Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study.

BACKGROUND: Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE: This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS: Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS: There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS: Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.

Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.

BACKGROUND: Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient's outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician's interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. OBJECTIVE: We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. METHODS: We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system's capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system's sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. RESULTS: Data collection began in July 2020 and is expected to continue through December 2022. CONCLUSIONS: If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36417.

Integrating Patient-generated Digital Health Data into Electronic Health Records (EHRs) in Ambulatory Care Settings: EHR Vendor Survey and Interviews.

Data traditionally collected in a clinic or hospital setting is now collected electronically in everyday environments from patients, known as patient-generated health data (PGHD). We conducted informal interviews and collected survey data from major ambulatory care EHR vendors that serve the majority of the U.S. market to collect information on how their clients are integrating PGHD into EHRs. Of the 9 EHR vendors contacted, 6 completed the survey and 5 participated in a 45-minute interview. Feedback from the vendors included how PGHD use has steadily risen over the past decade and how the COVID-19 pandemic accelerated PGHD use. Pathways for data from devices or surveys to be brought securely into the EHR are increasing. While promising, adoption of health IT systems has its challenges. There are disparities in EHRs, devices, and applications. We concluded that more supportive policies are needed to advance PGHD integration.

Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review.

BACKGROUND: Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient's self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia. METHOD: We conducted a systematic review using PubMed and Google Scholar. Relevant publications published before (including) December 2021 were identified and evaluated for inclusion. The objective was to conduct a systematic review of computer vision for facial behavior analysis in schizophrenia studies, the clinical findings, and the corresponding data processing and machine learning methods. RESULTS: Seventeen studies published between 2007 to 2021 were included, with an increasing trend in the number of publications over time. Only 14 articles used interviews to collect data, of which different combinations of passive to evoked, unstructured to structured interviews were used. Various types of hardware were adopted and different types of visual data were collected. Commercial, open-access, and in-house developed models were used to recognize facial behaviors, where frame-level and subject-level features were extracted. Statistical tests and evaluation metrics varied across studies. The number of subjects ranged from 2-120, with an average of 38. Overall, facial behaviors appear to have a role in estimating diagnosis of schizophrenia and psychotic symptoms. When studies were evaluated with a quality assessment checklist, most had a low reporting quality. CONCLUSION: Despite the rapid development of computer vision techniques, there are relatively few studies that have applied this technology to schizophrenia research. There was considerable variation in the clinical paradigm and analytic techniques used. Further research is needed to identify and develop standardized practices, which will help to promote further advances in the field.

Dementia Syndrome of Depression: Diagnostic Challenges and Clinical Relevance.

Late-life depression is frequently associated with cognitive impairment. Because of the overlap of symptoms, however, it can be challenging to discern a neurocognitive disorder (NCD) from a late-life depressive disorder. Although neuropsychological testing provides evidence, there are limited neurochemical or neuroimaging biomarkers for the etiological classification of NCD versus late-life depression. Without formal DSM-5 criteria for a dementia syndrome of depression (DSD), patients may be incorrectly diagnosed as having an NCD. Without recognition and appropriate aggressive treatment, patients may develop severe depression with cognitive impairment leading to significant morbidity. It is crucial that clinicians become aware of and assess for elements that differentiate DSD from neurocognitive disorders. In so doing, this syndrome can be identified and treated early in its course, allowing for the best patient outcomes. In this article, the authors demonstrate, through a case presentation, the diagnostic challenges and clinical value of accurately identifying and treating DSD.

Clinician Burnout Associated With Sex, Clinician Type, Work Culture, and Use of Electronic Health Records.

