This slide presents a short summary of my talk at ACM IUI 2023. You can download the full paper from this link - https://arxiv.org/abs/2302.10671.
Paper Title: Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations
Abstract: Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare. However, little research has compared the utility of different explanation methods in guiding healthcare experts for patient care. Moreover, it is unclear how useful, understandable, actionable and trustworthy these methods are for healthcare experts, as they often require technical ML knowledge. This paper presents an explanation dashboard that predicts the risk of diabetes onset and explains those predictions with data-centric, feature-importance, and example-based explanations. We designed an interactive dashboard to assist healthcare experts, such as nurses and physicians, in monitoring the risk of diabetes onset and recommending measures to minimize risk. We conducted a qualitative study with 11 healthcare experts and a mixed-methods study with 45 healthcare experts and 51 diabetic patients to compare the different explanation methods in our dashboard in terms of understandability, usefulness, actionability, and trust. Results indicate that our participants preferred our representation of data-centric explanations that provide local explanations with a global overview over other methods. Therefore, this paper highlights the importance of visually directive data-centric explanation method for assisting healthcare experts to gain actionable insights from patient health records. Furthermore, we share our design implications for tailoring the visual representation of different explanation methods for healthcare experts.
InfoWell Patient Portal: A Case of Patient-Centred Design [05 Cr2 1100 Chan]Gunther Eysenbach
This document summarizes the InfoWell Patient Portal project which aimed to develop a secure website to provide patients with personalized health information, care plans, education, and tools to better manage their health. It describes the objectives of assisting patients and enhancing their experience. It outlines the components of the portal and discusses the patient-centered design approach used, which involved engaging patients at each stage of concept development, design, testing, implementation, and evaluation. Usability testing found high completion and satisfaction rates among patients who used the portal.
This document discusses six healthcare trends and why user experience matters for addressing them. It provides definitions of usability and user experience, arguing that user experience is broader and requires understanding user needs from the start. The six trends are: 1) increased adoption of electronic medical records, 2) increased patient engagement, 3) mobile health becoming mainstream, 4) moving towards accountable care, 5) rise of retail health clinics, and 6) increased care at home solutions. For each trend, the document discusses challenges and how user-centered design can help address them.
Unleashing the Power of Data: Enhancing Physician Outreach through Machine Le...IRJET Journal
This document discusses how machine learning can enhance physician outreach efforts. It begins by outlining limitations of traditional outreach methods like mailings and calls. The document then explores how machine learning techniques like predictive modeling, recommender systems, natural language processing, and real-time analytics can optimize physician targeting, personalize communication, automate processes, and assess effectiveness. However, it notes challenges like data quality issues, privacy concerns, and physician resistance must be addressed. Case studies demonstrate benefits of customization, targeted communication, and increased referrals through machine learning-powered outreach.
The document outlines a root-cause analysis and safety improvement plan to address avoidable patient falls in medical facilities. It identifies key patient-related risk factors like age, gender, mobility issues, and incontinence as the root causes. The plan calls for administering a standardized fall risk assessment tool, implementing universal fall precautions, and documenting prevention strategies. Existing organizational resources that can support the plan include facility administrators, unit staff, unit champions, and an implementation team.
This document describes a capstone project to develop predictive models that rank healthcare providers based on their value by reducing readmissions. A team created models to: 1) Rank providers based on readmission rates and costs to categorize them from best to worst. 2) Predict the value of providers using a multivariate linear model with an R^2 of 0.54. 3) Predict reduced risk-based cost per patient of 1.2% for every 1% increase in the provider value score. The models and an interactive dashboard will help payers and consumers choose higher quality, lower cost providers to improve outcomes and lower healthcare costs.
1) The document discusses how technologies like machine learning, artificial intelligence, big data analytics, and natural language processing can be used to better engage patients and improve their healthcare experiences.
2) These technologies allow for more personalized care by understanding individual patients' needs and preferences. They also enable remote monitoring and participation in clinical trials to reduce barriers to care.
3) Challenges include overcoming risk-averse tendencies within healthcare and building patient trust, but these technologies overall have the potential to enhance patient safety, outcomes, and satisfaction if implemented effectively.
Explainable AI in Healthcare: Enhancing Transparency and Trust upon Legal and...IRJET Journal
This document discusses explainable artificial intelligence (XAI) and its importance in healthcare. It explores how XAI can enhance transparency and trust in AI systems used for clinical decision-making. Various XAI techniques like rule-based models, decision trees and model-agnostic methods are described. The document also examines the legal and ethical considerations of applying XAI in healthcare and discusses challenges and future directions.
A cost-benefit analysis worksheet is used to evaluate the costs and benefits of upgrading a health information system. It involves identifying gaps between the current system and future needs, analyzing costs and benefits of upgrading or not upgrading the system, and determining how evolving business trends may impact upgrade decisions. The analysis is used to make recommendations about whether to implement an upgraded system based on whether the benefits outweigh the costs. Patient decision aids can help involve patients in healthcare decisions by informing them of treatment options and helping them understand risks and benefits. This improves patient-centered care and shared decision making.
InfoWell Patient Portal: A Case of Patient-Centred Design [05 Cr2 1100 Chan]Gunther Eysenbach
This document summarizes the InfoWell Patient Portal project which aimed to develop a secure website to provide patients with personalized health information, care plans, education, and tools to better manage their health. It describes the objectives of assisting patients and enhancing their experience. It outlines the components of the portal and discusses the patient-centered design approach used, which involved engaging patients at each stage of concept development, design, testing, implementation, and evaluation. Usability testing found high completion and satisfaction rates among patients who used the portal.
This document discusses six healthcare trends and why user experience matters for addressing them. It provides definitions of usability and user experience, arguing that user experience is broader and requires understanding user needs from the start. The six trends are: 1) increased adoption of electronic medical records, 2) increased patient engagement, 3) mobile health becoming mainstream, 4) moving towards accountable care, 5) rise of retail health clinics, and 6) increased care at home solutions. For each trend, the document discusses challenges and how user-centered design can help address them.
Unleashing the Power of Data: Enhancing Physician Outreach through Machine Le...IRJET Journal
This document discusses how machine learning can enhance physician outreach efforts. It begins by outlining limitations of traditional outreach methods like mailings and calls. The document then explores how machine learning techniques like predictive modeling, recommender systems, natural language processing, and real-time analytics can optimize physician targeting, personalize communication, automate processes, and assess effectiveness. However, it notes challenges like data quality issues, privacy concerns, and physician resistance must be addressed. Case studies demonstrate benefits of customization, targeted communication, and increased referrals through machine learning-powered outreach.
The document outlines a root-cause analysis and safety improvement plan to address avoidable patient falls in medical facilities. It identifies key patient-related risk factors like age, gender, mobility issues, and incontinence as the root causes. The plan calls for administering a standardized fall risk assessment tool, implementing universal fall precautions, and documenting prevention strategies. Existing organizational resources that can support the plan include facility administrators, unit staff, unit champions, and an implementation team.
This document describes a capstone project to develop predictive models that rank healthcare providers based on their value by reducing readmissions. A team created models to: 1) Rank providers based on readmission rates and costs to categorize them from best to worst. 2) Predict the value of providers using a multivariate linear model with an R^2 of 0.54. 3) Predict reduced risk-based cost per patient of 1.2% for every 1% increase in the provider value score. The models and an interactive dashboard will help payers and consumers choose higher quality, lower cost providers to improve outcomes and lower healthcare costs.
1) The document discusses how technologies like machine learning, artificial intelligence, big data analytics, and natural language processing can be used to better engage patients and improve their healthcare experiences.
2) These technologies allow for more personalized care by understanding individual patients' needs and preferences. They also enable remote monitoring and participation in clinical trials to reduce barriers to care.
