Nursing

Please use only those 3 articles and the research statement to create 13-14 slides of PowerPoint to prove the statement.

My Nursing issue is Nurses that make medication errors due to nurse to patients’ ratio. Facilities like Nursing homes have a high workload, and the time that was expected for the Nurses to complete their tasks is impossible, which I believe is the main reason for their high level of medication errors. How can we change the system to make sure Nurses are not burned out and their patients are safe?

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Articles used for research.

Reference

1- Odberg, K., Hansen, B., & Wangensteen, S. (2019). Medication administration in nursing

homes: A qualitative study of the nurse role. Nursing Open, 6(2), 384–392.

https://doi.org/10.1002/nop2.216

2- Qureshi, S., Purdy, N., Mohani, A., & Neumann, W. (2019). Predicting the effect of

nurse–patient ratio on nurse workload and care quality using discrete event simulation. Journal of Nursing Management, 27(5), 971–980.

https://doi.org/10.1111/jonm.12757

3- Kim, L., Giannitrapani, K., Huynh, A., Ganz, D., Hamilton, A., Yano, E., Rubenstein, L.,

& Stockdale, S. (2019). What makes team communication effective: a qualitative analysis of interprofessional primary care team members’ perspectives. Journal of Interprofessional Care, 33(6), 836–838.

https://doi.org/10.1080/13561820.2019.1577809

The presentation should be 12-14 slides power point

(Excludes a title slide and a reference slide).

1. Introduction (6 points) – Introduce yourself, your NPI, PICO, and resulting practice question. Discuss the rationale of why your practice question is important and why it interests you.

2. Sharing of Evidence (15 points) – Share highlights and pertinent aspects of the evidence you found to answer nursing practice issue question. (Use the information you compiled in the Article Matrix). Discuss legal and ethical implications the evidence has for practice related to your NPI.

3. Discussion (18 points) – a) Discuss how your NPI research may affect practice outcomes at the individual, population and/or systems levels. b) Include information on how your nursing practice has been or will be affected as a result of completing the research for your NPI. C) Discuss how you would use relationship focused nursing care and teamwork to implement your NPI in order to foster mutual respect, decision-making and healthcare outcomes. Be specific to your NPI project.

4. Conclusion: (3 points) – Summarize the main points/focus of your presentation.

5. Research Process (3 points) – Demonstrate use of the research process in the presentation. Throughout the course, you will complete a series of JH tools. Your paper will need to include information and feedback from these tools.

384 | Nursing Open. 2019;6:384–392.wileyonlinelibrary.com/journal/nop2

1 | I N T R O D U C T I O N

Patient safety issues in primary health care are mainly related to di‐
agnosis and medication. It is generally acknowledged that adverse

events related to medication administration account for a significant
threat to overall patient safety (Kohn, Corrigan, & Donaldson, 2000;
Makeham, Dovey, Runciman, & Larizgoitia, 2008; Marchon & Mendes,
2014; Vogelsmeier, 2014). Medication administration involves an in‐
tricate mixture of various tasks and demands that temporally struc‐
ture the nurse’s workday (Carayon et al., 2014; Grigg, Garrett, &
Craig, 2011; Jennings, Sandelowski, & Mark, 2011; Moyen, Camiré, &
Stelfox, 2008; Odberg, Sætre Hansen, Aase, & Wangensteen, 2017).

Primary health care in the Western World reaches out to a broad
segment of the population and is the facet of the healthcare system
with which most people interface. Each municipality independently

governs Norwegian nursing homes, and there are local and re‐
gional variations in size, patient types and the style of management.
However, the basic principles of active treatment and ensuring the
basic needs of the residents are universal (Malmedal, 2014). Recent
reforms have led to increased collaboration between primary care
and specialist health care. Nursing homes experience increased pres‐
sure to receive more patients needing more complex active medical
treatment, compared with a few years back (Syse & Gautun, 2013).

2 | B A C K G R O U N D

The medication administration process consists of six stages: ordering
and prescription; transcribing; dispensing; preparing; administering; and
finally observing and documenting effects and side effects (Carayon et
al., 2014). Medication administration errors (MAE) may occur anywhere

Received: 20 April 2018 | Revised: 25 September 2018 | Accepted: 23 October 2018
DOI: 10.1002/nop2.216

R E S E A R C H A R T I C L E

Medication administration in nursing homes: A qualitative
study of the nurse role

Kristian Ringsby Odberg1 | Britt Sætre Hansen2 | Sigrid Wangensteen1

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2018 The Authors. Nursing Open published by John Wiley & Sons Ltd.

All authors contributed equally.

1Department of Health Sciences, Norwegian
University of Science and Technology
(NTNU), Gjøvik, Norway
2Faculty of Health sciences, SHARE—Centre
for Resilience in Healthcare, University of
Stavanger, Stavanger, Norway

Correspondence
Kristian Ringsby Odberg, Department of
Health Sciences, Norwegian University of
Science and Technology (NTNU), Gjøvik,
Norway.
Email: Kristian.odberg2@ntnu.no

Abstract
Aims: The objective of this study was to expand the knowledge of the nurse role dur‐
ing medication administration in the context of nursing homes. The following re‐
search question guided the study: How can the nurse role during medication
administration in nursing homes be described?
Design: A QUAL–qual mixed study design was applied.
Methods: Data were collected using partial participant observations and semi‐struc‐
tured interviews of all staff members involved in medication administration. An in‐
ductive content analysis was performed.
Results: Medication administration is a pervasive process ingrained in the day‐to‐day
activities of providing care to the patients. The nurse role is compensating, flexible
and adaptable. There is a dynamic interaction between several contributory factors,
those being shifting responsibility, a need for competence, invisible leadership, vary‐
ing available competence, staff stability and vulnerable shifts.

K E Y W O R D S

medication, nurses, nursing, nursing homes, older people

www.wileyonlinelibrary.com/journal/nop2

http://orcid.org/0000-0003-3456-9740

http://creativecommons.org/licenses/by/4.0/

mailto:Kristian.odberg2@ntnu.no

| 385ODBERG Et al.

along this chain and cause an adverse drug event (ADE; Carayon et
al., 2014; Choo, Hutchinson, & Bucknall, 2010; Odberg et al., 2017;
Smeulers, Onderwater, Zwieten, & Vermeulen, 2014). According to
WHO (2016), MAE’s are preventable at different levels.

Overall research acknowledges the importance of the nurse role
in maintaining and improving medication safety in health care (Choo
et al., 2010; Grigg et al., 2011; Kowalski & Anthony, 2017; Smeulers
et al., 2014). Many factors influence safe medication management.
Some argue that nurses (RN) may have insufficient knowledge and
skills to perform safe medication management (Andersson, Frank,
Willman, Sandman, & Hansebo, 2018; Simonsen, 2016); others
point to normalization of risk‐inducing behaviour and interruptions
(Odberg et al., 2017), or use of technology, design flaws, time con‐
straints, poor communication, lack of leadership, as well as outdated
policies and guidelines (Al‐Jumaili & Doucette, 2017; Carayon et al.,
2014; Keers, Williams, Cooke, & Ashcroft, 2013; Lapkin, Levett‐
Jones, Chenoweth, & Johnson, 2016; Marasinghe, 2015). There is
an apparent lack of studies investigating the nurse role during med‐
ication administration in nursing homes.

Due to the complexity of medication administration, the acknowl‐
edgement of MAE’s in primary care and the essential role of the RN,
the objective of this study was to expand knowledge of the nurse role
during medication administration in the context of nursing homes.
The following research question guided the study: How can the nurse
role during medication administration in nursing homes be described?

3 | M E T H O D

3.1 | Design

The study applied a qual‐qual mixed method design (Morse, 2016)
using partly participant observations (Hammersley & Atkinson,
2007) supplemented by semi‐structured interviews for data col‐
lection. The first author collected all the data in two nursing home
wards in Eastern Norway.

3.2 | Study setting and recruitment

The senior managers of the participating nursing homes were con‐
tacted by telephone in December 2015. They were informed of the
objective and content of the study and agreed to participate. Shortly
after, the first author briefed the entire staff on both wards during
regular staff meetings and asked whether they would consider par‐
ticipating in interviews. One nursing home ward with ten patients
was rurally based and catered mostly to patients suffering from de‐
mentia and minor disabilities. The other nursing home ward, with six
patients, was in a neighbouring urban municipality, with patients hav‐
ing multiple complex medical diagnoses and in need of palliative care.

3.3 | Data collection methods

A pilot study was conducted in a nursing home ward providing a sim‐
ilar contextual setting as the current study to test the data collection

methods. Experiences and findings from the pilot study resulted in a
more detailed observation guide and interview guide. No data from
the pilot study were used in the current study.

The data collection took place in 2016, consisting of 140 hr
of observations supplemented by 16 semi‐structured interviews
of staff members. Most observations took place in the daytime
shift and a few on the evening shift and opening hours of the
night shift. The first author, dressed in work attire, followed staff
members around conducting partly participating observations
during medication administration‐related tasks (Hammersley &
Atkinson, 2007). A semi‐structured observation guide based on
the elements in the work system of Human Factors theory (per‐
sons, tasks, physical environment, tools and technology, organi‐
zation) guided the researcher when observing the different stages
of medication administration (Carayon et al., 2006). Examples are,
observations of pre‐visitation, transcribing medicines or staff pre‐
paring medicines before administering them. Situations observed
were noted between sessions, while excerpts from relevant con‐
versations between staff members were written down verbatim
immediately. After each observational session, all notes were
transcribed and expanded on while the memory of the events was
clear in the mind.

Participants working more than a 50% position for more than a
year were interviewed. There were eight staff nurses, three nurse
assistants, two nurse managers and two doctors. The majority were
women (12). The reason for including professions apart from the
nurses was observations showing a strong dynamic interaction be‐
tween all staff members during medication administration. The in‐
terviews were digitally recorded and lasted from 30 min ‐ 1 hr. The
interview guide was constructed in line with observational findings
and from elements in the work system in Human Factors theory
(Carayon et al., 2006).

