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Background: Early information and support in dementia (FIDEM) is a cross-sectoral, general practitioner-centered network model for the improvement of community-based care of people with dementia and their caregivers by systematically assigning them to non-physician healthcare providers. Objective: To describe the implementation of FIDEM in Göttingen, Germany and to exploratorily evaluate satisfaction and relief vs. additional workload. Material and methods: FIDEM was established in Göttingen in 2017. Community-based and district-based networks consisted of medical (general practitioners) and non-medical cooperation partners (occupational therapists, care counselling, other non-medical care providers) and were instructed to efficiently share information. During biannual network meetings from August 2017 to October 2019, a self-developed questionnaire for quality management and evaluation of the aspects of the project described above was filled out by participants attending the meetings. Results: Consecutive recruitment resulted in 7 networks by October 2019, with participation of 29 general practitioners and 46 non-medical care providers, serving as cooperation partners. Quantitative evaluation of 80 FIDEM cooperation partners revealed high satisfaction ratings with all aspects of the model (M from 7.22 to 7.87 out of possible „10"), with partially higher ratings on the part of primary care physicians. Furthermore, all participants reported a reduction in workload due to participation, which was significantly higher for primary care physicians across all scales (all p-values < 0.001). Conclusion: FIDEM has been implemented in Göttingen beyond a funded pilot project phase. High satisfaction ratings and considerable relief in workload suggest the continuation of FIDEM along with a full evaluation and, provided positive results, its consolidation of and transfer to other regions in Germany. [ABSTRACT FROM AUTHOR]

Background: Treatment data from general practitioners (GP) in private practice have great potential for research into diagnostics, therapy and quality of care. The quality of these data influences not only the primary research results, but also the results of later secondary data analyses, but then for other research questions. Completeness and availability of data at the time of data collection are important quality characteristics that depend on medical documentation and technical data export. Using GP treatment data as an example, we examine these quality characteristics and propose a procedure for continuous quality assurance. Methods: A fictitious patient was defined with 22 treatment characteristics of routine general practitioners treatment (e.g. age, gender, medication, diagnoses, risk factors, inability to work, therapies, hospitalization, etc.). Without prior training, the data of the fictitious patient were electronically documented and exported by general practitioners. Comparisons between fictitious patient data, data documented electronically by general practitioners and exported data enabled the assessment of data completeness and availability. Results: Approximately 80% of the tasks could be completed without medical training. Exemplary studies with six practice software systems show that without software adaptation and medical training, only approx. 37% of the treatment data entered is available for secondary research in data export. An increase to 83.4% availability would be possible through software adaptations. If, in addition, medical training were carried out, a data availability of approx. 95% could be achieved. Discussion: Missingor incorrectly mapped data must be reckoned with in the data export prior to secondary research. The completeness and availability of the data must be tested individually for each practice software during the primary data collection and documented in the metadata of the research data. Fictitious patient data with defined characteristics can also be used for continuous quality assurance in quarterly software updates. Conclusions: Independent, continuous quality assurance of the data interfaces, medical training and investigations into the completeness of routine and treatment data can considerably improve the quality of subsequent secondary data research. [ABSTRACT FROM AUTHOR]