Patient and provider perspective with the use of a central intake system (CIS) for surgical waitlist management: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Our study aimed to summarise and reflect on current evidence around patient and surgeon perspectives regarding the use of a central intake system (CIS) as a strategy for managing surgical waitlists. SEARCH STRATEGY: A systematic review was conducted. Searches were performed on 9 October 2023. The strategies used key words such as 'central intake', 'surgery' and 'experience'. Medical and the Web of Science core databases were searched. INCLUSION CRITERIA: Titles and abstracts were assessed by two independent reviewers. Studies were included if: the study population was adult (age >18), and patients were referred for non-emergency surgery assessment. DATA EXTRACTION: Data were independently extracted by two reviewers using a standardised form. The Grading of Recommendations Assessment, Development and Evaluation Confidence in the Evidence from Reviews of Qualitative Research was used to assess study quality. Of 2805 studies identified, nine were included with a moderate to high confidence of evidence. Through thematic analysis, four patient and five surgeon themes were identified, with a further two common themes (although conceptualised differently). RESULTS: Patients value CISs for their potential to create an equitable referral process and clearer timelines, yet they emphasise the importance of preserving autonomy and personalised care by maintaining the option to choose their surgeon. Surgeons recognise the operational benefits of CISs in streamlining referrals and reducing wait times, but also caution that adequate resources, strong leadership and careful case selection are critical to sustain quality and engagement. CONCLUSIONS: These findings highlight the complex balance required to successfully implement CISs. The system-level gains in access and coordination must be carefully aligned with patient-centred values such as choice and trust and supported by organisational culture shifts and leadership commitment. Importantly, the study identifies gaps in end-user involvement and decision-making power that should be addressed to enhance acceptability and effectiveness.Future actions should consider a framework that incorporates clear governance with continued pilot programmes that include evaluation of patient satisfaction, quantitative and qualitative clinical outcomes, and impact on equity. Additionally, targeted strategies are needed to accommodate complex or specialised cases that may not fit the central intake model. Through careful implementation and continuous stakeholder engagement, central intake models have the potential to meaningfully improve surgical waitlist management while respecting the needs and preferences of both patients and surgeons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".