A Proof-of-concept Observational Study Evaluating the Acceptability and Utility of a Telemedicine Solution for the Post Anesthesia Care Unit
Phase: NA
Conditions: Elective Surgery
Sponsor: Washington University School of Medicine

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Protocol for a proof-of-concept observational study evaluating the potential utility 
and acceptability of a telemedicine solution for the Post-Anesthesia Care Unit 
 
 
 
AUTHOR LIST HERE 
 
Thaddeus P. Budelier* 
Christopher Ryan King* 
Shreya Goswami 
Anchal Bansal 
Stephen Gregory 
Troy Wildes 
Joanna Abraham 
Sherry McKinnon 
Amy Cooper  
Ivan Kangrga 
Jackie Martin  
Melissa Milbrandt  
Alex S. Evers 
Michael S. Avidan# 
 
 
 
 
 
 
 
Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO 63110, 
USA 
 
*These authors contributed equally to the drafting of this protocol and the design of the study.  
 
#To whom correspondence should be addressed 
Washington University School of Medicine 
Campus Box 8054 
660 South Euclid Avenue 
St. Louis, MO 63110 
Protocol version: 26 February, 2020 
 

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ABSTRACT 
 
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 3-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 3-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.  
 

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Registration Information: This trial is registered on clinicaltrials.gov, NCT04020887 
 
Keywords: 
Telemedicine, Post-Anesthesia Care Unit, Protocol, Proof-of-Concept, Observational Study 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Introduction 
After invasive procedures in the operating room (OR) or other procedure rooms, patients are 
usually transferred to a post-anesthesia care unit (PACU) for high acuity monitoring. The PACU 
period is important for patients, especially since they often are still in a vulnerable state.1,2 
Patients are prone to peri-procedural and post-anesthetic complications including dehydration, 
anemia, coagulopathy, bleeding, hypothermia, delirium, respiratory depression, airway 
obstruction, bronchospasm, hypotension, kidney injury, arrhythmias, metabolic acidosis, 
hypoxemia, glucose and electrolyte abnormalities, atelectasis, and pulmonary edema.3,4 These 
complications must be recognized and appropriately managed by PACU clinicians. 
Furthermore, PACU clinicians need to identify and manage patients’ adverse symptoms 
including pain, nausea, urine retention, weakness, and itching, which are common after invasive 
procedures, whether with or without general anesthesia.  
 
The ideal PACU environment provides close monitoring and prompt rescue for peri-procedural 
complications, while also efficiently transferring patients to their next phase of care. For 
example, when patients deteriorate in the PACU, it is important to recognize this early, intervene 
appropriately, and arrange transfer to a higher acuity area, such as an intensive care unit, when 
warranted.  
 
PACU clinicians are responsible for several clinical and organizational tasks5 including patient 
monitoring and treatment, promoting patient throughput, conducting hand-offs to and from the 
PACU, and documenting patient care information during the recovery period. As a result, PACU 
nurses and doctors can feel overwhelmed, and may not always be able to treat symptoms 
adequately, diagnose physiological derangements accurately, and detect patient deterioration 
expeditiously. Furthermore, in this high-pressure, high-turnover environment, communication 
among clinicians is often compromised, resulting in unreliable care coordination. Patient 

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satisfaction with PACU care varies, as the recognition and prompt treatment of symptoms 
depends on the availability of assigned clinicians.  
 
The necessity of operating room throughput creates a constant pressure on PACU clinicians to 
discharge patients rapidly, sometimes before they have recovered sufficiently. This workflow 
pressure can potentially compromise quality of care and patient safety. Nurses provide the 
majority of PACU care, typically for no more than two patients at a time during the initial phase 
of PACU care, in accordance with the American Society of PeriAnesthesia Nurses (ASPAN) 
guidelines6. Furthermore, physicians with competing responsibilities often provide oversight in 
the PACU. For example, a physician who has responsibility for patient assessment and 
management in the PACU is often simultaneously overseeing anesthetic care in operating 
rooms or other procedural suites. Surgical clinicians also participate in aspects of PACU care, 
but are often simultaneously engaged in surgical care of other patients. In addition, the 
coverage and oversight models can vary considerably across different PACUs, and even within 
the same PACU over the course of a single day. This is in stark contrast to other high acuity 
patient care settings, such as operating rooms and intensive care units, where roles and 
responsibilities of various clinicians are well defined, and staffing models are established.  
 