3.4 | Analysis

Shortly after finalizing the data collection, the authors read all the
material multiple times to reach a common understanding of the
data as a whole. The first author then coded openly in the margins
of the transcribed material, extracting meaning units pertaining to
the research question. These meaning units were condensed, coded
and grouped based on similarities, forming subcategories and main
categories in line with principles in inductive content analysis (Elo
& Kyngäs, 2008). Data from the observations and interviews were
handled and coded separately and integrated in the final stage of
the categorization process (Morse, 2016). Analytical discussions and
reflections with the co‐authors led to several iterations before ar‐
riving at a conceptual model. Observational data formed the core
for describing the day‐to‐day care and the structure of medication
administration. Excerpts from the interviews and observation notes
were chosen to illustrate the different main categories and subcat‐
egories. They are reported in italics throughout the Results section
and coded to differentiate the position (second and third letter) and
the individuals (final letter):

386 | ODBERG Et al.

IRN‐A = Interview Registered Nurse A
INA‐A = Interview Nurse Assistant A
INM‐A = Interview Nurse Manager A
IMD‐A = Interview Medical Doctor A

An example of analysis is shown in Table 1.

3.5 | Ethics

The Norwegian Social Science Data Service (NSD; No. 45389) ap‐
proved the study. Since there was no involvement of patients or use
of patient information, the study did not require approval from the
Norwegian Regional Committee for Medical Health Research Ethics.

The first author is a male registered intensive care nurse with no
prior familiarity with or knowledge of any of the wards or the partici‐
pants in the study. All participants gave their informed consent and were
informed of data confidentiality and of the opportunity to withdraw at
any time. No one chose to withdraw during or after data collection.

Before observations, the researcher informed all participants
that professional ethics overrode researcher neutrality, meaning
that the staff would be alerted if the researcher identified situations

where patient harm could be averted (Guillemin & Gillam, 2004). The
researcher encountered no such situations.

The paper was prepared according to SRQR guidelines (O’Brien,
Harris, Beckman, Reed, & Cook, 2014).

4 | R E S U LT S

When aiming to describe the nurse role in medication administra‐
tion, three main categories emerged: compensating, flexible and
adaptable. Each of these main categories contains subcategories
describing different aspects of the nurse role and the collabora‐
tion needed to perform medication administration. The results
reflect a dynamic interaction of several contributory factors and
how the nurse role is integral in medication administration as
shown in Table 2:

4.1 | Compensating

The roles of the individual staff members are affected by the com‐
petencies of the surrounding staff. The most striking finding is how

T A B L E 1 Analysis exemplified with one of three main categories and subsequent subcategories

Main category Sub‐category Condensed meaning Examples of meaning units

Compensating Need for competence Differences in individual competencies. Keeping up
to date is an individual responsibility

IRN‐D Yeah…internal education, we
have some of that. The previous
doctor used to spend some time with
us, refreshing competencies and
skill—not anymore though—and
sometimes we arrange some
educational stints

Shifting responsibility The nurse is regarded as pivotal for the running of
day‐to‐day business

IRN‐E It may be slow at times if the
doctor is uncertain. He does not take
hasty or quick decisions and may sow
doubt by the way he acts. Then you
feel more responsible as a nurse,
because you have to lead the way
somehow, and that is not how it
should be

T A B L E 2 Contributory factors influencing the nurse role during medication administration on different levels

Individual level
Compensating
Need for competence
Shifting responsibility

Team level
Flexible
Leadership
Available competence

Organizational level
Adaptable
Staff stability
The vulnerable shifts

• Varying competence
Need for updated competence

• Medication administration perceived as complex by RN’s
• Takes on more responsibility than necessary
• Administrative tasks take precedence
• The RN’s are natural leaders
• Do more tasks than obliged
• Inadequate resources

• Leadership is distributed and invisible
• Nurse managers are in a tight position
• Delegation of tasks

Available competence
Vulnerable
Random

• Informal leadership
• Random team composition
• RN’s prioritize administrative tasks

• Shifting workload
• Cannot plan for everything
• Staff stability important

Experience and personality
• Staff composition important
• Workarounds are normal
• Prepare in advance
• Contingency plans
• Continuity of care

| 387ODBERG Et al.

the nurse in charge is left to compensate for the degree of skills and
competencies of their team members.

4.1.1 | Shifting responsibility

NA’s perceive medication administration as an easy task, describing
it as only preparing and administering medicines. The nurses have a
fuller picture encompassing all six stages of the medication adminis‐
tration process, and they also consider it a much more complex pro‐
cess as documented in the following interview excerpts with a nurse:

IRN‐A I started out as an NA, which I appreciate.
It gave me a lot of the basic skills necessary, but of
course, there is a lot more responsibility as a nurse.
You do more of the same, but you have more respon‐
sibility and more tasks as a nurse.

The NA’s see themselves in the light of the nurses and perceive
their duty to assist the nurses. Consequently, they consider the nurses
to be their superior in all settings, referring to them if questions or
problems arise. Some nurses thrive on this, making them feel compe‐
tent and taking the role as leaders. This invisible role designation led to
a hierarchical structure, especially evident on shifts with a single nurse.
On shifts with several nurses, seniority seems to fall to the nurse with
most experience as illustrated in this observational excerpt:

There are three nurses in the nurse station, allocating
tasks at the start of the morning shift. It is hard to
identify who is the leader, but after a while, the nurse
with seniority becomes the centre of attention and
makes final decisions on which patients they will have
responsibility for.

The nurses have a considerable responsibility, and they tend to
take on tasks belonging to the other staff members as well as their
own. Observations document that the nurses often regard themselves
as being “the spoke of the wheel” and often define specific medication
administration tasks as more important than other tasks. A substantial
number of the tasks related to medication administration were dele‐
gated from the MD and could not be delegated to nurse assistants.

The nurses adjust dosages to patients with varying needs, for
example, when administering drugs for diabetes or pain manage‐
ment. Most often, they have a sheet of paper with pre‐authorization
from the doctor on various drugs. At other times, the nurses make
changes or adjustments themselves, based on observations and pa‐
tient needs and inform the doctor on a later occasion. Excerpt from
observational notes:

During pre‐visitation the nurse informs the doctor
that “we have made the following changes in some
medication prescriptions. The nurse then asks the
doctor if he may formalise the changes, which means
to transcribe them in the electronic medication

administration record. Then the nurse rationalises the
decision and the doctor agrees.

The MD generally accepts this as normal routine provided the RN’s
are able to substantiate the drug alterations. An excerpt from an inter‐
view with an MD follows:

IMD‐A I know how experienced the nurses on this
ward are when it comes to administering morphine,
so I probably often note the indication and give the
nurses space to be flexible. There is seldom a right
or wrong, but the nurses have to substantiate their
opinions or when they make alterations.

Observations documented that when the doctor was uncertain,
the nurses experienced more responsibility together with a feeling of
uneasiness. In cases where the doctor had strong opinions and openly
discussed the patients with the nurses, they were included and em‐
powered. This duality gave rise to the nurses compensating for how
the doctor behaved. If they considered the doctor to be “weak,” they
compensated by taking on tasks that were not theirs initially. If they
considered the doctor “strong,” they let the doctor handle things as
they stood. Examples of additional tasks could be how the nurse of‐
fered to take on documentation tasks belonging to the doctor (tran‐
scribing), merely to ensure that this was done.

4.1.2 | Need for competence

The staff often noted that patients have more diagnoses and are in
need of more advanced medication administration than before; they
had to take responsibility for patients before they were adequately
treated or diagnosed and in turn more complex tasks related to med‐
ication administration. This has led to more responsibility and a need
for updated competence.

There is limited funding to send staff to courses and conferences
and maintaining competence largely depends on personal initiative.
The staff complain that if they need more advanced competence,
they have to use their spare time, receiving no financial reimburse‐
ments or incentives. At the same time, all staff members acknowl‐
edge that complex healthcare environments and nursing sciences
are in constant flux due to advances both medically and procedurally.

The managers seemed aware of the inadequate resources that
inhibit competence development in the staff, placing them between
a rock and a hard place. One nurse manager described it in an inter‐
view as:

INM‐A We continuously receive new guidelines re‐
lating to medications, with new demands on docu‐
mentation. At the same time, we need to keep tabs
on everything; it always comes down to the economy,
who pays for what. Everything has consequences if
we are not thorough in following up. We have more
tasks and demands than ever.

388 | ODBERG Et al.

4.2 | Flexible

Flexibility mirrors the freedom staff members experience in struc‐
turing their workday and performing medication‐related activities.
Tasks in the workgroup on specific shifts are delegated differently
in line with changing circumstances. The nurse also compensates
for the other team members’ strengths and weaknesses. If a nurse
spots a weakness in a colleague or does not trust him or her to do
a specific task, they do it themselves instead. When they did, it
was not explicitly stated and was viewed by the others as expected
behaviour.

4.2.1 | Available competence

The team on a specific shift have a shared world of experience and
skill where the staff works. Available skills and competencies on a
given shift are demarcated partly by the professions in the team.

Some shifts may experience staff lacking the competencies
to administer certain medications. At other times, only one per‐
son, usually a nurse, has the necessary skills to perform specific
activities vital to a patient. This may lead to vulnerability as the
team may experience a lack of skill redundancy. Such vulnerabil‐
ity may lead to adverse events under adverse circumstances, for
example, staff shortage, or unexpected events in the ward. Some
shifts have only one nurse, and most administrative and medica‐
tion‐related tasks will fall on that nurse. Many tasks during a shift
are indirectly care‐related or related to medication administra‐
tion; these are perceived as administrative tasks. Administrative
tasks are often considered a nurse prerogative, and nurses may
find themselves swamped because of their inherent task flexibil‐
ity, being able to undertake a variety of roles. If there are NA’s
present, they are most often engaged in clinical work, close to the
patient, reporting verbally to the nurse on the team. The NA’s ac‐
knowledge the nurses’ workload:

INA‐A If you have the evening shift alongside a nurse,
they have a higher workload, because a majority of
the activity on this ward demands a nurse, because of
competence and such.

