Digital Advance Decision Making with Severe Mental Illness: A retrospective observational cohort analysis of the use of an Electronic Palliative Care Coordination System
Bibliographic record
Abstract
Abstract Background People living with severe mental illness (SMI) face significant health inequalities, including in palliative care. Advance Decision Making (ADM) is widely recommended by palliative care experts and could reduce inequalities. However, implementing palliative care ADM with this group is challenging. Electronic Palliative Care Coordination Systems such as Coordinate my Care (CMC) have been introduced to increase ADM uptake and improve care quality in the general population. This study explores the use of CMC amongst those with SMI and aims to describe the cohort of people with a primary diagnosis of SMI who have used CMC for ADM, how this cohort uses the service and make recommendations for future research and policy. Method A retrospective observational cohort analysis was completed of CMC records created 01/01/2010 - 31/09/2021. Descriptive statistics were used to report on characteristics including: age, diagnosis, prognosis and resuscitation status. Thematic analysis was used to report on the content of patients’ statements of preference. Results There were 1826 records where the service user had a primary diagnosis of SMI. Of this sample most (60.1%) had capacity to make treatment decisions, 47.8% were aged under 70, 86.7% were given a prognosis of ‘years’ and most (63.1%) wanted full CPR. Statements of preferences contained information about preferences for physical and mental health treatment care as well as information about patient presentation and capacity. This suggests that compared to usual CMC users the cohort of interest are relatively able, younger people using CMC to make long-term plans for active physical and mental health treatment. However, only a minority contained statements of patient wishes and where recorded these were often brief, and many did not obviously reflect authentic patient voice. Conclusions This digital tool is being used by people with SMI but to plan for more than palliative care. This cohort and supporting professionals have found a digital tool is helpful to plan for longer term physical and mental healthcare. Future research and policy should focus on development of tailored digital tools for people with SMI to plan for palliative, physical and mental healthcare and support expression of patient voice.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".