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Study protocol for the Anesthesiology Control Tower—Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot randomized controlled trial in intraoperative telemedicine

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Background : Each year, over 300 million people undergo surgical procedures worldwide. Despite efforts to improve outcomes, postoperative morbidity and mortality are common. Many patients experience complications as a result of either medical error or failure to adhere to established clinical practice guidelines. This protocol describes a clinical trial comparing a telemedicine-based decision support system, the Anesthesiology Control Tower (ACT), with enhanced standard intraoperative care. Methods : This study is a pragmatic, comparative effectiveness trial that will randomize approximately 12,000 adult surgical patients on an operating room (OR) level to a control or to an intervention group. All OR clinicians will have access to decision support software within the OR as a part of enhanced standard intraoperative care. The ACT will monitor patients in both groups and will provide additional support to the clinicians assigned to intervention ORs. Primary outcomes include blood glucose management and temperature management. Secondary outcomes will include surrogate, clinical, and economic outcomes, such as incidence of intraoperative hypotension, postoperative respiratory compromise, acute kidney injury, delirium, and volatile anesthetic utilization. Ethics and dissemination : The ACTFAST-3 study has been approved by the Human Resource Protection Office (HRPO) at Washington University in St. Louis and is registered at clinicaltrials.gov ( NCT02830126 ). Recruitment for this protocol began in April 2017 and will end in December 2018. Dissemination of the findings of this study will occur via presentations at academic conferences, journal publications, and educational materials.
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STUDYPROTOCOL
Study protocol for the Anesthesiology Control
Tower—Feedback Alerts to Supplement Treatments
(ACTFAST-3) trial: a pilot randomized controlled trial in
intraoperative telemedicine [version 2; referees: 2 approved]
StephenGregory , TeresaM.Murray-Torres , BradleyA.Fritz ,
ArbiBenAbdallah , DanielL.Helsten , TroyS.Wildes , AnshumanSharma ,
MichaelS.Avidan , ACTFASTStudyGroup
DepartmentofAnesthesiology,WashingtonUniversitySchoolofMedicine,St.Louis,Missouri,63110,USA
Equalcontributors
Abstract
:Eachyear,over300millionpeopleundergosurgicalproceduresBackground
worldwide.Despiteeffortstoimproveoutcomes,postoperativemorbidityand
mortalityarecommon.Manypatientsexperiencecomplicationsasaresultof
eithermedicalerrororfailuretoadheretoestablishedclinicalpractice
guidelines.Thisprotocoldescribesaclinicaltrialcomparinga
telemedicine-baseddecisionsupportsystem,theAnesthesiologyControl
Tower(ACT),withenhancedstandardintraoperativecare.
:Thisstudyisapragmatic,comparativeeffectivenesstrialthatwillMethods
randomizeapproximately12,000adultsurgicalpatientsonanoperatingroom
(OR)leveltoacontrolortoaninterventiongroup.AllORclinicianswillhave
accesstodecisionsupportsoftwarewithintheORasapartofenhanced
standardintraoperativecare.TheACTwillmonitorpatientsinbothgroupsand
willprovideadditionalsupporttothecliniciansassignedtointerventionORs.
Primaryoutcomesincludebloodglucosemanagementandtemperature
management.Secondaryoutcomeswillincludesurrogate,clinical,and
economicoutcomes,suchasincidenceofintraoperativehypotension,
postoperativerespiratorycompromise,acutekidneyinjury,delirium,and
volatileanestheticutilization.
:TheACTFAST-3studyhasbeenapprovedbytheEthics and dissemination
HumanResourceProtectionOffice(HRPO)atWashingtonUniversityinSt.
Louisandisregisteredatclinicaltrials.gov( ).RecruitmentforthisNCT02830126
protocolbeganinApril2017andwillendinDecember2018.Disseminationof
thefindingsofthisstudywilloccurviapresentationsatacademicconferences,
journalpublications,andeducationalmaterials.
Keywords
telemedicine,decisionsupport,protocol,randomizedcontrolledtrial
1* 1* 1
1 1 1 1
1
1
*
 
Referee Status:
 InvitedReferees

version 2
published
24Aug2018
version 1
published
22May2018
1 2
report report
,UniversityofMichigan,Leif Saager
USA
,UniversityofMichigan,Michael Burns
USA
1
,UniversityofMorten H. Bestle
Copenhagen,Denmark
,UniversityChristian Ari Dalby Sørensen
ofCopenhagen,Denmark
2
22May2018, :623(First published: 7
)https://doi.org/10.12688/f1000research.14897.1
24Aug2018, :623(Latest published: 7
)https://doi.org/10.12688/f1000research.14897.2
v2
Page 1 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
MichaelS.Avidan( )Corresponding author: avidanm@wustl.edu
 :Conceptualization,FundingAcquisition,Investigation,Methodology,Writing–OriginalDraftPreparation,Writing–Author roles: Gregory S
Review&Editing; :Conceptualization,FundingAcquisition,Investigation,Methodology,Writing–OriginalDraftPreparation,Murray-Torres TM
Writing–Review&Editing; :Conceptualization,Methodology,Writing–Review&Editing; :Conceptualization,FormalFritz BA Ben Abdallah A
Analysis,Methodology,Writing–Review&Editing; :Conceptualization,FundingAcquisition,Investigation,Writing–Review&Editing;Helsten DL
:Conceptualization,FundingAcquisition,Investigation,Methodology,Writing–Review&Editing; :Conceptualization,Wildes TS Sharma A
FundingAcquisition,Methodology,Writing–Review&Editing; :Conceptualization,FundingAcquisition,Investigation,Methodology,Avidan MS
Resources,Writing–Review&Editing;
Nocompetinginterestsweredisclosed.Competing interests:
TheACTFAST-3project,includingthisprotocol,hasbeenfundedbyagrantfromtheAgencyforHealthcareResearchandGrant information:
Quality(R21HS24581-01).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
©2018GregoryS .Thisisanopenaccessarticledistributedunderthetermsofthe ,whichCopyright: et al CreativeCommonsAttributionLicence
permitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
GregoryS,Murray-TorresTM,FritzBA How to cite this article: et al. Study protocol for the Anesthesiology Control Tower—Feedback
Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot randomized controlled trial in intraoperative telemedicine [version 2;
2018, :623( )referees: 2 approved] F1000Research 7https://doi.org/10.12688/f1000research.14897.2
22May2018, :623( )First published: 7 https://doi.org/10.12688/f1000research.14897.1
Page 2 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
      Amendments from Version 1
This new version of the ACTFAST-3 protocol addresses the
critiques of the referees of the initial version of the manuscript.
Specifically, this version expands the introduction to highlight
perioperative risk assessment and the role that deviation from
evidence-based standards of care plays in adverse perioperative
outcomes. In addition, this manuscript provides additional
detail on the rationale for the primary outcomes in the study
and attempts to address potential sources of bias raised by the
referees. This new version also contains a Supplementary File 1
that provides definitions for the postoperative surrogate outcomes
in the study.
See referee reports
REVISED
Introduction
Each year, over 300 million surgical procedures are performed
worldwide1. Unfortunately, many patients will experience sig-
nificant morbidity or mortality in the postoperative period2.
Research conducted at our institution and others has dem-
onstrated an early postoperative mortality rate ranging from
1–5% and 90-day to 1-year mortality rates between 5–10%213.
Additionally, 5–40% of patients will experience some type
of postoperative surgical complication, including surgical site
infection, respiratory complications, myocardial infarction,
stroke and acute kidney injury, resulting in a three- to seven-fold
increase in postoperative mortality3,4,11,12,14.
Despite the overall decline in surgical morbidity and mortality
over time, the risk of perioperative adverse events remains
substantial2. Some of this risk is a manifestation of either
underlying patient pathology or the complexity of the surgical
procedure itself, with increasingly complex registries and risk
score calculators available to provide assessment of periopera-
tive risk9,12,15,16. However, evidence also suggests that medical
errors contribute considerably to negative patient outcomes17,18.
Although some errors may be considered active, such as the
administration of an incorrect medication, the failure to follow
established clinical practice guidelines and recommendations
likely has a more significant overall detrimental effect on
patient outcomes. Prior studies have documented that
deviation from evidence-based standards of care is common
in a variety of settings. This, deviation appears to worsen
patient outcomes, including increases in surgical site infection,
postoperative pneumonia, and mortality1925.
Interventions to improve patient safety and outcomes remain
a major focus in anesthesiology. The complexity of anesthetic
practice can lead to frequent cognitive errors in the periop-
erative arena26,27, suggesting that the development of a real-time,
tailored feedback system to support intraoperative decision-
making may be valuable. The development of automated
feedback and alerting systems has been demonstrated to improve
adherence to a number of treatment guidelines2845. However,
the impact of decision support systems appears to decay over
time4649, and improvements in process variables may not translate
into improved patient outcomes50.
In the intensive care unit (ICU), the use of remote monitoring
to augment care, commonly referred to as “telemedicine,”
decreases ICU mortality and the length of ICU stay, and
improves adherence to clinical practice guidelines5155. While
this type of clinical decision support has seen robust adoption
in the critical care setting, its utilization in the intraoperative
care of surgical patients is limited53. In light of the benefits that
have been demonstrated from using telemedicine in the ICU
setting, we believe that the implementation of such a system
in the operating room has the potential to elevate the general
safety and quality of perioperative care.
We have designed a multifaceted approach for the development
and institution of an Anesthesiology Control Tower (ACT)
to provide real-time intraoperative telemedicine decision
support. In the first component of our approach, we outlined
a strategy of iterative usability testing and platform modifica-
tion that allowed us to develop a high-fidelity, user-centered
system56. We intend to continue separate usability analyzes over
the course of the pilot trial in order to evaluate the key usability
elements of effectiveness, efficiency, and satisfaction57 in a more
real-world setting. Because the impact of a clinical interven-
tion is dependent on the success of the process through which
it is implemented58, we will also evaluate implementation out-
comes that are relevant to the use of the ACT in the periop-
erative setting59,60. In the second component of our approach, we
will employ large-scale data analytics, integrating perioperative
information in order to create forecasting algorithms for nega-
tive patient trajectories61. In the current manuscript, we describe
the third element of our investigation: a pilot randomized con-
trolled trial that aims to demonstrate the superiority of the ACT
in improving adherence to best care practices when compared
to enhanced usual care.
