Notes

Substantial genetic selection in Plasmodium falciparum isolates between China migrant staff returnee from The african continent.
Informed by a review of the literature and observations across multiple implementations of population health strategies in community health, in this conceptual paper, we describe the steps (process), domains of team expertise (people), and health information technology components (technology) that contribute to the success of a population health strategy. We also explore future opportunities to expand the reach and impact of population health through patient engagement, analytics, interventions to address social determinants of health, responses to emerging public health priorities, and prioritization-of-use cases by assessing community-specific needs. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
The fourth sudden acute respiratory syndrome (SARS) virus, COVID-19, emerged in late 2019, leading to the most devastating pandemic since the Spanish influenza (H1N1) of 1918, which seized 50 million lives worldwide (https//www.cdc.gov/flu/pandemic-resources/1918-pandemic-h1n1.html). Elected officials must make critical system-level decisions for stymieing the spread of the virus. Businesspersons must make personnel, financial, and operational decisions to minimize transmission while preserving their business's vitality. Members of the public must make personal decisions about personal protective equipment and changing social, recreational, occupational, and spiritual behavior to protect themselves and others. The scientific community can shift how they illustrate the virus's behavior to the public in an appropriate and understandable way so that the public can make informed decisions. This article suggests the use of a single-case design and logarithmic analyses to improve the current methodologies for COVesspersons, and the public. Limitations and future directions for COVID-19 informatics are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Integrated health care is utilized in primary care clinics to meet patients' physical, behavioral, and social needs. Current methods to collect and evaluate the effectiveness of integrated care require refinement. Using informatics and electronic health records (EHR) to distill large amounts of clinical data may help researchers measure the impact of integrated care more efficiently. This exploratory pilot study aimed to (a) determine the feasibility of using EHR documentation to identify behavioral health and social care components of integrated care, using social work as a use case, and (b) develop a lexicon to inform future research using natural language processing.

Study steps included development of a preliminary lexicon of behavioral health and social care interventions to address basic needs, creation of an abstraction guide, identification of appropriate EHR notes, manual chart abstraction, revision of the lexicon, and synthesis of findings.

Notes (N = 647) were analyzed from a random sample ofcument and extract pertinent information about integrated team-based interventions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Transforming administrative health care data into meaningful metrics has been critical to the implementation of the Department of Defense's Primary Care Behavioral Health (PCBH) program.

Data from clinical encounters with PCBH providers are used to develop metrics of program performance collaboratively. Metrics focus on describing the PCBH program and patients, provider fidelity to the model, and provider performance. These metrics form two key deliverables a monitoring dashboard for program managers and a training dashboard for expert trainers conducting site visits.

Behavioral health consultants (BHCs) conducted nearly 200,000 encounters with more than 100,000 unique patients in fiscal year 2019 at more than 170 locations in 6 countries and 37 states. Administrative data derived from these encounters were used to create a variety of metrics that describe practice and performance at both the provider and program levels. These metrics are delivered through a variety of analytic products to stakeholders who use that information to make data-driven decisions about program direction and provider training.

We discuss examples of program management decisions and expert trainer actions based on these dashboards, highlighting the benefits of continued collaboration between analysts and program managers. Specifically, excerpts from several dashboards illustrate how penetration and productivity metrics yield specific, tailored action plans to improve care delivery and provider performance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
We discuss examples of program management decisions and expert trainer actions based on these dashboards, highlighting the benefits of continued collaboration between analysts and program managers. Specifically, excerpts from several dashboards illustrate how penetration and productivity metrics yield specific, tailored action plans to improve care delivery and provider performance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Frequent emergency department (ED) use has been operationalized in research, clinical practice, and policy as number of visits to the ED, despite the fact that this definition lacks empirical evidence and theoretical foundation. To date, there are no studies that have attempted to understand ED use empirically, without arbitrary use of "cut-points." This study was conducted to identify the best-performing, empirically grounded definition of frequent ED use. The performance of machine learning supervised clustering algorithms based on the most common definitions of frequent ED use in peer-reviewed literature (i.e., 3+, 4+, 5+ visits per year) were compared to unsupervised clustering algorithms that take into account numerous systemic factors associated with patients' ED use. All ED visits for the State of Florida, 2011-2015, including more than 100 clinical and payment-related variables per visit were employed in the model. Supervised algorithms using number of visits to the ED, alone, were unable to differentiate patients into clusters, while unsupervised models using all patient data formed clusters in which patients within a given cluster were alike, and patients between clusters were different. selleck kinase inhibitor Cluster size and characteristics were stable across years. The results of this study indicate that mean number of ED visits by patients differ between patient clusters, but this does not allow for accurate identification of ED patients. Machine learning algorithms using all systemic and biopsychosocial patient data can be used to identify and group patients for the purpose of developing and testing integrated, whole health interventions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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