High-value, data-informed, and team-based care for multimorbidity
Bibliographic record
Abstract
In most settings, care is traditionally physician-centred, but trends are shifting towards a patient-centred health-care system.1NEJM CatalystWhat is value-based health care?.https://catalyst.nejm.org/what-is-value-based-healthcare/Date: Jan 1, 2017Date accessed: December 4, 2019Google Scholar Patient-centred care focuses on the health-care needs and preferences of patients, by allowing patients to become active participants and ensuring that their values guide clinical decisions.1NEJM CatalystWhat is value-based health care?.https://catalyst.nejm.org/what-is-value-based-healthcare/Date: Jan 1, 2017Date accessed: December 4, 2019Google Scholar Jonathan Pearson-Stuttard and colleagues2Pearson-Stuttard J Ezzati M Gregg EW Multimorbidity—a defining challenge for health systems.Lancet Public Health. 2019; 4: e599-e600Summary Full Text Full Text PDF PubMed Scopus (63) Google Scholar called for a patient-centred approach of multimorbidity in the previous issue of The Lancet Public Health. However, we would like to stress that for such an approach to be truly implemented, health-care systems must be designed for the provision of high-value, data-informed, and team-based care. Firstly, the promotion of high-value care is needed for the long-term sustainability of health-care systems. Value-based health care aims to increase the value that is derived from the resources available for a population.3Gray M Value based healthcare.BMJ. 2017; 356: j437Crossref PubMed Scopus (75) Google Scholar Overdiagnosis, overtreatment, and more subtle forms of low-value care and waste are recognised as major threats. However, one major challenge is distinguishing high-value care from low-value care; a hierarchy of evidence-based recommendations needs to be generated through health technological assessments that integrate patients’ preferences. Secondly, the management of multimorbidity should be transformed through data-informed care. Care for multimorbidity is complex, calling for monitoring at the individual level with new health information technology, including through a better use of electronic health records. A surveillance system is also needed at the population level, and we agree with Pearson-Stuttard and colleagues that existing disease surveillance systems have not been used optimally to guide effective action for the management of multimorbidity. The digital transformation of health-care systems will help to improve health surveillance, but these systems have to use data effectively to avoid being “data rich but information poor”.4OECDHealth in the 21st century: putting data to work for stronger health systems. Organisation for Economic Co-operation and Development, Paris2019Crossref Google Scholar Thirdly, team-based care is needed for the integrated and coordinated management of chronic diseases and multimorbidity. Although several types of team-based intervention exist for the management of chronic diseases, such as diabetes or hypertension,5Santschi V Wuerzner G Chiolero A et al.Team-based care for improving hypertension management among outpatients (TBC-HTA): study protocol for a pragmatic randomized controlled trial.BMC Cardiovasc Disord. 2017; 17: 39Crossref PubMed Scopus (14) Google Scholar studies are now needed to evaluate how team-based care can help manage multimorbidity within a patient-centred approach. Digital transformation of health care can also offer opportunities to ease the implementation of team-based care through information systems leveraging data from shared electronic health records. In conclusion, we agree with Pearson-Stuttard and colleagues that several challenges need to be addressed to work towards the provision of high-value, data-informed, team-based, and patient-centred care of multimorbidity; overcoming these challenges will be necessary for the long-term sustainability of health-care systems in aging populations. We declare no competing interests. Multimorbidity—a defining challenge for health systemsMultimorbidity has emerged as one of the greatest challenges facing health services, both presently and in coming decades. Surveillance data on chronic diseases in Scotland, UK, estimate around one in four of their adult population to have two or more long-term conditions.1 By 2035, approximately 17% of the UK population is projected to have four or more chronic conditions, which is almost double the current prevalence (9·8%).2 Hypertension (prevalence 18·2%), depression or anxiety (10·3%), and chronic pain (10·1%) are among the leading contributors to the increasing prevalence of multimorbidity,3 demonstrating the breadth, heterogeneity, and interlinked nature of physical and mental health conditions. Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".