Targeting Telestroke: Benc

hmarking Time Performancein Telestroke Consultations

Julian P. Yang, MD,* Tzu-ChingWu, MD,† Charles Tegeler, MD,‡ Ying Xian, MD, PhD,*

DaiWai M. Olson, PhD, RN, CCRN,* and Brad J. Kolls, MD, PhD, MMCi*

From the *Departmen

Center, Durham,NC; †De

Houston; and ‡Departm

Medicine, Winston-Salem

Received August 24,

accepted March 8, 2013.

Disclosures: J.P.Y. has

vant disclosures. C.T. has

disclosures. D.M.O. recei

470

Objective: To describe the length of time physicians spend completing telestroke

consultations and examine factors associatedwith that period.Methods: This is a ret-rospective review of data from telestroke software. Clinical data obtained between

July 2010 and February 2011 from 8 hub and 24 spoke hospitals were abstracted

for 235 consecutive consultations and linked to timemetadata generated by software

interaction. Consult length was defined as the time logged on to the robot and was

exclusive of any telephone interaction or documentation time. Response time was

defined as patient arrival to physician log-on. Results: Mean consult length for

203 complete, time-stamped cases was 14.5 minutes. There was no independent as-

sociation between consult length and age, diagnosis, time of arrival from symptom

onset, neurological examfindings, known recombinant tissue plasminogen activator

(r-tPA) contraindications, and absence of vascular risk factors. Mean consult length

was statistically longer in r-tPA–recommended cases (20.0 versus 15.3 minutes; P5.04). Mean response timewas 76.3 minutes.Conclusions: The relatively short consultlength suggests a workflow model in which acute stroke care is largely completed

before telestroke consultation with a specialist rendering an expert opinion on pre-

viously gathered data performed off-line. The findings for prolonged response times

indicate an area for improvement. Future workflow models for telestroke consulta-

tion will need to be reconsidered to optimize quality of care and clinical

efficiency. Key Words: Acute stroke—telemedicine—telestroke—care delivery

models.

� 2013 by National Stroke Association

Background and Significance

Despite being a leading cause of death and disability in

the United States, stroke continues to be an under-

recognized and undertreated diagnosis in the emergent

setting.1 Treatment rates of ischemic stroke with intrave-

nous recombinant tissue plasminogen activator (r-tPA)

continue to be low in the United States with only 3%-

8% of ischemic stroke patients receiving treatment.2 As re-

cently as 2009, nearly two thirds of US hospitals did not

t of Neurology, Duke University Medical

partment of Neurology, University of Texas,

ent of Neurology, Wake Forest School of

, NC.

2012; revision received March 8, 2013;

no relevant disclosures. T.-C.W. has no rele-

no relevant disclosures. Y.X. has no relevant

ves funding from the Duke Clinical Research

Journal of Stroke and Ce

offer r-tPA treatment at all with a trend for smaller hospi-

tal size and rural location being the associated factors for

nontreatment.3 It is estimated that only half the US popu-

lation resides within timely access (,60 minutes) to

a primary stroke center.4

In recent years, the emergence of telestroke—systems

of care employing high-quality, 2-way, audio–video

technology—has been seen as a potential solution to

bridge the gap between stroke centers and underserved

Institute, which serves as the statistical co-ordinating center for Get

With The Guidelines. B.J.K. has no relevant disclosures.

Grant support: None.

Address correspondence to Brad J. Kolls, MD, PhD,MMCi, Depart-

ment of Medicine–Neurology, Duke University Medical Center, Box

2900, Durham, NC 27710. E-mail: kolls001@mc.duke.edu.

