ArticlePDF AvailableLiterature Review

ECG Interpretation Proficiency of Healthcare Professionals

Authors:

Abstract and Figures

Introduction: ECG interpretation is essential in modern medicine, yet achieving and maintaining competency can be challenging for healthcare professionals. Quantifying proficiency gaps can inform educational interventions for addressing these challenges. Methods: Medical professionals from diverse disciplines and training levels interpreted 30 12-lead ECGs with common urgent and non-urgent findings. Average accuracy (percentage of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on a scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]) were evaluated. Results: Among the 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FITs), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, and 249 (21%) allied health professionals (AHPs). Overall, participants achieved an average overall accuracy of 56.4 ± 17.2%, interpretation time of 142 ± 67 seconds, and confidence of 0.83 ± 0.53. Cardiology FITs demonstrated superior performance across all metrics. PCPs had a higher accuracy compared to nurses and APPs (58.1% vs. 46.8% and 50.6%; P<0.01), but a lower accuracy than resident physicians (58.1% vs. 59.7%; P<0.01). AHPs outperformed nurses and APPs in every metric and showed comparable performance to resident physicians and PCPs. Conclusions: Our findings highlight significant gaps in the ECG interpretation proficiency among healthcare professionals.
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&
Exploring Factors Influencing ECG
Interpretation Proficiency of Medical
Professionals
Anthony H. Kashou, MD
a
*, Peter A. Noseworthy, MD
a
,
Thomas J. Beckman, MD
b
, Nandan S. Anavekar, MD
a
,
Kurt B. Angstman, MD
c
, Michael W. Cullen, MD
a
,
Benjamin J. Sandefur, MD
d
, Paul A. Friedman, MD
a
,
Brian P. Shapiro, MD
e
, Brandon W. Wiley, MD
f
,
Andrew M. Kates, MD
g
, Andrew Braisted, MHSA
h
,
David Huneycutt, MD
h
, Adrian Baranchuk, MD
i
,
John W. Beard, MD
j
, Scott Kerwin, MN
j
,
Brian Young, MS
j
, Ian Rowlandson, MS
j
,
Stephen J. Knohl, MD
k
, Kevin OBrien, MD
l
, and
Adam M. May, MD
g
From the
a
Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA,
b
Internal Medicine,
Mayo Clinic, Rochester, Minnesota, USA,
c
Family Medicine, Mayo Clinic, Rochester, Minnesota,
USA,
d
Emergency Medicine, Mayo Clinic, Rochester, Minnesota, USA,
e
Cardiovascular Medicine,
Mayo Clinic, Jacksonville, Florida, USA,
f
Cardiovascular Medicine, Keck School of Medicine,
University of Southern California, Los Angeles California, USA,
g
Cardiovascular Medicine,
Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA,
h
Cardiovascular
Medicine, HCA Healthcare, Nashville, Tennessee, USA,
i
Cardiovascular Medicine, Queen’s Univer-
sity, Kingston, Ontario, Canada,
j
GE HealthCare, Wauwatosa, Wisconsin, USA,
k
Internal Medicine,
SUNY Upstate Medical University, Syracuse, New York, USA and
l
Internal Medicine, University of
South Florida, Tampa, Florida, USA.
Abstract: The electrocardiogram (ECG) is a crucial diag-
nostic tool in medicine with concerns about its interpreta-
tion proficiency across various medical disciplines. Our
study aimed to explore potential causes of these issues and
identify areas requiring improvement. A survey was con-
ducted among medical professionals to understand their
The funding supported in part by GE HealthCare (Milwaukee, WI) and NIH T32 HL007111.
*Corresponding author: Anthony H. Kashou, MD, Department of Cardiovascular Medicine, Mayo Clinic, 200
First Street SW, Rochester, MN, 55905. E-mail: kashou.anthony@mayo.edu
Curr Probl Cardiol 2023;48:101865
0146-2806/$ see front matter
https://doi.org/10.1016/j.cpcardiol.2023.101865
Curr Probl Cardiol, October 2023 1
experiences with ECG interpretation and education. A
total of 2515 participants from diverse medical back-
grounds were surveyed. A total of 1989 (79%) partici-
pants reported ECG interpretation as part of their
practice. However, 45% expressed discomfort with inde-
pendent interpretation. A significant 73% received less
than 5 hours of ECG-specific education, with 45% report-
ing no education at all. Also, 87% reported limited or no
expert supervision. Nearly all medical professionals (2461,
98%) expressed a desire for more ECG education. These
findings were consistent across all groups and did not
vary between primary care physicians, cardiology FIT,
resident physicians, medical students, APPs, nurses, physi-
cians, and nonphysicians. This study reveals substantial
deficiencies in ECG interpretation training, supervision,
and confidence among medical professionals, despite a
strong interest in increased ECG education. (Curr Probl
Cardiol 2023;48:101865.)
