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Understanding the causes of prescribing errors from a behavioural perspective
Abstract Introduction: While many attempts have been made to reduce prescribing errors (PEs), they persist. PE is not in itself a behaviour, but a consequence of a prescribing behaviour. Interventions aimed at prescribers should focus on understanding prescribers’ behaviours. The aim of this study was to use the capability, opportunity, motivation - behaviour (COM-B) model to explore the behaviours that could have caused PEs made by senior doctors in a speciality paediatric inpatient ward.Methods: A qualitative approach was used to investigate prescribers’ behaviours in a 26-bed paediatric oncology ward. Error data were collected over a two-month period and were presented during focus groups with prescribers, which were audio-recorded and transcribed verbatim. Thematic analysis was used to identify contributory factors to errors, which was used to identify sources of behaviours using the COM-B model.Results: Behaviours related to prescribers’ capabilities were: prescribers’ improper use of the software because of insufficient skills, and prescribers’ inability to prescribe correctly because of lack of knowledge. Behaviours related to opportunities in the environment were: prescribers’ inability to make an informed decision because of poor access to patient information, inability to properly complete a task because of heavy workload and interruption, and having to re-check doses frequently because of frequent change in patients’ weight and surface area. Those related to motivation were: prescribers unquestioningly following recommendations and not communicating with other specialists because they over-trusted them or feared a negative reaction, and prescribers inability to complete a task because of other competing and preferable tasks at the same time. Conclusion: Employing COM-B helped in identifying causes of PEs from a new perspective. Future work could focus on mapping identified sources of behaviour and errors against appropriate intervention functions and policies in order to design more successful interventions.
KeywordsCauses; prescribing errors; behavious; COM-B
INTRODUCTION
Behaviour can be defined as ‘anything a person does in response to internal or
external events.’ (1) Many factors contribute to individual decisions and thus the
behaviour that individuals express. Some of these factors relate to the individuals
themselves, such as knowledge and beliefs (i.e. some of the ‘internal’ events
mentioned above). Other factors could relate to the environment, such as hectic or
unfriendly surroundings (i.e some of the ‘external’ events). Of course, individual
behaviour in the presence of such factors varies, and while some of this behaviour can
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be predicted based on patterns or experience, other behaviours are more difficult to
predict.
As with many clinical activities, the behaviour involved in the prescribing process is
complex, as the process involves multiple linked steps that are interconnected and
interact in a nonlinear way, which could produce unpredictable results. (2) For many
reasons, prescribing for children is more complex than prescribing for adults. For
example, pharmacokinetic parameters differ between children of different ages, the
weight and height of young children can change dramatically over short periods, and
the ability of children to take different forms of medication can vary by age. (3, 4) In
addition, compared to prescribing for children in general, prescribing for children
with cancer is even more complex. Here, additional factors exist related to the nature
of the illness, the additional vulnerability of the patient group, and the use of a
combination of high-risk and toxic medications (such as chemotherapy drugs) over
long periods. (5, 6) The complexity of these conditions and their treatment plans make
hospitalised children with cancer particularly vulnerable to errors.
A prescribing error (PE) is the outcome that occurs when ‘as a result of a prescribing
decision or prescription writing process, there is an unintentional significant reduction
in the probability of treatment being timely and effective or increase in the risk of
harm when compared with generally accepted practice.’ (7) PEs are relatively
common in hospitals, (8) and the error rate for hospitalised children ranges from 16.8
to 86.5 errors per 100 medication orders. (9, 10) In Saudi Arabia (SA), studies on PEs
for children are limited, but the few published studies suggest that errors occurred
with 41.5% to 56% of total written medication orders. (11, 12)
As the definition suggests, PE occurs “as a result of a prescribing decision or
prescribing writing process”. Thus, an error is not in itself a behaviour that could be
targeted for change, but rather it can be considered as a consequence of a subset of
prescribing behaviours, some of which may have been conducted in an incorrect
fashion. To minimise errors, it is important to understand why errors have occurred.
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The causes of errors have been commonly investigated from a human error
perspective using models such as Reason’s model of active causation (i.e. active
failure, error provoking conditions and latent factors). (13-16) However, such models
typically focus on classifying causes of errors and offer little information on the
behaviours associated with the cause of these errors. For example, many studies have
identified performance deficits, knowledge deficits, and lack of communication as
causes of PEs in hospitalized patients. (17, 18) However, how prescribers behave
when they lack knowledge or how their behaviours are affected when they lack
communication with others are not always investigated.
Within behavioral psychology, there are multiple behaviour models and theories that
could have been appropriate for understanding prescribing behaviours. These include
the health belief model, (19-21) social cognitive theory, (22-24) and the theory of
reasoned action. (25, 26) However, they are largely stand-alone approaches that do
not, in themselves, provide guidance on how to link the actual behaviour change the
theories would suggest with the theory of developing interventions. The theoretical
domains framework has been used in healthcare studies, (27-29) but there is no formal
guidance on how to apply the framework for designing interventions. The Capacity,
Opportunity, Motivation – Behaviour (COM-B) model (30) was chosen for this study
as it is a component of a step-by-step intervention design framework called the
Behaviour Change Wheel, which focuses on both understanding and changing
behaviour. (1) The model states that behaviour is a result of interactions between the
three sources of behaviour: capability, opportunity, and motivation. The COM-B
model has been employed in healthcare settings both to understand behaviour and to
design interventions. For example, the COM-B model was used to explore facilitators
of and barriers to medication adherence, (31) to identify interventions that facilitate
the transfer of information on medication discharge summaries, (32) to understand
behaviours when using a stop smoking services, (33) to change behaviour of women
with gestational diabetes, (34) and to increase hearing aid use. (35) The aim of this
study was to use the COM-B model to explore the behaviours that could have caused
PEs made by senior doctors in a speciality paediatric inpatient ward. This work
constitutes a starting point for designing a behaviour intervention to reduce PEs.
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METHODS
Study Design and Population
A qualitative approach was used to investigate prescribers’ behaviour, to ensure that
an in-depth understanding could be achieved. Interviews and focus groups are two
commonly used approaches that are considered. (36-38) Focus groups were selected
as the primary data collection method for two reasons. First, the research ethics
committee argued that, for cultural reasons, doctors in SA would not talk about PEs in
one-to-one interviews due to concerns about sigma, privacy and negative impact on
relationships between the doctors and pharmacists. Focus groups about non-
attributable PEs were acceptable. Second, focus groups would allow researchers to
use discussion dynamics to capture participants’ perceptions and opinions about
concepts and topics as to why prescribers behave in such a way that resulted in errors.