1052-3057/$ - see front matter

� 2013 by National Stroke Association

http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.03.010

rebrovascular Diseases, Vol. 22, No. 4 (May), 2013: pp 470-475

Table 1. Patient demographics

Variable

Age, mean 69.3 6 15.9 y

Gender*

Male 85 (41.9%)

Female 111 (54.7%)

Present, n (%) Absent, n (%)

Diabetes 58 (28.6) 145 (71.4)

Hypertension 74 (36.5) 129 (63.5)

Prior stroke or TIA 63 (31.0) 140 (68.9)

Atrial fibrillation or

flutter

22 (10.8) 181 (89.2)

Other vascular disease 27 (13.3) 176 (86.7)

Abbreviation: TIA, transient ischemic attack.

*Gender not recorded in 7 cases.

TELESTROKE PERFORMANCE 471

populations. Telestroke networks have been implemented

around the world, and the feasibility and the efficacy of

telestroke systems have been effectively established.5-7

With the ongoing adaptation of telestroke systems, there

is a growing consensus among stroke specialists

regarding minimum hardware requirements.8 However,

little attention has been paid to the data that is a product

of software used to manage workflow, clinical informa-

tion, and documentation during the telestroke encounter.

The electronic capture and storage of this data may

greatly aid in describing the metrics of telestroke care.

Electronic metadata, essentially data about data, can

emerge as a powerful tool to analyze the performance

and efficiency of clinical processes. For example, time

stamping of physician interaction with software can yield

new metrics regarding physician behavior.

Some data exist in the current literature to describe the

time commitment required by stroke specialists involved

with telestroke consultation. Previous reports have

outlined target evaluation times for telestroke consulta-

tion.9 Prior efficacy trials have also listed average consul-

tations times and treatment times.10 However, they have

been limited to the controlled settings of formal telestroke

trials and may not reflect the ‘‘real-world’’ application

of teletechnology. Ultimately, time metrics, such as con-

sultation length and response time, may have large

implications on best practice standards of treating ische-

mic stroke patients with r-tPA. Previous studies do not ex-

plore which clinical variables may affect these times. The

purpose of this study was to examine these relationships.

Methods

This is a retrospective analysis of demographic

and clinical data of telestroke encounters captured elec-

tronically in StrokeRESPOND documentation software

(InTouch Technologies, Inc., Santa Barbara, CA) corre-

lated with metadata generated from data logs recording

time points at which physicians interacted with hardware

end point devices (ie, telestroke robots).

From 8 hub and 24 spoke hospitals, there were 235 dis-

tinct, consecutive telestroke encounters between July 2010

and February 2011. The clinical information in these en-

counters was matched to data logs from both StrokeRES-

POND and end point devices. Time data in these logs

were automatically captured at the point of care during

telestroke encounters. Times of physician log-on and

log-off to the end point devices were recorded to the near-

est centisecond on a global network clock. Cases in which

patient arrival times were not charted were excluded (n5

11). Additionally, cases in which charted arrival times

conflicted logically with available metadata were ex-

cluded (eg, arrival times charted after telestroke consulta-

tion initiation) (n 5 21).

After exclusion, there were 203 telestroke encounters

with complete, time-stamped data logs encompassing

14 physician users. Demographic data electronically ab-

stracted from StrokeRESPOND included age, gender,

and the presence of diabetes, hypertension, prior stroke

or transient ischemic attack (TIA), atrial fibrillation or

flutter, and/or other vascular disease. Other information

collected about telestroke encounters included presence

of normal or baseline exam, final consultation diagnosis,

administration of r-tPA, and patient disposition. All per-

sonal health information was removed, and data were

stored in a Microsoft Excel file, which was shared directly

with investigators. Because the data set was completely

de-identified, this study received exempt status from

the Institutional Review Board at Duke University Medi-

cal Center (protocol ID: Pro00038896). Statistical analysis

relating the effect of clinical variables on mean times

was performed with analysis of variance and paired t

test using SAS-JMP and SASv9.4 software (SAS Institute,

Inc., Cary, NC).

‘‘Response time’’ was defined as the length of time be-

tween patient arrival at a spoke hospital and physician

user log-on to the end point. ‘‘Consult length’’ was de-

fined strictly as the time a physician user was directly

logged on. This definition was exclusive of any telephone

interaction or documentation time that occurred either

before or after the telestroke consultation.