3) Challenges include overcoming risk-averse tendencies within healthcare and building patient trust, but these technologies overall have the potential to enhance patient safety, outcomes, and satisfaction if implemented effectively.
Explainable AI in Healthcare: Enhancing Transparency and Trust upon Legal and...IRJET Journal
This document discusses explainable artificial intelligence (XAI) and its importance in healthcare. It explores how XAI can enhance transparency and trust in AI systems used for clinical decision-making. Various XAI techniques like rule-based models, decision trees and model-agnostic methods are described. The document also examines the legal and ethical considerations of applying XAI in healthcare and discusses challenges and future directions.
A cost-benefit analysis worksheet is used to evaluate the costs and benefits of upgrading a health information system. It involves identifying gaps between the current system and future needs, analyzing costs and benefits of upgrading or not upgrading the system, and determining how evolving business trends may impact upgrade decisions. The analysis is used to make recommendations about whether to implement an upgraded system based on whether the benefits outweigh the costs. Patient decision aids can help involve patients in healthcare decisions by informing them of treatment options and helping them understand risks and benefits. This improves patient-centered care and shared decision making.
Required ResourcesThe following resources are required to comple.docxaudeleypearl
Required Resources
The following resources are required to complete the assessment.
· Vila Health: HIMS Cost Benefit Analysis | Transcript.
. This multimedia simulation will enable you to practice analyzing the short- and long-term costs and the benefits of implementing Vila Health's existing health information system at one of the newly-acquired rural hospitals.
SHOW LESS
Suggested Resources
The resources provided here are optional. You may use other resources of your choice to prepare for this assessment; however, you will need to ensure that they are appropriate, credible, and valid. The MHA-FP5064 Health Care Information Systems Analysis and Design for Administrators Library Guide can help direct your research, and the Supplemental Resources and Research Resources, both linked from the left navigation menu in your courseroom, provide additional resources to help support you.
Adoption of Health Information Systems
The following articles examine issues surrounding the adoption of health information systems, which may be helpful when considering cost-benefit analyses.
· Adler-Milstein, J., Everson, J., & Lee, S. D. (2015). EHR adoption and hospital performance: Time-related effects. Health Services Research, 50(6), 1751–1771.
. This study looked for evidence of a relationship between the adoption of an EHR by hospital and hospital performance.
· Ben-Assuli, O., Ziv, A., Sagi, D., Ironi, A., & Leshno, M. (2016). Cost-effectiveness evaluation of EHR: Simulation of an abdominal aortic aneurysm in the emergency department. Journal of Medical Systems, 40(6), 1–13.
. This study examines whether health information technologies are cost-effective by current standards and whether they improve decision making and the quality of care.
· Bergmo, T. S. (2015). How to measure costs and benefits of ehealth interventions: An overview of methods and frameworks. Journal of Medical Internet Research, 17(11), e254.
. Examines how to best apply cost-benefit evaluation methods to health information technologies.
· Price, M., & Lau, F. (2014). The clinical adoption meta-model: A temporal metamodel describing the clinical adoption of health information systems. BMC Medical Informatics & Decision Making, 14(1), 1–23.
. Presents an accessible model to aid implementers, evaluators, and others in the planning and implementation of health information systems.
Writing Resources
You are encouraged to explore the following writing resources. You can use them to improve your writing skills and as source materials for seeking answers to specific questions.
· APA Module.
· Academic Honesty & APA Style and Formatting.
· APA Style Paper Tutorial [DOCX].
Library Resources
· Journal and Book Locator.
· Journal and Book Locator Library Guide.
· Interlibrary Loan.
1/10/2020 Health Information System Cost-Benefit Analysis Scoring Guide
https://courserooma.capella.edu/bbcswebdav/institution/MHA-FP/MHA-FP5064/180700/Scoring_Guides/a04_scoring_guide.html 1/2
Health Information Sy ...
Discussion 3Select a topic for your Topic 3 Executive Summary as.docxduketjoy27252
Discussion 3
Select a topic for your Topic 3 Executive Summary assignment. Post your idea and basic thoughts about the topic using the assignment details from Topic 3. You should provide thoughts to your peers about their topics and ideas that may assist them in completing their projects.
In this assignment, you will propose a quality improvement initiative from your place of employment that could easily be implemented if approved. Assume you are presenting this program to the board for approval of funding. Write an executive summary (750-1,000 words) to present to the board, from which the board will make its decision to fund your program or project. Include the following:
The purpose of the quality improvement initiative.
The target population or audience.
The benefits of the quality improvement initiative.
The interprofessional collaboration that would be required to implement the quality improvement initiative.
The cost or budget justification.
The basis upon which the quality improvement initiative will be evaluated.
Reply 1
Weiner
1 posts
Re: Topic 2 DQ 3
For my executive summary assignment my idea for quality improvement in my workplace is the reduction of falls. I work in a skilled nursing facility and we have a lot of dementia patients as well as rehab patients. Basically, I think there are ways in which to decrease the number of falls that my facility could implement that wouldn’t be too difficult to accomplish. One would be to assign the fall risk patients rooms closer to the nurses’ station because we have a few right now that I think are too far and on more than one occasion, I have found certain patients halfway out of the bed. I know reducing falls is a very common project, but I do think it’s vitally important to do so.
Reply 2
Nkamse
1 posts
Re: Topic 2 DQ 3
The central electronic systems in hospitals are the electronic health record systems. These systems are clear evidence of the significance of information technology that continues to revolutionize various industries. These systems have great importance in enhancing healthcare services provided to the patients. Many healthcare organizations and facilities have shifted their focus to its use to improve their service delivery. These systems function as the key repository of all patients' files (Liberati et al., 2017). Nurses also utilize the appointment scheduling software to track and schedule shifts among them. The medical equipment management software helps doctors to monitor their patients who are in the ICU.
Healthcare has experienced some improvements in service delivery through these electronic systems, such as limiting medication errors and ensuring advanced health management. The continued adoption of electronics in the hospitals leads to increased delivery of quality care to patients and improvements in health and healthcare administration (Prezerakos, 2018). The most common electronic systems in hospitals include electronic health record (EHR.
Customer Journey Analytics: Cracking the Patient Engagement Challenge for PayersHealth Catalyst
Customer journey analytics uses machine learning and big data to track and analyze when and through what channels customers interact with an organization, with an aim to influence behavior (e.g., buying behaviors among retail customers). Similarly, healthcare organizations want to influence health-related behaviors, such a taking medication as prescribed and not smoking, to improve outcomes and lower the cost of care. In a partnership with an analytics services provider, a payer organization is leveraging customer journey analytics among healthcare consumers to identify the best opportunities and channels for patient outreach. With this analytics-driven engagement strategy, the payer has found an opportunity to significantly improve patient engagement—a predicted overall increase from 18 percent to 31 percent.
1. The document describes a proposed medicine recommendation system that would apply data mining techniques to analyze diagnosis data and provide personalized medication recommendations to reduce medical errors.
2. It would consist of modules for a database, recommendation models, model evaluation, and data visualization. Different recommendation algorithms like SVM, neural networks, and decision trees would be investigated and tested on an open diagnosis data set.
3. The goal is to build an accurate and efficient recommendation framework to help doctors prescribe the right medications, especially for inexperienced doctors and rare diseases, by leveraging patterns found in historical medical records and diagnosis data.
Generative AI in Health Care a scoping review and a persoanl experience.Vaikunthan Rajaratnam
A scoping review of the literature, its impact and challenges in healthcare, and a personal experience of its application in practice, teaching, and research.
Integrate RWE into clinical developmentIMSHealthRWES
With greater application of RWE throughout the pharmaceutical
lifecycle, learnings are emerging that offer guidance for
approaches to derive the maximum value. This article captures
the author’s experience at a leading international biotech, with
insights for smoothing RWE assimilation into clinical
development and realizing the benefits it brings.
