Innovations in technology have been a cornerstone in the advancement of anesthesiology and its contributions toward improving patient safety and outcomes. Burgeoning consumer technologies have expectedly moved into the health care space as both technology industry and health care delivery systems enthusiastically explore how new innovations improve patient care, workflow efficiencies, and reduce cost. Perioperative care generates a wealth of data matched by few other domains in medicine. Anesthesiologists are tasked with consuming a multitude of data quickly to avoid risk and keep patients safe. The development of tools to help clinicians collect and process information is rapidly increasing and central to the innovation moving through perioperative medicine, including telemedicine.1,2 Over the next decade, data tasks will be increasingly performed by computer-based systems, offering new, exciting pathways to improved care – if challenges that inevitably accompany such innovation can be overcome. Changes in how data are consumed and acted upon by anesthesiologists and others are under way, with the benefits, challenges, and impact of several key examples described here. In this article, we will focus on the technology innovations that are becoming part of everyday life and how they might impact anesthesiology practices in the future.

Cloud computing, wearable sensors, and the internet of things (IoT)

In place of local servers and expensive infrastructure, more health care systems are migrating their data to the cloud.3 Clouds are not just for passive data storage, they can also allow health care systems to develop applications that automate data migration through their information technology platforms, enabling decision-support systems in anesthesiology and perioperative practices. Infrastructure-, platform-, and software-as-a-service products provide a range of data services, from basic storage and access to more advanced features like embedding “intelligent” models that help users make sense of the information. Advantages include cost savings, data transparency with improved access to providers and patients, and greater capacity to manage “big data” and understand latent insights that are difficult to capture with current systems. The market estimate for cloud computing in health care was nearly $28 billion in 2020 and is expected to increase to $64 billion within the next four years.4 More than 80% of health care organizations have adopted at least one cloud service.5 Software applications and IoT devices, including wearable sensors, are more easily connected to the cloud than with traditional health information systems, creating new possibilities for patient surveillance and risk assessment. The Apple Watch has a single lead ECG function that may be used to detect atrial fibrillation, and Guardian Connect (Medtronic) predicts major blood glucose fluctuations before they occur using IBM Watson technology.6,7 Wearable mobile sensor technologies in uniquely customized form factors are in various phases of development and testing, including devices that can monitor blood pressure, cardiac output, ejection fraction, and other advanced cardiac measurements non-invasively. It is very conceivable that such devices will be in common use as the trend toward less invasive and longitudinal monitoring approaches gain greater footing in perioperative care. The simultaneous emergence of wearable sensors and cloud computing opens new opportunities for remote surveillance monitoring and biometric tracking of a variety of physiological indices, both inside and outside the hospital setting. Device measurements can be automatically sent to the cloud to alert clinicians of potential patient risks, making early, cost-saving interventions possible. Data security concerns, lack of specialists with expertise in developing and managing a health care system's cloud presence, and vendor lock-in where a customer is limited to a single vendor for practical, financial, or other reasons remain challenges to broad adoption of cloud technology.8 Its potential impact rests in capturing valuable new health information and increasing interoperability between devices and datasets, while making data more broadly available to stakeholders and artificial intelligence (AI) applications.9 

Machine learning (ML) and predictive analytics

Approval of diabetic retinopathy application based on machine learning (ML) by the FDA opened the door for the use of ML in clinical medicine.10 The confluence of cheap, available computing power, sophisticated machine-learning algorithms, and data at scale have made it possible to develop powerful, highly accurate models predicting poor outcomes after surgery. Those for mortality, hospital length of stay, and intermediate outcomes including acute kidney injury, deep venous thrombosis, re-intubation, and delirium, among others, have all been developed.11-14 Models predicting which patients require therapy, and the effect of treatment, have more recently been reported as well as those predicting adverse events.15,16 Perhaps the best example is intraoperative hypotension, a common adverse event with a clear relationship to multiple organ system injuries.17 The Hypotension Prediction Index (HPI) was one of the first models created that targeted hypotension and the only one demonstrated to reduce hypotension exposure when incorporated into care delivery, compared to care without it.18,19 New-age prediction models offer unprecedented opportunities to avoid poor outcomes with early interventions, but data sharing and standardization, patient safety, accountability and transparency for predictions made, and AI literacy for providers and patients are all areas where further work is required to bring these advances into routine clinical care.20 

