM) in November 1999

stated that 44,000-98,000 people die each year because of medical errors, over 7000 of these

deaths attributed to medication errors. Medication errors are considered the eighth leading

cause of death in US where more people die in a given year as a result of medication errors

than from motor vehicle accidents, breast cancer or AIDS (Kohn et al., 2000, Williams,

2007).

Medication errors are a significant issue affecting patient safety and costs in hospitals. Their

costs have been estimated between 17$-29$ billion per year in hospitals including the

expense of additional care needed to correct those errors, lost income and disability (Fogarty

11

and McKeon, 2006). The cost of each medication error is between 55$-146$ (Cohen, 2007).

Bate and colleagues estimated that the annual cost of serious medication errors was $2.9

million per hospital and a 17% decrease in incidence would result in $480,000 savings per

hospital (Bates et al., 1998).

Bates and colleagues estimated that, in 100 medication errors, 7 errors had significant

potential harm (Bates et al., 1995a). Medication errors have direct effects on the patients and

their families such as longer hospital stay, increased costs and mortality. These effects result

in loss of trust in the health care system by patients and diminished satisfaction by both

patients and health care professionals (Kohn et al., 2000). Medication errors are the second

cause for lawsuits involving nurses in US (Clayton, 1987, Wolf, 1989) and the most common

reason for removal from the nursing register in UK (Carlisle, 1996). The potential for

medication error in the medication administration stage is a problem of concern for the

nursing staff (Gladstone, 1995).

1.5. Categorization of Medication errors

The NCC MERP created an Index to standardize medication errors definition and outcome

severity categorization. This index is provided with four distinct levels of medication errors

based on harm; intervention; or a combination of both represented by letters of the alphabet.

The Index currently consists of nine categories from A to I (NCCMERP, 2005) (Figure 2).

The four levels are potential for error: (Category A, actual error without harm), (Categories

B, C, and D, actual error with no harm), (Categories E, F, G, and H, actual error with harm)

and (Category I, actual error that resulted in death).

12

Figure 1. National Coordinating Council for Medication Error Reporting and Prevention

Index for categorizing medication errors (NCCMERP, 2005)

Many studies have been used this index to categorize the medication errors, of those Vazin &

Delfani, Patanwala and colleagues reported that the majority of their detected errors were

categorized as C (Patanwala et al., 2010, Vazin and Delfani, 2012).The severity

categorization were varied among studies; Pastó-Cardona and colleagues reported that 84.4%

of the errors were as B (Pasto-Cardona et al., 2009). However it have been reported that

80.6% of errors reached the patient but not cause harm and 19.5% of errors didn’t reach the

patient (Kadam et al., 2009). Furthermore Karna and colleagues reported that 61.4% errors

reached the patient with no harm (Karna et al., 2012).

There is another classification system for medication errors clinical consequences which was

developed by Bates and colleagues it includes the following four-scale categories: potentially

13

fatal, potentially serious, potentially significant, and potentially non-significant (Bates et al.,

1995b, Lisby et al., 2005).

1.6. Contributing factors to the occurrence of medication errors

The first step toward preventing the incidence of medication errors is a proper understanding

of the contributing factors for the occurrence of these errors. (Aronson, 2009a, Bailey et al.,

2011). The contributing factors for medication errors are considered various and complex

(McBride-Henry and Foureur, 2006, Brady et al., 2009). The contributing factors may be

divided into two sub groups: those caused by systems and those caused by individual health

care professional (Reason, 2000, McBride-Henry and Foureur, 2006).

1.6.1. Contributing factors by the health care system

Each hospital has its own system for ordering, storing and monitoring medications.

Therefore, this system can have a significant influence on whether a medication error is

made, and whether it is noticed and reported (c.h.f.g, 2013). A number of systems' issues are

considered as contributing factors in medication errors, including work overload, staff

shortage with general inadequacy in checks and procedures during any stage of the

medication use process (O'Hare et al., 1995, O'Shea, 1999, Kelly, 2004).

Work environment

Environmental factors like work dynamics are important to be considered as contributing

causes to medication errors. Highly dynamic work situations such as (i.e., frequent changes

of orders, care plans, and procedures) create conditions in which nurses might be prone to

making medication errors. One of the health care work features is the multiple interruptions.

Situations like these make the nurses to be easily distracted; forget what they were doing, and

be more likely to commit errors (Conklin et al., 1990, Cohen et al., 2003).

14

Administration of medications is affected by the environment in which this process occurs, in

addition to the structures and systems that support this process (Pape, 2001, Stratton et al.,

2004, Mayo and Duncan, 2004, Mrayyan et al., 2007, Tang et al., 2007, Biron et al., 2009).

Therefore the environmental factors could be improved to prevent the occurrence of errors

(ASHP, 1993, Anderson and Webster, 2001).

Policy and procedure

Failure to follow policies and procedures resulted in lack of attention to prevent the

occurrence of medication errors during medication use process (O'Hare et al., 1995, Wirtz et

al., 2003, Cousins et al., 2005). Medication errors that resulted from not following a

procedure or rule are called Rule-based errors (Aronson, 2009a).

The drug distribution procedure can potentially determine vulnerable points at which

medication error can occur (Taxis et al., 1999). The distribution process in which medications

are being received from the pharmacy may contribute to errors. Issues such as late deliveries,

loss of orders and inadequate 24-hour cover shifts may all resulted in timing and omission

errors (Chua et al., 2010). Furthermore the absence of pharmacy staff (out of hours) can limit

the availability of drugs which may increase omission errors (Madegowda et al., 2007).

About 50–70% of all reported medication errors in hospitals are related to the procedure of

medication distribution and administration by nurses (CMR, 2013). Other studies have been

focused on identifying medication errors contributed factors that are difficult to modify and

related to practices and procedures. These include hospital characteristics such as number of

hospital beds, teaching status, design of technology (Brennan et al., 1991, Bruce and Wong,

2001, Anderson and Webster, 2001, Pape, 2001, Thornlow and Stukenborg, 2006).

15

Nursing shortage

According to a Jordanian study, the nursing shortage is identified as a significant factor that

influences medication errors because nurses are required to work with a large number of

patients who have different health conditions and severity of diseases. Therefore nurses are

more prone to commit medication errors under such stressful events (Mrayyan et al., 2007).

Other researchers have been reported that better staffing is associated with fewer medication

errors (Blegen et al., 1998, McGillis Hall et al., 2004). Bailey and Colleagues found that the

causes of medication errors are varied according to the several types of errors; such as staff

shortage, and work load (Bailey et al., 2011).

Workload

One of the major contribution factors for the occurrence of medication errors is the nursing

high workload (Tang et al., 2007, Fry and Dacey, 2007a, Fry and Dacey, 2007b), workload is

simply referred to the ratio of patients per nurse, the higher the workload the higher the risk

for error (Tissot et al., 2003). Madegowda and Colleagues found that the rate of medication

errors to be higher in winter due to higher census of patients during those months

(Madegowda et al., 2007). Factors related to workload such as number of consecutive hours

worked, rotating shifts, staffing mix and patient ratios. These factors may result in

distractions and interruptions among the working staff. Furthermore medication errors are

more likely to occur by busy and distracted staff(O'Hare et al., 1995, Hartley and Dhillon,

1998, Dean and Barber, 2001, Taxis and Barber, 2003b, Wirtz et al., 2003, Han et al., 2005,

Cousins et al., 2005).

Working shifts have been associated with higher rates of error according to (Rogers et al.,

2004). Rogers and colleagues also have found that working more than 12 hours was

16

contributed to medication errors. Working overtime with inadequate resources, and support

from the health care system all contributed to an increased risk of medication errors by nurses

(Wilkins and Shields, 2008).

1.6.2. Contributing factors by health care professionals

Many of medication errors contributing factors are human related (Kleinpell, 2001). Good

people can unintentionally commit errors due to inadequate experience or knowledge (Dugan

et al., 1996, Hand and Barber, 2000, Woods, 2001, Kelly, 2004, Rassin et al., 2005).

Nursing educational level and experience

Since nurses make up the large portion of the health care staff and medication errors are

mostly proportional to the nurse. Therefore the nursing level of education and experience are

the two factors that have contributed to medication errors (Yang, 2003, Benjamin, 2003,

Bailey et al., 2011). Chang and Mark reported that nurses’ educational level had a significant

relationship with severe medication errors (Chang and Mark, 2009). Furthermore a lack of

ward staff experience have been resulted in incorrect administration technique such as

reading the drug labels wrongly, misinterpreting prescriptions, infusion pumps not connected

properly, inaccurate measurements, and increased risk of contamination during parenteral

medication administration in particular (Chua et al., 2010).

Increased nursing level of experience (more than 5 years) has been attributed to lower

incidence of medication errors as their years of service increase (Kazaoka et al., 2007).

However nurses with less than 5 years experience have a low level of awareness regarding

medication errors. Blegen and colleagues found that increased registered nurse staffing was

associated with a lower incidence of adverse patient outcomes including medication errors

(Blegen et al., 1998).

17

Nursing Knowledge

Knowledge based errors that contributed to individual staff characteristics including lack of

knowledge to the patient’s diagnosis, medication names, purposes, and correct administration

of the medications (O'Hare et al., 1995, Wirtz et al., 2003, Cousins et al., 2005). Knowledge-

based errors or Failures of skill can be divided into; action-based errors (’slips’, including

technical errors), memory based errors (‘lapses’), where both divisions can be related the

individual knowledge (Aronson, 2009a).Good practice and adequate knowledge of

medication will definitely assist nurses in administering medications effectively and

correctly. Therefore nurses require the essential knowledge on pharmacology, and

competence in medication administration to prevent any error (Taxis and Barber, 2003a).

Medication knowledge deficiency

Poor medication knowledge was the most common reported contributory factor of medication

errors (Latter et al., 2000, Manias and Bullock, 2002, Morrison-Griffiths et al., 2002, Al

Khaja et al., 2005, Dibbi et al., 2006, Al Khaja et al., 2007, Koohestani et al., 2008, Eslamian

et al., 2010, Al-Dhawailie, 2011). Knowledge in pharmacology among the nurses was low

and inadequate (Latter et al., 2000, Manias and Bullock, 2002). Boggs and colleagues

reported that the mean score of pharmacology knowledge was only 46% among nurses. They

have insufficient knowledge on dosages, mechanism of action and pharmacokinetics (Boggs

et al., 1988). Inadequate knowledge about drugs compatibility has been found to be

associated with errors (Bruce and Wong, 2001). In addition to the inappropriate use of

antimicrobial drugs that could increase the risk of drug resistance (Kollef, 2001). Therefore

Lack of knowledge and experience with drugs or equipment were the cause of 79% of all

errors (Taxis and Barber, 2003a).

18

Confusion over medication names and packaging

One of the significant factors that associated with medication errors is the similarities

between drug names (Kelly, 2004). Therefore the need for staff training was highlighted as

they commonly confused about drug names and packages to avoid inappropriate selections

(Hoffman and Proulx, 2003). Drug factors that are related to medication errors, like

information resources, such as published drug guides, which may not be readily available or

up to date (Hartley and Dhillon, 1998, Wirtz et al., 2003, Cousins et al., 2005). In addition to

unclear labeling, confusing packaging of doses (similar packaging for different medications).

These factors have been associated to increase the incidence of medication errors (Bates et

al., 1995a, Hartley and Dhillon, 1998). According to Mrayyan et al study in Jordan, they

revealed that confusion over infusion devices was the most common perceived factor

contributing to medication errors (Mrayyan et al., 2007).