Centralization of Healthcare Insurance.docxwrite31
This document outlines an assessment for a course on health care leadership. Students are asked to propose a change to their local health care system and conduct a comparative analysis of two other countries' systems related to the proposed change. They must summarize their proposed change, the outcomes of the foreign systems, and how those systems compare to the current local system in a 4-5 page report. The report should address factors like who pays for care, outcomes, costs of implementing changes, and not implementing changes. Students are encouraged to examine systems with differing outcomes or innovative approaches related to their proposed change.
The Inclusion of Nurses in the Systems Development Life Cycle.docxwrite5
Nurses play an important role in healthcare organizations and should be included in all stages of the Systems Development Life Cycle when new health information technology systems are being planned and implemented. Excluding nurses can lead to poorly designed systems that do not meet the needs of end users and patients. At one healthcare organization, nurses provided feedback during the maintenance and evaluation stage of a new technology that streamlined the nursing assessment to be unit-specific, improving efficiency of documentation. Involving nurses in all stages of system development helps ensure technologies are designed to support safe, effective, and efficient patient care.
vincentbarner_HI-560-Health Care Data Analysis_Unit-9_assignmentvincent barner
This proposal aims to gather and analyze data on the efficiency of medical kiosks in clinical environments. Key objectives are to use statistical analysis to compare kiosk services to clinician services, and to test the potential for kiosks to evolve towards full automation. The proposal outlines stakeholders, background on kiosk benefits and challenges, a literature review on patient preferences for technology vs in-person care, and proposes measures to analyze financial impacts, technology performance, and fulfill meaningful use objectives. The budget table provides an example of costs that could be requested to support the proposed research.
This document summarizes a research fellowship project analyzing medical guidelines websites. It introduces the purpose of medical guidelines and common issues with online guidelines like lack of accessibility and usability. The project developed a quality index to evaluate and compare features of different medical guidelines websites. Data was collected from various websites and analyzed to identify trends and opportunities to make guidelines more patient-centered and effective. The goal is to modify guidelines websites to be more accessible, understandable and user-friendly to better educate patients.
The document provides instructions for a patient-centered care report assignment. Students are asked to evaluate the outcomes of a population health initiative presented in a multimedia scenario and develop an approach to personalizing patient care based on lessons learned. The report should address evaluating the initiative's outcomes, proposing strategies to improve outcomes, justifying evidence used, and proposing an evaluation framework. Students must integrate evidence, write clearly, and use APA style formatting.
Maureen Charlebois, Chief Nursing Director and Group Director, Canada Health ...Investnet
This document summarizes Canada Health Infoway's efforts to digitally transform healthcare across Canada in 3 waves: 1) Building foundational systems, 2) Providing digital tools for clinicians, and 3) Empowering patients. It highlights achievements in areas like EMR adoption and e-prescribing, and outlines ambitions to expand telehomecare and online patient services. Clinical leadership, change management, and ensuring clinicians have necessary digital skills are emphasized as key to adoption and realizing benefits of digital health innovations.
This document proposes a model for assessing the quality of knowledge used in clinical decision support systems. It discusses issues with current CDSSs, such as outdated or incomplete knowledge influencing decision making. The model uses Semantic Web technologies to discover high quality knowledge from different sources based on metrics like accuracy, reliability and relevance. Knowledge brokers would apply these metrics to evaluate knowledge and ensure only high quality sources are used to support clinical decisions.
AHRQ’s Health Care Innovations Exchange held a Web event on Promoting the Spread of Health Care Innovations on April 9, 2013. For more information, visit https://innovations.ahrq.gov/events/2013/04/promoting-spread-health-care-innovations.
1) The role of health care data analysts is evolving as the volume of available data grows exponentially. With zettabytes of data being generated, analysts must make sense of both structured and unstructured information.
2) Data analytics can provide insights to improve patient outcomes, lower costs, and enhance the health care experience. Examples show how visualizing data helps health systems better understand utilization and identify at-risk patients.
3) As incentives shift from fee-for-service to value-based models, health systems must transform to focus on population health. Advanced analytics and predictive modeling will be crucial to achieving the goals of better care, lower costs, and improved health.
Evolution Of Health Care Information SystemsLana Sorrels
The Defense Health Agency is a multi-service agency that enables the Army, Navy, and Air Force to provide medical services to members of the Department of Defense. It ensures the delivery of integrated, affordable, and high-quality healthcare to beneficiaries of the Military Health System. The Defense Health Agency drives greater integration of clinical and business processes across the system. It accomplishes this mission by implementing shared services with common functions and standards.
Master Your Value-Based Care Strategy: Introducing Health Catalyst Value Opti...Health Catalyst
The document provides an overview of Health Catalyst's Value Optimizer product, which is a web-based application that aims to help healthcare organizations develop data-informed strategies for value-based care. It describes three key capabilities of Value Optimizer: 1) Creating a comprehensive, benchmarked strategy using complete data, 2) Providing transparent and actionable insights into total cost of care and contracts, and 3) Enabling exploration of various clinical areas to uncover opportunities. The document includes several use cases that demonstrate how Value Optimizer can be used to analyze areas like inpatient utilization, pharmacy spending, imaging utilization, and more. It also discusses the support services that Health Catalyst provides to help customers implement and optimize the Value Optimizer
Artificial intelligence in healthcare revolutionizing personalized healthcare...Fit Focus Hub
Embark on a groundbreaking journey into the future of healthcare, where Artificial Intelligence (AI) is reshaping the landscape and ushering in a new era of personalized medicine tailored to the unique needs of each individual patient.
Explore the transformative power of AI as it becomes the catalyst for a healthcare revolution that goes beyond one-size-fits-all approaches.
In this illuminating exploration, we delve into how AI technologies are spearheading a paradigm shift in the delivery of healthcare services, putting patients at the center of attention.
Witness how machine learning algorithms analyze vast datasets, encompassing genetic information, medical histories, lifestyle choices, and environmental factors, to unlock insights that guide healthcare providers in crafting precise and personalized treatment plans.
Discover the pivotal role of AI in early disease detection, where predictive analytics and data-driven algorithms contribute to proactive interventions.
By identifying subtle patterns and potential risk factors, AI empowers healthcare professionals to intervene at the earliest stages, often before symptoms manifest, leading to more effective and targeted treatment strategies.
Explore the integration of wearable devices and IoT technologies, allowing for continuous patient monitoring beyond the confines of traditional healthcare settings.
AI-driven remote monitoring ensures real-time data analysis, enabling healthcare providers to make informed decisions and adjustments to individual care plans, promoting a proactive and patient-centric approach to healthcare.
Witness the acceleration of drug discovery and development through AI, as sophisticated algorithms analyze vast datasets to identify potential therapeutic targets and streamline the research and development process.
The result is a more efficient and tailored approach to pharmaceuticals, reducing trial-and-error methods and enhancing treatment outcomes.
Through captivating case studies and real-world examples, gain insights into how AI is optimizing resource allocation, improving patient engagement, and fostering a collaborative ecosystem between healthcare providers and patients.
Embrace the future of healthcare, where the marriage of human expertise and AI-driven insights paves the way for a more personalized, precise, and effective approach to individualized patient care.
Join us on this journey through the transformative impact of Artificial Intelligence in Healthcare, where the promise of personalized medicine becomes a reality, and each patient's unique characteristics guide the way towards a healthier and more tailored future.
The document provides instructions for a patient-centered care report based on a population health improvement initiative scenario. Key points include:
1. Evaluate the outcomes achieved and not achieved by the population health initiative and factors influencing shortfalls.
