Chapter 16Operating BudgetsBudget TypesAn org

Chapter 16:
Operating Budgets
Budget TypesAn organization’s objectives define specific
activitieshow they are assembledlevels of operationWhile an
organization’s performance standards set out performance
levelsA budget quantifies these activities into financial terms.
Budget Process Objectives
Objectives should provide:Written expression, quantified, of
policies and plansBasis to evaluate financial performance
according to policies and plans Useful tool for cost
controlCreation of cost awareness throughout the organization
Budget Types
There are basic differences between two budget types:Operating
budgetsCapital expenditure budgets
Operating BudgetsDeal with actual short-term operating
revenues and operating expensesGenerally cover the next year
(a 12-month period)

Capital Expenditure BudgetsDeal with capital expenditures for
the organization (not operating revenues or expenses)May also
cover the next year, but with a futuristic view; may cover a
five- or even
ten-year period
Responsibility CentersCost Centers (manager responsible for
controlling costs)Profit Centers (manager responsible for both
costs and revenue)
Budget ViewpointsTransactions outside the operating budget
may include:Grants received by the organizatio n Foundation
transactionsSo if transactions are “outside,” they would not be
part of the operating budget.
Budget ViewpointsGrants received by the organization may
have restricted funds that require separate accounting.If so, the
separate accounting requirement generally means their
transactions will be outside the operating budget.

Budget ViewpointsFoundation transactions should require
separate accounting because the foundation will be a legally
separate organizationThe separate accounting requirement
should mean their transactions will be outside the operating
budget.
Identifiable Versus Allocated
Budget CostsWithin a departmental budget, certain costs will be
specifically identifiable while others will be allocated instead.
Budget Basics Review
Regarding Identifiable versus Allocated Budget Costs: Mostly
identifiable = Direct patient care and supporting patient
careUsually allocated = general and administrative expense and
patient related expenseMaybe not included at all in a manager’s
budget = financial related expense
Fixed Versus Variable CostsVariable cost rises or falls in
proportion to a rise or fall in volume. (Examples of volume:
number of procedures or number of patient days.)Fixed cost
does not change even though volume rises or falls within a wide
range.

Building an Operating Budget: Construction PhasesPlan Gather
informationPrepare input Construct/submit draft version of
budgetMake required revisions to draftPresent preliminary
budgetMake required revisions to preliminary Submit final
budget
Building an Operating Budget: Construction ElementsFormat to
be used Budget scopeAvailable resources Levels of reviewTime
frame
Building an Operating Budget: Information SourcesFor the
Operating Expenditures Plan: Operating Revenue
ForecastStaffing Plan or Forecast Other Operating ExpensesFor
the Preliminary Operating Budget:Capacity Level Checkpoints
(See Figure 16-5.)
Building an Operating Budget: AssumptionsA series of
assumptions are made during construction; many key
assumptions are within forecasts used for the budget
construction process. Sufficient information at the proper level
of detail is essential.
Building an Operating Budget: Assumptions: Questions to
AskAre special projects going to use resources during the new
budget period?Are operations going to be placed under unusual
or inconvenient circumstances during the new budget period?
(Renovation is an example.)

Building an Operating Budget: ComputationsSupported by their
assumptions Capable of being replicated or reproduced by
another qualified individualComparable (as discussed in the
text)Budget assumptions and computations are intertwined in
the construction process.
Static BudgetsAre essentially based on a single level of
operations. That level of operations—or volume—is never
adjusted during the budget period.
(See example in Table 16-4.)It doesn’t move—therefore it is
“static.”
Flexible BudgetsAre based on a level of operation that will
change. In other words, the level of operations, or volume, is
adjusted to show change during the budget period.
(See example in Table 16-5.)It is adjusted, or flexed—therefore
it is “flexible.”
Budget Review
To review a budget, the manager needs to knowHow the budget
report format is constructedHow to annualize partial year
expenses
(More details are in the chapter.)
Building BudgetsThe budget process should begin with a review
of the strategy and objectives. Remember: Building a budget

means making a series of assumptions.
Building Budgets
To build a budget, a manager must considerThe workload
forecast (it must tie into the forecasted volume)Whether budget
projects will use resources during the budget periodWhether
budget operations will be placed under unusual or inconvenient
circumstances during the budget period (remodeling, for
example)
Budget: Example 16-A
$21,600,000
22,000,000
$ (400,000)
Revenue
Expenses
Excess of Expenses over Revenue
Step 1. Actual
Step 2. Budgeted
$24,000,000
22,400,000
$ 1,600,000
Revenue
Expenses
Excess of Revenue over Expenses