The number of medication existed that are supplied by different drug companies with

different brand names and packaging, in addition to the similar looking or sounding, and the

different routes for administering medications (Joshi et al., 2007, Sheu et al., 2009).

Therefore these varieties of routes and doses for the same medication increase the risk for

medication errors (Nuckols et al., 2008).

Poor calculation skills

Nurse's poor competency in drug calculations has been identified as a key cause of

medication administration errors (Calliari, 1995, Gladstone, 1995, Grandell-Niemi et al.,

2003, Wright, 2006, Jukes and Gilchrist, 2006, Lee, 2008). The NPSA (2007) report

highlighted that medication errors resulted from poor competency in drug calculations

accounts for 28.2% of all reported errors; those involving incorrect dosage, strength or

frequency (Agency, 2007). Medication errors resulting from poor calculation skills are an

19

international problem (Bayne and Bindler, 1988, Kapborg, 1995, Polifroni et al., 2003,

Wright, 2007) thus it is important for nurses to update their mathematical skills to be more

confident in drug calculations (Grandell-Niemi et al., 2003).

Medication Preparation related factors

Wrong drug preparation is resulted due to many reasons like all medications are prepared in

the unit by the nurses. Factors such as crowded environment and interruptions increase the

risk of medication errors during preparation (Font Noguera et al., 2008). Other contributing

factor that resulted in wrong drug preparation is the lack of standard protocol for preparation

and administration. This factor includes the inappropriate use of medical devices and

inappropriate antiseptic techniques (O'Hare et al., 1995).

Communication

Other contributing factor is poor communication among health care professionals (Kazaoka

et al., 2007). The ineffective communication among the professionals will lead to many

missed opportunities during the medication use process (Vogelsmeier et al., 2007). However,

conversations among staff and performance of multiple tasks during medication preparation

and administration can be considered as predisposing factors for medication errors

(Gladstone, 1995, Tang et al., 2007). This factor include illegible handwriting, incorrect

interpretation of physician’s orders, use of verbal orders, failure to document medications

given, missing medications, and unclear medication administration records MARs (O'Hare et

al., 1995, Bates et al., 1995b, Flynn et al., 1997, Hartley and Dhillon, 1998, Dean and Barber,

2001, Taxis and Barber, 2003b, Taxis and Barber, 2003a, Wirtz et al., 2003, Han et al., 2005,

Cousins et al., 2005) .

21

Illegible handwriting

Nurses and managers both found that incomplete or illegible prescriptions were associated

with medication errors (Gladstone, 1995, Kelly, 2004). One of medication errors contributing

factors revealed by Mayo & Duncan was poor physician handwriting (Mayo and Duncan,

2004). The Joint Commission revealed that the reason behind misinterpretations of

prescriptions was the overuse of abbreviations (Organizations., 2006). Therefore the health

care systems should seek to reduce the inappropriate use of abbreviations (Cohen and 2001,

Abushaiqa et al., 2007). Furthermore the use of hand written prescribing instead of

computerized physician order entry (COPE) was one of the factors that were associated with

the high incidence of prescribing errors in an Iranian ICU (Vazin and Delfani, 2012).

1.7. Detection methods of medication errors

Detection of errors should be a routine part of hospital practice. The purpose of detecting is to

discover errors, quantify the extent and types of errors. Health care systems that aimed to

reduce medication error occurrence, need to use reliable methods for detecting and

preventing them (Cohen, 2007). A number of detection methods have been proposed to detect

medication errors, as the following:

1.7.1. Direct Observation

Direct observation technique was developed by Barker and McConnell 54 years ago. This

method was used for detecting errors in drug administration (Barker and Mc, 1962). It is

considered the "gold" standard for detecting medication errors, in which a trained observer

accompanies the person giving the medications and witness the preparation and

administration of each dose (Barker et al., 2002a). It can be disguised where the nurse is

unaware of the precise goal of the observation (Barker et al., 2002a, Cohen, 2007).

21

Among medication errors detecting methods that have been used, direct observation was

more efficient and accurate than reviewing charts and incident reports (Barker et al., 2002b,

Lisby et al., 2005, Haw et al., 2007, Berdot et al., 2012, Vazin and Delfani, 2012). Disguised

observation method has some disadvantages (Barker, 1980). It is physically and mentally

demanding for the observer where the observer should be trained with knowledge of

medication names, and have the ability to read physician orders (Cohen, 2007). This method

requires events that are visible, predictable, and of limited duration in addition to employing

observers that are experienced, such as pharmacists and nurses. The observer should be

trained to be objective, unobtrusive, and nonjudgmental. The key advantages of observation

method are: easily understood; data are easy to use for problem identifying and available

within hours; objective and does not assign blame; and defensible, with all doses being

examined and errors witnessed (Barker et al., 2002a).

1.7.2. Chart review

Chart review method can be used to detect medication errors and adverse events related to the

medication use process (Cohen, 2007, Morimoto et al., 2011). The review focus on specific

areas of the medication use process (ordering, documenting and transcribing). This explains

why some studies found increased errors in particular areas compared to others (Wright,

2010). The review often involved a specially trained pharmacist or nurse to examine all the

charts (Cohen, 2007).

This method can be used along with observation method to detect medication errors (Barker

et al., 2002b, Pasto-Cardona et al., 2009, Fahimi et al., 2009). Furthermore it can be used in

combination with self reports and review of medication records to detect errors and evaluate

the number of adverse events occurring as a result of those errors (Holdsworth et al., 2003,

Dibbi et al., 2006, Haw et al., 2007, Ben-Yehuda et al., 2011).

22

1.7.3. Incident reports

An incident report ''is a legally recognized report of a medication error that is written by a

hospital staff member who detects a medication or other unwanted incident'' (Hartwig et al.,

1991, Stump, 2000). Voluntary medication error reporting systems depends on the ability and

willingness of all health care professionals to detect and report errors as part of their routine

practice (Wakefield et al., 2005). It is important to understand the nurses' perceptions of the

medication error reporting process as they play major role in the medication administration

(Evans et al., 2006). This method shows an important strategy to address growing concerns

about the incidence of errors in healthcare system (Leape, 2002, Rosenthal J and M., 2004).

A culture of safety that promotes voluntary reporting of medication errors without threats of

disciplinary action is provided by many groups including the Joint Commission on

Accreditation of Healthcare Organizations, the Food and Drug Administration (FDA), the

U.S. pharmacopeia (USP), and the Institute for Safe Medication Practices (ISMP) (Phillips,

2002). The advantages of an incident report are it provides an ongoing reporting mechanism

for an entire hospital, compared with observational studies of medication errors sample only

selected time periods in certain patient care areas (Tribble et al., 1985); and low cost 6.71$

(Lunik and Gaither, 1991). While the disadvantage of this method is that the time spent per

patient in reviewing incident reports was significantly more than the time spent per patient in

observational studies (Shannon and De Muth, 1987). There are two reporting programs the

Med Watch program that coordinated by the FDA, USP and the Medication Error Reporting

(MER) program (Watch, 2004, USP). In addition to the MEDMARX which is an anonymous

USP software reporting program that was designed to report, track, and detect medication

errors within the health care systems (USP).

23

1.7.4. Anonymous Self Reports (questionnaires)

Anonymous Self Reports such as questionnaires provide a mean by which the person

committing or witnessing an error can report the mistake but not be associated with it (Barker

and Mc, 1962). The limitation of this method is that it does not give the accurate perception

regarding medication errors (Sarvadikar et al., 2010). Many studies were conducted to detect

medication errors using the questionnaire method (Blegen et al., 2004, Evans et al., 2006,

Chiang and Pepper, 2006, Bayazidi et al., 2012). On the other hand low cost and the ability of

staff to avoid the fear of disciplinary action are the advantages of this method (Sarvadikar et

al., 2010).

In 1995, Jill Gladstone designed a questionnaire with the purpose of establishing nurses'

perceptions to the causes of drug errors, their views on the reporting of drug errors

(Gladstone, 1995). This questionnaire is used till now in many medication errors studies for

the same purposes. For example, in Jordan, few studies were conducted using the modified

form of Gladstone's questionnaire (Mrayyan et al., 2007, Al-Shara, 2011, Mrayyan and Al-

Atiyyat, 2011, Mrayyan, 2012).

1.7.5. The critical incident technique

The critical incident technique is an event-sampling method that involves in-depth analysis of

a large number of individual errors with the goal of identifying common causal factors

(Safren and Chapanis, 1960, JW., 1986). This method can involve direct observation of

subjects or interviews of people who have committed an error, of sample size ranges from

100 to several thousand critical incidents that is based on the complexity of the behavior

being evaluated, with minimum sample size that is reached when no new behaviors are

observed. The advantage of this method over observation is the consideration of subjective

information obtained from the participants relative to the causes of the errors detected.

24

However the difficulty of interpreting the data and developing appropriate solutions is

considered as a disadvantage (Flanagan, 1954).

1.7.6. Computerized Monitoring

Computerized monitoring can not only screen for orders and test results possibly associated

with an error or adverse events but can also alert hospital personnel that follow-up is needed

to confirm the error and treat the patient if necessary (Classen et al., 1991, Bates and

Gawande, 2003). However when comparing this method with chart review and voluntary

reporting in terms of monitoring adverse events, the chart review was more efficient than

computerized monitoring and voluntary reporting (Jha et al., 1998). Using a retrospective

computerized analysis of outpatient medication records resulted in detection of 5.5 adverse

events per 100 patients (Honigman et al., 2001).

Other error detection method include stimulated self report using interview (Barker et al.,

1984, Gladstone, 1995, Sanghera et al., 2007), attending medical rounds to listen for clues

that an error has occurred (Andrews et al., 1997), detecting omission errors on the basis of

doses returned on the medication cart (Goldstein et al., 1982, Hoffmann et al., 1984), urine

testing as evidence of omitted drugs and unauthorized drug administration (Ballinger et al.,

1974), examining death certificates (Phillips, 2002), attending nurse change of shift report

(Baker, 1997), comparing MARs with physician orders (Fontan et al., 2003), comparing

drugs removed from an automated dispensing device for a patient with physician order,

including overrides (Kester et al., 2006) and data mining (Runciman et al., 1993).

1.8. Prevention of medication errors

An analysis of medication errors can help healthcare professionals and managers identify

why they occur and provide insight into how to make improvements to prevent them (Bailey

et al., 2011). The reduction of medication errors is a very important issue for any health care

25

system, and hence it should work towards reducing medication errors through technology,

monitoring and education (Meadows, 2003). The goal of preventing medication errors is to

have a health care system where it is harder to do something wrong and easier to do

something right (Kohn et al., 2000).

1.8.1. Policies and procedures for prevention of medication errors

Medication use process is a part of everyday nursing practice. To ensure safe administration

of medications; nurses are subjected to a range of practices and procedures related to

administration. The major focus is directed toward prevention, control and management of

medication errors (Gibson, 2001). These policies and procedures are Normalizing judgment,

Hierarchical observation, and Examination (Foucault, 1977)

Normalizing judgment

''Is the process through which a norm is established, against individuals who commit an error''

(Foucault, 1977).