Introduction
&
Medical providers are required to interpret 12-lead electrocardio-
grams (ECGs) efficiently and accurately. Incorrect ECG inter-
pretation can lead to misdiagnosis and delayed treatment or use
of inappropriate treatment methods. Therefore, competency in ECG inter-
pretation is an expectation for almost all medical providers.
1-6
Although the issue of ECG interpretation proficiency among medical
professionals is a longstanding concern, the underlying causes and the
scope of these inadequacies remain unclear.
5-15
Our goals in this study
were to investigate factors contributing to proficiency deficiencies in
ECG interpretation and identify potential areas for improvement. A better
understanding of these factors will enable the development of compre-
hensive, scalable, and widely accessible educational solutions that can
effectively address gaps in ECG interpretation proficiency.
Methods
Study Design
We designed an electronic survey using Qualtrics software to gather
data. The survey contained questions about general demographics,
2 Curr Probl Cardiol, October 2023
profession, average number of weekly ECG interpretations, previous
ECG training, the extent of expert ECG interpreter supervision, and ECG
interpretation comfort level. The survey was part of the EDUcation Cur-
riculum Assessment for Teaching Electrocardiography (EDUCATE)
Trial and was conducted according to the Consensus-Based Checklist for
Reporting of Survey Studies (CROSS) proposed by the Enhancing the
QUAlity and Transparency Of health Research (EQUATOR)
Network.
16,17
Study Participants
We recruited medical professionals aged 18 or older, who already
completed their professional training or were currently enrolled in a train-
ing program. Participants included medical students, resident physicians
(including internal medicine, emergency medicine, and family medicine
residents), cardiology fellows-in-training (FIT), primary care physicians
(including family medicine and internal medicine physicians), nurses,
advanced practice providers (APPs, including physician assistants and
nurse practitioners), and allied health professionals (including ECG tech-
nicians, emergency medicine technicians, and paramedics). Physicians
were defined as primary care physicians, cardiology FIT, and resident
physicians, while nonphysicians were defined as APPs, nurses, and allied
health professionals. Practicing cardiologists and emergency medicine
physicians were excluded.
Ethical Considerations
We obtained approval from the educational review committee and
institutional review board (IRB) at the Mayo Clinic, the IRB at Washing-
ton University School of Medicine in St. Louis, SUNY Upstate Medical
University, and Keck School of Medicine at the University of Southern
California, as well as WCG IRB, an external IRB review company. Study
approval and support was obtained from all relevant academic and pro-
fessional leadership stakeholders or committees prior to recruitment.
Survey Questions
The survey comprised of multiple questions and response items that
delved into various aspects of participants’ current ECG interpretation
practices, prior ECG interpretation education, and their inclination
towards receiving additional ECG interpretation education. Table 1 out-
lines the items included in the survey.
Curr Probl Cardiol, October 2023 3
TABLE 1. Survey respondent characteristics
CharacteristicNo. (%)
Age distribution
18-25 years 419 (17)
26-35 years 1294 (51)
36-45 years 402 (16)
46-55 years 244 (10)
>55 years 156 (6)
Sex
Female 1184 (47)
Male 1331 (53)
Location
United States 1401 (56)
Outside United States 1114 (44)
Professional group
Primary care physicians 205 (8)
Cardiology fellows-in-training 255 (10)
Resident physicians 539 (21)
Medical students 350 (14)
Advanced practice providers 211 (8)
Nurses 250 (10)
Allied health professionals 705 (28)
Physicians*999 (40)
Nonphysicians
y
1166 (46)
Average ECG interpretations
0 per week 533 (21)
1-10 per week 1338 (53)
11-25 per week 416 (17)
>25 per week 228 (9)
ECG interpretation responsibility
Directly impacts patient care 1608 (64)
Indirectly impacts patient care 381 (15)
No impact on patient care 144 (6)
Not applicable 382 (15)
ECG interpretation comfort
Uncomfortable (low) 1125 (45)
Somewhat comfortable (moderate) 1082 (43)
Comfortable (high) 308 (12)
Prior dedicated ECG training
0 hours 1133 (45)
<5 hours 709 (28)
5-15 hours 436 (17)
>15 hours 237 (9)
Expert ECG interpreter supervision
None 992 (39)
Rarely 635 (25)
Somewhat often 548 (22)
Often 232 (9)
Very often 108 (4)
*Physicians include primary care physicians, cardiology fellows-in-training, and resident
physicians.
yNonphysicians include advanced practice providers, nurses, and allied health professionals.
4 Curr Probl Cardiol, October 2023
Statistical Analysis
All survey data were exported from Qualtrics into statistical analysis
software package, MedCalc for Windows, version 19.4 (MedCalc Soft-
ware, Ostend, Belgium). We used descriptive statistics to summarize the
survey data, with nominal variables reported as a count (percent of the
total). Comparison between groups were made using the Chi-square test
based on the frequency of expected values. We considered statistical sig-
nificance with a two-tailed alpha level of <0.05.