Importance: Electronic health records (EHRs) are considered a potentially significant contributor to clinician burnout. Objective: To describe the association of EHR usage, sex, and work culture with burnout for 3 types of clinicians at an academic medical institution. Design, Setting, and Participants: This cross-sectional study of 1310 clinicians at a large tertiary care academic medical center analyzed EHR usage metrics for the month of April 2019 with results from a well-being survey from May 2019. Participants included attending physicians, advanced practice providers (APPs), and house staff from various specialties. Data were analyzed between March 2020 and February 2021. Exposures: Clinician demographic characteristics, EHR metadata, and an institution-wide survey. Main Outcomes and Measures: Study metrics included clinician demographic data, burnout score, well-being measures, and EHR usage metadata. Results: Of the 1310 clinicians analyzed, 542 (41.4%) were men (mean [SD] age, 47.3 [11.6] years; 448 [82.7%] White clinicians, 52 [9.6%] Asian clinicians, and 21 [3.9%] Black clinicians) and 768 (58.6%) were women (mean [SD] age, 42.6 [10.3] years; 573 [74.6%] White clinicians, 105 [13.7%] Asian clinicians, and 50 [6.5%] Black clinicians). Women reported more burnout (survey score ≥50: women, 423 [52.0%] vs men, 258 [47.6%]; P = .008) overall. No significant differences in EHR usage were found by sex for multiple metrics of time in the EHR, metrics of volume of clinical encounters, or differences in products of clinical care. Multivariate analysis of burnout revealed that work culture domains were significantly associated with self-reported results for commitment (odds ratio [OR], 0.542; 95% CI, 0.427-0.688; P < .001) and work-life balance (OR, 0.643; 95% CI, 0.559-0.739; P < .001). Clinician sex significantly contributed to burnout, with women having a greater likelihood of burnout compared with men (OR, 1.33; 95% CI, 1.01-1.75; P = .04). An increased number of days spent using the EHR system was associated with less likelihood of burnout (OR, 0.966; 95% CI, 0.937-0.996; P = .03). Overall, EHR metrics accounted for 1.3% of model variance (P = .001) compared with work culture accounting for 17.6% of variance (P < .001). Conclusions and Relevance: In this cross-sectional study, sex-based differences in EHR usage and burnout were found in clinicians. These results also suggest that local work culture factors may contribute more to burnout than metrics of EHR usage.

Increased Clinician Time Using Electronic Health Records During COVID-19 Pandemic.

The COVID-19 pandemic challenged how healthcare systems provided care in socially distanced formats. We hypothesized that the COVID-19 era changes in clinical care delivery models contributed to increased Electronic Health Record (EHR) related work. To evaluate the changes in time and volume metrics of EHR usage, we segregated EHR audit log metric data into PreCOVID2019 March/April/May, initial COVID2020 March/April/May, and late COVID2021 March/April/May for 1262 physician providers. We discovered significant and pragmatically meaningful increases in total average time providers spent in the EHR in minutes mean(SD) PreCOVID2019=1958(1576), Mid-COVID2020=1709(1473), Late-COVID2021=2007(1563). Differences in total time in the EHR were significant Pre-mid:p-value=<0.001, but not Pre-Late:p=0.439. Total number of messages received across all specialties increased significantly mean(SD) PreCOVID=459(389), MidCOVID=400(362), LateCOVID 521(423) Pre-Mid p-value=<0.001 and Pre-Late p-value=<0.001. We additionally found changes in total time to differ significantly across select specialties. Based on these findings we recommend further assessment of physician workload and how new factors such as telehealth are contributing to EHR usage.

Law enforcement and mental health clinician partnerships in global mental health: outcomes for the Crisis Intervention Team (CIT) model adaptation in Liberia, West Africa.

BACKGROUND: The Crisis Intervention Team (CIT) model is a law enforcement strategy that aims to build alliances between the law enforcement and mental health communities. Despite its success in the United States, CIT has not been used in low- and middle-income countries. This study assesses the immediate and 9-month outcomes of CIT training on trainee knowledge and attitudes. METHODS: Twenty-two CIT trainees (14 law enforcement officers and eight mental health clinicians) were evaluated using pre-developed measures assessing knowledge and attitudes related to mental illness. Evaluations were conducted prior to, immediately after, and 9 months post training. RESULTS: The CIT training produced improvements both immediately and 9 months post training in knowledge and attitudes, suggesting that CIT can benefit law enforcement officers even in extremely low-resource settings with limited specialized mental health service infrastructure. CONCLUSION: These findings support further exploration of the benefits of CIT in highly under-resourced settings.