$(2,400,000)
(400,000)
$(2,000,000)
$24,000,000
22,400,000
$ 1,600,000
$21,600,000
22,000,000
$ (400,000)
Revenue
Expenses
Step 3. Actual
Budgeted Static Budget Variance
Budget Practice Exercise 16-1
$17,550,000
4,400,000
$21,950,000
$16,100,000
4,000,000
$20,000,000

$ 1,850,000
Revenue – Inpatient
Revenue – Outpatient
Subtotal
Expenses – Inpatient
Expenses – Outpatient
Subtotal
Actual
Step 1.
$19,500,000
4,000,000
$23,500,000
$18,000,000
3,800,000
$21,800,000
$ 1,700,000
Subtotal
Subtotal
Budgeted
Step 2.

$(1,950,000)
400,000
$(1,550,000)
$(1,900,000)
200,000
$(1,700,000)
$ 150,000
$19,500,000
4,000,000
$23,500,000
$18,000,000
3,800,000
$21,800,000
$ 1,700,000
$17,550,000
4,400,000
$21,950,000
$16,100,000
4,000,000
$20,000,000
$ 1,850,000
Subtotal

Subtotal
Static Budget Variance
Budgeted
Actual
Step 3.
Open Forum Infectious Diseases
Positive Deviance and Culture of Safety • OFID • 1
Open Forum Infectious Diseases®
Using a Positive Deviance Approach to Influence the

Culture of Patient Safety Related to Infection Prevention
Pranavi Sreeramoju,1,2, Lucia Dura,3 Maria E. Fernandez,1
Abu Minhajuddin,4 Kristina Simacek,5 Thomas B. Fomby,6 and
Bradley N. Doebbeling7
1Division of Medicine–Infectious Diseases, UT Southwestern
Medical Center, Dallas, Texas; 2Department of Infection
Prevention, Parkland Health and Hospital System, Dallas,
Texas; 3Department
of English, Rhetoric & Writing Studies, The University of
Texas at El Paso, El Paso, Texas; 4Department of Clinical
Sciences, UT Southwestern Medical Center, Dallas, Texas;
5Department of
Sociology, Indiana University, Bloomington, Indiana;
6Department of Economics, Southern Methodist University,
Dallas, Texas; 7Department of Biomedical Informatics, College
of Health
Solution
s,
Arizona State University, Phoenix, Arizona
Background. Health care–associated infections (HAIs) are a
socio-technical problem. We evaluated the impact of a social
change intervention on health care personnel (HCP), called
“positive deviance” (PD), on patient safety culture related to
infection
prevention among HCP.

Methods. This observational study was done in 6 medical wards
at an 800-bed public academic hospital in the United States.
Three of these wards were randomly assigned to receive PD
intervention on HCP. After a retrospective 6-month baseline
period, PD
was implemented over 9 months, followed by 9 months of
follow-up. Patient safety culture and social networks among
HCP were
surveyed at 6, 15, and 24 months. Rates of HAI were measured
among patients.
Results. The measured patient safety culture was steady over
time at 69% aggregate percent positive responses in wards with
PD vs decline from 79% to 75% in wards without PD (F statistic
10.55; P = .005). Social network maps suggested that nurses,
charge
nurses, medical assistants, ward managers, and ward clerks play
a key role in preventing infections. Fitted time series of
monthly HAI
rates showed a decrease from 4.8 to 2.8 per 1000 patient-days
(95% confidence interval [CI], 2.1 to 3.5) in wards without PD,
and 5.0
to 2.1 per 1000 patient-days (95% CI, –0.4 to 4.5) in wards with
PD.

Conclusions. A positive deviance approach appeared to have a
significant impact on patient safety culture among HCP who
received the intervention. Social network analysis identified
HCP who are likely to help disseminate infection prevention
informa-
tion. Systemwide interventions independent of PD resulted in
HAI reduction in both intervention and control wards.
Keywords. positive deviance; infection control; health care
personnel; patient safety culture; social networks.
Health care–associated infections (HAIs) continue to be a
significant public health problem [1, 2] despite advances in
tools and strategies available for reducing them. An explan-
ation for why HAIs continue to occur may be that they are a
socio-technical problem. Technical strategies to reduce HAIs,
such as making hand sanitizer and hand washing sinks avail -
able and establishing protocols for environmental cleaning
and safe insertion of catheters, may need an addition of adap-
tive strategies [3]. Strategies that address social and behav-
ioral norms among health care personnel related to their use
of infection prevention measures, collectively referred to as
“patient safety culture,” have not been studied well. Positive
deviance (PD) [4–8] is a strategy that has gained attention