Results
Respondent Characteristics
Table 1 provides an overview of the baseline characteristics of survey
respondents. Of 3500 medical professionals invited to take part, 2515
completed the entire survey, with 1184 (47%) of them being female.
Most participants were aged 26-34 years (1294, 51%) and from the
United States (1401, 56%). The cohort consisted of 205 (8%) primary
care physicians, 255 (10%) cardiology FIT, 539 (21%) resident physi-
cians, 350 (14%) medical students, 211 (8%) APPs, 250 (10%) nurses,
705 (28%) allied health professionals, 999 (40%) physicians, and 1166
(46%) nonphysicians.
Survey Results
Figure and Table 2 summarize survey results. All findings were consis-
tent across all medical professional groups (P<0.001) and did not vary
between (1) primary care physicians, cardiology FIT, resident physicians,
and medical students, (2) APPs and nurses, or (3) physicians and
nonphysicians.
ECG Interpretation Responsibility
Most participants (1338, 53%) reported interpreting an average of 1-10
ECGs per week. Meanwhile, 416 (17%) and 228 (9%) participants
reported interpreting an average of 11-25 and over 25 ECGs per week,
respectively. Of the 533 (21%) participants who reported zero average
weekly ECG interpretations, 219 (41%) were medical students.
A significant majority of medical professionals (1989, 79%) reported
that their ECG interpretation responsibilities have a direct (1608, 64%) or
indirect (381, 15%) impact on patient care. Only a small percentage of
Curr Probl Cardiol, October 2023 5
FIG. Survey results from all respondents, physicians, and nonphysicians. Survey responses regarding their (A) dedicated ECG training hours, (B) frequency of expert
interpreter supervision, (C) independent ECG interpretation comfort, and (D) desire for further ECG education. The top row represents the results ofallrespondents
(N = 2515). The middle row displays results of physicians (n = 999), which includes primary care physicians, cardiology fellows-in-training, and resident physicians. The
bottom row summarizes the results from nonphysicians (n = 1166), which includes advanced practice providers, nurses, and allied health professionals (Color version of
gure is available online.)
6 Curr Probl Cardiol, October 2023
TABLE 2. Respondent feedback on average number of weekly ECG interpretations, dedicated ECG training hours, independent ECG interpretation comfort,
and frequency of expert supervision
Professional group N Weekly interpretations (#) Dedicated education (hours) Interpretation comfort Expert supervision
0 1-10 11-25 >25 0 <5 5-15 >15 Low Moderate High None Rarely Somewhat
often
Often Very
Often
Primary care physicians 205 11
(5)
120
(59)
40
(20)
34
(16)
92
(45)
59
(29)
42
(20)
12
(6)
58
(28)
110
(54)
37
(18)
65
(32)
58
(28)
59
(29)
17
(8)
6
(3)
Cardiology fellows-in-training 255 6
(2)
69
(27)
102
(40)
78
(31)
105
(41)
63
(25)
46
(18)
41
(16)
38
(15)
132
(52)
85
(33)
61
(24)
72
(28)
59
(23)
37
(15)
26
(10)
Resident physicians 539 35
(7)
368
(68)
104
(19)
32
(6)
279
(52)
152
(28)
81
(15)
27
(5)
248
(46)
258
(48)
33
(6)
165
(31)
183
(34)
130
(24)
47
(9)
14
(3)
Medical students 350 220
(63)
123
(35)
5
(1)
2
(1)
167
(48)
121
(35)
48
(14)
14
(4)
262
(75)
72
(21)
16
(5)
204
(58)
77
(22)
45
(13)
18
(5)
6
(2)
Advanced practice providers 211 31
(15)
123
(58)
37
(18)
20
(9)
103
(49)
58
(28)
41
(19)
9
(4)
105
(50)
85
(40)
21
(10)
88
(42)
48
(23)
39
(19)
23
(11)
13
(6)
Nurses 250 86
(34)
136
(54)
17
(7)
11
(4)
144
(58)
68
(27)
24
(10)
14
(6)
155
(62)
81
(32)
14
(6)
153
(61)
45
(18)
29
(12)
13
(5)
10
(4)
Allied health professionals 705 144
(20)
399
(57)
111
(16)
51
(7)
243
(34)
188
(27)
154
(22)
120
(17)
259
(37)
344
(49)
102
(14)
256
(36)
152
(22)
187
(27)
77
(11)
33
(5)
Physicians*999 52
(5)
557
(56)
246
(25)
144
(14)
476
(48)
274
(27)
169
(17)
80
(8)
344
(34)
500
(50)
155
(16)
291
(29)
313
(31)
248
(25)
101
(10)
46
(5)
Nonphysicians
y
1166 261
(22)
658
(56)
165
(14)
82
(7)
490
(42)
314
(27)
219
(19)
143
(12)
519
(45)
510
(44)
137
(12)
497
(43)
245
(21)
255
(22)
113
(10)
56
(5)
All participants 2515 533
(21)
1338
(53)
416
(17)
228
(9)
1133
(45)
709
(28)
436
(17)
237
(9)
1125
(45)
1082
(43)
308
(12)
992
(39)
635
(25)
548
(22)
232
(9)
108
(4)
Group comparisons a, b, c, d a, b, c, d a, b, c, d a, b, c, d
a: P-value <0.001 between all medical professional groups (excluding physician and nonphysician groups)
b: P-value <0.001 between primary care physicians, cardiology fellows-in-training, resident physicians, and medical students
c: P-value <0.001 between advanced practice providers and nurses
d: P-value <0.001 between physicians
1
and nonphysicians
2
Values represent the number (%) of respondents.
