Growing the Psychiatry Workforce Through Expansion or Creation of Residencies and Fellowships: the Results of a Survey by the AADPRT Workforce Task Force

Qualitative application of the RE-AIM/PRISM framework to an educational intervention for improving the care of persons with behavioral and psychological symptoms of dementia

BACKGROUND

An academic detailing model has improved self-efficacy of memory clinic clinicians to identify and manage complex behavioral and psychological symptoms in persons with dementia (BPSD). The purpose of this report is to describe a systematic approach to adapting a clinician education program previously delivered in two primary care integrated memory clinics for improving the management of BPSD to also be deliverable outside a memory clinic setting, in generalist primary care clinics. The RE-AIM/PRISM implementation framework guided the approach.

METHODS

Application of the RE-AIM/PRISM framework to the academic detailing program for BPSD was mapped. Framework-guided qualitative interviews were completed with experienced (Champion) and inexperienced (Novice) program stakeholders including questions on perceived gaps in clinical care (BPSD management) and barriers and facilitators to the educational model. Inductive and deductive qualitative thematic analytic approaches were used, the latter organized by RE-AIM domains and multi-level context. Convergence or divergence in organized themes by stakeholder experience shaped examination of fit and interactions among domains, components and strategies of the model for pre-implementation adaptations planning for non-memory clinic primary care clinicians.

RESULTS

A pragmatic application of the RE-AIM/PRISM framework was completed for collecting qualitative feedback from stakeholders, identifying multi-level contextual barriers and facilitators, and planning adaptations to our clinician education program. A description of the clinician stakeholders, the approach and one example of a clinician and intervention-level theme identified across RE-AIM domains for the program, self-efficacy in the management of BPSD, and resulting planned adaptations were shared.

CONCLUSIONS

We provide a novel qualitative application of the RE-AIM/PRISM framework to inform adaptations for an intervention for primary care that incorporates feedback from both current experienced and future inexperienced program stakeholders. This approach can be used to identify multi-level contextual barriers and facilitators to reach, adoption, implementation, and effectiveness of this clinician education programs approach, academic detailing, for future primary care teams.

Quantifying Patient Demand for Orthopedics Care by Region Through Google Trends Analysis: Descriptive Epidemiology Study

Background

There is a growing gap between the supply of surgeons and the demand for orthopedic services in the United States.

Objective

We analyzed publicly available online data to assess the correlation between the supply of orthopedic surgeons and patient demand across the United States. The geographic trends of this gap were assessed by using the relative demand index (RDI) to guide precision public health interventions such as resource allocation, residency program expansion, and workforce planning to specific regions.

Methods

The data used were from the US Census Bureau, Association of American Medical Colleges (AAMC) through their 2024 Electronic Residency Application Service (ERAS) directory, AAMC State Physician Workforce Data Report, and Google Trends. We calculated the normalized relative search volume (RSV) and the RDI and compared them to the densities of orthopedic surgeons across the United States. We examined the disparities with the Spearman rank correlation coefficient.

Results

The supply of orthopedic surgeons varied greatly across the United States, with a significantly higher demand for them in southern states (P=.02). The orthopedic surgeon concentration, normalized to the highest density, was the highest in Alaska (n=100), the District of Columbia (n=96), and Wyoming (n=72); and the lowest in Texas (n=0), Arkansas (n=6), and Oklahoma (n=64). The highest RDI values were observed in Utah (n=97), Florida (n=88), and Texas (n=83), while the lowest were observed in Alaska (n=0), the District of Columbia (n=5), and New Hampshire (n=7). The 7 states of Alaska, Maine, South Dakota, Wyoming, Montana, Delaware, and Idaho lacked orthopedic surgery residencies. In 2023, New York (n=19), Michigan (n=17), Ohio (n=17), Pennsylvania (n=16), and California (n=16) had the most residency programs. Demand and supply, represented by the RDI and orthopedic surgeon concentration, respectively, were strongly correlated negatively (ρ=-0.791, P<.001). States that were in the top quartile of residency programs (≥4 residency programs) exhibited a high demand for orthopedic surgeons (ρ=.6035, P=.02).

Conclusions

This study showed that regional disparities in access to orthopedic care can be addressed by increasing orthopedic residencies. The study highlights the novel application of the RDI to mapping the regional need for orthopedics, and this map allows for better targeted resource allocation to expand orthopedic surgery training.

Overcoming the Impact of Students for Fair Admission v Harvard to Build a More Representative Health Care Workforce: Perspectives from Ending Unequal Treatment

Policy Points In a recently commissioned report on solutions for eliminating racial and ethnic health care inequities entitled Ending Unequal Treatment, the National Academies of Sciences, Engineering, and Medicine found a health workforce that is representative of the communities it serves is essential for health care equity. The Supreme Court decision to ban race-conscious admission constraints pathways toward health workforce representativeness and equity. This paper draws on the National Academies report's findings that health care workforce representativeness improves care quality, population health, and equity to discuss policy and programmatic options for various participants to promote health workforce representativeness in the context of race-conscious admissions bans.

Towards reliable forecasting of healthcare capacity needs: A scoping review and evidence mapping

BACKGROUND

The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, healthcare systems must also adapt to more gradual temporal changes in disease prevalence and demographic composition over time. To support proactive healthcare planning, statistical capacity forecasting models can provide valuable information to healthcare planners. This systematic literature review and evidence mapping aims to identify and describe studies that have used statistical forecasting models to estimate healthcare capacity needs within hospital settings.

METHOD

Studies were identified in the databases MEDLINE and Embase and screened for relevance before items were defined and extracted within the following categories: forecast methodology, measure of capacity, forecast horizon, healthcare setting, target diagnosis, validation methods, and implementation.

RESULTS

84 studies were selected, all focusing on various capacity outcomes, including number of hospital beds/ patients, staffing, and length of stay. The selected studies employed different analytical models grouped in six items; discrete event simulation (N = 13, 15 %), generalized linear models (N = 21, 25 %), rate multiplication (N = 15, 18 %), compartmental models (N = 14, 17 %), time series analysis (N = 22, 26 %), and machine learning not otherwise categorizable (N = 12, 14 %). The review further provides insights into disease areas with infectious diseases (N = 24, 29 %) and cancer (N = 12, 14 %) being predominant, though several studies forecasted healthcare capacity needs in general (N = 24, 29 %). Only about half of the models were validated using either temporal validation (N = 39, 46 %), cross-validation (N = 2, 2 %) or/and geographical validation (N = 4, 5 %).

CONCLUSION

The forecasting models' applicability can serve as a resource for healthcare stakeholders involved in designing future healthcare capacity estimation. The lack of routine performance validation of the used algorithms is concerning. There is very little information on implementation and follow-up validation of capacity planning models.

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.

Expansion of Psychosocial Care Centers and their relationship with health and human rights

OBJECTIVE

To analyze the factors associated with the expansion of the number of Brazilian Psychosocial Care Centers (CAPS) considering aspects related to the general health scenario and the institutionalization of human rights.

METHODS

An analytical document-based study, developed between February 2020 and May 2022, whose secondary data on the 27 Brazilian capitals were collected on platforms in the public domain, based on the time series from 2015 to 2020. Indicators were listed based on health system infrastructure and quality of life. For data analysis, descriptive statistics, Pearson's correlation test and Student's t test were used.

RESULTS

The capitals that expanded the number of CAPS in the analyzed period were the ones that presented the greatest political-legal framework for the protection of human rights.

CONCLUSION

The results suggest that the greater the commitment of governments in favor of human rights, the greater the investment for CAPS expansion.