Examination of Potential Differences in Reporting of Sensitive Psychosocial Measures via Diagnostic Evaluation Using Computer Video Telehealth
Publication: The Journal of Neuropsychiatry and Clinical Neurosciences
Abstract
Objective:
The authors compared baseline characteristics and reporting of psychosocial measures among veterans with seizures who were evaluated in-clinic or remotely via computer video telehealth (CVT). It was hypothesized that the CVT group would report less trauma history, drug use, and comorbid symptoms compared with veterans seen in-clinic.
Methods:
A cross-sectional design was used to compare 72 veterans diagnosed with psychogenic nonepileptic seizures (PNES) or concurrent mixed epilepsy and PNES who were consecutively evaluated by a single clinician at the Providence Veterans Affairs Medical Center (PVAMC) Neuropsychiatric Clinic. In-clinic evaluations of veterans were performed at the PVAMC Neuropsychiatric Clinic (N=16), and remote evaluations of veterans referred to the VA National TeleMental Health Center were performed via CVT (N=56). All 72 patients were given comprehensive neuropsychiatric evaluations by direct interview, medical examination, and medical record review. Veterans’ reporting of trauma and abuse history, drug use, and psychiatric comorbidities was assessed, along with neurologic and psychiatric variables.
Results:
No significant differences were found between veterans evaluated in-clinic or remotely with regard to baseline characteristics and reporting of potentially sensitive information, including trauma and abuse history, substance use, and comorbid symptoms.
Conclusions:
Veterans with PNES evaluated via telehealth did not appear to withhold sensitive or personal information compared with those evaluated in-clinic, suggesting that CVT may be a comparable alternative for conducting evaluations. Baseline evaluations are used to determine treatment suitability, and telehealth allows clinicians to gain access to important information that may improve or inform care.
In the United States, many patients have problems accessing medical and mental health care, especially those living in areas without medical professionals who have expertise specific to their disorder (1). This gap has led to the rise of the use of computer video telehealth (CVT), a live video conference between the patient and remote clinician.
The use of CVT has been growing in recent years. Many studies have investigated the use of telehealth in evaluating patients for various psychiatric disorders and delivering treatment, but none have compared telehealth (face-to-face) with in-clinic (eye-to-eye) encounters examining their effects on divulging sensitive history among neuropsychiatric patients with nonepileptic seizures. Given the nature of the remote interview, patients may view a video medium as less personal and may report trauma history, drug use, or comorbid symptoms differently because of the delicate nature of this information.
With the increasing popularity of CVT, a number of studies have evaluated the effectiveness of telehealth therapy to treat various mental disorders (2). Uniformly, these studies showed telehealth to be a comparable and reliable alternative to in-clinic treatment and, for certain disorders, found similar clinical outcomes for patients who received CVT treatment and those who were seen in-clinic (3–6); one study reported that patient satisfaction was higher among those who received CVT treatment (7). Extensive literature has also reported on use of telehealth in diverse patient populations, with a range of ages and diagnoses (8). In the Department of Veterans Affairs (VA) health care system, a number of similar studies have evaluated the efficacy of CVT for disorders in the veteran population (9–13). Feasibility studies have investigated neuropsychiatric evaluations using CVT (14, 15). Although studies have compared psychiatric diagnoses obtained via in-clinic versus CVT evaluations (16, 17), no study has addressed disclosure of sensitive history. In addition, this is the first and only study of telehealth evaluations of patients with nonepileptic seizures.
Clinically, patients may withhold information (18, 19), especially if rapport has not been established, and they could be more likely to do so if an interview is done remotely. Therefore, we hypothesized that patients interviewed remotely via telehealth may be less likely to report trauma history, drug use, or comorbid symptoms, compared with those seen in-clinic, given the delicate nature of this information and the remote nature of the interview. Thus this study aimed to examine use of CVT in patient evaluation at the Providence VA Medical Center (PVAMC) Neuropsychiatric Clinic and the VA National TeleMental Health Center (NTMHC) because patients might report symptoms or other sensitive information differently through teleconference, compared with in-clinic appointments. Patient evaluation is a critical component of care because the initial consultation informs many aspects of treatment. The purpose of this preliminary study was to examine psychosocial information disclosed during initial consultation obtained in-clinic or via telehealth by the same clinician for the same patient population. Examining any differences between these groups can provide insight about whether use of CVT affects patients when disclosing information to the clinician and whether evaluation via telehealth is a viable option before treatment of patients with psychogenic nonepileptic seizures (PNES).
Methods
Design
The study was approved by the PVAMC Institutional Review Board. Veterans diagnosed as having PNES were referred for consultative neuropsychiatric evaluation by means of a semistructured examination. This approach has been used to conduct deep phenotyping in studies of civilians with PNES (20–23). For this cross-sectional study, we conducted a comprehensive chart review of veterans diagnosed as having PNES at the PVAMC Neuropsychiatric Clinic.
Sample
All patients consecutively seen by a single clinician at the PVAMC Neuropsychiatric Clinic (N=72) from November 2012 to April 2018 met all enrollment criteria. Inclusion criteria for the sample were male and female veterans, ages 18–89, who were diagnosed as having video-EEG–confirmed PNES (a small percentage had routine EEG/ambulatory EEG–captured ictus and clinician-reviewed patient video of the patient’s typical events, similar to captured episodes). Seizure diagnosis was established according to the International League Against Epilepsy PNES standards (24). Patients with concurrent mixed PNES and epilepsy (N=4) were also included.