2. Propose a strategy to improve outcomes or ensure all are addressed, citing supporting evidence.
3. Develop an individualized care approach incorporating lessons from the initiative and addressing a patient's unique needs and evidence.
4. Justify the evidence used and propose an evaluation framework to determine applicability to other cases.
Explainable AI - making ML and DL models more interpretableAditya Bhattacharya
The document discusses explainable AI (XAI) and making machine learning and deep learning models more interpretable. It covers the necessity and principles of XAI, popular model-agnostic XAI methods for ML and DL models, frameworks like LIME, SHAP, ELI5 and SKATER, and research questions around evolving XAI to be understandable by non-experts. The key topics covered are model-agnostic XAI, surrogate models, influence methods, visualizations and evaluating descriptive accuracy of explanations.
More Related Content
Similar to Directive Explanations for Monitoring the Risk of Diabetes Onset - ACM IUI 2023
Required ResourcesThe following resources are required to comple.docxaudeleypearl
Required Resources
The following resources are required to complete the assessment.
· Vila Health: HIMS Cost Benefit Analysis | Transcript.
. This multimedia simulation will enable you to practice analyzing the short- and long-term costs and the benefits of implementing Vila Health's existing health information system at one of the newly-acquired rural hospitals.
SHOW LESS
Suggested Resources
The resources provided here are optional. You may use other resources of your choice to prepare for this assessment; however, you will need to ensure that they are appropriate, credible, and valid. The MHA-FP5064 Health Care Information Systems Analysis and Design for Administrators Library Guide can help direct your research, and the Supplemental Resources and Research Resources, both linked from the left navigation menu in your courseroom, provide additional resources to help support you.
Adoption of Health Information Systems
The following articles examine issues surrounding the adoption of health information systems, which may be helpful when considering cost-benefit analyses.
· Adler-Milstein, J., Everson, J., & Lee, S. D. (2015). EHR adoption and hospital performance: Time-related effects. Health Services Research, 50(6), 1751–1771.
. This study looked for evidence of a relationship between the adoption of an EHR by hospital and hospital performance.
· Ben-Assuli, O., Ziv, A., Sagi, D., Ironi, A., & Leshno, M. (2016). Cost-effectiveness evaluation of EHR: Simulation of an abdominal aortic aneurysm in the emergency department. Journal of Medical Systems, 40(6), 1–13.
. This study examines whether health information technologies are cost-effective by current standards and whether they improve decision making and the quality of care.
· Bergmo, T. S. (2015). How to measure costs and benefits of ehealth interventions: An overview of methods and frameworks. Journal of Medical Internet Research, 17(11), e254.
. Examines how to best apply cost-benefit evaluation methods to health information technologies.
· Price, M., & Lau, F. (2014). The clinical adoption meta-model: A temporal metamodel describing the clinical adoption of health information systems. BMC Medical Informatics & Decision Making, 14(1), 1–23.
. Presents an accessible model to aid implementers, evaluators, and others in the planning and implementation of health information systems.
Writing Resources
You are encouraged to explore the following writing resources. You can use them to improve your writing skills and as source materials for seeking answers to specific questions.
· APA Module.
· Academic Honesty & APA Style and Formatting.
· APA Style Paper Tutorial [DOCX].
Library Resources
· Journal and Book Locator.
· Journal and Book Locator Library Guide.
· Interlibrary Loan.
1/10/2020 Health Information System Cost-Benefit Analysis Scoring Guide
https://courserooma.capella.edu/bbcswebdav/institution/MHA-FP/MHA-FP5064/180700/Scoring_Guides/a04_scoring_guide.html 1/2
Health Information Sy ...
Discussion 3Select a topic for your Topic 3 Executive Summary as.docxduketjoy27252
Discussion 3
Select a topic for your Topic 3 Executive Summary assignment. Post your idea and basic thoughts about the topic using the assignment details from Topic 3. You should provide thoughts to your peers about their topics and ideas that may assist them in completing their projects.
In this assignment, you will propose a quality improvement initiative from your place of employment that could easily be implemented if approved. Assume you are presenting this program to the board for approval of funding. Write an executive summary (750-1,000 words) to present to the board, from which the board will make its decision to fund your program or project. Include the following:
The purpose of the quality improvement initiative.
The target population or audience.
The benefits of the quality improvement initiative.
The interprofessional collaboration that would be required to implement the quality improvement initiative.
The cost or budget justification.
The basis upon which the quality improvement initiative will be evaluated.
Reply 1
Weiner
1 posts
Re: Topic 2 DQ 3
For my executive summary assignment my idea for quality improvement in my workplace is the reduction of falls. I work in a skilled nursing facility and we have a lot of dementia patients as well as rehab patients. Basically, I think there are ways in which to decrease the number of falls that my facility could implement that wouldn’t be too difficult to accomplish. One would be to assign the fall risk patients rooms closer to the nurses’ station because we have a few right now that I think are too far and on more than one occasion, I have found certain patients halfway out of the bed. I know reducing falls is a very common project, but I do think it’s vitally important to do so.
Reply 2
Nkamse
1 posts
Re: Topic 2 DQ 3
The central electronic systems in hospitals are the electronic health record systems. These systems are clear evidence of the significance of information technology that continues to revolutionize various industries. These systems have great importance in enhancing healthcare services provided to the patients. Many healthcare organizations and facilities have shifted their focus to its use to improve their service delivery. These systems function as the key repository of all patients' files (Liberati et al., 2017). Nurses also utilize the appointment scheduling software to track and schedule shifts among them. The medical equipment management software helps doctors to monitor their patients who are in the ICU.
Healthcare has experienced some improvements in service delivery through these electronic systems, such as limiting medication errors and ensuring advanced health management. The continued adoption of electronics in the hospitals leads to increased delivery of quality care to patients and improvements in health and healthcare administration (Prezerakos, 2018). The most common electronic systems in hospitals include electronic health record (EHR.
Customer Journey Analytics: Cracking the Patient Engagement Challenge for PayersHealth Catalyst
Customer journey analytics uses machine learning and big data to track and analyze when and through what channels customers interact with an organization, with an aim to influence behavior (e.g., buying behaviors among retail customers). Similarly, healthcare organizations want to influence health-related behaviors, such a taking medication as prescribed and not smoking, to improve outcomes and lower the cost of care. In a partnership with an analytics services provider, a payer organization is leveraging customer journey analytics among healthcare consumers to identify the best opportunities and channels for patient outreach. With this analytics-driven engagement strategy, the payer has found an opportunity to significantly improve patient engagement—a predicted overall increase from 18 percent to 31 percent.
1. The document describes a proposed medicine recommendation system that would apply data mining techniques to analyze diagnosis data and provide personalized medication recommendations to reduce medical errors.
2. It would consist of modules for a database, recommendation models, model evaluation, and data visualization. Different recommendation algorithms like SVM, neural networks, and decision trees would be investigated and tested on an open diagnosis data set.
3. The goal is to build an accurate and efficient recommendation framework to help doctors prescribe the right medications, especially for inexperienced doctors and rare diseases, by leveraging patterns found in historical medical records and diagnosis data.
Generative AI in Health Care a scoping review and a persoanl experience.Vaikunthan Rajaratnam
A scoping review of the literature, its impact and challenges in healthcare, and a personal experience of its application in practice, teaching, and research.
Integrate RWE into clinical developmentIMSHealthRWES
With greater application of RWE throughout the pharmaceutical
lifecycle, learnings are emerging that offer guidance for
approaches to derive the maximum value. This article captures
the author’s experience at a leading international biotech, with
insights for smoothing RWE assimilation into clinical
development and realizing the benefits it brings.