In this protocol, we describe a proof-of-concept study in perioperative telemedicine that aims to 
demonstrate the (i) potential utility and (ii) acceptability of integrating telemedicine into the 
PACU environment. This proof-of-concept study will be conducted in the PACU located in 
Parkview Tower in Barnes-Jewish Hospital (BJH). If this proof-of-concept proves to be 
successful, we intend subsequently to show the impact of such a telemedicine solution on 
safety, quality of care, efficiency, and ultimately postoperative outcomes.  Our specific aims for 
the proposed proof-of-concept study are: 
 

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Aim 1 – Demonstrate the potential utility of a telemedicine center for the PACU, to assist 
with PACU functions 
We hypothesize that clinicians in the telemedicine center for the PACU will: 
1a. Detect physiological derangements and complications 
1b. Identify adverse symptoms requiring treatment  
1c. Recognize situations requiring emergency medical intervention 
1d. Determine when patients are ready for PACU discharge 
1e. Participate meaningfully in hand-off activity from the OR to the PACU 
 
Aim 2 – Identify barriers to and facilitators for the implementation of a telemedicine 
center for the PACU, as perceived by key stakeholders  
We will assess attitudes of key stakeholders towards a telemedicine center for PACU. The key 
stakeholders will include PACU nurses, anesthesiologists, surgeons, hospital administrators, 
and PACU-telemedicine center clinicians.  
 
Methods 
Study setting, design, and participants 
The study will be conducted at Barnes-Jewish Hospital (BJH) in St. Louis, Missouri, a large 
tertiary care academic medical center.  
 
We will conduct a single center prospective before-and-after proof-of-concept study to evaluate 
a telemedicine center for the PACU. Adults (18 years and older) undergoing elective surgery at 
Barnes Jewish Hospital in St. Louis, Missouri will be enrolled. Approximately 500 patients will be 
enrolled in this study over a 6-month duration, with an estimated 250 patients allocated to each 

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phase of the trial. The first phase is an Observation phase and the next phase is an Interaction 
phase. More information on these phases is provided below.  
 
This proof-of-concept study has been reviewed and granted a waiver of informed consent for all 
subjects enrolled by the Human Research Protection Office at Washington University in St. 
Louis (HRPO#201901180) and is registered at clinicaltrials.gov (NCT04020887).  It is infeasible 
to conduct this proof-of-concept study without a waiver of consent.  Additionally, this study has 
been determined to involve no more than minimal risk to participants, as study participation 
would not deviate from or delay current standards of peri-anesthesia care. Both the “Good 
ReseArch for Comparative Effectiveness” (GRACE) checklist7 and PICOTS framework8 (Table 
1) were used in designing this study. 
 
The conduct and reporting of this observational study will follow the “Reporting of studies 
Conducted using Observational Routinely-collected health Data” (RECORD)9 statement and the 
“Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE)10 statement 
guidelines for reporting observational studies. 
 
Table 1. PICOTS Framework 
PICOTS typology for a comparative effectiveness research protocol 
Population 
Adult (18 years and older ) patients undergoing elective surgery  
Intervention 
Telemedicine center for PACU 
Comparator 
Current post-anesthesia care unit practice 
Outcomes 
(i) potential utility, and (ii) acceptability of integrating telemedicine in the post-
anesthesia care unit environment 
Timing 
6-month study duration 

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Primary Intervention: Telemedicine Center for PACU 
Two bays in Barnes Jewish Hospital (BJH) in St. Louis, Missouri, will be equipped for 
telemedicine interaction (Figure 1).  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.11–14  
 
 
 
Figure 1. Image of post-anesthesia care unit bay in Barnes-Jewish Hospital with two-way video 
communication. 
Setting 
Hospital environment – Barnes-Jewish Hospital, St. Louis, Missouri 

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A station in our telemedicine center will be designated for monitoring patients assigned to the 
two PACU bays during this proof-of-concept study. Patient information flows to the telemedicine 
center through the electronic health record (EHR), physiological waveform tracings, and direct 
video observation. A version of AlertWatch® (AlertWatch, Ann Arbor, Michigan) decision-
support software, customized for the PACU environment (Figure 2), will assist clinicians in the 
telemedicine center in performing core PACU-related functions remotely (see Aim 1). 
 