Locations
The 72 subjects were divided into two categories: local and remote. Local patients were evaluated in-clinic at the PVAMC. Remote patients located at other VA medical centers and the VA Epilepsy Centers of Excellence (ECOE) (25) across the nation were evaluated via CVT through interfacility consults with the VA NTMHC. The VA NTMHC offers videoconferencing technology for clinicians at VA sites to provide medication management alone, with, or without telepsychotherapy services and diagnostic assessments to veterans around the country (26). Patients evaluated remotely used CVT at their local VA medical center or community-based outpatient clinic, where VA staff (telehealth clinical technicians) checked in the patient, set up the videoconferencing equipment, and were available via instant messaging if any clinical issues arose during the evaluation. Local staff utilized routine standard safety measures if a patient had a seizure during the session or if the patient disclosed acute safety concerns.
Neuropsychiatric Evaluation
Patients in the local group were evaluated in-clinic at PVAMC, and those in the remote group were seen via CVT at their local VA, which was facilitated by telehealth staff at the patient-side site. CVT was conducted using high-definition, encrypted video for all appointments, ensuring high-quality audio and video fidelity.
Evaluations for each veteran (local and remote) were completed over two appointments by a single clinician (W.C.L.), who is American Board of Psychiatry and Neurology double-board certified in neurology and in psychiatry, at the PVAMC Neuropsychiatry Consult Clinic. During these two appointments, the clinician interviewed the patient in the initial evaluation for symptom history, work-up, sociodemographic information, developmental history, and other clinical factors. In addition, past medical records were reviewed and documented for additional information. Sociodemographic characteristics obtained included age, sex, race/ethnicity, age at PNES onset, number of years of education, employment, disability status, marital status, and driving status. Developmental history included physical, verbal, emotional, and sexual trauma or abuse; history of psychotherapy; total number of current medications, such as antiepileptic drugs (AEDs), psychotropics, and over-the-counter medications, and past AEDs taken. Clinical factors that were collected included current and past substance use and abuse and history of traumatic brain injury. Seizure frequency was assessed by asking patients to estimate the total number of seizures they had the week and the month prior to the first examination and the average number of seizures they have per week. The second evaluation appointment consisted of mental status examination and neurocognitive testing; the diagnostic formulation and recommendations were then shared with the patient (and significant other, if present). Evaluations included neuropsychiatric cognitive tests, mental status and neurological examinations, and DSM-5 criteria psychiatric diagnoses review, which included all mood, anxiety, psychotic, somatic symptom, substance use, and personality disorders. Diagnoses were made through a semistructured history and systemic symptom review; all other examination procedures were administered during the evaluation appointments. For patients seen remotely via CVT, neurological examination results were obtained by past medical record review from patients’ local neurologists.
Psychosocial symptom measures were completed to evaluate comorbid symptoms and quality of life. They included the Beck Depression Inventory–II (BDI) (27), Beck Anxiety Inventory (BAI) (28), Family Assessment Device (29), Quality of Life in Epilepsy Inventory–31 (30), Global Assessment of Functioning (31), Short-Form 36 Health Survey (SF-36) (32), Symptom Checklist–90 (33), CAGE questionnaire (34), Posttraumatic Stress Disorder Checklist (35), Health Locus of Control (36), and Mini-Mental State Examination (37).
Finally, laboratory work-up was collected by reviewing past medical records. All records of brain MRI, video EEG, routine EEG, and ambulatory EEG and results were collected.
Statistical Analysis
All statistical analyses were conducted with SAS Software 9.4 (SAS, Inc. Cary, N.C.). Demographic and clinical factor differences that are categorical were compared between local and remote patients by using a nonparametric Fisher’s exact test with the FREQ procedure. Continuous and count demographic and clinical factors were examined by using generalized linear modeling, assuming a normal, negative binomial, and binomial distribution, when appropriate, with the GLIMMIX procedure. Time-to-event phenomena were examined by using median and the Wilcoxon test. Alpha was established a priori at the 0.05 level, and all interval estimates were calculated for 95% confidence. Because this study’s purpose was to estimate potential differences between the telehealth group and the in-clinic group to inform hypothesis generation and subsequent power analyses for future studies, alpha correction was not used because a type I error is less important in this context than is a type II error (i.e., failing to find differences that do exist).
Results
Analyses of various demographic characteristics, clinical factors, medical examination results, and diagnoses revealed no major differences between the local cohort (N=16) and the remote cohort (N=56). Remote patients were represented across the United States (California, N=11; Northwest, N=4; Southwest, N=8; Midwest, N=11; Southeast, N=7; Northeast, N=5; and Greater New England, N=10). All patients in both cohorts attended both evaluation appointments. Overall, no significant differences between the two cohorts were found in sociodemographic characteristics, clinical factors, current disorders, neurological examination results, neuropsychiatric cognitive test results, and laboratory results. Statistically significant between-group differences were found for current other somatoform disorders and panic disorder and in use of ambulatory EEG. These comparisons are shown in Tables 1–3.