Centralization of Healthcare Insurance.docxwrite31
This document outlines an assessment for a course on health care leadership. Students are asked to propose a change to their local health care system and conduct a comparative analysis of two other countries' systems related to the proposed change. They must summarize their proposed change, the outcomes of the foreign systems, and how those systems compare to the current local system in a 4-5 page report. The report should address factors like who pays for care, outcomes, costs of implementing changes, and not implementing changes. Students are encouraged to examine systems with differing outcomes or innovative approaches related to their proposed change.
The Inclusion of Nurses in the Systems Development Life Cycle.docxwrite5
Nurses play an important role in healthcare organizations and should be included in all stages of the Systems Development Life Cycle when new health information technology systems are being planned and implemented. Excluding nurses can lead to poorly designed systems that do not meet the needs of end users and patients. At one healthcare organization, nurses provided feedback during the maintenance and evaluation stage of a new technology that streamlined the nursing assessment to be unit-specific, improving efficiency of documentation. Involving nurses in all stages of system development helps ensure technologies are designed to support safe, effective, and efficient patient care.
vincentbarner_HI-560-Health Care Data Analysis_Unit-9_assignmentvincent barner
This proposal aims to gather and analyze data on the efficiency of medical kiosks in clinical environments. Key objectives are to use statistical analysis to compare kiosk services to clinician services, and to test the potential for kiosks to evolve towards full automation. The proposal outlines stakeholders, background on kiosk benefits and challenges, a literature review on patient preferences for technology vs in-person care, and proposes measures to analyze financial impacts, technology performance, and fulfill meaningful use objectives. The budget table provides an example of costs that could be requested to support the proposed research.
This document summarizes a research fellowship project analyzing medical guidelines websites. It introduces the purpose of medical guidelines and common issues with online guidelines like lack of accessibility and usability. The project developed a quality index to evaluate and compare features of different medical guidelines websites. Data was collected from various websites and analyzed to identify trends and opportunities to make guidelines more patient-centered and effective. The goal is to modify guidelines websites to be more accessible, understandable and user-friendly to better educate patients.
The document provides instructions for a patient-centered care report assignment. Students are asked to evaluate the outcomes of a population health initiative presented in a multimedia scenario and develop an approach to personalizing patient care based on lessons learned. The report should address evaluating the initiative's outcomes, proposing strategies to improve outcomes, justifying evidence used, and proposing an evaluation framework. Students must integrate evidence, write clearly, and use APA style formatting.
Maureen Charlebois, Chief Nursing Director and Group Director, Canada Health ...Investnet
This document summarizes Canada Health Infoway's efforts to digitally transform healthcare across Canada in 3 waves: 1) Building foundational systems, 2) Providing digital tools for clinicians, and 3) Empowering patients. It highlights achievements in areas like EMR adoption and e-prescribing, and outlines ambitions to expand telehomecare and online patient services. Clinical leadership, change management, and ensuring clinicians have necessary digital skills are emphasized as key to adoption and realizing benefits of digital health innovations.
This document proposes a model for assessing the quality of knowledge used in clinical decision support systems. It discusses issues with current CDSSs, such as outdated or incomplete knowledge influencing decision making. The model uses Semantic Web technologies to discover high quality knowledge from different sources based on metrics like accuracy, reliability and relevance. Knowledge brokers would apply these metrics to evaluate knowledge and ensure only high quality sources are used to support clinical decisions.
AHRQ’s Health Care Innovations Exchange held a Web event on Promoting the Spread of Health Care Innovations on April 9, 2013. For more information, visit https://innovations.ahrq.gov/events/2013/04/promoting-spread-health-care-innovations.
1) The role of health care data analysts is evolving as the volume of available data grows exponentially. With zettabytes of data being generated, analysts must make sense of both structured and unstructured information.
2) Data analytics can provide insights to improve patient outcomes, lower costs, and enhance the health care experience. Examples show how visualizing data helps health systems better understand utilization and identify at-risk patients.
3) As incentives shift from fee-for-service to value-based models, health systems must transform to focus on population health. Advanced analytics and predictive modeling will be crucial to achieving the goals of better care, lower costs, and improved health.
Evolution Of Health Care Information SystemsLana Sorrels
The Defense Health Agency is a multi-service agency that enables the Army, Navy, and Air Force to provide medical services to members of the Department of Defense. It ensures the delivery of integrated, affordable, and high-quality healthcare to beneficiaries of the Military Health System. The Defense Health Agency drives greater integration of clinical and business processes across the system. It accomplishes this mission by implementing shared services with common functions and standards.
Master Your Value-Based Care Strategy: Introducing Health Catalyst Value Opti...Health Catalyst
The document provides an overview of Health Catalyst's Value Optimizer product, which is a web-based application that aims to help healthcare organizations develop data-informed strategies for value-based care. It describes three key capabilities of Value Optimizer: 1) Creating a comprehensive, benchmarked strategy using complete data, 2) Providing transparent and actionable insights into total cost of care and contracts, and 3) Enabling exploration of various clinical areas to uncover opportunities. The document includes several use cases that demonstrate how Value Optimizer can be used to analyze areas like inpatient utilization, pharmacy spending, imaging utilization, and more. It also discusses the support services that Health Catalyst provides to help customers implement and optimize the Value Optimizer
Artificial intelligence in healthcare revolutionizing personalized healthcare...Fit Focus Hub
Embark on a groundbreaking journey into the future of healthcare, where Artificial Intelligence (AI) is reshaping the landscape and ushering in a new era of personalized medicine tailored to the unique needs of each individual patient.
Explore the transformative power of AI as it becomes the catalyst for a healthcare revolution that goes beyond one-size-fits-all approaches.
In this illuminating exploration, we delve into how AI technologies are spearheading a paradigm shift in the delivery of healthcare services, putting patients at the center of attention.
Witness how machine learning algorithms analyze vast datasets, encompassing genetic information, medical histories, lifestyle choices, and environmental factors, to unlock insights that guide healthcare providers in crafting precise and personalized treatment plans.
Discover the pivotal role of AI in early disease detection, where predictive analytics and data-driven algorithms contribute to proactive interventions.
By identifying subtle patterns and potential risk factors, AI empowers healthcare professionals to intervene at the earliest stages, often before symptoms manifest, leading to more effective and targeted treatment strategies.
Explore the integration of wearable devices and IoT technologies, allowing for continuous patient monitoring beyond the confines of traditional healthcare settings.
AI-driven remote monitoring ensures real-time data analysis, enabling healthcare providers to make informed decisions and adjustments to individual care plans, promoting a proactive and patient-centric approach to healthcare.
Witness the acceleration of drug discovery and development through AI, as sophisticated algorithms analyze vast datasets to identify potential therapeutic targets and streamline the research and development process.
The result is a more efficient and tailored approach to pharmaceuticals, reducing trial-and-error methods and enhancing treatment outcomes.
Through captivating case studies and real-world examples, gain insights into how AI is optimizing resource allocation, improving patient engagement, and fostering a collaborative ecosystem between healthcare providers and patients.
Embrace the future of healthcare, where the marriage of human expertise and AI-driven insights paves the way for a more personalized, precise, and effective approach to individualized patient care.
Join us on this journey through the transformative impact of Artificial Intelligence in Healthcare, where the promise of personalized medicine becomes a reality, and each patient's unique characteristics guide the way towards a healthier and more tailored future.
The document provides instructions for a patient-centered care report based on a population health improvement initiative scenario. Key points include:
1. Evaluate the outcomes achieved and not achieved by the population health initiative and factors influencing shortfalls.
2. Propose a strategy to improve outcomes or ensure all are addressed, citing supporting evidence.
3. Develop an individualized care approach incorporating lessons from the initiative and addressing a patient's unique needs and evidence.
