scholarly journals Designing for Accelerated Translation (DART) of emerging innovations in health

2019 ◽  
Vol 3 (2-3) ◽  
pp. 53-58 ◽  
Author(s):  
Alex T. Ramsey ◽  
Enola K. Proctor ◽  
David A. Chambers ◽  
Jane M. Garbutt ◽  
Sara Malone ◽  
...  
Keyword(s):  
Decision Making ◽  
Health System ◽  
Dynamic Process ◽  
Evidence Base ◽  
Dynamic Decision Making ◽  
Dissemination And Implementation ◽  
The Past ◽  
Learning Health System ◽  
Stakeholder Partnerships ◽  
Over Time

AbstractAccelerating innovation translation is a priority for improving healthcare and health. Although dissemination and implementation (D&I) research has made significant advances over the past decade, it has attended primarily to the implementation of long-standing, well-established practices and policies. We present a conceptual architecture for speeding translation of promising innovations as candidates for iterative testing in practice. Our framework to Design for Accelerated Translation (DART) aims to clarify whether, when, and how to act on evolving evidence to improve healthcare. We view translation of evidence to practice as a dynamic process and argue that much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation. The DART framework proposes that additional factors – demand, risk, and cost, in addition to the evolving evidence base – should influence the pace of translation over time. Attention to these underemphasized factors may lead to more dynamic decision-making about whether or not to adopt an emerging innovation or de-implement a suboptimal intervention. Finally, the DART framework outlines key actions that will speed movement from evidence to practice, including forming meaningful stakeholder partnerships, designing innovations for D&I, and engaging in a learning health system.

scholarly journals An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review (Preprint)

2019 ◽  
Author(s):  
Jodyn E Platt ◽  
Minakshi Raj ◽  
Matthias Wienroth
Keyword(s):  
Health Care ◽  
Health System ◽  
Thematic Analysis ◽  
Scoping Review ◽  
Rapid Learning ◽  
The Past ◽  
Learning From Data ◽  
Learning Health System ◽  
Over Time ◽  
Value Of Health

BACKGROUND In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care. OBJECTIVE This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward. METHODS We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial <i>Knowledge Gaps and Uncertainties</i> proposed in 2007. RESULTS We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy. CONCLUSIONS There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.

scholarly journals An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review

10.2196/17026 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e17026 ◽  
Author(s):  
Jodyn E Platt ◽  
Minakshi Raj ◽  
Matthias Wienroth
Keyword(s):  
Health Care ◽  
Health System ◽  
Thematic Analysis ◽  
Rapid Learning ◽  
The Past ◽  
Learning From Data ◽  
Learning Health System ◽  
Initial Knowledge ◽  
Over Time ◽  
Value Of Health

Background In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care. Objective This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward. Methods We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial Knowledge Gaps and Uncertainties proposed in 2007. Results We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy. Conclusions There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

scholarly journals Ball and Player Detection & Tracking in Soccer Videos Using Improved YOLOV3 Model

2021 ◽  
Author(s):  
Banoth Thulasya Naik ◽  
Mohammad Farukh Hashmi
Keyword(s):  
Decision Making ◽  
Kalman Filtering ◽  
Video Recording ◽  
The Past ◽  
Detection And Tracking ◽  
Area Of Interest ◽  
Sort Algorithm ◽  
Ball Detection ◽  
Player Detection ◽  
Over Time

Abstract Over the past few years, there has been a tremendous increase in the interest and enthusiasm for sports among people. This has led to an increase in the importance given to video recording of various sports that capture even the minutest detail using high-end equipment. Recording and analysis have thereby become extremely crucial in sports like soccer that involve several complex and fast events. Ball detection and tracking along with player analysis have emerged as an area of interest among a lot of analysts and researchers. This is because it helps coaches in performance assessment of the team and in decision making to obtain optimized results. Video analysis can additionally be used by coaches and recruiters to look for new, talented players based on their previously played games. Ball detection also plays a pivotal role in assisting the referees in making decisions at game-changing moments. However, as the ball is almost always moving, its shape-appearance keeps changing over time and it is frequently occluded by players, it makes it difficult to track it throughout the game. We propose a deep learning-based YOLOv3 model for the ball and player detection in broadcast soccer videos. Initially, the videos are processed and unnecessary parts like zoom-ins, replays, etc., are removed to obtain only the relevant frames from each game. Tracking is achieved using the SORT algorithm which employs a Kalman filtering and bounding box overlap.