in recent years. It was previously used to successfully solve
seemingly intractable and complex social and public health
problems. Through intentional inquiry, the PD approach
explores the social aspects of infection prevention practices
among health care personnel. In addition to identifying bar -
riers and potential solutions, the approach focuses on iden-
tifying and deploying peer role models to generate positive
peer pressure and mobilize change.
PD has been used to reduce infections caused by methicil -
lin-resistant Staphylococcus aureus in Veterans Affairs hospi -
tals [9], improve hand hygiene [10, 11], and reduce surgical
site infections [12]. However, these studies did not explore the
impact on patient safety culture among health care personnel
(HCP), especially in the setting of parallel system-wide hori-
zontal infection prevention interventions occurring independ-
ent of PD strategy. They also did not discuss the methodology
of
implementing PD in sufficient detail. In this study, we evaluate
the impact of PD on the patient safety culture related to infec-
tion prevention among HCP. Here we describe our implementa-
tion methodology so that future studies may replicate and refine
the approach as needed. Improvements in patient safety culture
among health care personnel are necessary for sustainment of

HAI reduction.
M A J O R A R T I C L E
© The Author(s) 2018. Published by Oxford University Press on
behalf of Infectious Diseases
Society of America. This is an Open Access article distributed
under the terms of the Creative
Commons Attribution License
(http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted reuse, distribution, and reproduction in any
medium, provided the original work
is properly cited.
DOI: 10.1093/ofid/ofy231
Received 25 August 2018; editorial decision 3 September 2018;
accepted 8 September 2018.
Correspondence: P. Sreeramoju, MD, 5323 Harry Hines Blvd,
MC 9113, Dallas, TX 75390-
9113 ([email protected]).
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http://orcid.org/0000-0002-1218-8974
mailto:[email protected]?subject=

2 • OFID • Sreeramoju et al
METHODS
The study was done at Parkland Memorial Hospital, an 800-
bed public academic medical center in Dallas, Texas, from April
2011 to March 2013. We conducted an observational prospec-
tive study with a retrospective baseline period in 6 wards. Three
of these wards were randomly selected for health care personnel
to receive positive deviance intervention. The baseline period
was April 2011 to September 2011. PD was implemented from
October 2011 to June 2012. The follow-up period was from
July 2012 to March 2013. The wards serve patients with gen-
eral medical problems, in addition to those needing specialty
care like geriatrics, hematology/oncology, and dialysis care.
Wards in the intervention group were not significantly differ-
ent in patient volume compared with the control wards. Other
attributes of the wards like HAI rates and culture of safety were
unknown at the time of random allocation of the intervention.
All employed HCP (nurses, patient care technicians, ward man-
agers, and clerks) and all patients receiving care in the study
wards were included. HCP who do not provide care exclusively
in a study ward, for example, physicians and respiratory thera-
pists, were excluded to prevent crossover between the interven-
tion and control wards.

Implementation of Positive Deviance Intervention
The intervention was implemented over 9 months using a sem-
istructured design in a series of 3 steps, where experience with
each step informed the next step. The decision on 9 months was
based on convenience, as there are no firm timelines for a rela -
tively novel intervention like PD. The steps of the intervention
are outlined in Box 1. In the first step, the chief medical officer
and chief nursing officer invited HCP to participate, with assur-
ance of voluntariness, freedom from negative consequences for
thoughts and ideas expressed, and anonymity, as desired. In the
second step, the research staff (P.S., M.E.F., and L.D.)
conducted
a positive deviance inquiry among HCP. The inquiry was con-
ducted in staff break rooms and nursing stations. The sessions
were evenly distributed between the day and night shifts. Drop
boxes and graffiti boards were also provided for HCP to offer
thoughts and ideas anonymously. The line list of patients with
HAI and unit level rates of HAI were discussed with HCP will -
ing to engage with the research team. During the PD inquiry,
the
HCP identified 12 “positive deviants” among themselves who
are “role models” for infection prevention practices in the wards
where they provide care. In the third step, which occurred at

6 months from start of intervention, the information gathered
by the study team was collated by an action planning group that
consisted of the “positive deviants,” managers of the 3 wards, 2
infection preventionists, and a research team member (M.E.F.).
The group organized and prioritized the large number of ideas
and made plans to disseminate and implement them. Most
importantly, they owned their plans, which included making
patients their partners in preventing infections and wanting to
improve their own infection prevention skills. This third step
lasted the remaining 4 months of the intervention period. All
activities and time spent on the interventio n were documented.
During the follow-up period, the research staff ceased involve-
ment in the intervention wards.
Outcomes
The outcome of positive deviance intervention was a culture
of safety, which was measured using the hospital survey of
patient safety climate [13], adapted to infection prevention
(see Box 2 for the tool). Social networks, that is, the networks
of relationships between HCP and other personnel with dif-
ferent job roles in their wards [14], were also measured to
understand the impact of intervention on HCP interactions.
The questions asked in the social network survey were: (1)

