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Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access. [ABSTRACT FROM AUTHOR]

International evidence indicates that older people with frailty are more likely to access social care services, compared to nonfrail older people. There is, however, no robust evidence on costs of social care provided for community‐dwelling older people living with frailty in their own homes. The main objective of this study was to examine the relationship between community‐dwelling older people living with frailty, defined using the cumulative deficit model, and annual formal social care costs for the 2012–2018 period. A secondary objective was to estimate formal social care spending for every 1% reduction in the number of older people who develop frailty over 1 year. Secondary analysis of prospective cohort data from two large nationally representative community‐based cohort studies in England was performed. Respondents aged ≥75 were used in the main analysis and respondents aged 65–74 in sensitivity testing. We used regression tree modelling for formal social care cost analysis including frailty, age, gender, age at completing education and living with partner as key covariates. We employed a minimum node size stopping criteria to limit tree complexity and overfitting and applied 'bootstrap aggregating' to improve robustness. We assessed the impact of an intervention for every 1% decrease in the number of individuals who become frail over 1 year in England. Results show that frailty is the strongest predictor of formal social care costs. Mean social care costs for people who are not frail are £321, compared with £2,895 for individuals with frailty. For every 1% of nonfrail people not transitioning to frailty savings of £4.4 million in annual expenditures on formal social care in England are expected, not including expenditure on care homes. Given considerably higher costs for individuals classed as frail compared to nonfrail, a successful intervention avoiding or postponing the onset of frailty has the potential to considerably reduce social care costs. [ABSTRACT FROM AUTHOR]

Background: Personalised care planning (PCP) interventions have the potential to provide better outcomes for older people and are a key focus in primary care practice. Behaviour change techniques (BCTs) can maximise effectiveness of such interventions, but it is uncertain which BCTs are most appropriate in PCP for older adults.Aim: To identify BCTs used in successful PCP interventions for older people aged ≥65 years.Design and Setting: Systematic review.Method: The authors searched 12 databases from date of inception to 30 September 2017. They identified randomised controlled trials (RCTs) of interventions involving participants aged ≥65 years, and contextually related to PCP. Five areas of risk of bias were assessed. The Michie et al, BCT taxonomy was used for coding.Results: Twenty-three RCTs involving 6489 participants (average age 74 years) described PCP interventions targeting the general older adult population and older people with specific long-term conditions (for example, heart disease, diabetes, stroke). Just over half of the studies were deemed to be at a low risk of bias. Eleven 'promising' BCTs were identified in five trials reporting significant improvements in quality of life (QoL). Six BCTs were reported in all five of these trials: 'goal setting', 'action planning', 'problem solving', 'social support', 'instructions on how to perform a behaviour', and 'information on health consequences'. Modes of delivery varied.Conclusion: Future PCP interventions to improve QoL for people aged ≥65 years may benefit from focusing on six specific BCTs. Better reporting of BCTs would enhance future design and implementation of such interventions. [ABSTRACT FROM AUTHOR]

Background the electronic frailty index (eFI) has been developed and validated using routine primary care electronic health record data. The focus of the original big data study was on predictive validity as a form of criterion validation. Convergent validity is a subtype of construct validity and considered a core component of the validity of a test. Objective to investigate convergent validity between the eFI and research standard frailty measures. Design cross-sectional validation study using data from the Community Ageing Research 75+ (CARE 75+) cohort. Setting multi-site UK community-based cohort study. Subjects three hundred fifty-three community-dwelling older people (median age 80 years, IQR 77–84), excluding care home residents and people in the terminal stage of life. Median eFI score of participants was 0.22 (IQR 0.14–0.31). Methods convergent validities between the eFI and: a research standard frailty index (FI); the phenotype model of frailty; Clinical Frailty Scale (CFS) and Edmonton Frail Scale were assessed using scatter plots and Spearman's rank tests to estimate correlation coefficients (Spearman's rho, ρ) and 95% confidence intervals. Results results indicate strong correlation between the eFI and both the research standard FI (ρ = 0.68, 95% CI 0.62–0.74) and Edmonton Frail Scale (ρ = 0.63, 95% CI 0.57–0.69). There was evidence for moderate correlation between the eFI and both the CFS (ρ = 0.59, 95% CI 0.49–0.65) and phenotype model (ρ = 0.51, 95% CI 0.42–0.59). Conclusions This study provides evidence for convergent validity of the eFI, a core component of test validity. [ABSTRACT FROM AUTHOR]

Presents several correspondence on health care in Great Britain. Contamination of urine and fecal specimens by using unsterile containers; Efficacy of glucagon as treatment of hypoglycemia; Number of deaths caused by tobacco smoking.