Methods and analysis
Overview of research design
The ACTFAST-3 study is a pragmatic comparative effectiveness
trial that is taking place at an academic university-affiliated
and adult tertiary care hospital in the United States that per-
forms over 19,000 surgeries a year. We plan to enroll approxi-
mately 12,000 patients over the study period, with approximately
6,000 patients in the control arm and 6,000 patients in the inter-
vention arm (Figure 1). Patients will be included with a waiver
of informed consent, as approved by the Human Research
Protection Office (protocol number 201603038), as the risk
associated with the ACT has been deemed to be minimal.
Randomization will occur at the level of individual operating
rooms on a daily basis.
The ACT will monitor all patients in both the control and
intervention operating rooms using information gathered from
the electronic medical record (EMR) and from a customized
version of a perioperative monitoring and alerting program called
AlertWatch® (Ann Arbor, MI). AlertWatch is an FDA-cleared
(KI3O4OI) system that displays integrated patient informa-
tion and alerts clinicians to physiologic derangements. It was
recently demonstrated that use of the AlertWatch software was
Page 3 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
Figure 1. Flow diagram of study population.
associated with improvements in several process measures,
although this did not translate into an effect on clinical
outcomes50. For the purposes of our intervention, the commer-
cially available AlertWatch platform was heavily modified through
usability testing56 to create a customized AlertWatch “Control
Tower” mode that is only available within the ACT (Figure 2
and Figure 3). The standard platform will remain available to
all OR clinicians during this study. The ACT will provide clini-
cians in the intervention ORs with real-time feedback based on
the available electronic resources, including AlertWatch Con-
trol Tower. Anesthesia providers in rooms assigned to the control
group will also be monitored but will not receive decision
support. Notably, the standard medical staffing models for
providing an anesthetic will not be affected with this inter-
vention, as the ACT is designed to augment decision-making,
rather than replace critical team members.
The primary outcome measures in the ACTFAST-3 pilot study
are compliance with best care practices for intraoperative core
temperature management and intraoperative blood glucose
management (Table 1). These outcomes were selected
because they are routinely and reliably tracked in the elec-
tronic medical record and optimal perioperative management of
temperature and blood glucose is known to influence clinical
outcome. We will also explore additional intraoperative process
measures in addition to surrogate outcomes (Table 2). The
incidence of intraoperative hypotension and the incidence of
postoperative renal dysfunction, atrial fibrillation, respiratory
failure and delirium will be assessed via review of the EMR.
Other postoperative complications, including intraoperative
awareness, surgical site infection, readmission, and death will be
assessed via analysis of the existing Center for Clinical Excellence
Registry, American College of Surgeons’ National Surgical
Quality Improvement Program (NSQIP) database, Society of
Thoracic Surgery (STS) database, and Systematic Assessment
and Targeted Improvement of Services Following Yearlong
Surgical Outcomes Surveys (SATISFY-SOS) database62. Outcomes
related to the usability of the ACT intervention, including
efficiency and efficacy of the software platform, will be obtained
from AlertWatch data logs. These logs will also be used to
obtain data related to the feasibility of implementing the pilot
ACT. User satisfaction will be assessed through surveys
administered to members of the anesthesia department.
Study population, randomization, and blinding
The trial will include all adult patients undergoing surgery at
two campuses of an academic university-associated hospital,
Barnes-Jewish Hospital (South Campus and Parkview Tower)
Page 4 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
Figure 2. Interface of the AlertWatch® Control  Tower system.  (A) AlertWatch® Control Tower Census View. This view shows summary
information for operating rooms with ongoing procedures. Physiological alerts (e.g., low blood pressure) are shown as black or red squares,
depending on the severity of the derangement, with red indicating a more severe abnormality. Checkmarks appear inside an operating room
when an alert is triggered that has been classified as actionable and requires a response on the part of the clinicians in the Control Tower (see
Figure 3). Control rooms are indicated with a “Do Not Contact” symbol. (B) AlertWatch® Control Tower Patient Display View. This deidentified
intraoperative patient display demonstrates organ-specific information individualized to each patient. Colors outlining organs indicate normal
(green), marginal (yellow) or abnormal function (red). Orange would indicate an organ system at risk due to pre-existing conditions. The left
side of the display shows patient characteristics and the case information. Lab values, if available, are listed beneath the kidneys. Alerts
generated by the AlertWatch® system are listed on the right-hand side of the display. Specific alerts, determined by the study team to be
clinically significant and actionable, trigger a checkmark to appear at the bottom left of the screen. This informs the Anesthesiology Control
Tower (ACT) clinician that an alert is present that must be addressed. Clicking on this checkmark allows clinicians in the ACT to review and
address these alerts (Figure 3).
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F1000Research 2018, 7:623 Last updated: 29 JAN 2019
Figure 3. AlertWatch® Control Tower Case Review  dialogue. Clinicians in the Anesthesiology Control Tower (ACT) use the Case Review
window to address actionable Control Tower alerts, indicated by checkmarks on the Census View and the Patient Display. Within this Case
Review window, clinicians document their assessment of the significant of each alert, what action they would recommend, and, in the case of
intervention operating rooms (ORs), the reaction of the clinician in the OR to the ACT support.
Table 1. Primary outcome measures and definitions.
Measure Outcome
Intraoperative temperature
management
Proportion of patients with final
recorded intraoperative core
temperature greater than 36°C
Intraoperative blood
glucose control
Proportion of cases with blood
glucose 180 mg/dl upon arrival
to the post-anesthesia recovery
area
(St. Louis, MI, USA), between 7:00 AM and 4:00 PM Monday
through Friday (Figure 1). This includes a total of 48 operating
room locations. The ACT will function on days when at least
two anesthesia providers are available, one of whom must
be an attending anesthesiologist. Patients undergoing surgical
procedures with greater than 50% of the case length occurring
outside of the ACT hours will be excluded from analysis. All
patients younger than 18 will also be excluded from the study.
Patients who undergo multiple surgeries in a single hospitaliza-
tion or who have a second surgical procedure within 30 days of
their initial surgery will be analyzed according to their initial
randomization assignment. Patients returning for a second sur-
gery more than 30 days after their initial surgical encounter will
be considered as separate patients in the analysis. We will also
obtain data from a group of historical control patients for
the 6 months prior to the initiation of the ACTFAST-3
study, as part of an analysis related to potential sources of bias
and contamination.
A randomization algorithm integrated into the AlertWatch sys-
tem will direct patient group allocation on a daily basis. Due to
the nature of the intervention in this study, clinicians work-
ing in the ACT and those randomized to receive support
cannot be blinded to the intervention. To minimize any risk of
bias with variation in ACT staff availability, we have ensured
that OR-level randomization will performed each day in a 1:1
ratio. Researchers responsible for extracting data during the
course of the study will be blinded to group allocation at the
time of extraction.
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Page 6 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
Primary intervention: ACT monitoring and decision support
A multidisciplinary team of clinicians in the ACT will
remotely monitor all active operating rooms at the campus
of interest. ACT clinicians will include attending anesthesi-
ologists, anesthesiology fellows, anesthesiology residents, and
certified and student registered nurse anesthetists. Information
will be obtained in near real-time from multiple complemen-
tary sources, including the AlertWatch Control Tower software
(Figure 2) and the EMR. The clinicians in the ACT will use this
information to communicate with OR clinicians to help main-
tain compliance with intraoperative best care practices and
to assist with the detection and management of physiological
derangements35,6366. These clinicians will evaluate all alerts
generated by the AlertWatch Control Tower notification system
(Figure 3), including alerts from both the intervention and the
control operating rooms. For ORs allocated to the intervention
arm, the ACT will deliver decision support to the primary per-
sonnel caring for the patient via text message or telephone call.
The clinician receiving the alert will determine the applicabil-
ity of the alert to the clinical situation and will choose whether
to carry out any recommendations sent by the ACT. In patients
with a persistent critical event, the ACT will offer real-time
assistance with crisis resource management.
Operating rooms assigned to the control group will undergo
the same monitoring and assessment by the ACT, but
clinicians in these ORs will not receive any contact from the
ACT. However, if clinicians staffing the ACT feel ethically
obliged to contact a room assigned to the control group due
to perceived potential for imminent and significant patient
harm, they will be able to do so. Although we anticipate that
this will be a rare occurrence, it will still be documented and
reported as part of our study outcomes.
Data collection and outcome measures
Data collection for this study will utilize multiple sources to
extract outcome measures67. All alert data generated by the Alert-
Watch Control Tower platform will be automatically logged
to a secure database, including all responses by the providers in
the ACT to individual alerts (Figure 3). Data from the periop-
erative period will be imported from Metavision® (iMDsoft,
Wakefield, Massachusetts, USA), the anesthesiology infor-
mation management software system currently in use by the
Department of Anesthesiology. In addition to capturing com-
prehensive intraoperative clinical data, Metavision® also stores
preoperative information, such as patient characteristics, clinical
and surgical history, comorbidities, and data from the
Table 2. Secondary outcome measures and definitions.
Intraoperative process measures Outcomes
Intraoperative blood pressure
management
Mean duration of time spent with Mean Arterial Pressure <60 mmHg
Temperature monitoring Proportion of procedures lasting greater than 1 hour with documented
temperature
Antibiotic dosing Proportion of procedures with appropriate administration of repeat doses of
antibiotics
Intraoperative blood glucose
management
Proportion of cases with at least one dose of insulin administered for blood
glucose greater than 180 mg/dl
Intraoperative measurement of blood glucose in patients with type 1
diabetes undergoing cases 1 hour in length and patients with type 2
diabetes undergoing cases 2 hours in length
Train of four documentation Proportion of cases with a train of four documented prior to extubation if a
nondepolarizing neuromuscular blocking agent was administered
Ventilator management Proportion of cases with median tidal volume less than 10 ml/kg ideal body
mass
Volatile anesthetic utilization Mean and standard deviation of fresh gas flow rates for cases with volatile
anesthetic use >80% of case duration
Postoperative surrogate measures Outcomes
Postoperative acute kidney injury Incidence of individual outcomes (Supplementary File 1)
Postoperative atrial fibrillation
Postoperative respiratory failure
Postoperative delirium
Intraoperative awareness
Surgical site infection
30-day readmission
30-day mortality
Page 7 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
immediate post-operative period. Of note, during the anticipated
duration of this trial, our hospital system will be transitioning
to Epic Systems software (Verona, WI, USA) for both the hos-
pital electronic health record and the anesthesiology infor-
mation management software. Postoperative data for patient
outcomes will be obtained from the inpatient EMR record
system, and from clinical registries (SATISFY-SOS, NSQIP, STS).