 
Figure 2. AlertWatch® decision-support software, customized for the PACU environment. 
 
 
Aim 1 – Demonstrate the potential utility of a telemedicine center for the PACU, to assist 
with PACU functions 

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The assessments in relation to PACU functions will include:  
1a. Detection of physiological derangements in PACU patients 
1b. Identification of symptoms requiring treatment in PACU patients 
1c. Recognition of situations requiring emergency medical intervention 
1d. Determination of patient readiness for PACU discharge 
1e. Participate meaningfully in hand-off activities 
 
Observation Phase (3 months) 
In the first three months (the Observation phase) of this proof-of-concept study, a 
telemedicine center for the PACU will monitor patients assigned to two PACU bays. Both 
the telemedicine center and nurses caring for patients in the PACU bays will separately 
document physiological derangements (Table 2), treatable symptoms (Table 3), or a 
situation requiring urgent medical intervention (telemedicine center only; Table 4) during 
the PACU stay. Clinicians in the telemedicine center will assess when the patient meets 
discharge criteria, based on the modified Aldrete scale15 and their clinical judgment. They 
will document the time that discharge criteria are met, the modified Aldrete scale score at this 
time, and any additional relevant information. If clinicians in the telemedicine center 
judge that they are unable to determine a patient’s readiness for discharge, they will 
document their reasons (Table 5). Clinical judgment will be used in determining 
appropriate discharge parameters for patients with pre-existing conditions.  The 
telemedicine center clinicians will document each patient’s information outlined in 
Tables 2-5 directly into REDCapTM (a secure web application for managing online surveys 
and databases) and AlertWatch. After a patient has been discharged from the PACU, the 
PACU nurse will fill out a form providing information outlined in Tables 2-5. This includes 

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information on physiological derangements, treatable symptoms, and discharge information. 
This form will be collected by the research team, and the information in the form will be 
documented in REDCap. During this phase of the study, clinicians in the telemedicine 
center will not communicate with clinicians in the PACU (nurses or physicians), unless there 
is a patient safety event. 
 
Table 2. Physiological derangements and complications  
Did the patient have any of the 
following physiological derangements: 
Definition: 
(for study purposes) 
Persistent confusion / delirium 
 
Tachycardia 
HR >120/min 
Bradycardia 
HR <45/min 
New onset atrial fibrillation 
 
Respiratory depression  
<8 respirations per minute 
Hypoxemia 
<90% 02 Saturation  
Hypotension 
MAP <55 
Weakness  
<5/5 power in limbs 
Emesis / vomiting 
 
Hyperglycemia 
Glucose >200mg/dL  
Hypothermia  
Temperature < 35.5 C 
Low urine output (for PACU stay >4h) 
<0.5 ml/kg per hour 
 
Table 3. Symptoms requiring treatment 
Did the patient complain of the following symptoms: 
Dizziness or lightheadedness 
Difficulty breathing 
Nausea 
Shivering 
Severe pain (Numerical Rating Scale 
>7/10) 
Itching 
Chest pain unrelated to surgery 
 
 
Table 4. Emergency medical interventions 
Did the telemedicine center contact PACU clinicians for any of the following 
interventions: 
Intubation 
Unplanned transfusion 
Assisted ventilation 
Naloxone administration 
Cardiopulmonary resuscitation  
Return to OR 
Cardioversion 
Other (free text box) 
If Yes, please check all that apply: 
□ PACU nurse already  aware of the situation 
□ PACU nurse unaware of the situation  
□ PACU nurse disagreed with the assessment 
□ PACU nurse had already spoken to the supervising physician regarding the situation  

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□ Other (please describe): 
*Only the telemedicine center will document the detection of urgent situations. 
 
