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1.
Gesundheitswesen ; 84(7): 581-596, 2022 Jul.
Artigo em Alemão | MEDLINE | ID: mdl-35679867

RESUMO

AIM OF THE STUDY: The digital transformation in healthcare is also of fundamental importance for healthcare research. For this reason, experts should agree on, prioritize and identify key topics for a medium-term strategy of the German Network for Health Services Research and classify the general development of digital health in the context of health services research. METHODS: Between April and September 2018, the working groups "Digital Health" and "Validation and Linkage of Secondary Data" of the German Network for Health Services Research were asked to submit their expertise online using the methodological approach of a Delphi study. For this purpose, a multi-stage modified Delphi method with quantitative and qualitative approaches was chosen. Initially, a list of theses was drawn from the network's published position papers on digital health applications and medical apps. A total of 131 statements were formulated. The final survey instrument included questions on the biographical background of the participants, 42 developed items (33 statements and 8 open-ended questions), and one free-text field to add further aspects. Items were evaluated with a five-point Likert scale. A statement was accepted if the agreement rate was 75% or higher. RESULTS: Of the 110 potential participants, 50 (46%) took part in the first round and 39 (36%) in the second round of the Delphi survey. In the first round, there was a clear result for 24 of 33 statements. There were 20 statements "agreed with" and four "disagreed with." Nine statements were between 60 and 75% and were presented to the participants again for evaluation in the second round. In round two, of these nine statements, four statements were "agreed with" and five statements were "disagreed with." Digital Health Literacy" emerged as a particular focus in this Delphi study. CONCLUSION: In this Delphi study, experts were involved in selecting and prioritizing possible topics for the Digital Health working group and assessing future developments in digital health in the context of health services research. The results reflect both the expectations and interests of the members and are largely consistent with the recommendations of the report "Digitalization for Health" made by the expert council for assessing developments in the health sector.


Assuntos
Atenção à Saúde , Pesquisa sobre Serviços de Saúde , Técnica Delphi , Alemanha , Humanos , Inquéritos e Questionários
2.
Stud Health Technol Inform ; 250: 266-267, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29857459

RESUMO

A dissertation project at the Witten/Herdecke University [1] is investigating which (nursing sensitive) patient characteristics are suitable for predicting a higher or lower degree of nursing workload. For this research project four predictive modelling methods were selected. In a first step, SUPPORT VECTOR MACHINE, RANDOM FOREST, and GRADIENT BOOSTING were used to identify potential predictors from the nursing sensitive patient characteristics. The results were compared via FEATURE IMPORTANCE. To predict nursing workload the predictors identified in step 1 were modelled using MULTINOMIAL LOGISTIC REGRESSION. First results from the data mining process will be presented. A prognostic determination of nursing workload can be used not only as a basis for human resource planning in hospital, but also to respond to health policy issues.


Assuntos
Modelos Logísticos , Recursos Humanos de Enfermagem Hospitalar , Máquina de Vetores de Suporte , Carga de Trabalho , Mineração de Dados , Hospitais , Humanos
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