-
Australasian Psychiatry : Bulletin of... Jun 2024To determine whether a brief educational intervention for Junior Medical Officers (JMOs), using teaching methods aimed at achieving higher outcomes on Bloom's Taxonomy,...
OBJECTIVE
To determine whether a brief educational intervention for Junior Medical Officers (JMOs), using teaching methods aimed at achieving higher outcomes on Bloom's Taxonomy, significantly improved participant confidence and knowledge in decision making about restrictive care.
METHOD
JMOs received a teaching session on restrictive medical and mental health care. Groups were randomly assigned to either sessions including a component of modern pedagogical interventions (Think-Pair-Share and SNAPPS), or sessions including a control period focusing on reviewing a condensed summary of relevant information. Pre- and post-intervention measures were recorded for subjective self-ratings of confidence and scores on standardized clinically relevant extended matching questions (EMQs).
RESULTS
There was no difference in subjective confidence improvement between groups; however, the group receiving the modern pedagogical intervention demonstrated significantly greater objective performance on knowledge-based EMQs.
CONCLUSIONS
A brief modern pedagogical intervention using interactive teaching methods shows promise for improving knowledge of restrictive care and the Mental Health and Guardianship Acts. In the control group, similarly increased confidence in knowledge did not equate to increased competence on a knowledge assessment. Refurbishing educational interventions presents opportunities for improving clinical outcomes and engaging junior doctors in psychiatry.
PubMed: 38876497
DOI: 10.1177/10398562241260170 -
NeuroImage. Clinical Jun 2024Past work has shown that people with schizophrenia exhibit more cross-subject heterogeneity in their functional connectivity patterns. However, it remains unclear...
UNLABELLED
Past work has shown that people with schizophrenia exhibit more cross-subject heterogeneity in their functional connectivity patterns. However, it remains unclear whether specific brain networks are implicated, whether common confounds could explain the results, or whether task activations might also be more heterogeneous. Unambiguously establishing the existence and extent of functional heterogeneity constitutes a first step toward understanding why it emerges and what it means clinically.
METHODS
We first leveraged data from the HCP Early Psychosis project. Functional connectivity (FC) was extracted from 718 parcels via principal components regression. Networks were defined via a brain network partition (Ji et al., 2019). We also examined an independent data set with controls, later-stage schizophrenia patients, and ADHD patients during rest and during a working memory task. We quantified heterogeneity by averaging the Pearson correlation distance of each subject's FC or task activity pattern to that of every other subject of the same cohort.
RESULTS
Affective and non-affective early psychosis patients exhibited more cross-subject whole-brain heterogeneity than healthy controls (ps < 0.001, Hedges' g > 0.74). Increased heterogeneity could be found in up to seven networks. In-scanner motion, medication, nicotine, and comorbidities could not explain the results. Later-stage schizophrenia patients exhibited heterogeneous connectivity patterns and task activations compared to ADHD and control subjects. Interestingly, individual connection weights, parcel-wise task activations, and network averages thereof were not more variable in patients, suggesting that heterogeneity becomes most obvious over large-scale patterns.
CONCLUSION
Whole-brain cross-subject functional heterogeneity characterizes psychosis during rest and task. Developmental and pathophysiological consequences are discussed.
PubMed: 38875745
DOI: 10.1016/j.nicl.2024.103630 -
JMIR AI Mar 2024[This corrects the article DOI: 10.2196/44358.].
[This corrects the article DOI: 10.2196/44358.].
PubMed: 38875681
DOI: 10.2196/57869 -
JMIR Biomedical Engineering Dec 2023Parkinson disease (PD) is the second most common neurodegenerative disease, affecting approximately 1% of the world's population. Increasing evidence suggests that...
BACKGROUND
Parkinson disease (PD) is the second most common neurodegenerative disease, affecting approximately 1% of the world's population. Increasing evidence suggests that aerobic physical exercise can be beneficial in mitigating both motor and nonmotor symptoms of the disease. In a recent pilot study of the role of exercise on PD, we sought to confirm exercise intensity by monitoring heart rate (HR). For this purpose, we asked participants to wear a chest strap HR monitor (Polar Electro Oy) and the Fitbit Charge 4 (Fitbit Inc) wrist-worn HR monitor as a potential proxy due to its convenience. Polar H10 has been shown to provide highly accurate R-R interval measurements. Therefore, we treated it as the gold standard in this study. It has been shown that Fitbit Charge 4 has comparable accuracy to Polar H10 in healthy participants. It has yet to be determined if the Fitbit is as accurate as Polar H10 in patients with PD during rest and exercise.