Primary outcome measures
The primary outcome measures in the ACTFAST-3 study are
compliance with recommendations for intraoperative core
temperature management and intraoperative blood glucose
management (Table 1). Data on primary outcomes measures will
be recorded to an SQL server.
Secondary outcome measures
Secondary intraoperative outcomes will include several process,
surrogate, clinical measures (Table 2). Intraoperative process
outcomes will include blood pressure management, compli-
ance with recommendations for repeat dosing of antibiotics and
for temperature monitoring, management of hyperglycemia,
documentation of train of four monitoring following neuromus-
cular blockade, and adherence to strategies for intraoperative
low tidal volume ventilation. Additionally, the impact of the
ACT on volatile anesthetic usage will be assessed. We will also
evaluate surrogate and clinical outcomes, specifically, the inci-
dence of postoperative acute kidney injury, postoperative atrial
fibrillation, postoperative respiratory failure, postoperative delir-
ium, intraoperative awareness, surgical site infection, 30-day
hospital readmission, and 30-day mortality. Data will be obtained
from review of electronic health records and cross-referencing
of patients in the ACTFAST study with other surgical
databases, as described above. We will also track the incidence of
provider-reported intraoperative adverse events via a review of
the departmental quality improvement database. Feasibility of
implementing the ACT will be determined in part by examining
the number of potentially staffed days versus the actual number
of staffed days. Usability outcomes will include metrics such as
the median number of alerts addressed by provider and across
time.
Data analysis
Comparisons between groups will be with parametric and non-
parametric statistical tests, as appropriate. Fisher’s exact or χ2 test
will be used to evaluate primary outcome measures with regards
to the following proportions: (i) the proportion of patients with
a last-documented intraoperative core temperature greater than
36 degrees Celsius; and (ii) the proportion of patients arriving to
the post-anesthesia care unit or ICU with a blood glucose greater
than 180 mg/dl. Contingency statistical tests will be used to com-
pare occurrence of hypothermia and hyperglycemia between
groups. Secondary outcomes will be compared between groups
using χ2 or Fisher’s exact test for categorical outcomes, and two-
sided t tests with unequal variances for comparison of means. By
convention, statistical significance will be based on a two-sided
p value <0.05. All statistical testing will performed using SAS®
version 9.4 (SAS Institute Inc., Cary, North Carolina, USA).
The small subset of rare patients in the control group whose pro-
vider may be contacted by the ACT clinicians out of concern
for a significant patient safety event will be included in the
control group in an intention-to-treat analysis. A sensitivity
analysis will also be performed with inclusion of these patients
in the intervention group. The frequency and rationale for
contacting these rooms will be reported as part of our trial results.
Once the ACT intervention is executed, we anticipate several
sources of contamination effect in the control group. There is
a high likelihood of a robust Hawthorne effect due to OR clini-
cian awareness of the ACT monitoring68,69. Also, all clinicians
in the OR will eventually be included in the intervention group,
due to the unit of randomization, and will likely become aware
of the best management practices of interest in this trial. There-
fore, even on days when they do not receive ACT support,
clinicians may change their behavior, leading to overlapping
improvements in both groups over the course of the study. Addi-
tionally, utilization of the AlertWatch software by clinicians in
the ORs may increase over time. Learning effects might mani-
fest most strongly among clinicians who staff the ACT and are
therefore sensitized to the interventions and outcomes in this
study. In order to evaluate the extent of the contamination and
Hawthorne effects, we will collect baseline data for the group
of historical controls. For categorical variables, contamina-
tion will be analyzed using logistic regression with a three-level
categorical variable representing group assignment (histori-
cal cohort, control group, or intervention group); continuous
variables will be analyzed using ANCOVA or non-parametric
ANCOVA70. Additionally, we will track which operating
rooms utilize the AlertWatch system intraoperatively, and will
plan to perform a subgroup analysis to assess the effect of the
ACT in this subset of patients. We will also perform an
analysis to ensure the integrity of the study data following our
institutional transition to the Epic electronic medical record.
Within the AlertWatch system, all alerts that are generated are
automatically logged to a secure database, as are all responses
of the ACT clinicians to these alerts (Figure 3). We will
analyze these logs to determine how clinicians in the ACT moni-
tor patients, address alerts, and interact with OR clinicians, and
how OR clinicians respond to the ACT support. This data will
allow us to explore aspects of the real-world usability of the
ACT intervention related to efficiency and effectiveness, and
will complement information gathered from qualitative usability
surveys administered to department members.
Sample size and power analysis
In this study, we plan to enroll a convenience sample of
12,000 patients over the course of the study period, based
on the staffing available for the ACT and the usual daily
surgical volume of approximately 125 cases. Power analysis
was based on the two primary outcomes defined for this study,
with the following assumptions:
i) Regarding the core-temperature outcome, we conserva-
tively assumed that only 80% of Barnes-Jewish Hospital
patients have their core temperature recorded during sur-
gery. Among patients with their temperature documented,
the target for this outcome was that the ACT intervention
Page 8 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
will increase the proportion of patients whose final recorded
intraoperative temperature is above 36°C from 60% to
95%. For this calculation we assumed a standard deviation
of core temperature of 0.9 degrees Celsius for both groups,
based on an unpublished EMR audit.
ii) Regarding the primary outcome of glucose control, we
assumed that the prevalence of diabetes mellitus among
Barnes-Jewish Hospital surgical patients is about 20%,
based on our EMR data over the past 5 years. Based on
the same data, we also assumed that currently 60% of our
diabetic patients reach a blood glucose >180 mg/dl at any
point during surgery. Our goal was that the ACT interven-
tion will reduce the proportion of patients arriving to the
Post Anesthesia Care Unit (PACU) with a blood glucose
value greater than 180 mg/dl from 60% to 40%.
A statistical power calculation based on the above assumptions
was performed for each of the two primary study outcomes to
determine whether the sample size (N=12,000) allocated for
this study is adequate. The effective sample size for the study
was defined as the largest sample needed to achieve any of the
two stated outcomes. We mainly powered all targeted outcomes
to detect a difference in proportions (adjusted for contamina-
tion between the two study groups) in a completely balanced
clustered-randomized design study (24 operating rooms in each
group) using two-sided Z-test statistics. We also assumed a mini-
mum to 90% power, a significance level of 0.05, an intracluster
correlation coefficient (ICC) varying between 0.01 and 0.05 by a
small increment of 0.005, and a coefficient of variation of cluster
sizes of 0.50. Table 3 shows the required sample per operating
room as well as the overall sample needed to achieve the
study targeted outcomes. The largest sample was required for
the proportion of patients whose last recorded intraoperative
core temperature is equal to or greater than 36°C (N=11,472).
This value was sufficient for the other primary outcome.
Substudy in educational curriculum
While the primary goal of the ACTFAST-3 study is to evalu-
ate the impact of the ACT on patient care and outcomes, the
structure and environment of the ACT has allowed for the crea-
tion of a novel curriculum in perioperative medicine. The
current educational paradigm for anesthesiology residents pri-
marily focuses on the management of individual patients in
the perioperative setting. However, the substantial increase in
requirements for surgical procedures, a projected shortage of
anesthesiologists, and financial constraints in healthcare suggest
that it will eventually be infeasible for anesthesiologists to provide
the level of supervision that is currently standard in the United States
(e.g. one anesthesiologist for every one to four ORs)71. There is
currently little emphasis in anesthesiology education on process
management and multitasking and caring for multiple patients
in a complex care environment. With the support of the resi-
dency program director and departmental chair, we have revised
the residency curriculum at our institute to allow each anesthe-
sia resident to spend 2 weeks in the ACT during their final
year of residency. We plan to implement an educational cur-
riculum in perioperative telemedicine, focusing on the utiliza-
tion of healthcare system resources to optimize intraoperative
management, improve quality, and provide oversight of multiple
patients undergoing complex surgical procedures.
Adverse events and safety monitoring
We do not anticipate the occurrence of significant adverse
events during this study. However, the primary investigator and
the study team will review any adverse events identified by the
departmental quality improvement program as potentially attrib-
utable to the ACT. The occurrence of any significant adverse
events will be reported to the HRPO, and the study team and
HRPO would decide together whether to halt the trial. No formal
data-monitoring committee will used. There will be no audit of
trial conduct during the investigation, although data recorded
via the AlertWatch system will be reviewed and analyzed to
determine appropriate group allocation and inclusion in the
final analysis. No interim data analysis is planned for this
pilot trial unless unanticipated safety issues are identified.
There are no provisions for post-trial care or compensation
to patients enrolled as part of this trial, as the intervention in
the ACTFAST-3 trial involves only the addition of real-time
decision-support tools and does not change existing anesthesia
care models.
Data management
The risk of breach of confidentiality will be minimized. The
data necessary for the completion of the trial will be pro-
tected by passwords and is contained in applications that are
compliant for protected healthcare information (PHI). Alert-
Watch meets this same standard of protection. Individual clinical
Table 3. Sample size assumptions and calculations for primary outcomes.
Outcome
Current 
practice
Cluster per group(size) Target level*Intracluster 
correlation 
coefficient
Total 
Sample 
RequiredIntervention Control Intervention Control
Core temperature:
proportion
reaching 36°C
50% 24(239) 24
(239)
95% 90% 0.0375 11,472
Post- operative
Blood Glucose
180 mg/dL
60% 24(59) 24
(59)
40% 50% 0.03 2,832
See Table 1 for full explanation of outcomes.
*High contamination effects were set to reach 67% as 2 out of 3 physicians will participate in the ACT.
Page 9 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
alerts and the ACT evaluation of these alert will be documented
using an electronic data capture tool in the AlertWatch system.
Outcomes data will be stored on one of two Washington Univer-
sity Department of Anesthesiology servers (a SQL server or a
Windows file server). Only trained employees of the Department of
Anesthesiology or Barnes Jewish Healthcare are granted access to
resources on this network. Access to the contents of this study will
be further restricted to approved personnel only, using server-level
permission access (for the SQL server), or Windows folder per-
mission settings (for the file server). It is a strict policy that PHI
cannot be saved or reviewed outside of this protected environ-
ment. Whenever possible, extracts for this project will avoid
the use of this information. Data extracts can be reconnected
to PHI using a special, non-PHI primary key, which this group
has successfully used with previous studies.