Who do you work with to prevent infections from occurring
on this unit? (Collaboration or Current network); (2) List pro-
jects and activities you are involved with and the people with
whom you work on those projects and activities (Prevention
or Project network); (3) From whom have you gotten new
ideas or inspiration that helped your infection prevention
efforts? (Innovation or Ideas network); (4) Who would you
like to work with in the future on preventing infections in
your unit? (Potential or Future network). We administered
the culture of safety survey and the social network survey
in September 2011 (before intervention), June 2012 (after
intervention), and March 2013 (end of follow-up period) to
the HCP of all 6 study wards via REDCap [15]. To assess the
impact of intervention on infection outcomes, the monthly
HAI rate—a composite of central line–associated bloodstream
infection (CLABSI), Clostridium difficile infection (CDI),
catheter-associated urinary tract infection (CAUTI), and hos-
pital-acquired pneumonia (HAP) per 1000 patient-days—was
measured among patients through review of medical records
by the study team and application of surveillance definitions
[16] per the Centers for Disease Control and Prevention
National Health Safety Network. Composite HAI rate was
chosen mainly because the PD inquiry was open-ended and
not targeted to any specific HAI. Patients who developed HAI
were also evaluated for development of any of the 3 complica-

tions during the remainder of their hospital stay, permanent
loss of organ or organ system function, transfer to higher level
of care, and death. Attributability was not assessed.
The University of Texas Southwestern Institutional Review
Board approved the study. Concurrently, during the study,
the infection prevention program for the hospital conducted
independent surveillance for publicly reportable HAI, as
required for acute care hospitals by the state of Texas and
nationally by the Centers for Medicare and Medicaid Services
(CMS), and pursued aggressive systemwide implementation
of hand hygiene because of a “systems improvement agree-
ment” [17, 18] with the CMS.
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Statistical Methods
We compared data at the level of groups, the group of 3 wards
where HCP received PD intervention and the group of 3 wards
where the HCP did not. We modeled binary outcomes of the
culture of safety survey using a logistic regression analysis
with group, period, and a group × period interaction term in
the model. We analyzed the social network survey data qual -

itatively and by measuring differences in the average number
of job roles of HCP each staff member interacts with to pre-
vent HAI. We mapped social networks using UCINET [19] and
NetDraw [20]. For analysis of differences in monthly rates of
HAI over time, we used the intervention modeling approach of
Box and Tiao [21] with inclusion of 6 months’ lag for detection
of intervention effect using the SAS PROC ARIMA procedure.
We used 0-inflated Poisson regression to analyze the rates of
complication in patients with HAI. We used SAS 9.3 software
(SAS Institute Inc., Cary, NC) for all analyses. All tests were
2-tailed, and the critical level of α was set at .05.
RESULTS
Positive Deviance Intervention
During PD inquiry, which lasted 5 months, a total of 54 PD
inquiry sessions, 13 meetings with positive deviants/peer role
models and ward managers, 15 meetings between positive devi -
ance consultant (L.D.) and ward managers, and 6 role-playing
sessions were conducted. In total, 110 HCP (77% of HCP in
intervention wards) voluntarily engaged with the research team,
for a total contact time of 116.5 HCP-hours (including 37 hours
spent with the mangers of 3 wards). During the action planning
phase, which lasted 4 months, the action planning group sifted

through more than 210 ideas and practices suggested by HCP as
potential solutions for reducing HAI during PD inquiry. Twelve
HCP were identified as positive deviants. The group named
their efforts “Stop a Bug, Save a Life.”
Culture of Safety Related to Infection Prevention
Detailed results are presented in Table 1. The measured culture
of safety at baseline was significantly more positive among
HCP
in the control group compared with the intervention group,
although the wards were randomly selected. However, in the
subsequent surveys, the culture of safety declined significantly
in the control group, which did not have positive deviance inter -
vention, whereas it remained unchanged in the intervention
group. The trends in overall culture over the duration of the
study period were significantly different between the 2 groups.
Social Network Analysis
Analysis of social networks revealed that, on average, each HCP
worked with 6 to 9 other HCP to prevent infections and reached
out to 3 to 5 other HCP for sharing ideas. The number of
relation-

