Automated Detection of Patients at High Risk of Polypharmacy including Anticholinergic and Sedative Medications.

Ensuring that medicines are prescribed safely is fundamental to the role of healthcare professionals who need to be vigilant about the risks associated with drugs and their interactions with other medicines (polypharmacy). One aspect of preventative healthcare is to use artificial intelligence to identify patients at risk using big data analytics. This will improve patient outcomes by enabling pre-emptive changes to medication on the identified cohort before symptoms present. This paper presents a mean-shift clustering technique used to identify groups of patients at the highest risk of polypharmacy. A weighted anticholinergic risk score and a weighted drug interaction risk score were calculated for each of 300,000 patient records registered with a major regional UK-based healthcare provider. The two measures were input into the mean-shift clustering algorithm and this grouped patients into clusters reflecting different levels of polypharmaceutical risk. Firstly, the results showed that, for most of the data, the average scores are not correlated and, secondly, the high risk outliers have high scores for one measure but not for both. These suggest that any systematic recognition of high-risk groups should consider both anticholinergic and drug-drug interaction risks to avoid missing high-risk patients. The technique was implemented in a healthcare management system and easily and automatically identifies groups at risk far faster than the manual inspection of patient records. This is much less labour-intensive for healthcare professionals who can focus their assessment only on patients within the high-risk group(s), enabling more timely clinical interventions where necessary.

A Business Analytics Framework for Primary Care.

In this paper, we present a Business Analytics (BA) framework, which addresses the challenge of analysing primary care outcomes for both patients and clinicians from multiple data sources in an accurate manner. A review of the process monitoring literature has been conducted in the context of healthcare management and decision making and its findings have informed the formulation of a BA conceptual framework for process monitoring and decision support in primary care. Furthermore, a real case study is conducted to demonstrate the application of the BA framework to implement a BA dashboard tool within one of the largest primary care providers in England. Findings: The main contributions of the presented work are the development of a conceptual BA framework and a BA dashboard tool to support management and decision making in primary care. This was evaluated through a case study of the implementation of the BA dashboard tool in London's largest primary care provider. This BA tool provides real-time information to enable simpler decision-making processes and to inform business transformation in a number of areas. The resulting increased efficiency has led to significant cost savings and improved delivery of patient care.

Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme.

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

Ethnic differences in the prevalence of frailty in the United Kingdom assessed using the electronic Frailty Index.

OBJECTIVE: There have been few studies in which the prevalence of frailty of different ethnic groups has been assessed in multiethnic countries. The aim of this study was to evaluate the prevalence of frailty in different ethnic groups in the United Kingdom. METHODS: Anonymized electronic health records (EHR) of 13 510 people aged 65 years and over were extracted from the database of a network of general practitioners, covering 16 clinical commissioning groups in London. Frailty was determined using the electronic Frailty Index (eFI), which was automatically calculated using EHR data. The eFI was used as a categorical variable with fit and mild frailty grouped together, and moderate and severe frailty grouped as frail. RESULTS: The overall prevalence of frailty was 18.1% (95% confidence interval [CI], 17.4%-18.9%). The prevalence of frailty increased with age (odds ratio [OR], 1.11; 95% CI, 1.10-1.12) and body mass index (BMI; OR, 1.05; 95% CI, 1.04-1.06). The highest prevalence of frailty was observed for Bangladeshis, with 32.9% classified as frail (95% CI, 29.2-36.7); and the lowest prevalence of 14.0% (95% CI, 12.6-15.5) was observed for the Black ethnic group. Stepwise logistic regression retained ethnicity, age, and BMI as predictors of frailty. CONCLUSION: This pilot study identified differences in the prevalence of frailty between ethnic groups in a sample of older people living in London. Additional studies are warranted to determine the causes of such differences, including migration and socioeconomic status. It would be worthwhile carrying out a validation study of the eFI in different ethnic populations.