Ein patientendatenbasiertes automatisiertes Vorschlagswesen zur Unterstützung der individualisierten Versorgung multimorbider Patienten in der hausärztlichen Praxis (Projekt ATMoSPHÄRE)

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung



Background The patient-and need-based care of multimorbid patients with external services or medical aids presents family physicians (FPs) with complex challenges. Various digitized information services can support FPs in decision making. Within the telemedicine project ATMoSPHÄRE, a patient data-based automated recommender system (ARS) of service offers was developed. The results of the plausibility check of the ARS form the basis for exploring the question of whether and to what extent it represents support for FPs. Methods To test the plausibility of the automated recommendations, a Delphi procedure (n = 8) and qualitative interviews (n = 4) were conducted with the FPs. Each FP indicated for two multi-morbid study patients which services were considered necess-ary and to what extent individual ARS proposals were rated as appropriate. Significant quantitative differences between FP-and ARS-recommendations were calculated with the Wilcoxon test for two dependent samples. Results In the Delphi procedure, a high variability of content as well as of quantity (1 to 10, MW = 5.6, SD = 2.8) of the indicated recommendations by the FP was shown. The number of ARS-based recommendations considered as appropriate ranged from 7 to 17 out of a total of 20 (MW = 11.9, SD = 2.5). The qualitative interviews showed that the additional ARS recommendations assessed as appropriate included social support services. Conclusions The ARS developed in ATMoSPHÄRE can be considered as a helpful supplement for the primary care of multimorbid pa-tients. It shows suitable recommendations for individual patient care.


Seiten (von - bis)276-281
FachzeitschriftZeitschrift fur Allgemeinmedizin
PublikationsstatusVeröffentlicht - 2020


Ziele für nachhaltige Entwicklung

ASJC Scopus Sachgebiete


  • Decision making, E-Health, Multimorbidity