Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals.

INTRODUCTION: Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions. METHODS AND ANALYSIS: A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants' attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants' opinions on how their experiences compare. Data will be analysed thematically using the Framework Method. ETHICS AND DISSEMINATION: This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.

Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records.

BACKGROUND: Primary care electronic health records (EHR) are widely used to study long-term conditions in epidemiological and health services research. Therefore, it is important to understand how well the recorded prevalence of these conditions in EHRs, compares to other reliable sources overall, and varies by socio-demographic characteristics. We aimed to describe the prevalence and socio-demographic variation of cardiovascular, renal, and metabolic (CRM) and mental health (MH) conditions in a large, nationally representative, English primary care database and compare with prevalence estimates from other population-based studies. METHODS: This was a cross-sectional study using the Clinical Practice Research Datalink (CPRD) Aurum primary care database. We calculated prevalence of 18 conditions and used logistic regression to assess how this varied by age, sex, ethnicity, and socio-economic status. We searched the literature for population prevalence estimates from other sources for comparison with the prevalences in CPRD Aurum. RESULTS: Depression (16.0%, 95%CI 16.0-16.0%) and hypertension (15.3%, 95%CI 15.2-15.3%) were the most prevalent conditions among 12.4 million patients. Prevalence of most conditions increased with socio-economic deprivation and age. CRM conditions, schizophrenia and substance misuse were higher in men, whilst anxiety, depression, bipolar and eating disorders were more common in women. Cardiovascular risk factors (hypertension and diabetes) were more prevalent in black and Asian patients compared with white, but the trends in prevalence of cardiovascular diseases by ethnicity were more variable. The recorded prevalences of mental health conditions were typically twice as high in white patients compared with other ethnic groups. However, PTSD and schizophrenia were more prevalent in black patients. The prevalence of most conditions was similar or higher in the primary care database than diagnosed disease prevalence reported in national health surveys. However, screening studies typically reported higher prevalence estimates than primary care data, especially for PTSD, bipolar disorder and eating disorders. CONCLUSIONS: The prevalence of many clinically diagnosed conditions in primary care records closely matched that of other sources. However, we found important variations by sex and ethnicity, which may reflect true variation in prevalence or systematic differences in clinical presentation and practice. Primary care data may underrepresent the prevalence of undiagnosed conditions, particularly in mental health.

Development and validation of the Quality of Work Life Scale for elderly workers.

BACKGROUND: With the rapid rise in the elderly population and their labour force participation, quality of work life (QoWL) of elderly workers becomes an important concept. A valid instrument to measure elderly workers QoWL is a prerequisite to further in this direction. OBJECTIVE: To develop and validate the Quality of Work Life Scale-Elderly (QoWLS-E) for elderly workers 60 years and above in Sri Lanka. METHODS: The development and validation of 35 items in QoWLS-E was carried out in two stages. Using a literature search and expert opinion, the items were developed in English language and later translated to Sinhala language. The initial scale consisted of 38 items and a principle component analysis (PCA) was conducted among 275 elderly workers in selected administrative divisions of Colombo district. Then a confirmatory factor analysis (CFA) was conducted among a separate group of 250 elderly workers to confirm the factor structure of the developed scale. RESULTS: PCA identified 9 principle components accounting for a variance of 71%, which was later confirmed in the CFA (RMSEA-0.07, SRMR-1.0, NNFI-0.87, GFI-0.82, CFI-0.96). The final QoWLS-E with a structure of 9 domains namely; physical health, psychological, welfare facility, safety, job content, co-worker, supervisor, flexibility and autonomy having 35 items correlated satisfactorily with Cronbach's alpha of 0.77 and test - retest reliability of 0.82. CONCLUSION: QoWLS-E is conceptually and culturally appropriate to assess Quality of Work Life Scale in elderly. It could be a useful tool to describe and monitor improvement of QOWL in elderly.