This study sought to improve undertriage and overtriage rates at a Level II Pediatric Trauma Center by updating outdated trauma team activation (TTA) criteria and improving adherence to the criteria. The study was conducted in two phases: Phase I focused on improving adherence to newly revised TTA criteria, while Phase II moved triage responsibility to nurses and included transfer patients. Undertriage decreased from 15% to under 5% by the end of the study, while overtriage rates stabilized within recommended ranges. Standardizing processes through evidence-based criteria updates and role changes led to more accurate trauma patient triage and resource utilization.
Implementation of a value driven outcomes program to identify high variability in clinical costs and outcomes and association with reduced cost and improved quality
The Importance of measuring outcomes, including Patient Reported Outcome Measures (PROMS)
BAOT Lifelong Learning Event
10 November 2010
Dr Alison Laver-Fawcett
Head of Programme, BHSC(Hons) Occupational Therapy
York St John University
Implementation of a value driven outcomes program to identify high variability in clinical costs and outcomes and association with reduced cost and improved quality
The Importance of measuring outcomes, including Patient Reported Outcome Measures (PROMS)
BAOT Lifelong Learning Event
10 November 2010
Dr Alison Laver-Fawcett
Head of Programme, BHSC(Hons) Occupational Therapy
York St John University
[HOW TO] Create High Performance Emergency DepartmentsEmCare
EmCare’s latest White Paper on implementing a system-wide approach to providing emergency care. At Baylor Health Care System, the initiative has fostered the development of numerous approaches to managing the challenges faced by its emergency departments, including an innovative protocol to manage overcrowding at the system’s flagship facility.
Clinical practice guidelines and quality metrics often emphasize effectiveness over patient-centered care. In this article, the authors offer three approaches to personalizing quality measurement to ensure patient preferences and values guide all clinical decisions.
An excellent article that uses predictive and optimization methods to reduce hospital readmissions.
Another great article, "Reducing hospital readmissions by integrating empirical prediction with resource optimization" (Helm, Alaeddini, Stauffer, Bretthaur, and Skolarus, 2016) describes how Machine Learning modeling tools were used to determine the root-causes and individualized estimation of readmissions. The post-discharge monitoring schedule and workplans were then optimized to patient changes in health states.
* Patient-level & wound-level parameters influencing wound
healing were identified from prior research and clinician input
* Probability of wound healing can be predicted with reasonable
accuracy in real-world data from EMRs
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
January-February 2016 • Vol. 25/No. 1 17
CPT (R) Gwendolyn Godlock, MS-PSL, BSN, RN, AN, CPHQ, is Field Representative Nurse
Surveyor, The Joint Commission, Oakbrook, Terrace, IL.
CPT Mollie Christiansen, BSN, RN, AN, CMSRN, is Clinical Nurse Officer in Charge, Burn
Progressive Care Unit, United States Army Institute of Surgical Research, Joint Base San
Antonio Fort Sam Houston, TX.
COL Laura Feider, PhD, RN, is Dean, School of Nursing Science and Chief, Department of
Nursing Science, Army Medical Department Center and School, Health Readiness Center of
Excellence, Joint Base San Antonio Fort Sam Houston, TX.
Acknowledgments: The team would like to thank nursing leaders COL (R) Sheri Howell, for-
mer Deputy Commander of Nursing and Chief of Staff; and COL Richard Evans, Assistant
Deputy Chief Army Nurse Corps, for their support. A special acknowledgment for the former
Chief, Medical Nursing Section, COL Vivian Harris, who remained a staunch supporter, advo-
cate, and cheerleader, the Medical Section nursing staff, and the Center for Nursing Science
and Clinical Inquiry.
Note: The view(s) expressed herein are those of the authors and do not reflect the official policy
or position of Brooke Army Medical Center, the U.S. Army Medical Department, the U.S. Army
Office of the Surgeon General, the Department of the Army, Department of Defense, or the U.S.
Government.
Implementation of an Evidence-Based
Patient Safety Team to Prevent Falls
in Inpatient Medical Units
T
he Centers for Medicare &
Medicaid Services identified
falls as a preventable health
care acquired condition (DuPree,
Fritz-Campiz, & Musheno, 2014). A
large portion of the medical-surgical
inpatient population is aging, and
therefore at high risk for falls (Boltz,
Capezuti, Wagner, Rosen berg, &
Secic, 2013). Falls have physical and
emotional implications for patients,
as well as increased financial costs for
facilities. Nationally, medical units
have the highest rates of falls
(Bouldin et al., 2013). Most notably,
falls can cause significant injuries
resulting in increased length of stay,
unexpected surgeries, and even death
(Williams, Szekendi, & Thomas,
2014). Historically medical-surgical
nurses care for a mix of complex
patients with an array of comorbidi-
ties and patient needs (Carter &
Burnette, 2011).