| In-clinic (N=16)b | Telehealth (N=56)c | ||||
|---|---|---|---|---|---|
| Characteristic | N | % | N | % | p |
| Age (years) (mean [95% CI]) | 51.4 | [44.9, 57.8] | 48.3 | [44.7, 51.7] | 0.3982 |
| Age at onset of seizures (years) (median [interquartile range]) | 41.0 | [23.0, 58.0] | 37.0 | [24.0, 48.0] | 0.6020 |
| Years from seizure onset to PNES diagnosis (median [interquartile range]) | 1.9 | [0.6, 23.8] | 5.2 | [1.3, 17.1] | 0.4364 |
| White | 15 | 93.8 | 47 | 83.9 | 0.506 |
| Hispanic or Latino | 0 | — | 3 | 5.4 | 0.4866 |
| Male | 11 | 68.8 | 47 | 83.9 | 0.1761 |
| Biological family history of seizures | 5 | 31.3 | 12 | 21.4 | 0.7789 |
| Employment status | 0.2670 | ||||
| Unemployed | 12 | 75.0 | 44 | 78.6 | |
| Employed | 2 | 12.5 | 10 | 17.9 | |
| Retired | 2 | 12.5 | 1 | 1.8 | |
| Receiving disability benefits | 12 | 75.0 | 45 | 80.4 | 0.1858 |
| Living situation | 0.3566 | ||||
| Alone | 3 | 18.8 | 10 | 17.9 | |
| With spouse and/or family | 9 | 56.3 | 33 | 58.9 | |
| With friends | 0 | — | 3 | 5.4 | |
| With significant other | 3 | 18.8 | 10 | 17.9 | |
| Currently driving | 6 | 37.5 | 22 | 39.3 | 0.6862 |
| Seizure counts | |||||
| Average per week (mean [95% CI]) | 10.7 | [4.9, 23.1] | 14.4 | [9.6, 21.7] | 0.4943 |
| Average per month (mean [95% CI]) | 23.9 | [11.3, 50.6] | 42.0 | [28.4, 62.3] | 0.1880 |
| Total in the week prior to evaluation (mean [95% CI]) | 6.4 | [3.0, 13.5] | 10.8 | [7.2, 16.3] | 0.2231 |
| Current substance abuse | 9 | 56.3 | 24 | 42.9 | 0.5241 |
| Past substance abuse | 11 | 68.8 | 36 | 64.3 | 0.7362 |
| Marijuana | 2 | 12.5 | 15 | 26.8 | 0.2354 |
| Cocaine | 0 | — | 8 | 14.3 | 0.1088 |
| Heroin | 1 | 6.3 | 1 | 1.8 | 0.3379 |
| Stimulants | 0 | — | 7 | 12.5 | 0.1366 |
| Prescription drugs | 1 | 6.3 | 7 | 12.5 | 0.4830 |
| Alcohol | 7 | 43.8 | 29 | 51.8 | 0.5708 |
| Current tobacco use | 4 | 25.0 | 19 | 33.9 | 0.3029 |
| Current alcohol use | 5 | 31.3 | 20 | 35.7 | 0.6062 |
| Current illicit drug use | 4 | 25.0 | 9 | 16.1 | 0.6332 |
| Current marijuana use | 4 | 25.0 | 8 | 14.3 | 0.3105 |
| Number of current medications (mean [95% CI]) | 10.9 | [8.2, 14.6] | 13.9 | [12.0, 16.2] | 0.1426 |
| Currently taking AEDs | 14.0 | 87.5 | 44.0 | 78.6 | 0.7210 |
| Number of AEDs (mean [95% CI]) | 2.7 | [1.9, 3.7] | 2.6 | [2.2, 3.1] | 0.8334 |
| Number of months taking AEDs (cumulative) (mean [95% CI]) | 56.9 | [22.1, 146.6] | 57.7 | [28.5, 116.8] | 0.9809 |
| Currently taking benzodiazepines | 6 | 37.5 | 12 | 21.4 | 0.2044 |
| Currently taking antidepressants | 13 | 81.3 | 34 | 60.7 | 0.1281 |
| Currently taking optimized antidepressants | 7 | 43.8 | 25 | 44.6 | 0.6419 |
| Currently taking antipsychotics | 1 | 6.3 | 6 | 10.7 | 0.5950 |
| History of psychotherapy | 9 | 56.3 | 44 | 78.6 | 0.0546 |
| Supportive | 6 | 37.5 | 20 | 35.7 | 0.5154 |
| Behavioral | 0 | — | 3 | 5.4 | 0.5154 |
| Psychodynamic | 0 | — | 1 | 1.8 | 0.5154 |
| Marital or family | 0 | — | 3 | 5.4 | 0.5154 |
| Group | 0 | — | 5 | 8.9 | 0.5154 |
| Substance abuse | 1 | 6.3 | 1 | 1.8 | 0.5154 |
| Cognitive-behavioral | 1 | 6.3 | 7 | 12.5 | 0.5154 |
| Current psychotherapy | 7 | 43.8 | 29 | 51.8 | 0.4841 |
| Supportive | 6 | 37.5 | 17 | 30.4 | 0.3206 |
| Behavioral | 0 | — | 3 | 5.4 | 0.3206 |
| Group | 0 | — | 3 | 5.4 | 0.3206 |
| Cognitive-behavioral | 1 | 6.3 | 1 | 1.8 | 0.3206 |
a
AEDs=antiepileptic drugs.
b
The in-clinic group included the following racial classification: Native American, N=1.
c
The telehealth group included the following racial classifications: Native American, N=1; Asian, N=1; African American, N=5; multiracial, N=2.