4.2.2 | Leadership

The nurse managers were in charge of the team composition on the
individual shifts, distributing staff across the various shifts, weeks in
advance. The teams were formed so that professions complemented
each other with the aim of always having a nurse on all shifts.

Although the staff are supposed to update on the patients on their
own by reading from the electronic medical record, they also had an
informal roundtable discussion before commencing each shift. This
discussion served to vent frustration, to reflect on recent events, but
also to discuss and delegate patients and specific tasks among the staff

members. The task‐allocation often took into account the wishes of the
staff members and was in contrast to the manager’s prior assignments:

INA‐A “Patients and tasks are in fact assigned in ad‐
vance, but we sit there during the time of the report
and distribute tasks and patients among ourselves as
well. It depends on the workload, if our wishes are
granted, we have to ensure that no one gets too much
to do, that we assign fairly. If we have a nurse on that
shift, she will have the final say. Otherwise, it’s like
the toss of the dice.”

The skills and competencies available on a particular shift result
from the managers’ pre‐planning but get randomized as circum‐
stances change; staff may become ill, forcing changes. The flexibility
of task assignment is therefore dependent on the skills and com‐
petencies needed in the various tasks related to medication admin‐
istration. Not all staff members can set up an intravenous line or
administer all type of medicines.

4.3 | Adaptable

The main category “adaptable” contains two related categories: Staff
stability and Vulnerable shifts. In short, adaptability is about how the
staff adapt to changing workloads during the various shifts and how
they perceive the relationship with their co‐workers as a critical factor
in collaborating and performing medication administration safely. An
alteration in work tasks and workload is sometimes predictable, but
most often not. Consequently, some shifts end up being vulnerable.

4.3.1 | Staff stability

Staff stability is critical to achieving optimal care for the patients, un‐
derlining the importance of knowing your co‐workers when working
in a demanding and complex environment. Working well together
depends on personality, and there are individual differences influ‐
encing cooperation. The freedom to ask colleagues for help dur‐
ing medication administration is reported as crucial by most staff
members and depends on a shared understanding of the situation
and that all staff members report on their location at all times. Also,
sharing experiences together seems vital, allowing the staff to form
bonds that would not otherwise have formed. The relationship with
co‐workers is illustrated in the following excerpt from an interview
with a nurse assistant:

INA‐B “We experience a lot together, stressful and
taxing situations…for the most part we are good at
talking to each other, but there are variations, it de‐
pends on who you’re working with; it’s all about per‐
sonal chemistry.”

Having good personal chemistry with colleagues was necessary
for the staff to thrive. When the staff know each other, they are less

| 389ODBERG Et al.

vulnerable if something unpredictable happens. The quality of the care
depends on the stability of the staff and when staff members know
each other, there seems to be less need for direct communication and
delegation of tasks. A stable staff also know the patients and can work
more efficiently and may provide better care. The opposite happens
if there are many substitute nurses; the continuity of care may be dis‐
rupted and a proportionally higher fraction of the total workload is
taken on by the regular staff members.

4.3.2 | The vulnerable shifts

In periods of high workload, the staff seems to work with great ef‐
ficiency and they describe the work as going smoothly. Like one
nurse said: IRN‐B “When it’s busy we are like well‐oiled machinery.”
Another nurse stated that it is a balancing act. “If it’s too hectic, we
do not work so well together”. Such high workloads may have posi‐
tive professional outcomes, as the staff claim to work more smoothly.
It may also lead to adverse patient outcomes in that the healthiest
patients receive less attention and care. One nurse (IRN‐C) said dur‐
ing observations that “when it is busy we prioritise medication to the
patients most needing it.” At the same time, several stated that they
like working when it is busy since it gives them a feeling of higher
self‐worth.

Both nursing home wards reported staff levels to be adequate
during the day shifts on weekdays. Evening shifts, night shifts and
weekends were often reported as vulnerable depending on work‐
load and status of the current patients. This vulnerability was di‐
rectly linked to the professions and competencies of the staff at
work. Working vulnerable shifts seemed to invoke negative emo‐
tions in the staff and an excerpt from an interview with a nurse de‐
scribes it as follows:

IRN‐D “This is the way it is. I feel very alone during
my weekend shifts, being a single nurse and the only
regular staff member. That is not okay. I feel that I lose
control and when Monday finally arrives, I send a si‐
lent thanks that everything went well.”.

Some night shifts had no nurse on duty, and all medications had
to be prepared in advance. The staff were aware of the vulnerable
shifts in advance and did their best to plan accordingly, as shown in
this observation note:

The nurse in charge realises that there are no nurse
set up on the next shift and that they have a patient
suffering from pains hard to relieve. They decide to
prepare a dose of morphine in advance, doing the
double‐checking now.

This proactive engagement seems to be due partly to the unpre‐
dictable nature of working in a complex healthcare system; the staff
expected the unexpected.

Because the vulnerable shifts could be particularly unpredict‐
able, the staff prepared medications in advance or sent notice to the
staff on the neighbouring wards that they might need assistance. In
coping with the provision of medicines around the clock, the staff
knowingly bent guidelines and procedures to fit the reality of their
work environment. An excerpt from an interview with a nurse elabo‐
rates on how she would handle a potential situation on a vulnerable
shift:

IRN‐E If I needed to administer morphine and was
alone on my shift, I might have taken a photo with my
cell phone and sent it to a colleague for confirmation.
I would have done something like that if the situation
demanded it.

5 | D I S C U S S I O N

The main findings indicate that the RN has a central role at all the
stages of medication administration and that this role goes beyond
the job description. Varying workload, staff stability, the degree of
leadership, available competence and dynamic events in the work‐
day are compensated by the RN’s to ensure fulfilment of all tasks
related to medication administration at all times.

5.1 | Resilience

Medication administration in nursing homes is a complex process
taking place in a complex system with inherent vulnerabilities, plac‐
ing high demands on the sociotechnical work system and the staff
(Carayon et al., 2014; Choo et al., 2010; Grigg et al., 2011; Odberg
et al., 2017). Findings in the current study document this complex‐
ity and elaborate on how the staff and particularly the RN’s adjust
to shifting circumstances in their work environment. Human Factors
focus on the interaction of the elements in the sociotechnical work
system and how people perform processes in this system (Carayon et
al., 2006). Workarounds and adaptations are often described as “filling
in the gaps” to cover for design flaws or internal or external pressure
and complexity (Rankin, Lundberg, Woltjer, Rollenhagen, & Hollnagel,
2014). The main categories in the current study describe role compen‐
sation, flexibility and adaptability as crucial when describing the nurse
role in medication administration. These categories reflect an intrinsic
ability to confront and adjust to a dynamic and challenging workday.

If one adopts a resilience engineering perspective, work pro‐
cesses in complex systems are recognized by variations, driving
people to change and adapt behaviour to meet the fluctuations
both long‐term and short‐term (Hoffman & Woods, 2011). Everyday
adaptations to cope with dynamic events can be described as per‐
formance variability, encompassing individual adaptations and how
the surroundings react to them (Hollnagel, 2009, 2014 ). The nurse
role is highly regulated, but the unpredictable nature of healthcare

390 | ODBERG Et al.

systems often forces RN’s to improvise, to find workarounds and
adapts to the conditions offered by the current situation (Lindblad,
Flink, & Ekstedt, 2017). Sometimes these adaptations may lead to
unsafe situations, but most often they will have a successful out‐
come (Hollnagel, 2009).

Performance variability in a system should aim to be propor‐
tional to the system complexity, meaning that the staff of the
nursing homes should have appropriate skills, resources and flexi‐
bility at hand to meet any unforeseen events (Braithwaite, Wears,
& Hollnagel, 2016; Grigg et al., 2011). The current study identified
six areas (subcategories) necessitating adaptive behaviour to en‐
sure safe medication administration. These areas are on an individ‐
ual level (Need for Competence and Shifting Responsibility), team
level (Leadership and Available Competence) and organizational
level (Staff Stability and The Vulnerable Shifts). Figure 1 illustrates
the balancing act of safe medication administration documented
in the study.

5.2 | The nurses are compensating

Individual adaptive behaviour manifested itself in the degree of flex‐
ibility nurses exhibited about the medication administration respon‐
sibility and how they compensated for the other staff members. This
flexibility depended on the capabilities of the workgroup on a spe‐
cific shift, as well as their training and competence. Other attributes
usually associated with nurses’ performance are motivation, fatigue
and stress (Al‐Jumaili & Doucette, 2017; Carayon et al., 2006; Grigg
et al., 2011). Furthermore, the training and skill maintenance in medi‐
cation administration‐related tasks are to some degree random in
that it is voluntary to participate. Consequently, the staff members
may have different skill sets and competencies. Over time, this may
contribute to lowering the overall competence of the staff.

Individual characteristics of the staff, therefore, vary signifi‐
cantly from shift to shift, having a impact on performance variability

and degrading the ability to prepare for unexpected conditions.
Changing circumstances meant that the staff had to improvise and
prioritize. At the same time, the staff were obliged to undertake
a variety of tasks, not all of them clinically related. These findings
seem universal as RN’s often are required to undertake multiple
tasks simultaneously in stress‐inducing physical environments,
making them more prone to making errors (Carayon et al., 2014;
Monroe & Graham, 2005; Odberg et al., 2017). Under high work‐
load, administrative tasks related to medication administration took
precedence for the RN’s, thus delegating the remaining workload
to the other staff members. In effect, administration of drugs and
the subsequent observations were delegated to RN’s or NA’s with‐
out first‐hand knowledge of the patients. A lack of task redundancy
often resulted in task vulnerability, and medications or treatments
sometimes had to be postponed or were interrupted. Breaks in the
medication administration chain may increase the risk of committing
MAE’s and potential ADE’s (Carayon et al., 2014).