Results

This sample had 85 males and 111 females; gender was

not recorded in 7 cases. Mean age was 69.3 years with

a standard deviation of 15.9 years (Inter Quartile Range

[IQR] 58-81). In terms of vascular risk factors, including

diabetes, hypertension, prior stroke or TIA, atrial fibrilla-

tion or flutter, and other vascular disease, 158 patients

(77.8%) had at least 1 risk factor present at admission,

and 92 patients (45.3%) were noted to have the presence

of 2 or more risk factors (see Table 1). Of note, 63 patients

(31.0%) were known to have a history of a stroke before

telestroke consultation.

Table 2. Mean response time by arrival times

Time of patient

arrival

Number of

cases

Mean response

time (minutes) P value

6:01 AM-12:00 PM 80 (39.4%) 74.6 .82*

12:01 PM-6:00 PM 84 (41.4%) 77.0

6:01 PM-12:00 AM 30 (14.8%) 74.2

12:01 AM-6:00 AM 9 (4.4%) 91.9

Total 203 76.3

*Omnibus test of means.

J.P. YANG ET AL.472

A final diagnosis at the end of the telestroke consulta-

tion was not recorded in 51 cases (25.1%). Categories of

consultation diagnoses consisted of ischemic stroke or

TIA (60/203, 29.6%), hemorrhagic stroke (6/203, 2.9%),

psychiatric illness (3/203, 1.5%), other medical illness

(6/203, 2.9%), and undiagnosed neurological symptoms

(77/203, 37.9%). In this sample, there were no docu-

mented diagnoses of seizure or other specified neurolog-

ical disease (eg, brain tumor). In the cases of ischemic

stroke or TIA, the administration of intravenous r-tPA

was recommended in 13 cases (13/60, 21.7%). Reasons

for not recommending r-tPA were inconsistently re-

corded, but in several cases, low National Institutes of

Health Stroke Scale and rapidly improving symptoms

were listed as reasons.

Mean response time was 76.3 minutes (IQR 39.4-94.0) in

this sample. Most telestroke consultations occurred dur-

ing daytime hours with 80 cases logged between 6:01

AM and 12:00 PM (39.4%) and 84 cases logged between

12:01 PM and 6:00 PM (41.4%) (see Table 2). In the 9 cases

in which patients arrived to emergency department in be-

tween 12:01 AM and 6:00 AM, mean response times were

Table 3. Mean consult leng

Variable

Age Age #55 (

15.4 m

Age $80 (

13.4 m

Arrival time #4.5 h (

15.0 m

Neurological exam Normal/baseli

15.6 m

tPA contraindications Absent (

14.7 m

Vascular risk factors Absent (

13.5 m

tPA recommended (all patients) Yes (

20.0 m

tPA recommended (ischemic stroke patients) Yes (

20.0 m

Abbreviation: tPA, tissue plasminogen activator.

noted to be longer (91.9 minutes). However, this differ-

ence was not statistically significant (P 5 .82).

Mean consult length for the entire cohort was 14.5 min-

utes (IQR 9.2-18.4). Mean consult length was significantly

longer in cases in which r-tPA was recommended (20.0

versus 15.3 minutes, P 5 .04). In the subset of patients

with a final diagnosis of TIA or ischemic stroke was

made, the association between mean consult length and

r-tPA recommendation was found to be stronger (20.0

versus 14.2 minutes, P 5 .02) (see Table 3). There was no

independent association between consult length andmul-

tiple clinical variables, including age, time of arrival from

symptom onset, absence of vascular risk factors, and con-

sultation diagnosis. Factors thought to favor shorter con-

sultation times, such as normal or baseline neurological

exam or presence of r-tPA contraindications, were also

not independently associated with consult length.