 
 
 
 
 
Table 5. Patient discharge readiness 
At what time did the patient sufficiently recover to be discharged (case attending 
anesthesiologist contacted for discharge)?  
PACU Nurses 
Telemedicine Center for PACU 
Time anesthesiologist is contacted for 
discharge evaluation 
Time ready for discharge 
Aldrete Score at discharge 
Aldrete Score at discharge 
 
Unable to determine patient’s readiness for 
discharge 
Modified Aldrete Scale Component and 
Scoring Parameters 
If Unable to fully assess, select reason(s) why 
(Checkbox) 
Respiration 
□ More patient information needed 
2 – Able to take deep breath and cough 
□ Equipment issues 
1 – Dyspnea / Shallow Breathing 
□ Patient cooperation 
0 – Apnea 
□ Other (free text box) 
02 Saturation 
 
2 – Maintains > 92% on room air 
 
1 – Needs 02 inhalation to maintain 02 
saturation > 90% 
 
0 – Saturation <90% even with 
supplemental 02 
 
Consciousness 
 
2 – Fully awake 
 
1 – Arousable on calling 
 
0 – Not responding 
 
Circulation 
 
2 – BP ± 20mmHg pre-op 
 
1 – BP ± 20-50mmHg pre-op 
 
0 – BP ± greater than 50mmHg pre-op 
 
Motor Activity 
 
2 – Able to move 4 extremities voluntarily 
or on command 
 
1 – Able to move 2 extremities voluntarily 
or on command 
 
0 – Able to move 0 extremities voluntarily 
or on command 
 
 
 

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Interaction Phase (3 months) 
In the three months following the observation phase, clinicians in the telemedicine center will 
interact with patients and clinicians associated with the designated PACU bays using audio-
visual technology. PACU clinicians and clinicians in the telemedicine center will become a 
“fused” team, and the telemedicine center will continue to document information on 
physiological derangements (Table 2), treatable symptoms (Table 3), situations requiring urgent 
medical intervention (Table 4), and discharge readiness (Table 5). 
 
The telemedicine center clinicians will assess patients’ discharge readiness throughout their 
PACU stay. A modified Aldrete scale along with clinical judgment will guide the telemedicine 
center clinicians in determining readiness for discharge (Table 5).  After discharge readiness 
has been determined by the telemedicine center, the attending anesthesiologist in the 
telemedicine center will document discharge readiness in AlertWatch and REDCap, and contact 
the relevant anesthesiologist.  The telemedicine center for PACU will document when this 
information was communicated. At any point clinicians in the telemedicine center might decide 
to contact PACU clinicians (nurse or physician) if they have specific concerns regarding 
patients. If the telemedicine center clinicians feel that they cannot adequately assess a patient’s 
clinical status, they will notify the PACU clinicians. This will be documented together with a 
relevant explanation (Table 5). 
 
Final determination and sign-off regarding discharge suitability will be made by the 
anesthesiologist in the PACU. With this proof-of-concept research project, there will be no 
change in relation to which clinicians have responsibility for decision making and clinical care. 
The telemedicine center clinicians will not write any orders in the medical record, and will 
provide opinions only to physicians and nurses who are responsible for patient care in the 

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PACU. The responsibility to call for help when patients are deteriorating will remain with the 
PACU nurses, as is the current standard in that environment. The notion is that the telemedicine 
center will not lead to any decrement in the care that PACU patients are currently receiving from 
nurses and physicians in that environment.   
 
The successful integration of the telemedicine center into each of the core PACU functions 
will be measured in the following ways: 
Physiological derangements – Success will be measured (in the observation phase) 
by the ability of the telemedicine center clinicians to identify physiological 
derangements as they are occurring in the PACU. The extent to which the 
telemedicine center clinicians can identify these physiological derangements will 
be measured by comparing PACU nurse and telemedicine center assessment 
surveys for each patient (Figure 3).   
 
