Barriers and facilitators to implementing priority setting and resource allocation tools in hospital decisions: A systematic review

Ahumada-Canale, Antonio; Jeet, Varinder; Bilgrami, Anam; Seil, Elizabeth; Gu, Yuanyuan; Cutler, Henry

doi:10.1016/j.socscimed.2023.115790

Cited by 4 publications

(5 citation statements)

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“…We then discuss how to start closing this evidence gap by identifying lessons learned from the development and implementation of previous disruptive technologies that faced similar unique challenges, such as audit and feedback applications like dashboards 7 . Closing AI's evidence gap can help to reduce the 'chasm' between AI-development and AI-implementation and adoption in healthcare 8 , reducing global spending and time-to-market for relevant applications that can make a difference.…”

Section: Table Of Contentsmentioning

confidence: 99%

“…Such technologies, like AI applications, evaluate and visualize data on local processes or outcomes, and provide situated guidance intended to steer clinical action 7,22 .…”

Section: Closing the Evidence Gapmentioning

confidence: 99%

“…ignore or override the advice provided by the application if they think it is unsuitable to the given circumstances. Lack of trust, testing, or embedding of these technologies have all been found to act as barriers to use, preventing causal changes in outcomes, processes, or performance 7 . For these reasons, we argue that the implementation of non-AI digital performance applications can provide significant insights for the production of relevant evidence relating to AI implementation.…”

Section: Closing the Evidence Gapmentioning

confidence: 99%

“…One key lesson from the implementation of audit and feedback applications is that the commonly conducted quantitative studies, designed to establish sought-after validity evidence, were often insufficient to gain a good understanding of the performance of the applications in situ 7 . One reason for this was that the performance of these applications varied across contexts, and quantitative investigations were not sensitive enough to the role of these contexts 23 .…”

Section: Closing the Evidence Gapmentioning

confidence: 99%

“…In agreement with Vasey et al 8 , we argue for the introduction of early and small-scale clinical evaluations that sit between the insilico algorithm development and the larger-scale clinical trials. The focus of such evaluations would be to assess the real-world impact of an AI application on its users' decisions at an early stage when changes to the application are still feasible, as this has been found to facilitate the implementation of performance dashboards 7,8 . Furthermore, this could help to identify the application's safety profile therefore avoiding exposing large groups of patients to harm understanding of what AI applications can realistically achieve, but also make it more difficult for AI developers to embellish their AI's performance for profitability.…”

Section: Closing the Evidence Gapmentioning

confidence: 99%

See 4 more Smart Citations

Mind the gap! Overcoming the lack of evidence to support the innovation journey of AI in healthcare (Preprint)

Silkens,

Leusder,

Woznitza

et al. 2024

Preprint

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UNSTRUCTURED The rapid development of artificial intelligence (AI) applications for the healthcare setting confronts providers and practitioners with the challenge of choosing those applications that have the best chance to reduce the burden of care in their context. This is challenging due to a general lack of evaluation metrics and because the evidential claims provided by AI vendors are not always in line with the forms of evidence needed by healthcare providers and practitioners. This evidence gap currently harms the development of trust in and acceptability of AI, and thereby hampers the successful implementation and adoption of AI in healthcare. In this viewpoint, we argue that closing this evidence gap is crucial to helping AI achieve its full potential in the healthcare context and we provide practical guidance towards this objective.

