Evidence map›Paper›PMID 37738050›Full record

ArticleJAMA network open2023

Economic Evaluation of Novel Models of Care for Patients With Acute Medical Problems.

Orlanda Q M Goh, Xiaohui Xin, Wan Tin Lim, Michelle W J Tan, Juliana Y L Kan, Hartini Bte Osman, Wanyi Kee, Tse Yean Teo, Wee Boon Tan, Mei Ling Kang and 1 more

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Orlanda Q M GohDepartment of Internal Medicine, Singapore General Hospital, Singapore.
Xiaohui XinHealth Services Research Unit, Singapore General Hospital, Singapore.
Wan Tin LimDepartment of Internal Medicine, Singapore General Hospital, Singapore.
Michelle W J TanDepartment of Family Medicine and Continuing Care, Singapore General Hospital, Singapore.
Juliana Y L KanDepartment of Internal Medicine, Singapore General Hospital, Singapore.
Hartini Bte OsmanDepartment of Nursing Administration, Singapore General Hospital, Singapore.
Wanyi KeeDepartment of Internal Medicine, Singapore General Hospital, Singapore.
Tse Yean TeoDepartment of Internal Medicine, Singapore General Hospital, Singapore.
Wee Boon TanPopulation Health and Integrated Care Office, Singapore General Hospital.
Mei Ling KangDepartment of Internal Medicine, Singapore General Hospital, Singapore.
Nicholas GravesProgramme in Health Services and Systems Research, Duke-NUS Medical School, Singapore.
Singapore General Hospital · SGDuke-NUS Medical School · SGSingHealth Duke-NUS Academic Medical Centre · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: During COVID-19, Singapore simultaneously experienced a dengue outbreak, and acute hospitals were under pressure to lower bed occupancy rates. This led to new models of care to treat patients with acute, low-severity medical conditions either at home, in a hospital-at-home (HaH) model, or in a clinic-style setting sited at the emergency department in an ambulatory care team (ACT) model, but a reliable cost analysis for these models is lacking. Objective: To compare personnel costs of HaH and ACT with inpatient care. Design, Setting, and Participants: In this economic evaluation study, time-driven activity-based costing was used to compare the personnel cost of inpatient care with treating dengue via HaH and treating chest pain via ACT. Participants were patients with nonsevere dengue and chest pain unrelated to a coronary event admitted via the emergency department to the internal medicine service of a tertiary hospital in Singapore. Exposures: HaH for dengue and ACT for chest pain. Main Outcomes and Measures: A process map was created for the patient journey for a typical patient with each condition. The amount of time personnel spent on delivering care was estimated and the cost per minute determined based on their wages in 2022. The total cost of care was calculated by multiplying the time spent by the per-minute cost of the personnel resource and summing all costs. Results: Compared with inpatient care, HaH used 50% less nursing time (418 minutes, 95% uncertainty interval [UI], 370 to 465 minutes) but 80% more medical time (303 minutes, 95% UI, 270 to 338 minutes) per case of dengue. If implemented nationally, HaH would save an estimated 56 828 SGD per year (95% UI, -169 497 to 281 412 SGD [US $41 856; 95% UI, -$124 839 to $207 268]). The probability that HaH is cost saving was 69.2%. Compared with inpatient care, ACT used 15% less nursing time (296 minutes, 95% UI, 257 to 335 minutes) and 50% less medical time (57 minutes, 95% UI, 46 to 69 minutes) per case of chest pain. If implemented nationally, ACT would save an estimated 1 561 185 SGD per year (95% UI, 1 040 666 to 2 086 518 SGD [US $1 149 862; 95% UI, $766 483 to $1 536 786]). The probability that ACT is cost saving was 100%. Conclusions and Relevance: This economic evaluation found that the HaH and ACT models decreased the overall personnel cost of care. Reorganizing hospital resources may help hospitals reap the benefits of reduced hospital-acquired infections, improved patient recovery, and reduced hospital bed occupancy rates.

Indexed as

COVID-19DengueChest PainCost-Benefit AnalysisHumansTertiary Care Centers

Identifiers

PMID37738050
PMCPMC10517377
OpenAlexW4386944583

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.