Symptom identification and management – Success will be measured (in the 
observation phase) by the ability of the telemedicine center clinicians to identify 
treatable symptoms as they arise in the PACU. The extent to which the 
telemedicine center clinicians can identify these treatable symptoms will be 
measured by comparing PACU nurse and telemedicine center assessment 
surveys for each patient (Figure 3).   
 
Emergency situations – Success will be measured (in the observation phase) by 
the ability of the telemedicine center clinicians to identify situations requiring 
emergency medical intervention as they are occurring in the PACU. By 
construction, any time the telemedicine center feels that an emergency situation 
is present, preserving patient safety mandates contacting the bedside clinician. 

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During each such contact, the telemedicine center clinician will ask if the PACU 
nurse was already aware of the situation, disagreed with the assessment, and 
had already spoken to the supervising physician regarding it. The occurrence of 
emergency medical situations will be extracted from the electronic health record, 
and the agreement between telemedicine center and PACU nurse assessments 
will be quantitated.  
 
PACU discharge – Success will be measured by the ability of the telemedicine 
center clinicians to identify when patients are ready for discharge (observation 
phase [without communication] and interaction phase [active communication with 
patient and PACU clinicians]) (Figure 3). The impact of the telemedicine center on 
this key function will be examined based on feedback from key stakeholder focus 
groups (interaction phase; see Aim 2).  The difference between sign-out times in 
the observation and the interaction phases will be compared. 
 

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Figure 3: Overview of data collection methods and outcome measures during the interaction 
phase of a before-and-after proof-of-concept study for a telemedicine center for the PACU.   
 
Hand-off Activity 
The telemedicine center clinicians will participate in hand-off activities to and from the PACU.  
This includes ensuring appropriate transfer of information from operating rooms to the PACU. 

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The telemedicine center clinicians will remotely join the hand-off conversations, and review 
patients’ medical history and intraoperative course to identify potential missed transfer of 
information.  
 
During the observation phase, the telemedicine center clinicians will observe the hand-off 
workflow, gain familiarity with the current hand-off routine, and identify possible areas of missed 
information transfer where the telemedicine center clinicians may have adjunct utility.  An 
example of potential adjunct utility would be communicating the importance of appropriate 
insulin and glucose management in the PACU for a patient with type I diabetes.  
 
In the interaction phase of the study, the telemedicine center clinicians will try to fill gaps in 
information transfer during the hand-off procedure.  In addition to remotely joining the hand-off 
conversation, the telemedicine center clinicians will share pertinent additional patient or 
procedural information, especially if this could inform the patient’s PACU medical treatment. 
After the completion of the hand-off procedure, the PACU nurse who interacted with the 
telemedicine center clinicians will complete a short survey to assess the telemedicine center’s 
involvement in that patient’s transfer of care.  
 
The successful integration of the telemedicine center clinicians’ hand-off activity will be 
measured in the following way: 
Hand-off activity – Success will be measured (in the interaction phase) by the ability of 
the telemedicine center clinicians to join and contribute meaningfully to the hand-off 
discussion. The impact of the telemedicine center clinicians on this key function will be 
determined from feedback from key stakeholder focus groups (see Aim 2) and PACU 
nurse surveys. These stakeholders will comment on utility of the telemedicine center’s 
involvement and provide suggestions for improvement. A binary assessment of hand-off 

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adequacy will be provided by the PACU nurse hand-off survey. The telemedicine center 
clinician will use a hand-off content checklist to record the number of mandatory items 
not discussed and number of recommended non-mandatory items discussed. For each 
of the observation and intervention phase, for 50 randomly selected cases a trained 
observer (not the participant in hand-off) will use the hand-off communication 
assessment tool of Weinger and others16 substituting the telemedicine center hand-off  
content checklist. A run-in phase of 1 month during the intervention will elapse before 
any of the 50 detailed communication evaluations are performed. 
 