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Section: Table Of Contentsmentioning

confidence: 99%

“…Such technologies, like AI applications, evaluate and visualize data on local processes or outcomes, and provide situated guidance intended to steer clinical action 7,22 .…”

Section: Closing the Evidence Gapmentioning

confidence: 99%

Section: Closing the Evidence Gapmentioning

confidence: 99%

Section: Closing the Evidence Gapmentioning

confidence: 99%

Section: Closing the Evidence Gapmentioning

confidence: 99%

See 3 more Smart Citations

Mind the gap! Overcoming the lack of evidence to support the innovation journey of AI in healthcare (Preprint)

Silkens,

Leusder,

Woznitza

et al. 2024

Preprint

View full text Add to dashboard Cite

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Open and opaque? The performativity of open priorities in a Norwegian hospital

Melberg

2024

Social Science & Medicine

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Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods

Ma,

Li,

Xue

et al. 2024

INQUIRY

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Performance evaluation is important for improving medical quality and services. But, there is a lack of research for medical quality in traditional Chinese medicine (TCM) hospitals. This study examines the medical quality and various indicators of tertiary public traditional Chinese medicine hospitals in Gansu Province, to establish a foundation for improving the medical and management standards of these hospitals. This study collected performance assessment data from 10 tertiary TCM hospitals in Gansu Province from 2019 to 2022. Thirteen indicators with TCM characteristics were selected and categorized into 3 aspects: control of medical costs, internal operational dimensions, and comprehensive management. The level of medical quality in different hospitals and in different years were determined using the TOPSIS method for ranking and the RSR method for grading. Firstly, in terms of TCM characteristic indicators, hospital H had the highest control of medical costs and comprehensive management among different hospitals, with 45.87% and 24.20% respectively. The highest values for control of medical costs and comprehensive management were observed in 2020, with 40.65% and 18.69% respectively among different years. When evaluating the medical quality of different hospitals using the TOPSIS method, it was found that hospital H had the highest ranking from 2020 to 2022, with Ci values of 0.725, 0.778, and 0.667 respectively. Additionally, the RSR method indicated that hospital H had a high level of grading from 2020 to 2022, with Pi values of 0.687, 0.690, and 0.723 respectively. These findings suggest that the medical quality of hospital H is at a high and stable level of development. Based on the TOPSIS method to evaluate the performance appraisal results and ranking of different hospitals from 2019 to 2022. The results showed that the highest ranking was hospital B(Ci = 0.913) in 2019. The highest ranking was hospital C(Ci = 0.809)in 2020. The highest ranking was hospital D(Ci = 0.689) in 2021. The highest ranking was hospital J(Ci = 0.865) in 2022. The RSR method indicated that high grading level were hospitals B(Pi = 0.899),F(Pi = 0.795) in 2019. The highest grading level was hospital C(Pi = 0.809) in 2020. The highest grading level were hospitals A(Pi = 0.868), D(Pi = 0.813), E(Pi = 0.689), G(Pi = 0.873), J(Pi = 0.813), K(Pi = 0.842) in 2022. Based on the above results indicate that there is a large variation in the medical quality profile of different hospitals from 2019 to 2022. By comparing the results of TOPSIS and RSR method from 2019 to 2022, we found that the hospitals with identical ranking were D and J, and the hospitals with ≤2 difference in ranking was A,B,C,E in 2019, the hospitals with >2 ranking was A, F in 2020, the hospitals with >2 ranking were C, G in 2021, and the hospitals with identical ranking results were B,D,E,G,J in 2022. Comparing the ranking results of TOPSIS and RSR methods, showed that the hospitals with identical rankings were B, F from 2019 to 2022. The difference in ranking results ≤2 were A, C, D, E, G, H, J, K, indicating that high consistency between TOPSIS and RSR methods and credible results. The findings reveal significant fluctuations in medical quality across different years, while the overall level of medical quality remains relatively stable among the various hospitals. It is recommended that TCM hospitals focus on improving management efficiency, optimizing hospital operations, enhancing the utilization of medical resources, and fostering the efficient development of hospitals.

show abstract

Barriers and facilitators to implementing priority setting and resource allocation tools in hospital decisions: A systematic review

Cited by 4 publications

References 88 publications

Mind the gap! Overcoming the lack of evidence to support the innovation journey of AI in healthcare (Preprint)

Mind the gap! Overcoming the lack of evidence to support the innovation journey of AI in healthcare (Preprint)

Open and opaque? The performativity of open priorities in a Norwegian hospital

Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods

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