scholarly journals Using Reproducible Data Visualizations to Augment Decision-Making During Suppression of Small Counts

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Andriy Koval ◽  
Kate Smolina ◽  
Anthony Leamon
Keyword(s):  
Decision Making ◽  
British Columbia ◽  
Quality Control ◽  
Chronic Diseases ◽  
Human Error ◽  
Data Systems ◽  
Quality Control Tool ◽  
Human Decision ◽  
And Function ◽  
Function Dependency

IntroductionWhen reporting disease rates to the public, a health system must take precaution to protect released data from re-identification risks. While specific guidelines and methods vary across data systems and governances 1 , redaction of cells with small values is a key component in any approach for preparing data for public release. These preparations, when conducted manually, have proven to be arduous, time consuming, and prone to human error. Although finding a “small” value (e.g. “< 5 ” ) is straightforward, detecting conditions in which suppressed values could be recalculated from related cells involves human judgement. Objectives and ApproachGuided by the real-world objective to reports the rates of chronic diseases in British Columbia, we aimed to design a reproducible workflow that would augment human decision-making and offer a nimble quality control tool, approachable by epidemiologists without technical background. Our workflow (1) splits data into disease-by-year data frames of a specific form, (2) applies a sequence of algorithms trained to recognize conditions that made recalculation of suppressed values possible and (3) prints a graph for each case of suggested automatic redaction to be confirmed by a human. ResultsThe augmented suppression system was successfully integrated into the maintenance of Chronic Disease Dashboard, an online reporting tool of the Observatory for Population and Public Health designed to address the gap in surveillance of chronic diseases in British Columbia. Anticipating the evolution of suppression logic, we isolated the logical tests responsible for redaction and provided several options to vary the degree of preserved information. Conclusion / ImplicationsInstead of employing a complex generalizable solution, we make a case for organizing the procedure for small cell redaction as a data visualization task, allowing for straightforward quality control of suppression decision and thus more approachable to a non-technical audience, as well as for employing such learning devices as workflow maps and function dependency trees for structuring applied projects and ensuring their reproducibility.

Reducing biases of decision-making processes in complex organizations

2017 ◽  
Vol 40 (3) ◽  
pp. 270-291 ◽  
Author(s):  
Matteo Cristofaro
Keyword(s):  
Decision Making ◽  
Quality Control ◽  
Research Question ◽  
Decision Processes ◽  
Decision Makers ◽  
Problem Structuring Methods ◽  
Complex Organizations ◽  
Content Type ◽  
Quality Control Tool ◽  
Decision Making Processes

Purpose This paper aims to study how biases in decision-making processes could be reduced. In this vein, over the past 30 years, scholars interested in decision-making have been raising their interest in the development of quality control tools to mitigate the effects of cognitive distortions. However, they have often neglected the use of psychological instruments for understanding the role of decision-makers’ personality in the quality of the decision-making processes. Design/methodology/approach This is an intrinsic case study about an Italian complex organization (i.e. Consorzio ELIS) which tries to shed light on the identified research question. Three decision-makers responsible for the decision processes of three new business initiatives were interviewed using a recent quality control tool (i.e. checklist) and their personality types were tracked by performing MBTI® tests. The thematic analysis, approached by using NVivo software, and after six months of direct observations inside the organization, allowed an understanding of the decision processes and their distortions. Findings The results of this study show how initiatives with frequent quality control mechanisms and different stakeholders are more able to pass the decision phase than initiatives with no controls, few participants and little difference between personalities. Originality/value The results of this work show how reducing biases of decision-making processes in complex organizations can benefit from the simultaneous use of the checklist and MBTI® test. As demonstrated, when used together, they can make more effective use of and provide better results for both, as well as providing a better quality control of the decision-making processes. From that, an approach is proposed that both takes into account the two perspectives and can work together with other cognitive problem structuring methods.