Literature Review
The literature search was limited
to keyword searches on falls, team-
work, patient safety, nursing, hourly
rounding, and communication. Data -
bases included PubMed, EBSCO,
Agency for Healthcare Research and
Quality, CINAHL, and The Joint
Commission for years 2008-2014.
Use of fall prevention teams was an
emerging evidence-based practice
(EBP) intervention to decrease the
incidence of inpatient falls (Graham,
2012). Consistently, the evidence
demonstrated ineffective communi-
cation, situation awareness, team-
work, assessment, hourly rounding,
and environmental challenges as key
factors related to preventable inpa-
tient falls.
Collectively, research.
[HOW TO] Create High Performance Emergency DepartmentsEmCare
EmCare’s latest White Paper on implementing a system-wide approach to providing emergency care. At Baylor Health Care System, the initiative has fostered the development of numerous approaches to managing the challenges faced by its emergency departments, including an innovative protocol to manage overcrowding at the system’s flagship facility.
Clinical practice guidelines and quality metrics often emphasize effectiveness over patient-centered care. In this article, the authors offer three approaches to personalizing quality measurement to ensure patient preferences and values guide all clinical decisions.
An excellent article that uses predictive and optimization methods to reduce hospital readmissions.
Another great article, "Reducing hospital readmissions by integrating empirical prediction with resource optimization" (Helm, Alaeddini, Stauffer, Bretthaur, and Skolarus, 2016) describes how Machine Learning modeling tools were used to determine the root-causes and individualized estimation of readmissions. The post-discharge monitoring schedule and workplans were then optimized to patient changes in health states.
* Patient-level & wound-level parameters influencing wound
healing were identified from prior research and clinician input
* Probability of wound healing can be predicted with reasonable
accuracy in real-world data from EMRs
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
January-February 2016 • Vol. 25/No. 1 17
CPT (R) Gwendolyn Godlock, MS-PSL, BSN, RN, AN, CPHQ, is Field Representative Nurse
Surveyor, The Joint Commission, Oakbrook, Terrace, IL.
CPT Mollie Christiansen, BSN, RN, AN, CMSRN, is Clinical Nurse Officer in Charge, Burn
Progressive Care Unit, United States Army Institute of Surgical Research, Joint Base San
Antonio Fort Sam Houston, TX.
COL Laura Feider, PhD, RN, is Dean, School of Nursing Science and Chief, Department of
Nursing Science, Army Medical Department Center and School, Health Readiness Center of
Excellence, Joint Base San Antonio Fort Sam Houston, TX.
Acknowledgments: The team would like to thank nursing leaders COL (R) Sheri Howell, for-
mer Deputy Commander of Nursing and Chief of Staff; and COL Richard Evans, Assistant
Deputy Chief Army Nurse Corps, for their support. A special acknowledgment for the former
Chief, Medical Nursing Section, COL Vivian Harris, who remained a staunch supporter, advo-
cate, and cheerleader, the Medical Section nursing staff, and the Center for Nursing Science
and Clinical Inquiry.
Note: The view(s) expressed herein are those of the authors and do not reflect the official policy
or position of Brooke Army Medical Center, the U.S. Army Medical Department, the U.S. Army
Office of the Surgeon General, the Department of the Army, Department of Defense, or the U.S.
Government.
Implementation of an Evidence-Based
Patient Safety Team to Prevent Falls
in Inpatient Medical Units
T
he Centers for Medicare &
Medicaid Services identified
falls as a preventable health
care acquired condition (DuPree,
Fritz-Campiz, & Musheno, 2014). A
large portion of the medical-surgical
inpatient population is aging, and
therefore at high risk for falls (Boltz,
Capezuti, Wagner, Rosen berg, &
Secic, 2013). Falls have physical and
emotional implications for patients,
as well as increased financial costs for
facilities. Nationally, medical units
have the highest rates of falls
(Bouldin et al., 2013). Most notably,
falls can cause significant injuries
resulting in increased length of stay,
unexpected surgeries, and even death
(Williams, Szekendi, & Thomas,
2014). Historically medical-surgical
nurses care for a mix of complex
patients with an array of comorbidi-
ties and patient needs (Carter &
Burnette, 2011).
Literature Review
The literature search was limited
to keyword searches on falls, team-
work, patient safety, nursing, hourly
rounding, and communication. Data -
bases included PubMed, EBSCO,
Agency for Healthcare Research and
Quality, CINAHL, and The Joint
Commission for years 2008-2014.