As we expand our understanding of ML algorithms, models, and databases in building the models, we will further refine such technologies in daily clinical decision-making.21,22 While ML is a way for the computer systems to learn patterns, the professional societies and individual practitioners are investigating how ML could add value above their clinical decision-making skills23-25 A recent work using EEG as the target to drive remifentanil and propofol titration during anesthetic cases argues that ML-based approaches could supplement our clinical decision trees.26 However, there are areas of concern for the use of ML in anesthesiology and perioperative medicine due to its potential impact on patient safety and reliability, removing the autonomy of clinicians, and negative impact on clinical decision heuristics.27-31 

1) The very fact that the data used to build the models underlying the mythical ML applications are the drivers of ML read-outs tells us the varying degree of patient safety and reliability of ML.27 While models are now being refined with increased data points, we cannot deny the future where we interact with these algorithms. One then questions who (anesthesiologists) or (machine) carries the accountability.10 Because of the potential need for rescuing interventions, it is likely that anesthetic delivery systems would not be fully autonomous, but require some clinician supervision. However, what accountability, and therefore liability, lies with the anesthesiologist versus with the ML system needs to be established.

2) The notion of transferring most of the clinical decision autonomy to a computer system is a very real possibility in specialties such as oncology. However, we believe that in anesthesiology and perioperative medicine, the various acute interventions make it unlikely that the clinical autonomy is transferred to ML systems.32 

3) The potential for any negative impact of ML on clinical decision heuristics would need to be mitigated. Could we forget how to perform a safety check in our anesthesia machines?33 Unfortunately, case reports are published in the literature as we rely more on automated systems. Such potential needs to be mitigated using high-fidelity simulation or prolonging the time to learn the clinical skills.34-36 

Future of ML in anesthesiology: Clinical skills such as central venous catheter placement are not easily replaced by computers or robots, but clinical decision-making will have ML-driven applications in addition to our presence.37,38 The models for the ML should apply multiple variables for developing the algorithms tailored for individual patients, e.g., disparate ethnic groups or residents of different regions may have unique physiologies and environmental factors impacting their clinical presentations.

Automated systems

Incorporating themes described above, but moving beyond systems designed to support human activity, are those that operate independent, or nearly so, of clinicians to deliver care autonomously. In 2018, the FDA approved the first autonomous AI diagnostic system, IDx-DR, which serves as a screening tool for more severe forms of diabetic retinopathy in undiagnosed adults 22 years of age and older using digital photographs of a patient's retina.39 There are similar, albeit less developed, systems that maintain hemodynamic and hypnotic targets during surgery, primary tasks of modern anesthesia providers, and use AI-guided administration of vasoactive, fluid, and anesthetic therapies.26,40 Early testing suggests systems outperform humans acting alone, consistent with prior findings that humans and machines perform best when working together.41 Fully automated systems share the same risks as their component technologies (see above) but possess additional risks, including marginalizing the physician-patient relationship and skill atrophy among clinicians.42 Nonetheless, transitioning low-risk, time-consuming tasks to machines would allow humans to focus on more complex activities to increase efficiency and optimize resources.

Telemedicine in anesthesiology

Telemedicine has steadily grown over the past decade, but the COVID-19 pandemic dramatically accelerated its adoption. It is expected that even with the receding of pandemic-related restrictions, most outpatient practices will continue virtual patient care at a significant level. Many anesthesiology preoperative clinics similarly have adapted and will grow this practice through a variety of technologies. Telemedicine pre-anesthesia evaluation could provide for safe patient care while reducing patient inconvenience, time away from work, travel time, and cost associated with an extra in-person presurgical evaluation visit. The initial studies in telemedicine requested patients to report to a telemedical site that housed equipment to accomplish both the history and physical examination portion, but patients were not willing to do this. Additionally, there was marked concern about the potential for no or poor physical examination and both cardiopulmonary and airway exams. But as technology has developed with increased access, telemedicine has gradually moved into the patient's home, where mobile, tablet, or computer devices with an internet connection are all that is necessary for an evaluation.43 

