IT and the Quality and Efficiency of Mental Health Care in a Time of COVID-19: Case Study of Mental Health Providers in England
Bibliographic record
Abstract
BACKGROUND: In England, COVID-19 has significantly affected mental health care and tested the resilience of health care providers. In many areas, the increased use of IT has enabled traditional modes of service delivery to be supported or even replaced by remote forms of provision. OBJECTIVE: This study aimed to assess the use and impact of IT, in remote service provision, on the quality and efficiency of mental health care during the pandemic. We drew on sociotechnical systems theory as a conceptual framework to help structure the gathering, analysis, and interpretation of data. METHODS: We conducted a national scoping survey that involved documentary analysis and semistructured interviews with 6 national stakeholders and case studies of 4 purposefully selected mental health providers in England involving interviews with 53 staff members. RESULTS: Following the outbreak of COVID-19, mental health providers rapidly adjusted their traditional forms of service delivery, switching to digital and telephone consultations for most services. The informants provided nuanced perspectives on the impact on the quality and efficiency of remote service delivery during the pandemic. Notably, it has allowed providers to attend to as many patients as possible in the face of COVID-19 restrictions, to the convenience of both patients and staff. Among its negative effects are concerns about the unsuitability of remote consultation for some people with mental health conditions and the potential to widen the digital divide and exacerbate existing inequalities. Sociotechnical systems theory was found to be a suitable framework for understanding the range of systemic and sociotechnical factors that influence the use of technology in mental health care delivery in times of crisis and normalcy. CONCLUSIONS: Although the use of IT has boosted mental health care delivery during the pandemic, it has had mixed effects on quality and efficiency. In general, patients have benefited from the convenience of remote consultation when face-to-face contact was impossible. In contrast, patient choice was often compromised, and patient experience and outcomes might have been affected for some people with mental health conditions for which remote consultation is less suitable. However, the full impact of IT on the quality and efficiency of mental health care provision along with the systemic and sociotechnical determinants requires more sustained and longitudinal research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".