Law Enforcement and Clinician Partnerships: Training of Trainers for CIT Teams in Liberia, West Africa.

The crisis intervention team (CIT) model was developed in the United States to align law enforcement goals with those of mental health advocates and service users. Liberia is the first low-income country where CIT has been implemented. After preliminary training of law enforcement officers and mental health clinicians by U.S. CIT experts, the program is now entirely implemented by Liberian personnel. In this column, the authors describe topics addressed in the 5-day training-of-trainers process to prepare Liberian mental health clinicians and law enforcement officers to conduct the program, along with feedback received from participants. They hope that this model can guide future initiatives aimed at fostering collaboration of law enforcement and mental health services in global mental health.

Re-titration rates after clozapine-induced neutropenia or agranulocytosis: A case report and literature review.

Clozapine-induced neutropenia occurs in 3-5% of individuals treated with clozapine. Current US guidelines require interruption of clozapine when the absolute neutrophil count (ANC) drops below 1000 cells/mm3. There is minimal available guidance for what dosing schedule to use when restarting clozapine after an episode of neutropenia. Here, we present a case of a 50-year-old Caucasian female with a history of schizoaffective disorder who was successfully rechallenged on clozapine one month after developing clozapine-induced neutropenia (ANC 600 cells/mm3). To understand published re-titration rates of clozapine after neutropenia, we conducted a literature review using a using the PubMed database and found only seven case reports that unambiguously reported a clozapine dosing schedule during re-challenge. All were successful except one, a case of clozapine rechallenge after agranulocytosis. Including this case presentation, six out of eight cases restarted clozapine more cautiously than recommended by the US guidelines for a new clozapine initiation. We cannot comment what role a slower or more rapid titration plays in a successful rechallenge after neutropenia with the available evidence. We encourage researchers to publish their dosing schedule in detail after an episode of neutropenia or agranulocytosis.

An Additive Mix? Acute Urinary Retention in a Patient With Benign Prostatic Hyperplasia Treated With Suboxone, Lurasidone, and Trazodone.

Treatment of psychiatric patients is frequently complicated by medical comorbidities, complex pharmacologic regimens, and side effects occurring secondarily to those regimens. Acute urinary retention is an infrequently discussed side effect of such regimens. This report describes the development of acute urinary retention (AUR) in a 60-year-old man with a history of benign prostatic hyperplasia. The patient developed AUR during treatment with combination buprenorphine/naloxone, trazodone, and lurasidone. We discuss the potential relationship of these agents to the development of this patient's AUR, the complicated neurochemical dynamic of the voiding process, and the pathologic consequences that psychotropic agents can have on that process.

Exploring the role of miRNAs in renal cell carcinoma progression and metastasis through bioinformatic and experimental analyses.

Metastasis results in most of the cancer deaths in clear cell renal cell carcinoma (ccRCC). MicroRNAs (miRNAs) regulate many important cell functions and play important roles in tumor development, metastasis and progression. In our previous study, we identified a miRNA signature for metastatic RCC. In this study, we validated the top differentially expressed miRNAs on matched primary and metastatic ccRCC pairs by quantitative polymerase chain reaction. We performed bioinformatics analyses including target prediction and combinatorial analysis of previously reported miRNAs involved in tumour progression and metastasis. We also examined the co-expression of the miRNAs clusters and compared expression of intronic miRNAs and their host genes. We observed significant dysregulation between primary and metastatic tumours from the same patient. This indicates that, at least in part, the metastatic signature develops gradually during tumour progression. We identified metastasis-dysregulated miRNAs that can target a number of genes previously found to be involved in metastasis of kidney cancer as well as other malignancies. In addition, we found a negative correlation of expression of miR-126 and its target vascular endothelial growth factor (VEGF)-A. Cluster analysis showed that members of the same miRNA cluster follow the same expression pattern, suggesting the presence of a locus control regulation. We also observed a positive correlation of expression between intronic miRNAs and their host genes, thus revealing another potential control mechanism for miRNAs. Many of the significantly dysregulated miRNAs in metastatic ccRCC are highly conserved among species. Our analysis suggests that miRNAs are involved in ccRCC metastasis and may represent potential biomarkers.