(39, 40)
All senior doctors (n=18) working in the study ward, and who were authorised to
prescribe medication (i.e. consultants, assistant consultants, staff physicians, and
clinical fellows), were eligible to participate in this study. Unlike in some nations or
even other settings in SA, junior doctors in their first foundation year of postgraduate
training (i.e. interns and junior residents) were not authorised to prescribe in this
hospital, and thus were not included in the study.
All of these 18 potential participants were informed about the study both via email
from the department secretary and through a presentation of the project by researchers
at a staff meeting. The participants were then given participant information sheets and
consent forms. Based on individual preference, the consent forms were collected
either during a subsequent staff meeting or just before the beginning of focus group
sessions. Participation was voluntary. Ethical approval was granted both by the
Institutional Review Board (IRB) in SA and by the University of Manchester
Research Ethics Committee (UREC) in the UK.
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Setting
The study was conducted in a 26-bed paediatric oncology ward in a tertiary hospital in
SA. Two types of prescribing software systems were used to order medications in this
ward, which were given the pseudonyms of O-software and C-software. The O
software was the standard software used by all healthcare professionals working
throughout the hospital. This software was used to order all medications except
chemotherapy protocols. These protocols had not been incorporated within the O-
software and therefore they had to be prescribed using the oncology-specific C-
software. The major differences between the two software systems, such as who had
access and the proportion of available patient data captured by each system, are shown
in Table 1. The two systems were not integrated or connected with each other, and
patient information and medication orders in one system could not be displayed in the
other.
Table 1: Description of the two prescribing software systems
Software Brief description
O-software - Recently introduced (2016)- Contained all patient’s information such as laboratory results, progress notes,
consultation, and imagining- Contained medication orders except chemotherapy protocols, due to the
software being unable to cope with the complexity of chemotherapy protocols
- Prescribers in all wards in the hospital had access to and used the O-software while prescribing
C-software - Had been used since 2015- Contained some of the patient information- Used solely for prescribing chemotherapy protocols- Only oncology prescribers had access to the C-software
Data Collection
Data collection proceeded in two phases. For the first phase, data were collected on
PEs on the ward over a two-month period prior to the conduction of the focus groups.
The purpose of this phase was to collect actual cases of PEs that could be used for
focus groups discussions. One researcher (AA), the clinical pharmacist in charge of
the ward, reviewed all medication charts and orders for all patients during normal
workdays (Sunday-Thursday) for any issues (including PEs), as part of her usual
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practice. All patient safety problems were addressed with the prescriber in the usual
way. Errors were then recorded on a standardised form, using previously validated
criteria (15). The form had two pages: the first contained patient information such as
the name, age, gender, and condition, which allowed the pharmacist to review patient
notes if needed. The second page described the error(s) in detail. After both pages
were coded with the same identification number, all first pages were removed and
stored in a locked hospital cabinet in order to protect patient and doctor
confidentiality. The researcher (DB) then used the second pages for data analysis.
As Dean et al.’s definition of PEs was used when collecting errors, the equivalent
error classification system (7) was used to categorized errors collected. The system
classified errors into five categories: need for drug therapy; selection of dosage
regimen; selection of specific drug; administration of drug; and provide drug product.
For the second phase, four focus groups were conducted with the senior doctors
described above. One week before the conduct of each focus group (focus groups 1-
3), the department secretary emailed six prescribers, as evidence suggests that the
optimum size for a focus group is between six and eight. (41-43) An important
characteristic of the sample was to have prescribers with different grades (consultants,
assistant consultants, staff physicians, and clinical fellows). Therefore, the secretary
randomly emailed two consultants, two assistant consultants, and two staff physicians
currently working on the ward and invited them to participate in the focus groups.
Different assistant consultants and staff physicians were invited to each focus group.
However, the same consultant could be invited more than once to participate in
different focus groups, as only five were working on the ward at that time. Only one
fellow was practicing in the ward. Thus, the fellow was invited once. The number of
participants attending the focus groups ranged from 4-6 participants. For the fourth
focus group, all prescribers who had been invited to the previous three focus groups
were invited to attend, as this provided respondent validation after preliminary data
analysis on the first three focus groups was complete (see below for more details).
The composition of the focus groups ranged from higher-grade doctors (i.e.
consultants) to lower-grade doctors (i.e. staff physicians and fellows), although all
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were considered “senior doctors” in the hospital. This was important to get the
perspective of different doctors and to understand the causes of errors made by
different doctors. However, this also ran the risk of affecting the openness of either
group to talk freely about the errors or their causes.
The research team chose common examples of each category of errors, created case
scenarios and presented these during focus groups 1-3 (see Appendix A). Different
error examples were presented to the participants in the different focus groups.
Examples related to one classification of error were discussed with each group –
examples of errors related to ‘need for drug therapy’ were discussed in focus group 1,
'selection of dosage regimen' in focus group 2 and 'selection of specific drug' in focus
group 3.
Two topic guides were developed. The first topic guide (see Appendix B) was used
for focus groups 1-3. This topic guide focused on discussing examples of common
errors committed in the ward (based on the case scenarios) and on identifying
contributory factors to PEs. For the purpose of this study, contributory factors refer to
any factor that prescribers believe could contribute to the occurrence of errors.
Participants were asked very broad questions such as why they thought that the errors
in the case scenarios had error occurred, what might have contributed to the
occurrence and what they thought might have been done differently to prevent the
errors happening. Errors during these focus groups discussions were presented
without knowing whether the prescriber who made the errors in question had been
invited to the focus group or not. Unless the prescriber who made the error self-
identified during the discussion (and they were asked not to), neither the facilitators
nor the participants knew who they were.
The fourth focus group was conducted for respondent validation, after preliminary
data analysis on the first three focus groups was complete. This fourth session (see
Appendix C for the fourth focus group topic guide) began with a presentation of the
preliminary analysis results based on the application of the COM-B model. To add
validity to the researchers’ interpretations, participants provided feedback and any
disagreements between the participant meaning from data collected from earlier focus
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groups and the researchers’ interpretations or representation were resolved. The
second part of this session involved discussing questions that were developed based
on the COM-B model application, explicitly to connect the identified contributory
factors from the first three focus groups to COM-B components.
