|Thèse, 8 avenue Rockefeller, 69373 Lyon.|
|Entreprise/Organisme :||University Claude Bernard Lyon 1|
|Niveau d'études :||Master|
|Sujet :||This doctoral thesis will be conducted within the project "Variation, Change, Complexity", funded by IDEXLYON. This project aims to develop algorithms for visualization of medication histories and calculation of adherence to medication from Electronic Healthcare Data to provide international researchers with validated algorithms for transparent and reproducible research (www.adherer.eu). These algorithms will be integrated into clinical software to provide feedback on past behaviour and support shared decision making on adherence management in the consultation. This work is part of an ongoing international collaborative effort that brings together experts in data science, pharmacoepidemiology, medicine, health psychology, and health services research to advance research methodology in adherence to medications.|
|Date de début :||01.09.2019|
|Durée du contrat :||3 years|
|Rémunération :||monthly gross salary 1768 euros|
|Secteur d'activité :||scientific research; public sector|
|Description :||The Université Claude Bernard Lyon 1 and the Health Services and Performance Research laboratory (HESPER; EA7425) in Lyon, France, invite applications for a three-year PhD position on the use of electronic healthcare data for estimating patterns of medication use for research and clinical decision making support.
Adherence to medication is a key behaviour in managing chronic conditions, and difficult to sustain long-term. Giving feedback on medication use has been proven effective in managing adherence, yet implementation in routine care remains a challenge. Data on medication prescription and dispensation are collected automatically in routine care and commonly used to estimate adherence in research contexts. For this purpose, we developed AdhereR, an R package for computation of adherence to medication and visualisation of medication histories (www.adherer.eu). These data also become increasingly available for use in routine primary care and may be used for feedback to patients and healthcare professionals for clinical decision making and behaviour change support within the consultation. These new developments open up the possibility to integrate AdhereR into routine care and develop transparent adherence algorithms for both research and clinical practice.
For this purpose, adherence algorithms and visualisations need to be tailored to different clinical areas, data characteristics, and use patterns. The position advertised here will involve the development and validation of such algorithms and plots, using a mixed methods approach. First, a qualitative Delphi consensus study will be performed to reach expert consensus on clinically-meaningful visualisations and estimates, based on examples of medication histories from routine care data discussed within an interdisciplinary group of clinicians and researchers with expertise in adherence and/or the target clinical area. Second, validation of (alternative) algorithms will be performed on datasets representative of the target population, and results shared with expert group. Usability tests will be performed with healthcare providers and patients to inform the selection of appropriate user-interface characteristics and develop training materials. These will be further implemented and evaluated in routine care as part of wider projects. Three clinical areas (epilepsy, asthma, chronic pain) have been selected for initial work within an ongoing primary care-focused project. Further applications may be required in different clinical contexts within other collaboration projects.
We are looking for a talented and motivated master graduate in public health, pharmacy, epidemiology, statistics, data science or similar scientific fields with a strong quantitative background. A solid programming experience is a major advantage, but at a minimum we require foundations of programming and a willingness to quickly reach a level of expert programming in R. Familiarity with managing large and complex datasets is an advantage, in particular concerning electronic healthcare records and/or longitudinal analysis. Experience in medication adherence research, health behaviour change, patient trajectories, and/or patient-provider communication is a plus. A good track record of working independently and creatively is highly valued, as are team work and communication, analytical and project management skills. An excellent command of English (speaking and writing) is required.|
|En savoir plus :||https://euraxess.ec.europa.eu/jobs/405203|