Hierarchical observation

''Is the process that helps the health care professionals who are in charge to be able to view all

other professionals below them in identifying individuals who have committed errors''

(Foucault, 1977). There are a range of observational procedures and techniques specifically

directed at nurses to prevent them from making errors. These include the ‘five rights’ and the

‘10 golden rules’ (Wolf, 1989, Morris, 1999). The 'five rights’ procedure is "the right

medication to the right patient in the right dose, by the right route, at the right time''(Wolf,

1989). Following the '' five rights'' rule, errors will not occur (Wolf, 1989, Sullivan et al.,

2005). Morris stated that failing to adhere to a basic rule like this not only increases the

chance of a drug error, but it is also important in court if the nurse is being sued for

26

negligence (Morris, 1999). Furthermore this procedure has been expanded to the 10 golden

rules of the medication administration namely, the right medication in the right dose, to the

right person, by the right route, using the right dosage form, at the right time, with the right

documentation, monitoring, patient history profile, and patient education (Gibson, 2001).

Examination

Is a measurement technique that differentiates and judges the competence, knowledge or skill

of an individual, comparing one with another (Foucault, 1977).

The health care systems should respond to prevent medication errors with safety rules and

procedures. These systems should involve adequately trained and supervised personnel,

adequate communications, appropriate work environments, reasonable workloads, effective

drug handling systems, and quality management (ASHP, 1993, Katz-Navon et al., 2005).

Care and consideration must be given in assigning personnel involved in the medication use.

Furthermore the quality improvement program should include a system for monitoring,

reviewing, and reporting medication errors to identify and eliminate the factors contributed to

the occurrence of these errors. Additionally, the educational programs should be directed to

discuss medication errors, their causes, and how to prevent them (Anderson, 1971, ASHP,

1980, Davis et al., 1981, Ingrim et al., 1983, ASHP, 1984, Barker et al., 1984, ASHP, 1985,

JW., 1986, ASHP, 1988, Fuqua and Stevens, 1988, ASHP, 1989, Cohen and Davis, 1990,

ASHP, 1991, ASHP, 1992, Organizations, 1992).

27

1.8.2. Using Information Technology to prevent medication errors

Information Technology (IT) can provide tasks not possible with manual processes (Kohn et

al., 2000). The use of IT can reduce the frequency of different types of medication errors

(Leape et al., 1995, Bates et al., 1998, Bates et al., 1999, Bates, 2000, Bates and Gawande,

2003). The strategies for preventing errors include tools that can improve communication;

make knowledge more readily accessible; perform checks in real time; assist with

monitoring; and provide decision support (Bates and Gawande, 2003). These include

computerized physician order entry, robots for filling prescriptions, bar coding, automated

dispensing devices, and computerization of the medication administration record (Figure, 2)

(Bates, 2000). Use of IT will not replace people but allow the health care professionals to do

their job in the best and safe manner (Sheridan and Thompson, 1994).

28

Figure 2. The role of Information Technology by stage in the medication use process

(Bates, 2000)

29

2. Aim and objectives of the study

In view of the fact that medication errors in Jordan are underestimated, therefore the aim of

this study is to detect and evaluate medication errors using disguised direct observation and

chart review methods in Jordan University Hospital.

Objectives of this study include the following:

1- To detect and evaluate the rate and frequency of all types of medication errors.

2- To categorize the severity of medication errors, and

3- To determine the associated risk factors of medication errors.

31

Chapter Two

Methodology

31


1. Approval of the study

The study was approved by the Institutional Review Board (IRB) at Jordan University

Hospital (JUH) (Appendix 1). Conducting this study was endorsed by Dr. Nathir Obeidat, a

Consultant Pulmonologist and director of the internal medicine department at JUH.

2. Description of the drug distribution system at JUH

Since this study aimed to evaluate and detect medication errors; it was essential for

researchers to be familiar with the drug distribution policy at JUH. The drug distribution

system used at JUH is the ward stock system. Figure 3 illustrate the mechanism of this drug

distribution system. The first step includes physician handwritten prescription(s) on the

Medication Administration Record (MAR). An example of regular prescription is showed in

(Figure 4). Other types of prescriptions on MAR are shown in (Appendix 2). Then

prescriptions are sent to the pharmacy, where all prescriptions are transcribed into the

pharmacy information system that is provided with a labeling machine (step 2). The

transcribed orders are dispensed by the pharmacist. Each medication is dispensed individually

inside a see-through suitably sized plastic bag with a printed label sticker quoting the name of

medication, dosage, expiry date, and time for use (step 3). Examples of transcribed printed

labels and dispensed medication are shown in (Appendix 3). For each ward side, one staff

nurse is responsible for medication preparation and administration. The medications are

prepared and administered by the nurse regarding each medication cycle using a tray (steps

4&5).

32

*MAR=Medication Administration Record.

Figure 3. Mechanism of drug distribution system at JUH

Figure 4. Example of a regular physician order

1-Physician orders (Handwritten

prescriptions on MAR*)

2-Transcription of prescriptions by the pharmacist

from MAR to the patient's

computerized file

3- Dispensing of medications

by the pharmacist

according to the transcribed

labels

4-Preparation of medications by the nurse according to the physician order

5- Administeration of medications by

the nurse

33

3. Study population

3.1. Patient selection and recruitment

Patients in the internal medicine ward (sixth floor) of JUH were selected. Up to 10 inpatients

were selected for observation during medication administration session on daily basis and

informed consent was obtained (Appendix 4). The selection of patients was based on the

study's inclusion and exclusion criteria.

3.2. Inclusion Criteria

Adult inpatients (age ≥ 18), Arabic speaking and have the ability and willingness to provide

information.

3.3. Exclusion Criteria

Inpatients aged below than 18 and without MAR, those patients with mentally diseased

conditions, and those who refused to participate in the study were excluded.

4. Study design

This study is a cross-sectional prospective study of medication errors using two methods,

disguised direct observation and chart review methods over 6 months (from June to

December 2013) at the internal medicine ward of JUH.

4.1. Direct observation

Based on the direct observation method established by Barker and McConnell, this method

was conducted during the morning shift (10:00AM-3:00PM) for five days/week over the

study period. The observation included the nurse who prepared and administered the

medications. The observed nurses were not aware of the study's aim (disguised). The

observations were recorded on a data collection form specially designed for this study

(Appendix 5). All prepared and administered medications of the selected patients were

34

recorded and compared with eligible prescriptions in the patient's MAR. Any discrepancy

between the prepared/administered medication and that prescribed in the MAR was recorded

as an administration error according to the study's criteria of medication errors identification

(Appendix 6).

4.2. Chart review

Following the chart review method that was published previously (Barker et al., 2002b, Lisby

et al., 2005), the reviewing verified that all prescriptions in the MAR were identical to the

prescriptions in the transcribed labels, and examined whether the prescriptions in the MAR

were unambiguous. The MARs that were included in the observational step screened for

medication errors during the same shift (8:00-10:00) at the ward's pharmacy were medication

transcription and dispensing over (five days/week). All reviews were recorded on a data

collection form specially designed for this study (Appendix 7). Any discrepancy between the

prescribed medication and that transcribed by the pharmacist was recorded as a transcription

error. In addition, any discrepancy between the transcribed label of prescribed medication and

that dispensed was considered as a dispensing error (Appendix 6).

5. Data collection

5.1. Characteristics of the ward and nurses

The internal medicine ward at the sixth floor in JUH was examined regarding the number of

beds, number of shifts, and number of nurses per shift. The characteristics of the nurses

working in the ward were reviewed regarding the number of staff nurses; nurse assistants;

nursing students; and registered nurses. The nursing experience in the ward was also

examined.

35

5.2. Characteristics of the nurse observed

The characteristics pertinent to each observed nurse were recorded on a data collection form

specially designed for this study (Appendix 8). These involved the nurse's age; gender; years

of experience in the ward; and educational level. The characteristics of workload pertinent to

each observed nurse was also collected, these included the patient to nurse ratio, I.V infusion

per nurse, and number of patient admission/discharge.

5.3. Demographic characteristics of patients and their medical profile

Data was collected for each patient from his/her medical files, and personal interview. All

information used was gathered using a patient data collection form designed for this study

(Appendix 9).

5.3.1. Interviews (patient questionnaire)

- Demographic details (age, gender, marital status, nationality, and education level).

- Life style (smoking, caffeine intake, and alcohol consumption).

5.3.2. Medical files

Information that was obtained from the patient medical files included:

- Chief Complaints (primary diagnosis).

- Comorbidities (co-existing acute or chronic medical conditions).

- Prescribed medications (Physician orders on the MAR).

5.4. Medication error detection

Medication error detection was done by the observer (a well trained clinical pharmacist in

medication errors). During each observation day, a list of up to 10 patients' MARs was

selected for error reviewing using the chart review method at the ward's pharmacy where

36

medications were dispensed once daily. The following sections were evaluated: Physician

orders in the MAR, transcribed labels, and dispensed medications. Then the nurse responsible

for the selected MARs was directly observed in a disguised manner during medication

preparation at the ward's treatment room and administration of medications to the ward's

inpatients. The nurse prepared and administered the medications during his/her shift only. In

regard to the observation method used, the following sections were evaluated: Physician

orders on MAR, prepared and administered medications.

The criteria for identification of medication errors in this study were similar to that used by

(Lisby et al., 2005). Medication administration errors detected were classified into ten

categories similar to that used by other authors (Chua et al., 2010, Barker et al., 2002b, Vazin

and Delfani, 2012) (Appendix 6). Figure 8 illustrate the error detection mechanism using

disguised direct observation and chart review methods.

Resources used during the medication error detection process:

Clinician's Pocket Drug Reference, 2013.

Med Notes 3rd

Edition (Pocket Drug Guide).

37

*MAR=Medication Administration Records

Figure 5. Medication errors detection mechanism using disguised direct observation and chart

review methods

38

6. Data analysis

To measure the identified medication errors, the collected data were entered and tabulated

using the Statistical Package for Social Sciences (SPSS) software package 11.5.

6.1. Descriptive analysis

The collected data of this study included:

Patients' demographic data (as mentioned in data collection), chief compliant for

hospitalization (diagnosis), co-existing medical conditions, doses of different

medications and types of detected medication errors.

Nurses' data: age, gender, educational level, and experience level in the ward.

Workload characteristics included: patient to nurse ratio, number of I.V. infusion per

nurse and the number of patient admission/discharge during the observation shift.

Descriptive analysis was run on all the variables in both data sets. Some of these variables

were described as (%), others as means. Moreover, some of patients' variables were

converted to charts to give more clarified picture about the characteristics of patients.

6.2. Medication error rate (MER) and frequency of detected errors

The medication error rate (MER) was calculated by dividing the number of errors by the

number of opportunities for errors and multiplied by 100. An opportunity of error (OE)

included any dose given as well as any dose ordered but omitted. The frequency of each type

of medication errors was described along with associated percentage.

39

6.3. Severity assessment of medication errors

The seriousness of detected medication errors was identified using the NCC MERP index for

categorization of medication errors. The categories were tabulated for each error stage

detected in this study and described as frequencies. This was done by the observer, consultant

physician and two specialists in clinical pharmacy.

6.4. Regression analysis

As a result to the nature of the data collection process, a compiled data set was generated by

aggregating the two data sets. Accordingly a multiple Univariate analysis was conducted by

adopting inter-method for regression analysis to determine the risk factors associated with the

detected errors. Correlation coefficient matrix was generated for all independent variables as

well as the dependent variable. A 95% confidence interval (CI) was used for the individual

value of the response variable.