Strengths and limitations
The ACTFAST pilot study has important strengths. It is a ran-
domized clinical trial conducted in a high volume, real world
clinical setting and can be conducted efficiently, as many com-
ponents of the proposed study are incorporated into existing
infrastructures and processes at Washington University. This
includes access to existing information technology resources and
to established and ongoing registries (SATISFY-SOS, NSQIP
and STS). The data required for analysis of the primary out-
come measures are routinely recorded on every patient undergo-
ing surgery at our institution, and the databases used for analysis
of secondary surrogate and clinical outcomes also all have high
levels of data fidelity.
Randomization of anesthesiology care teams can be easily
implemented, and the process for providing feedback alerts
does not require any advanced preparation on the part of clini-
cians working in the OR. These clinicians will participate in
the ACTFAST trial in the course of their routine clinical work,
and the impact on overall workflow and workload will be mini-
mized through the testing in our first phase of the study56. We
anticipate that it will be feasible to staff the ACT during the pilot
RCT. The feasibility is enhanced by participation of a highly
committed cadre of attending anesthesiologists and all of
the residents in the anesthesiology department, as well as an
experienced team of investigators that has established a track
record of collaboration and completion of major clinical trials.
The following limitations should be considered. The Alert-
Watch software is currently available on all computers in
the OR, and in-room provider utilization of AlertWatch may
increase over the course of the study. In response, we plan to
conduct a subgroup analysis with user log-in data to ascertain
the impact of in-room software utilization, defined as docu-
mentation of intraoperative provider log-in to the AlertWatch
system. Also, the ACTFAST study will be vulnerable both to
Hawthorne and contamination effects. While we do not think
that these effects can be eliminated, we have considered
how best to account for them in the analyses. An important
constraint and possible source of bias will be that it will not be
possible to ensure blinding of OR clinicians as any communica-
tion from the ACT will inform them that their operating room
is in the intervention group on that day72. However, clinicians
outside of the OR, and the researchers responsible for extracting
data, will be blinded to group assignment.
Another potential source of bias involves the existing surgi-
cal databases that will be used during analysis (i.e. STS, NSQIP,
SATISFY-SOS). These registries themselves may be biased
according to which patients choose to participate, with indi-
vidual patients’ outcomes impacting their willingness or ability
to provide reliable information, and which patients are contacta-
ble. We have been attempting to mitigate this source of bias by
employing three modalities (e-mail, telephone and mail) to reach
patients postoperatively in one such study62. Overall, the regis-
tries have impressive response rates, and there does not appear
to be systematic bias in any of these registries based on baseline
patient characteristics. Therefore, we expect our data sources
to be robust, with minimal deficiencies.
Ethics and dissemination
This study was approved by the HRPO at Washington Univer-
sity (St. Louis, MI, USA, protocol number 201603038). This
protocol is written in compliance with the Standard Protocol
Items: Recommendations for Interventional Trials (SPIRIT)
checklist with consideration of the Consolidated Standards of
Reporting Trials (CONSORT) guidelines73,74.
If the results of the pilot ACTFAST-3 trial show benefit, the
pilot study will likely be replicated as a larger, multicenter study
for further validation that this intervention remains beneficial
and that it is feasible to institute at other centers. We also antici-
pate the expansion of the ACT into the surrounding healthcare
facilities within our hospital system. Larger trials could focus
on expanded clinical and patient-reported outcomes (e.g. death,
renal failure, delirium, duration of mechanical ventilation, inten-
sive care length of stay, post-discharge disposition, postoperative
falls, return to work, disability-free survival). The ACT infra-
structure could also be used to explore current controversies
in perioperative care by testing candidate experimental
interventions (e.g., fluid management strategies, blood trans-
fusion triggers). We envision that national implementation
of the ACT concept would occur, which would be
comparable to the path that similar programs for intensive care
units have followed.
Any significant changes to the protocol or the analysis plan
during the trial will be communicated directly to the Washing-
ton University HRPO, as well as via update of the ACTFAST-3
registration at clinicaltrials.gov (ClinicalTrials.gov Identifier:
NCT02830126). We also plan to publish any modifications
made to this protocol during dissemination of the results of the
trial. Authorship for the final trial data will be determined in
accordance with International Committee of Medical Journal
Editors (ICMJE) guidelines.
Data sharing
Data from the ACTFAST-3 trial will be made available for analy-
sis in compliance with the recommendations of the ICMJE75. For
this study, individual participant data that underlie the results of
Page 10 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
the trial will be made available after appropriate deidentifica-
tion, along with the study protocol and statistical analysis plan.
We plan to make this information accessible to researchers who
provide a methodologically appropriate proposal for the purpose
of achieving the aims of that proposal. Data will be available
beginning 9 months and ending 36 months following trial publi-
cation at a third-party website. Data requestors will need to sign
a data access agreement to gain access to trial data. Proposals
should be directed to avidanm@wustl.edu.
Conclusions
Despite aggressive efforts aimed to improve the quality of peri-
operative care, the risk of morbidity and mortality following a
major surgical procedure remains substantial. In this protocol,
we describe a pilot pragmatic, randomized, controlled trial in
intraoperative telemedicine that examines the ability of a novel
system of real-time feedback to improve adherence to periopera-
tive best care practices. We hypothesize that the implementation
of the ACT will be feasible and that it will increase clinician
compliance with clinical practice standards. The development of
the ACT, as described in this protocol, will also lay the ground-
work for a subsequent large randomized controlled trial exam-
ining the utility of the ACT in improving patient outcomes
following surgical procedures.
The findings from the trial will be disseminated in the form
of posters and oral presentations at scientific conferences, as
well as publications in peer-reviewed journals. Updates and
results of the study will be available at https://clinicaltrials.gov/
ct2/show/NCT02830126.
Data availability
No data is associated with this study.
Competing interests
No competing interests were disclosed.
Grant information
The ACTFAST-3 project, including this protocol, has been funded
by a grant from the Agency for Healthcare Research and Quality
(R21 HS24581-01).
The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Acknowledgements
Members of the ACTFAST study group are as follows: Stephen
Gregory, Teresa M. Murray-Torres, Bradley A. Fritz, Arbi Ben
Abdallah, Daniel L. Helsten, Troy S. Wildes, Anshuman Sharma,
Yixin Chen*, Mary Politi*, Alex Kronzer*, Bernadette Henrichs*,
Brian A. Torres*, Sherry McKinnon*, Thaddeus Budelier*,
Walter Boyle*, Bruce Hall*, Benjamin Kozower*, Sachin
Kheterpal*, Michael S. Avidan
*Contributor
References
Supplementary material
Supplementary File 1: Definitions of postoperative surrogate measures for the ACTFAST-3 clinical trial.
Click here to access the data.
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Page 13 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
Open Peer Review
Current Referee Status:
Version 1
29 June 2018Referee Report
https://doi.org/10.5256/f1000research.16217.r35259
 , Morten H. Bestle Christian Ari Dalby Sørensen
DepartmentofAnaesthesiologyandIntensiveCare,NordsjællandsHospital-Hillerød,Universityof
Copenhagen,Hillerød,Denmark
Thankyoufortheopportunitytoreviewthispaper.
Inmanuscripttheauthorsdescribeapilotrandomizedcontrolledtrialthataimstodemonstratethe
implementationandutilityoftheanesthesiologycontroltower(ACT)inimprovingadherencetobestcare
practiceswhencomparedtoenhancedusualcare.Theauthorsproposetorandomize12,000patients
overthestudyperiod,withapproximately6,000patientsinthecontrolarmand6,000patientsinthe
interventionarm.Cliniciansgroupedintheinterventionarmwillbeprovidedwithreal-timefeedback
basedontheavailableelectronicresources.Primaryandsecondaryoutcomeswillbecomparedtothe
controlgroup.

Page3paragraph2:Theauthorsstatethatsomeoftherisksofperioperativeadverseeventsmaybea
manifestationofeitherunderlyingpatientpathologyorthecomplexityofthesurgicalprocedureitself.The
authorscouldconsiderelaboratingthatstatementinmoredetails.Howbigistheproportionofunderlying
patientpathologyandcomplexsurgicalprocedures?
Page3paragraph2:Theauthorsstatethatpriorstudieshavedocumentedthatdeviationfrom
evidence-basedstandardsofcareiscommon,andthatdeviationresultsinpoorerpatientoutcomes.
Whichoutcomeshasbeenthefocusofpriorstudies?
Page3paragraph8:Whyhaveyouchosentheseoutcomestobetheprimaryoutcomes?
Page4paragraph2:Theauthorsmentionsthatonlypatientsundergoingsurgerybetween7:00AMand
4:00PMMondaythroughFridaywillbeincluded.Haveyouconsideredtherecouldbeadifference
betweenelectiveandacutesurgery.Arecliniciansmorepronetofollowclinicalguidelinesatdaytime
comparedtonighttime?
Is the rationale for, and objectives of, the study clearly described?
Yes
Is the study design appropriate for the research question?
Yes
Are sufficient details of the methods provided to allow replication by others?
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Are sufficient details of the methods provided to allow replication by others?
Yes
Are the datasets clearly presented in a useable and accessible format?
Yes
Nocompetinginterestsweredisclosed.Competing Interests:
Referee Expertise:Clinicalresearchinintensivecaremedicine
We have read this submission. We believe that we have an appropriate level of expertise to
confirm that it is of an acceptable scientific standard.
AuthorResponse16Jul2018
,WashingtonUniversityinSaintLouis,USATeresa Murray-Torres
Thankyoufortakingthetimetoreviewourmanuscriptandprovidefeedback.Wehavesubmitteda
revisedversionofourprotocoladdressingthereviewer'scomments.Ourchangesinresponseto
therefereeareasfollows:
Page3paragraph2:Theauthorsstatethatsomeoftherisksofperioperativeadverseeventsmay
beamanifestationofeitherunderlyingpatientpathologyorthecomplexityofthesurgical
procedureitself.Theauthorscouldconsiderelaboratingthatstatementinmoredetails.Howbigis
theproportionofunderlyingpatientpathologyandcomplexsurgicalprocedures?
We have expanded this sentence to highlight the development of complex surgical risk calculators
to evaluate perioperative risk using both patient pathology and the surgical procedure.
Page3paragraph2:Theauthorsstatethatpriorstudieshavedocumentedthatdeviationfrom
evidence-basedstandardsofcareiscommon,andthatdeviationresultsinpoorerpatient
outcomes.Whichoutcomeshasbeenthefocusofpriorstudies?