ships per HCP did not change significantly in either group dur -
ing the study period. Social network maps suggested that
nurses,
charge nurses, patient care technicians, ward managers, and
ward
clerks play a key role in preventing infections as they are
located
toward the center of the map, an indicator of high network cen-
trality (representative social network map shown in Figure 1).
HAI and Complications
During the study period of 2 years, the 6 wards provided care
for 11 069 unique patients during 16 876 encounters. Two hun-
dred thirty patients developed 255 HAI: 48 (18.8%) CLABSI,
Box 1. Steps of Positive Deviance Intervention
Step 1: Leader extends invitation to health care personnel to
participate, with assurances that participation in the
intervention
is voluntary, that their responses will not be judged in any way,
that there will be no negative consequences, that they are free
to speak their minds, and that they are free to leave the
conversation at any time.

Step 2: Positive deviance inquiry or “Discovery and Action
Dialogues” through 1:1 conversations, focus groups, anonymous
drop box reporting, and anonymous graffiti boards in break
rooms. The study team asks the following open-ended questions,
listens while taking notes, asks any clarifying questions from
the participants, and reviews data with the participants if they
request. Study team collates responses once the inquiry stops
yielding new information.
a. How do you know or recognize when health care–associated
infection is present?
b. How do you protect yourself, patients, and others from
transmission of any microorganisms?
c. What prevents you from taking these actions all the time?
d. Is there any group or anyone you know who is able to
overcome the barriers frequently and effortlessly? How?
e. Do you have any ideas?
f. What initial steps need to be pursued to make it happen? Any
volunteers?
g. Who else needs to be involved?
Step 3: An action planning group is formed by the “positive
deviants” and may include the ward managers and other key
per-
sonnel. This group owns the organization of ideas and

prioritization, implementation, and evaluation of results. The
group
may implement ideas using Plan-Do-Study-Act cycles, as in
traditional quality improvement. Steps 1–3 may be repeated as
necessary.
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53 (20.8%) CAUTI, 79 (31%) HAP, and 75 (29.4%) CDI. Actual
monthly HAI rates fluctuated widely in both the intervention
and control groups of wards. The patterns of HAI reduction
were clearer in the fitted time series graphs (Figure 2). The HAI
rate in the control group showed an abrupt and 1-time decrease
at the end of the baseline period, from a baseline rate of 4.8 per
1000 patient-days to 2.8 (95% confidence interval [CI], 2.1 to
3.5), and did not change for the remaining 18 months of the
study. The HAI rate in the intervention group declined from
a baseline rate of 5.0 per 1000 patient-days in a gradual and
exponential fashion throughout the intervention and follow-up
periods to 2.1 per 1000 patient-days (95% CI, –0.4 to 4.5) with-
out reaching a stable rate at the end of the study period, which
might suggest continued intervention effects. The differences
between the 2 study groups were not statistically significant.

The rates of individual HAIs are shown in Table 2, and the
differences in rates between intervention and control groups
were not statistically significant. Of the 230 patients with HAI,
65 (28.3%) patients had 92 complications: 5 (5.4%) permanent
loss of organ or organ system function, 55 (59.8%) transfers
to higher level of care, and 32 (34.8%) deaths. The baseline
rate of HAIs associated with complications was 1.57 and 0.87
per 1000 patient-days in the control and intervention groups,
respectively. Both groups experienced a decrease in the rate
of complications over time, to 0.47 and 0.53 per 1000 patient-
days respectively, during the follow-up period (P = .03), but the
difference between the groups over time was not statistically
significant.
Box 2. Modified Hospital Survey of Patient Safety Climate
Adapted to Infection Prevention
A. Think about your hospital work area/unit.
1. People support one another in this unit.
2. When a lot of work needs to be done quickly, we work
together as a team to get the work done.
3. In this unit, people treat each other with respect.
4. We are actively doing things to improve/prevent health care–
associated infections.
5. Mistakes in preventing infections have led to positive

changes here.
6. When one area in this unit gets really busy, others help out.
7. After we make changes to improve infection prevention, we
evaluate their effectiveness.
B. Your Supervisor/Manager
1. My supervisor/manager says a good word when he/she sees a
job done according to established infection prevention
procedures.
2. My supervisor/manager seriously considers staff suggestions
for improving infection prevention.
3. Whenever pressure builds up, my supervisor wants us to work
faster, even if it means taking shortcuts.
4. My supervisor/manager overlooks infection prevention
problems that happen over and over.
C. Communications
1. Staff will freely speak up if they see something that may
negatively affect infection prevention.
2. Staff feel free to question the decisions or actions of those
with more authority.
3. Staff are afraid to ask questions when something does not
seem right.
D. Frequency of Events Reported
1. When a mistake is made, but is caught and corrected before it
causes an infection in a patient, how often is this reported?