4. Justify the evidence used and propose an evaluation framework to determine applicability to other cases.
Similar to Directive Explanations for Monitoring the Risk of Diabetes Onset - ACM IUI 2023 (20)
Explainable AI - making ML and DL models more interpretableAditya Bhattacharya
The document discusses explainable AI (XAI) and making machine learning and deep learning models more interpretable. It covers the necessity and principles of XAI, popular model-agnostic XAI methods for ML and DL models, frameworks like LIME, SHAP, ELI5 and SKATER, and research questions around evolving XAI to be understandable by non-experts. The key topics covered are model-agnostic XAI, surrogate models, influence methods, visualizations and evaluating descriptive accuracy of explanations.
Accelerating Data Science and Machine Learning Workflow with Azure Machine Le...Aditya Bhattacharya
Accelerating Data Science and Machine Learning Workflow with Microsoft Azure Machine Learning
Microsoft User Group Hyderabad AIML Day 2020
https://aditya-bhattacharya.net/
https://www.eventbrite.com/e/microsoft-user-group-hyderabad-aiml-day-2020-tickets-123940376001
MUGH
For more details please follow:
https://medium.com/datadriveninvestor/a-powerful-tool-for-demand-planning-segmentation-a66bfa729360
https://towardsdatascience.com/effective-approaches-for-time-series-anomaly-detection-9485b40077f1
https://aditya-bhattacharya.net/2020/07/20/sales-and-demand-forecast-analysis/
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This document discusses using Mask RCNN to segment brain hemorrhage from CT scan images. It begins by outlining the objectives of discussing the importance of AI for analyzing CT scans and using deep learning for automated segmentation. Next, it describes how Mask RCNN works in a two-stage process, first generating proposals for regions of interest and then classifying and generating masks for each. A demo is then provided of Mask RCNN segmenting brain hemorrhage from a sample CT scan image.
For this talk, I will be discussing about various approaches to accelerate deep learning solutions from notebooks or research environment to production environment and how these solutions can be transformed as an enterprise level end to end Deep Learning Solution, which can be consumed as a service by any software application, with a practical use-case example.
This document discusses various computer vision topics including convolutional neural networks (CNNs), popular CNN architectures, data augmentation, transfer learning, object detection, neural style transfer, generative adversarial networks (GANs), and variational autoencoders (VAEs). It provides overviews and explanations of each topic with examples. The goals are to introduce new concepts, discuss practical use cases, develop and improve intuitions, and provide tips for working on projects and participating in the community.
The document introduces various computer vision topics including convolutional neural networks, popular CNN architectures, data augmentation, transfer learning, object detection, neural style transfer, generative adversarial networks, and variational autoencoders. It provides overviews of each topic and discusses concepts such as how convolutions work, common CNN architectures like ResNet and VGG, why data augmentation is important, how transfer learning can utilize pre-trained models, how object detection algorithms like YOLO work, the content and style losses used in neural style transfer, how GANs use generators and discriminators, and how VAEs describe images with probability distributions. The document aims to discuss these topics at a practical level and provide insights through examples.
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
3. Explainable Decision Support Systems in Healthcare
ML-based
Decision Support Systems
XAI Methods
Explainable Decision Support Systems
4. Explainable Decision Support Systems in Healthcare
ML-based
Decision Support Systems
XAI Methods
Explainable Decision Support Systems
Healthcare Experts Explainable Interface for
monitoring the risk of
diabetes onset for patients
Understand the rationale
behind the predicted risk
of diabetes onset
Monitoring the Risk of
Type 2 Diabetes Onset
11. Feature Importance Explanations (Model-Centric Explanations)
• Feature importance explainability is a model-centric explanation method as it estimates the
importance of features in the model that have the most influence on its output or prediction.
• Examples of feature importance methods are permutation importance, partial dependence
plots, LIME based feature importance and Shapley values (SHAP) based feature importance.
12. Data-Centric Explanations
• Data-centric explainability focuses on examining the data used to train the model rather than the model's internal
workings. The idea is that by analyzing the training data, we can gain insights into how the model makes its
predictions and identify potential biases or errors.
• Examples of data-centric explanation approaches include summarizing datasets using common statistical methods
like mean, mode, and variance, visualizing the data distributions to compare feature values to those across the
remaining dataset, and observing changes in model predictions through what-if analysis to probe into the
sensitivity of the features.
• Additionally, data-centric explanations include creating more awareness about the data quality by sharing more
insights about the various data issues, such as data drift, skewed data, outliers, correlated features and etc., that
can impact the overall performance of the ML models.
13. Counterfactual Explanations (Example-based Explanations)
• Counterfactual explanations are example-based methods that provide minimum
conditions required to obtain an alternate decision.
• Rather than explaining the inner working of the model, counterfactuals can guide users to
obtain their desired predictions.
* Applied Machine Learning Explainability Techniques, A.
Bhattacharya
*
15. Research Questions
RQ1. In what ways do patients and HCPs find our visually directive explanation
dashboard useful for monitoring and evaluating the risk of diabetes onset?
RQ2. In what ways do HCP and patients perceive data-centric, model-centric, and
example-based visually directive explanations in terms of usefulness, understandability,
and trustworthiness in the context of healthcare?
RQ3. In what ways do visually directive explanations facilitate patients and HCPs to take
action for improving patient conditions?
16. Iterative User-Centric Design and Evaluation Process
Low-fidelity prototype High-fidelity prototype
Figma click-through prototype
Interactive web application prototype
11 healthcare experts
Qualitative study through 1:1 interviews
45 healthcare experts and 51 diabetes patients
Mixed-methods study through online questionnaires
Thematic analysis for evaluation
Evaluation through descriptive statistics, test of
proportion, and analyzing participant-reported
Likert scale question
18. Combining XAI methods to address different dimensions of explainability
* Applied Machine Learning Explainability Techniques, A.
Bhattacharya
*
19. Tailoring Directive Explanations for Healthcare Experts
o Increasing actionability through interactive what-if analysis
o Explanations through actionable features instead of non-actionable features
o Color-coded visual indicators
o Data-centric directive explanations
* These design implications are aligned with the recommendations from Wang et al. [2019] -
Designing Theory-Driven User-Centric Explainable AI
20. Summarizing the contribution of this research
1. Combining XAI methods to address different dimensions of explainability
2. Visually directive data-centric explanations that provide local explanations with a global overview
3. The design of a directive explanation dashboard that combines different explanation methods and
further compared them in terms of understandability, usefulness, actionability, and trustworthiness
with healthcare experts and patients.
4. Design implications for tailoring visually directive explanations for healthcare experts
21. Summarizing the contribution of this research
1. Combining XAI methods to address different dimensions of explainability
2. Visually directive data-centric explanations that provide local explanations with a global overview
3. The design of a directive explanation dashboard that combines different explanation methods and
further compared them in terms of understandability, usefulness, actionability, and trustworthiness
with healthcare experts and patients.
4. Design implications for tailoring visually directive explanations for healthcare experts
22. Summarizing the contribution of this research
1. Combining XAI methods to address different dimensions of explainability
2. Visually directive data-centric explanations that provide local explanations with a global overview
3. The design of a directive explanation dashboard that combines different explanation methods and
further compared them in terms of understandability, usefulness, actionability, and trustworthiness
with healthcare experts and patients.
4. Design implications for tailoring visually directive explanations for healthcare experts
23. Summarizing the contribution of this research
1. Combining XAI methods to address different dimensions of explainability
2. Visually directive data-centric explanations that provide local explanations with a global overview
3. The design of a directive explanation dashboard that combines different explanation methods and
further compared them in terms of understandability, usefulness, actionability, and trustworthiness
with healthcare experts and patients.