Recommendation for patient disposition (ie, whether to

transfer a patient for further care at a hub hospital or to

keep a patient at a spoke hospital for continued care)

was documented in only 67 cases. Diagnoses were noted

as follows: ischemic stroke or TIA (20/67, 29.9%), hemor-

rhagic stroke (3/67, 4.5), psychiatric illness (1/67, 1.5%),

other medical illness (1/67, 1.5%), and undiagnosed neu-

rological symptoms (25/67, 37.3%). Diagnosis was miss-

ing in 17 cases (25.4%).

In the subgroup of 67 cases with reported disposition,

transfer of care to hub hospital was recommended for

35 patients (52.2%). Transfer rates varied by diagnosis: is-

chemic stroke or TIA (15/20, 75%), hemorrhagic stroke

(3/3, 100%), psychiatric illness (0/1, 0%), other medical

illness (0/1, 0%), undiagnosed neurological symptoms

(11/25, 44%), and missing diagnosis (6/17, 35.3%). In

this group, all 4 cases in which r-tPA administration

th by clinical variables

P value

n 5 39) Age .55 (n 5 160)

in 14.3 min .43

n 5 66) Age ,80 (n 5 133)

in 15.1 min .13

n 5 79) .4.5 h (n 5 110)

in 14.1 min .44

ne (n 5 23) New symptoms (n 5 137)

in 14.8 min .65

n 5 61) Present (n 5 142)

in 14.4 min .77

n 5 45) Present (n 5 158)

in 14.8 min .33

n 5 13) No (n 5 115)

in 15.3 min .04

n 5 13) No (n 5 47)

in 14.2 min .02

TELESTROKE PERFORMANCE 473

was recorded and recommendedwere also recommended

to transfer care.

Discussion

Using temporal parameters obtained directly from tele-

stroke network data presents a unique opportunity to as-

sess telestroke quality of care. The American Stroke

Association’s ‘‘TARGET: Stroke’’ initiative with its goal

of door-to-needle times of 60 minutes or less may serve

as a frame of reference for judging the timeliness of tele-

stroke.11 The administration of intravenous r-tPAwas rec-

ommended in relatively few cases in this study, but the

times found for consult length and response time may in-

dicate how well telestroke might perform to help ensure

the quick delivery of r-tPA.

Consult Length

The mean consult length of 14.5 minutes suggests that

telestroke consultations are quick and efficient; however,

it is important to note that this figure solely represents

the time logged by a physician user directly interacting

with hardware end points and was exclusive of any un-

documented time spent communicating over the tele-

phone before or after telestroke consultation. Physician

time for documentation itself was also unaccounted for.

Consult length, found to be consistent across multiple

independent clinical variables, is relatively brief in rela-

tion to a 60-minute goal for completion of acute stroke

protocols. This finding has 2 important implications

about the use of telestroke in real-world settings. First,

the use of telestroke technology (ie, managing a patient

encounter via a robot interface) does not appear to be

overly cumbersome for physician users. Previous pub-

lished studies have documented software problems and

physician difficulty with technical troubleshooting that

limited the efficacy of telestroke consultations.12 The

group in this study, however, benefitted fromusing a stan-

dardized telestroke platform with software and hardware

end points integrated with a network managed by a third

party for connectivity and technical issues (SureCON-

NECT; InTouch Technologies, Inc.).

Second, the relatively brief consult lengths suggest

that the workflow model for managing acute stroke via

telestroke systems may be very different from stroke codes

conducted as live encounters in person. In our interpreta-

tion, physician users do not appear to be using telestroke

technology to manage stroke codes from start to finish in

entirety. Rather, after a patient’s arrival in the emergency

department of a spoke hospital, much of the initial triage

and workup, including neuroimaging, likely is conducted

off-line before telestroke consultation. On completion of

this workup, stroke specialists are then called in via tele-

stroke to review already collected clinical information,

perform a confirmatory neurological examination, and

then render an expert opinion regarding appropriate

management and disposition. The recommendation for ad-

ministration of r-tPA was the only statistically significant

variable associated with longer consult times (20.0 min-

utes), which possibly represent time needed to recheck

exam findings, personally review neuroimaging, or in-

struct spoke-site staff about drug dosing and delivery.