Many patients have indeed received successful telemedical preoperative evaluations, with histories being done virtually and electronic stethoscopes allowing cardiopulmonary examination. In general, studies that compare telemedicine visits to in-person visits report high concordance of physical examinations, reduced cancellations on the day of surgery, improved patient satisfaction, reduction in cost, and less travel time and time away from work.44,45 

Intraoperative applications of telemedicine exist, although more compelling evidence on the utility and at-scale implementation needs to be generated for these innovations to significantly change practices. An example of a successful intraoperative tele-anesthesia set up incudes a telemedicine unit that integrated all physiologic data (ECG, oxygen saturation, carbon dioxide, blood pressure, breath and heart sounds) for bidirectional voice and live videoconferencing over a low-bandwidth satellite between Ecuador and the United States.46 And a pilot study in patients undergoing surgery reported the feasibility of EEG-controlled closed-loop administration of propofol over a distance of 200 km, using a teletherapeutic propofol infusion, connected through the internet with a remote computer, while using EEG monitoring to adjust the propofol dosage.47 

In summary, trends toward automation and algorithmic clinical and operational decision-support systems are strongly evident in most health care practices, including anesthesiology.48 In the future, as troves of data are generated from electronic records, notes, images, sensors and devices, it will become imperative for health care systems to employ AI-enabled technologies to help clinicians and researchers develop greater insights from these multidimensional data sets in order to improve monitoring, diagnostics, and therapies in patient care. While technology innovations are likely to have a significant impact on anesthesiology practices, challenges in adoption remain and will require focused attention. These include a perceived threat and competition with anesthesiologists' practices and expertise, data privacy concerns, the ethics of machine and algorithm performance versus guiding clinical decision-making, the evolving regulatory landscape for digital diagnostics and therapeutics, and technical and financial challenges in IT interoperability and integration.

Michael Schnetz, MD, PhD, Assistant Professor, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

Michael Schnetz, MD, PhD, Assistant Professor, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

Murat Kaynar, MD, MPH, Professor, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

Murat Kaynar, MD, MPH, Professor, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

Stephen Esper, MD, MBA, Associate Professor, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

Stephen Esper, MD, MBA, Associate Professor, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

Aman Mahajan, MD, PhD, MBA, Peter and Eva Safar Professor and Chair, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

Aman Mahajan, MD, PhD, MBA, Peter and Eva Safar Professor and Chair, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center and School of Medicine.