*Physicians include primary care physicians, cardiology fellows-in-training, and resident physicians.
yNonphysicians include advanced practice providers, nurses, and allied health professionals.
Curr Probl Cardiol, October 2023 7
participants (526, 21%) reported no impact on patient care, with the
majority (350, 67%) of those being medical students.
ECG Interpretation Comfort
Most participants (2207, 88%) were uncomfortable or only somewhat
comfortable with independent ECG interpretation, while only 308 (12%)
reported feeling comfortable. This trend was consistent across all medical
professional groups, including physicians and nonphysicians. Medical
students and nurses reported the lowest comfort levels. Cardiology FIT
reported the highest level of comfort, with 84 (33%) feeling comfortable
in independent ECG interpretation.
Prior ECG Interpretation Education
Overall, most medical professionals (1842, 73%) reported receiving
less than 5 hours of dedicated ECG education during training, with nearly
half of them (1133, 45%) reporting no education. This trend was consis-
tent across participants within and outside the United States, with 1041
(74%) and 801 (72%) reporting less than 5 hours of dedicated education,
and 641 (46%) and 492 (44%) reporting no education, respectively.
Among practicing and training physicians, 1038 (77%) reported less
than 5 hours of dedicated education, with 643 (48%) reporting no dedi-
cated education during medical training. Even after excluding medical
students, there was virtually no difference, with 750 (75%) and 476
(48%) of physicians reporting less than 5 hours or no dedicated educa-
tion, respectively. Similar findings were observed from nonphysicians
with 804 (69%) and 491 (42%) reporting less than 5 hours or no dedicated
education, respectively. Similarly, 212 (85%) nurses and 161 (76%) APPs
reported less than 5 hours of dedicated education, with 145 (58%) and
103 (49%) reporting no education, respectively. In contrast, a greater pro-
portion of allied health professionals (465, 66%) reported receiving dedi-
cated education, with 276 (39%) reporting receiving 5 hours or more.
Expert ECG interpretation supervision
Table 2 summarizes the respondents’ feedback on the amount of expert
ECG interpretation supervision. Most respondents (2175, 87%) reported
limited expert ECG interpreter oversight, with 992 (39%), 635 (25%),
and 548 (22%) reporting no, rare, or somewhat frequent review of their
ECG interpretations, respectively. Even 192 (75%) cardiology FIT
reported limited expert ECG interpretation supervision, with 61 (24%),
8 Curr Probl Cardiol, October 2023
72 (28%), and 59 (23%) reporting no, rare, and somewhat frequent review
of their ECG interpretations, respectively. Only 340 (14%) medical pro-
fessionals reported receiving frequent supervision from an expert ECG
interpreter, with 232 (9%) and 108 (4%) reporting supervision often or
very often, respectively.
Desire for ECG Interpretation Training
The overwhelming majority of surveyed participants (2461, 98%)
expressed a desire for more ECG education and training.
Discussion
The issue of inadequate ECG interpretation competency among medi-
cal professionals is not a new problem. Despite the longstanding impor-
tance of ECGs to medical practice, concerns about ECG interpretation
inadequacies have been raised for nearly 50 years,
13-15
and multiple
reports have demonstrated significant deficiencies in ECG interpretation
proficiency across numerous medical disciplines.
5,7-10
Our study sheds
light on a critical reason why major shortcomings might exist.
Our investigation identified notable insufficiencies in the amount of
training and supervision provided to medical professionals in ECG inter-
pretation. Despite the acknowledged importance of ECGs in patient care
and their frequent exposure in clinical practice, our survey respondents
reported major deficits in their comfort level, training, and expert supervi-
sion in ECG interpretation. Most medical professionals reported low or
moderate comfort levels with independent ECG interpretation. A substan-
tial majority received less than 5 hours of dedicated training, with nearly
half stating they had not received any education or training. These find-
ings are particularly alarming because even among those for whom expert
supervision is a widely recommended approach, respondents reported a
lack of expert ECG interpreter supervision.
1-5
Moreover, given the seem-
ingly widespread lack of ECG interpretation education and training,
expert supervision or oversight may be the only means by which patients
receive a skilled ECG interpretation. The dual findings of inadequate
training and limited expert supervision may not only directly contribute
to poor ECG interpretation in clinical practice but also to the widespread
lack of comfort in performing ECG interpretation among medical pro-
viders, as previously reported.
