Using the Integrated Cognitive Assessment (ICA) to improve the efficiency of primary care referrals to memory services
Bibliographic record
Abstract
Abstract Background Existing primary care cognitive assessment tools are crude or time‐consuming screening instruments which can only detect cognitive impairment when it is well established, impacting carers and patient quality‐of‐life. We initiated the Accelerating Dementia Pathway Technologies (ADePT) study to develop a real‐world evidence basis to support the adoption of the Integrated Cognitive Assessment (ICA), a 5 minute computerised cognitive test that employs artificial intelligence to improve its accuracy, as an inexpensive screening tool for the detection of cognitive impairment and improving the efficiency of the dementia care pathway. Method Patients referred to memory clinics from primary care General Practitioners (GPs) were recruited. Participants completed the ICA either at home or in the clinic along with medical history and usability questionnaires. The GP referral and ICA outcome were compared with the specialist diagnosis obtained at the memory clinic. The clinical outcomes as well as costing data were used as part of an economic analysis to assess the potential health economic benefits of the use of the ICA in the dementia diagnosis pathway in the United Kingdom. Result 87 participants referred to memory clinics were recruited who completed all assessments (40 dementia, 19 mild cognitive impairment, 12 inconclusive, 5 healthy, 3 non‐dementia conditions). From these patients the ICA was able to identify cognitive impairment with a sensitivity of ∼90%. The results of the health economics model, utilising real world data collected from the ADePT study estimates that if the ICA were introduced in a primary care setting then it could result in a cost saving to the health and social care system of approximately £147 per patient over a lifetime horizon (∼£44m of direct costs to the health care system) or £283 per patient if introduced into a secondary care setting. Conclusion The results from this study demonstrate the potential of the ICA as a screening tool to support accurate referrals from primary care settings to memory clinics. The introduction of disease modifying treatments for Alzheimer’s and dementia will further improve the case for earlier detection of the condition and therefore increase the cost‐effectiveness of more accurate screening using tests such as the ICA tool.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".