Suchergebnisse

Chapter 1: "Two patients are dead and Foothills Hospital staff are to blame." BlameChapter 2: "And she died because of one of the most dreadful medical mistakes ever revealed in Alberta, or all of Canada." From memory and information processing to errors, violations and sabotageChapter 3: "Don't make me sue you." Apology, disclosure and supportChapter 4: "All Intensive Care Units in Calgary were notified to look out for similar difficulties."It's mainly about sharing information Chapter 5: "But what are we going to do? Hang a pharmacist?" Supporting healthcare providersChapter 6: "It is vital we learn from these mistakes." Systems, systems thinking and investigatingChapter 7: "Get something positive out of this tragedy." The Region's patient safety strategyChapter 8: "A major shake-up" The journey never endsAfterword #1 -- Jack Davis MScAfterword #2 -- Deborah E Prowse QC

IntroductionWorldwide large cohort studies have invested in community engagement to promote studies and aidrecruitment. HealthWise Wales, a national population study, aims to create a register of 'researchready' participants and provide long-term follow up data on health behaviours, outcomes andwider social and environmental determinants. Public involvement and engagement was key to thedevelopment of HealthWise Wales. We describe how a model for promoting HealthWise Wales wasco-produced with members of the public.
MethodsMembers of the public were invited to take part in a workshop, either in North or South Wales,to discuss public involvement in long-term cohort studies. Information on community engagement,projects that had used the concept of "citizen scientists" to promote involvement, and other largelongitudinal studies was provided to 15 members of the public prior to the meeting. Eight ofthese attended the workshops, to explore the concept of citizen scientist and how it may relateto HealthWise Wales.
ResultsData from two workshops was used to draft a protocol for involvement that was reviewed and refinedby members of the public. The protocol describes two levels of public involvement, HealthWise WalesChampion or Supporter. The Champion is a more formal role that requires promoting the projectat public events, whereas Supporters pledge to promote the study to friends and family. Trainingwas provided to 17 of the 26 members of the public who had expressed interest in becoming HWWChampions. Twelve trained Champions attended 41 events to promote the study and collect 'consentto contact' forms from members of the public.
Conclusions
It is possible to develop a model of community engagement with members of the public to promoteand raise awareness of a national population study in Wales. It is essential that adequate resourceis provided to support the concept.

The COVID-19 pandemic has tested the resilience of health systems broadly and primary care (PC) specifically. This paper begins by distinguishing the technical and political aspects of resilience and then draws on a documentary analysis and qualitative interviews with health system and PC stakeholders to examine competing resilience-focused responses to the pandemic in Alberta, Canada. We describe the pre-existing linkages between the province's central service delivery agency and its independent PC clinics. Together, these central and independent elements make up Alberta's broader health system, with the focus of this paper being on PC's particular vision of how resilience ought to be achieved. We describe two specific, pandemic-affected areas of activity by showing how competing visions of resilience emerged in the central service delivery agency and independent PC responses as they met at the system's points of linkage. At the first point of linkage, we describe the centralized activation of an incident management system and the replies made by independent PC stakeholders. At the second point of linkage, we describe central efforts to disseminate infection prevention and control guidance to PC clinics and the improvisational efforts of staff at those independent clinics to operationalize the guidance and ensure continuity of operations. We identify gaps between the resilience visions of the central agency and independent PC, drawing broadly applicable policy lessons for improving responses in present and future public health emergencies. Finding ways to include PC in centralized resilience policy planning is a priority.

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed