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Record W4416413180 · doi:10.2196/79767

Implementation of the German Clinical Practice Guideline for Multimorbidity Using a Digital Tool in Primary Care: Pilot Cluster Randomized Clinical Trial

2025· article· en· W4416413180 on OpenAlexvenueno aff
Julia Nothacker, Valentina Paucke, Susanne Lezius, Antonia Zapf, Dagmar Lühmann, Martin Scherer, Ingmar Schäfer

Bibliographic record

VenueJMIR Aging · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsClinical PracticeGuidelineRandomized controlled trialGermanCluster (spacecraft)General practiceClinical trial

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical practice guidelines (CPGs) summarize the best available evidence in a specific field. To improve patient-centered outcomes, guidelines have to be implemented, using, for example, information and communications technology. Although there are CPGs addressing multimorbidity, there is still a lack of studies investigating their implementation. OBJECTIVE: This study aimed to evaluate whether the implementation of a CPG for multimorbidity using a digital tool is feasible and explore possible effects of this intervention. METHODS: A pilot cluster randomized clinical trial based on telephone interviews was conducted from October 25, 2023, to September 8, 2024. Patients enrolled in any disease management program who were aged ≥65 years and had at least 2 additional chronic conditions were randomly selected from 20 general practitioner (GP) practices and contacted for informed consent. Each practice was randomized after baseline interviews of all participating patients in the practice were finished. The use of a web application facilitating collection and documentation of treatment-relevant data in accordance with the German CPG for multimorbidity was compared with treatment as usual. The primary outcome was time spent in hospital. As a secondary outcome, the number of patients with at least one hospital admission was calculated. Further secondary outcomes included outpatient health care use, quality of life, patient satisfaction, and quality of care. Feasibility assessment included examination of sample size, participation rate, and compliance with the study protocol. Outcome measures were analyzed using linear, logistic, and negative binomial regressions with random intercepts for practices. RESULTS: Of 384 patients who were contacted, 123 (32%) agreed to participate, and 120 (31.3%, including 54/120, 45% in the intervention group and 66/120, 55% in the control group) completed baseline and follow-up assessments. Mean age was 75.4 (SD 6.6) years, and 51.7% (62/120) were women. The compliance rate, or the proportion of patients who were treated per protocol, was 89% (48/54). In our data, the incidence rate of hospital days was comparable in both groups (incidence rate ratio [IRR] 0.94, 95% CI 0.09-9.42; P=.96), but the odds of hospital admission were almost reduced by half in the intervention group (odds ratio 0.51, 95% CI 0.17-1.54; P=.23). Our data also suggest a higher incidence rate of GP contacts (IRR 1.13, 95% CI 0.83-1.53; P=.43) and lower incidence rate of contacts with outpatient specialists (IRR 0.79, 95% CI 0.54-1.15; P=.24) in the intervention group compared to usual care. Moreover, patients and GPs reported a better quality of care (mean difference 0.51, 95% CI -0.12 to 1.14; P=.12 and mean difference 1.19, 95% CI 0.13-2.25; P=.03, respectively) in the intervention group. CONCLUSIONS: Implementation of the CPG using a digital tool was feasible. Our data suggest that the probability of hospital admissions and contacts with outpatient specialists might be reduced and quality of care might be improved. TRIAL REGISTRATION: ClinicalTrials.gov NCT06061172; https://clinicaltrials.gov/study/NCT06061172.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.528
Teacher spread0.414 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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