Two members of the research team ran the focus groups. One (MA) led the
discussions for focus groups 1-3, while the other (DB) assisted and took notes on the
major points that emerged. A different facilitator (DB) ran the fourth focus group. All
focus groups lasted between 60-75 minutes. With participant consent, the discussions
were taped and transcribed verbatim by the main researcher (DB). Participant names
were anonymised using codes (P1, P2, P3, etc.), and any other identifying personal
information was removed. The focus groups were numbered in the order in which
they were conducted: FG1, FG2, FG3, and FG4. Those focus groups were conducted
over a four-month period (one month between focus groups 1-3 and two-month
between focus groups 3-4).
In Saudi hospitals, the formal language of communication between doctors when
discussing cases is English. Nonetheless, it was common for the participants to switch
between English and Arabic or add isolated Arabic words in the middle of a
conversation. The original verbatim transcripts are available from the authors upon
request. Transcripts with Arabic words were translated into English by the bilingual
first author (DB) prior to data analysis.
Analysis
The translated transcripts were read by the research team; DB, MA and MT and coded
iteratively by DB. The researchers discussed this analysis repeatedly and any
differences of interpretation were resolved. Transcribed focus groups data were
analysed in two ways. First, thematic analysis based on the work of Braun and Clarke
(44) was used to analyse the focus groups transcripts, identify and code potential
themes. Second, the COM-B model was used to conduct an analysis of the
prescribers’ behaviours and what led up to those behaviours.
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Thematic analysis was chosen because it provides a ‘thick description’ of data sets
and allows researchers to identify themes within data. (44) The transcripts were read
and re-read multiple times in order to be familiar with the dataset. Notes were taken of
relevant points from the focus groups discussions, and points made for further
clarification in subsequent focus groups. Then coding was applied using the
comments facility within Word, so that data that were related to the contributory
factors for and the causes of PEs could be identified. An extract could be coded in
several ways as appropriate. (45) The final step was searching for themes related to
the contributory factors or causes of PEs. The focus was not on identifying which
pattern was the most frequent, but rather capturing the most meaningful elements
necessary to explore the behaviours that could have caused PEs.
As these factors provided no behavioural insights when considered in isolation, a
second analysis using the COM-B model was used. This well-established model (30)
is used in behavioral psychology for analysing and describing behaviour. (31, 36, 46,
47) The identified themes that emerged from the initial analysis were then mapped
against the appropriate COM-B model components. This allowed the researcher to
understand prescribers’ behaviours and what could have resulted in these behaviours.
For example, if lack of knowledge was identified as a contributory factor, prescriber
behaviours leading to a PE that indicated a lack of knowledge (such as Prescribers’
inability to prescribe correctly due to lack of knowledge of dose) were identified and
mapped against the appropriate component of the COM-B model. This process
allowed the researchers to determine how a prescriber behaves when their capability
was affected, when an environmental factor was present, or when they were
motivated/not motivated to do certain behaviour.
Findings were presented using the three COM-B model components. Quotes were
selected to highlight the discussion content. Where quotes were taking out of context,
a few words or phrases were added in parentheses for the reader to understand the
situation.
RESULTS
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Fifteen doctors (nine male and six female) participated in the focus groups.
Approximately half of the participants were staff physicians (n=7); the others were
consultants (n=5), an assistant consultant (n=1), and a fellow (n=1). The number of
participants in the first three focus groups was similar, ranged from 4-6 participants -
FG1 (n=4), FG2 (n= 6) and FG3 (n=5). For the fourth focus groups, eight participants
attended. Those who were invited to attend but could not gave reasons such as dealing
with a patient and being busy in other meetings.
The focus groups provided rich data about prescriber behaviour and contributory
factors that could have led to the errors in the case scenarios. By the end of the third
focus group, no new themes had emerged and a data saturation point was reached.
The fourth focus groups provided validation of the initial analysis and additional rich
data about prescribers’ behaviour in relation to capability, motivation, and
opportunities.
During focus groups 1-3, the error scenarios were presented and discussed freely.
Participants appeared motivated to discuss these errors and other issues related to the
overall prescribing process and software systems. The focus groups environment was
friendly and blame-free. During all of the focus groups, participants were willing to
disclose contributory factors, even those that would cast themselves in a negative
light. For example, when participants believed that one contributory factor to
behaviour could be a lack of knowledge, they freely discussed this topic, even
concerning medications that were commonly prescribed to cancer patients, such as
antibiotics and anti-emetics. In fact, in two instances, the prescribers who had made
the error being discussed identified themselves and explained in greater detail why the
error was made.
Before the start of this study, some ward doctors stated that they made either very few
errors (i.e. one every few months) or none at all, perhaps because they misunderstood
the meaning of the term ‘prescribing error.’ In addition, some participants connected
the occurrence of errors to patient harm. Therefore, for those doctors, errors that were
intercepted before medication was administered or before a patient was harmed were
not considered to be errors at all. However, the more error cases that were discussed
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in the focus groups, the clearer the meaning of ‘prescribing error’ became, and the
more participants appeared to believe that errors were indeed common and that the
paediatric oncology ward was no exception. In addition, the more these examples of
actual errors were discussed in the focus groups, the more open the participants
became.
The themes identified from the thematic analysis were: lack of knowledge, lack of
training, lack of integration between EP software systems, heavy workload,
interruption and distraction, low staffing rate, poor communication between teams,
and poor access to guidelines. Prescribers’ behaviours in the presence of these factors
are presented below using the three main components of the COM-B model:
capability, opportunity, and motivation. The application of the COM-B model was
presented in Table 2. Because the model components interacted with each other,
incorrect prescribing (i.e. behaviour) sometimes occurred because one component was
affected, such as the prescriber capability due to lack of knowledge, or because two or
more components affected each other. For example, an opportunity in the
environment (e.g., heavy workloads) may have caused prescribers to focus solely on
chemotherapy drugs during medication reconciliations (i.e. motivation), resulting in
omission of other medications (i.e. behaviour).
