Evaluation of participants' reaction and learning in a taught analytics and modelling academy program in U.K.‘s National Health Service
Evaluation of participants' reaction and learning in a taught analytics and modelling academy program in U.K.‘s National Health Service
Recent research has highlighted the need to invest in the development of healthcare analytics capability. However, the contents of such programs and how they should be delivered to maximize the learning outcome are unclear. In this paper, we provide insights into the learning within the first two cohorts of modelling fellows successfully trained in an analytics and modelling academy run within the National Health Service (NHS) Wales, U.K. The participants followed a taught healthcare analytics and mathematical modelling program tailored for senior staff members including managers and clinicians. We build our learning evaluation framework on Kirkpatrick's training evaluation model and participants filled in questionnaires with respect to their level 1 (reaction) and level 2 (learning) experience after each module. In addition, we asked the participants about their self-assessments during three time points in the program. The qualitative feedback results revealed that the participants appreciate the learning and reflect where they could use the new developed skills in practice. They also provided useful suggestions for improving the program. The participants' aggregated quantitative self-assessments show a statistically significant increase in competence. In conclusion, this may lead to a behavior change in applying the methods on the job (level 3) and, ultimately, improve level 4 outcomes through analytics-driven healthcare improvement.
computer and information science education, healthcare, modeling and prediction, modeling methodologies, optimization of service systems, queuing theory, simulation
591-596
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Spernaes, Izabela
99791cea-ac5f-4184-8308-06afe260bec5
England, Tracey J.
8f99b32a-1670-4e20-b6c6-30ae96940ca2
Behrens, Doris A.
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Buchanan, Joanne
4ad70eaf-5feb-4308-8776-c94fb84abe1b
Harper, Paul R.
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14 June 2022
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Spernaes, Izabela
99791cea-ac5f-4184-8308-06afe260bec5
England, Tracey J.
8f99b32a-1670-4e20-b6c6-30ae96940ca2
Behrens, Doris A.
b3064ed3-26df-40fc-bc41-677147289d62
Buchanan, Joanne
4ad70eaf-5feb-4308-8776-c94fb84abe1b
Harper, Paul R.
8cba8a2d-4088-4112-abc9-da6100e414b9
Gartner, Daniel, Spernaes, Izabela, England, Tracey J., Behrens, Doris A., Buchanan, Joanne and Harper, Paul R.
(2022)
Evaluation of participants' reaction and learning in a taught analytics and modelling academy program in U.K.‘s National Health Service.
2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), Rochester, MN, USA, United States.
11 - 14 Jun 2022.
.
(doi:10.1109/ICHI54592.2022.00116).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Recent research has highlighted the need to invest in the development of healthcare analytics capability. However, the contents of such programs and how they should be delivered to maximize the learning outcome are unclear. In this paper, we provide insights into the learning within the first two cohorts of modelling fellows successfully trained in an analytics and modelling academy run within the National Health Service (NHS) Wales, U.K. The participants followed a taught healthcare analytics and mathematical modelling program tailored for senior staff members including managers and clinicians. We build our learning evaluation framework on Kirkpatrick's training evaluation model and participants filled in questionnaires with respect to their level 1 (reaction) and level 2 (learning) experience after each module. In addition, we asked the participants about their self-assessments during three time points in the program. The qualitative feedback results revealed that the participants appreciate the learning and reflect where they could use the new developed skills in practice. They also provided useful suggestions for improving the program. The participants' aggregated quantitative self-assessments show a statistically significant increase in competence. In conclusion, this may lead to a behavior change in applying the methods on the job (level 3) and, ultimately, improve level 4 outcomes through analytics-driven healthcare improvement.
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More information
e-pub ahead of print date: 11 June 2022
Published date: 14 June 2022
Additional Information:
Funding Information:
The authors thank The Health Foundation for their financial support in the evaluation of the program.
Publisher Copyright:
© 2022 IEEE.
Venue - Dates:
2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), Rochester, MN, USA, United States, 2022-06-11 - 2022-06-14
Keywords:
computer and information science education, healthcare, modeling and prediction, modeling methodologies, optimization of service systems, queuing theory, simulation
Identifiers
Local EPrints ID: 473433
URI: http://eprints.soton.ac.uk/id/eprint/473433
PURE UUID: 74efe3af-501c-4d31-a22c-c8f685e95ca7
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Date deposited: 18 Jan 2023 17:32
Last modified: 18 Jun 2024 02:00
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Contributors
Author:
Daniel Gartner
Author:
Izabela Spernaes
Author:
Doris A. Behrens
Author:
Joanne Buchanan
Author:
Paul R. Harper
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