5.3 | The nurses are flexible

An important finding was how the leadership was distributed and
invisible, leading to flexibility when delegating tasks and responsi‐
bilities. Nurse managers had indirect control of staff allocation and
task delegation in that the staff often made their own decisions and
planned contrary to prior assignments. The leadership and style of
management seem to affect how the staff perform and delegate
tasks. A clear leader with a hands‐on approach may impose more di‐
rect control and strictures in relation to the myriad of regulations and
guidelines on medication administration, while a more distant leader
lets the staff regulate more independently. In terms of resilience, this
resembles the terms work‐as‐done (WAD) and work‐as‐imagined
(WAI; Braithwaite et al., 2016). Human Factors theory often uses
the analogues “blunt end” and “sharp end” to encapsulate much of
the same meaning (Rankin et al., 2014; Reason, 2000). In the current

F I G U R E 1 The balancing act of safe
medication administration

| 391ODBERG Et al.

study, the nurse managers of both nursing homes “imagined” how
the wards should be run (WAI), something that not always translated
to how it was actually done (WAD). This discrepancy underlines the
importance of communication across levels and management capa‐
ble of addressing the needs of the staff (Backman, Sjögren, Lövheim,
& Edvardsson, 2017; Hollnagel, 2012). Examples in the current study
indicate that even though managers endeavour to structure the
workday of the staff, they simultaneously encourage flexible behav‐
iour without giving clear indications of where this delineation ought
to be. The staff may perceive this as distant management and thus
use considerable internal resources to structure their workday. This
entails the staff forming ad hoc teams with a random team‐structure
and performing many of the tasks of the regular nurse manager.

5.4 | The nurses are adaptable

The vulnerable shifts are to some degree predictable, but still pose
challenges to the staff. Staff shortage, lack of competence and
scarce resources may impede the staff’s ability to be adaptive and
find workarounds (Hollnagel, 2009). Over time, this behaviour may
evolve to be a part of normal operations, stretching the boundaries
of safe medication administration. As a consequence, the staff may
be balancing precariously close to unsafe medication administration
in their daily routines without knowing. If something unpredictable
happens during a vulnerable shift, the border may be crossed and
ADE’s occur. Some staff members expressed gratitude when they
finished a so‐called vulnerable shift and opined that sometimes it
was due to luck or coincidence that no ADE’s occurred.

Staff stability and shared mental models are often recognized as
a key factor to ensure safe care in healthcare environments (Salas
& Frush, 2013). When the staff know each other’s skills and com‐
petencies and trust each other, there is less need for communica‐
tion to coordinate medication administration tasks. They describe
it as working in silent agreement. It may lead to increased freedom
and flexibility when performing tasks, but may also lead to less
structure, less use of guidelines, checks and regulations. The law
of requisite variety states that WAI should be as complex or varied
as WAD, meaning that one should strive to increase the knowledge
and competence of the staff to enable them to cope with unfore‐
seen activities. Another approach is to seek to minimize unforeseen
events through rules, regulations, standardizations and guidelines
(Braithwaite et al., 2016). To balance the complexity of the WAD
and WAI, one needs an in‐depth understanding of the organization.
Without it, medication administration may spiral into an unregulated
activity, having both positive and negative effects—the positive ef‐
fects being apparently increased resilience when facing unexpected
events, the negative effects being the erasing of borders between
safe and unsafe acts. Erasing the borders may continue and even‐
tually breach the bounds of safe medication administration without
the staff knowing. This may be exemplified by the RN who in a po‐
tential situation would consider using the mobile phone to message
an image to a colleague rather than asking the manager to double‐
check a medication to be a reasonable solution.

6 | L I M I TAT I O N S

Data collection was performed by a single researcher with a
nursing background, which may introduce bias. This was coun‐
tered by a research team, discussing and reflecting on the data
throughout the research process. Having a nursing background
may influence preconceptions, but also allows for rapidly gain‐
ing insights that might otherwise be missed. The researcher was
aware of the potential Hawthorne effect throughout the obser‐
vations. The two nursing home wards included were intention‐
ally different, to provide a broad picture of the nurse role in
medication management.

7 | C O N C L U S I O N

Medication administration is ingrained in normal clinical activities,
and isolated work processes may be challenging to define. Work
system factors such as competence, leadership and staffing may
influence the ability to perform safe medication administration. To
counter this, nurses exhibit role compensation and flexibility and are
highly adaptable during all the stages of administering medicines.
The seeming resilience nurses exhibit, may be brittleness, extending
the boundaries of day‐to‐day clinical activities close to the borders
of safe medication administration.

By identifying normal operations, one may learn, adapt and develop
appropriate safety measures in the future. The study underscores the
importance of first‐hand knowledge of the clinical setting before im‐
plementing interventions or enforcing any organizational changes.

C O N F L I C T O F I N T E R E S T

There are no conflict of interests.

O R C I D

Kristian Ringsby Odberg http://orcid.org/0000-0003-3456-9740

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Journal of Interprofessional Care

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What makes team communication effective: a
qualitative analysis of interprofessional primary
care team members’ perspectives

Linda Y. Kim, Karleen F. Giannitrapani, Alexis K. Huynh, David A. Ganz, Alison
B. Hamilton, Elizabeth M. Yano, Lisa V. Rubenstein & Susan E. Stockdale

To cite this article: Linda Y. Kim, Karleen F. Giannitrapani, Alexis K. Huynh, David A. Ganz,
Alison B. Hamilton, Elizabeth M. Yano, Lisa V. Rubenstein & Susan E. Stockdale (2019)
What makes team communication effective: a qualitative analysis of interprofessional primary
care team members’ perspectives, Journal of Interprofessional Care, 33:6, 836-838, DOI:
10.1080/13561820.2019.1577809

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SHORT REPORT

What makes team communication effective: a qualitative analysis of
interprofessional primary care team members’ perspectives
Linda Y. Kima, Karleen F. Giannitrapanib, Alexis K. Huynha, David A. Ganza, Alison B. Hamiltona, Elizabeth M. Yanoa,
Lisa V. Rubensteina, and Susan E. Stockdalea

aCenter for the Study of Healthcare Innovation, Implementation & Policy (CSHIIP), VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA;
bCenter for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Palo Alto, Menlo Park, CA, USA

ABSTRACT
Although numerous scholars have emphasized the need for effective communication between
members of interprofessional teams, few studies provide a clear understanding of what constitutes
effective team communication in primary care settings, specifically where patient-centered medical
home (PCMH) teams have been implemented. This paper describes the elements of effective
communication as perceived by members of interprofessional PCMH primary care teams, and
identifies elements of effective communication that have persisted over time. Using transcribed
text from 75 semi-structured interviews, we applied the grounded theory method of constant
comparison to categorize emergent themes relating to elements of team communication.
Interprofessional PCMH team members described the elements of effective communication as: 1)
shared knowledge, 2) situation/goal awareness, 3) problem-solving, 4) mutual respect; and commu-
nication that is 5) transparent, 6) timely, 7) frequent, 8) consistent, and 9) parsimonious. Parsimony is
an emergent theme that may be especially relevant for interprofessional PCMH teams challenged
with structured clinic schedules. Future work could focus on understanding how to teach and
sustain effective parsimonious communication. Comprehensive quality improvement efforts incor-
porating a variety of strategies, including team communication training, information and commu-
nication technologies, and standardized communication tools may facilitate communication of
pertinent patient information in a brief and concise manner.

ARTICLE HISTORY
Received 19 February 2018
Revised 14 January 2019
Accepted 18 January 2019

KEYWORDS
Qualitative method;
team-based practice;
interprofessional team
communication;
patient-centred practice

Introduction

With the implementation of the patient-centered medical
home (PCMH), traditional physician-centric primary care
practices are changing to environments where physicians
work collaboratively with other members of an interprofes-
sional team, sharing responsibilities for common face-to-face
tasks such as health promotion and coaching, medication
reconciliation, and reviewing test results and other findings
with the patient and family, as well as non-face-to-face tasks
such as telephone and email communication, processing clin-
ical reminders, and handling of forms (Department of
Veterans Affairs, Veterans Health Administration, 2014;
Edwards et al., 2015; Kim et al., 2017). This increased com-
plexity of PCMH team structure opens up new possibilities
for shared care of patients, but also raises new issues about the
most effective means of communication in an environment
where more team members are interacting. Few studies pro-
vide a clear understanding of what is perceived to be effective
primary care team communication since the transition to
PCMH. The objectives of this paper are to identify the ele-
ments of effective communication as perceived by interprofes-
sional PCMH team members in the primary care setting, and
to identify whether these perceptions have persisted over time.

Methods

In 2010, the Veterans Health Administration (VHA) implemented
the patient aligned care team (PACT), a PCMH model of patient
care delivery, in primary care settings. PACTs consist of a primary
care provider (PCP) – a physician, nurse practitioner (NP), or
physician assistant (PA) – and three supporting team members
including a registered nurse (RN) care manager, a licensed voca-
tional nurse (LVN) or a health technologist, and a medical support
assistant (MSA) or clerk (Department of Veterans Affairs,
Veterans Health Administration, 2014). Teams are supported by
ancillary staff, such as clinical pharmacists, nutritionists/dietitians,
and social workers. Participants for this study were recruited from
three VHA primary care practices implementing PACTs.

This study was conducted as part of a larger qualitative study
(Rubenstein et al., 2014), which employed a semi-structured inter-
view protocol to evaluate the early implementation experiences of
PACT members. The interview guides covered multiple domains,
including perceptions of team formation, team functioning, team
member roles and responsibilities, and team communication.
Team member interviews were conducted in two waves (wave 1:
January-June 2012; wave 2: September 2013-January 2014).

For the purposes of this study, we used output from all text
segments referring to team communication. A grounded theory

CONTACT Linda Kim linyskim@ucla.edu
This article has been republished with minor changes. These changes do not impact the academic content of the article.