Use of fall prevention teams was an
emerging evidence-based practice
(EBP) intervention to decrease the
incidence of inpatient falls (Graham,
2012). Consistently, the evidence
demonstrated ineffective communi-
cation, situation awareness, team-
work, assessment, hourly rounding,
and environmental challenges as key
factors related to preventable inpa-
tient falls.
Collectively, research.
Our current approach to root causeanalysis is it contributi.docxgerardkortney
Our current approach to root cause
analysis: is it contributing to our
failure to improve patient safety?
Kathryn M Kellogg,1 Zach Hettinger,1 Manish Shah,2 Robert L Wears,3
Craig R Sellers,4 Melissa Squires,5 Rollin J Fairbanks1
ABSTRACT
Background Despite over a decade of efforts to
reduce the adverse event rate in healthcare, the
rate has remained relatively unchanged. Root
cause analysis (RCA) is a process used by
hospitals in an attempt to reduce adverse event
rates; however, the outputs of this process have
not been well studied in healthcare. This study
aimed to examine the types of solutions
proposed in RCAs over an 8-year period at a
major academic medical institution.
Methods All state-reportable adverse events
were gathered, and those for which an RCA was
performed were analysed. A consensus rating
process was used to determine a severity rating
for each case. A qualitative approach was used
to categorise the types of solutions proposed by
the RCA team in each case and descriptive
statistics were calculated.
Results 302 RCAs were reviewed. The most
common event types involved a procedure
complication, followed by cardiopulmonary
arrest, neurological deficit and retained foreign
body. In 106 RCAs, solutions were proposed.
A large proportion (38.7%) of RCAs with
solutions proposed involved a patient death. Of
the 731 proposed solutions, the most common
solution types were training (20%), process
change (19.6%) and policy reinforcement
(15.2%). We found that multiple event types
were repeated in the study period, despite
repeated RCAs.
Conclusions This study found that the most
commonly proposed solutions were weaker
actions, which were less likely to decrease event
recurrence. These findings support recent
attempts to improve the RCA process and to
develop guidance for the creation of effective
and sustainable solutions to be used by RCA
teams.
INTRODUCTION
The problem of morbidity and mortality
from adverse events in healthcare has
undergone over 15 years of intense scru-
tiny, funding, regulation and research
worldwide. Despite dramatically intensi-
fied efforts to increase the safety of the
healthcare system, reports have suggested
that safety has not improved. The adverse
event rate has remained essentially the
same, suggesting that our current solu-
tions to the problem are not working.1–10
This lack of progress persists despite the
devotion of a tremendous amount of
financial and human resources at the
local, state and national levels in an effort
to reduce errors and patient harm.11
One common, resource-intensive, prac-
tice is the root cause analysis (RCA)
process, which is used by most hospitals
in the USA.12–15 The RCA process has
been mandated in response to sentinel
events by the Joint Commission since
1997.16 Although the RCA process has
been presumed to induce change, its
effectiveness has been questioned and
there is not robust literature to support
its efficacy.17 18 In healthcare, there are
reports of difficul.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.There are a total of thirteen hospitals included in this review. These facilities have implemented vitals capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
The best way to enhance patient safety is to build a culture of safety at the hospital. The Johns Hopkins Hospital Comprehensive Unit-based Safety Program (CUSP)
Standardized Bedside ReportingOne of the goals of h.docxwhitneyleman54422
Standardized Bedside Reporting
One of the goals of healthcare is to ensure that the patients get the best service possible while not compromising on the satisfaction and goodwill of the nurses and other healthcare professionals. A key aspect of ensuring quality healthcare is the consistent handling of patient information from nurse to nurse during shifts; information handled wrongly can jeopardize the patients’ health (Baker, 2010). It is important to implement procedures that ensure consistent and smooth handling of patient information from nurse to nurse to increase patient safety and improve nurse satisfaction. This paper will explore the merits of standardized bedside reporting as opposed to board reporting in ensuring a positive outcome and consistent quality healthcare.
Change model overview
A key aspect in determining whether bedside shift reporting has any merits over board reporting is the John Hopkins Nursing Evidence-Based Practice Process (JHNEBP). The John Hopkins Nursing Evidence-Based Practice Process is a framework for guiding the translation and synthesis of evidence into valid healthcare practice. JHNEBP has three cornerstones that include research, education, and practice; the framework ensures that research evidence is the basis of clinical decision-making. (Dearholt & Dang, 2012) The implementation of the John Hopkins Nursing Evidence-Based Practice Process has three key phases, the first phase is the identification of an important question, the second phase involves the systematic review of research evidence, and the third phase is translating the results into action. Nurses should use the JHNEBP process because it provides a clear way for healthcare professionals to translate research results into healthcare practice.