Table 2: Application of the COM-B to incorrect prescribing behaviour
COM-B components
Behaviour* Anything a person does in response to internal or external events.In this study, behaviour was anything a prescriber does that eventually leads to a prescribing error
Capability
Definition* Knowledge, skills and abilities to engage in the behaviourFactors - Prescribers’ improper use of the software because of Insufficient skills
- Prescribers’ inability to prescribe correctly because of lack of knowledge
Opportunity
Definition* Outside factors which make the behaviour possibleFactors - Prescribers’ inability to make an informed decision because patient’s
information not available, can not be accessed, or can not be retrieved quickly- Prescribers’ inability to double check prescription because of manpower- Prescribers’ inability to complete a task because of workload and interruption- Prescribers’ having to re-check the doses frequently because of frequent
change in patients’ weight and surface areaMotivation
Definition* Thought processes and perceptions which direct decisions and behaviours
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Factors - Prescribers’ following recommendations by specialists because they over-trusted the specialist
- Prescribers’ not communicating with other teams because they feared the specialist would get angry, or because the medication was topical (e.g., ear drops) so no need to communicate
- Prescribers’ not completing a certain task because of competing behaviour- Prescribers’ not performing medication reconciliation because they did not
want to do extra effort
*Definition adapted from Michie et al 2011 (30)
In general, all prescribers recognised the ideal types of behaviours that should be
followed. However, the reality of their workplace often meant that these ideals could
not happen.
“[When I am not busy and I don’t have lots of patients], I will think [about] every patient slowly and I will check all the medication [and] the interactions. I will double check the doses properly maybe once or twice, [and] even at the end of the day I will check the patients again, all medications expired or not. If I am very busy, I have a lot of sick patients, I don’t have time. I will try my best. I am not saying I will intentionally [make mistakes], but by default because I will be too busy, I will do mistakes—maybe I will forget to add the medications” P11, FG4
Capability
Insufficient skills and lack of knowledge were two factors that affected prescriber
behaviour and their ability to engage in error-free prescribing processes. For example,
prescriber ability to properly use the EP software was hindered by prescribers’
insufficient skills due to improper training on the O- software system. The training
was improper because it was offered after the launch of the software and it was
described as ‘not structured’. Lack of knowledge was another factor that affected
prescribers’ capability in making appropriate medical decisions when prescribing. For
example, lack of knowledge of the correct course of action or the condition of a
patient could have influenced prescriber behaviour resulting in errors.
Participants discussed their own lack of knowledge as well as the lack of knowledge
of others, such as lower-grade doctors (in comparison to more experienced doctors
such as consultants), new staff coming to the ward, and certain specialists, particularly
surgeons and dentists with ostensibly less knowledge about medications such as
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antibiotics. However, the perception that these doctors lacked required knowledge
was based on participants’ opinions and was not supported by any other evidence.
“Unfortunately, many juniors [lower-grade doctors] have not got much experience compared to you, or compared to others who are working with you. They [junior doctors] think that it is like what they memorised, as long as [the patient is] fungal afebrile, neutrophils recover, CT scan negative, [then] stop antifungal.” P4, FG2
Lack of knowledge accompanied by other factors such as fatigue or excessive
workload (i.e. opportunity afforded in the environment) also sometimes influenced
prescriber behaviour. As new workdays in the ward began at midnight, ordering
processes were found to be particularly problematic at night, when on-call doctors
were often tired (i.e. opportunity), treated multiple patients (i.e. opportunity) and were
not familiar with the condition of every patient (i.e. capability). As one participant
explained, even if a written progress note stated that a medication had expired on the
morning of a given day and thus there was no need for a renewal, if a nurse asked for
a renewal at night when a doctor was already fatigued and asked, “please renew”, the
doctor might agree and “automatically renew, that’s it.”
Apart from a lack of knowledge, some participants pointed out that prescriber
competency levels could cause errors. Regardless of workplace environment, a few
participants stated that errors should not happen unless there was a problem with the
prescriber, their competency, or their efforts.
“In reality, it [errors] should not happen because when you are trained as a doctor, you go from junior to senior [and] you are told how to respect roles, doses, methods of administration and all of that. You cannot go beyond the dose, unless your training is a big problem, really. OK. Or you recommend that acetaminophen [is] used once a day, for an example, unless there is a big problem with you, ok? But paracetamol is given in this dose 10-15mg/kg orally or as an IV. For example—you give it every 4-6 hours for pain for fever for whatever. OK. This is the recommendation, but if you want to give it for something else, there is a problem with you. Really. Who would do that? This, in reality, this should not happen.” P4, FG4
In summary, participants identified that prescriber’s ability to engage in error-free
prescribing was affected by insufficient skills, and lack of knowledge among
oncology prescribers, new staff, and certain specialists.
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Opportunity
The results showed that prescriber behaviour can also be influenced by opportunities
afforded in the environment, which helped to perpetuate some behaviours that
subsequently led to errors or made it difficult for less error-prone behaviour to occur.
For example, access to patient information at the time of prescribing is necessary to
make an informed decision. However, in some circumstances, prescribers did not
access information because it was either unavailable or difficult to retrieve in a timely
fashion.
Participants reported that patient information was sometimes not available because it
was not documented in the first place. Prescribers were not always able to document
this information fully because healthcare providers sometimes do not communicate
with each other. In addition, sometimes prescribers only partially document
information due to the way in which their software system was configured. For
example, the pre-set problem list in the EP software system offered no space to add
information, which sometimes resulted in a prescriber not being able to document
relevant information that were not included in this list
In other circumstances, information was available but prescribers did not frequently
look for it because it was difficult to retrieve in time. For example, medication orders
are available in both the O-software and C-software systems. However, these orders
are often spread across these two different systems, which require prescribers to open
both systems every time they prescribe a medication, which does not always happen.
Some evidence suggests that when decisions were made based on no access to
information, errors occurred. For example, omission of a treatment occurred because
prescribers assumed that the drug was ordered in the other software system when in
fact it was not, or duplication errors occurred because prescribers forgot that a
medication had been ordered in the other software system.
Guidelines were also common sources of information. However, the content and
availability of guidelines had an effect on access to this information. The participants
stated that they always used the customised chemotherapy protocols and guidelines
for children, such as those incorporated within the C-software system, and were one
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of many safety checks that reduced the opportunity for errors. In contrast, guidelines
for supportive care medications were available but were not commonly accessed by
prescribers because these guidelines were located in the outpatient clinic and were not
incorporated in the O-software system. Having to physically go and check these
guidelines could have made it more difficult to access information when needed.