JOURNAL OF INTERPROFESSIONAL CARE
2019, VOL. 33, NO. 6, 836–838
https://doi.org/10.1080/13561820.2019.1577809

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method of constant comparison was used to categorize emergent
themes relating to elements of team communication. All analyses
were conducted using ATLAS.ti software, Version 7 (Scientific
Software Development, 2013). All procedures were approved by
the Institutional Review Board of the VHA Greater Los Angeles
Healthcare System (2011-070725).

Results

The themes presented emerged via analysis of 75 interprofessional
PACT team member interviews: PCPs (wave 1: n = 14; wave 2:
n = 13), RNs (wave 1: n = 8, wave 2: n = 8), LVNs (wave 1: n = 9,
wave 2: n = 11), and MSAs (wave 1: n = 7, wave 2: n = 5).
Interprofessional PCMH team members described the elements
of effective communication as: 1) shared knowledge, 2) situation/

goal awareness, 3) problem-solving, 4) mutual respect; and com-
munication that is 5) transparent (open), 6) timely (prompt), 7)
frequent (often), 8) consistent (regular), and 9) parsimonious
(concise). These nine elements along with the description of the
elements and team members’ quotes are listed in Table 1.

Shared knowledge was described as sharing information
and knowledge about patient care issues, as well as strategies
to achieve positive patient and clinic outcomes. Situational
awareness involved giving a “heads up” to team members
about what is currently happening and what needs to happen
to accomplish team goals. Problem-solving was described as
the process of joint decision-making, resolution of patient
care issues, and conflict resolution/management, which was
often facilitated through shared knowledge within and
between teams. Mutual respect was a sense of being respected
and valued, regardless of one’s relative position on the team.

Table 1. Summary of elements of interprofessional team communication.

Elements of
Interprofessional
Team Description of Element Sample Quotes

Shared Knowledge Giving and receiving: “We talk about different roles and any problems that came up, any news that people have from
other meetings they’ve gone to that they bring in to share information.” [PCP]• Information

• Knowledge
• Ideas/strategies for

improvement
“We sometimes share, you know, like this is how we do it. Like when we develop like a group, so we
get some ideas from other groups also and sometimes those who haven’t had, we give them like tips
how we do it, so we kind of share that way…” [RN]

Situation/Goal
Awareness

Preparing (giving “heads-up”)
about:

“We try to, talk to each other as far as what we require from each other and what we need from each
other and if there’s any questions about what’s going on and if we need any extra help whether –
what our schedule looks like and so forth.” [LVN]• What is currently going on

• What is to come “These people are coming in. You know, I’m anticipating, you know, this guy is going to need this
and, you know, this. Let’s make sure that that happens.” [PCP]

Problem-Solving Joint (with members of the team): “We talk in the interdisciplinary group about, you know, whether it was a compliance problem or
frequencies and why he keeps going to the ER for psychiatric reasons or pain medicine or whatever,
and we’ll come up with some sort of plan or solution…” [RN]

• Decision-making
• Resolution to patient care

issues
• Conflictresolution/

management
“It’s a team meeting since we’re a smaller group… So often things get hammered out in that
meeting or if they need to get tossed to someone up above for input, then that decision is made
there.” [PCP]

Mutual Respect Giving and receiving/feeling: “And, again, just try to be very respectful to each other.” [RN]
• Respect
• Valued “I value the input of both my LVN and RN. If they feel like… we really need to sit down and really

carve out a time, we’ll do that…” [PCP]
Transparent (Open) Honest and open ““We talk about, you know, our own concerns. We give ideas. We share our ideas with the team and

basically, you know, we’re pretty open with a lot of suggestions.” [MSA]Without hidden agendas or
motives
Having a voice and being heard
Giving and accepting:
• Input/feedback/suggestions “I think that all concerns are taken seriously and we do discuss them veryopenly at our meetings so

nothing’s really hidden. I mean we always put everything out on the table.” [RN]
Timely (Prompt) Information is shared promptly

Resolving issues quickly
“I think it’s also, yeah, really [the LVN] being able to communicate with the provider either by
messaging me, getting back to me in a timely fashion about stuff, handling stuff quickly…” [PCP]
(Ex: Untimely Communication) And the communication… Well, I know with one clerk [MSA], she will,
she’ll get a call from the patient who has a problem. She’ll try to pass it to the RN, but the RN isn’t
very, doesn’t do it in a timely manner so then the patient ends up coming in because you know,
there’s no really communication with what needs to be done in however amount of time [MSA]

Frequent (Often) Communicating often “My RN, I meet with her on a multiple daily basis, usually before clinic, mid-morning, lunchtime,
afternoon, end of the day. I mean it’s constant.” [PCP] “We huddle a lot… I don’t know, maybe more
than ten times a day… That’s why we know what’s going on with the patients, even our clerk [MSA]
… one of the patients need this, or there’s a problem with this, then we communicate.” [RN]

Consistent (Regular) Regular (schedule of)
communication Consistency Routine

“Well, actually I meet with my doctor every day. I do my huddle with my doctor every day. Before
I leave for work I make sure that I give him a copy of his appointment for the following day.” [LVN]
(Ex: Inconsistent Communication) I think sometimes when, for example, if my LVN has been told by us
to do certain things and she sees that the other teamlets are not doing it, she may work really well
for a week or two, but then, she falls right back to her old ways. And the same thing with the clerk
[MSA]… she will be really good maybe two weeks after the meeting, then it goes right back to the
old ways. [PCP]

Parsimonious
(Concise)

Concise communication: “It could be just, you know, a couple of minutes, but those couple of minutes are really productive.”
[PCP]• To the point

• Not overly detailed I’m huddling and I have patients like waiting, I don’t like all the chit-chatty. I got people waiting on
me. So in my case, this [short huddle] works out very well. It’s black and white, the nitty gritty, and
then do what we need to do. [LVN]

JOURNAL OF INTERPROFESSIONAL CARE 837

Communication features included transparency, which was
described as sharing concerns, suggestions, and team goals hon-
estly and openly, without worrying about ridicule or scorn, which
further promoted knowledge-sharing and situational awareness.
Timely communication allowed for information to be shared
promptly and questions to be answered quickly so that all mem-
bers of the team have situational awareness. Frequent communi-
cation promoted exchange of timely information throughout
the day about immediate issues, while consistent information-
sharing allowed team members to have team awareness of what
is planned for the future. Finally, parsimonious communication
was described as information that is accurate, yet concise.

Discussion

Our study findings highlight a new element of team commu-
nication–parsimony, which has not been noted in previous
literature. Parsimonious communication, in which team
members accurately and succinctly convey all relevant infor-
mation to other team members, may be especially critical in
primary care settings where patients have scheduled appoint-
ment times for their visits. Due to this time limitation, team
members not only need to communicate patient information
promptly, frequently, and consistently, they also need to com-
municate parsimoniously.

To enhance team function in PCMH, future work should
assess 1) what are the critical pieces of information that need
to be shared/transmitted to allow for better PCMH team
functioning and 2) what communication mechanisms and
processes facilitate parsimonious interactions. For instance,
a comprehensive quality improvement program incorporating
the use of communication strategies such as SBAR (situation,
background, assessment, recommendation), along with var-
ious information and communication technology (e.g.,
secured text and/or instant messaging) may facilitate commu-
nication of pertinent patient information in an accurate, yet
concise manner. Further research should clarify what parsi-
mony in the context of PCMH really means.

Our findings also provide support for elements of team
communication seen across other non-primary care settings
(e.g., acute, long-term, and palliative care) being present in
primary care in the PCMH era. Our study findings show that
the elements of communication are not mutually exclusive;
rather, communication effectiveness and relationships
between members of the interprofessional PCMH team are
interrelated. Another notable finding was that all the commu-
nication elements identified in Wave 1 were also present in
Wave 2. These results provide tentative evidence of commu-
nication elements that may persist over time. Evaluation of
longitudinal data from subsequent studies and future studies
applying a survey or an interview guide specifically designed
to measure these team communication domains, may further
strengthen conclusions about these and future findings.

One limitation of this study is that this analysis represents
a secondary analysis of interviews collected for a broader imple-
mentation evaluation; therefore, the interview guide was not
designed specifically to address team members’ perceptions of
elements of effective team communication (e.g., parsimony).

Nonetheless, various elements of team communication were
discussed and emerged from the interviews and given the large
sample, thematic saturation was reached. Another limitation
was that the interviews in this study were conducted in three
primary care practice sites in one VA administrative region.

Conclusion

In identifying what makes team communication effective in
primary care settings, we identify one underexplored element –
parsimony, which may be especially relevant in interprofessional
PCMH teams. In addition, we found that elements of effective
communication identified in other settings also may be found in
primary care in the PCMH era. Future studies evaluating the
effectiveness of targeted interventions aimed at improving these
elements of team communication over time, are critical for
sustainment of effective communication between interprofes-
sional PCMH team members, which promotes better team
function and delivery of coordinated, patient-centered care.

Acknowledgments

We would like to thank Dr. Danielle Rose for reviewing drafts of this
manuscript.

Disclosure Statement

The authors report no conflicts of interest.

Funding

Funding for this project was supported through a grant from the VA
Veterans Assessment and Improvement Laboratory for Patient-
Centered Care (VAIL-PCC) Patient Aligned Care Team (PACT)
Demonstration Lab (#XVA 65-018). LK’s effort was supported
through a grant from the Agency for Healthcare Research and
Quality (#T32HS00046) and salary support from the Quality
Scholars Program funded by the VA Office of Academic Affiliations
(#TQS 65-000). A part of KG’s effort was supported through the VA
Locally Initiated Project (LIP #65162). The content does not represent
the views of the U.S. Department of Veterans Affairs or the United
States Government.

References

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Analyzing tasks for the patient centered medical home. Retrieved from
http://www.va.gov/HEALTH/services/primarycare/pact/resources.asp.