We have updated this section to highlight that deviation from evidence-based standards of care is
ubiquitous across a variety of health care settings and may be associated with an increase in a
number of adverse patient outcomes, including surgical site infection, pneumonia, and mortality.
Page3paragraph8:Whyhaveyouchosentheseoutcomestobetheprimaryoutcomes?
These outcomes were selected because they are routinely and reliably tracked in the electronic
medical record and optimal perioperative management of temperature and blood glucose is known
to influence clinical outcome. We have added this information to the manuscript.
Page4paragraph2:Theauthorsmentionsthatonlypatientsundergoingsurgerybetween7:00AM
and4:00PMMondaythroughFridaywillbeincluded.Haveyouconsideredtherecouldbea
differencebetweenelectiveandacutesurgery.Arecliniciansmorepronetofollowclinical
guidelinesatdaytimecomparedtonighttime?
We do recognize that this is a limitation of our current study, but we have attempted to account for
any variation in guideline compliance during off-hours by equally applying time exclusion criteria to
both our control and intervention ORs. Additionally, we have designated that patients having a
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both our control and intervention ORs. Additionally, we have designated that patients having a
surgical procedure with >50% of the operative time occurring outside of ACT hours will be
excluded from analysis. Evaluating variations in compliance with perioperative guidelines outside
of normal working hours is an interesting proposal, and may be considered as part of a future
expansion of the ACT concept.
N/A.Competing Interests:
05 June 2018Referee Report
https://doi.org/10.5256/f1000research.16217.r34272
 , Leif Saager Michael Burns
UniversityofMichigan,AnnArbor,MI,USA
Thankyouverymuchfortheopportunitytoreviewthisinnovativeandtimelysubmission.
Themanuscriptiseloquentlywrittenandthestudyprotocolcomprehensivelydescribed;ourcomments
arethereforefewandminor.
Inthisarticletheauthorspresentastudyprotocolforarandomizedcontroltrialinthefieldof
intraoperativeclinicaldecisionsupport.Theauthorsproposetorandomize12,000patientstoeither
intraoperativeclinicaldecisionsupportorenhancedintraoperativeclinicaldecisionsupportbyutilizinga
novelAnesthesiaControlTower(ACT)concept.Throughoutthearticletheauthorsthoroughlypresent
theirpragmaticstudywithadequatedetailsandathoughtfulpatient-centricapproach.Theiridentification
ofthecomplexityoftheanestheticpracticeandcognitiverequirementsiswellfounded,andtheir
referencetotheICUremotemonitoringsystemsisestablished.

Onpage3,paragraph1,theauthorsstatethat“10-40%ofpatientswillexperiencesomesortof
postoperativesurgicalcomplication”.Thecitationsmostlyrefertoelderlyand/orhigh-risksurgical
patients.Perhapstheauthorscouldconsideraddingareferenceforageneralsurgicalpopulation.
Onpage4,theauthorsstatetheACTwillfunctiononlyondayswithatleast2anesthesiaproviders
available.CouldthisintroducebiasintothestudyasonORdayswithhighvolume,orcomplexcases
requiringlowerstaffingratios,theavailabilityofstafffortheACTwouldbelesslikely?
Onpage7,paragraph2,theauthorsstateananticipatedtransitioninelectronichealthrecords.Inour
experience,implementationofanewrecordkeepingsystemcanincreasecognitiveload,documentation
errors,andlagsindataacquisition.Ourconcernwouldbeapossiblecompromiseofstudydata.Dothe
authorshaveacontingency/transitionplanavailable?
Onpage8,theauthorsbasethesamplesizecalculationoncoretemperaturemeasurements.Therestof
themanuscriptislessspecificastothesiteoftemperaturemeasurement.Willonlycoretemperaturesbe
utilizedinthisstudy?
Onpage9,paragraph2,theauthorsproposeaninnovativeeducationalcurriculum.Wouldtheauthors
considerprovidingmoredetailontheimplementationandevaluationofthiscomponent?
InTable2,theauthorsdescribesecondaryoutcomes.Woulditbepossibletoaddanappendixtoprovide
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InTable2,theauthorsdescribesecondaryoutcomes.Woulditbepossibletoaddanappendixtoprovide
definitionsfortheseparametersorreferenceNSQIP/STSdocumentsasthesourceofthesedefinitions?
Is the rationale for, and objectives of, the study clearly described?
Yes
Is the study design appropriate for the research question?
Yes
Are sufficient details of the methods provided to allow replication by others?
Yes
Are the datasets clearly presented in a useable and accessible format?
Yes
Nocompetinginterestsweredisclosed.Competing Interests:
We have read this submission. We believe that we have an appropriate level of expertise to
confirm that it is of an acceptable scientific standard.
AuthorResponse16Jul2018
,WashingtonUniversityinSaintLouis,USATeresa Murray-Torres
Thankyoufortakingthetimetoreviewourmanuscriptandprovidefeedback.Wehavesubmitteda
revisedversionofourprotocoladdressingthereviewer'scomments.Ourchangesinresponseto
therefereeareasfollows:
Onpage3,paragraph1,theauthorsstatethat“10-40%ofpatientswillexperiencesomesortof
postoperativesurgicalcomplication”.Thecitationsmostlyrefertoelderlyand/orhigh-risksurgical
patients.Perhapstheauthorscouldconsideraddingareferenceforageneralsurgicalpopulation.
We have updated this statistic to “5-40%,” including a reference examining NSQIP complication
rates in patients undergoing orthopedic surgical procedures, primarily elective total joint
procedures.
Onpage4,theauthorsstatetheACTwillfunctiononlyondayswithatleast2anesthesiaproviders
available.CouldthisintroducebiasintothestudyasonORdayswithhighvolume,orcomplex
casesrequiringlowerstaffingratios,theavailabilityofstafffortheACTwouldbelesslikely?
We have attempted to minimize the risk of bias secondary to ACT staff availability by performing
OR randomization each day with a 1:1 allocation. We anticipate that this will allow for any staffing
variations to equally affect both the intervention and control groups to minimize bias. We have
updated the manuscript to specifically address this point.
Onpage7,paragraph2,theauthorsstateananticipatedtransitioninelectronichealthrecords.In
ourexperience,implementationofanewrecordkeepingsystemcanincreasecognitiveload,
documentationerrors,andlagsindataacquisition.Ourconcernwouldbeapossiblecompromise
ofstudydata.Dotheauthorshaveacontingency/transitionplanavailable?
Fortunately, the data required to evaluate the primary and secondary outcomes in this study is
Page 17 of 18
F1000Research 2018, 7:623 Last updated: 29 JAN 2019
Fortunately, the data required to evaluate the primary and secondary outcomes in this study is
electronically populated from patient monitoring data (temperature) or autopopulated into the
electronic medical record following measurement (glucose). Although we do not anticipate any
significant difficulties with ensuring the integrity of the study data, we do plan to perform an
analysis to confirm that there has been no significant compromise of study data.
Onpage8,theauthorsbasethesamplesizecalculationoncoretemperaturemeasurements.The
restofthemanuscriptislessspecificastothesiteoftemperaturemeasurement.Willonlycore
temperaturesbeutilizedinthisstudy?
Yes, we plan to only utilize core temperature in our analysis of temperature as a primary outcome.
This has been updated in the manuscript.
Onpage9,paragraph2,theauthorsproposeaninnovativeeducationalcurriculum.Wouldthe
authorsconsiderprovidingmoredetailontheimplementationandevaluationofthiscomponent?
At present time, we are still actively developing the educational curriculum for residents rotating
through the ACT. The specific endpoints for the protocol assessing this substudy are not yet
defined.
InTable2,theauthorsdescribesecondaryoutcomes.Woulditbepossibletoaddanappendixto
providedefinitionsfortheseparametersorreferenceNSQIP/STSdocumentsasthesourceof
thesedefinitions?
We have added an appendix to define the postoperative surrogate outcomes for the study.
Nocompetinginterestsweredisclosed.Competing Interests:
ThebenefitsofpublishingwithF1000Research:
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Page 18 of 18
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... In addition, because AWOB retrieves vital signs directly from the monitoring network, AWOB detected nine severe cases of hemorrhage that MEWS did not detect [8]. An overview display mode used in the anesthesia, obstetrics and nursing workrooms allowed the simultaneous monitoring of multiple patients, perhaps in a manner more analogous to a control tower maintaining vigilance of multiple aircraft [30,31]. AWOB has been well accepted by clinicians in Labor & Delivery with a majority of providers feeling the system should remain in use and that it improved patient safety [9]. ...
Article
Full-text available
Background Multifunction surveillance alerting systems have been found to be beneficial for the operating room and labor and delivery. This paper describes a similar system developed for in-hospital acute care environments, AlertWatch Acute Care (AWAC). Results A decision support surveillance system has been developed which extracts comprehensive electronic health record (EHR) data including live data from physiologic monitors and ventilators and incorporates them into an integrated organ icon-based patient display. Live data retrieved from the hospitals network are processed by presenting scrolling median values to reduce artifacts. A total of 48 possible alerts are generated covering a broad range of critical patient care concerns. Notification is achieved by paging or texting the appropriated member of the critical care team. Alerts range from simple out of range values to more complex programing of impending Ventilator Associated Events, SOFA, qSOFA, SIRS scores and process of care reminders for the management of glucose and sepsis. As with similar systems developed for the operating room and labor and delivery, there are green, yellow, and red configurable ranges for all parameters. A census view allows surveillance of an entire unit with flashing or text to voice alerting and enables detailed information by windowing into an individual patient view including live physiologic waveforms. The system runs via web interface on desktop as well as mobile devices, with iOS native app available, for ease of communication from any location. The goal is to improve safety and adherence to standard management protocols. Conclusions AWAC is designed to provide a high level surveillance view for multi-bed hospital units with varying acuity from standard floor patients to complex ICU care. Alerts are generated by algorithms running in the background and automatically notify the selected member of the patients care team. Its value has been demonstrated for low acuity patients, further study is required to determine its effectiveness in high acuity patients.
... Video cameras and monitors have been installed in each of these bays to allow for remote monitoring, as well as two-way video communication during the interaction phase. The telemedicine center is staffed by attending anesthesiologists along with certified registered nurse anesthetists (CRNAs), anesthesiology residents, and student registered nurse anesthetists (SRNAs), and is currently providing evidence-based support to clinicians in the operating rooms [11][12][13][14] . ...