| In-clinic (N=16) | Telehealth (N=56) | ||||
|---|---|---|---|---|---|
| Current diagnosis | N | % | N | % | pa |
| Anxiety disorder | 14 | 87.5 | 53 | 94.6 | 0.3216 |
| Posttraumatic stress disorder | 10 | 62.5 | 33 | 58.9 | 0.7973 |
| Generalized anxiety disorder | 6 | 37.5 | 28 | 50.0 | 0.3771 |
| Obsessive-compulsive disorder | 2 | 12.5 | 8 | 14.3 | 0.8555 |
| Panic disorder | 2 | 12.5 | 25 | 44.6 | 0.0192 |
| Mood disorder | 12 | 75.0 | 47 | 83.9 | 0.4129 |
| Other somatoform disorder | 13 | 81.3 | 55 | 98.2 | 0.0090 |
| Learning disorder | 3 | 18.8 | 14 | 25.0 | 0.6037 |
| Eating disorder | 0 | — | 1 | 1.8 | 0.5904 |
| Attention-deficit disorder | 2 | 12.5 | 2 | 3.6 | 0.1691 |
| Adjustment disorder | 1 | 6.3 | 2 | 3.6 | 0.6363 |
| Substance use disorder | 11 | 68.8 | 33 | 58.9 | 0.4773 |
| Schizophrenia | 0 | — | 1 | 1.8 | 0.5904 |
| Cognitive disorder, not otherwise specified | 11 | 68.8 | 48 | 85.7 | 0.1198 |
| Personality disorder | 12 | 75.0 | 30 | 53.6 | 0.1252 |
| Personality cluster trait | 8 | 50.0 | 32 | 57.1 | 0.6121 |
| Axis II diagnosis (cumulative frequency)b | |||||
| Histrionic personality disorder | 0 | 1 | |||
| Avoidant personality disorder | 2 | 4 | |||
| Obsessive-compulsive personality disorder | 11 | 30 | |||
| Borderline personality disorder | 1 | 1 | |||
| Narcissistic personality disorder | 0 | 2 | |||
| Dependent personality disorder | 1 | 3 | |||
| Mixed personality disorder | 0 | 1 | |||
| Personality disorder not otherwise specified | 1 | 3 | |||
| Cluster B traits | 5 | 18 | |||
| Cluster C traits | 3 | 15 | |||
| More than one anxiety disorder | 12 | 75.0 | 36 | 64.3 | 0.3163 |
| More than one mood disorder | 4 | 25.0 | 15 | 26.8 | 0.7347 |
| More than one substance use disorder | 8 | 50.0 | 18 | 28.1 | 0.1434 |
a
Bold indicates statistically significant difference between groups.
b
Cumulative frequencies show the number of times a certain disorder was diagnosed in either group.
| In-clinic (N=16) | Telehealth (N=56) | ||||
|---|---|---|---|---|---|
| Variable | N | % | N | % | pa |
| Brain MRI | 14 | 87.5 | 48 | 85.7 | 0.8555 |
| Normal | 4 | 25.0 | 18 | 32.1 | 0.6021 |
| Abnormal | 9 | 56.3 | 29 | 51.8 | |
| Routine EEG | 12 | 75.0 | 32 | 57.1 | 0.1963 |
| Abnormal | 4 | 25.0 | 11 | 34.4 | 0.8954 |
| Epileptiform activity | 0.7735 | ||||
| None | 9 | 56.3 | 23 | 41.1 | |
| Spikes | 0 | — | 1 | 1.8 | |
| Sharps | 1 | 6.3 | 2 | 3.6 | |
| Slowing | 0 | — | 2 | 3.6 | |
| Generalized epileptiform discharge | 2 | 12.5 | 2 | 3.6 | |
| Ambulatory EEG | 5 | 31.3 | 5 | 8.9 | 0.0228 |
| Abnormal | 1 | 20.0 | 2 | 40.0 | 0.1819 |
| Epileptiform activity | 0.2439 | ||||
| None | 4 | 25.0 | 3 | 5.4 | |
| Slowing | 0 | — | 1 | 1.8 | |
| Video EEG | 14 | 87.5 | 52 | 92.9 | 0.4941 |
| Abnormal | 4 | 28.6 | 13 | 25.0 | 0.8813 |
| Epileptiform activity | 0.5296 | ||||
| None | 10 | 62.5 | 41 | 73.2 | |
| Spikes | 0 | — | 2 | 3.6 | |
| Sharps | 1 | 6.25 | 0 | — | |
| Slowing | 0 | 0.0 | 1 | 1.8 | |
| Generalized epileptiform discharge | 1 | 6.3 | 2 | 3.6 | |
| Other | 0 | — | 1 | 1.8 | |
| Elemental neurological examination: abnormal | 12 | 75 | 32 | 57 | 0.3166 |
a
Bold indicates statistically significant difference between groups.
Analysis of reporting of comorbid symptoms and developmental histories, including trauma-abuse and substance use, also showed no major differences between local and remote groups. However, scores on the BAI, BDI, and certain subscales of the SF-36 differed significantly between the groups (Table 4). No between-group significant differences were found in verbal, physical, and emotional trauma as an adult or as a child (Table 5). Patients seen via CVT did not report difficulties during the video teleconferencing with history or examination questions or procedures. Most patients seen via telehealth showed high levels of satisfaction on quality assessment surveys administered from the national telehealth office.
