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

Forecasting the future of smart hospitals: findings from a real-time delphi study

BACKGROUND

In concert with other digital technologies, artificial intelligence (AI) is shaping the vision of smart hospitals. The transformation into smart hospitals, however, is all but trivial due to the lack of financial and human resources, digital skills, and supporting policies. Thus, the extent to which the vision of smart hospitals will eventually become reality is uncertain. In this context, our study provides a multidimensional conceptualization of the immediate future of smart hospitals to 2042.

METHODS

This study employs an iterative mixed-methods approach, including expert workshops and a Delphi study. We conducted a real-time Delphi study to forecast the evolution of smart hospitals in 5-year steps from 2027 to 2042. A total of 39 experts in healthcare, artificial intelligence, and management participated.

RESULTS

Our understanding of a technology-enabled smart hospital in this study includes four dimensions: artificial intelligence (AI), sustainability, ecosystems, and human-centeredness. Our findings underscore the critical need to address the shortage of hospital staff and general practitioners that models predict will peak by 2032. Additionally, our results show a significant shift to individualized medicine and home care. This shift indicates that smart hospitals are expected to leverage AI and digital technologies to tailor care to each patient. Furthermore, the roles and responsibilities of hospital staff will undergo significant changes. Healthcare personnel will have to adapt to new technologies that facilitate more efficient workflows and improve patient engagement in evolving healthcare environments. The results of our study suggest a shift in care to individualized medicine and home care, with corresponding changes in the roles and responsibilities of hospital staff who will employ new technologies.

CONCLUSIONS

The findings from our real-time Delphi study suggest that the vision of smart hospitals is gradually becoming reality over the next 20 years. Advancements in artificial intelligence should enhance operational efficiency and patient-centric care, while facilitating the integration of sustainability practices and fostering collaborative ecosystems. However, addressing challenges such as staff shortages, ethical considerations, and the need for robust digital skills will be essential. A deep pool of expert healthcare practitioners, clear ethical guidelines, and robust digital skills are essential to fully realize this vision and ensure that smart hospitals can meet the evolving needs of healthcare delivery.

Benefits of the Modified Version of the Illness-Wellness Continuum Model for Nurse Education

With rapid advancements in sciences and the expanding scope of nursing practice, faculty are challenged to connect the experiences of learners with abstract concepts of science and to cultivate clinical reasoning skills. Rationale for the adoption of the illness-wellness continuum model includes the following: (a) the relationships among illness, health, and the nursing process are relevant in many health situations, and (b) levels of wellness are steeped in holistic principles of self-care, which are consistent with educational and clinical goals. The modified version of the illness-wellness continuum model can serve as the groundwork that prepares nursing students in a constantly changing workplace.

Development of an instrument to measure the competencies of health professionals in the process of evidence-based healthcare: A Delphi study

AIMS

To identify and reach consensus on dimensions and criteria of a competence assessment instrument for health professionals in relation to the process of evidence-based healthcare.

DESIGN

A two-round Delphi survey was carried out from April to June 2023.

METHODS

Consensus was sought from an expert panel on the instrument preliminarily established based on the JBI Model of Evidence-Based Healthcare and a rapid review of systematic reviews of relevant literature. The level of consensus was reflected by the concentration and coordination of experts' opinions and percentage of agreement. The instrument was revised significantly based on the combination of data analysis, the experts' comments and research group discussions.

RESULTS

Sixteen national and three international experts were involved in the first-round Delphi survey and 17 experts participated in the second-round survey. In both rounds, full consensus was reached on the four dimensions of the instrument, namely evidence-generation, evidence-synthesis, evidence-transfer and evidence-implementation. In round-one, the instrument was revised from 77 to 61 items. In round-two, the instrument was further revised to have 57 items under the four dimensions in the final version.

CONCLUSION

The Delphi survey achieved consensus on the instrument. The validity and reliability of the instrument needs to be tested in future research internationally.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE

Systematic assessment of nurses and other health professionals' competencies in different phases of evidence-based healthcare process based on this instrument provides implications for their professional development and multidisciplinary team collaboration in evidence-based practice and better care process and outcomes.

IMPACT

This study addresses a research gap of lacking an instrument to systematically assess interprofessional competencies in relation to the process of EBHC. The instrument covers the four phases of EBHC process with minimal criteria, highlighting essential aspects of ability to be developed. Identification of health professionals' level of competence in these aspects helps strengthen their capacity accordingly so as to promote virtuous EBHC ecosystem for the ending purpose of improving global healthcare outcomes.

REPORTING METHOD

This study was reported in line with the Conducting and REporting of DElphi studies (CREDES) guidance on Delphi studies.

PATIENT AND PUBLIC CONTRIBUTION

No patient or public contribution.

[The Delphi technique: Methodology, variants and usage examples]

In the field of medicine and health sciences, Delphi methods are applied mainly in the exploratory or evaluative phases of a research process. Explicit and implicit knowledge of respected experts from research and practice is systematically synthesized. Originally developed as a method for structuring a group communication process, Delphi techniques have been established in the health sector as a consensus method. The findings are used to improve the evidence and acceptance of planned interventions or necessary standards or guidelines and to increase the probability of successful implementation in practice. However, different variants of Delphi methods have been developed in recent years, which are systematically contrasted and reflected in this paper with regard to key epistemological and methodological research activities. Based on this overview, researchers should be enabled to select the most suitable Delphi technique for their own research questions and research endeavors.

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

OBJECTIVE

The 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.

DESIGN

We 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.

SETTING

The Delphi consultation questionnaire was distributed to the leaders of PMIs in districts D, F and S in Beijing, China.

PARTICIPANTS

Nineteen 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.

RESULTS

The 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.

CONCLUSIONS

An 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.

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

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.