Practice Question
The team includes several key stakeholders who will benefit greatly from my research. Among the team members include myself as ER nurse, charge nurse, ERT ( Emergency room tech), nurse case manager, nurse supervisor, physician and hospital manager.
The evidence-based practice question that the team members will explore is "Does the use of a standardized bedside report versus board reporting help increase patient safety, nurse satisfaction, and positive outcome?" The evidence-based practice question assesses the ability of bedside shift reporting to improve healthcare provision. The practice area of the question is clinical. The practice issue came about because of assessing risk management concerns in ensuring good health practices. To answer the question, the team members gathered evidence from patient preferences, peer-reviewed journals, and clinical guidelines. The team members searched peer-reviewed journal databases to gather relevant information from previous research that could affect the results.
Understanding the merits of bedside shift reporting as opposed to board reporting is important as most healthcare organization use either strategy in collecting and passin.
A Dartmouth Microsystem Assessment was conducted to examine a hospital unit\\’s functionality and to highlight opportunities for improvement. To enhance the gathering of data, a statistical tool was created to measure a wider sample population. The CNL student implemented a more reliable and valid data gathering system. The nurse educator asked to use the graduate student’s tool on the unit and throughout the hospital.
2. programs establish a goal to maintain undertriage below 5–10%, and
they defined an acceptable overtriage rate of up to 30–50% at the time
of the study. Currently, the ACS-COT recommends undertriage b5%
and overtriage 25–35% [2]. Undertriage is defined as a triage decision
that classifies patients as not needing a TTA, when in fact they do.
Undertriage is a medical problem, which may result in adverse patient
outcomes. When a trauma case is overtriaged, a TTA is activated when
criteria was not met, over utilizing resources.
To that end the Eastern Association for the Surgery of Trauma (EAST)
has published recommendations based on Level 3 recommendations.
Pediatric triage should include:
• A two-tiered triage system in the ED by physicians can effectively re-
duce unnecessary resource utilization.
• Mechanism of injury alone may not be useful in triaging pediatric
patients.
• A combination of physiologic and anatomic parameters with mecha-
nism provides better triage utilizing age-appropriate vital signs.
These recommendations were based on a systematic review of mod-
erate quality data [3].
The authors sought to improve the undertriage and overtriage rates
at our community Level II Pediatric Trauma Center by 1) improving ac-
curacy in following established trauma team activation criteria and
2) modifying established trauma team activation criteria in an
evidence-based fashion to better identify severely injured children.
We implemented a process improvement patient safety (PIPS) project
utilizing a Lean 4-step problem solving approach methodology to better
understand our current triage rates. We then asked the question if
undertriage would improve further if we adjusted the countermeasure
by moving leveling responsibility to Emergency Department (ED)
nurses for all pediatric trauma (using current TTA criteria and a revised
Base Station form) (Figs. 2 and 3, respectively). Increasing awareness of
the importance of appropriate resource utilization prompted the devel-
opment of a systematized procedure for impacting under- and
overtriage rates within our large, community health care system.
1. Methods
1.1. Lean process
Pediatric trauma services led a PIPS project at Mary Bridge Children's
Hospital (MBCH), a Level II Pediatric Trauma Center in Tacoma,
Washington from 2011 through June 30, 2012, with data analysis com-
pleted in September 2012. The baseline phase of the study was Q1 YTD
2011. A Lean 4-Step Problem Solving Approach (Plan-Do-Check-Adjust)
was developed by the Trauma Program Manager (TPM - CJM) and ap-
proved by the Trauma Medical Director (TMD - MAE) [4]. Trauma ser-
vices worked in collaboration with MBCH Emergency Department/
Base Station leadership to provide education and training to staff
(MDs & RNs) that determine the level of trauma team activation. The
Trauma Registrar tracked data through the trauma registry and made
clerical revisions to the TTA criteria and Base Station documents as nec-
essary. The TPM routinely reported PIPS progress at MBCH pediatric
trauma quality assurance (QA) and Multidisciplinary Committee
Meetings.
