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Background: Hospital readmission rates are very high in patients with chronic obstructive pulmonary disease (COPD). Continuity of care (CoC) with general practitioners (GPs) and ambulatory specialists can impact readmission rates. This study aimed to identify shared patient networks of ambulatory care physicians and to examine the effect of provider connectedness on CoC and hospital readmissions. Methods: A retrospective observational study was conducted in claims data from the years 2016 to 2018 in patients with COPD (aged 40 years or older; hospital stay in 2017). Linkages between GPs, pneumologists, and cardiologists were determined on the basis of shared patients. Multilevel regression models were used to analyze the impact of provider connectedness, operationalized by several social network characteristics, on continuity of care (sequential continuity [SECON] index) and hospital readmission rates. Results: A total of 7294 patients linked to 3673 GPs were available for analysis. Closeness centrality (β=- 0.029) and the external-internal (EI)-index (β =0.037) impacted on the SECON index. The EI-index (odds ratio [OR]=1.25) and degree centrality (OR=1.257) impacted 30-day readmission. Network density (OR=0.811) and the SECON index (OR=1.121) affected the likelihood of a 90-day readmission. None of the predictors had a significant impact on 180-day and 365-day readmissions. Conclusions: Ambulatory care providers’ connectedness showed some effects on hospital readmissions and CoC in patients with COPD up to 90 days after hospital discharge, but the additional predictive power is limited.

Additional file 1. German translation of the Nijmegen Continuity Questionnaire used in the study.

Additional file 3. Detailed breakdown of missing values per item in the Nijmegen Continuity Questionnaire.

Additional file 1. Guide for focus groups.

Additional file 3. Guide for interviews with practice staff.

Additional file 2. Guide for interviews with other stakeholders.

Introduction: Previous studies show that reading behavior varies with the readers’ Levels of Expertise (LoE) in a task area. Except for LoE, other factors like acquired information plays a role in this process. In the area of health policymaking, people read supporting documents to inform their decisions. This leads to a natural question: could it be possible to predict the decisions based on the reading pattern of the supporting document on top of their LoE? Method: We collected eye tracker data from a group of people with various LoE. We used the heatmaps as the primary pattern of reading. These were prepared using the average fixation duration of the individuals. First, we performed a hierarchical cluster analysis with the pairwise correlation matrix between the heatmaps, to see whether heatmaps as A single feature were effective to reach our goal. In the second step, we made an ensemble of the features of the reading patterns from the heatmaps and pupillometric features, and LoE, with the decision made by the participants as an outcome, using AdaBoost regressor. In this decision-making task, one could choose one among expensive, prudent, and midway. Result: The first analysis reveals to us that there are a minority number of individuals who read less than the majority group. This minority group tends to make decisions in the extremities. The result of AdaBoost-regressor, shows us 1. the LoE is a stronger feature than the patterns of reading to predict the decision to be taken. 2. the pupillometric features are weaker feature than the reading patterns from the heatmaps for our task. Conclusion: Reading patterns could be useful for forecasting a decision, given the LoE of the individual. Heatmaps can be used as both qualitative and quantitative measures for reading patterns., First Version

Hintergrund/Fragestellung/Problem: Zahlreiche Versorgungsberichte werden erstellt um gesundheitspolitische Entscheidungen zu informieren. Dabei wird angenommen, dass die adressierten Entscheidungsträger*innen bestverfügbare wissenschaftliche Erkenntnisse in ihren Entscheidungsprozess [zum vollständigen Text gelangen Sie über die oben angegebene URL]

Hintergrund/Fragestellung/Problem: Ohne die Zusammenarbeit zwischen Leistungserbringern kann die Gesundheitsversorgung nicht optimal funktionieren. Traditionelle Forschungsmethoden können die zugrunde liegenden Muster schlecht aufzeigen und damit Fragen wie: „Wer arbeitet zusammen [zum vollständigen Text gelangen Sie über die oben angegebene URL]

Additional file 4. Heatmaps of all 46 participants.

Additional file 2. Indicator assessment questions (example).

Additional file 2. Questionnaire.

Additional file 2. Multivariable logistic regression: subgroup non-prescriber 2017.

Additional file 1. Reporting Guideline.

Additional file 5. Graphics: Average fixation duration and time spent per report section.

Additional file 1. Quantitative data report.

Additional file 3. Interview guide.

Additional file 2. Questionnaire_GPs_provider.

Additional file 5. NCQ_german.

Additional file 1. Questionnaire_cardiologists.