Aim 2 – Identify barriers to and facilitators for the implementation of a telemedicine 
center for the PACU, as perceived by key stakeholders  
We will assess the attitudes of key stakeholders in order to identify barriers to and facilitators for 
implementation of a telemedicine center for the PACU. (Figure 3) 
 
Stakeholder Focus Groups 
We will conduct focus groups with stakeholders to gain insights regarding their perceptions of 
barriers and facilitators related to the above-noted PACU functions before and after the 
implementation and use of a telemedicine center for the PACU. We will also gather perspectives 
from the stakeholders on the role and impact of the telemedicine center on their individual and 
team workflows in the PACU and between units during care transitions. Focus group 
participants will include nurses, anesthesiologists, surgeons, hospital administrators, and PACU 
telemedicine center clinicians. Our focus groups will be homogeneous in order to understand 
the clinician workflow based on their professional role, and their use of the telemedicine center 
in supporting their role and responsibilities. Each focus group will comprise five to six 
participants. This will allow in-depth discussions of the workflow problems and unintended 

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consequences caused by the implementation and use of the telemedicine center for the PACU. 
The focus group sessions will be guided by a semi-structured interview guide focused on the 
following themes: (1) PACU core functions, (2) PACU patient workflow, (3) PACU clinician 
activities and tasks, (4) tools and technologies used to support the PACU workflow, (5) major 
barriers to PACU functions, (6) use of a telemedicine intervention as a potential mechanism to 
support effective and efficient functioning of the PACU. We plan to conduct 6-8 focus group 
sessions (4 pre-intervention during observation phase, and 4 post-intervention during interaction 
phase) or until data saturation is attained.  
 
Study Size 
Patients are allocated to PACU bays according to the discretion of the nurse in charge of the 
PACU. Currently, approximately two patients per day are cared for in each bay in the 
participating PACU. Therefore, the telemedicine team will monitor approximately four patients 
per day over the course of the proof-of-concept study. We estimate that 500 patients will be 
included in this proof-of-concept study (250 per monitored phase) (Figure 4). 
 

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Figure 4. Allocation of sample size and PACU layout for proof-of-concept study 
 
Statistical Methods 
Primary Outcome 
This is a proof-of-concept study and will only address surrogate outcomes. The primary 
outcome (time to PACU discharge readiness) will use two comparison groups. First, historical 
controls will be drawn from the observation phase. A propensity score for inclusion into the 
study will be generated as a function of (minimally) surgery performed, day of week, time of day, 
age, and sex. 3:1 matched control patients will be included. The outcome will be analyzed with 
interrupted time series methods with flexible functions of calendar time used to adjust for 
secular trends; the study hypothesis is a non-zero discontinuity at telemedicine implementation. 

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That is, if Yi is the outcome for the ith patient at time ti with covariate vector Xi, while the 
implementation time is t0, and I() is the indicator function, 
𝑌௜ൌ𝑓ଵሺ𝑡௜ሻ𝐼ሺ𝑡௜൏𝑡଴ሻ൅𝑓ଶሺ𝑡௜ሻ𝐼ሺ𝑡௜൒𝑡଴ሻ൅𝑋௜𝛽൅𝜖𝐻଴: 𝑓ଵሺ𝑡଴ሻൌ𝑓ଶሺ𝑡଴ሻ 
where f1 and f2 are smooth functions. Other patient factors known to strongly influence PACU 
length of stay (age, ASA physical status, number of co-morbidities, morbid obesity, obstructive 
sleep apnea, surgical specialty, primary anesthesia type, history of postoperative nausea and 
vomiting, preoperative pain, and scheduled case duration) will be included as covariates. The 
minimization criteria will be least squares or trimmed least squares or other robust criteria if 
there are substantial outliers. Outcomes will be examined for residual auto-correlation, and if 
non-negligible, auto-correlation robust standard errors (such as Newey-West errors) and an 
ARIMA model will be reported. Confidence intervals will be generated by non-parametric 
bootstrap sampling where possible. No adjustment will be made for matching, but bootstrap 
methods will respect the matched “units.” P-values will be generated both by likelihood ratio 
tests and by using non-deployment times as a null distribution; that is, we will run the same 
analysis looking for discontinuity at times remote from the true implementation time. We will 
conduct sensitivity analyses with transformations of the outcome variable. We will use an 
excluded run-in period of 1 month as a sensitivity analysis. Because hospital length of stay is 
unlikely to be meaningfully affected by a telemedicine center for the PACU, but does track 
overall acuity and surgical severity, we will use hospital length of stay as a control time series. 
 