5,7
Our investigation uncovers an unsettling discrepancy between the
responsibility placed on medical professionals for ECG interpretation
Curr Probl Cardiol, October 2023 9
and the amount of training and supervision they receive. In other words,
we reveal a significant gap between the training expected of medical pro-
fessionals and what is needed in real-life clinical practice.
To achieve major improvements in clinical ECG interpretation profi-
ciency, there must be commensurate improvements in the quality and
extent of education and training provided to medical professionals. Fortu-
nately, our study findings have identified significant interest among medi-
cal professionals to enhance their ECG interpretation skills through
dedicated education and training. This presents an opportunity for the
development of contemporary and standardized learning solutions tai-
lored to different learners, with the aim of bridging these gaps and pro-
moting improved ECG interpretation and patient care. Further research
should investigate various educational approaches to identify effective
methods for teaching, acquiring, and maintaining this skill.
Limitations
We must acknowledge the limitations of our work. Firstly, we relied
on self-reported data, which is susceptible to recall and response bias.
Secondly, we did not survey practicing cardiology and emergency medi-
cine providers. Although some similarities could be expected for certain
questions, we cannot generalize our findings to these provider groups.
Lastly, while our study identified deficiencies in ECG training and super-
vision, we cannot definitively conclude that these findings directly corre-
spond to low ECG interpretation proficiency among the surveyed
medical professionals.
Conclusions
This survey of medical professionals from different discipline and
training levels exposed noteworthy inadequacies in ECG interpretation
training, expert supervision, and confidence. Nearly all participants
expressed a desire for more ECG interpretation education and training.
Declaration of Competing Interest
The authors declare the following financial interests/personal relation-
ships which may be considered as potential competing interests: Author
(Anthony Kashou, MD) is the founder and CEO of The EKG Guy, and
has received research funding from GE HealthCare (Milwaukee, WI).
10 Curr Probl Cardiol, October 2023
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Curr Probl Cardiol, October 2023 11
... 4,5 Over the last five decades, numerous reports have identified significant gaps in ECG interpretation skills. [6][7][8][9][10][11][12]2,14 Yet, previous studies have been limited by their lack of participant diversity, narrow topic range, and small sample size, which have constrained a thorough understanding of the issue. Recently, our team conducted a large-scale study 6 with a diverse group of healthcare professionals, demonstrating widespread and pervasive deficits in ECG interpretation skills. ...
... [6][7][8][9][10][11][12]2,14 Yet, previous studies have been limited by their lack of participant diversity, narrow topic range, and small sample size, which have constrained a thorough understanding of the issue. Recently, our team conducted a large-scale study 6 with a diverse group of healthcare professionals, demonstrating widespread and pervasive deficits in ECG interpretation skills. These findings provide yet another compelling reason to address pressing need for effective and widely accessible educational interventions for healthcare professionals. ...
... Furthermore, this study supports and builds on previous research that highlighted significant gaps in ECG interpretation proficiency among healthcare professionals. [6][7][8][9][10][11][12]2,14 Despite consistent improvements made by participants in the intervention groups, we observed persistent shortcomings in ECG interpretation performance. Several factors could have contributed to these lingering deficiencies. ...
... Despite its importance, prior research reveals significant deficiencies in this skill amongst healthcare professionals. [1][2][3][4][5][6][7][8][9][10][11][12][13] It is widely accepted that learners require education, training, and practice to attain ECG interpretation proficiency. Despite several proposed educational interventions, limited evidence-based strategies exist. ...
... The methods of survey data collection and test score tabulation were described in prior work. 12,20 The primary outcome was the overall test score, representing the percentage of correctly identified findings. A total of 69 commonly taught and encountered urgent and nonurgent findings were evaluated in the 30 ECG test set. ...
Article
Introduction: Accurate ECG interpretation is vital, but variations in skills exist among healthcare professionals. This study aims to identify factors contributing to ECG interpretation proficiency. Methods: Survey data and ECG interpretation test scores from participants in the EDUCATE Trial were analyzed to identify predictors of performance for 30 sequential 12-lead ECGs. Non-modifiable factors (being a physician, clinical experience, patient care impact) and modifiable factors (weekly interpretation volume, training hours, expert supervision frequency) were analyzed. Bivariate and multivariate analyses were used to generate a Comprehensive Model (incorporating all factors) and Actionable Model (incorporating modifiable factors only). Results: Among 1206 participants analyzed, there were 72 (6.0%) primary care physicians, 146 (12.1%) cardiology fellows-in-training, 353 (29.3%) resident physicians, 182 (15.1%) medical students, 84 (7.0%) advanced practice providers, 120 (9.9%) nurses, and 249 (20.7%) allied health professionals. Among them, 571 (47.3%) were physicians and 453 (37.6%) were non-physicians. The average test score was 56.4 ± 17.2%. Bivariate analysis demonstrated significant associations between test scores and >10 weekly ECG interpretations, being a physician, >5 training hours, patient care impact, and expert supervision but not clinical experience. In the Comprehensive Model, independent associations were found with weekly interpretation volume (9.9 score increase; 95% CI, 7.9-11.8; P<0.001), being a physician (9.0 score increase; 95% CI, 7.2-10.8; P<0.001), and training hours (5.7 score increase; 95% CI, 3.7-7.6; P<0.001). In the Actionable Model, scores were independently associated with weekly interpretation volume (12.0 score increase; 95% CI, 10.0-14.0; P<0.001) and training hours (4.7 score increase; 95% CI, 2.6-6.7; P<0.001). The Comprehensive and Actionable Models explained 18.7% and 12.3% of the variance in test scores, respectively. Conclusion: Predictors of ECG interpretation proficiency include non-modifiable factors like physician status and modifiable factors such as training hours and weekly ECG interpretation volume.