The relative lack of hemorrhagic stroke diagnoses (6/

203, 2.9%) recorded in this sample indirectly seems to

support this postulated workflow model for telestroke.

In live stroke codes, hemorrhagic strokes are encountered

far more frequently as both ischemic and hemorrhagic

stroke present similarly initially with focal neurological

deficits and are then distinguished by the presence of in-

tracranial blood on neuroimaging obtained during the

stroke code. Because of the relative ease in diagnosing

hemorrhagic stroke with positive neuroimaging findings,

these cases at the spoke hospitals are presumed to have

been triaged without any stroke specialist input and

that telestroke consultations were largely not initiated in

these cases. Consequently, hemorrhagic stroke appears

to be under-represented in this study’s sample.

Telestroke networks, at least in this sample, appear to

be used mainly as a way to connect to stroke specialists

for the clinical diagnosis of ischemic stroke in a confirma-

tory capacity and, if warranted, decision support regard-

ing thrombolytic therapy. This model is very dissimilar to

live-encounter stroke protocols found in many academic

centers in which stroke specialists manage the entire

stroke code from patient arrival to treatment. If our inter-

pretation of clinical workflow in this sample is correct, tel-

estroke networks may not be optimally providing

patients with early access to specialist expertise.

Indeed, a remarkable finding in this sample is that is-

chemic stroke or TIA was found to be the final diagnosis

in only 29.6% of cases. Teletechnology in its application

for telestroke networks may be underused as a means of

addressing the larger scope of neurological emergencies,

including hemorrhagic strokes, which are prone to the

same limitations in acute care because of a lack of timely

access to expertise. For example, quick evaluation by

a neurointensivist or neurosurgeon may change treat-

ment or triage plans for hemorrhagic strokes. The role

of telestroke beyond r-tPA treatment may need further

consideration as telestroke networks are developed and

implemented. Given the frequent activation of telestroke

encounters for nonstroke diagnoses, one may argue that

telestroke protocols in real-world execution are actually

functioning in a broader scope of practice as teleneurol-

ogy services. The results and outcomes of this practice,

however, do not appear to be followed and remain un-

known at this time. Teleneurology itself is a nascent prac-

tice, and the lack of publications indicates an area of

needed research and growth.13

In general, further insights garnered from research di-

rected toward elucidating workflow patterns in telestroke

consultations will become important on several levels: the

J.P. YANG ET AL.474

development of clinical protocols for best practice, the

design of software that best supports workflow efficiency,

and future research in the larger scope of acute stroke

care. Additionally, there are implications for telestroke re-

imbursement schema. Traditional, time-based billing

schedules may not ensure fair compensation for stroke

specialists managing acute and highly complex clinical

encounters.

Response Time

The mean response time of 76.3 minutes and its consid-

erable range (IQR 39.4-94.0) in this group clearly indicate

an area for improvement and a need for additional data.

Even in a model of care in which response time may corre-

spond to productive, off-line work being performed at the

spoke hospital prior to telestroke consultations, the pro-

tracted figures in this study do not support achievement

of adequate door-to-needle times less than 60 minutes. In

light of the relative brevity of consult length, the response

times in this data set suggest that quality improvement

may need to shift focus to an earlier point in the clinical en-

counter. Initial evaluation and triage of stroke patients

should be directed toward rapid acquisition of pertinent

history and quick identification of those patients requiring

specialist consultation. The efficiency of the process by

which the request for consultation is made also deserves

consideration (eg, transfer center versus direct page).

Last, the factors that may delay consultants from promptly

answering requests need to be identified.

Time of day is one important variable that may affect all

these aspects of early acute stroke care. Recent attention to

‘‘off-hour admissions’’ has shown outcome differences for

stroke patients.14,15 The small sample in this study did not

show a statistically significant trend; however, the 9 cases

arriving in early morning hours (12:01 AM to 6:00 AM) did

have longer mean response times. The reasons for longer

response times are not documented, but future studies

could be directed toward collecting information about

physician user practices in regards to ‘‘off-hour’’ call

structure.

