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Effective Implementation of digital health technologies

  • Full or part time
  • Application Deadline
    Friday, January 31, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

The PhD project will be located within the work of the National Institute for Health Research Applied Health Research Collaboration Greater Manchester (ARC-GM) Digital Health Theme. The focus of the theme is to generate and share knowledge to support the development, deployment and adoption of digital health and care technologies in Greater Manchester. Research in the theme focuses on involving end-users of digital transformation, and encompasses both technologies targeted at clinicians, as well as community/patient facing technologies (such as smart phones, wearables and sensors) to address the needs of the health and care service, public health challenges and support the management of long-term conditions.

The successful candidate will have the flexibility to identify a specific research topic within the broad focus of the theme research projects. Potential studies include:
• Strategies for supporting the implementation of decision support technologies in pre-hospital care.
• Evaluation of a regional health and care record to support data sharing and care planning for patients with dementia and frailty
• Methods for evaluating digital health technologies that are agile and optimise adoption and impact (e.g. A/B testing)
• How to implement Artificial Intelligence systems for decision making in health and social care settings
• The implementation of digital health solutions for risk assessment, screening, triage and (self-) management for conditions identified as being a GM priority.
• How new digital technologies, such as remote monitoring of long term conditions, can become economically sustainable over time.

Entry Requirements
Applicants are expected to hold, or about to obtain, a minimum upper second class undergraduate degree (or equivalent) in either biomedicine/health (e.g. medicine; nursing; pharmacy), behavioural science (e.g. psychology; sociology), or information/computing sciences (e.g. computer science; mathematics; bioengineering). A Masters degree in a relevant subject and/or experience in health informatics, health data science, epidemiology or applied health research is desirable.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). If you are interested, please make direct contact with the Supervisor to discuss the project. You MUST also submit an online application form - choose PhD Health Informatics.

Funding Notes

This project is funded by NIHR ARC and The University of Manchester. Studentship funding is for a duration of three years to commence in September 2020 and covers UK/EU tuition fees and a UKRI stipend (£15,009 per annum 2019/20). Due to funding restrictions the studentship is open to UK and EU nationals.

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Austin L, Sharp CA, van der Veer SN, Machin M, Humphreys J, Mellor P, McCarthy J, Ainsworth J, Sanders C, Dixon WG. Providing 'the bigger picture': benefits and feasibility of integrating remote monitoring from smartphones into the electronic health record. Rheumatology 2019. pii: kez207. doi: 10.1093/rheumatology/kez207.

Tully MP, Bozentko K, Clement S, Hunn A, Hassan L, Norris R, Oswald M, Peek N. Investigating the extent to which patients should control access to patient records for research: A deliberative process Using Citizens' Juries. J Med Internet Res. 2018 Mar 28;20(3):e112.

Randell R, Honey S, Alvarado N, Greenhalgh J, Hindmarsh J, Pearman A, Gardner P, Gill A, Kotze A, Dowding D. Factors supporting and constraining the implementation of robot-assisted surgery: a realist interview study. BMJ Open 2019;9:e028635. doi: 10.1136/bmjopen-2018-028635

Wu, D. T. Y., Chen, A. T., Manning, J. D., Levy-Fix, G., Backonja, U., Borland, D., Caban, J. J., Dowding, D., Hochheiser, H., Kagan, V., Kandaswamy, S., Kumar, M., Nunez, A., Pan, E. & Gotz, D., Evaluating visual analytics for health informatics applications: A systematic review from the AMIA VIS working group task force on evaluation. Journal of the American Medical Informatics Association. 2019 Apr 1;26(4):314-323

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