2. When a mistake is made, but has no potential to cause
infection in the patient, how often is this reported?
3. When a mistake is made that could cause infection in the
patient but does not, how often is this reported?
E. Preventing Health Care–Associated Infections Grade
Please give your work area/unit in this hospital an overall grade
on preventing health care–associated infections.
F. Your Hospital
1. Hospital units do not coordinate well with each other.
2. There is good cooperation among hospital units that need to
work together.
3. It is often unpleasant to work with staff from other hospital
units.
4. Hospital units work well together to provide the best care for
patients.
For each question in Sections A, B, C, and F, respondents are
asked to choose 1 of the following 5 responses: strongly dis-
agree = 1, disagree = 2, neither = 3, agree = 4, and strongly
agree = 5. Note that questions B.4, C.3, F.1, and F.3 are
negatively
worded. For each question in section D, respondents are asked
to choose 1 of the following 5 responses: never = 1, rarely = 2,
sometimes = 3, most of the time = 4, and always = 5. For
question E, the choices are; A = excellent, B = very good,
C = accept-

able, D = poor, E = failing.
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DISCUSSION
Our study gave us several insights into the implementation of
positive deviance as a strategy to influence social behaviors
among health care personnel and potentially change patient
safety culture with the goal of influencing rates of health care–
associated infections from the grassroots level in a busy aca-
demic medical center. Our paper adds to the methodology of
implementing positive deviance intervention described in pre-
vious publications [22, 23]. The invitation to participate in PD
was received well and generated a lot of conversation among the
HCP in intervention wards. PD strategy, although labor inten-
sive and “high touch,” allowed for increased engagement and
awareness among frontline HCP. The culture of safety did not
worsen among the HCP who received positive deviance inter -
vention as it did among HCP in the control wards, and the dif-
ference between trends in the intervention and control wards
was statistically significant. It is possible that open dialogues
regarding challenges and potential solutions for preventing
infections in the 3 intervention wards prevented a downward

slide in the culture of safety, as was the case in the 3 control
wards, during a time of organizational stress and aggressive
system improvement that was being implemented at that time.
This study was unable to determine whether the differences in
culture of safety between the intervention and control wards
were clinically impactful because the secular effects of system-
wide hand hygiene efforts might have been a major contributor
to reduction in HAI in both the intervention and control wards.
There were no other significant events in the study wards that
might have potentially explained the differences observed in
patient safety culture between the groups. Any potential differ -
ences in patient complexity between the intervention and con-
trol wards and the study results were not studied. The variation
in culture of safety between wards of similar size and the
changes over time suggest that hospital culture of safety results
need to be examined carefully at the level of each ward and that
it might be helpful to measure them serially over time.
Network maps helped us identify connection patterns among
HCP in the wards to prevent infections among the patients they
care for. We found a concentration of attention in the patient’s
nurse, patient care technician, charge nurse, ward manager,
and ward clerk. This qualitative information was valuable.
Communication intended to have a wide impact in any ward

may be disseminated better through HCP with the highest
number of relationships within the ward. Inclusion of social
Table 1. Results of Hospital Survey of Patient Safety Climate in
Health Care Personnel in Intervention and Control Groups
Before and After Intervention
and At the End of the Follow-up Period
Before Intervention,
%
End of Intervention,
%
End of
Follow-up, %
Average Difference
Before and After
Intervention
Average
Difference

Between Groups
Difference
Between Groups
Before and After
Intervention
F Statistic
(P Value)
F Statistic
(P Value)
F Statistic
(P Value)
Participation (% invited) Control 32.40 81.20 69.60 13.10
(<.001)a 33.64 (<.001)a 26.65 (<.001)a
Intervention 46.30 50.80 45.80
Aggregate percent positive Control 79.30 72.70 75.00 36.19
(<.001)a 4.62 (.099) 10.55 (.005)a

Percent positive responses
per domain
A. Unit where HCP works Control 90.60 83.20 82.50 1.75
(0.19) 0.45 (0.64) 0.09 (0.91)
B. HCP’s supervisor/
manager
Control 68.80 74.70 77.50 0.00 (0.97) 1.95 (0.14) 0.10 (0.90)
C. Communication Control 79.60 62.00 72.80 0.01 (0.93) 0.53
(0.59) 0.07 (0.93)
D. Frequency of events
reported

Control 82.40 73.90 82.60 6.89 (0.009)a 1.41 (0.25) 0.55 (0.58)
E. Overall HAI prevention
grade for HCP’s unit
Control 80.60 75.60 74.60 1.24 (0.27) 0.75 (0.47) 0.33 (0.72)
F. Entire hospital Control 67.40 58.70 55.30 0.01 (0.91) 0.11
(0.90) 0.33 (0.72)
Positive response was defined as agree, strongly agree, most of
the time, always, very good, and excellent for all questions
except the negatively worded questions, for which disagree
and strongly disagree are considered positive responses.
Abbreviations: HAI, health care–associated infection; HCP,
health care personnel.
aDifferences between intervention and control groups
statistically significant at <.05.