Evaluating Human Decision-Making Theories With Monkey Data

2013 ◽  
Author(s):  
Scott D. Brown ◽  
Pete Cassey ◽  
Andrew Heathcote ◽  
Roger Ratcliff
Keyword(s):  
Decision Making ◽  
Human Decision

History among the Junipers: Hosea 14:2–10 as Metahistoriography

2019 ◽  
Vol 63 (1) ◽  
pp. 105-116
Author(s):  
Mark W. Hamilton
Keyword(s):  
Decision Making ◽  
Moral Formation ◽  
Divine Agency ◽  
Human Decision

Abstract The dual endings of Hosea promoted reflection on Israel’s history as the movement from destruction to restoration based on Yhwh’s gracious decision for Israel. It thus clarifies the endings of the prior sections of the book (chs. 3 and 11) by locating Israel’s future in the realm of Yhwh’s activities. The final ending (14:10) balances the theme of divine agency in 14:2–9 with the recognition of human decision-making and moral formation as aspects of history as well. The endings of Hosea thus offer a good example of metahistoriography, a text that uses non-historiographic techniques to speak of the movements of history.

Network Theory of Human Decision Making

2012 ◽  
Author(s):  
Paolo Grigolini ◽  
Bruce J. West
Keyword(s):  
Decision Making ◽  
Network Theory ◽  
Human Decision

scholarly journals Personalized treatment options for chronic diseases using precision cohort analytics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenney Ng ◽  
Uri Kartoun ◽  
Harry Stavropoulos ◽  
John A. Zambrano ◽  
Paul C. Tang
Keyword(s):  
Decision Making ◽  
Chronic Diseases ◽  
Primary Care Physicians ◽  
Treatment Guidelines ◽  
Point Of Care ◽  
Data Extraction ◽  
Treatment Options ◽  
Personalized Treatment ◽  
Medical Decision ◽  
Similarity Model

AbstractTo support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.

Comparative study on e-bikes’ decision-making behaviors under flashing green and green countdown

2021 ◽  
pp. 1-8
Author(s):  
Haoyang Meng ◽  
Sheng Dong ◽  
Jibiao Zhou ◽  
Shuichao Zhang ◽  
Zhenjiang Li
Keyword(s):  
Decision Making ◽  
Critical Issue ◽  
Trajectory Data ◽  
Decision Points ◽  
Binary Logit ◽  
The One ◽  
Common Signal ◽  
And Function ◽  
Decision Behaviors ◽  
Stop Line

Green flash light (FG) and green countdown (GC) are the two most common signal formats applied in green-red transition that provides drivers additional alert before termination of green phase. Due to their importance and function in stop-pass decision-making process, proper use of them has become a critical issue to greatly improve the safety and efficiency of signalized intersections. Gradually e-bike riders have become more important commuters in China, however, the influence of FG or GC on them is not clear yet and need pay more attention to it. This study chooses two almost identical intersections to obtain highly accurate trajectory data of e-bike riders to study their decision-making behaviors under FG or GC. The e-bike riders’ behavior is classified into four categories and is to identify their stop-pass decision points using the acceleration trend. Two binary-logit models were built to predict the stop–pass decision behaviors for the different e-bike rider groups, explaining that the potential time to the stop-line is the dominant independent factor of the different behaviors of GC and FG. Furthermore empirical analysis of decision points indicated that GC provides the earlier stop-pass decision point and longer decision making duration on the one side while results in more complexity of decision making and greater risk of stop-line crossing than FG on the other side.

scholarly journals Range probing as a quality control tool for CBCT based synthetic CTs: in vivo application for head and neck cancer patients

2021 ◽  
Author(s):  
Carmen Seller Oria ◽  
Adrian Thummerer ◽  
Jeffrey Free ◽  
Johannes A. Langendijk ◽  
Stefan Both ◽  
...  
Keyword(s):  
Quality Control ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Quality Control Tool ◽  
Control Tool
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