SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing. It can simulate a population of 1 million people in seconds per day allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

This article presents a systematic literature review done to evaluate the feasibility and benefits of home-based information and communications technology enabled interventions for chronic disease management, with emphasis on their impact on health outcomes and costs. Relevant articles were retrieved from PubMed and evaluated using quality worksheets with pre-identified inclusion and exclusion criteria. Of the 256 articles retrieved, 27 were found to concord with the study criteria. Evaluation of the identified articles was conducted irrespective of study design, type of home-based intervention or chronic disease involved. The review demonstrates that HBIs applied to chronic disease management improve functional and cognitive patient outcomes and reduce healthcare spending. However, further research is needed to assess benefit in terms of evidence-based outcome indicators (that can provide a basis for meta-analysis), to confirm sustainable cost benefits, and to systematically collect data on physician satisfaction with patient management.

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data-a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented.

Poor adherence in long-term use of statins is commonplace, but a number of key predictors-including age, language other than English spoken at home, smoking status and psychological distress-are readily assessable by the prescribing practice.

Background Dialog agents (chatbots) have a long history of application in health care, where they have been used for tasks such as supporting patient self-management and providing counseling. Their use is expected to grow with increasing demands on health systems and improving artificial intelligence (AI) capability. Approaches to the evaluation of health care chatbots, however, appear to be diverse and haphazard, resulting in a potential barrier to the advancement of the field. Objective This study aims to identify the technical (nonclinical) metrics used by previous studies to evaluate health care chatbots. Methods Studies were identified by searching 7 bibliographic databases (eg, MEDLINE and PsycINFO) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. The studies were independently selected by two reviewers who then extracted data from the included studies. Extracted data were synthesized narratively by grouping the identified metrics into categories based on the aspect of chatbots that the metrics evaluated. Results Of the 1498 citations retrieved, 65 studies were included in this review. Chatbots were evaluated using 27 technical metrics, which were related to chatbots as a whole (eg, usability, classifier performance, speed), response generation (eg, comprehensibility, realism, repetitiveness), response understanding (eg, chatbot understanding as assessed by users, word error rate, concept error rate), and esthetics (eg, appearance of the virtual agent, background color, and content). Conclusions The technical metrics of health chatbot studies were diverse, with survey designs and global usability metrics dominating. The lack of standardization and paucity of objective measures make it difficult to compare the performance of health chatbots and could inhibit advancement of the field. We suggest that researchers more frequently include metrics computed from conversation logs. In addition, we recommend the development of a framework of technical metrics with recommendations for specific circumstances for their inclusion in chatbot studies.

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

PurposeDeficiencies in medication adherence are a major barrier to effectiveness of chronic condition management. Continuity of primary care may promote adherence. We assessed the association of continuity of primary care with adherence to long-term medication as exemplified by statins.Research DesignWe linked data from a prospective study of 267,091 Australians aged 45 years and over to national data sets on prescription reimbursements, general practice claims, hospitalisations and deaths. For participants having a statin dispense within 90 days of study entry, we computed medication possession ratio (MPR) and usual provider continuity index (UPI) for the subsequent two years. We used multivariate Poisson regression to calculate the relative risk (RR) and 95% confidence interval (CI) for the association between tertiles of UPI and MPR adjusted for socio-demographic and health-related patient factors, including age, gender, remoteness of residence, smoking, alcohol intake, fruit and vegetable intake, physical activity, prior heart disease and speaking a language other than English at home. We performed a comparison approach using propensity score matching on a subset of the sample.Results36,144 participants were eligible and included in the analysis among whom 58% had UPI greater than 75%. UPI was significantly associated with 5% increased MPR for statin adherence (95% CI 1.04–1.06) for highest versus lowest tertile. Dichotomised analysis using a cut-off of UPI at 75% showed a similar effect size. The association between UPI and statin adherence was independent of socio-demographic and health-related factors. Stratification analyses further showed a stronger association among those who were new to statins (RR 1.33, 95% CI 1.15–1.54).ConclusionsGreater continuity of care has a positive association with medication adherence for statins which is independent of socio-demographic and health-related factors.

There is potential for general practices to identify substantial levels of long-term medication adherence problems through their electronic prescribing records. Significant further adherence problems could be detected if an e-pharmacy network allowed practices to match dispensing against prescriptions.