Article
Introduction: The post-anesthesia care unit (PACU) is a clinical area designated for patients recovering from invasive procedures. There are typically several geographically dispersed PACUs within hospitals. Patients in the PACU can be unstable and at risk for complications. However, clinician coverage and patient monitoring in PACUs is not well regulated and might be sub-optimal. We hypothesize that a telemedicine center for the PACU can improve key PACU functions. Objectives: The objective of this study is to demonstrate the potential utility and acceptability of a telemedicine center to complement the key functions of the PACU. These include participation in hand-off activities to and from the PACU, detection of physiological derangements, identification of symptoms requiring treatment, recognition of situations requiring emergency medical intervention, and determination of patient readiness for PACU discharge. Methods and analysis: This will be a single center prospective before-and-after proof-of-concept study. Adults (18 years and older) undergoing elective surgery and recovering in two selected PACU bays will be enrolled. During the initial three-month observation phase, clinicians in the telemedicine center will not communicate with clinicians in the PACU, unless there is a specific patient safety concern. During the subsequent three-month interaction phase, clinicians in the telemedicine center will provide structured decision support to PACU clinicians. The primary outcome will be time to PACU discharge readiness determination in the two study phases. The attitudes of key stakeholders towards the telemedicine center will be assessed. Other outcomes will include detection of physiological derangements, complications, adverse symptoms requiring treatments, and emergencies requiring medical intervention. Registration: This trial is registered on clinicaltrials.gov, NCT04020887 (16 th July 2019).
... We are currently investigating the usability of the Anesthesiology Control Tower, and are also conducting a randomised trial to assess whether the Control Tower intervention reduces the fraction of patients with hypothermia or hypoglycaemia at the end of surgery. 26,27 We anticipate that predictions from our model could help supervising anaesthesiologists and telemedicine clinicians identify which patients are at the highest risk of complications and would potentially benefit from their attention. The model can be run repeatedly during surgery (perhaps every 10 or 15 min). ...
Article
Full-text available
Background: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. Methods: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. Results: Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). Conclusions: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.
... Just as an air traffic control tower monitors individual aircraft and delivers additional information and alerts to the pilot and co-pilot, the ACT functions as a clinical support system for teams of anesthesia clinicians, engaging with them to assist in providing safe, effective, and efficient care for their patients [36]. The ACT is currently being evaluated in the form of a proofof-principle pragmatic trial (NCT02830126) [37]. ...
Preprint
BACKGROUND Major postoperative morbidity and mortality remain common despite efforts to improve patient outcomes. Health information technologies, such as decision support systems, have the potential to advance the standard of perioperative patient care. Failure to evaluate the usability of these technologies and barriers to their implementation can limit their acceptance within health systems. OBJECTIVE This manuscript describes the usability and acceptability of and systematic process for developing and adapting an innovative telemedicine based clinical support system, the Anesthesiology Control Tower. It also reports stakeholders’ perceptions of the barriers and facilitators the implementation of the intervention. METHODS Three phases of testing were conducted in an iterative manner in order to evaluate both the individual components of the Anesthesiology Control Tower and their integration as a whole. Phase 1 testing employed a “think-aloud” protocol analysis to identify surface level usability problems with individual software components of the ACT, in addition to the entirety of the structure. Phase 2 testing involved an extended qualitative and quantitative in-situ usability analysis. Phase 3 sought to identify major barriers and facilitators to implementation of the ACT through semi-structured interviews with key stakeholders. RESULTS Numerous usability problems with the software components of the ACT were identified in the Phase 1 and Phase 2 usability testing sessions. In response to these problems, seven iterations of the ACT software platform were developed. Initial satisfaction with the ACT, as measured by standardized measures, was below commonly accepted cutoffs for these measures. Satisfaction improved to acceptable levels over the course of revision and testing. A number of barriers to implementation were identified and addressed during the refinement of the ACT intervention. CONCLUSIONS The Anesthesiology Control Tower system has the potential to improve the standard of perioperative anesthesia care. Through our thorough and iterative usability testing process and stakeholder assessment of barriers and facilitators, we were able to maximize the acceptability of this novel technology, thus improving our ability to implement this innovation into the model of care for perioperative medicine.
Article
Background: Delirium is an acute neuropsychological disorder that is common in hospitalised patients. It can be distressing to patients and carers and it is associated with serious adverse outcomes. Treatment options for established delirium are limited and so prevention of delirium is desirable. Non-pharmacological interventions are thought to be important in delirium prevention. OBJECTIVES: To assess the effectiveness of non-pharmacological interventions designed to prevent delirium in hospitalised patients outside intensive care units (ICU). Search methods: We searched ALOIS, the specialised register of the Cochrane Dementia and Cognitive Improvement Group, with additional searches conducted in MEDLINE, Embase, PsycINFO, CINAHL, LILACS, Web of Science Core Collection, ClinicalTrials.gov and the World Health Organization Portal/ICTRP to 16 September 2020. There were no language or date restrictions applied to the electronic searches, and no methodological filters were used to restrict the search. Selection criteria: We included randomised controlled trials (RCTs) of single and multicomponent non-pharmacological interventions for preventing delirium in hospitalised adults cared for outside intensive care or high dependency settings. We only included non-pharmacological interventions which were designed and implemented to prevent delirium. DATA COLLECTION AND ANALYSIS: Two review authors independently examined titles and abstracts identified by the search for eligibility and extracted data from full-text articles. Any disagreements on eligibility and inclusion were resolved by consensus. We used standard Cochrane methodological procedures. The primary outcomes were: incidence of delirium; inpatient and later mortality; and new diagnosis of dementia. We included secondary and adverse outcomes as pre-specified in the review protocol. We used risk ratios (RRs) as measures of treatment effect for dichotomous outcomes and between-group mean differences for continuous outcomes. The certainty of the evidence was assessed using GRADE. A complementary exploratory analysis was undertaker using a Bayesian component network meta-analysis fixed-effect model to evaluate the comparative effectiveness of the individual components of multicomponent interventions and describe which components were most strongly associated with reducing the incidence of delirium. Main results: We included 22 RCTs that recruited a total of 5718 adult participants. Fourteen trials compared a multicomponent delirium prevention intervention with usual care. Two trials compared liberal and restrictive blood transfusion thresholds. The remaining six trials each investigated a different non-pharmacological intervention. Incidence of delirium was reported in all studies. Using the Cochrane risk of bias tool, we identified risks of bias in all included trials. All were at high risk of performance bias as participants and personnel were not blinded to the interventions. Nine trials were at high risk of detection bias due to lack of blinding of outcome assessors and three more were at unclear risk in this domain. Pooled data showed that multi-component non-pharmacological interventions probably reduce the incidence of delirium compared to usual care (10.5% incidence in the intervention group, compared to 18.4% in the control group, risk ratio (RR) 0.57, 95% confidence interval (CI) 0.46 to 0.71, I2 = 39%; 14 studies; 3693 participants; moderate-certainty evidence, downgraded due to risk of bias). There may be little or no effect of multicomponent interventions on inpatient mortality compared to usual care (5.2% in the intervention group, compared to 4.5% in the control group, RR 1.17, 95% CI 0.79 to 1.74, I2 = 15%; 10 studies; 2640 participants; low-certainty evidence downgraded due to inconsistency and imprecision). No studies of multicomponent interventions reported data on new diagnoses of dementia. Multicomponent interventions may result in a small reduction of around a day in the duration of a delirium episode (mean difference (MD) -0.93, 95% CI -2.01 to 0.14 days, I2 = 65%; 351 participants; low-certainty evidence downgraded due to risk of bias and imprecision). The evidence is very uncertain about the effect of multicomponent interventions on delirium severity (standardised mean difference (SMD) -0.49, 95% CI -1.13 to 0.14, I2=64%; 147 participants; very low-certainty evidence downgraded due to risk of bias and serious imprecision). Multicomponent interventions may result in a reduction in hospital length of stay compared to usual care (MD -1.30 days, 95% CI -2.56 to -0.04 days, I2=91%; 3351 participants; low-certainty evidence downgraded due to risk of bias and inconsistency), but little to no difference in new care home admission at the time of hospital discharge (RR 0.77, 95% CI 0.55 to 1.07; 536 participants; low-certainty evidence downgraded due to risk of bias and imprecision). Reporting of other adverse outcomes was limited. Our exploratory component network meta-analysis found that re-orientation (including use of familiar objects), cognitive stimulation and sleep hygiene were associated with reduced risk of incident delirium. Attention to nutrition and hydration, oxygenation, medication review, assessment of mood and bowel and bladder care were probably associated with a reduction in incident delirium but estimates included the possibility of no benefit or harm. Reducing sensory deprivation, identification of infection, mobilisation and pain control all had summary estimates that suggested potential increases in delirium incidence, but the uncertainty in the estimates was substantial. Evidence from two trials suggests that use of a liberal transfusion threshold over a restrictive transfusion threshold probably results in little to no difference in incident delirium (RR 0.92, 95% CI 0.62 to 1.36; I2 = 9%; 294 participants; moderate-certainty evidence downgraded due to risk of bias). Six other interventions were examined, but evidence for each was limited to single studies and we identified no evidence of delirium prevention. AUTHORS' CONCLUSIONS: There is moderate-certainty evidence regarding the benefit of multicomponent non-pharmacological interventions for the prevention of delirium in hospitalised adults, estimated to reduce incidence by 43% compared to usual care. We found no evidence of an effect on mortality. There is emerging evidence that these interventions may reduce hospital length of stay, with a trend towards reduced delirium duration, although the effect on delirium severity remains uncertain. Further research should focus on implementation and detailed analysis of the components of the interventions to support more effective, tailored practice recommendations.