1.2. Trauma team activation redesign
The TTA was critically reviewed and revised during the baseline
phase of the study. The authors assumed leadership of the Trauma De-
partment in 2010. It was noted at that time that the TTA had not been
reviewed, revised, or renewed since 2007 (Fig. 1). Three tiers of activa-
tion existed at that time, and several new pediatric criteria were not
considered in the original TTA. Furthermore, mechanism of injury was
the branching point in the decision making tree for TTA. The TTA criteria
was updated based on guidelines published by the American College of
Surgeons, Committee of Trauma (ACS-COT), WA State DOH Governor's
Steering Committee on EMS and Trauma (WA DOH-EMS/Trauma) and
CDC with a focus on head injuries, the primary finding in undertriage
patients pre-study [1,2]. Other drivers for the selected changes included
a thorough review of the then current literature and Washington State
Administrative Code (WAC) [5–8]. The Society of Trauma Nurses List-
Serve was also used to obtain and review outside pediatric trauma hos-
pital policy/procedures for determining appropriate levels of activation.
The initial changes implemented in the TTA are listed in Table 1.
The Trauma QA Committee and the WA DOH-EMS/Trauma ap-
proved the MBCH trauma team activation criteria for use in triaging
EMS transport from the scene, arrivals to the ED by privately owned ve-
hicles (POV), and trauma transfers. The TTA criteria outlined in boxes A-
B-C (and eventually box D) determined the appropriate level of activa-
tion (major or modified for our two-tiered system). The elements of the
newly designed TTA were then imbedded in the Base Station report, so
that when EMS called in a report, the elements could quickly be checked
to help determine a) the need for activation and b) what level of activa-
tion was required. Under- and overtriage rates were then calculated by
using the Cribari grid (Fig. 4) [2].
1.3. PIPS design
Data was obtained from our trauma registry as follows: (1) 2011 Q1
YTD data was used as a Baseline (pre-study); (2) Phase I (April 1
through June 30, 2011) of the study involved using the newly updated
TTA. The MBCH Base Station form was revised to reflect key components
of TTA criteria (Fig. 3). All pediatric trauma activations were evaluated
using the Cribari grid for triage accuracy (Fig. 4) [2]. Essentially,
undertriage was defined as patients with an ISS N15 for which a major
or modified was not activated, and overtriage was defined as patients
with an ISS b16 for which a major was activated.
Structured education and training for MDs and RN/charge nurses oc-
curred during this phase, and signatures were required of the MD and
RN filling out the Base Station report. (3) Phase II (July 1, 2011 through
June 30, 2012) of the study moved the trauma team activation respon-
sibility primarily to nursing (with a component of collaboration with
MBED MDs). Data was analyzed during Q3 2012 and continuing trends
were documented. A second wave of structured education occurred
with all RN staff. Box (D) was added to the TTA criteria to address
transfers-in from outside hospitals (Fig. 2).
2. Results
The MBCH undertriage rate during Q1 2011 YTD at baseline was 15%.
The Cribari grid for the baseline data is presented as Table 2. 72 trauma
cases (4 major [6%], 18 modified [25%]) were evaluated at baseline, and
10/68 cases were undertriaged. Our overtriage rate was 75%, which in-
dicated criteria were not being applied consistently or accurately in 3/
4 cases. We defined accuracy of the use of the TTA as 85% based on
the undertriage rate.
Phase I was April 1 through June 30, 2011 (Q2 2011). The main
goal during this phase was to assess accuracy of the use of the TTA
tool and track under-/overtriage rates. During this phase, there was
90% use of the newly redesigned Base Station report. There were
123 total traumas during Q2 2011 (5 major [b1%], 36 modified
[29%]), and the Cribari grid for Q2 2011 data is presented as
Table 3. Undertriage rates improved to 10% (12/118), and overtriage
dropped to 20% (1/5) during Phase I.
Phase II was July 1, 2011 through June 30, 2012, with an analysis
completed and data check through September 30, 2012. Phase II was
evaluated for Q3 and Q4 2011 (IIa) and Q1 and Q2 2012 (IIb)
(Tables 4a and 4b). There were 503 total traumas during Phase II, 26
(5%) of which were Major, and 180 (36%) of which were modified
traumas. Phase II continued to demonstrate improving under- and
overtriage rates. Phase IIa (concluding the data collection for 2011)
1519M.A. Escobar Jr., C.J. Morris / Journal of Pediatric Surgery 51 (2016) 1518–1525
3. demonstrated an undertriage rate of 8.4% (19/226) and an overtriage
rate of 38% (5/13). Data during Phase IIb indicated an undertriage rate
of 4.7% (12/251 pts) and overtriage rate of 54% (7/13). During Phase II,
there was 100% use of the newly redesigned Base Station report, and
MD/RN signatures validated this. Fig. 5 demonstrates the under-/
overtriage trends throughout the study.