... Similar to prior work, our study also reveals significant gaps in ECG interpretation performance among various medical professional groups, even in the context of CEI usage (30). While CEI plays a role in improving ECG interpretation, it does not fully supplant the impact of ECG interpretation deficiencies. ...
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Introduction: The interpretation of electrocardiograms (ECGs) involves dynamic interplay between computerized ECG interpretation (CEI) software and human overread. However, the impact of computer ECG interpretation on the performance of healthcare professionals remains largely unexplored. The aim of this study was to evaluate the interpretation proficiency of various medical professional groups, with and without access to the CEI report. Methods: Healthcare professionals from diverse disciplines, training levels, and countries sequentially interpreted 60 standard 12-lead ECGs, demonstrating both urgent and non-urgent findings. The interpretation process consisted of two phases. In the first phase, participants interpreted 30 ECGs with clinical statements. In the second phase, the same 30 ECGs and clinical statements were randomized and accompanied by a CEI report. Diagnostic performance was evaluated based on interpretation accuracy, time per ECG (in seconds [s]), and self-reported confidence (rated 0 [not confident], 1 [somewhat confident], or 2 [confident]). Results: A total of 892 participants from various medical professional groups participated in the study. This cohort included 44 (4.9%) primary care physicians, 123 (13.8%) cardiology fellows-in-training, 259 (29.0%) resident physicians, 137 (15.4%) medical students, 56 (6.3%) advanced practice providers, 82 (9.2%) nurses, and 191 (21.4%) allied health professionals. The inclusion of the CEI was associated with a significant improvement in interpretation accuracy by 15.1% (95% confidence interval, 14.3 to 16.0; P<0.001), decrease in interpretation time by 52 s (-56 to -48; P<0.001), and increase in confidence by 0.06 (0.03 to 0.09; P=0.003). Improvement in interpretation accuracy was seen across all professional subgroups, including primary care physicians by 12.9% (9.4 to 16.3; P=0.003), cardiology fellows-in-training by 10.9% (9.1 to 12.7; P<0.001), resident physicians by 14.4% (13.0 to 15.8; P<0.001), medical students by 19.9% (16.8 to 23.0; P<0.001), advanced practice providers by 17.1% (13.3 to 21.0; P<0.001), nurses by 16.2% (13.4 to 18.9; P<0.001), allied health professionals by 15.0% (13.4 to 16.6; P<0.001), physicians by 13.2% (12.2 to 14.3; P<0.001), and non-physicians by 15.6% (14.3 to 17.0; P<0.001). Conclusion: CEI integration improves ECG interpretation accuracy, efficiency, and confidence among healthcare professionals.
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The widespread prevalence of cardiovascular diseases (CVDs) mandates meticulous and continuous monitoring for effective management and treatment. Wearable technologies have garnered substantial attention due to their seamless integration with bodily movements and biological systems. Researchers are actively exploring wearable technology from multidimensional angles, encompassing materials, design, and bioelectronics, to enhance CVD detection with greater sophistication and comfort. Enduring challenges, notably those surrounding material selection, persist, encompassing biocompatibility, conductivity, sensitivity, accuracy, and flexibility. Addressing these challenges is pivotal for adequate progress in wearable devices across many applications. Here, our review highlights the advancements in developing novel materials tailored for wearable technologies to detect cardiovascular diseases. The paper explicitly accentuates potential materials, architectural designs, operative mechanisms, and recent breakthroughs in flexible wearable sensors for CVD detection. The discussion explores diverse sensing mechanisms to monitor vital cardiac indicators, including piezoelectric, piezoresistive, capacitive, and triboelectric modalities. Furthermore, the paper provides a consolidated overview of contemporary efforts by different research teams in pulse wave sensors, heart sound sensors, ultrasound sensors, wearable ECG electrodes, and electro-biochemical sensors. We envision that the comprehensive analysis and juxtaposition of these distinct sensing mechanisms provide a more nuanced comprehension of their potential applications, constraints, and performance attributes within the wearable CVD health monitoring device framework.