References

1
Seger
C
,
Cannesson
M
.
Recent advances in the technology of anesthesia
.
F1000Res
2020
;
9
. https://doi.org/10.12688/f1000research.24059.1
2
Rothman
BS
,
Gupta
RK
,
McEvoy
MD
.
Mobile Technology in the Perioperative Arena: Rapid Evolution and Future Disruption
.
Anesth Analg
2017
;
124
:
807
18
. https://doi.org/10.1213/ane.0000000000001858
3
O'Dowd
E
.
Understanding HIPAA-Compliant Cloud Options for Health IT
. HITInfrastructure website. https://hitinfrastructure.com/features/understanding-hipaa-compliant-cloud-options-for-health-it
4
Mehra
A
.
Healthcare Cloud Computing Market worth $64.7 billion by 2025
. Health Cloud Solutions website. https://healthcloudsolutions.org/healthcare-cloud-computing-market-expected-to-grow-to-64-7-billion-by-2025.
5
Cloud Computing Used by 83% of Healthcare Organizations. Health care global website. https://healthcareglobal.com/technology-and-ai-3/cloud-computing-used-83-healthcare-organizations.
6
Seshadri
DR
,
Bittel
B
,
Browsky
D
,
Houghtaling
P
,
Drummond
CK
,
Desai
MY
,
Gillinov
AM
.
Accuracy of Apple Watch for Detection of Atrial Fibrillation
.
Circulation
2020
;
141
:
702
3
. https://doi.org/10.1161/circulationaha.119.044126
8
Kuo
AM
.
Opportunities and challenges of cloud computing to improve health care services
.
J Med Internet Res
2011
;
13
:
e67
. https://dx.doi.org/10.2196%2Fjmir.1867
9
Barton
J
,
Chin
F
,
Dotson
C
,
Edwards
M
,
Greer
M
,
Koumpan
E
,
Meegan
J
,
Melrose
P
,
Mujib
R
,
Osaigbovo
O
,
Parker
B
,
Sanders
J
,
Schalk
K
,
Scott
K
,
Thimsen
J
,
Underwood
M
.
Impact of Cloud Computing on Healthcare
. Version 2.0. 2017. https://www.omg.org/cloud/deliverables/CSCC-Impact-of-Cloud-Computing-on-Healthcare.pdf
10
Abramoff
MD
,
Lavin
PT
,
Birch
M
,
Shah
N
,
Folk
JC
.
Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
.
NPJ Digit Med
2018
;
1
:
39
. https://doi.org/10.1038/s41746-018-0040-6
11
Fritz
BA
,
Cui
Z
,
Zhang
M
,
He
Y
,
Chen
Y
,
Kronzer
A
,
Ben Abdallah
A
,
King
CR
,
Avidan
MS
.
Deep-learning model for predicting 30-day postoperative mortality
.
Br J Anaesth
2019
;
123
:
688
95
. https://doi.org/10.1016/j.bja.2019.07.025
12
Jo
YY
,
Han
J
,
Park
HW
,
Jung
H
,
Lee
JD
,
Jung
J
,
Cha
HS
,
Sohn
DK
,
Hwangbo
Y
.
Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study
.
JMIR Med Inform
2021
;
9
:
e23147
. https://doi.org/10.2196/23147
13
Xue
B
,
Li
D
,
Lu
C
,
King
CR
,
Wildes
T
,
Avidan
MS
,
Kannampallil
T
,
Abraham
J
.
Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications
.
JAMA Netw Open
2021
;
4
:
e212240
. https://doi.org/10.1001/jamanetworkopen.2021.2240
14
Hofer
IS
,
Lee
C
,
Gabel
E
,
Baldi
P
,
Cannesson
M
.
Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set
.
NPJ Digit Med
2020
;
3
:
58
. https://doi.org/10.1038/s41746-020-0248-0
15
Jalali
A
,
Lonsdale
H
,
Zamora
LV
,
Ahumada
L
,
Nguyen
ATH
,
Rehman
M
,
Fackler
J
,
Stricker
PA
,
Fernandez
AM
,
Pediatric Craniofacial Collaborative G
.
Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset
.
Anesth Analg
2021
;
132
:
160
71
. https://doi.org/10.1213/ane.0000000000004988
16
Hale
AT
,
Riva-Cambrin
J
,
Wellons
JC
,
Jackson
EM
,
Kestle
JRW
,
Naftel
RP
,
Hankinson
TC
,
Shannon
CN
,
Hydrocephalus Clinical Research N
.
Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network
.
Childs Nerv Syst
2021
;
37
:
1485
94
. https://doi.org/10.1007/s00381-021-05061-7
17
Wijnberge
M
,
Schenk
J
,
Bulle
E
,
Vlaar
AP
,
Maheshwari
K
,
Hollmann
MW
,