“[Was the treatment of irinotecan induced diarrhoea different?] Yes, of course. It is complex. Usually, to know what you should [do, as] a supportive care of irinotecan, you have to read it all [the guideline], and once you read it, you know how to deal with it [the condition]. They [doctors] should go and read this, irinotecan supportive care. For me to access this, I have to print it [out] from the out-patient [clinic].” P5, FG1
In addition, most of these guidelines contained information that was not specific to
children with cancer. This might have resulted in prescribers not commonly accessing
these guidelines, as they did not have specific information for their patients (i.e.
motivation).
Even when prescribers had physical access to these guidelines, the lack of consensus
on the use of the same guideline among doctors of different specialities sometimes led
to confusion between prescribers as to which guidelines should be followed. This
often resulted in following specialist recommendations, even when they were not
acceptable. One observed example concerned a lack of consensus between OB/GYN
doctors and oncologists about the suitable method for menses suppression for an 11-
year old girl. The OB/GYN guidelines for menses suppression did not take into
account patients on chemotherapy or possible drug interactions with chemotherapy
drugs. As a result, following the recommendations of OB/GYN doctors based on the
OB/GYN guidelines resulted in prescribing a medication that was not suitable for a
cancer patient.
“We consulted the OB/GYN. The OB/GYN insisted [on giving] this medication; they even convinced the mother to give it. We asked the mother—we actually refused to discharge her [the patient] on this patch [medication]. But they insisted, the OB/GYN, to give [it].” P13, FG2
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Participants also stated that having prescriptions double-checking by another
prescriber (to check the work of the first prescriber) was a recognised behaviour that
could potentially reduce errors. However, prescribers were not always able to perform
this behaviour due to lack of manpower and the frequent unavailability of a second
senior doctor in some areas. One prescriber highlighted that to be able to double
check prescriptions, other factors must also be present at the time of ordering:
“The culture of double checking before each prescription needs time, less interruptions, and accessible guidelines. For each medication you need to prescribe, you need to have, like, easy, easy I mean, easy to search for whatever you are looking for in the guidelines and you [have to] know where to look in a quick way, and you [need] enough time to look for that. And, less interruptions so you are not interrupted while prescribing specifically highlighted medications or high-risk medications.” P16, FG4
Participants reported that interruptions were another opportunity for errors, especially
if prescribers had many tasks to finish (i.e. high workload), which could divide their
attention or interfere with the successful completion of tasks. Rather than giving
specific details, prescribers talked about interruption in more general terms.
“There is no specific time, ah, for the physician to sit in peace without interruption to write these medications—patient medications. It just comes from the nurses’ side that each time you are doing your job, you are interrupted by something else to do.” P16, FG2
In summary, participants identified that incorrect prescribing and errors occurred
because they were unable to access information, their high workload, they were
frequently distracted, and the frequent unavailability of a second senior doctor in
some areas.
Motivation
Some of the prescribers’ perceptions directed their decisions (i.e. motivated them) to
perform certain behaviours or engage in competing behaviours. The relationship
between these perceptions and prescriber behaviour were discussed in the focus
groups.
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Performing medication reconciliation was accepted as an important behaviour in
reducing the opportunity for errors. However, not all prescribers performed
medication reconciliation to the same degree. Some prescribers thoroughly checked
all patients’ medications, whereas others did not ask patients about all medications
taken because prescribers either did not have time or did not make an effort to ask
patients. As one participant explained:
“The clinic is busy and [when] we admit a patient, for example, for a new [course of treatment], the time for reconciliation that you really need to sit with the patient is not there. How many minutes you see the patient really with all the number of patients? So, you concentrate on what he is coming for, for the chemo, and you leave the other tiny minute things which might be of importance.” P6, FG1
The results also showed that it was common practice to ask for consultations from
other specialists when patient conditions were outside a doctor’s scope of expertise.
Prescriber perceptions about the knowledge of the specialist contacted or the reaction
of the specialist when contacted affected prescriber behaviour. In fact, prescribers
sometimes followed recommendations that were not appropriate for a patient because
they over-trusted a specialist’s level of knowledge.
“The problem [the medication involved with error discussed] is that the medication is their speciality. So, if something interferes with my speciality, I will know better than them, I will just question it. But if it’s their speciality and they are the one prescribing!” N9, FG3
Over-trusting specialists was influenced by the grade of the specialist and the type of
medication recommended. When the impact of a specialist’s grade was discussed, not
all prescribers believed that it affected their decisions. However, one high-grade
prescriber believed that if a peer was consulted, they were required to follow their
recommendations unless something was obviously wrong with these
recommendations.
“This will influence me very much, I believe. Not a personal relationship, some colleagues working with you, you know him, ok. And you know that he is almost perfect in everything, [so] when he tells me about a medication [to] do it this way, I do it.” P4, FG4
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The type of medication recommended by specialists could also influence prescriber
decisions in terms of whether or not they follow these recommendations. In a case of
a topical medication that was contraindicated, yet prescribed, one participant was
asked if they would accept the recommendations by others as guaranteed, they
replied:
“If a specialist recommends, for example, systemic steroids, I will not take it. I will first discuss [it] with them. But, if an ENT prescribes local [topical medication], definitely I will trust them and I will not go and check.” P9, FG3
In addition, the reaction of a specialist when contacted by oncology prescribers had
affected prescriber behaviour. Prescribers were hesitant and sometimes decided not to
contact specialists because they felt that the specialists could become agitated if
contacted for further clarification.
“Sometimes they [doctors from other teams] get angry. We wrote our recommendation, why are you calling me now. You see [it] in the computer. They will tell you like this.” P11, FG4
Finally, the opportunity to pursuing competing behaviours at the time of performing
the behaviour of interest sometimes motivated prescribers to prioritise tasks they had
to perform within given time frames. This could have resulted in an original
behaviour of interest not being performed or being only partially performed. For
example, prescribers stated that treating patients and stabilising their condition as their
priorities, particularly during on call periods, and shifted the responsibility of ordering
additional medications or completing documentation to other teams. As one
participant explained:
“In admission and discharge, it’s probably related to a patient coming [in] as an emergency or when you are dealing with certain emergency, and given the medication for a certain emergency, and you think or push that responsibility to the morning team, which is wrong. OK. But [it is] like this where a miss can happen. Or you are just dealing with an emergency right now, [so] admit him, get settled, get over the fever neutropenia, then all his day medications can happen with the morning team and they can sort it out. That is pushing [off] the problem.” P1, FG4
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Some evidence showed that heavy workloads and having large numbers of patients to
treat were reasons that prescribers prioritised necessary tasks. For example, when
busy, prescribers stated that they focused on immediate concerns and left other
problems for later:
“You postpone until you are free—then you are a human being. There are other things to do, you are too busy and you forget to document. You think you’ve done it, but you haven’t done it.” P4, FG4
In summary, participants’ over-trusting other prescribers or specialists, and
prioritising tasks to perform were some of the motivations that could cause incorrect
prescribing and errors.
