3 results
Search Results
2. Perceptions of Barriers and Facilitators to a Pilot Implementation of an Algorithm-Supported Care Navigation Model of Care: A Qualitative Study.
- Author
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Pang, Rebecca K., Andrew, Nadine E., Srikanth, Velandai, Weller, Carolina D., and Snowdon, David A.
- Subjects
HEALTH facility employees ,MEDICAL quality control ,HEALTH facilities ,MATHEMATICAL models ,RESEARCH methodology ,PATIENT readmissions ,PATIENT-centered care ,INTERVIEWING ,HEALTH outcome assessment ,HUMAN services programs ,RISK assessment ,QUALITATIVE research ,CONCEPTUAL structures ,MEDICAL care research ,PSYCHOSOCIAL factors ,THEORY ,QUALITY assurance ,PATIENT care ,DATA analysis software ,ALGORITHMS ,DISCHARGE planning - Abstract
We aimed to explore managerial and project staff perceptions of the pilot implementation of an algorithm-supported care navigation model, targeting people at risk of hospital readmission. The pilot was implemented from May to November 2017 at a Victorian health service (Australia) and provided to sixty-five patients discharged from the hospital to the community. All managers and the single clinician involved participated in a semi-structured interview. Participants (n = 6) were asked about their perceptions of the service design and the enablers and barriers to implementation. Interviews were transcribed verbatim and analysed according to a framework approach, using inductive and deductive techniques. Constructed themes included the following: an algorithm alone is not enough, the health service culture, leadership, resources and the perceived patient experience. Participants felt that having an algorithm to target those considered most likely to benefit was helpful but not enough on its own without addressing other contextual factors, such as the health service's capacity to support a large-scale implementation. Deductively mapping themes to the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework highlighted that a formal facilitation would be essential for future sustainable implementations. The systematic identification of barriers and enablers elicited critical information for broader implementations of algorithm-supported models of care. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Regional Flood Frequency Analysis Using the FCM-ANFIS Algorithm: A Case Study in South-Eastern Australia.
- Author
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Zalnezhad, Amir, Rahman, Ataur, Vafakhah, Mehdi, Samali, Bijan, and Ahamed, Farhad
- Subjects
RUNOFF ,QUANTILE regression ,FLOODS ,ALGORITHMS ,QUANTILES - Abstract
Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Both linear and non-linear methods are adopted in RFFA. The development of the non-linear RFFA method Adaptive Neuro-fuzzy Inference System (ANFIS) using data from 181 gauged catchments in south-eastern Australia is presented in this study. Three different types of ANFIS models, Fuzzy C-mean (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) were adopted, and the results were compared with the Quantile Regression Technique (QRT). It was found that FCM performs better (with relative error (RE) values in the range of 38–60%) than the SC (RE of 44–69%) and GP (RE of 42–78%) models. The FCM performs better for smaller to medium ARIs (2 to 20 years) (ARI of five years having the best performance), and in New South Wales, over Victoria. In many aspects, the QRT and FCM models perform very similarly. These developed RFFA models can be used in south-eastern Australia to derive more accurate flood quantiles. The developed method can easily be adapted to other parts of Australia and other countries. The results of this study will assist in updating the Australian Rainfall Runoff (national guide)-recommended RFFA technique. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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