Article
Background Clinical decision support systems and telemedicine for remote monitoring can together support surgical patients' intraoperative decision-making and care management. However, there has been limited investigation on patient perspectives about advanced health information technology use in intraoperative settings, particularly within an intraoperative telemedicine setting (eOR). Purpose Our study objectives were: (1) to identify participant-rated items contributing to patient attitudes, beliefs, and level of comfort with eOR monitoring; (2) to highlight barriers and facilitators to eOR use; and (3) to develop guidelines for eOR implementation that improve patient buy-in. Methods We surveyed 324 individuals representing surgical patients across the United States using Amazon Mechanical Turk, an online platform supporting internet-based work. The structured survey questions examined the level of agreement and comfort with eOR for remote patient monitoring. We calculated descriptive statistics for demographic variables and performed a Wilcoxon matched-pairs signed-rank test to assess whether participants were more comfortable with familiar clinicians from local hospitals or health systems monitoring their health and safety status during surgery than clinicians from hospitals or health systems in other regions or countries. We also analyzed open-ended survey responses using a thematic approach informed by an eight-dimensional socio-technical model. Results Participants’ average age was 34.07 (SD = 10.11). Most were white (80.9%), male (57.1%), and had a high school degree or more (88.3%). Participants reported a higher level of comfort with clinicians they knew monitoring their health and safety than clinicians they did not know, even within the same healthcare system (z = -4.012, p<.001). They reported significantly higher comfort levels with clinicians within the same hospital or health system in the United States than those in a different country (z = -10.230, p<.001). Facilitators and barriers to eOR remote monitoring were prevalent across four socio-technical dimensions: 1) organizational policies, procedures, environment, and culture; 2) people; 3) workflow and communication; and 4) hardware and software. Facilitators to eOR use included perceptions of improved patient safety through a safeguard system and perceptions of streamlined care. Barriers included fears of incorrect eOR patient assessments, decision-making conflicts between care teams, and technological malfunctions. Conclusions Participants expressed significant support for intraoperative telemedicine use and greater comfort with local telemedicine systems instead of long-distance telemedicine systems. Reservations centered on organizational policies, procedures, environment, culture; people; workflow and communication; and hardware and software. To improve the acceptability of remote monitoring by an OR telemedicine team and address these concerns, we highlight evidence-based guidelines applicable to telemedicine use within the context of OR workflow. Guidelines include backup plans for technical challenges, rigid care, and privacy standards, and patient education to increase understanding of telemedicine’s potential to improve patient care.
Article
Background: Delirium is an acute neuropsychological disorder that is common in hospitalised patients. It can be distressing to patients and carers and it is associated with serious adverse outcomes. Treatment options for established delirium are limited and so prevention of delirium is desirable. Non-pharmacological interventions are thought to be important in delirium prevention. OBJECTIVES: To assess the effectiveness of non-pharmacological interventions designed to prevent delirium in hospitalised patients outside intensive care units (ICU). Search methods: We searched ALOIS, the specialised register of the Cochrane Dementia and Cognitive Improvement Group, with additional searches conducted in MEDLINE, Embase, PsycINFO, CINAHL, LILACS, Web of Science Core Collection, ClinicalTrials.gov and the World Health Organization Portal/ICTRP to 16 September 2020. There were no language or date restrictions applied to the electronic searches, and no methodological filters were used to restrict the search. Selection criteria: We included randomised controlled trials (RCTs) of single and multicomponent non-pharmacological interventions for preventing delirium in hospitalised adults cared for outside intensive care or high dependency settings. We only included non-pharmacological interventions which were designed and implemented to prevent delirium. DATA COLLECTION AND ANALYSIS: Two review authors independently examined titles and abstracts identified by the search for eligibility and extracted data from full-text articles. Any disagreements on eligibility and inclusion were resolved by consensus. We used standard Cochrane methodological procedures. The primary outcomes were: incidence of delirium; inpatient and later mortality; and new diagnosis of dementia. We included secondary and adverse outcomes as pre-specified in the review protocol. We used risk ratios (RRs) as measures of treatment effect for dichotomous outcomes and between-group mean differences for continuous outcomes. The certainty of the evidence was assessed using GRADE. A complementary exploratory analysis was undertaker using a Bayesian component network meta-analysis fixed-effect model to evaluate the comparative effectiveness of the individual components of multicomponent interventions and describe which components were most strongly associated with reducing the incidence of delirium. Main results: We included 22 RCTs that recruited a total of 5718 adult participants. Fourteen trials compared a multicomponent delirium prevention intervention with usual care. Two trials compared liberal and restrictive blood transfusion thresholds. The remaining six trials each investigated a different non-pharmacological intervention. Incidence of delirium was reported in all studies. Using the Cochrane risk of bias tool, we identified risks of bias in all included trials. All were at high risk of performance bias as participants and personnel were not blinded to the interventions. Nine trials were at high risk of detection bias due to lack of blinding of outcome assessors and three more were at unclear risk in this domain. Pooled data showed that multi-component non-pharmacological interventions probably reduce the incidence of delirium compared to usual care (10.5% incidence in the intervention group, compared to 18.4% in the control group, risk ratio (RR) 0.57, 95% confidence interval (CI) 0.46 to 0.71, I2 = 39%; 14 studies; 3693 participants; moderate-certainty evidence, downgraded due to risk of bias). There may be little or no effect of multicomponent interventions on inpatient mortality compared to usual care (5.2% in the intervention group, compared to 4.5% in the control group, RR 1.17, 95% CI 0.79 to 1.74, I2 = 15%; 10 studies; 2640 participants; low-certainty evidence downgraded due to inconsistency and imprecision). No studies of multicomponent interventions reported data on new diagnoses of dementia. Multicomponent interventions may result in a small reduction of around a day in the duration of a delirium episode (mean difference (MD) -0.93, 95% CI -2.01 to 0.14 days, I2 = 65%; 351 participants; low-certainty evidence downgraded due to risk of bias and imprecision). The evidence is very uncertain about the effect of multicomponent interventions on delirium severity (standardised mean difference (SMD) -0.49, 95% CI -1.13 to 0.14, I2=64%; 147 participants; very low-certainty evidence downgraded due to risk of bias and serious imprecision). Multicomponent interventions may result in a reduction in hospital length of stay compared to usual care (MD -1.30 days, 95% CI -2.56 to -0.04 days, I2=91%; 3351 participants; low-certainty evidence downgraded due to risk of bias and inconsistency), but little to no difference in new care home admission at the time of hospital discharge (RR 0.77, 95% CI 0.55 to 1.07; 536 participants; low-certainty evidence downgraded due to risk of bias and imprecision). Reporting of other adverse outcomes was limited. Our exploratory component network meta-analysis found that re-orientation (including use of familiar objects), cognitive stimulation and sleep hygiene were associated with reduced risk of incident delirium. Attention to nutrition and hydration, oxygenation, medication review, assessment of mood and bowel and bladder care were probably associated with a reduction in incident delirium but estimates included the possibility of no benefit or harm. Reducing sensory deprivation, identification of infection, mobilisation and pain control all had summary estimates that suggested potential increases in delirium incidence, but the uncertainty in the estimates was substantial. Evidence from two trials suggests that use of a liberal transfusion threshold over a restrictive transfusion threshold probably results in little to no difference in incident delirium (RR 0.92, 95% CI 0.62 to 1.36; I2 = 9%; 294 participants; moderate-certainty evidence downgraded due to risk of bias). Six other interventions were examined, but evidence for each was limited to single studies and we identified no evidence of delirium prevention. AUTHORS' CONCLUSIONS: There is moderate-certainty evidence regarding the benefit of multicomponent non-pharmacological interventions for the prevention of delirium in hospitalised adults, estimated to reduce incidence by 43% compared to usual care. We found no evidence of an effect on mortality. There is emerging evidence that these interventions may reduce hospital length of stay, with a trend towards reduced delirium duration, although the effect on delirium severity remains uncertain. Further research should focus on implementation and detailed analysis of the components of the interventions to support more effective, tailored practice recommendations.
Article
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Introduction: Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. With advances in technology, there is an opportunity to research the utility of a telemedicine-based control center for anesthesia clinicians that assess risk, diagnoses negative patient trajectories, and implements evidence-based practices. Objectives: The primary objective of this trial is to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Secondary objectives are to determine whether the ACT improves perioperative quality of care metrics including management of temperature, mean arterial pressure, mean airway pressure with mechanical ventilation, blood glucose, anesthetic concentration, antibiotic redosing, and efficient fresh gas flow. Methods and analysis: We are conducting a single center, randomized, controlled, phase 3 pragmatic clinical trial. A total of 58 operating rooms are randomized daily to receive support from the ACT or not. All adults (eighteen years and older) undergoing surgical procedures in these operating rooms are included and followed until 30 days after their surgery. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses. Differences between groups will be presented with 99% confidence intervals; p-values <0.005 will be reported as providing compelling evidence, and p-values between 0.05 and 0.005 will be reported as providing suggestive evidence. Registration: TECTONICS is registered on ClinicalTrials.gov, NCT03923699 ; registered on 23 April 2019.
Article
Background: Despite efforts to improve patient outcomes, major morbidity and mortality remain common after surgery. Health information technologies that provide decision support for clinicians might improve perioperative and postoperative patient care. Evaluating the usability of these technologies and barriers to their implementation can facilitate their acceptance within health systems. Objective: This manuscript describes usability testing and refinement of an innovative telemedicine-based clinical support system, the Anesthesiology Control Tower (ACT). It also reports stakeholders' perceptions of the barriers and facilitators to implementation of the intervention. Methods: Three phases of testing were conducted in an iterative manner. Phase 1 testing employed a think-aloud protocol analysis to identify surface-level usability problems with individual software components of the ACT and its structure. Phase 2 testing involved an extended qualitative and quantitative real-world usability analysis. Phase 3 sought to identify major barriers and facilitators to implementation of the ACT through semistructured interviews with key stakeholders. Results: Phase 1 and phase 2 usability testing sessions identified numerous usability problems with the software components of the ACT. The ACT platform was revised in seven iterations in response to these usability concerns. Initial satisfaction with the ACT, as measured by standardized instruments, was below commonly accepted cutoffs for these measures. Satisfaction improved to acceptable levels over the course of revision and testing. A number of barriers to implementation were also identified and addressed during the refinement of the ACT intervention. Conclusions: The ACT model can improve the standard of perioperative anesthesia care. Through our thorough and iterative usability testing process and stakeholder assessment of barriers and facilitators, we enhanced the acceptability of this novel technology and improved our ability to implement this innovation into routine practice. International registered report identifier (irrid): RR2-10.1186/s40814-018-0233-4.
