Systems medicine 2030: A Delphi study on implementation in the German healthcare system

Lemmen, Clarissa; Woopen, Christiane; Stock, Stephanie

doi:10.1016/j.healthpol.2020.11.010

Cited by 9 publications

(7 citation statements)

References 41 publications

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“…The aim was to find a variety of perspectives for idea aggregation and hypothesis generation to operationalize items for the Delphi process (Phase 4). In the Delphi process, arguments and hypotheses on potential influencing factors and chances of success for the implementation of systems medicine were evaluated and assessed by experts from medical care, politics, business, and science and research [ 10 ]. The results of Phase 2 are presented below.…”

Section: Methodsmentioning

confidence: 99%

“…Systems medicine is not a medical discipline in the traditional sense, but terms a relatively young interdisciplinary approach that brings together (bio)medical knowledge and digital technologies for systems-oriented thinking and action [ 9 , 10 ]. It uses and combines extensive molecular biological, clinical, and demographic data, including environmental factors, to evaluate complex biological relationships.…”

Section: Introductionmentioning

confidence: 99%

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A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

Lemmen

Simić

Stock

2021

IJERPH

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Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

Lemmen

Simić

Stock

2021

IJERPH

Self Cite

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“…This method is validated, systematic, effective, reliable and comprehensive, and it has been widely used to construct index systems. 20 21 The research process of this study is shown in figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Development of an index system for evaluating the organisational capabilities of primary medical institutions: a modified Delphi study in China

Cui

Meng

2021

BMJ Open

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ObjectiveThe low performance of primary medical institutions (PMIs) in China is a significant issue. The WHO proposed that the main reason for the failure of the healthcare system in developing countries is poor organisational capabilities. However, there is no international tool for evaluating the organisational capabilities of PMIs. Therefore, this study aimed to develop an index system for evaluating the organisational capabilities of PMIs.DesignWe searched the literature (English and Chinese) published before June 2020 in the PubMed, China National Knowledge Infrastructure and Wanfang databases to conduct a literature review and develop a preliminary indicator pool. Then, two rounds of Delphi expert consultations were conducted by email from June to September 2020, followed by screening, revision and supplementation of the indicators using the boundary value method. Finally, the analytic hierarchy process was used to determine the weight of the indicators.SettingThe Delphi consultation questionnaire was distributed to the leaders of PMIs in districts D, F and S in Beijing, China.ParticipantsNineteen leaders of PMIs who had a profound understanding of PMI operations and management and were able to participate in Delphi research from a professional and comprehensive perspective were included in this study.ResultsThe Cr values were 0.76 (first-level indicators) and 0.78 (second-level indicators), indicating that the expert consultation results were accurate and reliable. The result of the expert coordination coefficient test was significant at the p<0.01 level, suggesting that the experts’ views were consistent. The organisational capability index system includes 3 first-level indicators, 9 second-level indicators and 37 third-level indicators.ConclusionsAn index system for the organisational capabilities of PMIs was developed. This index system is a scoring system that focuses on basic service capabilities, management capabilities and sustainable development capabilities, and it can determine the priority of improvement areas for PMIs.

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“…These data are required to transition clinical reasoning to clinical coherence, and precision medicine, and to support meaningful, scalable, and sustainable DHI. [46][47][48]…”

Section: Social Value Of Digital Health Interventionsmentioning

confidence: 99%

Telehealth’s Role Enabling Sustainable Innovation and Circular Economies in Health

Kalogeropoulos¹

2023

THMT

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Digital health interventions, including the use of telehealth augmented by artificial intelligence (AI), support an increasingly broad range of improvement goals for prevention and treatment. Limitations obstructing the many digital benefits of the targeted healthcare innovations from reaching their full potential include the lack of robust usability and user-centered design, nimble regulatory policy, and lack of adequate high-quality evidence and methodologies to evaluate the performance generalization and clinical robustness. We explore health innovation using different value systems and solutions proposed to overcome the fundamental limitations arising in the data value system. We propose a new telehealth paradigm and incorporate intervention designs, which combine clinical innovation with innovation in data resource development. Machine learning and AI have the potential to enable circular economies for digital and health innovation, in which sustainable solutions can be offered within a data-enabled collaborative and shared digital ecosystem. Alignment of industry standards, adjustments to regulatory policies, and the embrace of new governance models for telehealth-based innovation are essential for this new approach for health innovation scaling, clinical adoption, and social innovation.

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Systems medicine 2030: A Delphi study on implementation in the German healthcare system

Cited by 9 publications

References 41 publications

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

Development of an index system for evaluating the organisational capabilities of primary medical institutions: a modified Delphi study in China

Telehealth’s Role Enabling Sustainable Innovation and Circular Economies in Health

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