An analysis was retrospectively completed at the time of manuscript
preparation to understand more about whether the patients in the
major group really needed that level of service based on resource
needs. Or, in other words, if they were activated as a major, did they re-
quire a trip to the OR, PICU or other immediate interventions? Baseline
had 4 majors with the following dispositions: OR (2), Med-Surg (1), and
home (1). 15 modifieds went home, and 3 were unable to be located
within the registry at time of retrospective review. Phase I had 5 majors
with the following dispositions: OR (1) and ICU (4). 16 modifieds went
home, 16 were admitted to Med-Surg, one went to the OR, and three
were admitted to the ICU. Finally, Q2 2012 was evaluated (the last quar-
ter of Phase II – conclusion of the study). There were 8 majors and 79
modifieds. The major dispositions included: OR (2), ICU (4), Med-Surg
(1), and home (1). 46 modifieds went home, 24 were admitted to
Med-Surg, five went to the OR, and three were admitted to the ICU.
One was transferred out because of lack of plastic surgery coverage at
the time.
3. Discussion
The “Orange Book” (Resources for Optimal Care of the Injured Pa-
tient) clearly states the prehospital trauma system is driven by the
goal of getting the right patient to the right place at the right time [2].
As noted above undertriage is a medical problem, and overtriage is a re-
source utilization issue. In general, priority has been given to reduction
of undertriage, because undertriage may result in preventable mortality
or morbidity from delays in definitive care [2]. Overtriage typically im-
pacts the improper (and costly) inappropriate use of resources. This
Fig. 1. Trauma team activation tool - 2007.
Table 1
Initial changes implemented to the Trauma Team Activation (TTA) tool during the baseline
phase of the study.
• Reorganizing the algorithm flowchart to reflect physiologic compromise first
rather than mechanism of injury
• Removal of the third tier of activation – “Limited Activation”.
• Decreasing fall height from N20 ft to N10 ft.
• Maintaining transferred patient form other hospital receiving blood to maintain
VS.
• Addition of suspected non-accidental trauma.
• Addition of isolated injury (femur fx, liver or spleen injury, open/depressed
skull fx).
• Addition of impending operation from any surgical service for a patient at risk
for multisystem injury.
• Addition of high energy mechanism with potential for occult injury.
• Addition of ED MD Discretion.
• Addition of altered mental status associated with injury.
• Addition of age-specific hypotension in children.
• Addition of GSW(s) to the neck, chest, abd, or groin requiring automatic major
activation.
• Addition of allowing for a Modified Activation for other stable penetrating
injury.
• Addition of anticipated arrival of N3 seriously injured patients.
• Addition of inability to intubate in prehospital setting with suspected need for
surgical airway.
1520 M.A. Escobar Jr., C.J. Morris / Journal of Pediatric Surgery 51 (2016) 1518–1525
4. does not typically affect the trauma care of a patient directly, except in
situations of disaster and mass casualty events.
The Resources for Optimal Care of the Injured Patient handbook pro-
poses two ways to evaluate undertriage (and overtriage) within a trau-
ma region [2]. One method is to identify all the potentially preventable
deaths that occur within a regionalized trauma system. Another method
is to determine how many major trauma patients were transported
incorrectly to a non-trauma center. Utilizing the latter method we
adapted the Matrix method or Cribari grid (Fig. 4) to calculate our inter-
nal overtriage and undertriage rates by tracking the accuracy of our TTA
based on our TTA tool (Fig. 2) [2]. It should be noted that the Cribari
method is based on ISS, and the suggested protocol in the text is primar-
ily based on consensus opinion because of limited literature available to
assist in developing the protocol [9]. The authors believe the inclusion of
Fig. 2. Trauma team activation tool - 2012.
1521M.A. Escobar Jr., C.J. Morris / Journal of Pediatric Surgery 51 (2016) 1518–1525
5. inappropriately activating a major trauma as a modified (box E in Fig. 4)
in the undertriage rate calculation reflects the inappropriate activation
of trauma at the appropriate tier, rather than just using box H in the cal-
culation (no TTA). Many institutions may not have their trauma, gener-
al, or pediatric surgeon automatically respond to a midlevel or modified
TTA, and box E suggests that a major trauma did not have a surgeon ap-
propriately respond in a timely manner (a requirement for every major
trauma). While this has certainly been the gold standard in the past,
newer studies reviewing the actual utilization of resources (not just
the severity of the injury) suggest that perhaps a better way to review
use of trauma resources are based on the need for intervention rather
than ISS [9,10].