Each independent varible was measured with the model of the dependent variable (total

detected errors). These independent variables were: doses given, doses prescribed, length of

hospitalization, nurses' characteristics (age, gender, educational level, experience in the ward)

and nurses' workload (patient to nurse rasio, patient admission, patient discharge, and number

of I.V. infusions/nurse).

6.4.1. Assumptions for the Univariate regression

The following assumptions for the regression models were all validated: Linearity of the

relationship between dependent and independent variables; and nature of the collected data

which both of these two assumptions were addressed through using the Univariate regression

analysis.

41

6.4.2. "Goodness of fit" of regression model

R2 is the "coefficient of determination" indicating how much of the variance in the dependent

variables is explained by the model. An R2 value, however, tends to give an optimistic

overestimate of the true value in the population. Hence an adjusted R2 value given by SPSS

can "correct" this value to provide a better estimation of the true population value. Therefore,

the adjusted coefficient of variation was used to measure the goodness of fit of the regression

model.

6.4.3. Interpreting the results from the regression models

The regression model in the results section presented the variables included in each original

Univariate regression model (as summary for each Univariate model). Variables are

presented with their R2, and p values to show whether each variable was making a

statistically unique contribution to the model (P<0.05) or not. R2 values ranged from 0 to 1,

with 1 representing a perfect fit between the variables and the regression line, and 0

representing no statistical explanation between the variables and the regression line.

41

Chapter Three

Results

42


Two hundred and eighty three patients were included in this study. Fifteen nurses were

observed during the morning shift. Rate of errors detected was 12.6% (803) over the six-

month period (from June to December 2013) of data collection.

1. Characteristics of the ward and nurses

This study was conducted in 54-beds internal medicine ward (sixth floor) of JUH. There were

3 rotating shifts in the ward (morning, evening, and night) with relatively 6-8 nurses working

during each shift. The total number of nurses working in the ward was 37 (16 males and 21

females) including 25 staff nurse, and 12 nurse assistants. Of total 37 nurses, 26 (13 males

and females) had more than 2 years of experience in the ward and 4 nurses had less than 1

year experience. General characteristics of the ward and nurses are illustrated in (Table 2).

There were

Table 2. Characteristics of the ward and nurses

Characteristics of the ward

Number of beds

Number of shifts

Number of nurses per shift

Characteristics of the nurses

Registered nurses working full-time in the ward

Interns and temporary staffing agency nurses

Number of staff nurses

Number of nurse assistants

Nursing school students

Number of nurses working in the ward

> 2 years experience in the ward

1-2 years experience in the ward

< 1 year experience in the ward

No

54

3

6-8

1

0

25 (14 F¹, 11² M)

12 (7 F,5 M)

10

37 (21 F, 16 M)

26(13 F,13M)

7 (4 F,3 M)

4 (F)

¹Female, ²Male

43

2. Characteristics of the nurses observed

This study covered observation of 15 staff nurses during 84 observation days. It is to be noted

that staff nurses are authorized to prepare/administer medications according to the JUH

policies and procedures. Female nurses represented 53.3% of the total observed nurses.

Average age was 25.86 years (SD= 0.83) with the majority of nurses being in the age group

of 20-30. Majority of the nurses had baccalaureate degree in nursing (n=14, 93.3%) and 1

nurses with master degree in nursing (6.7%). During the study period, the median number of

patient admission to the ward and discharge was 1; in addition, the median number of patients

and I.V. infusion prepared by each nurse was 22. The given nurses' work experience was

measured by the number of years the nurse reported working in the ward during his/her

career. Four nurses reported less than 1 year experience (26.7%), six nurses reported 1-2

years of experience (40%), and five nurses reported more than 2 years of experience (33.3%).

Table 3 represents nurses’ characteristics.

Table 3. Characteristics of the nurses observed (n=15)

Nurses

Age, Mean ±SD (range), years

Gender, no. (%)

Male

Female

Educational level, no. (%)

B.Sc¹ Nursing

M.Sc² Nursing

Experience in the ward, no. (%)

< 1 yr

1-2 yrs

> 2 yrs

Nurse workload Patients per nurse

I.V drug infusions per nurse

Patient's admission during observation period

Patient's discharge during observation period

N=15

25.86± 0.83 (20-30)

7 (46.7%)

8 (53.3%)

14 (93.3%)

1 (6.7%)

4 (26.7%)

6 (40%)

5 (33.3%)

Median

22

22

1

1

¹ Bachelor of Science, ² Master of Science

44

3. Demographic characteristics of patients and their medical profile

3.1. Demographic characteristics of patients

The study population consisted of 283 patients observed during the same 84 days observation

period. Average age was 54.2 years (SD 17.59) with the majority of patients being in the age

group of 51-60. Female patients represented 70% of the population. Majority of the patients

were Jordanian (n=280, 98.9%) and 3 patients were from other Arabic nationalities.

Majority of patients reported university level education (n=178, 62.9%), and 13 patients

reported lower secondary level education (4.6%). Majority of patients were married (n=229,

80.9%) and 2 were divorced (7%). Few patients observed were smokers (n=36, 12.7%) and

12 were ex-smokers (4.2%). Nearly all patients were caffeine consumers (n=274, 96.8%),

drinking about 1-2 cups of coffee and/or tea a day. The mean length of patient hospitalization

per day was 7.39 days (SD 8.39), ranging from 1 to 65 days. Table 4 present the

characteristics of the patients included in the study.

Table 4. Demographic characteristics of the patients (n=283)

Patient demographics (n= 283)

Age, Mean ±SD (range) , yr

Gender, no. (%)

Male

Female

Nationality, no (%)

Jordanian

Iraqi

Syrian

Palestinian

Educational level, no (%)

Lower secondary school

Upper secondary school

University tertiary level

54.2±17.59 (51-60)

85 (30%)

198 (70%)

280 (98.9%)

1 (0.4%)

1 (0.4%)

1 (0.4%)

13 (4.6%)

92 (32.5%)

178 (62.9%)

45

Marital status, no (%)

Single

Married

Widow

Divorced

Smoking, no (%), (range of cigarettes per day)

Ex-smoking, no (%)

Caffeine intake, no (%), (range of coffee/tea

cups/glasses per day)

Alcohol consumption, no (%)

Length of hospitalization, Mean ±SD (range),

day

46 (16.3%)

229 (80.9%)

6 (2.1%)

2 (7%)

36 (12.7%), (20 cigarettes (1 pack)

12 (4.2%)

274 (96.8%), (1-2 cups/glasses)

1 (0.4%)

7.39± 8.39 (1-65)

3.2. Patients' chief complaints for hospitalization

Regarding patients' chief complaints (primary diagnosis), results showed that the most

frequent chief complaints encountered were infection (n=72, 25.4%), cancer (n=58, 20.5%),

and gastrointestinal disorders (n=28, 9.9%), while the least frequent chief complaint was

orthopedic disorders (n=5, 1.8%), (Figure 6).

46

*General diseases included unspecified reasons for hospitalization.

Figure 6. Patients' chief complaint for hospitalization at study entry (n=283)

3.3. Patients' comorbidities

Regarding patients' comorbidities (co-existing acute or chronic medical conditions), results

showed that the most frequent comorbidities encountered were hypertension (n=136, 48.1%),

diabetes mellitus (n=118, 41.7%) and Coronary Artery Disease (CAD) (n=65, 23%). The

least frequent comorbidity encountered was peptic ulcer disease (n=1, 0.4%), (Figure 7).

(12)* 4.2%

(58) 20.5%

(72) 25.4%

(20) 7.1%

(7) 2.5%

(25) 8.8% (21) 7.4%

(28) 9.9%

(16) 5.7%

( 9) 3.2% (5) 1.8%

(14) 4.9% (10) 3.5%

0.0

5.0

10.0

15.0

20.0

25.0

30.0P

erc

en

tage

Diseases Category

47

*CAD=Coronary artery disease, CVD=Cerebrovascular disease.

Figure 7. Patients' comorbidities at study entry (n=283)

3.4. Prescribed medications

During the 84 shifts observed, with each shift taking 7 hours, the observation of 2729 doses

of prescribed medications administered to the 283 patients involved was completed. The

mean number of doses prescribed of different classes of medications for each patient was

4.53 doses (SD 2.22), ranging from 2 to 12 doses per observation day. Among the different

classes of medications prescribed during this study, antimicrobials were the most frequently

prescribed in the ward (n=555, 20.34%), followed by gastrointestinal (n=334, 12.24%) and

anticoagulants (n=283, 10.37%). It was found that 615 of the prescribed medications were

(136)* 48.1%

(65) 23.0%

(118) 41.7%

(1) 0.4%

(16) 5.7% (7) 2.5%

(30) 10.6%

(11) 3.9%

(36) 12.7%

(3) 1.1%

0.0

10.0

20.0

30.0

40.0

50.0

60.0P

erc

en

tage

Comorbidities

48

given by the oral route of administration (27.53%) while 5 were given by the I.M. route

(0.22%). Table 5 represents the characteristics of drugs prescribed per each observation day.

Table 5. Characteristics of prescribed medications (n=283)

Drug Characteristics (n=2729)

No. of doses (of different medications) prescribed /per

patient during the study period, mean ±SD (range)

Drug class, no. (%)

Antimicrobials

Gastrointestinal

Anticoagulants

Cardiovascular

Diabetics

Vitamins

Chemotherapeutics

CNS¹

Respiratory

Electrolytes

Sedatives/analgesics

Hematologic

Hormones

Others²

Route of administration

Oral

I.V3. Bolus

I.V. Infusion

Subcutaneous

Inhalation

Intraocular

Rectal

I.M4.

Others5

4.53±2.22 (2-12)

555 (20.34%)

334 (12.24%)

283 (10.37%)

238 (8.72%)

235 (8.61%)

134 (4.91%)

100 (3.66%)

98 (3.59%)

93 (3.41%)

93 (3.41%)

86 (3.15%)

19 (0.70%)

1 (0.04%)

460 (16.86%)

615 (27.53%)

433 (19.38%)

432 (19.34%)

406 (18.17%)

96 (4.30%)

63 (2.82%)

14 (0.63%)

5 (0.22%)

170 (7.61%)

¹CNS=Central Nervous System

²Other drug class, (e.g. Corticosteroids, anti gout agents, antihistamines, and miscellaneous agents) 3Intra-Venous 4Intra-Muscular 5Other route of administration, (e.g. local, gargles)

49

4. Medication error rate (MER) and frequency of detected errors

Out of the existing 6396 total opportunities for error, 803 errors were detected (12.6%) with

2.8 errors per patient. Of 3667 opportunities (doses given plus doses omitted) identified

during observation session, there were (20.2%, n=739) administration errors. During the chart

review session, a total of 2729 opportunities there were (1.5%, n=40) transcription errors,

(0.8%, n=21) dispensing errors, and (0.1, n=3) prescribing errors. Table 6 represents the rates

and frequencies of all types of detected medication errors.