scholarly journals The Swiss Learning Health System: a national initiative supporting evidence-informed decision-making

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Mantwill ◽  
T Kasper Wicki ◽  
S Boes
Keyword(s):  
Decision Making ◽  
Health System ◽  
Research Policy ◽  
Informed Decision ◽  
Supporting Evidence ◽  
Informed Decision Making ◽  
Policy And Practice ◽  
Learning Health System ◽  
Learning Cycles ◽  
Swiss Health

Abstract Issue Over the last years, there has been an ever-increasing interest in how to promote and support uptake of evidence and evidence-informed decision-making in health-systems related policy and practice. However, only few efforts have been made that aim at fully leveraging the knowledge and expertise of bigger networks to address this issue more comprehensively. Description of the problem Also, in Switzerland, the need to address this matter has been increasingly emphasized by actors in the health system. In particular, the lack of comprehensive coordination efforts in the field of health services research, and subsequent knowledge translation activities, has been stressed. Results In response, the Swiss Learning Health System (SLHS) was established as a nationwide project in 2017, currently involving 10 academic partners. One of the overarching objectives of the SLHS is to bridge research, policy, and practice by providing an infrastructure that supports learning cycles. Learning cycles enable the continuous integration of evidence into policy and practice by: continuously identifying issues relevant to the health system, systemizing relevant evidence, presenting potential courses of action, and revising and reshaping responses. Key features of learning cycles in the SLHS include the development of policy/evidence briefs that serve as a basis for official stakeholder dialogues, which are structured dialogues during which the best course of action is discussed and evaluated. Issues that are identified to be further pursued are monitored for potential implementation and eventually evaluated to inform new learning cycles and to support continuous learning within the system. Lessons The SLHS is an important mechanism to institutionalize learning cycles within the Swiss health system by leveraging the expertise and knowledge of a comprehensive network, going beyond individual initiatives, and to respond effectively to newly occurring health system challenges. Key messages The Swiss Learning Health System is the first nationwide project that aims to establish learning cycles that support evidence-informed decision-making in health-system-related policy and practice. The Swiss Learning Health System is an infrastructure that supports the continuous flow of evidence into policy and practice, providing a mechanism to respond to occurring health system challenges.

Quantifying Dynamic Resilience using First-process Autoregressive Modelling: An Empirical Study

2020 ◽  
Vol 64 (1) ◽  
pp. 770-774
Author(s):  
Luke Crameri ◽  
Imali Hettiarachchi ◽  
Samer Hanoun
Keyword(s):  
Decision Making ◽  
Concurrent Validity ◽  
Dynamic Decision Making ◽  
Autoregressive Modelling ◽  
First Order ◽  
Induced Stress ◽  
Temporal Process ◽  
Seismic Resilience ◽  
Over Time

Dynamic resilience is a temporal process that reflects individuals’ capability to overcome task-induced stress and sustain their performance during task-related events. First-order autoregressive (AR(1)) modelling is posited for measuring individuals’ dynamic resilience over time. The current research investigated this by testing 30 adults in a dynamic decision-making task. AR(1) modelling was conducted on the data, and was compared against a modified seismic resilience metric for concurrent validity purposes. Results revealed that AR(1) modeled parameters are applicable in assessing participants’ dynamic resilience, with analyses supporting their use to distinguish between individuals that can overcome task-induced stress and those that cannot, as well as, in the classification of individuals’ dynamic resilience.

Who Goes to Prison?

