Hospital at Home and Point-of-care diagnostics, creating sustainability within healthcare.
Bibliographic record
Abstract
Hospital at Home (HaH) embodies a transformative approach to healthcare delivery, facilitating early discharge and admission avoidance by offering hospital-level diagnostics and treatment within the community. This innovative approach not only expands treatment options for patients but also accelerates the goals outlined in the NHS 10-year plan, aiming to enhance the sustainability of our healthcare system. By bringing care closer to home, HaH reduces the strain on secondary care services whilst optimising outcomes, particularly for frail patients who are vulnerable to adverse effects associated with hospital admission. The Guys and St Thomas’ H@H team explored the use of point-of-care (POC) blood testing as an initiative to provide rapid diagnostics. We were keen to understand whether introducing POC diagnostics would support early discharge, enhancing patient flow as well as allowing us to optimise our capacity to admit new patients to the service and prevent admission thus reducing our carbon footprint by minimising hospital admissions and associated costs. This includes not only the direct expenses of ambulance transfers and hospital stays but also the indirect costs related to adverse effects associated with admission. In this retrospective cohort study, we evaluated 152 patients referred to H@H for urgent assessment within a 2-hour response time. The patients underwent POC diagnostics as part of their assessment, and the collected data underwent retrospective analysis. Key outcome measures were assessed, focusing on the extent to which POC diagnostics facilitated early discharge and admission avoidance through timely diagnosis and appropriate intervention. Additionally, demographic data were collected to assess the prevalence of severe frailty among the patient population. The results of our study demonstrate the significant impact of POC diagnostics on clinical outcomes and treatment interventions. We observed a notable change in clinical outcome in 67% of patients, with nearly 40% receiving earlier therapeutic interventions as a result. Analysis of demographic data revealed that a substantial portion (74%) of our cohort exhibited a clinical frailty score exceeding 6, underscoring the vulnerability of elderly individuals within our patient population. Remarkably, out of the 152 patients assessed, only nine required hospitalisation for further treatment. These findings emphasise the transformative potential of implementing POC diagnostics in managing the health of elderly individuals and optimising treatment outcomes. In conclusion, our study underscores the pivotal role of POC testing in enhancing early diagnoses and broadening the scope of therapeutic interventions for patients managed within their homes. The investment in community-based care infrastructure not only bolsters the resilience of our healthcare system but also addresses the evolving needs of the population. With a significant proportion of patients experiencing tangible improvements in clinical outcomes, our findings highlight the potential for POC testing to revolutionise care delivery, particularly for patients contending with severe frailty. Overall, our commitment to home-based care contributes to a sustainable NHS by optimising resource utilisation, enhancing patient outcomes, and fostering a patient-centred approach to healthcare delivery. This paradigm shift aligns with broader healthcare system objectives of improving efficiency, reducing costs, and elevating the overall quality of care, thereby laying the groundwork.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".