Table 6. Rate and Frequency of all types of detected medication errors (n=803)

Type of error No. of

errors

% within the

identified

errors

% within total

errors

Medication

Error Rate

Administration errors (no of

opportunities=3667)

Dose omission

Unauthorised dose

Wrong route

Wrong administration technique

Wrong time

Extra dose error

Total administration error

No of opportunities=2729

Transcription errors Omission

Wrong frequency

DC* order

Total transcription error

Dispensing errors

Wrong drug error

Wrong dosage form

Wrong quantity error

Total dispensing error

Prescribing errors

Wrong route

Wrong prescription instructions

Total prescribing error

Total errors (total no of

opportunities=6396)

55

3

1

7

667

6

739

33

5

2

40

4

2

15

21

1

2

3

803

7.44

0.41

0.14

0.95

90.26

0.81

100.0

82.50

12.5

5.00

100.0

19.05

9.52

71.43

100.0

33.33

66.67

100.0

6.85

0.37

0.12

0.87

83.06

0.75

92.03

4.11

0.62

0.25

4.98

0.50

0.25

1.87

2.62

0.12

0.25

0.37

100.0

1.5

0.1

0.03

0.2

18.2

0.2

20.2

1.2

0.2

0.1

1.5

0.1

0.1

0.5

0.8

0.04

0.1

0.1

12.6

*DC=Discontinued.

51

Administration errors were the most common errors detected (92.03%); followed by

transcription (4.98%), dispensing (2.62%), and prescribing (0.37%) as illustrated in Figure 8.

Figure 8. Types of detected medication errors (n=803)

Administration errors were the most common detected errors in this study. Of the total 3667

opportunities for error, a rate of medication administration errors of 20.2% was found. This

rate was decreased to 2% when wrong time errors were excluded. Wrong time errors were the

most frequent administration errors found (n=667, 90.26%), followed by omission (n=55,

7.44%), wrong administration technique (n=7, 0.95%), extra dose errors (n=6, 0.81%),

unauthorized dose (n=3, 0.41%), and wrong route (n=1, 0.14%), (Figure 9).

Out of the different classes of medications prescribed to the patients during this study,

antimicrobials (mainly Tienam® (Imipenem & Cilastatin)) were associated with most of the

detected errors (p<0.001, Chi square test). Corticosteroids (mainly Hydrocortisone) came

next in this regard (p<0.033), followed by the antihistamines (mainly Allerfine®

(Chlorphenamine Maleate) (p<0.033) and anti-gout agents (mainly Zyloric®, Allopurinol)

(p<0.033).

92.03 %

4.98 % 2.62 % 0.37 %

Administration errors

Transcription errors

Dispensing errors

Prescribing errors

51

Figure 9. Types of medication administration errors (739)

Transcription errors were the second errors detected. Of the total 2729 prescriptions were

screened, there were 1.5% transcription errors. Omission was the most frequent error type

during transcription (n=33, 82.5%) followed by wrong frequency error (n=5, 12.5%) and

transcription of discontinued order (n=2, 5%), (Figure 10).

Figure 10. Types of transcription errors (n=40)

Errors in dispensing and prescribing stages were also identified in this study. The rate of

dispensing errors was 0.8% with the most frequent type of dispensing errors found being

7.44% (55) 0.41% (3) 0.14% (1) 0.95% (7)

90.26% (667)

0.81% (6) 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Omission UnauthorizedDose

Wrong Route Wrong AdminTechnique

Wrong Time Extra DoseError

Freq

uen

cy o

f er

rors

82.5% (33)

5% (2) 12.5% (5)

0

10

20

30

40

50

60

70

80

90

Omission Discontinued order Wrong FrequencyFreq

uen

cy o

f tr

ansc

rip

tio

n e

rro

rs

52

wrong quantity error (n=15, 71.43%) followed by wrong drug error (n=4, 19.05%) and wrong

dosage form (n=2, 9.52%), (Figure 11).

Figure 11. Types of dispensing errors (21)

Additionally the rate of prescribing errors was 0.1% including wrong instruction errors (n=2,

66.67%) and using the wrong route (n=1, 33.33%), (Figure 12).

Figure 12. Types of prescribing errors (n=3)

19.05% (4)

9.52% (2)

71.43% (15)

0

10

20

30

40

50

60

70

80

Wrong Drug Error Wrong Dosage Error Wrong Quantity Error

Freq

uen

cy o

f d

isp

ensi

ng

erro

rs

33.33% (1)

66.67% (2)

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

1 2

Freq

uen

cy o

f p

resc

rib

ing

typ

es

Wrong Route

Wrong PrescriptionInstructions

53

5. Severity of medication errors

The detected medication errors were evaluated for the severity using the NCC MERP Index

for categorization of medication errors (NCCMERP, 2005). The majority of detected errors

(n=743, 92.5%) were categorized as (C= error reached the patient with no harm). Only 1

detected error was categorized as require monitoring (0.1%, category D). The other levels of

severity such as (error with permanent harm, error resulted in death) were not identified in

this study. Table 7 presents the level of medication errors and their severity categorization.

Table 7. Severity categories of detected medication errors

Harm category*

Administration errors (n=739)

Dispensing errors (n=21)

Transcribing errors (n=40)

Prescribing errors (n=3)

Total % (n=803)

No error (A) Error didn't reach the patient (B) Error reach the patient with no harm (C) Error require monitoring (D) Error with temporary harm (E) Error require hospitalization (F) Error with permanent harm (G) Error require intervention to sustain life (H) Error resulted in death (I)

1 (0.1%) 737 (99.8%) 1 (0.1%)

20 (95.2%) 1 (4.8%)

35 (87.5%) 5 (12.5%)

3 (100%)

59 (7.3%) 743 (92.5%) 1 (0.1%)

*According to the National Coordinating Council for Medication Error Reporting and Prevention

54

6. Multiple Univariate regression analysis identifying risk factors for the identified

medication errors

Table 8 shows the independent variables included in the multiple Univariate regression

modelling the dependent variable (total detected errors); the R2 values, and p values of each

model at 95% Confidence Interval (CI). The risk factors associated with the total detected

errors in this study included: nurse's experience in the ward (R2=0.456, p<0.042), no of doses

given to the patient (R2=0.451, p<0.025), patient to nurse ratio (R

2=0.409, p<0.010 at 99%

CI), length of hospitalization (R2=0.399, p<0.049). Nurses' gender was not significantly

associated with errors (R2=0.0, p<0.059).

Table 8. Multiple Univariate regression analysis showing risk factors for the identified

medication errors (dependent variable is the total detected errors=803)

Variables R2

p value

Nurse characteristics

Age

Gender

Educational level

Experience in the ward

Nurse workload

Patient admission during observation

Patient discharge during observation

Patient to nurse ratio

No of I.V. infusion per nurse

Other factors

No of doses given to the patient

No of doses prescribed

Length of hospitalization

0.121

0.0

0.148

0.456

0.315

0.231

0.409

0.154

0.451

0.015

0.399

0.045

0.059

0.027

0.042

0.039

0.047

0.010*

0.021

0.025

0.001*

0.049

*99% CI was obtained for this value. ''R2'' is the coefficient of determination, ''R

2'' values with their ''p'' values

show whether each variable is making a statistically unique contribution to the model (p<0.05) or not. ''R2''

ranges from 0 to 1, with 1 representing a perfect fit between the variables and regression line, and 0 representing

no statistical explanation between the variables and the regression line.

55

7. Examples of detected medication errors

Examples were listed for all the detected medication errors in this study for each category and

type of error. These examples are presented as listed tables and pictures (photos were taken

during the observation).

7.1. Examples of medication administration errors

Table 9 presents the examples of medication administration types of errors that have been

detected in this study.

Table 9. Examples of medication administration errors (n=739)

Type of

administration error

Examples

Dose omission¹ (n=55)

Unauthorized dose²

(n=3)

Wrong route³ (n=1)

Oral doses not given: Fluconazole 150mg, Flagyl® (Metronidazole) 500mg,

Aspirin 100mg, MST* 30mg, Myogesic® (Paracetamol 350mg+Orphenadrine

45mg), Ofloxacin 200mg, Warfarin 3mg, Vesicare® (Solifenacin) 5mg,

Pilocarpine 4% oral dropper.

Forgot to administer Decadrone® (Dexamethasone) Nebulizer to the patient

Forgot to administer Flixonase® (Fluticasone, nasal spray) to the patient

S.C*. doses not given: Clexane® (Enoxaparin) 40mg, Insulin (Act rapid®, 40

I.U), Insulin (Mixtard®, 5 I.U), Heparin 5000 I.U*

Forgot to give Glycerin suppository to the patient

I.V* bolus doses not given: Hydrocortisone 100mg, Oprazole (Omeprazole)

40mg,

I.V infusion doses not given: Tienam® 500mg, Albumin 10g%

Lactulose 15ml soln., Sopa-K® (Potassium gluconate) 15ml oral soln. syrup

not given

Clexane® 60mg S.C inj. administered although the order was on hold

Hydrocortisone 100mg I.V bolus administered to the patient without an order

Oprazole® 40mg I.V bolus administered to the patient without an order

Oprazole® 20mg tablet given to the patient instead of Oprazole® 40mg I.V

bolus

56

Wrong administration

technique4 (n=7)

Wrong time5 (n=667)

Administering Clexane® 40mg S.C inj. without wiping the administration site

Administering Heparin® 5000 I.U S.C inj. without wiping the administration

site

Not wiping the injection site before administering Neubogen® 300mcg S.C

inj. to the patient

Oral doses given at 10am-11am instead of 12md: Betaserc® (Betahistine,

16mg), Forlax® (Marcogol) 10g oral solution, Fluconazole 150mg

Oral doses given at 11am-12md instead of 2pm: Adalat® (Nifedipine) 40mg,

Amiodarone 200mg, Augmentin® (Amoxicillin+Clavulanic acid) 625mg,

Calcium oxalate 30g, Apo-K ®(Potassium chloride) 160mg, Asacol®

(Mesalazine) 400mg, Bismuth 120mg, Deflat® (Simethicone) 125mg,

Cefuroxime 500mg, Diazepam 5mg, Dipyridomole 75mg, Domperidone

10mg, Dulcolax® (Bisacodyl) 5mg, Flagyl 500mg, Gabatrex® (Gabapentin)

400mg, Lactulose 15ml soln., Nocuf syrup 15ml soln., Methyldopa 250mg,

Phenytoin 100mg, Plavix® (Clopidogrel) 75mg, Warfarin 5mg, Sopa-K 15ml

soln., Tegretol® (Carbamazepine) 200mg, Famodar® (Famotidine)40mg

I.V infusion doses given at 11-12 am instead of 2pm: Albumin 10g%,

Augmentin 1.2g, Tazocin® (Piperacillin+tazobactam) 4.5g, Flagyl® 500mg,

Tienam® 250mg, Tragocid® (Teicoplannin) 1g, Vancomycin 1g

S.C doses given 11-12am instead of 2pm: Allerfine® (Chlorpheniramine

maleate) 10mg, Clexane® 40mg, Innohip® (Tinzaparin) 4500 I.U,

Neubogen® (Filgrastim) 300mcg

I.V bolus doses given at 11-12am instead of 2pm: Antivote® 10mg,

Buscopan® (Hyoscine butyl bromide) 10mg, Decadrone® 16mg,

Augmentin® 1.2g, Hydrocortisone 100mg, Oprazole® (Omeprazole) 40mg,

Maxil® (Cefuroxime sodium) 750mg, Phenobarbital 60mg, Plasil

(Metoclopramide HCL) 10mg, Zofran® (Ondasetron) 24mg, Rocephine®

(Ceftriaxone) 1g

Cansidas® (Caspofungin) 50mg I.V Infusion given at 3pm instead of 12md

Deparn® (Citalopram) 10mg tablet given at 12md instead of 10am

Insulin Act rapid® 38 I.U S.C inj. administered after meal

Insulin Mixtard® 40 I.U administered with meal while it should be given 30

minutes before meal

Lansotec® (Lansoprazole) 30 mg capsule at 6am order given at 10am

57

Extra dose error6

(n=6)

Lasix (Furosemide) 40mg I.V bolus given at 11:30am instead of 10am

Oral doses given at 11am instead of 10am: Candesartan 16mg, Aspirin® plus

(Vit. C) 325mg, Amlocard® (Amlodipine) 5mg, Augmentin® 625mg tablet ,

Dexamed® (Dexamethasone) 0.5mg, Myogesic®, Loten® (Atenolol) 50mg,

Prednisolone 5mg, Vesicare® 5mg, Zomax® (Azithromycin) 250mg,

Prograf® (Tacrolimus) 1.0g.

