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Peri‐operative care pathways: re‐engineering care to achieve the ‘triple aim’
Author(s) -
Grocott M. P. W.,
Edwards M.,
Mythen M. G.,
Aronson S.
Publication year - 2019
Publication title -
anaesthesia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.839
H-Index - 117
eISSN - 1365-2044
pISSN - 0003-2409
DOI - 10.1111/anae.14513
Subject(s) - medicine , psychological intervention , quality management , health care , intervention (counseling) , perioperative , population , analytics , patient satisfaction , nursing , operations management , management system , surgery , environmental health , data science , computer science , economics , economic growth
Summary Elective surgical pathways offer a particular opportunity to plan radical change in the way care is delivered, based on patient need rather than provider convenience. Peri‐operative pathway redesign enables improved patient experience of care (including quality and satisfaction), population/public health, and healthcare value (outcome per unit of currency). Among physicians with the skills to work within peri‐operative medicine, anaesthetists are well positioned to lead the re‐engineering of such pathways. Re‐engineered pre‐operative pathways open up opportunities for intervention before surgery including shared decision‐making, comorbidity management and collaborative behavioural change. Individualised, risk‐adapted, intra‐operative interventions will drive more reliable and consistent care. Risk‐adapted postoperative care, particularly around transitions of care, has a significant role in improving value through peri‐operative medicine. Improved integration with primary care providers offers the potential for minimising errors around transitions of care before and after surgery, as well as maximising opportunities for population health interventions, including lifestyle modification (e.g. activity/exercise, smoking and/or alcohol cessation), pain management and sleep medicine. Systematic data collection focused on quality improvement is essential to drive continuous clinical improvement and will be enabled by technological development in predictive analytics, systems modelling and artificial intelligence.