| In-clinic (N=16) | Telehealth (N=56) | ||||
|---|---|---|---|---|---|
| Measure | M | 95% CI | M | 95% CI | pa |
| Beck Depression Inventory–IIb | 25.0 | 23.0, 27.0 | 28.9 | 27.7, 30.1 | 0.0016 |
| Beck Anxiety Inventoryb | 23.3 | 21.4, 25.3 | 28.5 | 27.3, 29.7 | <0.001 |
| Global Assessment of Functioning | 51.1 | 48.6, 53.6 | 50.4 | 49.1, 51.7 | 0.6470 |
| CAGE questionnaireb | 0.6 | 0.3, 1.1 | 0.7 | 0.5, 1.0 | 0.6655 |
| Posttraumatic Stress Disorder Checklistb | 57.2 | 55.1, 59.3 | 56.8 | 55.5, 58.2 | 0.7719 |
| Health Locus of Controlb,c | 57.3 | 53.2, 61.3 | 53.7 | 51.6, 55.8 | 0.1250 |
| Quality of Life in Epilepsy Inventory–31c | 37.6 | 34.7, 40.6 | 35.8 | 34.4, 37.3 | 0.2753 |
| Family Assessment Device | |||||
| Problem solving | 2.4 | 1.8, 3.0 | 2.1 | 1.8, 2.5 | 0.4270 |
| Communication | 2.4 | 1.8, 2.9 | 2.2 | 1.9, 2.5 | 0.6463 |
| Roles | 2.4 | 1.8, 2.5 | 2.2 | 1.9, 2.5 | 0.5695 |
| Affective responsiveness | 2.4 | 1.8, 3.0 | 2.3 | 2.0, 2.6 | 0.7811 |
| Affective involvement | 2.3 | 1.7, 2.9 | 2.2 | 1.9, 2.5 | 0.6704 |
| Behavior control | 1.9 | 1.3, 2.5 | 1.8 | 1.5, 2.1 | 0.7456 |
| Global functioning | 2.3 | 1.7, 2.9 | 2.1 | 1.8, 2.4 | 0.4596 |
| Symptom Checklist–90b,c | |||||
| Somatization | 70.0 | 63.2, 76.8 | 71.7 | 69.1, 74.3 | 0.6430 |
| Obsessive-compulsive | 72.0 | 64.6, 79.4 | 74.3 | 71.5, 77.1 | 0.5596 |
| Interpersonal sensitivity | 63.8 | 54.3, 73.3 | 68.6 | 65.1, 72.2 | 0.3454 |
| Depression | 63.8 | 56.1, 71.6 | 73.0 | 70.1, 75.9 | 0.0309 |
| Anxiety | 62.2 | 53.3, 71.1 | 72.9 | 69.5, 76.3 | 0.0279 |
| Hostility | 63.8 | 54.1, 73.6 | 66.7 | 63.1, 70.4 | 0.5764 |
| Phobic anxiety | 66.7 | 57.3, 76.1 | 71.2 | 67.7, 74.8 | 0.3642 |
| Paranoid ideation | 59.2 | 49.7, 68.6 | 63.7 | 60.1, 67.3 | 0.3695 |
| Psychoticism | 67.3 | 60.0, 74.6 | 72.2 | 69.4, 74.9 | 0.2196 |
| Global severity index | 70.8 | 64.2, 77.5 | 74.9 | 72.4, 77.4 | 0.2576 |
| Positive symptom distress index | 67.8 | 61.2, 74.4 | 68.5 | 66.0, 71.0 | 0.8500 |
| Short-Form 36 Health Surveyc | |||||
| Physical functioning | 47.5 | 44.6, 50.4 | 40.3 | 38.8, 41.7 | <0.001 |
| Pain | 32.7 | 30.0, 35.4 | 33.7 | 32.3, 35.2 | 0.4932 |
| General health | 32.0 | 29.4, 34.8 | 36.3 | 34.8, 37.7 | 0.0083 |
| Vitality | 27.5 | 25.0, 30.2 | 27.3 | 26.0, 28.6 | 0.8761 |
| Social functioning | 39.6 | 36.8, 42.5 | 37.8 | 36.3, 39.3 | 0.2605 |
| Mental health | 44.7 | 31.8, 57.5 | 43.5 | 36.7, 50.2 | 0.8673 |
| Mini-Mental State Examination | 27.3 | 22.0, 30.0 | 28.0 | 25.0, 30.0 | 0.1345 |
a
Bold indicates statistically significant difference between groups.
b
For these scales, a higher score indicates a worse condition. (Otherwise, higher scores indicate a better condition.)
c
Scores are reported as T scores.
| Trauma type and | In-clinic (N=16) | Telehealth (N=56) | |||
|---|---|---|---|---|---|
| developmental stage | N | % | N | % | p |
| Physical | 8 | 50.0 | 28 | 50.0 | 0.9490 |
| As a child | 6 | 37.5 | 25 | 44.6 | 0.5900 |
| As an adult | 4 | 25.0 | 9 | 16.1 | 0.3849 |
| Verbal | 4 | 25.0 | 23 | 41.1 | 0.2635 |
| As a child | 4 | 25.0 | 23 | 41.1 | 0.1953 |
| As an adult | 1 | 6.3 | 1 | 1.8 | 0.3411 |
| Emotional | 8 | 50.0 | 28 | 50.0 | 0.9191 |
| As a child | 6 | 37.5 | 24 | 42.9 | 0.8056 |
| As an adult | 4 | 25.0 | 11 | 19.6 | 0.5873 |
| Sexual | 6 | 37.5 | 23 | 41.1 | 0.8992 |
| As a child | 5 | 31.3 | 18 | 32.1 | 0.9494 |
| As an adult | 3 | 18.8 | 10 | 17.9 | 0.9182 |
| Traumatic brain injury | 9 | 56.3 | 40 | 71.4 | 0.2876 |
Discussion
Overall, the major characteristics of the cohorts evaluated in-clinic or via telehealth did not differ significantly. In terms of reported sensitive psychosocial information and comorbid symptoms, we found no statistically significant differences between the cohorts. Although a few statistically significant differences were found between the cohorts, examining multiple aspects of the two cohorts could lead to a type I error. However, in general, patients from both groups were similar in demographic characteristics and in their reporting of clinical factors and developmental histories.
Statistically significant differences between cohorts included the presence of somatoform disorders and panic disorder, scores on mood and anxiety symptom scales, and use of ambulatory EEG. The remotely evaluated CVT group had a higher frequency of panic disorder and other somatoform disorder diagnoses. However, all other medical diagnoses did not differ significantly between groups. Given the similarities between the two groups across all other major psychiatric diagnoses and demographic factors, we did not expect to find these diagnostic differences between veterans in New England (i.e., those evaluated locally) and veterans in other parts of the country; however, the difference might be due to differences in patients’ reports of paroxysmal symptoms in different clinics. In clinics across the country that are not seizure specialty clinics, some PNES semiologies may have been diagnosed as panic disorder previously, and patients may have described some symptoms as anxiety or other somatic symptoms.
A significant between-group difference in the use of ambulatory EEG was also found. The frequency of ambulatory EEG in the local group was higher, compared with the remote group, which may simply reflect a difference in practice and availability of equipment at different VA locations.
No significant between-group differences were found in patients’ reports of abuse and trauma in their developmental history, which may indicate that use of CVT does not deter patients from reporting instances of trauma.
Another difference observed was in anxiety and depression symptoms, as indicated by symptom scale scores, which were higher in the remote group, compared with the local group. The greater number of panic disorder diagnoses in the remote group may have influenced the higher BAI scores in that group. Both BAI and BDI scores were in the moderate-to-low-severity level in both groups, and the groups were not clinically different in severity. Ultimately, patients seen via CVT did not seem to underreport their comorbid symptoms, compared with those evaluated in-clinic. Statistically significant differences in certain subscales of the SF-36—physical functioning and general health—were noted. SF-36 general health scores were higher in the remote group, indicating better health, whereas physical functioning scores were higher in the local group, indicating better functioning. This may be a result of slight differences in physical symptoms or reporting of symptoms in local and remote groups. We did not find a response bias favoring either group. Because the symptom scales were administered and scored similarly across groups, we do not believe these differences were due to acquisition errors.
As noted in the introduction, prior studies have examined patients with other diagnoses, including depression and posttraumatic stress disorder (PTSD), seen in-clinic and via CVT. In a review of 452 studies, Hubley et al. (2) reported that establishment of rapport was much less of a concern to the patients than it was to their clinicians. Furthermore, in a study of a large sample (N=666), Engel et al. (9) showed that use of telecare in the management of PTSD and depression among military personnel improved outcomes, compared with usual care. Divulging sensitive information and establishing rapport, although not explicitly measured in prior studies, are integral components of successful treatment in such rapport-dependent psychiatric cases. In the study reported here, we built on this literature by assessing divulgence of sensitive topics directly and addressing not only psychiatric symptoms but also neurologic ones in a novel diagnostic group in which CVT evaluation has not yet been reported and in which stigma among patients, families (38), and clinicians (39) may limit full disclosure of information related to difficult topics.
In this study, we found similarities across groups in commonly reported clinical factors. AEDs were prescribed in this sample, sometimes to “cover” seizures (N=28) and also for reasons other than epilepsy. The reasons reported included migraine prophylaxis (N=7), mood stabilization (N=11), and pain treatment (N=16). The literature indicates that in the absence of one of these indications, patients with PNES and no epilepsy can be tapered off AEDs safely (40) because AEDs do not treat PNES. Seizure rates were similar across groups in our study. As in prior studies, we found variability in reported monthly and weekly seizure rates. We have found similar fluctuations in seizure occurrence rates over time in our prospective studies (41). Past studies have reported a diagnostic delay, with a mean of 7.2 years (SD=9.3 years) from symptom onset to diagnosis (42). In the study reported here, symptom onset to PNES diagnosis ranged from a median of approximately 2–5 years. We believe this shortened delay is a result of active efforts to address PNES by the VA ECOE and the NTMHC.
Limitations of this study included the relatively small sample size of the local group, and thus the results of this preliminary study are intended to be used to inform further evaluations of telehealth utility. We acknowledge that other CVT comparison studies have larger samples for different populations (i.e., patients with psychiatric conditions such as depression, PTSD, or bulimia); however, this is the first study to assess neuropsychiatric complexities in a cohort of patients with the conversion disorder PNES. The design was limited intentionally to patients evaluated by a single clinician at the PVAMC Neuropsychiatry Clinic to be able to compare the same diagnostic approach for evaluations locally and remotely. The study may be subject to selection bias. The sample may not be representative of the entire veteran population in the United States, either the local sample or the veterans using CVT; however, veterans in the remote group were referred through the VA ECOE and were distributed across the United States. We did not determine or categorize urban versus rural residents, which could be done in the future to inform development of distance-delivery methods. Given the national distribution of the CVT group, it was likely a representative sample of U.S. veterans. The seizure rate reports may have been subject to recall bias. As noted, the estimates of the number of seizures per week and per month were collected retrospectively. Prospectively collected daily seizure logs may better reflect seizure frequency; however, this approach was not part of the cross-sectional study.
Overall, the absence of significant major differences between the two cohorts in sociodemographic factors, medical diagnoses, and medical examination findings may mitigate potential concerns of reporting bias between patients evaluated in-clinic and remotely. Our results mirror those of in-person-only assessments conducted with patients with PNES in a well-described sample in the Northwest (43–47).
Conclusions
An analysis of comorbid symptoms, substance abuse, medication use, history of trauma and abuse, and history of therapy did not show significant reporting differences between veterans evaluated in-clinic and via CVT. Patients evaluated remotely disclosed the same amount of sensitive information as those evaluated in-clinic, and use of telehealth did not seem to be a barrier to evaluation. Thus, this study shows promising early results for the use of CVT for evaluating complex, chronic, paroxysmal disorders such as PNES and epilepsy. Despite the prevalence of PNES, expertise about PNES is limited, and thus remote access to clinicians knowledgeable about the disorder is of utmost importance to overcome treatment gaps. Findings from this study may help clinicians gain more confidence about remote clinical evaluations, which over time, may become a standard, alternative method of evaluating patients with neuropsychiatric disorders. Such use of CVT may eliminate one of the many barriers to accessing mental health care encountered by patients with seizures.
Acknowledgments
The authors thank the PVAMC Center for Neurorestoration and Neurotechnology for assistance with this study, the staff of the VA National Telemental Health Center, and the VA Epilepsy Centers of Excellence.
Footnote
The contents of this article do not represent the views of the U.S. Department of Veterans Affairs (VA) or the United States government.
References
1.
Bashshur RL, Shannon GW, Krupinski EA, et al: National telemedicine initiatives: essential to healthcare reform. Telemed J E Health 2009; 15:600–610
2.
Hubley S, Lynch SB, Schneck C, et al: Review of key telepsychiatry outcomes. World J Psychiatry 2016; 6:269–282
3.
Bouchard S, Paquin B, Payeur R, et al: Delivering cognitive-behavior therapy for panic disorder with agoraphobia in videoconference. Telemed J E Health 2004; 10:13–25
4.
Day SX, Schneider PL: Psychotherapy using distance technology: a comparison of face-to-face, video, and audio treatment. J Couns Psychol 2002; 49:499–503
5.
Hilty DM, Marks S, Wegelin J, et al: A randomized, controlled trial of disease management modules, including telepsychiatric care, for depression in rural primary care. Psychiatry 2007; 4:58–65
6.
O’Reilly R, Bishop J, Maddox K, et al: Is telepsychiatry equivalent to face-to-face psychiatry? Results from a randomized controlled equivalence trial. Psychiatr Serv 2007; 58:836–843
7.
Simon GE, Ludman EJ, Tutty S, et al: Telephone psychotherapy and telephone care management for primary care patients starting antidepressant treatment: a randomized controlled trial. JAMA 2004; 292:935–942
8.
Chakrabarti S: Usefulness of telepsychiatry: a critical evaluation of videoconferencing-based approaches. World J Psychiatry 2015; 5:286–304
9.
Engel CC, Jaycox LH, Freed MC, et al: Centrally assisted collaborative telecare for posttraumatic stress disorder and depression among military personnel attending primary care: a randomized clinical trial. JAMA Intern Med 2016; 176:948–956
10.
Franklin CL, Cuccurullo LA, Walton JL, et al: Face to face but not in the same place: a pilot study of prolonged exposure therapy. J Trauma Dissociation 2017; 18:116–130
11.
Frueh BC, Monnier J, Yim E, et al: A randomized trial of telepsychiatry for post-traumatic stress disorder. J Telemed Telecare 2007; 13:142–147
12.
Luxton DD, Pruitt LD, Wagner A, et al: Home-based telebehavioral health for US military personnel and veterans with depression: a randomized controlled trial. J Consult Clin Psychol 2016; 84:923–934
13.
Maieritsch KP, Smith TL, Hessinger JD, et al: Randomized controlled equivalence trial comparing videoconference and in person delivery of cognitive processing therapy for PTSD. J Telemed Telecare 2016; 22:238–243
14.
Shore JH, Hilty DM, Yellowlees P: Emergency management guidelines for telepsychiatry. Gen Hosp Psychiatry 2007; 29:199–206
15.
Turner TH, Horner MD, Vankirk KK, et al: A pilot trial of neuropsychological evaluations conducted via telemedicine in the Veterans Health Administration. Telemed J E Health 2012; 18:662–667
16.
Kirkwood KT, Peck DF, Bennie L: The consistency of neuropsychological assessments performed via telecommunication and face to face. J Telemed Telecare 2000; 6:147–151
17.
Ruskin PE, Reed S, Kumar R, et al: Reliability and acceptability of psychiatric diagnosis via telecommunication and audiovisual technology. Psychiatr Serv 1998; 49:1086–1088
18.
Bugge C, Entwistle VA, Watt IS: The significance for decision-making of information that is not exchanged by patients and health professionals during consultations. Soc Sci Med 2006; 63:2065–2078
19.
Sankar P, Jones NL: To tell or not to tell: primary care patients’ disclosure deliberations. Arch Intern Med 2005; 165:2378–2383
20.
LaFrance WC Jr, Alosco ML, Davis JD, et al: Impact of family functioning on quality of life in patients with psychogenic nonepileptic seizures versus epilepsy. Epilepsia 2011; 52:292–300
21.
LaFrance WC Jr, Deluca M, Machan JT, et al: Traumatic brain injury and psychogenic nonepileptic seizures yield worse outcomes. Epilepsia 2013; 54:718–725
22.
LaFrance WC Jr, Leaver K, Stopa EG, et al: Decreased serum BDNF levels in patients with epileptic and psychogenic nonepileptic seizures. Neurology 2010; 75:1285–1291
23.
LaFrance WC Jr, Syc S: Depression and symptoms affect quality of life in psychogenic nonepileptic seizures. Neurology 2009; 73:366–371
24.