Hydrocortisone 100mg I.V bolus administered to the patient instead of 50mg

Tienam® 500mg I.V Infusion administered instead of Tienam® 250mg

Warfarin 7.5mg tablet was given although the physician discontinued the

order

*MST=Morphine sulphate tablet, S.C=Subcutaneous, I.V. =Intravenous, I.U=International Unit 1Failure to administer an ordered does to the resident

2The administration of a medication to a resident for which the physician did not write an order or the

administration of a medication that is not authorized by a legitimate prescriber 3 The administration of a medication to a resident by a route other than that ordered by the physician

4 The use of an inappropriate procedure or improper technique in the administration of a drug

5 The failure to administer a medication to a resident within an hour before or after the scheduled administration

time 6 The administration of duplicate doses to a resident or administration of one or more dosage units in addition to

those that were ordered

7.2. Examples of transcription errors

Table 10presents the examples of transcription types of errors detected in this study

Table 10. Examples of transcription errors (n=40)

Type of transcription

error1

Examples

Omission (n=33)

Forgot to transcribe Albumin 10g% (1*3) order

Forgot to transcribe Gabatrex® 300mg (1*2) order

Forgot to transcribe Carvidolol 6.25mg (1*2) order

Forgot to transcribe Decadrone® 8mg (1*2)

Forgot to transcribe Isoket® (Isosorbide nitrate) 20mg (1*2) order

Forgot to transcribe Tavanic (Levofloxacin) 750mg I.V Infusion order

58

Wrong frequency (n=5)

Discontinued order

(n=2)

Thyroxin (100mcg) tablet (1*1) order was not transcribed

Valsartan 160mg tablet (1*2) order not transcribed

Myogesic® 2*3 order not transcribed

Calcicar® (Calcitriol) 500mg (2*3) order, transcribed by the pharmacist as

Calcicar® 500mg (1*3)

Carvidolol 6.25mg (1*2), transcribed by the pharmacist as Carvidolol

6.25mg (1*1)

Gabatrex® 400mg (1*3) order, transcribed into Gabatrex® 400mg (1*1)

Metformin 1g (1*3) order, the pharmacist transcribed the frequency as (1*2)

without asking the physician

Rocephine® 1g (1*2) order transcribed in (1*1)

Warfarin 7.5mg (1*1) order was transcribed although it was discontinued the

day before

Zyloric (Allopurinol) 100mg tablet (1*1) order was transcribed although it's

DC order

1 A transcription error is any discrepancy in: medication name; dose; dosage form; dosing regimen (dose, route,

frequency and duration); dose omission; and unordered drug

7.3. Examples of dispensing errors

Table 11 presents the examples of dispensing types of errors that were detected in this study.

Table 11. Examples of dispensing errors (n=21)

Type of dispensing

errors

Examples

Wrong drug error1 (n=4)

Antivote® (Metoclopramide HCL) 10mg dispensed instead of

Decadrone® 16mg (look-alike)

Isoket ® 40mg tablet dispensed instead of Inderal® (propranolol) 40mg

tablet (look-alike)

Lasix ® 20mg ampoule was dispensed instead of Antivote® 10 mg

ampoule

59

Wrong dosage form2

(n=2)

Wrong quantity error3

(n=15)

Vastarel® (Trimetazidine) 35mg tablet dispensed instead of Vesicare ®

5mg tablet

Nexium® (esomeprazole) 40 mg tablet was dispensed instead of

Oprazole® 40mg vial for inj.

Oprazole® 40mg vial dispensed instead of Oprazole® 20mg tablet

Rocephine® 500mg, 1 vial dispensed instead of 2

Tavanic® , 1 vial dispensed instead of 2

2 tablets of Glucophage® (Metformin, 500mg) dispensed instead of 3

3 vials of Amikacin 500mg dispensed instead of 2 vials

Augmentin® 1.2g vial for inj. (1*3) order, 2 vials dispensed instead of 3

Carvidolol 6.25mg tablet (2*2) order, 3 tablet were dispensed instead of 4

Creon® (pancrelipase, 6000 Units) capsules (3*3) order, 8 capsules

dispensed instead of 9

Doxidar® (Doxycycline, 500mg) capsule (1*3) order, 2 capsules

dispensed instead of 3

Hydrocortisone 100mg (1*3) vial for inj. 2 vials dispensed instead of 3

Omedar® (Omeprazole) 20mg tablet (1*2) order, 1 tablet dispensed of 2

One-alpha® (alfacalcidol) 1mcg tablet (1*2) order, the pharmacist

dispense 1 tablet instead of 2

Oprazole® 40mg vial for inj. (1*1) order, 2 vials dispensed instead of 1

Prednisolone 5mg tablet (1*2) order, 3 tablets dispensed instead of 2

Tienam® 500mg vial for inj. (1*4) order, 1 vial dispensed instead of 4

1 Occurs when a medication different that named in writing on a prescription is used to fill the prescription

2Occurs when the form of the medication used to fill the prescription differs from what the prescriber wrote

3 Occurs when the amount of medication dispensed to a patient differs from the amount ordered without

acceptable reason

61

7.4. Examples of prescribing errors

Table 12 presents the examples of prescribing types of errors that have been detected in this

study.

Table 12. Examples of prescribing errors (n=3)

Type of prescribing error1

Examples

Wrong route (n=1)

Wrong prescription

instructions (n=2)

Phyentoin10 mg capsule (1*1) ordered to patient with nasogastric tube

Order of Cytrabine 3.2g I.V Infusion over 12hrs written (1*1) instead

of (1*2)

(Taxotere, Docetaxel®) 160mg in 500ml Normal Saline N/S 0.9% as

I.V Infusion over 1 hr, the nurse confused whether it's over 1hr or 7hrs.

1A prescribing error is any discrepancy in: medication name; drug formulation; route; dose; dosing regimen;

date; signature; and instructions for use

The figures (13-14) below represent prescribing errors that were corrected by the physician as

soon as the nurse discovered them.

Figure 13. Prescribing error (1), Zyloric® (Allopurinol) to be prescribed for oral

administration only; however it was prescribed for I.V. administration

61

Figure 14. Prescribing error (2), Tienam® (Imipenem/Cilastatin) to be given 3-4 times daily;

however it was prescribed as to be given once daily

62

Chapter Four

Discussion

63

Chapter Four: Discussion

Medication error is a vital common problem in all healthcare systems around the world. This

problem may result in patient injury, increased health costs and liability claims. All health

care professionals have a responsibility in ensuring patient safety, eliminating risk factors and

implementing strategies to prevent the occurrence of medication errors (Lustig, 2000,

Jennane et al., 2011).

This study looked into the medical care delivered in one Jordanian hospital ward, by 15

nurses during 84 observation days. Comparing results of this study with previous studies is

not straight forward due to the many differences between the studies conducted in this field.

For example, in Prot et al, 485 nurses were observed during 271 observation days in 4

pediatric wards (Prot et al., 2005). In Berdot et al, 28 female nurses were observed during 72

rounds in 4 medical wards (Berdot et al., 2012). In Patanwala et al, 18 nurses were observed

for a total of 28 shifts in the emergency department (Patanwala et al., 2010). While Agalu et

al observed 9 nurses during one month period in one ICU ward (Agalu et al., 2012).

Differences in the study design led to these dissimilarities.

Differences in the nursing level of education and experience were also noted. In this study,

the majority of nurses observed were baccalaureate degree nurses with 1-2 years of

experience in the ward studied. While in Agalu et al, the majority of nurses observed were

diploma degree nurses with 3-6 months of experience in the ward. In Berdot et al, the nurses

observed had a median of 5 years of nursing experience and 3 years in the ward. The nurse

work load reported in this study was also different, for example it was higher than that seen in

Prot et al, where the median number of patient per nurse interaction and IV infusion delivery

was only 3 and 2 respectively compared to 22 for both in our study. These variations make

64

the comparisons more challenging, hence we recommend for future studies to consider a

uniform methodology that would allow for proper cross comparisons amongst the countries.

Direct observation method was chosen for this study based on the findings of previous reports

that observation is more efficient, reliable and objective when compared to other detecting

methods for medication errors (Barker et al., 2002b, Lisby et al., 2005, Haw et al., 2007,

Berdot et al., 2012). There were few studies conducted in Jordan regarding medication errors

(Mrayyan et al., 2007, Mrayyan and Al-Atiyyat, 2011, Mrayyan, 2012), whereas this study is

the first disguised observational study detecting medication errors. This method of detection

has many benefits mainly in detecting the true actions performed by the medical team, who

would otherwise perform their job accurately when they know that their actions are being

observed by an observer (Hawthorne effect) (Allan and Barker, 1990). A proof to this claim

was reported by Tissot et al 1999 later on, in an internal ICU in France where the rate of

administration errors using direct observation method in which nurses were aware of the

study's goal were less than that when disguised observational method was used (Tissot et al.,

1999). Although Observation is very time-consuming and tiring, however it is the most

effective method of diagnosing the maximum number of cases by the exhaustive revision of

medical orders and the most valid way for identifying the causes of administration errors.

This is in agreement with previous studies that followed this method for detecting medication

errors (Barker et al., 2002a, Barker et al., 2002b, Haw et al., 2007, Pasto-Cardona et al., 2009,

Chua et al., 2010, Berdot et al., 2012, Vazin and Delfani, 2012).

Absence of a standard definition of ‘medication errors’ across the studies adds to the

difficulty in comparing and contrasting the different result presented. Some of the studies

reported ‘medication errors’ per 1000 patient-day (Pasto-Cardona et al., 2009, Wilmer et al.,

2010, Jennane et al., 2011), while others took into account the opportunities for errors within

stages of medication use process (Tissot et al., 1999, Bruce and Wong, 2001, Dean et al.,

65

2002, Cousins et al., 2005, Lisby et al., 2005, Haw et al., 2007, Fahimi et al., 2008, Chua et

al., 2010, Al-Jeraisy et al., 2011, Berdot et al., 2012, Vazin and Delfani, 2012). This study

followed the final definition of ‘medication errors’, because it was the most popular.

The number of opportunities found in this study was high compared to that seen in similar

observational studies (Vazin and Delfani, 2012). The number of patients involved in this

study was 283, resulting in 6396 opportunities, while in Vazin and Delfani study, 38 patients

were involved only, with the number of opportunities being 5785. Other studies reported

lower number of opportunities per sample size. Lisby et al for example reported a lower

number of opportunities 2467 for the 64 patients involved in her study. All other studies in

this area reported lower number of opportunities (from 900 to 1423) for a variety sample size

of patients (from 108 to 336 patients) (Prot et al., 2005, Haw et al., 2007, Chua et al., 2010,

Berdot et al., 2012). It is clear that in comparison to the literature, this study reports one of

the highest opportunities which may reflect in more accurate results.