OBJECTIVE
This study aimed to compare Fitbit Charge 4 to Polar H10 for monitoring HR in patients with PD at rest and during an intensive exercise program.
METHODS
A total of 596 exercise sessions from 11 (6 male and 5 female) participants were collected simultaneously with both devices. Patients with early-stage PD (Hoehn and Yahr ≤2) were enrolled in a 6-month exercise program designed for patients with PD. They participated in 3 one-hour exercise sessions per week. They wore both Fitbit and Polar H10 during each session. Sessions included rest, warm-up, intense exercise, and cool-down periods. We calculated the bias in the HR of the Fitbit Charge 4 at rest (5 min) and during intense exercise (20 min) by comparing the mean HR during each of the periods to the respective means measured by Polar H10 (HRFitbit - HRPolar). We also measured the sensitivity and specificity of Fitbit Charge 4 to detect average HRs that exceed the threshold for intensive exercise, defined as 70% of an individual's theoretical maximum HR. Different types of correlations between the 2 devices were investigated.
RESULTS
The mean bias was 1.68 beats per minute (bpm) at rest and 6.29 bpm during high-intensity exercise, with an overestimation by Fitbit Charge 4 in both conditions. The mean bias of the Fitbit across both rest and intensive exercise periods was 3.98 bpm. The device's sensitivity in identifying high-intensity exercise sessions was 97.14%. The correlation between the 2 devices was nonlinear, suggesting Fitbit's tendency to saturate at high values of HR.
CONCLUSIONS
The performance of Fitbit Charge 4 is comparable to Polar H10 for assessing exercise intensity in a cohort of patients with PD (mean bias 3.98 bpm). The device could be considered a reasonable surrogate for more cumbersome chest-worn devices in future studies of clinical cohorts.
PubMed: 38875680
DOI: 10.2196/51515 -
JMIR AI Dec 2023Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this...
BACKGROUND
Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increase the efficiency of service delivery.
OBJECTIVE
In this study, we examined whether artificial intelligence (AI)-enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes.
METHODS
In this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK's National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients.
RESULTS
We showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects.
CONCLUSIONS
Our results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.
PubMed: 38875569
DOI: 10.2196/44358 -
JMIR AI Oct 2023Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying...
BACKGROUND
Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs' psychological challenges is crucial to addressing HCWs' mental health needs effectively, now and for future large-scale events.
OBJECTIVE
In this study, we used natural language processing methods to examine deidentified psychotherapy transcripts from telemedicine treatment during the initial wave of COVID-19 in the United States. Psychotherapy was delivered by licensed therapists while HCWs were managing increased clinical demands and elevated hospitalization rates, in addition to population-level social distancing measures and infection risks. Our goal was to identify specific concerns emerging in treatment for HCWs and to compare differences with matched non-HCW patients from the general population.
METHODS
We conducted a case-control study with a sample of 820 HCWs and 820 non-HCW matched controls who received digitally delivered psychotherapy in 49 US states in the spring of 2020 during the first US wave of the COVID-19 pandemic. Depression was measured during the initial assessment using the Patient Health Questionnaire-9, and anxiety was measured using the General Anxiety Disorder-7 questionnaire. Structural topic models (STMs) were used to determine treatment topics from deidentified transcripts from the first 3 weeks of treatment. STM effect estimators were also used to examine topic prevalence in patients with moderate to severe anxiety and depression.
RESULTS
The median treatment enrollment date was April 15, 2020 (IQR March 31 to April 27, 2020) for HCWs and April 19, 2020 (IQR April 5 to April 27, 2020) for matched controls. STM analysis of deidentified transcripts identified 4 treatment topics centered on health care and 5 on mental health for HCWs. For controls, 3 STM topics on pandemic-related disruptions and 5 on mental health were identified. Several STM treatment topics were significantly associated with moderate to severe anxiety and depression, including working on the hospital unit (topic prevalence 0.035, 95% CI 0.022-0.048; P<.001), mood disturbances (prevalence 0.014, 95% CI 0.002-0.026; P=.03), and sleep disturbances (prevalence 0.016, 95% CI 0.002-0.030; P=.02). No significant associations emerged between pandemic-related topics and moderate to severe anxiety and depression for non-HCW controls.
CONCLUSIONS
The study provides large-scale quantitative evidence that during the initial wave of the COVID-19 pandemic, HCWs faced unique work-related challenges and stressors associated with anxiety and depression, which required dedicated treatment efforts. The study further demonstrates how natural language processing methods have the potential to surface clinically relevant markers of distress while preserving patient privacy.
PubMed: 38875560
DOI: 10.2196/47223 -
JMIR AI Dec 2023Drug-induced mortality across the United States has continued to rise. To date, there are limited measures to evaluate patient preferences and priorities regarding...