Binnekade
JM
,
Geerts
BF
,
Veelo
DP
.
Association of intraoperative hypotension with postoperative morbidity and mortality: systematic review and meta-analysis
.
BJS Open
2021
;
5
. https://doi.org/10.1093/bjsopen/zraa018
18
Hatib
F
,
Jian
Z
,
Buddi
S
,
Lee
C
,
Settels
J
,
Sibert
K
,
Rinehart
J
,
Cannesson
M
.
Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis
.
Anesthesiology
2018
;
129
:
663
74
. https://doi.org/10.1097/aln.0000000000002300
19
Wijnberge
M
,
Geerts
BF
,
Hol
L
,
Lemmers
N
,
Mulder
MP
,
Berge
P
,
Schenk
J
,
Terwindt
LE
,
Hollmann
MW
,
Vlaar
AP
,
Veelo
DP
.
Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial
.
JAMA
2020
;
323
:
1052
60
. https://doi.org/10.1001/jama.2020.0592
20
He
J
,
Baxter
SL
,
Xu
J
,
Xu
J
,
Zhou
X
,
Zhang
K
.
The practical implementation of artificial intelligence technologies in medicine
.
Nat Med
2019
;
25
:
30
6
. https://doi.org/10.1038/s41591-018-0307-0
21
Diao
JA
,
Kohane
IS
,
Manrai
AK
.
Biomedical informatics and machine learning for clinical genomics
.
Hum Mol Genet
2018
;
27
:R
29
34
. https://doi.org/10.1093/hmg/ddy088
22
USFDA: Artificial Intelligence and Machine Learning in Software as a Medical Device, US Food & Drug Administration edition. 2021. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
23
Chen
PC
,
Liu
Y
,
Peng
L
.
How to develop machine learning models for healthcare
.
Nat Mater
2019
;
18
:
410
4
. https://doi.org/10.1038/s41563-019-0345-0
24
Suleiman
D
,
Al-Zewairi
M
,
Naymat
G
.
An Empirical Evaluation of Intelligent Machine Learning Algorithms under Big Data Processing Systems
.
Procedia Computer Science
2017
;
113
:
539
44
. https://doi.org/10.1016/j.procs.2017.08.270
25
Alexander
JC
,
Joshi
GP
.
Anesthesiology, automation, and artificial intelligence
.
Proc (Bayl Univ Med Cent)
2018
;
31
:
117
9
. https://doi.org/10.1080/08998280.2017.1391036
26
Lee
HC
,
Ryu
HG
,
Chung
EJ
,
Jung
CW
.
Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach
.
Anesthesiology
2018
;
128
:
492
501
. https://doi.org/10.1097/aln.0000000000001892
27
Char
DS
,
Shah
NH
,
Magnus
D
.
Implementing Machine Learning in Health Care - Addressing Ethical Challenges
.
N Engl J Med
2018
;
378
:
981
3
. https://dx.doi.org/10.1056%2FNEJMp1714229
28
Fenton
JJ
,
Taplin
SH
,
Carney
PA
,
Abraham
L
,
Sickles
EA
,
D'Orsi
C
,
Berns
EA
,
Cutter
G
,
Hendrick
RE
,
Barlow
WE
,
Elmore
JG
.
Influence of computer-aided detection on performance of screening mammography
.
N Engl J Med
2007
;
356
:
1399
409
. https://doi.org/10.1056/nejmoa066099
29
Gambus
P
,
Shafer
SL
.
Artificial Intelligence for Everyone
.
Anesthesiology
2018
;
128
:
431
3
. https://doi.org/10.1097/aln.0000000000001984
30
Banga
B
.
Global Precision Medicine Market to Reach $216.75 Billion by 2028
. PR Newswire website. 2019. https://www.prnewswire.com/news-releases/global-precision-medicine-market-to-reach-216-75-billion-by-2028-891830298.html
31
Hughes
TM
,
Dossett
LA
,
Hawley
ST
,
Telem
DA
:
Recognizing Heuristics and Bias in Clinical Decision-making
.
Ann Surg
2020
;
271
:
813
4
. https://dx.doi.org/10.3389%2Ffpsyg.2020.02080
32
Bitterman
DS
,
Aerts
H
,
Mak
RH
.
Approaching autonomy in medical artificial intelligence
.
Lancet Digit Health
2020
;
2
:
e447
9
. https://doi.org/10.1016/s2589-7500(20)30187-4
33
Eisenkraft
JB
.
Editorial comment: mask induction despite circuit obstruction: an unrecognized hazard of relying on automated machine check technology
.
A A Case Rep
2014