Telestroke will benefit from the development of

standardized clinical protocols that are optimized for effi-

ciency. Beyond the installation of high-quality technolog-

ical platforms, a recurring theme of successful telestroke

systems is the necessity of education and continual re-

education of personnel at both hub and spoke sites.

Clinical protocols would require all personnel involved

to gain familiarity with the aims of telestroke consulta-

tions and the best practice standards of acute stroke

care. Despite the availability of clinical expertise via tech-

nology, quality care is still dependent on the ready and

able local execution of recommendations made remotely.

Limitations and Future Directions

The findings of this study should be interpreted with

caution given several limitations. Most notably, no datum

or information was available regarding the occurrence or

timing of any telephone interactions and how initial con-

tact from spoke hospital to hub hospital was made. Con-

sequently, potentially important factors, such as the

amounts of time elapsed between patient arrival, decision

to request consultation, first contact with consulting

physicians, and then actual telestroke initiation, were

not considered. Also, the time required for off-line docu-

mentation or follow-up was not recorded.

In general, the retrospective design and small sample

size may have introduced information bias and selection

bias. Individual cases in this sample were not indepen-

dent in that repeated observations were taken from only

14 physician users at 8 hub hospitals. A clear example

of selection bias in this sample is the likely under-

identification of hemorrhagic strokes, which can be ex-

plained by a lack of uniform clinical criteria for telestroke

initiation. Further selection bias may have occurred from

data loss by excluding 32 cases from the initial sample

with conflicting or illogical charted arrival times.

The source and method of data acquisition for different

variables figure largely into the limitations of this study.

Outcomes, such as consult length, were calculated strictly

with time points obtained from automatically generated

electronic metadata and reflect a certain measure of data

integrity. Conversely, response time was calculated using

patient arrival times that were charted into electronic doc-

umentation. Because these arrival times essentially repre-

sent retrospective chart review, they are prone to human

error in both initial documentation and retrospective

data abstraction. In this way, the presence of conflicting

or illogical times is explained. Similarly, there is an incon-

sistent record of basic demographic information with 7

cases missing any notation of patient gender.

As an initial foray into analyzing the metrics of tele-

stroke efficacy from software audit trails, the results of

this study can serve as a point of comparison for future

studies. A very important point to distinguish in the de-

sign of telestroke studies is to standardize clinical proto-

cols so that the technology is used in the same way,

from user to user and from site to site. The conjectured

model of care in this study with physician users logging

in only at the end of the clinical pathway for acute stroke

management, if true, should be formally outlined into

prospective research design. Future studies should also

require clear documentation about key points in the off-

line workflow, including any telephone interactions be-

tween hub and spoke hospitals, and also all traditional

acute stroke protocol metrics at the spoke hospital. Other-

wise, a different model of care may be more appropriate

for research.

This study should draw attention to the importance of

telestroke software design to take advantage of a unique

opportunity to study acute stroke care and physician de-

cision making. The elimination of secondhand data ab-

straction with electronic data acquisition can minimize

TELESTROKE PERFORMANCE 475

data corruption. More importantly, the ability to capture

metadata can yield powerful information: by analyzing

the sequence and patterns of clinical information entry

and utilization, the actual thought process of a physician

user can be investigated. Physician decision-making pat-

terns and other telestroke metrics, such as consult length

and response times in this study, would ideally be tied di-

rectly to patient outcome measures.

Ultimately, the information garnered in future

outcome-focused research could aid in the optimization

of not only telestroke systems but also acute stroke care

protocols as a whole.

Acknowledgments: We gratefully acknowledge the ef-

forts of Andre Grujovski and TimWright in helping to gather

and prepare the initial data set. Additionally, we would

thank Kristina Riemen for her assistance in manuscript prep-

aration.

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