Patient variables were included in this study, from demographic characteristics to their

chronic medical conditions. Complete picture of detected medication errors were prepared.

Previous studies didn’t include many of the patient’s variables included in this study (e.g.

nationality, educational level, life style, and chronic existing conditions) (Bruce and Wong,

2001, Barker et al., 2002b, Lisby et al., 2005, Haw et al., 2007, Mrayyan et al., 2007, Font

Noguera et al., 2008, Fahimi et al., 2009, Kadam et al., 2009, Pasto-Cardona et al., 2009,

Chua et al., 2010, Patanwala et al., 2010, Karna et al., 2012, Berdot et al., 2012). This fact

makes this study unique when it comes to exploring associations between different patient

factors and medication errors detected.

Medication errors were common in the ward where this study was conducted. A total of 803

errors were detected, equaling to 2.8 errors per patient. The overall rate of errors obtained

66

during the study period was 12.6%, which is comparable to what was reported by the

different previous studies ranged from 7% to 43% (Lisby et al., 2005, Kadam et al., 2009,

Karna et al., 2012, Vazin and Delfani, 2012). Reasons behind the differences between the

errors rates reported across the studies could be attributed to the differences in the hospital

setup, number of beds, number of patients followed, severity of the complicated medical

conditions and number of drugs required by the patients in the medical wards investigated.

Discrepancies were found between the rate of medication errors of this study and that

reported in the study by (Mrayyan et al., 2007), where Gladstone's questionnaire was used for

data collection in 24 Jordanian hospitals. Although both studies were conducted in Jordanian

hospitals, these discrepancies are obviously attributed to the differences between their

objectives and the methodologies used. Records of Jordanian nurses' perceptions about

medication errors related issues as well as a 42.1% rate of medication errors reporting to the

nurse managers were reported in their study.

In comparison with previous studies, the number of prescribed medications during the study

period was higher than that seen in similar studies (Vazin and Delfani, 2012). The highly

prescribed drug class seen during the study period was antimicrobials (20.34%) followed by

gastrointestinal (12.24%) and anticoagulants (10.37%). Hormones were the least drug class

prescribed (0.04%). Those prescribed medication were mainly administered orally (27.53%)

followed by I.V. bolus (19.38%) and I.V infusion (19.34%). In comparison with the current

literature, antimicrobials have also been ranked the highest drug class prescribed (Chua et al.,

2010, Vazin and Delfani, 2012, Agalu et al., 2012). The oral route of administration has also

been reported previously as the route mostly used for drug administration (Prot et al., 2005,

Vazin and Delfani, 2012).

Most of errors occurred in this study were associated with antimicrobial use (n=555,

20.34%), especially Tienam® (Imipenem and Cilastatin). This comes in line with the fact that

67

most of the patients observed were diagnosed with an infection (25.4%) (e.g. Pneumonia,

Urinary Tract Infection) at study entry. Cancer was the second mostly diagnosed condition

(20.5%) among the observed patients (e.g. Breast cancer, Nasopharyngeal carcinoma, and

Gastric carcinoma), and as expected, immunocomprimised patients require more prophylactic

antimicrobial agents to prevent any suspected infection. Other classes of medications with

high frequency of use were the corticosteroids, anti-gout drugs and miscellaneous agents that

were mostly given to patients on chemotherapy for allergy and anemia.

Medication administration errors were the most common type of errors detected in the ward

under study, giving a total of 3667 opportunities for error; (rate of 20.2%), decreasing to 2%

when wrong time errors were excluded. The overall medication administration errors rate in

this study was relatively low in comparison with previous studies where they ranged from

6.6% to 51.8% (Tissot et al., 1999, Lisby et al., 2005, Font Noguera et al., 2008, Pasto-

Cardona et al., 2009, Patanwala et al., 2010, Vazin and Delfani, 2012, Karna et al., 2012,

Agalu et al., 2012). Runciman and colleagues however reported that medication

administration error rates ranged from 15%-20% in hospitals that utilize the ward stock

system for drug distribution (Runciman et al., 1993); thereby making the results of this study

within the expected range, since this drug distribution system is used at JUH. This study

showed lower rates even after excluding ‘wrong time errors’ from the other studies, which

decreased their range about (5.9%-17.6%) (Bruce and Wong, 2001, Prot et al., 2005, Chua et

al., 2010, Kelly et al., 2011, Berdot et al., 2012).

The most frequent administration errors found in this study was wrong time (n=667,

90.26%), followed by omission (n=55, 7.44%), wrong administration technique (n=7,

0.95%), extra dose errors (n=6, 0.81%), unauthorized dose (n=3, 0.41%), and wrong route

(n=1, 0.14%). Results of this study agrees with findings reported in a current systematic

review, where it was reported that wrong time errors followed by omission are the most

68

frequent types of errors reported in similar studies (Vazin and Delfani, 2012, Berdot et al.,

2012).In this study, wrong time errors are exceptionally important in consideration that most

of these errors were associated with the use of antimicrobial agents, where the timing of

administration is vital in achieving optimal therapeutic effects and preventing bacterial

resistance.

Medication administration errors were higher than other errors reported in this study, which

could be due to the higher efficiency of direct observation methodology in detecting errors as

compared with chart review method.

Using the chart review method, the rate of transcription errors identified in 2729 prescriptions

was 1.5%. Other previous studies reported a large range of transcription errors, from 0.7% to

56% (Barker et al., 2002b, Lisby et al., 2005, Fahimi et al., 2009, Pasto-Cardona et al., 2009,

Patanwala et al., 2010, Vazin and Delfani, 2012). The low rate of transcription errors reported

in this study may be attributed to the lower efficiency of this method compared with

observation technique. In addition, double checking of the transcribed labels by the nurse was

conducted in the study ward, decreasing the chance for transcription errors. The most

frequent type of transcription errors in this study was omission (n=33, 82.5%), followed by

wrong frequency error (n=5, 12.5%) and transcription of discontinued order (n=2, 5%). These

findings were similar to those reported by many previous studies (Vazin and Delfani, 2012,

Fahimi et al., 2009, Pasto-Cardona et al., 2009, Hartel et al., 2011).

Dispensing and prescribing errors were also detected in this study. The rate of dispensing

errors was 0.8% which is considered low in comparison with previous studies where

dispensing errors ranged from 0.6% to 48% (Lisby et al., 2005, Pasto-Cardona et al., 2009,

Patanwala et al., 2010, Vazin and Delfani, 2012, Karna et al., 2012). This low rate could be

due to the good pharmaceutical management found within the hospital under study and the

69

presence of a pharmaceutical care unit. Many of the transcribing and dispensing errors

detected were corrected as soon as discovered, yet the type of errors detected in this study

should be taken into consideration to prevent such types of errors from reaching the patient

and causing harm in the future.

A rate of 0.1% prescribing errors was also found. This rate is very low compared with

previous findings where prescribing errors ranged from 1.5% to 53% ;(Dean et al., 2002,

Lisby et al., 2005, Pasto-Cardona et al., 2009, Kadam et al., 2009, Patanwala et al., 2010,

Vazin and Delfani, 2012); This can be attributed to the nurse continuously checking of all the

prescriptions in the patient's MAR and reporting back to the physician in case of any

discrepancy (such as unclear or incomplete order) found. Most of the dispensing and

prescribing errors detected in this study were corrected as soon as discovered, which is

normal considering that this is a teaching hospital with updated health care professionals.

The severity categorization of the detected medication errors in this study showed that the

majority of errors (n=743, 92.5%) were categorized as C (error that reached the patient with

no harm). Additionally 59 errors (7.3%) were categorized as B error that didn’t reach the

patient), and 1 error (0.1%) that required monitoring. Vazin & Delfani reported similar results

with regards to severity of detected errors, as the majority of their errors were also

categorized as C (Vazin and Delfani, 2012). Kadam et al reported that 80.6% errors reached

the patient but no harm was caused (Kadam et al., 2009), while Karna et al reported that

61.4% errors reached the patient with no harm caused (Karna et al., 2012). Similar results

were seen in the emergency department as well, as Patanwala et al reported a majority of

errors reaching the patient with no harm caused (within the C category) (Patanwala et al.,

2010). Few studies reported different results, such as the study conducted by Pastó-Cardona

et al, which reported that 84.4% of the errors detected were category B (they did not reach the

patient) (Pasto-Cardona et al., 2009). Generally, the severity of detected errors in this study

71

was consistent with the findings of current literature as most of the medication errors detected

did not result in any clinically significant harm to patients.

One of the risk factors that showed association with the errors detected in this study is

nursing characteristics such as age, education and experience level in the ward. This is in

agreement with many previous findings in the current literature. Nurses make up the largest

portion of the health care staff at hospital, and medication errors are mostly associated with

their treatment delivery. Nursing level of education and experience are two important factors

that have strong association with the rate of medication errors happening (Walters, 1992,

Yang, 2003, Benjamin, 2003, Kazaoka et al., 2007, Chua et al., 2010, Bailey et al., 2011).

Gender of the nurse showed no significant association with the rate of medication errors

reported in this study, unlike what was reported in a previous Jordanian study (Mrayyan et

al., 2007).

Workload factors, such as patient to nurse ratio, patient admission/discharge rate, and number

of I.V infusion per nurse showed significant association with medication errors detected in

this study. Workload has been defined by Tissot et al as the ratio of patients per nurse.

Significant statistical relationship between medication errors and nurse workload has been

reported in many previous studies (O'Hare et al., 1995, Hartley and Dhillon, 1998, Dean and

Barber, 2001, Tissot et al., 2003, Taxis and Barber, 2003a, Wirtz et al., 2003, Han et al.,

2005, Cousins et al., 2005). Higher patients: nurse and I.V infusion: nurse ratios are

triggering factors for medication errors in this study. Higher ratios are associated with

increased nurse distraction and confusion. Hussain and Kao reported previously that

medication errors occur when nurses are confused by the different types and functions of

infusion devices (Hussain and Kao, 2005). Furthermore, no. of patient admission/discharge

during the shift have also been associated with higher medication errors (Girotti et al., 1987),

71

since newly admitted patients need more care and attention from the nurse. The case is

similar with discharged patients.

A significant relationship between the length of hospitalization and errors was found. This is

in agreement with Kohn et al findings (Kohn et al., 2000), as this factor is considered a direct

consequence of medication errors. Furthermore, a previous study by Shannon and De Muth

revealed a direct relationship between the hospital stay and risk of errors (Shannon and De

Muth, 1987). Since such relationship contribute to inducing in patients a sense of uncertainty,

discomfort, and possibly mistrust of the health care professionals; therefore strong strategies

and effective systems are needed to eliminate or decrease medication errors.

Other risk factors for medication errors identified in this study were the number of doses

given and doses prescribed. As reported previously, medication errors may occur due to the

large no of doses prescribed and administered during a shift (Raju et al., 1989). Physicians at

the JUH as it is the practice worldwide are responsible for prescribing medications,

pharmacists are responsible for transcribing, dispensing, and storing of medications, and

nurses are responsible for preparing and administering medications. Although the rates of

medication errors reported in this study are attributable to all health care professionals, (the

physicians, pharmacists, and the nurses); it's obvious that nurses play the pivotal role in the

process of medication administration and they are at a higher risk of committing errors

(Benjamin, 2003, Prot et al., 2005, Mrayyan et al., 2007, Karna et al., 2012).Understanding

the associated factors that increase the incidence of medication errors is the first step towards

preventing them (Aronson, 2009b, Bailey et al., 2011). Therefore, identified risk factors are

of important consideration in the current context, and ''making a mistake is not a sign of

weakness or unprofessionalism'' (Wolf, 1989). New need to build in systems that would

monitor medication errors, investigates causes, and identifies strategies for improvement of

medication practices to ensure maximal patient safety.

