Melek Somai. Research in areas ranging from Clinical Computing, Patient Remote Monitoring, Neuro-Epidemiology, to Blockchain.

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain

Healthcare organizations must implement effective methods to increase the reach, adoption, implementation, and maintenance of PCD tools across all patient populations. Assisting people, particularly racial minorities, with PCD Tool registration and actively supporting clinician use are critical steps in implementing technology that facilitates care.

In this paper published in JAMA Open, we report on the clinical outcomes of implementing a scalable Remote Patient Monitoring (RPM) platform for COVID-19 patients at Froedtert & Medical College of Wisconsin. In nutshell, we found that RPM was effective and associated with fewer hospitalizations, helping patients stay safely at home and helping manage hospital bed capacity.

In this narrative case, we identify issues related to patients’ use of technology, make comparisons between telehealth adoption and the deployment of electronic health records, and propose that building intuitive and supported digital care experiences for patients is required to make virtual care sustainable.

This quality improvement study of 137 846 video visits showed an overall 90% success rate. Patient rather than clinician factors were more systematically associated with successful completion of video visits, and clinician comfort with technology was associated with successful video visits or conversion to telephone visits. The findings suggest that, as policy makers consider expanding telehealth coverage and hospital systems focus on investments, consideration of patient support, equity, and friction should be kept in the forefront.

Metformin, an antidiabetic drug, triggers anti-aging cellular responses. Aging is the principal risk factor for dementia, but previous observational studies of the diabetes drugs metformin vs. sulfonylureas have been mixed. We tested the hypotheses that metformin improves survival and reduces the risk of dementia, relative to the sulfonylureas, by emulating target trials in electronic health records of diabetic patients at an academic-centered healthcare system in the US and a wide-ranging group of primary care practices in the UK. To address metformin’s potentially dual influences on dementia risk—that it might reduce the hazard of death and put more people at risk of developing dementia while reducing the hazard of dementia by slowing biological aging, we used a competing risks approach and carefully grounded that within a causal inference emulated trial framework. To identify candidate biomarkers of metformin’s actions in the brain that might mediate reduced dementia risk, we conducted an in-vitro systems pharmacology evaluation of metformin and glyburide on differentiated human neural cells through differential gene expression. We named our multi-dimensional approach DRIAD-EHR (Drug Repurposing in Alzheimer’s Disease-Electronic Health Records). In intention-to-treat analyses, metformin was associated with a lower hazard of all-cause mortality than sulfonylureas in both cohorts. In competing risks analyses, there was also a lower cause-specific hazard of dementia onset among metformin initiators. In in-vitro studies, metformin reduced human neural cell expression of SPP1 and APOE, two secreted proteins that have been implicated in Alzheimer’s disease pathogenesis and whose levels can be quantified in the CSF. Together, our findings suggest that metformin might prevent dementia in patients without type II diabetes. In addition, our results inform the design of clinical trials of metformin in non-diabetics and suggest a pharmacodynamic CSF biomarker, SPP1, for metformin’s action in the brain.

The outbreak of coronavirus disease 2019 (COVID-19) further accelerated the deployment and utilization of telemedicine services. An analysis of the socioeconomic characteristics of telemedicine users to understand potential socioeconomic gaps and disparities is critical for improving the adoption of telemedicine services among patients.. This study aims to measure the correlation of socioeconomic determinants with the use of telemedicine services in Milwaukee metropolitan area.

At one academic health system, a digital mental health program was deployed in primary care and outpatient behavioral health programs to help patients meet needs identified through screening or clinical visits.

The inclusion of PCD enhanced essential aspects of patient-provider communication but did not affect patient activation. Outcomes did not differ by race.

Desired outcomes include real-time error identification and better-shared understanding and decision-making, leading to better downstream follow-through with care plans.

Recently, there has been increased attention to the role of health information technology tools to enable patients to collaborate with providers through the sharing of PCD. The adoption of such tools may overcome the barriers of current EHRs by directly engaging patients to submit their contextual data.

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Online triage tools are increasingly being adopted in health care to aid patients in identifying the appropriate care level. However, there is a lack of empirical evidence on how patients use virtual triage and whether these tools influence care-seeking behavior. Using data from a free online triage tool, we describe the common symptoms queried by users and analyze whether the tool was associated with the level of care that patients intended to seek.

A low dissemination rate for results and a high rate of study non-completion, as well as lack of geographic dispersion of trials appear to be major challenges in the field.

The ever-increasing pace of technological advancements, rising costs, and new entrants into the health care marketplace are part of the challenge health care incumbents face today. With no alternative but to adapt, health care organizations must find effective methods to embrace innovation, which we define as the delivery of new patient and clinician value. Embedding and accelerating innovation in health care, however, has proven to be difficult. In health care, most current processes of governance, business planning, and information technology implementation are designed to minimize risk to organizations and are often inflexible to adapt quickly to new technological changes, netting incremental changes that fail to deliver much needed transformation.

Advances in predictive analytics and machine learning supported by an ever-increasing wealth of data and processing power are transforming almost every industry. Accuracy and precision of predictive analytics have significantly increased over the past few years and are evolving at an exponential pace. There have been significant breakthroughs in using Predictive Analytics in healthcare where it is held as the foundation of precision medicine. Yet, although the research in the field is expanding with the profuse volume of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Regardless of the status of its current contribution, the field of predictive analytics is expected to fundamentally change the way we diagnose and treat diseases, as well as the conduct of biomedical science research. In this review, we describe the main tools and techniques in predictive analytics and will analyze the trends in application of these techniques over the recent years. We will also provide examples of its application in medicine and more specifically in stroke and neurovascular research and outline current limitations.

The UK is one of the largest funders of health research in the world, but little is known about how health funding is spent. Our study explores whether major UK public and charitable health research funders support the research of UK-based scientists producing the most highly-cited research.

GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view—and subsequent provision—of quality health care for all populations.

Today’s health and social care systems are facing a challenge in how to effectively address caregiving for ageing populations facing cognitive disorders and frailty. Scholars and policy makers are now identifying a rise of “hidden form of care”, e.g. informal caregiving, as a phenomenon in support for ageing populations.

The focus of e-cigarette research has changed over time, from toxicology studies and journals to public health issues.

Through this study we were able to quantify some of the most important benefits of teledermatology. We found evidence of a total reduction in costs, both for the patients (US $76·36 per patient) and the healthcare system by decreasing referrals to tertiary-care centres by 75% in the intervention group. This study also shows a significant reduction in the time to receive dermatological care; the patients in the intervention group saved a total of 19 892 km and 269 h of travel when compared with patients in the control group. Finally, healthcare delivery was likely improved as GPs in the intervention group were supported by a specialist consultant for all patients who needed dermatological care and thus did not need to refer them as often. Patients in the intervention group were also given a significantly wider variety of diagnoses and treatments than those in the control group (P =0·03), and we think this could indirectly be a marker of better healthcare delivery.

In order to ensure the continued, safe administration of pharmaceuticals, particularly those agents that have been recently introduced into the market, there is a need for improved surveillance after product release. This is particularly so because drugs are used by a variety of patients whose particular characteristics may not have been fully captured in the original market approval studies.