Falcone, et al. [10] reported an analysis of injured children undergo-
ing trauma activation and examined the number of high resource inter-
ventions needed to validate the need for trauma team activation. Those
most predictive of using a high-level resource were a gunshot wound to
the abdomen, blood given before arrival, traumatic arrest, tachycardia/
poor perfusion, and age-appropriate hypotension. The addition of
tachycardia/poor perfusion and pretrauma center resuscitation (with
greater than 40 mL/kg) resulted in eight criteria with an overtriage of
Fig. 3. Mary Bridge base station form.
1522 M.A. Escobar Jr., C.J. Morris / Journal of Pediatric Surgery 51 (2016) 1518–1525
6. 39% and an undertriage of 10.5%. Nevertheless, outcomes studies related
to trauma team activation have not been standardized in terms of re-
sources analyzed [11,12].
A recent multicenter study attempted to define a consensus-based
criterion standard definition for the highest-level pediatric trauma
team activation by using a modified Delphi technique to develop a list
of criteria that would form the criterion standard definition for the
highest-level pediatric trauma team activation [9]. The expert panel
agreed upon 12 criteria that included time-sensitive interventions. In-
terventions meeting criteria within two hours of arrival included:
need for advanced airway, tube thoracostomy, blood transfusion,
pericardiocentesis, and thoracotomy. Interventions meeting criteria
within four hours of arrival included: major operative intervention, in-
terventional radiology, cesarean section, vasopressors, or burr hole.
The follow criteria were included regardless of time of presentation:
spinal cord injury or spinal fracture and death in the ED resulting from
their injury [9].
Our PIPS presented in the current study was not designed to evalu-
ate the outcomes of patients activated during the study period. Rather,
the purpose of this project was to analyze the effect of a Lean-
designed PIPS on under- and overtriage rates to determine if triage
rates could be improved at a community pediatric trauma center. This
weakness in the study is further discussed below. Nevertheless, a retro-
spective evaluation of the trauma registry was performed during the
preparation of the manuscript. A review of the data demonstrates that
during the baseline phase of the study, although 75% of our major pa-
tients were overtriaged according to ISS, 50% went to the OR from the
ER, suggesting our tool was not appropriately matching patients to
their needed resources. During Phase I all major activations did in fact
require admission to the PICU (4) or the OR (1). Finally, Q2 2012 was
Fig. 4. Cribari grid methodology.
Fig. 5. Percentage of undertriage/overtriage during the study period.
1523M.A. Escobar Jr., C.J. Morris / Journal of Pediatric Surgery 51 (2016) 1518–1525
7. evaluated (the last quarter of Phase II). 25% of majors went to OR (2/8),
50% to ICU (4/8), 12.5% to Med-Surg (1/8), and 12.5% to home (1/8).
This did suggest that improving our triage rates did correlate with prop-
er utilization of resources and may have impacted patient outcomes.
The PIPS was designed using Lean methodology (PDCA cycle). The
Plan was reviewing our current situation at baseline and designing the
study. The Do was implementing the change in the TTA and the Base
Station report and assessing the accuracy of their use. The Check was
reviewing the changes in undertriage and overtriage rates following im-
plementation. The Adjust involved shifting responsibility for trauma
team activation solely from the ED physicians to a shared responsibility
with the ED charge nurses.
A “5 Whys Analysis” was done to understand (1) if our triage tool
was accurate and (2) if a standardized triage process would improve
our metrics. The point of this exercise is to iteratively ask the question
“Why?” to determine the root cause analysis of a problem, of which ask-
ing why 5 times is typically required to solve the problem. To test this,
ED physicians triaged pediatric trauma patients from April 1 through
June 30, 2012 (Phase I) using the revised/approved trauma team activa-
tion criteria (Fig. 2) [4]. Accuracy improved from baseline 85% to 90% in-
dicating tool effectiveness. This reflected an undertriage rate of 9.4%
(Fig. 5). Phase I of our study demonstrated accuracy of our TTA Criteria.
However, the goal was N95% usage of the TTA as reflected by the use of
the Base Station report and b5% resulting undertriage. The pilot was
then moved to Phase II.
We sought to understand if triage accuracy would improve further if
we adjusted the countermeasure by moving leveling responsibility to
the ED charge nurses for all pediatric trauma (by using current TTA
criteria and the revised Base Station form). Beginning July 1, 2012
through September 30, 2012 responsibility moved to RNs who were en-
couraged to collaborate with MDs for consensus. Only MDs were able to
upgrade the TTA level. Individual feedback and coaching was provided
by the TPM. During this phase, it was determined that trauma transfers
into MBCH from outside hospitals were either (1) not getting triaged
per our TTA Criteria upon arrival to the ED or (2) getting directly admit-
ted to the hospital, bypassing the ED without a complete ATLS trauma
evaluation, resulting in undertriage of severely-injured children. Our
TTA Criteria was revised to include box D for trauma transfers (Fig. 2).