72

One of the main limitations of this study arises from the involvement of one ward only, so it

may not be representative of all medical wards in the hospital, as the results obtained from

this ward may not necessarily be the same as that in the other wards. Many of the patients

observed in this study have repeated admissions to the ward during the study period, which

could affect the results. The nurses were observed only during the morning shift and on

weekdays, hence the results could be not as accurate as if the observations were completed

over 24 hours for all weekdays.

73

Chapter Five

Conclusion and Future Implications

74

Chapter five: Conclusion and Future Implications

Conclusions:

Medication errors are occurring in the Jordan University Hospital, as this study

reports an overall rate of 12.6% medication errors at the internal medicine ward.

Using two detecting methods of medication errors (observation and chart review)

revealed medication errors occurring mainly during the administration and

transcription stages of medication use process.

Wrong time and omission errors were the most frequent administration errors.Wrong

time errors are specifically important because most of these errors were associated

with the use of antimicrobial agents, where the timing of administration is important

to achieve optimal therapeutic effects as well as to prevent bacterial resistance.

The majority of detected errors in this study were categorized as errors reaching the

patient with no harm (Category C).

Certain nurses' characteristics, mainly workload, have been significantly associated

with the rate of detected errors. Longer nurse experience and lower job pressure lead

to lower rate of medication errors.

75

Future Implications

An area of future detection of medication errors could be further extends to include

governmental and private hospitals in Jordan. Future studies may find an explanation for the

lack of association between gender of nurses and medication errors. Evaluation of the

relationship between patient characteristics and medication errors as well as drug

characteristics and medication errors are all areas that have not been research in Jordan as yet.

Long term comparative studies for detection and evaluation of medication errors rather than

cross-sectional studies can give more insight into the factors that associate with the

occurrence of medication errors, granting more insight into how best to prevent them.

76

Recommendations:

Despite the fact that the medication errors' rates reported in this study only represent the

situation in one medical ward in the JUH, their extent leads to underpin considering

medication errors as a concern that needs to be addressed with serious, proactive, and well

structured alleviation measures and practices. This is particularly important as the rates

recorded in this study represent trends that most probably exist in all other wards and

hospitals in the country. The following alleviation measures in a priority order are

recommended:

Raising the medical staff' awareness of medication errors:

This is achieved through conducting professional training programs and seminars aimed at

ensuring a safe health care system free of ''medication errors''. Such programs should address

the sequential steps in the medication use process; standards for medication administration

times; standards for working shifts, and the importance of having reasonable workload on the

nurses.

Upgrading the drug distribution system:

Changing the existing drug distribution system in the JUH from ward stock to unit dose

system by implementing a pilot study in this regard which would have a positive impact on

the reduction of medication errors. The unit dose system is a pharmacy coordinated method

of dispensing and controlling medications. It may differ in form, depending on the specific

needs of the organization. However, the following distinctive elements are basic to all unit

dose systems: medications are contained in single unit packages; they are dispensed in as

ready-to-administer form as possible for most medications; and not more than a 24-hour

supply of doses is delivered to or available at the patient-care area at any time.

77

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111

Appendices

111

Appendix 1

Official approval form of the study

112

Appendix 2

Examples of physician handwritten prescriptions on MAR (photos were taken during

the observation period)

Example (1): PRN order '' Pro re nata, in Latin'', to be carried out when the patient requires it

Example (2): Order for I.V. ''Intravenous'' infusion therapy

Example (3): STAT order ''Latin word statim; meaning immediately'' an order to be given at

once

113

Example (4): Order for chemotherapy

Example (5): Hold order; this type of order means stop the medication for short period of

time and to be re-carried out

Example (6): Discontinued order ''DC''; this type of order means stop giving the medication

and not to be re-carried out

114

Appendix 3

Examples of transcribed printed labels and dispensed medication

Example (1): Transcribed order labels for dispensing

115

Example (2): The plastic bag containing the dispensed medication

116

Appendix 4

نموذج معلومات للمشارك في الدراسة وموافقته على المشاركة

عزيزي المشترك:

إن اخذ االدوية بالطريقة الصحيحة هي جزء أساسي من العملية العالجيه. فنرجو مشاركتك في مشروع مراجعة اخطاء اخذ

عمان، والذي يقوم به الباحث من جامعة البترا الخاصة. وقد يحتاج الباحث لالتصال مع كل من العالج لحاالت صحية في

الطبيب المعالج للمريض والممرض المسؤل عنه لجمع المعلومات بعد موافقة كال منهم .

سوف يتم المحافظة على خصوصية وسرية المعلومات التي يحصل عليها الطالب من المشترك بشكل كامل.

حال نشر أي جزء من البحث في المجالت العلمية فإن هذا النشر سيتم دون ذكر اسم المشارك مطلقا، وال ذكر أي من في

المعلومات الدالة عليه.

او 4581906970جامعة البترا الخاصة -كلية الصيدلة-إذا كان لديك أي سؤال فيُرجى االتصال بالدكتور سليم حمادي

4588409440جامعة العلوم التطبيقية -لية الصيدلةك-الدكتورة إيمان أمين بشيتي

يرجى التكرم بقراءة نموذج الموافقة التالي والتوقيع عليه:

إنني ___________________ أوافق باختياري على المشاركة في مشروع مراجعة اخطاء اخذ العالج لحاالت

ة بإشراف أصحاب االختصاص.صحية، والذي يقوم به الباحث الصيدالني في جامعة البترا الخاص

لقد قام الطالب/الطالبة _______________ بشرح هدف الدراسة شرحا مستوفيا.

إنني على علم بهدف الدراسة وما يترتب على اشتراكي فيها. أعلم أن المعلومات المجموعة لهذه الدراسة ستبقى سرية تماما

و لن تُستخدم في محاولة التعرف على أي مشارك.

قد تم إخباري أن المعلومات المجموعة من هذا المشروع قد تُستخدم في بحث مستقبلي أو يتم نشرها في المجالت العلمية. ل

______________________االسم:

________________________التوقيع:

_______________________رقم الهاتف )اختياري(:

117

Appendix 5

Direct observation form

Physician order (prescription) in the patient's MAR:

Medication name(trade and generic)

Dose

Dosage form

Route

Time

With/out meal

Frequency of administration

DC

Preparation notes:

Preparation techniques (washing hands, wiping syringe)

Drug formulation ( tablet, capsule, solution, suspension)

Dose calculation ( extra dose, under dose)

Improper uses ( crushing medication, I.V adjustment )

Physician instructions

Comments

118

Administration notes

Administration techniques: washing hands, wiping an injection site with alcohol, using

disposable administration tools, and cleaned place.

Improper administration technique: manipulation of inhalers, rate of liquid products, rate of

I.V fluids.

Patient

Medication

Dose

Dosage form

Time

Route


Unordered dose

Omission

Physician note (discontinued, DC)

Nurse signature

Comments

119

Appendix 6

Criteria for medication errors

Stage Definition

Prescribing Discrepancy in: medication name; drug formulation; route; dose;

dosing regimen; date; signature; and instructions for use

Transcription Discrepancy in: medication name; dose; dosage form; dosing

regimen ( dose, route, frequency and duration); dose omission; and

unordered drug

Dispensing Discrepancy in: medication name; dose; dosage form; unordered

dose; dose omission; dose quantity

Administering Discrepancy in: patient name; medication name; dose (± 10% of

prescribed dose); dosage form; dose omission; unordered dose;

administration technique; route; time (± 60 min)

111

Criteria for classification of medication administration errors

Type of error Definition

Dose omission error The failure to administer an ordered dose to a resident by the time

the next dose is due, assuming there has been no prescribing error.

Exceptions would include a resident’s refusal to take the

medication and failure to administer the dose because of

recognized contraindications.

Wrong dose error When the resident receives an amount of medication that is greater

than or less than the amount ordered by the prescriber.

(Doses beyond ±10% of the prescribed dose).

Unauthorized drug

error

The administration of a medication to a resident for which the

physician did not write an order or the administration of a

medication that is not authorized by a legitimate prescriber.

This category includes a dose given to the wrong resident, dose

given that was not ordered, administration of the wrong drug or a

discontinued drug, and doses given outside a stated set of clinical

parameters or protocols.

Extra dose error The administration of duplicate doses to a resident or

administration of one or more dosage units in addition to those that

were ordered.

May include administration of a medication dose after the order

was

Discontinued (which could also be considered an Unauthorized

Drug Error).

Wrong route

The administration of a medication to a resident by a route other

than that ordered by the physician or doses administered via the

correct route but at the wrong site (e.g., left eye instead of right

eye).

111

Wrong

administration

technique

Use of an inappropriate procedure or improper technique in the

administration of a drug.

Examples of wrong technique errors include: incorrect

manipulation of inhalers, failure to maintain sanitary technique

with medications, not wiping an injection site with alcohol, failure

to use proper technique when crushing medications, failure to

check nasogastric tube placement or flushing NG tube before and

after administration of medication, failure to wash hands or

improper hand washing technique used.

Incorrect technique of administration included wrong route and

wrong rate of administration.

Wrong rate error The incorrect rate of administration of a medication to a resident

may occur with intravenous fluids or liquid products.

Wrong dosage form The administration of a medication in a dosage form different

from the one that was ordered by the prescriber.

This could include crushing a tablet prior to administration without

an order form the prescriber.

Wrong time error The failure to administer a medication to a resident within an hour

before or after the scheduled administration time.

112

Appendix 7

Chart review form

Physician order (prescription) in the patient's MAR:

Medication name(trade and generic)

Dose

Dosage form

Route

Time

With/out meal


Discontinued DC

Transcription notes:

Medication name (trade and generic)

Dose

Dosage form

Route

Time


Physician note (discontinued, DC)

Pharmacy label

Quantity dispensed

comments

113

Appendix 8

Characteristics form of each nurse observed

Date: _____________

Name (code):__________

Age_________

Gender__________

Nurse educational level

B.Sc. Nursing

M.Sc. Nursing

Nurse experience in the ward

More than 2 yrs

1-2 yrs

Less than 1 yr

Number Work characteristics

Patient to nurse ratio

No of patient's admission/day during observation

No of patient's discharge/day during observation

114

Appendix 9

Demographic characteristics and medical profile form of each patient observed

Date _______________, length of hospitalization__________

Chief Complaint (diagnosis)

Demographics:

Name (code):

Gender (Male Female)

Year of birth ______________

Nationality _________

Marital status_________

Smoker Yes (cigarettes per day ________________) No

Ex-smoker Yes No

Caffeine intake Yes No

No. of (coffee-tea) cups/glasses per day___________________

Education level:

Primary level (School years1-4)

Lower secondary school (School years 5-8)

Upper secondary school (School years 9 and higher)

University tertiary level

Postgraduate level (MSc or PhD)

115

Patient co-existing chronic condition(s):

Hypertension Yes No

Coronary artery disease/post MI Yes No

Diabetes mellitus Yes No

Peptic ulcer disease Yes No

Asthma Yes No

COPD Yes No

Renal insufficiency Yes No

Liver impairment Yes No

Cancer Yes No

Cerebrovascular disease (post stroke) Yes No

Alcohol consumption Yes No

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