4. Design implications for tailoring visually directive explanations for healthcare experts
24. Thank you for your attention!
Directive Explanations for Monitoring the Risk of Diabetes
Onset:
Introducing Directive Data-Centric Explanations and
Combinations to Support What-If Explorations
Aditya Bhattacharya
aditya.bhattacharya@kuleuven.be
@adib0073
Jeroen Ooge
jeroen.ooge@kuleuven.be
@JeroenOoge
Gregor Stiglic
gregor.stiglic@um.si
@GStiglic
Katrien Verbert
katrien.verbert@kuleuven.be
@katrien_v
Editor's Notes
Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare
Existing XAI methods such as LIME, SHAP, Saliency Maps and others are predominantly designed for ML experts and little research has compared the utility of these different explanation methods in guiding healthcare experts who may not have technical ML knowledge, for patient care.
Additionally, current XAI methods provide explanations through complex visualizations which are static and difficult to understand for healthcare experts.
These gaps highlight the necessity for analyzing and comparing explanation methods with healthcare professionals (HCPs) such as nurses and physicians. (1 min)
Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare
Existing XAI methods such as LIME, SHAP, Saliency Maps and others are predominantly designed for ML experts and little research has compared the utility of these different explanation methods in guiding healthcare experts who may not have technical ML knowledge, for patient care.
Additionally, current XAI methods provide explanations through complex visualizations which are static and difficult to understand for healthcare experts.
These gaps highlight the necessity for analyzing and comparing explanation methods with healthcare professionals (HCPs) such as nurses and physicians. (1 min)
Our research particularly focuses on providing an explainable interface for an ML-based system used for monitoring the risk of diabetes onset which could be used by healthcare experts such as nurses and physicians.
To understand the real needs of our users in detail, we first conducted an exploratory focus group discussion with 4 nurses.
Method – We first showed them SHAP based explanations for explaining the model predicted risk of diabetes onset
We then conducted a codesign session with our participants to understand the key components of the explainable interface.
Results: As a result of this study, we formulated the responses of our participants into the following User Requirements:
Additionally, our user conveyed that visualizations for SHAP based explanations are complex and they need simpler visualizations to communicate with patients
* Is it important to highlight about the tasks? (2 slides, 1.5 mins)
Our research particularly focuses on providing an explainable interface for an ML-based system used for monitoring the risk of diabetes onset which could be used by healthcare experts such as nurses and physicians.
To understand the real needs of our users in detail, we first conducted an exploratory focus group discussion with 4 nurses.
Method – We first showed them SHAP based explanations for explaining the model predicted risk of diabetes onset
We then conducted a codesign session with our participants to understand the key components of the explainable interface.
Results: As a result of this study, we formulated the responses of our participants into the following User Requirements:
Additionally, our user conveyed that visualizations for SHAP based explanations are complex and they need simpler visualizations to communicate with patients
* Is it important to highlight about the tasks? (2 slides, 1.5 mins)
This research work presents our Visually Directive Explanation Dashboard, which we developed following an iterative user-centric design process to satisfy our user requirements.
We included model-agnostic local explanation methods to meet our explanation goals considering our user requirements.
Our dashboard included feature importance explanations – Important Risk Factors
Data Centric explanations – VC1, VC2 and V5
Counterfactual Explanations – Recommendations to reduce risk
Another video – separate (30-45 secs)
We further tailored the representation of these explanation methods.
We mainly included interactive explanations that supported what-if explorations instead of static representations. Our users can alter the selected feature value to observe any change in the predicted risk
We also separated emphasized on actionable health variables over non-actionable ones as the users can alter these actionable variable to obtain their favourable outcome.
We also categorized the actionable features as patient measures – these provide information patient vitals like blood sugar, BMI etc. and patient behaviours – which provides information from behavioral information captured through FINDRISC questionnaires
Our customizations also include providing information about feasibility and impact of counterfactual information presented as recommendations.
This research work presents our Visually Directive Explanation Dashboard, which we developed following an iterative user-centric design process to satisfy our user requirements.
We included model-agnostic local explanation methods to meet our explanation goals considering our user requirements.
Our dashboard included feature importance explanations – Important Risk Factors
Data Centric explanations – VC1, VC2 and V5
Counterfactual Explanations – Recommendations to reduce risk
Another video – separate (30-45 secs)
We further tailored the representation of these explanation methods.
We mainly included interactive explanations that supported what-if explorations instead of static representations. Our users can alter the selected feature value to observe any change in the predicted risk
We also separated emphasized on actionable health variables over non-actionable ones as the users can alter these actionable variable to obtain their favourable outcome.
We also categorized the actionable features as patient measures – these provide information patient vitals like blood sugar, BMI etc. and patient behaviours – which provides information from behavioral information captured through FINDRISC questionnaires
Our customizations also include providing information about feasibility and impact of counterfactual information presented as recommendations.
This research work presents our Visually Directive Explanation Dashboard, which we developed following an iterative user-centric design process to satisfy our user requirements.
We included model-agnostic local explanation methods to meet our explanation goals considering our user requirements.
Our dashboard included feature importance explanations – Important Risk Factors
Data Centric explanations – VC1, VC2 and V5
Counterfactual Explanations – Recommendations to reduce risk
Another video – separate (30-45 secs)
We further tailored the representation of these explanation methods.
We mainly included interactive explanations that supported what-if explorations instead of static representations. Our users can alter the selected feature value to observe any change in the predicted risk
We also separated emphasized on actionable health variables over non-actionable ones as the users can alter these actionable variable to obtain their favourable outcome.
We also categorized the actionable features as patient measures – these provide information patient vitals like blood sugar, BMI etc. and patient behaviours – which provides information from behavioral information captured through FINDRISC questionnaires
Our customizations also include providing information about feasibility and impact of counterfactual information presented as recommendations.
This research work presents our Visually Directive Explanation Dashboard, which we developed following an iterative user-centric design process to satisfy our user requirements.
We included model-agnostic local explanation methods to meet our explanation goals considering our user requirements.
Our dashboard included feature importance explanations – Important Risk Factors
Data Centric explanations – VC1, VC2 and V5
Counterfactual Explanations – Recommendations to reduce risk
Another video – separate (30-45 secs)
We further tailored the representation of these explanation methods.
We mainly included interactive explanations that supported what-if explorations instead of static representations. Our users can alter the selected feature value to observe any change in the predicted risk
We also separated emphasized on actionable health variables over non-actionable ones as the users can alter these actionable variable to obtain their favourable outcome.
We also categorized the actionable features as patient measures – these provide information patient vitals like blood sugar, BMI etc. and patient behaviours – which provides information from behavioral information captured through FINDRISC questionnaires
Our customizations also include providing information about feasibility and impact of counterfactual information presented as recommendations.
We wanted to address the following research questions using our Visually Directive Explanation Dashboard
- In general we wanted to analyze and compare the understandability, usefulness, actionability, and trust of the different explanation methods included in our dashboard with HCPs who are our primary users and patients who could be our potential users.
We followed an iterative user-centric design process for the design and evaluation of our dashboard.
We first designed a low-fidelity click-through prototype in Figma in multiple iterations. Here you can see the final version of the low-fidelity process
We conducted a qualitative user study through 1:1 interviews with 11 healthcare experts.
We evaluated our qualitative interview data using thematic analysis.
We also utilized the feedback to perform design changes for our high-fidelity prototype
Particularly, we improved the discoverability of our interactive visual explanation methods through tooltips and explicit visual indicators
Overall, the healthcare experts were positive about the utility of this dashboard and further suggested that patients can directly use this as a self-monitoring tool.
So we included patients as our participants in the next study.
We then designed and developed our high-fidelity web application prototype.
We conducted a mixed-methods study with 45 healthcare experts and 51 patients through online questionnaires.
We evaluated the data gathered through descriptive statistics, test of proportion and analyzing participant reported Likert scale questions and their justifications.