Edwards, S. T., Rubenstein, L. V., Meredith, L. S., Hackbarth, N. S.,
Stockdale, S. E., Cordasco, K. M., & Yano, E. M. (2015). Who is
responsible for what tasks within primary care: Perceived task alloca-
tion among primary care providers and interdisciplinary team
members? Healthcare, 3(3), 142–149. doi:10.1016/j.hjdsi.2015.05.002

Kim, L. Y., Rose, D. E., Soban, L. M., Stockdale, S. E., Meredith, L. S.,
Edwards, S. T., … Rubenstein, L. V. (2017). Primary care tasks asso-
ciated with provider burnout: Findings from a veterans health admin-
istration survey. Journal of General Internal Medicine, 33(1), 50-56.

Rubenstein, L. V., Stockdale, S. E., Sapir, N., Altman, L., Dresselhaus, T.,
Salem-Schatz, S., … Yano, E. M. (2014). A patient-centered primary care
practice approach using evidence- based quality improvement: Rationale,
methods, and early assessment of implementation. Journal of General
Internal Medicine, 29(2), 589–597. doi:10.1007/s11606-013-2703-y

838 L. Y. KIM ET AL.

http://www.va.gov/HEALTH/services/primarycare/pact/resources.asp

http://dx.doi.org/10.1016/j.hjdsi.2015.05.002

http://dx.doi.org/10.1007/s11606-013-2703-y

Abstract

Introduction
Methods
Results
Discussion
Conclusion
Acknowledgments
Disclosure Statement
Funding
References

1 | I N T R O D U C T I O N

Managing healthcare systems to achieve better quality of care,

while reducing cost is a global concern (Bragadóttir, Kalisch,
Smáradóttir, & Jónsdóttir, 2015). Contributing to fiscal pres‐
sures are an increased demand for healthcare services due to a

significant increase in an ageing population, the need for newer
and costly healthcare technologies, and the increasing complexity
of hospital care and treatment processes (Letiche, 2008). Amidst
these challenges, hospital managers are faced with the competing
priorities of improving cost efficiency in their operations while
improving care quality, patient safety and maintaining a safe work

Received: 29 June 2018 | Revised: 14 January 2019 | Accepted: 5 February 2019
DOI: 10.1111/jonm.12757

O R I G I N A L A R T I C L E

Predicting the effect of nurse–patient ratio on nurse workload
and care quality using discrete event simulation

Sadeem Munawar Qureshi MEng1 | Nancy Purdy RN, PhD2 | Asad Mohani BEng1 |
W. Patrick Neumann PhD, LEL,EurErg1

1Human Factors Engineering Lab,
Department of Mechanical and Industrial
Engineering, Ryerson University, Toronto,
Ontario, Canada
2Daphne Cockwell School of
Nursing, Ryerson University, Toronto,
Ontario, Canada

Correspondence
Sadeem Munawar Qureshi, Human Factors
Engineering Lab, Department of Mechanical
and Industrial Engineering, Ryerson
University, Toronto, ON, Canada.
Email: s1qureshi@ryerson.ca

Funding information
The Natural Sciences and Engineering
Research Council of Canada (NSERC) for
their generous Discovery grant(s)—Grant #
341664 and Grant # 2018‐05956.

Abstract
Aim: A novel nurse‐focused discrete event simulation modelling approach was tested
to predict nurse workload and care quality.
Background: It can be challenging for hospital managers to quantify the impact of
changing operational policy and technical design such as nurse–patient ratios on
nurse workload and care quality. Planning tools are needed—discrete event simula‐
tion is a potential solution.
Method: Using discrete event simulation, a demonstrator “Simulated Care Delivery
Unit” model was created to predict the effects of varying nurse–patient ratios.
Modelling inputs included the following: patient care data (GRASP systems data), in‐
patient unit floor plan and operating logic. Model outputs included the following: nurse
workload in terms of task‐in‐queue, cumulative distance walked and Care quality in
terms of task in queue time, missed care.
Results: The model demonstrated that as NPR increases, care quality deteriorated
(120% missed care; 20% task‐in‐queue time) and nursing workload increased (120%
task‐in‐queue; 110% cumulative walking distance).
Conclusions: DES has the potential to be used to inform operational policy and tech‐
nical design decisions, in terms of impacts on nurse workload and care quality.
Implications for Nursing Management: This research offers the ability to quantify
the impacts of proposed policy changes and technical design decisions, and provide
a more cost‐effective and safe alternative to the current trial and error
methodologies.

K E Y W O R D S

discrete event simulation, human factors, nurse management, nurse–patient ratio, quality of
care

www.wileyonlinelibrary.com/journal/jonm

mailto:

https://orcid.org/0000-0003-4309-6674

https://orcid.org/0000-0002-5294-0729

https://orcid.org/0000-0002-1560-3870

mailto:s1qureshi@ryerson.ca

http://crossmark.crossref.org/dialog/?doi=10.1111%2Fjonm.12757&domain=pdf&date_stamp=2019-04-25

972 | QURESHI Et al.

environment for the healthcare professionals delivering care.
Since direct labour costs are the highest cost budget item, hos‐
pital managers are challenged to limit or reduce the direct labour
costs despite potential adverse effects. Adverse effects arising
from understaffing can lead to overtime and excessive workload
giving rise to stress, fatigue, work‐related musculoskeletal dis‐
orders (WMSD), absenteeism and eventually burnout or injury
(Silas, 2015). Therefore, hospital processes need to improve in
ways that do not negatively impact the work environment or
worker safety.

In 2016, nurses in Canada worked over 20.1 million hours of
overtime, which is equivalent to 11,100 full‐time positions with
an estimated cost of $968 million dollars (Canadian Federation of
Nurses Unions, 2017b). In 2011, the rate of injuries and illness re‐
sulting in days away from work in the United States was highest
for hospital workers among all industries, with an incident rate of
157.8/10,000 workers (Bureau of Labor Statistics, ). In the same
year, the United Kingdom’s National Health Service reported that
76% of the registered nurses worked extra hours; in 2012, this in‐
creased to 80% (International Council of Nurses, 2015). Nurses are
absent from work at almost twice the rate of all other professions
(Canadian Federation of Nurses Unions, 2017a). Nursing is a highly
stressful job, where the incidence of burnout is high (Rizo‐Baeza et
al., 2018). Furthermore, the healthcare sector was reported to have
the highest number of lost time injuries including WMSD, workplace
violence, exposures and falls; making nursing the highest risk job in
2014, over manufacturing and mining industries (CNFU, 2015). It
is imperative that proposed changes to operational policies do not
worsen working conditions for nurses. Hence, tools are needed to
help understand the possible negative consequences of proposed
changes.

1.1 | Industrial engineering and human factors
in healthcare

To improve healthcare processes, several healthcare organizations
have implemented industrial engineering (IE) techniques such as

Lean interventions. But, long‐term negative effects have been re‐
ported such as an increased potential for making mistakes, injuries
and missing less urgent care tasks which led to a drop‐in the quality
of care (Moraros, Lemstra, & Nwankwo, 2016). Process improve‐
ment strategies to increase efficiency are sometimes accompanied
by negative effects such as the degradation of the healthcare pro‐
fessional’s (HCP) health, satisfaction and engagement, workload is‐
sues, availability of supplies, increased stress and reduced safety for
patients (Carayon et al., 2014). Engineering tools applied in health‐
care processes have shortcomings when they do not consider the
impact on the HCP. However, human factors engineering, and ergo‐
nomic principles and methodologies may help as most techniques
are user‐centred (Carayon, 2016).

Human factors (HF) are the scientific discipline concerned with
the understanding of interactions among humans and other ele‐
ments of a system to optimize system performance and human well‐
being (International Ergonomics Association, 2018). The conceptual
model used in this study builds on the Systems Engineering Initiative
for Patient Safety (SEIPS 2.0) model by Holden et al. (2013). SEIPS
2.0 is a framework for understanding the role of HF in healthcare
systems. In this study, a more design‐oriented approach to the SEIPS
2.0 model was used with a broader goal to support efforts in im‐
proving performance in existing healthcare units. As illustrated in
Figure 1, this design‐oriented approach addresses the needs of both
the HCPs and patients in the improvement process. HCPs are central
and factors affecting them affect the overall performance of these
complex healthcare systems.

In the design and management of healthcare systems, ignoring
HCP’s workload means a lack of focus on the HCP’s performance
with a possible impact on efficiency, productivity, injury, burnout
and increased costs (Alghamdi, 2016; Kalisch & Williams, 2009).
Current approaches to testing design and management decisions
such as the real‐life trial and error methods can be very expensive
and hazardous (Gaba, 2007), as workers would be exposed to un‐
safe and untested environments that can not only affect their health
but also negatively impact productivity and process efficiency with
possible long‐lasting consequences. Hence, there is a need for a tool

F I G U R E 1 Illustrates how a design‐orientated approach can address the needs of the healthcare professionals (HCP) and patients, by
focusing on the health and workload of HCP, and the quality of care that gets delivered to patients

| 973QURESHI Et al.

that can “virtually” assess and predict the effects of design changes
such as staffing on HCP and patients without the risk of real trials
or at the expense of HCP’s health. Simulation is a potential solution
to this challenge.

1.2 | Discrete event simulation

Simulation imitates real‐world scenarios over time (Banks,
Carson, Nelson, & Nicol, ). Discrete Event Simulation (DES) is an
operational research technique used to assess, predict and op‐
timize the efficiency of a proposed or existing system (Clancy &
Delaney, 2005; Jun, Jacobson, & Swisher, 1999). It is useful to
study complex systems with emergent characteristics and com‐
plexity issues. DES uses mathematical formulas as a means of
representing complex structures of a system/unit as a sequence
of ordered events and stages, in which the variable(s) change at
a discrete set of points (Banks, Carson, Nelson, & Nicol, ; Clancy
& Delaney, 2005; Dode, Greig, Zolfaghari, & Neumann, 2016).
When applied to health care, the interactions of interest could
be between acute care patients and HCPs to examine patient or
nurse outcomes.

