Evidence map›Paper›PMID 40165231›Full record

Observational studyBMC medical informatics and decision making2025

Association between patient characteristics and recommendations by medical on-call service 116117 in Germany: a cross sectional observational study.

Heike Hansen, Agata Menzel, Jan Hendrik Oltrogge-Abiry, Dagmar Lühmann, Martin Scherer, Ingmar Schäfer

Abstract readObservational Study
In one paragraph

Observational study in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

6 authors.

Heike HansenDepartment of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. h.hansen@uke.de.
Agata MenzelDepartment of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Jan Hendrik Oltrogge-AbiryDepartment of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Dagmar LühmannDepartment of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Martin SchererDepartment of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Ingmar SchäferDepartment of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUse of emergency departments has increased in recent years. Different efforts address this problem, eg, medical on-call services. The basis of the DEMAND intervention is computer-assisted initial telephone assessment implemented at regional associations of statutory health insurance physicians in Germany. In this intervention, recommendations for healthcare settings were given over the telephone by medical staff. Recommendations were provided using the software SmED which calculates neural networks. This study aimed to analyse if patient characteristics are associated with the output of the intervention, ie, specific setting recommendations.

methodsBetween January 2020 and March 2021, patients aged 18 years and older of the DEMAND intervention from eight intervention sites received a standardised postal survey. Recommended and used settings, and data on sociodemography, health status at the time of the emergency call, past health service use, and health literacy were collected by self-report. Multilevel, multivariable logistic regression models adjusted for random effects at the level of regions and months of observation within regions were conducted.

resultsOf 9473 contacted individuals, 1756 (18.5 %) participated in the survey. Median age was 66 years, 59.0% were women and 30.2% living alone. The most frequently recommended service was emergency home visits (40.1%). Recommendations for this setting were associated with worse self-rated health (odds ratio 0.67, 95% confidence interval: 0.55/0.81, p < 0.001). Telephone counselling was associated with lower age (0.71, 0.59/0.85, p < 0.001), lower subjective treatment urgency (0.65, 0.51/0.82, p < 0.001) and health problems not classified as symptoms and complaints (0.41, 0.25/0.68, p = 0.001) or infections (0.22, 0.09/0.57, p = 0.002). Emergency departments were associated with better self-rated health (1.37, 1.11/1.70, p = 0.003) and health problems classified as injuries (3.12, 1.67/5.83, p < 0.001). Rescue service were associated with higher age (1.44, 1.15/1.81, p = 0.002) and higher subjective treatment urgency (2.51, 1.83/3.43, p < 0.001). General practices were associated with lower subjective treatment urgency (0.58, 0.44/0.76, p < 0.001) and health problems not classified as injuries (0.26, 0.10/0.68, p = 0.006). Emergency practices were associated with lower age (0.60, 0.48/0.74, p < 0.001), and specialist practices were associated with health problems classified as symptoms or complaints (3.75, 1.49/9.45, p = 0.005).

conclusionsMost associations between patient characteristics and recommendations were comprehensible and in line with the aim of the intervention. However, it should be clarified why patients with better self-rated health were more likely to receive recommendations for emergency departments.

Indexed as

Emergency Service, HospitalAdolescentAdultAgedAged, 80 and overCross-Sectional StudiesFemaleGermanyHumansMaleMiddle AgedTelephoneYoung AdultCross sectional observational studyPatient characteristicsRecommendations by medical on-call serviceSpecific setting recommendations

Identifiers

PMID40165231
PMCPMC11959809

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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.