DISCUSSION
The COM-B model facilitated an understanding of how prescriber behaviour resulted
in PEs. By using this model, we were able to describe how prescriber behaviours had
been impacted upon by their capabilities, by opportunities in the environment, or by
how they were motivated to behave. This study sheds light on two novel aspects
related to prescribing errors: 1) the use of the COM-B model to understand the causes
of PEs, and 2) the behaviours of senior prescribers that led to PEs.
The COM-B model (30) is becoming a well-recognised approach to understanding
and changing behaviour – in mid-2018 it had been cited over 500 times in articles
indexed on PubMed. It has advantages over other models used to understand causes of
PEs, as it enabled the researchers to identify behaviours that led to errors and explain
the interrelationship between contributory factors and behaviours expressed. This in-
depth understanding of the sources of the problem allowed the researcher to
understand why errors occurred, which could provide a foundation for the
development of an intervention.
Using the COM-B model also showed that prescribing error is a complex problem and
that often multiple sources of behaviour were affected. For example, prescriber lack
of knowledge of a patient (affects capability) was sometimes accompanied by
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prescriber fatigue, heavy workload, and presence of multiple tasks to complete
(opportunities in an environment). This suggests the importance of using the model in
its entirety, rather than considering only single constructs. Thus, interventions
designed to reduce errors should consider the complexity of this problem and the
different sources of behaviour that could be targeted, as different sources can be
targeted by different intervention functions. For example, to improve prescriber
knowledge of patients (i.e. psychological capability), interventions that include
education, training, or enabling functions could be used. (30, 48) However, to affect
behaviour when prescribers are busy and fatigued (i.e. physical opportunity),
interventions that incorporate training, restriction, environmental restructuring, or
enabling functions could be used. (30, 48)
According to the literature, interventions used to reduce PEs often have similar
functions, but little information appears on whether the selection of intervention
functions was made based on a systematic understanding of sources of behaviour. For
example, a systematic review that used the Behavioural Change Wheel as a
framework for describing the content of 17 bundle interventions used to reduce PEs in
hospitalised children found that all 17 bundles contained an intervention with an
environmental restructuring function and 16 bundles contained an intervention with
an educational function. (49) Environmental restructuring is an intervention function
aimed at physical/social opportunities and automatic motivation, whereas education is
a function aimed at psychological capability and reflective motivation. Although a
bundle of interventions that addressed these two functions would cover almost all
sources of behaviour (except physical capability), PEs still occur in settings where
these interventions were implemented. Thus, one could hypothesise that targeting
only relevant sources of behaviour, rather than a broad range, would have a greater
impact on incorrect prescribing and errors; this hypothesis needs to be tested.
However, to date limited research exists on interventions designed based on identified
sources of incorrect prescribing. Understanding the causes of errors and then
designing interventions based on behavioural sources to be changed could help to test
this hypothesis.
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Causes of PEs in hospitalised patients have been investigated, and the majority of the
studies have looked at causes of errors made by doctors who were one or two year’s
post-qualification. (17, 50) Causes of errors made by senior doctors in specialised
areas, such as paediatrics, have rarely been investigated. While one study in a large
UK hospital did investigate the causes of PEs made by both senior and junior doctors,
(51) any differences in causes of errors between doctors of different grades were not
reported. The contributory factors to errors identified in our study are similar to those
reported elsewhere in the literature in terms of prescribing for both adults and
children. Examples of contributory factors include lack of knowledge and training,
poor communication, poor access to guidelines, heavy workload, and interruptions.
(14, 17, 52)
On the other hand, the behaviours expressed by prescribers that result in prescribing
errors have rarely been explicitly described in the literature. It may be possible to
infer behaviours from the literature on causes of errors. However, this may not
necessarily be transferrable to the context of senior prescribers because, as previously
stated, such research has been conducted with the most junior doctors. The effect of
the contributory factors on behaviour may vary for senior and junior doctors. For
example, for both senior and junior doctors, interruption is a contributory factor that
can serve as an opportunity for error. (16) However, it is not known if interruptions
affect the behaviour of these two groups to the same extent. In addition, the relative
lack of research focusing on the behaviour of different grades of prescribers that
resulted in errors makes it difficult to compare findings from this study to other
studies.
The study has both strengths and limitations. Focus groups sessions were used to
explore the contributory factors of incorrect prescribing and the associated behaviours
that potentially resulted in PEs. The dynamics and interactions between participants in
discussion were useful in clarifying perspectives, something the researchers would not
have been able to achieve with individual interviews. In addition, it is possible that
presenting examples of real errors that prescribers could relate to may have improved
their engagement in the process and enriched the data.
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The study also has a few limitations. First, the study was conducted in a single
specialised ward, with only a small group of prescribers from which to draw the study
sample. Second, the study involved only senior doctors, as junior doctors were not
allowed to prescribe. Junior doctors might be authorised to prescribe in other settings
in SA or elsewhere, and they might express different behaviours than those expressed
by senior doctors. Therefore, these findings may not be generalisable to that group of
doctors. Third, one researcher coded the transcripts. However, the researchers
discussed this analysis repeatedly and any differences of interpretation were resolved.
Fourth, the design of the focus groups might have affected the openness of the
participants to talk about prescribing behaviours. Although participants were all
senior doctors, they were of different grades, such as staff physicians and consultants.
Having this mixture of doctors with different experience might have affected the
openness of consultants to admit their lack of knowledge in front of staff physicians,
and vice versa. Finally, the clinical pharmacist who collected error data (AA) was the
clinical pharmacist on charge of monitoring patients’ treatment at the ward. The close
relationship between the participants and the clinical pharmacist (i.e. the clinical
pharmacist being part of the oncology team) might have affected the openness of
participants to talk about causes of errors made. However this effect was not assessed.