DES has been widely used to model patient flow in hospi‐
tal units such as the discharge process in a paediatric hospital
(Lambton, Roeder, Saltzman, Param, & Fernandes, 2017). DES
has also been used to examine two family physician practice
clinics by focusing on staffing and size of the facility (Swisher &
Jacobson, 2002). DES was also used to model the operations of
an emergency department to understand the system behaviour
and examine the causes of excessive waiting times (Komashie &
Mousavi, 2005), and injury levels of healthcare workers (Duguay
& Chetouane, 2007). DES models have been used to predict the
effect of different layouts of hospital units in term of nurses’
movements (Boucherie, Hans, & Hartmann, 2012; Choudhary,
Bafna, Heo, Hendrich, & Chow, 2010). DES has also been used
to monitor wait times in a Québec‐based haematology–oncology
clinic using a patient‐flow simulation experiment (Baril, Gascon,
Miller, & Bounhol, 2016). These DES studies have, however, gen‐
erally been limited to a focus on physicians and/or patients and
not on nurses’ care delivery (Barnes, Golden, & Price, 2013), de‐
spite the fact that nurses are the largest group of healthcare pro‐
viders and nurses deliver over 75% of the hospital care (Nursing
Task Force, 1999). While there has been a significant number of
publications of DES research in healthcare settings, there has
been a scarcity of research specifically focused on the healthcare
professional and none that have modelled nursing workload, care
quality and work environments.

1.3 | Aim

The aim of this study was to create and test a novel nurse‐focused
DES modelling approach that could proactively assess care quality
and the workload for nurses, by modelling the delivery of care for
patients under different technical design and operational policies.

Specifically, the demonstrator model quantified the effect of chang‐
ing nurse–patient ratios on care quality and nurse workload.

2 | M E T H O D S

“The Simulated Care Delivery Unit” (SCDU) is a computerized simu‐
lation model created using a commercial DES environment software
(Rockwell ARENA). The SCDU is the representation of the HCP’s
work processes. The demonstration model was created in consulta‐
tion with a subject specialist—a registered nurse with extensive re‐
search and practical experience.

As illustrated in Figure 2, the inputs of the model consist of pa‐
tient care data, operating logic and virtual layout. The outputs con‐
sist of task in queue time and missed care, used here as care quality
indicators and, task queue and cumulative distance walked as nurse
workload indicators. These are further expanded in the next section.

2.1 | Patient care data

As illustrated in Figure 2, patient care data entail essential details of
the daily patient care tasks that a nurse performs. These data were
obtained from an inpatient unit of a large urban academic health
centre in Canada for a period of one month. The data were part of a
workload report generated from the hospital’s GRASP software sys‐
tem (Grace Reynolds Application of the Study of PETO) (Farrington,
Trundle, Redpath, & Anderson, 2000; Song et al., 2004). GRASP is
a proprietary management information‐processing system used to
collect data for analysis of nursing workload. Data contain informa‐
tion pertaining to the patient care tasks that were performed by
nurses. The definitions of each task are specific to GRASP meth‐
odology; for example, assessment refers to the completion of the
Braden Scale, Morse Fall Assessment, etc. and does not refer to the
ongoing assessment that nurses conduct when delivering care. Data
are manually entered by nurses at the end of their shift. For each
sub‐task (such as: IV Maintenance), there is a pre‐set standardized
time duration. Approximately 70% of the hospitals in Ontario use
the GRASP system (Song et al., 2004). Patient care data are com‐
prised of task information, task frequency and task duration. (a) Task
information includes basic task information such as task group, for in‐
stance nutrition; sub‐tasks within this category include feeding with
minimal assistance; shift and date stamp. (b) Task frequency entails
how frequently a certain task is completed along with the day and
time stamps. Task frequency was calculated using an average of the
task count for each task group across all patients per day for a period
of one month. (c) Task duration is the amount of time required by the
nurse to complete the task. Task duration for each of the task groups
was calculated using a frequency‐weighted average of GRASP’s
standardized time duration for all sub‐tasks of in a Task group. Since
the GRASP system uses a standardized time duration for each sub‐
task, a frequency‐weighted average was used in this study to reduce
the volume of sub‐task programming in the model. Table 1 contains
the cumulative time durations of the tasks for the SCDU model.

974 | QURESHI Et al.

2.2 | Operating logic

As illustrated in Figure 3, the model’s operating logic of the SCDU
model consists of task priorities, nurse priorities, task schedules, task
location and call tasks. Task priorities indicate which tasks have an
increased priority for completion relative to other tasks in queue.
As mentioned in Section 2.1, nursing care delivery task names, and
associated task definitions, were taken from GRASP systems. For
GRASP reporting purposes—the nurses are only required to enter
the tasks delivered during that shift (i.e. task frequency), not their
sequence or timing. The priority levels for these nursing care deliv‐
ery tasks were developed in consultation with a subject matter ex‐
pert (registered nurse with 25 years of experience) who considered
the importance and urgency of each task based on their professional
experience (Hendry & Walker, 2004). As illustrated in Table 1, the
“Admission” task bears a priority level of 6 because it was believed
that the nurse must attend to the immediate care needs of pa‐
tients already present in the unit (such as: delivering “Medication,”
“Treatments”), before starting the admission process (recognizing
that the acuity of the admission may vary between settings and
model scenarios). It is anticipated that nurses may assign a different
priority than those listed in Table 1 and this can be easily changed in
the model to test alternatives. Nurse priorities can also be referred
to as the “brain of the simulated nurse”—the logic rule identifies
which task a nurse performs in retrospect of the task priorities. In
this demonstrator model, the simulated nurse is programmed to do
the highest priority task first. There may be occasions where more
than one task bears the same priority. In this case, the task logic was
built to direct the simulated nurse to perform the task for the closest

patient (at the least distance). Figure 3 represents the operating logic
of this model.

As illustrated in Table 1, Task schedule refers to tasks that fol‐
low either an established schedule or those that occur randomly
throughout the shift, or both. For example, hygiene is scheduled
for once a day. However, the hygiene task can happen at any time
(randomly) as well as the need arises. In this model, nutrition, hy‐
giene, admission and discharge are identified as both, scheduled
and random tasks. Within the simulated environment, there are
also “call” tasks that are called directly by the patient. For exam‐
ple, within the task group of Vascular Access, a patient’s IV may
become blocked. Therefore, the nurse performs IV maintenance, a
task that was not scheduled or a random task but in fact this was a
task that was called directly by the patient. The Task location was
determined for each task, that is occurring at the nurses’ station or
patient bedside. Task priority level and task scheduling for the SCDU
model are listed in Table 1.

2.3 | Virtual layout of the simulated care
delivery unit

The virtual layout was developed using Microsoft Visio software to
define the overall floor plan details of an inpatient unit such as the
nurse station location, total beds and the distances reflecting the
simulated unit layout in the DES model. The virtual layout is also
used for visual verification while running the simulation. It allows
the software to display the nurse’s movement on the layout diagram
that helps to visually verify the simulated‐nurse’s movement pat‐
terns during simulation trials.

F I G U R E 2 Inputs are patient care data, operating logic and virtual layout, and outputs to the model are care quality (task in queue time,
missed care) and nurse workload (task queue and cumulative distance walked)

| 975QURESHI Et al.

2.4 | Outputs

In this demonstrator simulation model, nurse workload is assessed
by task queue, a mental workload indicator representing the num‐
ber of pending tasks which has been associated with medical er‐
rors (Potter et al., 2009). Tasks are generated stochastically by the
model according to the frequency and schedule of the unit’s histori‐
cal GRASP data (per 2.1). These tasks are recorded in a sequence/
queue as a “stack” for the simulated nurse to perform according to
the task priority rules, this stack is called the task queue. Cumulative
distance walked by nurse, the total distance walked by the nurse
during a shift in metres. Care quality is assessed by calculating the
amount of missed care, the number of pending tasks that were not
started by the nurse before the end of the shift, and task in queue
time, the average amount of time a task has been in queue waiting
to be completed.

2.5 | Demonstrator model testing

NPR is defined as the number of patients assigned to a nurse. The
SCDU model was simulated on different NPR conditions: Low (1
nurse: 2 patients), Medium (1:4) and High (1:6), each for a period of
252 shifts which is approximately the total working days in a year.
Each shift consists of 12 hr which is the standard shift length in
nursing for North America. Data for 10 replications were recorded
for each operating condition to calculate warm‐up period for the
model and to analyse 10 years of nursing data for each operating
condition. Warm‐up times are used in simulation for the model
to reach an optimal operating state. For this model, a warm‐up
period of 41 days was established using Welch’s method (Hoad,
Robinson, & Davies, 2008); averages across shifts were taken for
missed care, task in queue time, task queue and cumulative distance
walked.

Task group
Priority
level (rank)

Task schedule
type

Task delivery
location

Time duration
(min)

Medication 1 Random intervals Bedside 6.51

Vital signs 2 Random intervals Bedside 5.26

Assessment and
planning

3 Random intervals Bedside and
nurse station

6.93

Vascular access 4 Random intervals Bedside 31.50

Treatments 5 Random intervals Bedside 9.50

Activity 6 Random intervals Bedside 26.10

Consultation 6 Random intervals Bedside 6.00

Hygiene 6 Random intervals
and Scheduled
interval
(8:00 a.m.)

Bedside 13.32

Nutrition 6 Random intervals
and Scheduled
intervals (8 a.m.,
12 p.m., 5 p.m.)

Bedside 17.05

Other direct
nursing care

6 Random intervals Bedside 25.65

Admission 6 Scheduled
interval
(7:30 a.m.)

Bedside 32.10

Discharge 6 Scheduled
interval
(7:30 a.m.)