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Introduction: The electrocardiogram (ECG) is a crucial diagnostic tool in medicine with concerns about its interpretation proficiency across various medical disciplines. Our study aimed to explore potential causes of these issues and identify areas requiring improvement. Methods: A survey was conducted among medical professionals to understand their experiences with ECG interpretation and education. Results: A total of 2515 participants from diverse medical backgrounds were surveyed. 1989 (79%) participants reported ECG interpretation as part of their practice. However, 45% expressed discomfort with independent interpretation. A significant 73% received less than 5 hours of ECG-specific education, with 45% reporting no education at all. Also, 87% reported limited or no expert supervision. Nearly all medical professionals (2461, 98%) expressed a desire for more ECG education. These findings were consistent across all groups and did not vary between primary care physicians, cardiology FIT, resident physicians, medical students, APPs, nurses, physicians, and non-physicians. Conclusion: This study reveals substantial deficiencies in ECG interpretation training, supervision, and confidence among medical professionals, despite a strong interest in increased ECG education.
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Aims Identify and synthesize evidence of nurses’ competency in electrocardiogram interpretation in acute care settings. Design Systematic mixed studies review. Data sources Cumulative Index to Nursing and Allied Health Literature, Medline, Scopus and Cochrane were searched in April 2021. Review methods Data were selected using the updated Preferred Reporting Items for Systematic Reviews and Meta‑Analysis framework. A data-based convergent synthesis design using qualitative content analysis was adopted. Quality appraisal was undertaken using validated tools appropriate to study designs of the included papers. Results Forty-three papers were included in this review. Skills and attitudes were not commonly assessed, as most studies referred to ‘competency’ in the context of nurses’ knowledge in electrocardiogram interpretation. Nurses’ knowledge levels in this important nursing role varied notably, which could be partly due to a range of assessment tools being used. Several factors were found to influence nurses’ competency in electrocardiogram interpretation across the included studies from individual, professional and organizational perspectives. Conclusion The definition of ‘competency’ was inconsistent, and nurses’ competency in electrocardiogram interpretation varied from low to high. Nurses identified a lack of regular training and insufficient exposure in electrocardiogram interpretation. Hence, regular, standard training and education are recommended. Also, more research is needed to develop a standardized and comprehensive electrocardiogram interpretation tool, thereby allowing educators to safely assess nurses’ competency. Impact This review addressed questions related to nurses’ competency in electrocardiogram interpretation. The findings highlight varying competency levels and assessment methods. Nurses reported a lack of knowledge and confidence in interpreting electrocardiograms. There is an urgent need to explore opportunities to promote and maintain nurses’ competency in electrocardiogram interpretation.
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Objective To develop an artificial intelligence (AI)–enabled electrocardiogram (ECG) algorithm capable of comprehensive, human-like ECG interpretation and compare its diagnostic performance against conventional ECG interpretation methods. Methods We developed a novel AI-enabled ECG (AI-ECG) algorithm capable of complete 12-lead ECG interpretation. It was trained on nearly 2.5 million standard 12-lead ECGs from over 720,000 adult patients obtained at the Mayo Clinic ECG laboratory between 2007 and 2017. We then compared the need for human over-reading edits of the reports generated by the Marquette 12SL automated computer program, AI-ECG algorithm, and final clinical interpretations on 500 randomly selected ECGs from 500 patients. In a blinded fashion, 3 cardiac electrophysiologists adjudicated each interpretation as (1) ideal (ie, no changes needed), (2) acceptable (ie, minor edits needed), or (3) unacceptable (ie, major edits needed). Results Cardiologists determined that on average 202 (13.5%), 123 (8.2%), and 90 (6.0%) of the interpretations required major edits from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 958 (63.9%), 1058 (70.5%), and 1118 (74.5%) interpretations as ideal from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 340 (22.7%), 319 (21.3%), and 292 (19.5%) interpretations as acceptable from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. Conclusion An AI-ECG algorithm outperforms an existing standard automated computer program and better approximates expert over-read for comprehensive 12-lead ECG interpretation.
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Background Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. Methods We used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model. Findings Our training and validation dataset comprised 180 112 ECGs from 70 692 patients, collected between Jan 1, 2012, and Apr 30, 2019. The test dataset comprised 828 ECGs corresponding to 828 new patients, recorded between Sept 11, 2012, and Aug 30, 2019. At the multilabel level, our deep learning approach to diagnosing heart abnormalities resulted in an exact match in 658 (80%) of 828 ECGs, exceeding the mean performance of physicians (552 [67%] for physicians with 0–6 years of experience; 571 [69%] for physicians with 7–12 years of experience; 621 [75%] for physicians with more than 12 years of experience). Our model had an overall mean F1 score of 0·887 compared with 0·789 for physicians with 0–6 years of experience, 0·815 for physicians with 7–12 years of experience, and 0·831 for physicians with more than 12 years of experience. The model had a mean AUC ROC score of 0·983 (95% CI 0·980–0·986), sensitivity of 0·867 (0·849–0·885) and specificity of 0·995 (0·994–0·996). Promising F1 scores were also obtained from the external public database using our proposed model without any model modifications (mean F1 scores of 0·845 in multilabel and 0·852 in single-label ECGs). Interpretation Our model is more accurate than physicians working in cardiology departments at distinguishing a range of distinct arrhythmias in single-label and multilabel ECGs, laying a promising foundation for computational decision-support systems in clinical applications. Funding National Natural Science Foundation of China and Hubei Science and Technology Project.