In conclusion, we believe that the use of COM-B helped to identify the causes of PEs
from a new perspective. Future work could focus on mapping identified sources of
behaviour and errors against intervention functions and policies in order to design an
effective intervention.
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Appendix A
Focus Groups 1-3 Error Scenarios ‘Cases’
Box 1: Cases presented at focus group 1
During data collection 44 errors related to the ‘need of drug therapy’ were found. The following are examples. We are interested in understanding why these errors or errors like these could have occurred and what could be done to prevent their recurrence.
Case 1: An 11-year old male with Hodgkin Lymphoma suffering from distal right internal jugular vein thrombosis admitted for thrombosis treatment. The patient has a history of insulin resistance, glucose-6-phosphate dehydrogenase deficiency, and obesity. On admission home medication (Metformin 500mg twice a day; Budesonide inhaler 1 puff twice a day; and Salbutamol inhaler as needed) were not prescribed.
Case 2: A 3-year old male with acute lymphoblastic leukaemia was on Bactrim 240mg orally every 12 hours two days/week for pneumocystis pneumonia prophylaxis. The drug was put on hold during high dose Methotrexate treatment. The drug remained on hold for six weeks.
Case 3: A 12-year old female with diffuse large B-cell lymphoma on percutaneous transhepatic cholangiography drainage with fluid and wound infection. The antifungal was discontinued based on negative fungal search by Computerized Tomography scan, although patient has fungal infection with candida glabrata from wound and fluid drain.
Case 4: A 2-year old female with acute lymphoblastic leukaemia suffering from lice was prescribed Permethrin topical once weekly with no end date or instructions.
Case 5: A 3 and half year old male with monoclonal B-cell lymphocytosis suffering from tooth abscess was given Tazocin and cefazolin. These antimicrobials have the same coverage.
Case 6: A 12-year old female with Burkitt lymphoma was started on a regimen for non- Hodgkin’s lymphoma. Pre-medications (pre Rituximab) were not included in the patient Performa and no justification for not prescribing was given in the medical record
Case 7: An 11-year old female with acute lymphoblastic leukaemia (post paediatric intensive care unit discharge) was on Acetylcysteine. Bronchodilator was not prescribed prior to Acetylcysteine.
Case 8: A 7-year old male with lymphoma on chemotherapy. The patient was on Methylprednisolone 21mg intravenous every 24 hours with no gastrointestinal prophylaxis.
Case 9: A 10-year old male with pelvic synovial sarcoma on Doxorubicin and Ifosfamide was not prescribed an antiemetic.
Case 10: A 13-year old male with rhabdomyosarcoma suffering from diarrhea (6 days after he received a dose of Voriconazole/Irinotecan) was given Metronidazole 400mg intravenous every 8 hours.
Box 2: Cases presented at focus group 2
During data collection 88 errors related to the ‘selection of dosage regimen’ were found. The following are examples. We are interested in understanding why these errors or errors like these could have occurred and what could be done to prevent their recurrence.
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Case 1: A 6-year old male with mediastinal mass (T-cell acute lymphoblastic leukaemia) is on Prednisolone 23 mg orally every 24 hours for acute lymphoblastic leukaemia induction and Ranitidine 36 mg orally every 24 hours for gastrointestinal prophylaxis. Doses were divided incorrectly.
Case 2: A 7-year old female with acute lymphoblastic leukaemia was prescribed Voriconazole 160mg orally every 24 hours as prophylaxis from fungal infection. Case 3: A 9-year old male (actual weight.: 51.5kg, ideal body weight: 32kg) with acute lymphoblastic leukaemia suffering from febrile neutropenia and septic shock was prescribed Amikacin 380mg intravenous every 8 hours (correct dose 7.5mg/kg)
Case 4: A 10-year old male (surface area >1m2) with acute lymphoblastic leukaemia was
prescribed Bactrim 7.5ml (360mg) for pneumocystis pneumonia prophylaxis.
Case 5: A 13-year-old male with acute lymphoblastic leukaemia was prescribed Caspofungin 125mg intravenous every 24 hours as prophylaxis, the dose was then changed to 90mg intravenous every 24 hours, 88mg intravenous every 24 hours, and finally reduced to 64mg
intravenous every 24 hours. (maintenance dose is 50mg/m2/day with maximum of 50mg)
Case 6: A 12-year old male (surface area=1.16m2) was prescribed dexamethasone 4mg
intravenous every 8 hours as pre- chemo antiemetic. Patient was also on high emetogenic chemotherapy (Aprepitant +5H3 receptor anatagonist+steroid).
Case 7: A 19-month old male (weight=9.5kg) with acute lymphoblastic leukaemia suffering from constipation was given Lactulose 2.5ml.
Case 8: An 11-year old female with osteosarcoma was given Evra 1 patch weekly for menses suppression. Estrogen and patch formulation are not recommended for oncology patient due to side effects and chemotherapy interaction but the gynaecologist insisted on the patch.
Box 3: Cases presented at focus group 3
During data collection 11 errors related to the ‘Selection of a specific drug’ and 14 errors related to wrong drug choice were found. The following are examples. We are interested in understanding why these errors or errors like these could have occurred and what could be done to prevent their recurrence.
Case 1: An 11-year old male with T-cell acute lymphoblastic leukaemia was prescribed Amikacin 249mg intravenous every 8 hour although the patient was labelled Gentamicin allergic. Evidence showed that if it is true allergy for Gentamicin, then Amikacin will have 50% chnace of cross reactivity as all aminoglycosides shows strong structural similarities.
Case 2: A two and half year old male with langerhans cell histiocytosis was prescribed Waxol
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3 drops every 8 hours for wax removal after consultation with an otolaryngologist. The use of Waxol is contra-indicated in case of ear perforation and inflammation.
Case 3: An 8-year-old male with acute myeloid leukemia was given Etoposide with Fluconazol. This might might reduce the execretiion of Etoposide and increase side effects.
Case 4: A 4-year-old male was given Etoposide with Carbamazepine. This might alter metabolism and decrease level or effect of Etoposide.
Case 5: An 11-year-old female with acute lymphoblastic leukaemia on Voriconazol was given Omeprazole. This might increase the level of Omeprazole and side effects.
Case 6: A 9-year-old male with relapsed acute lymphoblastic leukaemia was given Tazocin for Salmonella eradication.