Bedside 21.40

Evaluation 6 Random intervals Bedside and
nurse station

3.00

Non‐patient care 6 Random intervals Bedside and
nurse station

13.79

Elimination 7 Random intervals Bedside 19.91

Teaching and
emotional
support

8 Random intervals Bedside 19.68

T A B L E 1 List of tasks programmed in
the SCDU model. The list contains the
task name along with their respective
priority levels, task schedule type and
time duration where 1 = highest task
priority. Time duration for each task group
is calculated using a frequency‐weighted
average of the sub‐tasks for each group,
as reported by GRASP systems

976 | QURESHI Et al.

3 | R E S U LT S

A nurse‐focused DES modelling approach was developed, the
SCDU, that demonstrated the ability to assess the impact of
changing nurse–patient ratios on care quality and nurse work‐
load. The demonstrator model exhibited that as the NPR in‐
creased (Low, Medium, High), nursing workload increased (tasks
in queue: 2, 15, 33 tasks, respectively; cumulative walking distance:
279, 269, 595 metres, respectively) and care quality deteriorated
(missed care: 17, 24, 53 tasks, respectively; task in queue time: 0.3,
1.0, 1.2 hr, respectively). A summary of these results is presented
in Table 2.

3.1 | Nurse workload indicators

As illustrated in Figure 4, the demonstrator model showed an in‐
crease in the number of tasks in queue by 120% when the NPR is
increased from medium to high and decreased by 86% when NPR
levels changed from medium to low. However, the cumulative dis‐
tance walked increased in both cases, that is when the NPR is in‐
creased from medium to high and medium to low by 110% and 3%,
respectively. With the increase in NPR (Low, Medium, High), nurs‐
ing workload increased in terms of task queue by 2, 15, 33 tasks,
respectively, and cumulative distance walked by 279, 269 and 595 m,
respectively.

F I G U R E 3 Flow chart representing the operating logic of the simulated care delivery unit model

| 977QURESHI Et al.

3.2 | Care quality indicators

When the NPR is increased from medium to high, the demonstrator
model showed an increase in missed care by 120%, and a decrease in
missed care by 86% when NPR levels changed from medium to low.
Furthermore, task in queue time increased by 20% when the NPR is
increased from medium to high and decreased by 70% when NPR
levels changed from medium to low. With the increase in NPR (Low,
Medium, High), care quality deteriorated–task queue time increased
by 0.3, 1.0, 1.2 hr and missed care increased by 17, 24, 53 tasks, re‐
spectively. This effect is illustrated in Figure 5.

4 | D I S C U S S I O N

In this study, a nurse‐focused DES modelling approach was devel‐
oped, to evaluate the impact of healthcare system design policy

choices on nurse workload and care quality. This is a novel approach
in DES as previous simulation studies have only focused on model‐
ling patient flow.

The number of missed care tasks generated from the demon‐
strator model was found to be consistent with the RN4CAST study
conducted in medical and/or surgical units of 488 hospitals across
12 European countries where missed care was estimated before the
end of the shift (Ausserhofer et al., 2014). The top three missed care
tasks reported in this international study were comfort/talking, care
planning and patient education which is consistent with the most
prevalent areas of missed care identified by the simulated model
(“teaching and emotional support” and “assessment and planning”).
Therefore, the simulation model was able to demonstrate similar
results regarding the types of missed care adding to the validity of
this test of DES. Since “teaching and emotional support” were as‐
signed the lowest task priority level, it was expected to have a higher
occurrence within missed care tasks. However, “assessment and

Nurse–patient
ratio (NPR)

Care quality indicators Nurse workload indicators

Missed care
(no. of task)

Task in queue
time (hr)

Task in queue
(no. of task)

Cumulative walking
distance (m)

Low (1:2) 17 0.3 2 279

Medium (1:4) 24 1.0 15 269

High (1:6) 53 1.2 33 595

T A B L E 2 The results for care quality
(missed care, task in queue time) and
nurse workload indicators (task in queue
time, cumulative walking distance)

F I G U R E 4 The nurse workload
indicators: mean and St. Deviation of “no.
of task queue” (left) and “distance walked
by nurse” (right)

F I G U R E 5 The care quality indicators:
mean and St. Deviation of “missed care”
(left) and “task in queue time” (right)

978 | QURESHI Et al.

planning” with a priority level of 3 also constituted a greater propor‐
tion of missed care tasks—an unanticipated finding. This is due to the
higher task frequency or a number of occurrences for “assessment
and planning” in comparison with others. High‐frequency unsched‐
uled tasks that occur towards the end of shift are less likely to be
completed in time if any higher priority tasks remain to be done. The
quantity of missed care in the simulated model is much larger (17–64
missed care tasks) than reported in the RN4CAST study (range of
1.5–7.5 and mean of 3.6 missed care tasks). One possible expla‐
nation is that the simulated model measured actual missed care,
whereas the RN4CAST study measured nurse perceptions of missed
care. Another possible reason for the large volume of missed care in
the model could be caused by real nurses rushing to keep up with a
heavy workload (with possible quality implications) while the simu‐
lated‐nurse only ever operates at the designated GRASP time. The
effects of varying task priority levels and the sources of difference
between modelled and reported missed care estimates need further
research.

Dabney and Kalisch (2015) reported that increased nurse–pa‐
tient ratios were associated with a greater incidence of missed care.
A similar relation was observed with the demonstrator modelling
results of missed care as high NPR had greater missed care in compar‐
ison with lower NPR. Chapman, Rahman, Courtney, and Chalmers
(2016) reported that increased missed care led to increased overtime
which can lead to increased workload for nurses (Alghamdi, 2016;
McGillis Hall et al., ; Silas, 2015). As illustrated in Figure 5, a small
fraction of “missed care” can also be observed for low NPR. Even
though a NPR of 1:2 may be lower than is realistic in such wards, it
still shows that there are still missed tasks. This was caused by the
arrival of tasks at the end of shift that the simulated nurse was un‐
able to complete before shift‐end.

In this model, each room consists of two patient beds; the oper‐
ational logic is programmed in a way that the simulated‐nurse can
walk to the nurse station only when all patient bedside priority tasks
are completed. For medium NPR level, the simulated‐nurse had to
walk between two rooms and a nurse station. Since the two rooms
are arranged closely to each other, the simulated‐nurse walked less.
However, for low NPR level since there is just one room and a nurse
station, the simulated‐nurse walked relatively more (i.e. 4% more).
The virtual layout programmed consists of a hypothetical floor
layout with scaled drawings of patient rooms and a nurse station.
Further research is needed to estimate the impact of floor layout and
bed assignment on workload and care quality.

In this study, task(s) in queue is treated as a mental workload in‐
dicator (Potter et al., 2009), but it also related to care quality. The
number of tasks in queue has a direct impact on care quality indica‐
tors. If the number of tasks in queue is substantial, then task in queue
time and missed care will also be greater, as observed in high NPR.

4.1 | Implications to nursing management

The ability to create a computerized model to simulate nursing care,
staffing conditions and related outcomes offers a promising strategy

to test the impact of various administrative decisions on a range of
nurse and patient outcomes. For instance, the implementation of en‐
gineering techniques such as Lean may lead to an increased poten‐
tial for making mistakes, injuries and missing less urgent care tasks
which lead to a drop‐in the quality of care (Moraros et al., 2016). This
novel nurse‐focused approach to DES modelling can provide insight
to the impact of this new design policy proactively. This framework
for nurse‐focused DES modelling can be adapted to proactively
quantify the impacts of proposed policy changes and technical de‐
sign decisions. This could be useful for hospital managers, healthcare
practitioners, researchers, architects, engineers and policymakers,
and provide a more cost‐effective and safe alternative to the current
trial and error methodologies.

4.2 | Methodological issues

Like all computer models, the current model will suffer from the “gar‐
bage‐in, garbage‐out” (GIGO) phenomenon. The current modelling
approach needs to be further developed to test and adjust for possi‐
ble in‐data errors. The current demonstrator model was built on ex‐
isting 1‐month data (GRASP) from a metropolitan area hospital and
from a single inpatient unit. This data set (GRASP) consisted of only
standardized task durations, lacked variability in terms of nurse skill
level (novice/expert), and did not reflect patient acuity. If the GRASP
data set failed to capture other nurse activities, then the workload
in the model would be an underestimate and the care quality would
likely decline. Further study is needed on the extent to which the
GRASP system captures all relevant nurse activities. Other limita‐
tions included the use of a single subject matter expert to construct
the nurse operating logic in the model, and the use of scaled draw‐
ings rather than actual floor plans. Further field validation studies
are needed to address these issues. The modelling method itself al‐
lows for testing of the potential impacts of different task prioritiza‐
tion strategies or changes in task mix and this issue required further
research to as well. The authors are currently working with a nursing
team to explore and refine the approach to establishing and testing
different task priority logics.

Future work includes exploring additional indicators for work‐
load and care quality, such as fatigue, biomechanical loading and
error rates, testing other unit layouts and design factors such as
patient acuity. Using up to 1 year of historical care delivery data
(GRASP). A field validation study incorporating nurse experience/
competency levels (novice, expert) and using acuity sensitive time
duration inputs would be a needed next step in the development of
this DES tool. The model needs to be extended, validated and tested
for utility to support real‐world management and decision‐making.

5 | C O N C L U S I O N

This study demonstrated the capability of a novel nurse‐focused
simulation approach that simulated the nurse’s process of care deliv‐
ery to help hospital administrators understand, quantify and predict

| 979QURESHI Et al.

the impact of changing NPRs in terms of nurse workload and care
quality. In this simulation, as the NPR increased (from Low, Medium,
High), nursing workload increased (120% increase in task in queue;
110% increase in walking distance) and care quality deteriorated
(120% increase in missed care; 20% increase in task in queue time). A
field validation study is needed to support further development of
the SCDU model.

E T H I C A L A P P R O VA L

This research has been reviewed by Ryerson’s Research Ethics Board
(REB).

O R C I D

Sadeem Munawar Qureshi https://orcid.
org/0000‐0003‐4309‐6674

Nancy Purdy https://orcid.org/0000‐0002‐5294‐0729

W. Patrick Neumann https://orcid.org/0000‐0002‐1560‐3870

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