We finally addressed our research questions and summarized our research findings considering collective feedback from our two user studies.
Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare
Existing XAI methods such as LIME, SHAP, Saliency Maps and others are predominantly designed for ML experts and little research has compared the utility of these different explanation methods in guiding healthcare experts who may not have technical ML knowledge, for patient care.
Additionally, current XAI methods provide explanations through complex visualizations which are static and difficult to understand for healthcare experts.
These gaps highlight the necessity for analyzing and comparing explanation methods with healthcare professionals (HCPs) such as nurses and physicians. (1 min)
We share our design implications for tailoring the visual representation of directive explanations for healthcare experts from our observations and results
Our modified design of this visual component (VC3) used in our high-fidelity prototype enabled them to perform interactive what-if analysis, i.e. allowed them to change the feature values and observe the change in the overall prediction. Hence, we recommend the usage of interactive design elements that allows what-if analysis for representing directive explanations for HCPs. This recommendation also supports hypothesis generation
In our approach, we included only actionable variables for visual components which supports what-if interactions and better identification of coherent factors [57 ]. We anticipated that allowing the ability to alter values of non-actionable variables can create confusion for HCPs, especially for representing counterfactual explanations.
HCPs indicated that the color-coded representations of risk factors were very useful for getting quick insights. Hence, we recommend the usage of color-coded representations and visual indicators to highlight factors that can increase or decrease the predictor variable. This further facilitates the identification of coherent factors.
HCPs indicated that our representation of data-centric explainability through the patient summary was very informative. They could easily identify how good or bad the risk factors are for a specific patient. Additionally, they could get an overview of how other patients are doing as compared to a specific patient through the data-distribution charts. Thus, our representation of data-centric explainability provided a local explanation but with a global perspective. Furthermore, data-centric directive explanations support forward reasoning by providing accessto source and situational data and yet can be easily integrated with multiple explanation methods.
We share our design implications for tailoring the visual representation of directive explanations for healthcare experts from our observations and results
Our modified design of this visual component (VC3) used in our high-fidelity prototype enabled them to perform interactive what-if analysis, i.e. allowed them to change the feature values and observe the change in the overall prediction. Hence, we recommend the usage of interactive design elements that allows what-if analysis for representing directive explanations for HCPs. This recommendation also supports hypothesis generation
In our approach, we included only actionable variables for visual components which supports what-if interactions and better identification of coherent factors [57 ]. We anticipated that allowing the ability to alter values of non-actionable variables can create confusion for HCPs, especially for representing counterfactual explanations.
HCPs indicated that the color-coded representations of risk factors were very useful for getting quick insights. Hence, we recommend the usage of color-coded representations and visual indicators to highlight factors that can increase or decrease the predictor variable. This further facilitates the identification of coherent factors.
HCPs indicated that our representation of data-centric explainability through the patient summary was very informative. They could easily identify how good or bad the risk factors are for a specific patient. Additionally, they could get an overview of how other patients are doing as compared to a specific patient through the data-distribution charts. Thus, our representation of data-centric explainability provided a local explanation but with a global perspective. Furthermore, data-centric directive explanations support forward reasoning by providing accessto source and situational data and yet can be easily integrated with multiple explanation methods.
We share our design implications for tailoring the visual representation of directive explanations for healthcare experts from our observations and results
Our modified design of this visual component (VC3) used in our high-fidelity prototype enabled them to perform interactive what-if analysis, i.e. allowed them to change the feature values and observe the change in the overall prediction. Hence, we recommend the usage of interactive design elements that allows what-if analysis for representing directive explanations for HCPs. This recommendation also supports hypothesis generation
In our approach, we included only actionable variables for visual components which supports what-if interactions and better identification of coherent factors [57 ]. We anticipated that allowing the ability to alter values of non-actionable variables can create confusion for HCPs, especially for representing counterfactual explanations.
HCPs indicated that the color-coded representations of risk factors were very useful for getting quick insights. Hence, we recommend the usage of color-coded representations and visual indicators to highlight factors that can increase or decrease the predictor variable. This further facilitates the identification of coherent factors.
HCPs indicated that our representation of data-centric explainability through the patient summary was very informative. They could easily identify how good or bad the risk factors are for a specific patient. Additionally, they could get an overview of how other patients are doing as compared to a specific patient through the data-distribution charts. Thus, our representation of data-centric explainability provided a local explanation but with a global perspective. Furthermore, data-centric directive explanations support forward reasoning by providing accessto source and situational data and yet can be easily integrated with multiple explanation methods.
We share our design implications for tailoring the visual representation of directive explanations for healthcare experts from our observations and results
Our modified design of this visual component (VC3) used in our high-fidelity prototype enabled them to perform interactive what-if analysis, i.e. allowed them to change the feature values and observe the change in the overall prediction. Hence, we recommend the usage of interactive design elements that allows what-if analysis for representing directive explanations for HCPs. This recommendation also supports hypothesis generation
In our approach, we included only actionable variables for visual components which supports what-if interactions and better identification of coherent factors [57 ]. We anticipated that allowing the ability to alter values of non-actionable variables can create confusion for HCPs, especially for representing counterfactual explanations.
HCPs indicated that the color-coded representations of risk factors were very useful for getting quick insights. Hence, we recommend the usage of color-coded representations and visual indicators to highlight factors that can increase or decrease the predictor variable. This further facilitates the identification of coherent factors.
HCPs indicated that our representation of data-centric explainability through the patient summary was very informative. They could easily identify how good or bad the risk factors are for a specific patient. Additionally, they could get an overview of how other patients are doing as compared to a specific patient through the data-distribution charts. Thus, our representation of data-centric explainability provided a local explanation but with a global perspective. Furthermore, data-centric directive explanations support forward reasoning by providing accessto source and situational data and yet can be easily integrated with multiple explanation methods.
This paper presents three primary research contributions
Visually directive data-centric explanations that provide local explanations of the predicted risk for individual patients with a global overview of risk factors for the entire patient population.
The design of a directive explanation dashboard that combines visually represented data-centric, feature-importance, and counterfactual explanations and further compared the different visual explanations in terms of understandability, usefulness, actionability, and trustworthiness with healthcare experts and patients.
Design implications for tailoring explanations for healthcare experts based on observations of our user-centered design process and an elaborate user study
This paper presents three primary research contributions
Visually directive data-centric explanations that provide local explanations of the predicted risk for individual patients with a global overview of risk factors for the entire patient population.
The design of a directive explanation dashboard that combines visually represented data-centric, feature-importance, and counterfactual explanations and further compared the different visual explanations in terms of understandability, usefulness, actionability, and trustworthiness with healthcare experts and patients.
Design implications for tailoring explanations for healthcare experts based on observations of our user-centered design process and an elaborate user study
This paper presents three primary research contributions
Visually directive data-centric explanations that provide local explanations of the predicted risk for individual patients with a global overview of risk factors for the entire patient population.
The design of a directive explanation dashboard that combines visually represented data-centric, feature-importance, and counterfactual explanations and further compared the different visual explanations in terms of understandability, usefulness, actionability, and trustworthiness with healthcare experts and patients.
Design implications for tailoring explanations for healthcare experts based on observations of our user-centered design process and an elaborate user study
This paper presents three primary research contributions
Visually directive data-centric explanations that provide local explanations of the predicted risk for individual patients with a global overview of risk factors for the entire patient population.
The design of a directive explanation dashboard that combines visually represented data-centric, feature-importance, and counterfactual explanations and further compared the different visual explanations in terms of understandability, usefulness, actionability, and trustworthiness with healthcare experts and patients.
Design implications for tailoring explanations for healthcare experts based on observations of our user-centered design process and an elaborate user study