;
2
:
147
8
. https://doi.org/10.1213/xaa.0000000000000035
34
Casner
SM
,
Geven
RW
,
Recker
MP
,
Schooler
JW
.
The retention of manual flying skills in the automated cockpit
.
Hum Factors
2014
;
56
:
1506
16
. https://doi.org/10.1177/0018720814535628
35
Walsh
K
.
Simulation: the need for a balanced view
.
J Biomed Res
2013
;
27
:
243
4
. https://dx.doi.org/10.7555%2FJBR.27.20130048
36
Fraser
AB
,
Stodel
EJ
,
Chaput
AJ
.
Curriculum reform for residency training: competence, change, and opportunities for leadership
.
Can J Anaesth
2016
;
63
:
875
84
. https://doi.org/10.1007/s12630-016-0637-7
37
Wang
X
,
Tao
Y
,
Tao
X
,
Chen
J
,
Jin
Y
,
Shan
Z
,
Tan
J
,
Cao
Q
,
Pan
T
.
An original design of remote robot-assisted intubation system
.
Sci Rep
2018
;
8
:
13403
. https://dx.doi.org/10.1038%2Fs41598-018-31607-y
38
Cheng
Z
,
Davies
BL
,
Caldwell
DG
,
Barresi
G
,
Xu
Q
,
Mattos
LS
.
A hand-held robotic device for peripheral intravenous catheterization
.
Proc Inst Mech Eng H
2017
;
231
:
1165
77
. https://doi.org/10.1177/0954411917737328
39
FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. FDA news release website. 2018. https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye
40
Joosten
A
,
Rinehart
J
,
Van der Linden
P
,
Alexander
B
,
Penna
C
,
De Montblanc
J
,
Cannesson
M
,
Vincent
JL
,
Vicaut
E
,
Duranteau
J
.
Computer-assisted Individualized Hemodynamic Management Reduces Intraoperative Hypotension in Intermediate- and High-risk Surgery: A Randomized Controlled Trial
.
Anesthesiology
2021
;
135
:
258
72
. https://doi.org/10.1097/aln.0000000000003807
41
Lakhani
P
,
Sundaram
B
.
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
.
Radiology
2017
;
284
:
574
82
. https://doi.org/10.1148/radiol.2017162326
42
Char
DS
,
Burgart
A
.
Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges
.
Anesth Analg
2020
;
130
:
1709
12
. https://doi.org/10.1213/ane.0000000000004656
43
Dilisio
RP
,
Dilisio
AJ
,
Weiner
MM
.
Preoperative virtual screening examination of the airway
.
J Clin Anesth
2014
;
26
:
315
7
. https://doi.org/10.1016/j.jclinane.2013.12.010
44
Mullen-Fortino
M
,
Rising
KL
,
Duckworth
J
,
Gwynn
V
,
Sites
FD
,
Hollander
JE
.
Presurgical Assessment Using Telemedicine Technology: Impact on Efficiency, Effectiveness, and Patient Experience of Care
.
Telemed J E Health
2019
;
25
:
137
42
. https://doi.org/10.1089/tmj.2017.0133
45
Applegate
RL
, 2nd
,
Gildea
B
,
Patchin
R
,
Rook
JL
,
Wolford
B
,
Nyirady
J
,
Dawes
TA
,
Faltys
J
,
Ramsingh
DS
,
Stier
G
.
Telemedicine pre-anesthesia evaluation: a randomized pilot trial
.
Telemed J E Health
2013
;
19
:
211
6
. https://doi.org/10.1089/tmj.2012.0132
46
Cone
SW
,
Gehr
L
,
Hummel
R
,
Merrell
RC
.
Remote anesthetic monitoring using satellite telecommunications and the Internet
.
Anesth Analg
2006
;
102
:
1463
7
. https://doi.org/10.1213/01.ane.0000204303.21165.a4
47
Fiadjoe
J
,
Gurnaney
H
,
Muralidhar
K
,
Mohanty
S
,
Kumar
J
,
Viswanath
R
,
Sonar
S
,
Dunn
S
,
Rehman
M
.
Telemedicine consultation and monitoring for pediatric liver transplant
.
Anesth Analg
2009
;
108
:
1212
4
. https://doi.org/10.1213/ane.0b013e318198f786
48
Barry
ME
,
Hochman
BR
,
Lane-Fall
MB
,
Zappile
D
,
Holena
DN
,
Smith
BP
,
Kaplan
LJ
,
Huffenberger
A
,
Reilly
PM
,
Pascual
JL
.
Leveraging Telemedicine Infrastructure to Monitor Quality of Operating Room to Intensive Care Unit Handoffs
.
Acad Med
2017
;
92
:
1035
42
. https://doi.org/10.1097/acm.0000000000001590