Case 7: A 4-year-old male was prescribed Calcium Carbonate as Phosphate binder. Calcium level was 2.44, Phosphate level was 2.22, Calcium x Phosphate: 5.4. the chnaces of precipitation and oral calcification was high.
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Appendix B
Focus Groups 1-3 Topic Guide
No one knows the real cause of a prescribing error more than the prescribers. The purpose of the focus group is to explore your perspective of the cause(s) of prescribing errors intercepted in prescriptions written in the ward. The focus group will last approximately 60-90 minutes and will be audio-recorded. All information discussed during this focus group will be treated in confidence. No one outside the research team will know what you have said or that you are participating in the study. Data will be analysed anonymously and any identifying information including names of anyone mentioned during the focus group will be removed.
Please do not identify yourself as responsible for the error and do not lay blame for the error on your colleagues. Please respect the confidentiality of your colleagues, as they may be sharing examples of errors that they have been aware of. The purpose is to understand why the errors occur and learn from the errors so that these errors do not happen again.
The focus group will be divided into three parts: The first part is about the prescribing system in the hospital and the next two parts are about examples of the common types of prescribing errors on the ward and not the errors themselves.
Part One
The focus of this part is talk in general about the prescribing system in the hospital to get prescribers’ view and perception of the prescribing process (system) and if the process contributes to the occurrence of errors
Tell me about the process for medication prescribing at your hospital? Do you think the prescribing process contributes to the occurrence of error?
[probe as necessary, e.g. In what way?] Why else do you think prescribing errors occur?
Part Two
This part of the focus group will focus on the error(s) identified in phase 1. The same process in part two will be repeated for each type of error identified in phase 1
I would like to talk to you about an example of an error we identified in one of the prescriptions written in the ward. The error was …………..
From your knowledge and experience, why do you think that error occurred? What factors do you think contributed to the occurrence of errors like that? What do you think could’ve been done differently to prevent the error
happening?
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Is this type of error something you usually encounter?
Further questions will be asked according to the case and the answers of the participants to get as much information as possible about the causes of prescribing errors.
Part Three
This part focuses on safety measures/system improvements prescribers believe could mitigate prescribing errors in their setting
What safety measures could be implemented to reduce prescribing errors such as the ones you made?
What systems improvements can be used to make the prescribing process safer?
Is there anything else you want to add?
Thank you for participating, your time is appreciated.
(Stop recording)
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Appendix C Focus Group 4 Topic Guide
No one knows the real cause of a prescribing error more than the prescribers. The purpose of the focus group is to explore your perspective of the cause(s) of prescribing errors intercepted in prescriptions written in the ward. The focus group will last approximately 60-90 minutes and will be audio-recorded. All information discussed during this focus group will be treated in confidence. No one outside the research team will know what you have said or that you are participating in the study. Data will be analysed anonymously and any identifying information including names of anyone mentioned during the focus group will be removed.
Please do not identify yourself as responsible for the error and do not lay blame for the error on your colleagues. Please respect the confidentiality of your colleagues, as they may be sharing examples of errors that they have been aware of. The purpose is to understand why the errors occur and learn from the errors so that these errors do not happen again.
The focus group will be divided into three parts: The first part is a PowerPoint presentation about the preliminary results from focus groups 1-3 and an introduction about the concepts of capability, opportunity, and motivation. The second part is about what enable/disable you from prescribing properly in terms of capability, opportunity and motivation.
Part One
PowerPoint presentation (Prescribing errors in the Paediatric Oncology Ward)
Part Two
Q: In terms of individual capability, what do you think are the barriers that affect the ability of doctors on the unit to prescribe properly? [Prompts: knowledge, physical and psychological skills)
Q: What do you think are the opportunities in the environment that makes errors easy to happen?
[Probes: time, resources, more staff, support from others]
Q: What about motivation? What do you think are the factors that motivate doctors to prescribe the way they prescribe? Or discourage doctors from doing some tasks?
When we analysed the data, the majority of errors were related to supportive meds rather than chemotherapy meds and the majority was dosing errors.
Q: Why do you think that is?
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One of the contributory factors that physicians talked about was the transition from one electronic prescribing system to another without being properly trained to use it.
Q: Do you think not knowing how to use the system affected your ability to prescribe properly?
Q: Is there any other example were you were not trained properly?
Q: what other tasks related to prescribing do you think doctors should have training or more training?
You talked about some errors that occurred during admission and discharge because of problems with the medication reconciliation or patient counselling.
Q: How much do you think it is needed to check all patients’ medication (chemo and others) upon admission and discharge?
Q: Do you think this is part of the job of the doctor? [Probes; who’s job it is?]
Q: Why do you think this job is not being done?
Q: Doctors may be discouraged to check all patients’ medications upon admission or discharge, why do you think that is?
[Prompts: heavy workload, not having enough time]
Q: What other factors affect you?
Q: What would encourage doctors to spend more time on medication reconciliation?
Some of you talked about communication with specialists
Q: Are there particular problems prescribing after a consultation by someone else?
Q: Does the name or level (e.g., consultant vs. resident) of the person who made the consultation affect your decision to whether you follow the recommendations or not?
Q: Do you think maintaining the relationship with other doctors has an effect on the doctor’s decision to follow the recommendations?
Q: How can you improve the communication with doctors from other specialities?
Some of you mentioned the lack of knowledge of doctors from other specialities (e.g., surgeons and dentists) especially when it comes to antibiotics.
Q: When the other doctor makes the consultation, do you know at that point if the order is correct or has an error?
Q: If you know, do you communicate with the other doctor? Why not?
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Q: How much do you think the lack of knowledge of others contributed to errors made in the oncology ward?
Some of you raised the issue of poor documentation.
Q: What hinders your ability to document everything you did?
Q: What sort of an impact does poor documentation have on how you work?
You have said that some of the tasks that the doctors do every day are tedious and time consuming such as re-entering and renewing orders in the system every day or every few days for all the patients in the ward.
Q: How much do you feel you need to do this task?
Q: How can you improve this process (renewing orders)?
Q: What other tasks you think is time consuming?
Just before we finish, can I ask a few general questions:
Q: What tasks do you think doctors should focus on to prescribe effectively and properly?
Q: What are the environmental factors surrounding doctors that contribute to error occurring or affect the way doctors prescribe?
Q: What do you think can be done to improve the prescribing process?
Thank you for participating, your time is appreciated.
(Stop recording)
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