Article
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Introduction Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forecast an individual patient’s trajectory in real time remains poor. We propose to apply machine learning techniques to perioperative time-series data to develop algorithms for predicting adverse perioperative outcomes. Methods and analysis This study will include all adult patients who had surgery at our tertiary care hospital over a 4-year period. Patient history, laboratory values, minute-by-minute intraoperative vital signs and medications administered will be extracted from the electronic medical record. Outcomes will include in-hospital mortality, postoperative acute kidney injury and postoperative respiratory failure. Forecasting algorithms for each of these outcomes will be constructed using density-based logistic regression after employing a Nadaraya-Watson kernel density estimator. Time-series variables will be analysed using first and second-order feature extraction, shapelet methods and convolutional neural networks. The algorithms will be validated through measurement of precision and recall. Ethics and dissemination This study has been approved by the Human Research Protection Office at Washington University in St Louis. The successful development of these forecasting algorithms will allow perioperative healthcare clinicians to predict more accurately an individual patient’s risk for specific adverse perioperative outcomes in real time. Knowledge of a patient’s dynamic risk profile may allow clinicians to make targeted changes in the care plan that will alter the patient’s outcome trajectory. This hypothesis will be tested in a future randomised controlled trial.
Article
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Background Major postoperative morbidity and mortality remain common despite efforts to improve patient outcomes. Health information technologies have the potential to actualize advances in perioperative patient care, but failure to evaluate the usability of these technologies may hinder their implementation and acceptance. This protocol describes the usability testing of an innovative telemedicine-based intra-operative clinical support system, the Anesthesiology Control Tower, in which a team led by an attending anesthesiologist will use a combination of established and novel information technologies to provide evidence-based support to their colleagues in the operating room. Methods Two phases of mixed-methods usability testing will be conducted in an iterative manner and will evaluate both the individual components of the Anesthesiology Control Tower and their integration as a whole. Phase I testing will employ two separate “think-aloud” protocol analyses with the two groups of end users. Segments will be coded and analyzed for usability issues. Phase II will involve a qualitative and quantitative in situ usability and feasibility analysis. Results from each phase will inform the revision and improvement of the Control Tower prototype throughout our testing and analysis process. The final prototype will be evaluated in the form of a pragmatic randomized controlled clinical trial. DiscussionThe Anesthesiology Control Tower has the potential to revolutionize the standard of care for perioperative medicine. Through the thorough and iterative usability testing process described in this protocol, we will maximize the usefulness of this novel technology for our clinicians, thus improving our ability to implement this innovation into the model of care for perioperative medicine. Trial registrationThe study that this protocol describes has been registered in clinicaltrials.gov as NCT02830126.
Article
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Background: The impact of surgery on health is only appreciated long after hospital discharge. Furthermore, patients' perceptions of postoperative health are not routinely ascertained. The authors instituted the Systematic Assessment and Targeted Improvement of Services Following Yearlong Surgical Outcomes Surveys (SATISFY-SOS) registry to evaluate patients' postoperative health based on patient-reported outcomes (PROs). Methods: This article describes the methods of establishing the SATISFY-SOS registry from an unselected surgical population, combining perioperative PROs with information from electronic medical records. Patients enrolled during their preoperative visit were surveyed at enrollment, 30 days, and 1-yr postoperatively. Information on PROs, including quality of life, return to work, pain, functional status, medical complications, and cognition, was obtained from online, mail, or telephone surveys. Results: Using structured query language, 44,081 patients were identified in the electronic medical records as having visited the Center for Preoperative Assessment and Planning for preoperative assessment between July 16, 2012, and June 15, 2014, and 20,719 patients (47%) consented to participate in SATISFY-SOS. Baseline characteristics and health status were similar between enrolled and not enrolled patients. The response rate for the 30-day survey was 62% (8% e-mail, 73% mail, and 19% telephone) and for the 1-yr survey was 71% (13% e-mail, 78% mail, and 8% telephone). Conclusions: SATISFY-SOS demonstrates the feasibility of establishing a PRO registry reflective of a busy preoperative assessment center population, without disrupting clinical workflow. Our experience suggests that patient engagement, including informed consent and multiple survey modalities, enhances PROs collection from a large cohort of unselected surgical patients. Initiatives like SATISFY-SOS could promote quality improvement, enable efficient perioperative research, and facilitate outcomes that matter to surgical patients.
Article
Background: The authors hypothesized that a multiparameter intraoperative decision support system with real-time visualizations may improve processes of care and outcomes. Methods: Electronic health record data were retrospectively compared over a 6-yr period across three groups: experimental cases, in which the decision support system was used for 75% or more of the case at sole discretion of the providers; parallel controls (system used 74% or less); and historical controls before system implementation. Inclusion criteria were adults under general anesthesia, advanced medical disease, case duration of 60 min or longer, and length of stay of two days or more. The process measures were avoidance of intraoperative hypotension, ventilator tidal volume greater than 10 ml/kg, and crystalloid administration (ml · kg · h). The secondary outcome measures were myocardial injury, acute kidney injury, mortality, length of hospital stay, and encounter charges. Results: A total of 26,769 patients were evaluated: 7,954 experimental cases, 10,933 parallel controls, and 7,882 historical controls. Comparing experimental cases to parallel controls with propensity score adjustment, the data demonstrated the following medians, interquartile ranges, and effect sizes: hypotension 1 (0 to 5) versus 1 (0 to 5) min, P < 0.001, beta = -0.19; crystalloid administration 5.88 ml · kg · h (4.18 to 8.18) versus 6.17 (4.32 to 8.79), P < 0.001, beta = -0.03; tidal volume greater than 10 ml/kg 28% versus 37%, P < 0.001, adjusted odds ratio 0.65 (0.53 to 0.80); encounter charges $65,770 ($41,237 to $123,869) versus $69,373 ($42,101 to $132,817), P < 0.001, beta = -0.003. The secondary clinical outcome measures were not significantly affected. Conclusions: The use of an intraoperative decision support system was associated with improved process measures, but not postoperative clinical outcomes.
Article
Background: Residual postoperative paralysis from nondepolarizing neuromuscular blocking agents (NMBAs) is a known problem. This paralysis has been associated with impaired respiratory function, but the clinical significance remains unclear. The aims of this analysis were two-fold: (1) to investigate if intermediate-acting NMBA use during surgery is associated with postoperative pneumonia and (2) to investigate if nonreversal of NMBAs is associated with postoperative pneumonia. Methods: Surgical cases (n = 13,100) from the Vanderbilt University Medical Center National Surgical Quality Improvement Program database who received general anesthesia were included. The authors compared 1,455 surgical cases who received an intermediate-acting nondepolarizing NMBA to 1,455 propensity score-matched cases who did not and 1,320 surgical cases who received an NMBA and reversal with neostigmine to 1,320 propensity score-matched cases who did not receive reversal. Postoperative pneumonia incidence rate ratios (IRRs) and bootstrapped 95% CIs were calculated. Results: Patients receiving an NMBA had a higher absolute incidence rate of postoperative pneumonia (9.00 vs. 5.22 per 10,000 person-days at risk), and the IRR was statistically significant (1.79; 95% bootstrapped CI, 1.08 to 3.07). Among surgical cases who received an NMBA, cases who were not reversed were 2.26 times as likely to develop pneumonia after surgery compared to cases who received reversal with neostigmine (IRR, 2.26; 95% bootstrapped CI, 1.65 to 3.03). Conclusions: Intraoperative use of intermediate nondepolarizing NMBAs is associated with developing pneumonia after surgery. Among patients who receive these agents, nonreversal is associated with an increased risk of postoperative pneumonia.
Article
Medical error is not included on death certificates or in rankings of cause of death. Martin Makary and Michael Daniel assess its contribution to mortality and call for better reporting The annual list of the most common causes of death in the United States, compiled by the Centers for Disease Control and Prevention (CDC), informs public awareness and national research priorities each year. The list is created using death certificates filled out by physicians, funeral directors, medical examiners, and coroners. However, a major limitation of the death certificate is that it relies on assigning an International Classification of Disease (ICD) code to the cause of death.1 As a result, causes of death not associated with an ICD code, such as human and system factors, are not captured. The science of safety has matured to describe how communication breakdowns, diagnostic errors, poor judgment, and inadequate skill can directly result in patient harm and death. We analyzed the scientific literature on medical error to identify its contribution to US deaths in relation to causes listed by the CDC.2 Medical error has been defined as an unintended act (either of omission or commission) or one that does not achieve its intended outcome,3 the failure of a planned action to be completed as intended (an error of execution), the use of a wrong plan to achieve an aim (an error of planning),4 or a deviation from the process of care that may or may not cause harm to the patient.5 Patient harm from medical error can occur at the individual or system level. The taxonomy of errors is expanding to better categorize preventable factors and events.6 We focus on preventable lethal events to highlight the scale of potential for improvement. The role of error can be complex. While …
Article
Background: Recent studies have identified processes that are associated with more favorable length of stay outcomes when an ICU telemedicine program is implemented. Despite these studies, the relation of the acceptance of ICU telemedicine management services by individual ICUs to length of stay (LOS) outcomes is unknown. Methods: This is a single ICU telemedicine center study that compares length of stay outcomes among 3 groups of intensivist staffed mixed medical surgical ICUs that used alternative co-management strategies. The proportion of provider orders recorded by an ICU telemedicine provider to all recorded orders were compared among ICUs that used a monitor and notify co-management approach, a direct intervention with timely notification process, and ICUs that used a mix of these two approaches. The primary outcome was acuity adjusted hospital length of stay. Results: ICUs that used the direct intervention with timely notification strategy had a significantly larger proportion of provider orders recorded by ICU telemedicine physicians than the mixed methods of co-management group, which had a larger proportion than ICUs that used the monitor and notify method (p < 0.001). Acuity-adjusted hospital LOS was significantly lower for the direct intervention with timely notification co-management strategy 0.68 (.65 to .70) compared to the mixed methods group (0.70 (.69 to .72); p =0.01), which was significantly lower than the monitor and notify group (0.83 (.80 to .86); p< 0.001). Conclusions: Direct intervention with timely notification strategies of ICU telemedicine co-management were associated with shorter LOS outcomes than monitor and notify co-management strategies.
Article
Background: Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain. Objectives: To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals. Research design: We performed a multicenter retrospective case-control study using 2001-2010 Medicare claims data linked to a national survey identifying US hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 nonadopting hospitals (controls) based on size, case-mix, and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach. Results: A total of 132 adopting case hospitals were matched to 389 similar nonadopting control hospitals. The preadoption and postadoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, P=0.07) and control hospitals (23.5% vs. 23.7%, P<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios=0.96; 95% CI, 0.95-0.98; P<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios=1.01; interquartile range, 0.85-1.12; range, 0.45-2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions postadoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (P<0.001) and be located in urban areas (P=0.04) compared with other hospitals. Conclusions: Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.