LaFrance WC Jr, Baker GA, Duncan R, et al: Minimum requirements for the diagnosis of psychogenic nonepileptic seizures: a staged approach: a report from the International League Against Epilepsy Nonepileptic Seizures Task Force. Epilepsia 2013; 54:2005–2018
25.
Altalib H, Cavazos J, Pugh MJ, et al: Providing quality epilepsy care for veterans. Fed Pract 2016; 33:26–32
26.
Deen TL, Godleski L, Fortney JC: A description of telemental health services provided by the Veterans Health Administration in 2006–2010. Psychiatr Serv 2012; 63:1131–1133
27.
Beck AT, Steer RA, Brown GK: Manual for the Beck Depression Inventory, 2nd ed. San Antonio, Tex, Psychological Corp, 1996
28.
Beck AT, Epstein N, Brown G, et al: An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol 1988; 56:893–897
29.
Epstein NB, Baldwin LM, Bishop DS: The McMaster Family Assessment Device. J Marital Fam Ther 1983; 9:171–180
30.
Vickrey BG, Perrine KR, Hays RD, et al: Quality of Life in Epilepsy—QOLIE-31: Scoring Manual, Version 1.0 ed. Santa Monica, Calif, RAND, 1993, pp 1–32
31.
Endicott J, Spitzer RL, Fleiss JL, et al: The Global Assessment Scale: a procedure for measuring overall severity of psychiatric disturbance. Arch Gen Psychiatry 1976; 33:766–771
32.
Ware JE Jr, Sherbourne CD: The MOS 36-item Short-Form Health Survey (SF-36): I. conceptual framework and item selection. Med Care 1992; 30:473–483
33.
Derogatis LR: Symptom Checklist 90–R: Administration, Scoring, and Procedures Manual, 3rd ed. Minneapolis, National Computer Systems, Inc, 1994
34.
Ewing JA: Detecting alcoholism: the CAGE questionnaire. JAMA 1984; 252:1905–1907
35.
Blanchard EB, Jones-Alexander J, Buckley TC, et al: Psychometric properties of the PTSD Checklist (PCL). Behav Res Ther 1996; 34:669–673
36.
Wallston BS, Wallston KA, Kaplan GD, et al: Development and validation of the Health Locus of Control (HLC) scale. J Consult Clin Psychol 1976; 44:580–585
37.
Folstein MF, Folstein SE, McHugh PR: “Mini-Mental State”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189–198
38.
Friedman JH, LaFrance WC Jr: Psychogenic disorders: the need to speak plainly. Arch Neurol 2010; 67:753–755
39.
Tolchin B, Baslet G, Dworetzky B: Psychogenic seizures and medical humor: jokes as a damaging defense. Epilepsy Behav 2016; 64(pt A):26–28
40.
Oto M, Espie C, Pelosi A, et al: The safety of antiepileptic drug withdrawal in patients with non-epileptic seizures. J Neurol Neurosurg Psychiatry 2005; 76:1682–1685
41.
Baird GL, Harlow LL, Machan JT, et al: Identifying seizure clusters in patients with psychogenic nonepileptic seizures. Epilepsy Behav 2017; 73:142–147
42.
Reuber M, Fernández G, Bauer J, et al: Diagnostic delay in psychogenic nonepileptic seizures. Neurology 2002; 58:493–495
43.
Salinsky M, Evrard C, Storzbach D, et al: Psychiatric comorbidity in veterans with psychogenic seizures. Epilepsy Behav 2012; 25:345–349
44.
Salinsky M, Rutecki P, Parko K, et al: Psychiatric comorbidity and traumatic brain injury attribution in patients with psychogenic nonepileptic or epileptic seizures: a multicenter study of US veterans. Epilepsia 2018; 59:1945–1953
45.
Salinsky M, Spencer D, Boudreau E, et al: Psychogenic nonepileptic seizures in US veterans. Neurology 2011; 77:945–950
46.
Salinsky M, Storzbach D, Goy E, et al: Traumatic brain injury and psychogenic seizures in veterans. J Head Trauma Rehabil 2015; 30:E65–E70
47.
Salinsky M, Storzbach D, Goy E, et al: Health care utilization following diagnosis of psychogenic nonepileptic seizures. Epilepsy Behav 2016; 60:107–111
Information & Authors
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History
Received: 10 August 2019
Revision received: 28 October 2019
Accepted: 18 November 2019
Published online: 14 February 2020
Published in print: Summer 2020
Keywords
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Funding Information
Matthew Siravo Memorial Foundationhttp://dx.doi.org/10.13039/100012219:
Dr. LaFrance has served or currently serves on the editorial boards of Epilepsia, Epilepsy and Behavior, the Journal of Neurology, Neurosurgery, and Psychiatry, and the Journal of Neuropsychiatry and Clinical Neurosciences; he has received royalties from Cambridge University Press and Oxford University Press; he has received research support from the American Epilepsy Society, Brown University, the Center for Neurorestoration and Neurotechnology, the Department of Defense, the Epilepsy Foundation, NIH, Providence Veterans Affairs Medical Center, Rhode Island Hospital, and the Siravo Foundation; he serves on the Professional Advisory Board of the Epilepsy Foundation New England; he has received honoraria from the American Academy of Neurology for teaching an annual meeting annual course; and he has served as a clinic development consultant for Cleveland Clinic, Emory University, Oregon Health Sciences University, Spectrum Health, and the University of Colorado Denver. The other authors report no financial relationships with commercial interests.Supported by the Providence VA Center for Neurorestoration and Neurotechnology and a Siravo Foundation grant (to Dr. LaFrance).This study is the result of work supported with resources and use of facilities at PVAMC.Metrics & Citations
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