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science experts in the multidisciplinary work groups for patient
safety initiatives may be helpful.
Our study has some limitations. Interpretation of changes in
culture of safety and the network survey is limited by the vol -
untary nature of participation in these surveys. The study was
conducted at a single institution. Process measure data such as
adherence to central line bundle and urinary catheter bundle
were not available during the study period. Because physicians
provide services throughout the hospital and rotate frequently,
we were unable to include them in the intervention or the
VP
SVP
othhosp
1
1

1
1
1 1 1
1
1
1
1
1 1
1
1
1
1
1
1

1 1
1
1
1 1
1
1
1
1
1 1
LVN MA
1
1
1
1
1

1
1
1
1
1
1
1
1 1
1
1
1
1
1
RN

PtCare Tch
NursStudt
psyctech
DON
speech
medstudt
NursPrac
HousStaf
attndMD phlebots
othstudt
occthast
clinasst
surgtech

sonograp
microbi
pharm
clnsuptc
patient
houskeep
radasst
specprth
ptsafety
ptfam
infprev
diet
respther
chaplain
transpor

radtech
ChgNurse
UnitClrkUnitMgr
1
1
1
Figure 1. Representative social network map: current network in
the intervention group before start of intervention. Round nodes
labeled 1 represent health care per-
sonnel (HCP) in the intervention group, while square, black
nodes represent the job roles of other personnel in the ward they
worked with. The size of the nodes is directly
proportional to how frequently they worked with someone in
that job role. Roles that were not chosen by anyone are not
connected to any HCP and are shown in the upper
left corner. Abbreviations: attndMD, attending physician;
chasst, clinic staff assistant; ChgNurse, charge nurse;
chsuptc, clinical support tech; diet, dietician; DON, director of
nursing; houskeep, housekeeper; HousStaf, resident physician;

infprev, infection prevention; medstudt, medical student;
microbi, microbiologist; NursPrac, nurse practitioner;
NursStudt, nursing student; occthasst, occupational therapy
assistant; othhosp, someone in another hospital; othstudt, other
student; pharm, pharmacist; phlebots, phleboto-
mist; PtCareTch, patient care tech; psyctech, psychiatry
technician; ptfam, patient’s family; ptsafety, patient safety;
radasst, radiology assistant; radtech, radiology technician;
respther, respiratory therapist; RN, registered nurse;
specprth, special procedures technician; speech, speech
therapist; sonograp, sonographer; surgtech, surgery technician;
SVP, senior vice president; transport, transporter;
UnitClrk, unitclerk; UnitMgr, unit manager; VP, vice president.
8
7
6
5
4
3

2
1
0
1 2 3 4 5 6 7 8 9 10 11 12 13
Month
Actual Time Series Fitted Time Series
14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13
Month
14 15 16 17 18 19 20 21 22 23 24
8
Change in Control Wars: 4.8 to 2.8
Change in Intervention Wards: 5.0 to 2.1
Di-erence Not Statistically Significant
7

6
5
4
3
2
1
0
Figure 2. Rates of health care–associated infections (HAIs) in
intervention and control groups. Solid line: HAI rate in control
wards. Dashed line: HAI rate in wards in which
health care personnel received intervention. Y-axis: Rate of
health care–associated infections per 1000 patient-days.
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culture of safety surveys and network surveys in the study. The

impact of PD on physicians’ perceptions of culture of safety is
worth evaluating in future studies. Another limitation is that
9 months each for PD inquiry and follow-up may not have been
sufficient in hindsight. We hope that the lessons learned in our
study will be applied in future studies.
HAI rates decreased in both the intervention and control
wards, indicating the effectiveness of systemwide aggressive
hand hygiene measures. The average monthly hand hygiene
observations in the entire hospital and health system increased
23-fold compared with the months before systems improve-
ment agreement, and adherence increased from 93% to 98.5%
(P < .05). Similarly, the environmental rounding observations
increased 26-fold, and adherence increased from 91.9% to
92.8% (P < .05) (Parkland Infection Prevention Department,
unpublished data 2013). The patterns of reduction were differ-
ent, with gradual-exponential decline in the intervention wards,
in contrast to 1-time, abrupt improvement in the control wards.
We are unable to ascertain whether the differences in patterns
could be explained by intervention effect.
Valuable insights we gained from conducting this study were:
1. It took time to gain the necessary trust from the frontline
HCP in order for them to share freely their perceived barri -