Contemporaneous control patients will also be gathered. A propensity score for study inclusion 
will be generated as a function of (minimally) surgery performed, calendar time, time of day, 
age, and sex. 3:1 matched control patients will be included.  Differences will be analyzed by t-
tests using permutation calibration. Confidence intervals on the difference in mean time to 
discharge readiness will be generated by nonparametric bootstrap. We will include a sensitivity 

22 
 
analysis where the interrupted time series method includes historical and contemporaneous 
control patients with the treatment indicator T for study patients, 
𝑌௜ൌ𝑓ଵሺ𝑡௜ሻ𝐼ሺ𝑡௜൏𝑡଴ሻ൅𝑓ଶሺ𝑡௜ሻ𝐼ሺ𝑡௜൒𝑡଴ሻ൅𝑋௜𝛽൅𝑇௜൅𝜖. 
 
Based on data from our EHR, patients are currently in PACU for a mean of 150 min (standard 
deviation = 65 min) before they are determined to be suitable for discharge. Based on these 
values, with 250 patients in each phase (observation and interaction), this observational before 
and after study will have >70% power with an alpha <0.005 and > 90% power with an alpha 
<0.05 to detect a mean decrease in 20 min (from 150 min to 130 min) to PACU discharge 
readiness time. Statistical testing will be with appropriate statistical software. Using non-
parametric bootstrap of historical data and a 3:1 control sampling ratio, the average standard 
error on the difference in means under the null hypothesis was 5.5 minutes, giving an 
anticipated 95% confidence interval width of 22 minutes. A somewhat larger standard error will 
be encountered when adjusting for covariates or secular trends; however, this suggests that we 
will be able to resolve differences in PACU readiness times of 20-25 minutes. This difference of 
approximately a third a standard deviation is usually regarded as a “small-moderate” sized 
effect. 
 
Secondary Outcomes 
Hand-off quality assessment from the PACU nurse binary survey response will be analyzed 
using a logistic regression model adjusting for surgical service, age, and sex. Because 
observation resources are required for hand-off evaluations, no matching will be performed, and 
no contemporaneous controls will be gathered. Adjusted differences in rates of inadequate 
hand-off will be summarized with 95% confidence intervals and model-based p-values. 

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Observed reported hand-off communication quality will be presented as a purely descriptive 
result. 
 
The accuracy of physiologic, symptom, and status assessments is less straightforward to 
analyze. At the heart of the proposal is the belief that telemedicine assistance will detect some 
abnormalities not caught (or caught later) by the bedside team and detect that the patient has 
adequate status for PACU discharge before the bedside nurse. Using the bedside assessment 
as a gold standard is therefore limited. Similarly, although we believe that abnormalities 
detected by either bedside or telemedicine are unlikely to be false positives, we have no way of 
assuring that. We also cannot reliably determine the timing of the bedside nurse’s detection of 
an abnormality, as they may document it much later if they believe it does not require an 
immediate intervention.  
 
Each status assessment event can occur multiple times for each patient; however, we are 
unlikely to accurately capture the bedside nurse’s impression of the number of times an event 
occurred. We will therefore binarize the presence of each assessment type and display 
confusion matrices (count tabulations) for each assessment type, which we will summarize with 
Jaccard indicies. The “null hypothesis” that these measures do not agree at all is not meaningful 
or the subject of this study. As described above, neither is a directional superiority hypothesis 
possible to evaluate. Final Aldrete scores will be assessed with pearson correlation, and a t-test 
of the difference in scores presented. Differences in ready-for-discharge times will be 
summarized as mean and standard deviation, with the null hypothesis of zero mean tested by t-
test with a robust standard error. 
 