BACKGROUND
Drug-induced mortality across the United States has continued to rise. To date, there are limited measures to evaluate patient preferences and priorities regarding substance use disorder (SUD) treatment, and many patients do not have access to evidence-based treatment options. Patients and their families seeking SUD treatment may begin their search for an SUD treatment facility online, where they can find information about individual facilities, as well as a summary of patient-generated web-based reviews via popular platforms such as Google or Yelp. Web-based reviews of health care facilities may reflect information about factors associated with positive or negative patient satisfaction. The association between patient satisfaction with SUD treatment and drug-induced mortality is not well understood.
OBJECTIVE
The objective of this study was to examine the association between online review content of SUD treatment facilities and drug-induced state mortality.
METHODS
A cross-sectional analysis of online reviews and ratings of Substance Abuse and Mental Health Services Administration (SAMHSA)-designated SUD treatment facilities listed between September 2005 and October 2021 was conducted. The primary outcomes were (1) mean online rating of SUD treatment facilities from 1 star (worst) to 5 stars (best) and (2) average drug-induced mortality rates from the Centers for Disease Control and Prevention (CDC) WONDER Database (2006-2019). Clusters of words with differential frequencies within reviews were identified. A 3-level linear model was used to estimate the association between online review ratings and drug-induced mortality.
RESULTS
A total of 589 SAMHSA-designated facilities (n=9597 reviews) were included in this study. Drug-induced mortality was compared with the average. Approximately half (24/47, 51%) of states had below average ("low") mortality rates (mean 13.40, SD 2.45 deaths per 100,000 people), and half (23/47, 49%) had above average ("high") drug-induced mortality rates (mean 21.92, SD 3.69 deaths per 100,000 people). The top 5 themes associated with low drug-induced mortality included detoxification and addiction rehabilitation services (r=0.26), gratitude for recovery (r=-0.25), thankful for treatment (r=-0.32), caring staff and amazing experience (r=-0.23), and individualized recovery programs (r=-0.20). The top 5 themes associated with high mortality were care from doctors or providers (r=0.24), rude and insensitive care (r=0.23), medication and prescriptions (r=0.22), front desk and reception experience (r=0.22), and dissatisfaction with communication (r=0.21). In the multilevel linear model, a state with a 10 deaths per 100,000 people increase in mortality was associated with a 0.30 lower average Yelp rating (P=.005).
CONCLUSIONS
Lower online ratings of SUD treatment facilities were associated with higher drug-induced mortality at the state level. Elements of patient experience may be associated with state-level mortality. Identified themes from online, organically derived patient content can inform efforts to improve high-quality and patient-centered SUD care.
PubMed: 38875553
DOI: 10.2196/46317 -
JMIR AI Oct 2023Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in...
BACKGROUND
Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance.
OBJECTIVE
This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine.
METHODS
Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set.
RESULTS
Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams.
CONCLUSIONS
These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.
PubMed: 38875536
DOI: 10.2196/47449 -
Revista Brasileira de Psiquiatria (Sao... Jun 2024We aim to investigate whether: 1) social skills (SS) are impaired in obsessive-compulsive disorder (OCD); 2) SS would change over the course of treatment; and 3)...
OBJECTIVE
We aim to investigate whether: 1) social skills (SS) are impaired in obsessive-compulsive disorder (OCD); 2) SS would change over the course of treatment; and 3) severity of OCD, age of onset of OCD symptoms and illness duration would be associated with SS impairments.
METHODS
41 treatment-naive patients with OCD and 34 control participants (CP) were assessed using a SS inventory. Patients were reevaluated 12-weeks after standardized treatment. Group differences, as well as the treatment effect on OCD symptomatology over time, were analyzed with independent and paired tests, respectively. OCD severity, age at illness onset and illness duration were tested as predictors of SS.
RESULTS
Patients had lower total SS scores compared to controls (p-value < 0.001). After treatment, although OCD symptomatology (p-value < 0.001) improved, there was no statistical difference in SS performance (p-value = 0.673). Earlier age of onset of OCD symptoms predicted worse SS total score (p-value = 0.016).
CONCLUSION
This study suggests that, despite the amelioration of OCD symptomatology, there was no alteration in Social Skills (SS) performance. Subsequent treatment investigations incorporating larger sample sizes and extended follow-up periods could elucidate whether enhancements in social skills are likely to manifest over time.
PubMed: 38875529
DOI: 10.47626/1516-4446-2024-3577 -
Revista Brasileira de Psiquiatria (Sao... Jun 2024
PubMed: 38875511
DOI: 10.47626/1516-4446-2024-3719