2016 ◽  
Author(s):  
Daniel P. Mears ◽  
Joshua C. Cochran
Keyword(s):  
Decision Making ◽  
Black Males ◽  
The United States ◽  
Demographic Variables ◽  
Rational Decision Making ◽  
The Past ◽  
Race Ethnicity ◽  
Substantive Rationality ◽  
Over Time ◽  
Age And Sex

This essay discusses changes in the composition of inmate populations in the United States over the past several decades based on legal factors (i.e., types of offenses and offenders) and demographic variables (i.e., race, ethnicity, age, and sex) and examines why variation in inmate composition matters. In particular, black incarceration rates are substantially greater than those of whites and Hispanics, and over time these differences have become more pronounced for black males in particular as compared to other groups. Possible reasons for these changes are considered such as the roles of police and courts in shaping inmate demographics and the implications of the shift from decision-making based on substantive rationality to more “structured“ (formally rational) decision-making.

scholarly journals Patient Perspectives on the Learning Health System: The Importance of Trust and Shared Decision Making

2015 ◽  
Vol 15 (9) ◽  
pp. 4-17 ◽  
Author(s):  
Maureen Kelley ◽  
Cyan James ◽  
Stephanie Alessi Kraft ◽  
Diane Korngiebel ◽  
Isabelle Wijangco ◽  
...  
Keyword(s):  
Decision Making ◽  
Health System ◽  
Shared Decision Making ◽  
Patient Perspectives ◽  
Shared Decision ◽  
Learning Health System

Future of medical engagement

2016 ◽  
Vol 40 (4) ◽  
pp. 443 ◽  
Author(s):  
Helen Dickinson ◽  
Marie Bismark ◽  
Grant Phelps ◽  
Erwin Loh
Keyword(s):  
Health System ◽  
Patient Experience ◽  
System Performance ◽  
Crucial Role ◽  
Evidence Base ◽  
Health System Performance ◽  
The Past ◽  
International Attention ◽  
Effectiveness And Efficiency ◽  
Key Questions

Although it has long been recognised that doctors play a crucial role in the effectiveness and efficiency of health organisations, patient experience and clinical outcomes, over the past 20 years the topic of medical engagement has started to garner significant international attention. Australia currently lags behind other countries in its heedfulness to, and evidence base for, medical engagement. This Perspective piece explores the link between medical engagement and health system performance and identifies some key questions that need to be addressed in Australia if we are to drive more effective engagement.

scholarly journals GPs’ mindlines around deprescribing antihypertensives in older patients with multimorbidity: qualitative study

2020 ◽  
pp. bjgp21X714305
Author(s):  
Karolina Kuberska ◽  
Fiona Scheibl ◽  
Carol Sinnott ◽  
James Sheppard ◽  
Mark Lown ◽  
...  
Keyword(s):  
Decision Making ◽  
Older Patients ◽  
Evidence Base ◽  
Drug Event ◽  
Care Practice ◽  
Multiple Sources ◽  
Qualitative Interview Study ◽  
Practice Methods ◽  
Multimorbid Patients ◽  
Over Time

Background: Optimal management of hypertension in older patients with multimorbidity is a cornerstone of primary care practice. Despite emphasis on personalised approaches to treatment in older patients, there is little guidance on how to achieve medication reduction when GPs are concerned that possible risks outweigh potential benefits of treatment. Mindlines – tacit, internalised guidelines developed over time from multiple sources – may be of particular importance in such situations. Aim: To explore GPs’ decision-making on deprescribing antihypertensives in multimorbid patients over 80 years old, drawing on the concept of mindlines. Design: Qualitative interview study. Setting: English general practice. Methods: Thematic analysis of face-to-face interviews with a sample of 15 GPs from 7 practices in the East of England, using a chart-stimulated recall approach to explore approaches to treatment for older multimorbid patients with hypertension. Results: GPs are typically confident making decisions to deprescribe antihypertensive medication in older multimorbid patients when prompted by a trigger, such as a fall or adverse drug event. GPs are less confident to attempt deprescribing in response to generalised concerns about polypharmacy, and work hard to make sense of multiple sources (including available evidence, shared experiential knowledge, and non-clinical factors) to guide decision-making. Conclusion: In the absence of a clear evidence base on when and how to attempt medication reduction in response to concerns about polypharmacy, GPs develop ‘mindlines’ over time through practice-based experience. These tacit approaches to making complex decisions are critical to developing confidence to attempt deprescribing, and may be strengthened through reflective practice.

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