Telepsychiatry has also been reported to have a high level of ac-
curacy and value as an assessment tool by Singh et al.
in 2007. In
this study, substantial agreement was found between face-to-face
assessment and telepsychiatry, and the intermethod agreement for
Cohen’s kappa was high. Regardless of how service was delivered,
results for this study revealed that diagnosis, risk assessment, and
nondrug and drug intervention were all greater than 0.76 with a
combined overall accuracy of >0.81.
Two meta-analyses have been conducted on the delivery of mental
health services using telehealth technologies. Hyler et al.
results from 14 studies encompassing 500 total cases, to compare the
accuracy of in-person psychiatric evaluation with psychiatric evalua-
tion delivered using live two-way interactive video. No significant
differences in diagnostic accuracy were reported between the two
modalities, even when low versus high bandwidth conditions were
compared. The authors concluded that evaluations conducted via tele-
psychiatry were accurate enough to replace in-person evaluations in
In a more recent study, Osenbach et al.
conducted a meta-
analysis that included 14 studies of depression treatment delivered
via telehealth versus in-person. No significant systematic difference
in effect size was found between the two modalities, although studies
that indicated the use of ‘‘care as usual’’ as the comparison group
differed from those using a face-to-face comparison treatment, sug-
gesting that ‘‘care as usual’’ was inferior to treatment delivered using
Despite this growing consensus regarding its
utility, telemedicine adoption still faces significant hurdles with re-
gard to reimbursement and sustainability. A recent report by Williams
revealed that only small numbers of emergency telemental
health evaluation programs are functioning in hospital emergency
departments nationwide, and most of these programs must rely on
grants to sustain them.
The current study evaluated a telemedicine program implemented
in a rural hospital ER. The project compared time to treatment, length
of stay, and door-to-consult time among patients presenting to the
ER before and after implementation of an emergency telemental
health evaluation service. This article presents important descriptive
data on the impact of integrating telemental health services into the
work flow processes at a CAH ER.
Materials and Methods
Data were collected retrospectively from hospital charts of a naturally
occurring serial sample of patients. All those who presented in the ER
over the 13-month study period from January 1, 2009 through January
31, 2010 and who had an emergency mental health evaluation ordered
and completed were included in the study (n = 62). These patients were
divided into a pretelemedicine group that contained 24 patients and a
telemedicine group that contained 38 patients. Time to treatment, door-
to-consult time, and length of stay were extracted from charts and
electronic records and compared for the two groups of patients.
Time to treatment was defined as the time interval (in hours) from
the time the clinician ordered a mental health consultation to the time
the consultation was completed. Door-to-consult time was the time
interval from the time the patient arrived (triage date and time) to the
time the consult was completed. Length of stay was defined as the
time interval from the time the patient presented in the ER (triage date
and time) to the time the patient was discharged from the hospital,
including any time spent as a registered inpatient.
Data were gathered for 7 months prior to implementation of the
telemental health program and for 6 months following implementa-
tion. To ensure that preimplementation data and postimplementation
data were collected in a standardized fashion, data collection tem-
plates were developed and imported into the facility’s electronic
medical record. Following Institutional Review Board approval, the
emergency department manager and the project team used the elec-
tronic medical record to collect the researchers’ variables of interest
using the data collection templates. Work flow studies were conducted
prior to project implementation to ensure that new clinical and doc-
umentation protocols presented minimal disruption to the facility’s
standard operating procedures.
Following collection of b aseline data, interactive videocon-
ferencing equipment was installed, and all involved hospital staff
were trained in the use of the equipment. A contingenc y plan
was put in place should the technology fail. Extensive records of
the process were kept, and necessary documentation was pro-
vided in easy-to-l ocate, color-coded envelopes to facilitate ease
of use and staff buy-in. Training was not required for staff at the
community mental health center because they were already
highly skilled in rendering urgent mental h ealth consults via
videoconferencing. Following implementation of the emergency
telemental health program, the same variables of interest were
collected on patients who had a mental health consult ordered by
the provider on duty.
Tests of normality were conducted on outcome variables to ensure
the assumptions for parametric tests were met. Welch two-sample
t tests were used to determine differences between the variables of
interest. Data manipulation took place in Excel (Microsoft,
Redmond, WA), whereas all calculations were performed using SPSS
version 17.0 (IBM, Armonk, NY) and SAS System 9.0 for Windows
(SAS Institute, Cary, NC).
In total, 67 patients received urgent mental health consultations
between January 1, 2009 and January 31, 2010. Of these 67 patients, 5
were excluded from the study because they presented in an atypical
fashion and were directly admitted to the hospital rather than entering
through the ER. The mean age of the sample was 37.1 years, with a
median and standard deviation of 40.0 and 15.9 years, respectively.
The range was 8–76 years. The population was 56% female and 44%
male. Half of the sample was single, with 27% married, 21.0% di-
vorced, and 1.6% widowed. Forty-five percent were unemployed,
with 21.0% employed, 12.9% disabled, 12.9% students, and 8.1%
retired. The payer mix of the sample was 41.9% self-pay, 22.6%
commercial, 14.5% Medicare, and 21.0% Medicaid. Percentages in
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