Optimisation: defining and exploring a concept to enhance the impact of public health initiatives

Health Research Policy and Systems

卷:17

Samantha Batchelor¹  Chris Doran²  Robyn Mildon³  Maria Fernandez⁴  Joanie Sims-Gould⁵  Heather McKay⁵  Bianca Albers⁶  Joanna Moullin⁷  Chris Rissel⁸  Natalie Taylor⁸  Adrian Bauman⁸  Alix Hall⁹  Katarzyna Bolsewicz¹⁰  Nicole Nathan¹⁰  Rebecca Wyse¹⁰  Rachel Sutherland¹⁰  John Wiggers¹⁰  Sze Lin Yoong¹⁰  Rebecca Kate Hodder¹⁰  Alice Grady¹⁰  Luke Wolfenden¹⁰  Sam McCrabb¹⁰  Melanie Kingsland¹⁰  Cara Lewis¹¹  Andrew Bailey¹²  Andrew Milat¹³  Jeremy Grimshaw¹⁴  John Attia¹⁵  Penny Reeves¹⁵ 

[1] Central Coast Local Health District;

[2] Central Queensland University;

[3] Centre for Evidence and Implementation;

[4] Centre for Health Promotion and Prevention Research, School of Public Health, University of Texas Health Science Centre;

[5] Centre for Hip Health and Mobility, Robert H N Ho Research Centre, University of British Columbia;

[6] European Implementation Collaborative;

[7] Faculty of Health Sciences, School of Pharmacy and Biomedical Sciences, Curtin University;

[8] Faculty of Medicine and Health, University of Sydney;

[9] Hunter Medical Research Institute;

[10] Hunter New England Local Health District;

[11] Kaiser Permanent Washington Health Research Institute;

[12] Mid North Coast Local Health District;

[13] NSW Ministry of Health;

[14] Ottawa Hospital Research Institute;

[15] School of Medicine and Public Health, University of Newcastle;

关键词: Optimisation;    implementation;    public health;    intervention;    Delphi study;    consensus process;   

DOI  :  10.1186/s12961-019-0502-6

来源: DOAJ

【 摘 要 】

Abstract Background Repeated, data-driven optimisation processes have been applied in many fields to rapidly transform the performance of products, processes and interventions. While such processes may similarly be employed to enhance the impact of public health initiatives, optimisation has not been defined in the context of public health and there has been little exploration of its key concepts. Methods We used a modified, three-round Delphi study with an international group of researchers, public health policy-makers and practitioners to (1) generate a consensus-based definition of optimisation in the context of public health and (2i) describe key considerations for optimisation in that context. A pre-workshop literature review and elicitation of participant views regarding optimisation in public health (round 1) were followed by a daylong workshop and facilitated face-to-face group discussions to refine the definition and generate key considerations (round 2); finally, post-workshop discussions were undertaken to refine and finalise the findings (round 3). A thematic analysis was performed at each round. Study findings reflect an iterative consultation process with study participants. Results Thirty of 33 invited individuals (91%) participated in the study. Participants reached consensus on the following definition of optimisation in public health: “A deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints”. A range of optimisation considerations were explored. Optimisation was considered most suitable when existing public health initiatives are not sufficiently effective, meaningful improvements from an optimisation process are anticipated, quality data to assess impacts are routinely available, and there are stable and ongoing resources to support it. Participants believed optimisation could be applied to improve the impacts of an intervention, an implementation strategy or both, on outcomes valued by stakeholders or end users. While optimisation processes were thought to be facilitated by an understanding of the mechanisms of an intervention or implementation strategy, no agreement was reached regarding the best approach to inform decisions about modifications to improve impact. Conclusions The study findings provide a strong basis for future research to explore the potential impact of optimisation in the field of public health.

【 授权许可】

Unknown