Phase II of our study demonstrated continued improvement by moving
leveling (triage) responsibility primarily to nursing and adding box D
regarding transfers-in from outside hospitals.
The novel (although certainly not unique) idea occurred during
Phase II of our study. We certainly saw improvement after we embed-
ded our activation criteria within the Base Station report (MB serves
as pediatric medical control for Pierce County, Washington). But the
significant decrease in undertriage to b5% occurred when the charge
nurses were empowered to activate a trauma using the TTA.
This drop in undertriage rate was monitored closely. The inherent
risk of becoming too sensitive is seeing a concomitant increase in
overtriage. This was not the case, however. The overtriage dropped
from 75% to 54% with fluctuations as low as 25%, but by the end of the
study period it never again reached 75% [2]. Box D was added to the
TTA to guide activations of trauma transfers. This also addressed the
complex issue of secondary overtriage in pediatric trauma transfers
(unnecessary transfers to a pediatric trauma center based on ISS and
lengths of stay) [13].
Our performance was sustained by hardwiring a standardized pro-
cess resulting in improved patient safety and delivery of quality trauma
care (Fig. 5). One of the key aspects to success was engaging the ED MDs
and RNs. The TPM provided continual feedback and education to the
charge nurses. Autonomy, mastery, and purpose were stressed with
the ultimate goal being to activate the patient at the appropriate level
to have the patient get the right care at the right time. The PIPS project
resulted in increased engagement and strengthening relationships be-
tween the trauma department and the ED.
The authors acknowledge several weaknesses of the current article.
First, this is clearly a PIPS project rather than a hypothesis driven
study. Nevertheless, we feel that it is important to note that community
Level II Pediatric Trauma centers can execute a project of this magnitude
that should improve the resource utilization for the care of injured chil-
dren in their region. The changes to the TTA resulted from a thorough
review of the then current literature and evaluation of ACS-COT and
CDC recommendations, and Washington State DOH requirements [5–8].
Second, the project is an analysis of the effect of PIPS on under- and
overtriage rates. Outcomes of patients were not analyzed formally at the
time, and thus the authors are only able to report the basic outcomes
within our cohort of injured patients. Nevertheless, the trauma commit-
tee did review the literature following the conclusion of the PIPS project
to further refine the TTA. This prompted a second revision of the TTA
after noting an increased overtriage rate in our specific subset of pa-
tients that were transferred in (the newly created box D during the
study) resulting better utilization of resources at our Level II Pediatric
Trauma center.
In conclusion, the authors believe patient safety should be improved
by reducing harm to individual patients from undertriage of severe in-
jures versus overutilization of systems resources because of overtriage
of lesser injuries. Modifying the TTA and decreasing the variation in its
application resulted in improved TTA accuracy. Further, the appropriate
use of the TTA resulted in appropriate matching of resource utilization
to injury severity. Finally, empowering the nursing staff in the ED to
Table 2
Baseline Cribari grid (Q1 2011).
Baseline Cribari: Quarter 1 2011 (January 1–March 31, 2011)
ISS 1–15 ISS 16–75 Total
Full trauma team activation 3 1 4
Modified trauma team activation 15 3 18
No trauma team activation 43 7 50
72
Table 3
Phase I Cribari grid (Q2 2011).
Quarter 2 2011 (April 1–June 30, 2011)
ISS 1–15 ISS 16–75 Total
Full trauma team activation 1 4 5
Modified trauma team activation 33 3 36
No trauma team activation 73 9 82
123
Table 4a
Phase IIa Cribari grid (Q3 & Q4 2011).
July 1–December 31, 2011
ISS 1–15 ISS 16–75 Total
Full trauma team activation 5 8 13
Modified trauma team activation 61 6 67
No trauma team activation 146 13 159
239
Table 4b
Phase IIb Cribari grid (Q1 & Q2 2012).
January 1, 2012–June 30, 2012
ISS 1–15 ISS 16–75 Total
Full trauma team activation 7 6 13
Modified trauma team activation 109 4 113
No trauma team activation 130 8 138
264
1524 M.A. Escobar Jr., C.J. Morris / Journal of Pediatric Surgery 51 (2016) 1518–1525
8. appropriately activate a trauma was the adjustment that ultimately re-
sulted in sustainable improvement.
Acknowledgements
The authors gratefully acknowledge and thank Rachel Parker and
Heidi Mallrie, our Trauma Registrars past and present, respectively, for
their contribution to this project.
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