ers and potential solutions. Initiatives to reduce HAIs need to
consciously factor in the time it takes to establish trust with
participants in the intervention.
2. Once the HCP recognized the potential value of PD inquiry,
they became engaged and shared many ideas with the study
team. They found the conversation refreshing and made
statements like, “I did not know my opinion mattered.” They
also solved many seemingly small issues between themselves
during the course of the conversation. For example, they
came up with a color scheme to label intravenous tubing to
identify which tubes needed to be changed on which day of
the week. Staff modeled complex workflows to each other
and learned from each other. Examples are workflows asso-
ciated with drawing blood from a patient in contact isola-
tion or cleaning up a patient with explosive diarrhea who has
soiled himself (without contaminating catheter sites).
3. The ward managers of the intervention wards pointed to
issues of time and the challenge of keeping a united front
between different shifts. Local factors like these may be
important for ongoing evaluation of infection prevention
initiatives.

4. Although PD is a grassroots-level intervention, leadership
buy-in and permission to engage with the research team were
essential.
5. The research team and ward managers recognized that the
PD approach is similar to the traditional quality improve-
ment approach using Plan-Do-Study-Act cycles in that they
have similar steps, but different in that the PD approach has
an upfront phase of in-depth inquiry that is intended to get at
the root of the problem, identify local role models for acceler-
ating change, and emphasize local ownership of the problem
and solutions.
6. Although the ward managers and frontline personnel even-
tually accepted PD after initial hesitation, the investigators
identified a need for better tools to assess setting, context,
and preconditions that might be more conducive for imple-
menting PD. The approach also needs to be more developed
and adapted for use in infection prevention because there is
not a 1:1 correlation between any single practice by HCP and
infection outcome in the patient. Another challenge in large,
complex health systems is that peer role models are harder
to identify and any single HCP may not have a deep reach
within the area where they work.

In summary, when organizational conditions were the same
for hospital wards that received positive deviance intervention
and those that did not, PD inquiry of health care personnel was
associated with a statistically significant impact on culture of
safety. Mapping of social networks yielded several important
insights about patterns of connections among frontline health
care personnel. Rates of health care–associated infections and
complications reduced in both intervention and control wards
due systemwide interventions. Further studies are needed to
evaluate social dynamics among health care personnel and their
impact on culture of safety and HAI outcomes.
Acknowledgments
The authors would like to acknowledge support received from
John Jay
Shannon, MD, Josh Floren, Judith Herrington, D. Jane Clinton,
Jonathan
Talaguit, Sheila Sims, Jimmy Donahue, Jacob Pietersen,
Deborah Reliford,
Dale Kemp, Sylvia Trevino, Thomas Button, John Michael
Ashworth,
Robert Haley, MD, and health care personnel in the 6 study
wards. These
individuals received no compensation for their work other than

their usual
salary. They have no conflicts of interest relevant to this article.
Table 2. Rates of Individual Health Care–Associated Infections
Group
6-mo Baseline
Period
9-mo
Intervention
Period
9-mo
Follow-up
Period
Rate of CLABSI Control 0.84 0.49 0.48
Rate of CAUTI Control 1.05 0.56 0.68

Rate of HAP Control 1.36 1.05 1.16
Rate of CDI Control 1.25 0.77 0.82
Differences in rates of individual HAIs between intervention
and control groups were not
statistically significant.
Abbreviations: CAUTI, catheter-associated urinary tract
infection; CDI, Clostridium difficile
infection; CLABSI, central line–associated bloodstream
infection; HAI, health care–associ-
ated infection; HAP, hospital-acquired pneumonia; HCP, health
care personnel.
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Financial support. This work was supported by the University of
Texas
System (Patient Safety Grant #137911; PI: Pranavi Sreeramoju,
MD) and
in-kind support provided by Parkland Health and Hospital
System, Dallas,
Texas.
Potential conflicts of interest. All authors: no reported conflicts
of
interest. All authors have submitted the ICMJE Form for
Disclosure of
Potential Conflicts of Interest. Conflicts that the editors
consider relevant to
the content of the manuscript have been disclosed.
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