Agreement of emergency medical status is unlikely to have enough events to be statistically 
compared. We will present cross-tabulations of (emergency detected by telemedicine center: 

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yes/no) and (PACU nurse: disagree, investigate and agree, already aware, physician 
contacted). The absolute rate of telemedicine center false positives (team disagrees), true 
positives (team unaware), true positives (team aware), and false negatives (team aware > 15 
minutes prior or t never detects) will be presented with 95% confidence intervals. 
 
Data Collection 
Multiple sources will be utilized for data collection from which outcome measures will be 
extracted.  Data from AlertWatch will be automatically logged to a secure database.  
Preoperative patient characteristics, comorbidities, surgical and clinical history, as well as 
perianesthesia information will be captured using Epic Systems software (Verona, WI, 
USA). Prospective data will be collected from Epic Systems for the datapoints mentioned 
throughout the proof-of-concept study. 
 
Relevant PACU information outlined in Tables 2-5 for patients in this study will be collected and 
entered into a REDCap database managed by Washington University. Data will not be shared 
with others outside the research team. 
 
Methodological strengths and limitations 
A strength of this study is its pragmatic approach as a real-world study with measurable aims. 
Feasibility will be determined, and information will be provided regarding logistical implications 
of establishing a telemedicine solution for the PACU. Many telemedicine solutions have been 
implemented without considering barriers and facilitators, such as cultural and political 
obstacles. This study proactively addresses these concerns, which might facilitate future 
successful implementation and generalization of similar telemedicine initiatives. Specific 

25 
 
functions of the PACU have been detailed, and the methods of this study will allow assessment 
of the ability of the telemedicine center to facilitate the accomplishment of these functions.  
This study also has important limitations. First, as a proof-of-concept, it will only include two 
PACU bays. Thus, its applicability to a large PACU will not be resolved. Second, PACU 
clinicians will be aware of the initiative, which could modify their behavior during the conduct of 
the study. Third, as the study design is observational with a before and after approach, 
improvements (for example in time to discharge) cannot be causally attributed to the 
intervention; there could be confounding explanations. Fourth, the current discharge criteria for 
the PACU do not have a firm evidential foundation (there is no gold standard measure for 
discharge readiness), and clinician gestalt plays an important role. This limitation can be 
addressed through development of rigorous, reliable and practical criteria. Finally, as a single 
center study, results will not necessarily be broadly generalizable.    
 
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 attributable to this proof-of-concept 
study. 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 be used. There will be no audit of trial conduct during the 
investigation. No interim data analysis is planned for this proof-of-concept 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 this proof-of-concept 
trial involves only the addition of real-time decision-support tools and does not change existing 
care models. 
 

26 
 
 
 
Conclusions 
Recovery in the PACU is an important phase in most patients’ surgical course. In this study, we 
propose a new model for future PACU care. Thought has been given to assess important 
barriers to and facilitators for the implementation of a telemedicine solution for the PACU. 
Potential key findings of this study might include decreased length of stay for patients in the 
PACU, as well as acceptance by identified key stakeholders of the telemedicine solution. 
Following successful pilot implementation of a telemedicine solution for the PACU, we 
subsequently intend to expand this model to more PACU bays, and possibly other PACU 
locations in order to study relevant clinical outcome measures. 
 
The impact of this this study, and subsequent future studies, may be far reaching.  The current 
PACU model is not well defined. A telemedicine solution for this important recovery environment 
has the potential to improve safety, clinical outcomes, and quality of care for patients recovering 
from invasive procedures. A telemedicine solution for the PACU might also provide a suitable 
solution for PACU environments in under-resourced or remote locations, and decrease 
healthcare costs for hospital systems.  
 
 
Data availability 
 
Underlying data 
No data are associated with this article. 
 
 
Supplementary material 
 
XXX Other supporting documents will be submitted in this section, in a file repository 
(surveys, etc…) XXX 
 
 
 

27 
 
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