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Introduction: Peer-assisted learning has been shown to be an effective teaching and learning method. However, this technique has not been proven in Thai medical school. We aimed to compare the effectiveness of peer-assisted learning and self-study in interpreting an electrocardiogram in Thai medical students. Methods: This is a prospective, randomized controlled trial, conducted in Chonburi teaching hospital, a community hospital affiliated with Chulalongkorn University. All medical students from the fourth and fifth years, a total of eighty students, were randomly assigned to two groups of peer-assisted learning (PAL) and self-study (SS) via stratified randomization done by computer-generated randomization. The two groups were matched for sex and grade point average. In the PAL group, teaching was performed by the fourth and fifth year medical students. We conducted five weekly study sessions. Different topics of electrocardiogram interpretation were assigned to tutors for teaching. SS group would separately study the same topic on their own. Constructed response questions were used to assess the students at the beginning as a pre-test and after a five-week session as a post-test. Online self-assessment was delivered to students one month after the study. Results: Mean pre-test and post-test score was put into the analysis and compared across groups using t-test. No significant difference in pre-test score was observed between the two groups in the same academic year. There was a significant difference between the mean post-test score between the fourth year PAL and SS groups. Also, the mean difference score in the fourth year PAL group was higher than the fourth year SS group. However, in the fifth year group, there was no significant difference between the PAL and SS groups in the mean post-test score and mean difference score. Conclusion: In conclusion, peer-assisted learning is an interesting method to improve understanding and interpreting skills of basic ECG better than self-study in the early clinical year medical students.
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Background: Electrocardiogram (ECG) interpretation training is a fundamental component of medical education across disciplines. However, the skill of interpreting ECGs is not universal among medical graduates, and numerous barriers and challenges exist in medical training and clinical practice. An evidence-based and widely accessible learning solution is needed. Design: The EDUcation Curriculum Assessment for Teaching Electrocardiography (EDUCATE) Trial is a prospective, international, investigator-initiated, open-label, randomized controlled trial designed to determine the efficacy of self-directed and active-learning approaches of a web-based educational platform for improving ECG interpretation proficiency. Target enrollment is 1000 medical professionals from a variety of medical disciplines and training levels. Participants will complete a pre-intervention baseline survey and an ECG interpretation proficiency test. After completion, participants will be randomized into one of four groups in a 1:1:1:1 fashion: (i) an online, question-based learning resource, (ii) an online, lecture-based learning resource, (iii) an online, hybrid question- and lecture-based learning resource, or (iv) a control group with no ECG learning resources. The primary endpoint will be the change in overall ECG interpretation performance according to pre- and post-intervention tests, and it will be measured within and compared between medical professional groups. Secondary endpoints will include changes in ECG interpretation time, self-reported confidence, and interpretation accuracy for specific ECG findings. Conclusions: The EDUCATE Trial is a pioneering initiative aiming to establish a practical, widely available, evidence-based solution to enhance ECG interpretation proficiency among medical professionals. Through its innovative study design, it tackles the currently unaddressed challenges of ECG interpretation education in the modern era. The trial seeks to pinpoint performance gaps across medical professions, compare the effectiveness of different web-based ECG content delivery methods, and create initial evidence for competency-based standards. If successful, the EDUCATE Trial will represent a significant stride towards data-driven solutions for improving ECG interpretation skills in the medical community.
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Background: Accurate electrocardiogram (ECG) interpretation is key to quickly providing attention to patients, and the first health staff who evaluate ECGs are nurses. Method: This was a prospective study with a pre-posttest design. The study test included 15 ECGs related to primary cardiac arrhythmias. After pretest nurses were instructed on arrhythmia interpretation using the Cardiac Rhythm Identification for Simple People (CRISP) method, posttests were completed. Results: There was a significant difference between the pretest scores of nurses who had postgraduate education on ECG interpretation and who did not (p = .002). Median test score increased from 3 (interquartile range [IQR] = 2-5) to 7 (IQR = 5-9) (p < .001). Participants mostly missed questions about heart blocks and were most successful with questions about fatal arrhythmias after education. Conclusion: The CRISP method is an effective, simple, and easy method for accurate ECG interpretation by nurses. The posttest scores of the participants, especially accurate interpretation of fatal arrhythmias, increased significantly after training. [J Contin Educ Nurs. 2020;51(12):574-580.].
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