This paper discusses the notion of quality measurement and describes the role of an enterprise‐wide, technology‐based continuous Quality Measurement System (QMS2000) in the quality assurance program at the University of Louisville. QMS2000 is a relational, interactive information system that includes data from 273 student, alumni, faculty, staff, and employer satisfaction surveys that are linked to corresponding databases at the university. QMS2000 is on‐line, operating in a networked, client‐server environment that permits licensed users access to designated components of the system at any time from designated desktops at the university. QMS2000 users generate reports and perform advanced statistical analyses drawing from the databases. The data and reports are integrated into the accreditation, strategic planning, budgeting, outcomes assessment, and program review processes at the university. The paper closes with a discussion of the initial impacts of QMS2000 on the university’s efforts at quality assurance.

In light of today's COVID-19 crisis, it is argued that new ways to collaborate among all nations for pandemic preparedness may be the next big thing. A workable solution for pandemic preparedness requires leaders of all nations to be on the same page (all for one), aiming at a swift turnaround of the crisis in tandem that can only benefit everyone on a global scale (one for all). After all, a public health crisis of this magnitude involves all humankind, demanding not only the most sensible and intelligent adoption of progressive policies and innovative technology, but an effective balancing of emergency supply chain management (SCM) reserve of personal protective equipment (PPE), professional workers and/or other urgently needed resources (e.g., ICU beds) to save lives. Above all, accurate sharing of information and massive-scale testings vis-à-vis targeted isolations must be sustained. Notably, such a framework may not just be limited to infuenza. Here, the authors elaborate on several key strategies and aim to provide grounds for scientific innovations and clearer policy thinking across international boundaries to combat a global public health pandemic via a league of nations conceived as IPPO: Intercontinental Pandemic Preparedness Organization.

In recent years, non-communicable diseases (NCDs) have become epidemic in Bangladesh. Behaviour changing interventions are key to prevention and management of NCDs. A great majority of people in Bangladesh have low health literacy, are less receptive to health information, and are unlikely to embrace positive health behaviours. Mass media campaigns can play a pivotal role in changing health behaviours of the population. This review pinpoints the role of mass media campaigns for NCDs and the challenges along it, whilst stressing on NCD preventive programmes (with the examples from different countries) to change health behaviours in Bangladesh. Future research should underpin the use of innovative technologies and mobile phones, which might be a prospective option for NCD prevention and management in Bangladesh.

Summary Introduction: Various health-related data, subsequently called Person Generated Health Data (PGHD), is being collected by patients or presumably healthy individuals as well as about them as much as they become available as measurable properties in their work, home, and other environments. Despite that such data was originally just collected and used for dedicated predefined purposes, more recently it is regarded as untapped resources that call for secondary use. Method: Since the secondary use of PGHD is still at its early evolving stage, we have chosen, in this paper, to produce an outline of best practices, as opposed to a systematic review. To this end, we identified key directions of secondary use and invited protagonists of each of these directions to present their takes on the primary and secondary use of PGHD in their sub-fields. We then put secondary use in a wider perspective of overarching themes such as privacy, interpretability, interoperability, utility, and ethics. Results: We present the primary and secondary use of PGHD in four focus areas: (1) making sense of PGHD in augmented Shared Care Plans for care coordination across multiple conditions; (2) making sense of PGHD from patient-held sensors to inform cancer care; (3) fitting situational use of PGHD to evaluate personal informatics tools in adaptive concurrent trials; (4) making sense of environment risk exposure data in an integrated context with clinical and omics-data for biomedical research. Discussion: Fast technological progress in all the four focus areas calls for a societal debate and decision-making process on a multitude of challenges: how emerging or foreseeable results transform privacy; how new data modalities can be interpreted in light of clinical data and vice versa; how the sheer mass and partially abstract mathematical properties of the achieved insights can be interpreted to a broad public and can consequently facilitate the development of patient-centered services; and how the remaining risks and uncertainties can be evaluated against new benefits. This paper is an initial summary of the status quo of the challenges and proposals that address these issues. The opportunities and barriers identified can serve as action items individuals can bring to their organizations when facing challenges to add value from the secondary use of patient-generated health data.

This paper demonstrates the application of machine learning (ML) to predict patients with hypertension. The data was gathered from the New York City community health survey database for the 2018 survey year, which contains self-reported socio-demographic and health-related items. The study predicted individuals who were at risk of hypertensive conditions. Hypertensive respondents were identified using a battery of questions. The objective was to predict these individuals using social determinants of health (SDH) and clinical attributes. The analysis also shows the importance of clinical or pseudo-clinical measures to improve prediction accuracy. Our planet is under a severe pandemic, COVID-19. While this paper is on hypertension, a secondary conclusion was drawn. The world lacks a global database with clinical attributes for COVID-19 infected, recovered, and deceased patients. Machine learning with clinical data would immensely increase the potential for effective testing and a vaccine.