Standards
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The document discusses the need for universal standards in healthcare to improve quality, reduce costs, and enable comparisons across providers. It notes that while leadership is aligned with the objectives, management and governance structures are not fully in place. Standards are currently missing, particularly for measurements, processes, and auditing. The recommendation is that standard definitions be adopted for measurements, processes, and auditing similar to ISO quality standards in order to enable comparisons, allow best practices to spread, and support continuous improvement across the industry.
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Transactional Blackbelts are different
1. Transactional Black Belts often face different challenges than manufacturing Black Belts as processes are less defined and changes are harder to reverse in human-centered environments.
2. A key difference is that cycle time is a more useful overall measure than defect count for selecting projects in service/transaction environments where establishing a baseline process is often needed.
3. The training presented teaches how to model service and transaction processes, understand decision-making reliability, measure and improve cycle time, and manage change to achieve greater process efficiency.
BuildingEffectiveDecisionMakingFramework_v1.05
1) The document discusses adopting an effective decision making framework using common methods and processes. It emphasizes capturing analytic insight as an asset and improving collaboration.
2) Decision Model and Notation (DMN) is presented as a common language that can be used to model decisions. DMN provides constructs to define decisions, their requirements, and relationships in diagrams.
3) Examples of applying DMN to decisions around retail conversion rate are shown. Components such as decisions, data sources, and knowledge bases are modeled visually.
Quality Management and NABH.pptx
The document discusses various quality management tools and techniques. It provides descriptions and examples of 7 basic quality tools:
1. Cause and effect diagrams help identify potential causes of defects. An example shows causes of a project going over budget.
2. Control charts detect trends in data over time by showing if a process is in or out of control. An example explains the rule of 7.
3. Flow charts visually show inputs, processes, and outputs in a system. An example maps the process of deciding to get out of bed.
4. Histograms show data distributions to analyze characteristics like defect severities. An example shows most bugs are high priority.
5. Pareto charts identify the most
Images of Information Systems
The document discusses strategic planning and management. It outlines the typical planning cycle which includes analyzing the current situation, forecasting future trends, setting objectives, developing an action plan, implementing the plan, monitoring progress, and starting a new cycle. It also describes the different levels of management - operational, tactical, and strategic - and their respective information needs and decision-making responsibilities.
Accelerating Your Move to Value-Based Care
The document discusses accelerating healthcare organizations' move to value-based care through achieving information management maturity. It describes three key steps:
1. Developing an information management strategy, including conducting a data asset inventory, business workload analysis, and architectural component mapping to create a 3-5 year execution roadmap.
2. Implementing better data governance and improving data quality through evaluating and enhancing processes.
3. Modernizing existing business intelligence and data investments to achieve a more mature "Data 3.0" environment where data is actionable, explainable, trusted and contextualized.
The summary highlights the main points about the three key steps discussed in the document for achieving information management maturity to support the transition
Total Quality Management in Healthcare
Now days, Healthcare systems are of fundamental interests to all level of Hospitals in our societies. Eventually, increasing importance and reliance are placed on total quality management in healthcare systems. Due to this rising importance that is also reflected in the increasing percentage of national and international resources for both private and public sector to allocated in hospital management systems. Hospitals and other healthcare organization across the globe have been progressively implementing TQM to reduce costs, improve efficiency and provide high quality patient care.
MEASURING-SAFETY-PERFORMANCE-PPT-GOOD.pdf
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U Of I Slides 11 09
The document summarizes an industrial engineering project at Cermak Health Services, which provides healthcare to detainees at Cook County Jail. The project aimed to improve the medication administration process, which was not meeting requirements to deliver medications within 24 hours. Using Six Sigma methodology, the industrial engineers mapped current processes, identified inefficiencies, and are implementing improvements such as standardizing processes, reducing non-value-added steps, and leveraging a new computer system. The project demonstrates how industrial engineering techniques can be applied in healthcare to improve quality and efficiency.
Performance Measurement
The document discusses key concepts in performance measurement and strategic information management. It emphasizes that consistent, accurate data across business areas provides real-time information to evaluate processes, products and services to meet objectives and customer needs. It also discusses leading practices like developing performance indicators reflecting customer needs, using comparative data to improve, and involving all employees in measurement activities.
Data drift and machine learning
A short presentation on basic methodologies to deal and address data drift in automated Machine Learning
Data management (1)
The document discusses various methods for collecting and analyzing data to inform quality improvement projects. It describes process mapping to analyze current processes, brainstorming to generate ideas, surveys to understand stakeholder perspectives, audits to measure performance against standards, and cause and effect diagrams to identify root causes of problems. The goal of using these techniques is to thoroughly diagnose issues to identify opportunities for improving processes and outcomes.
Using an ocp to accomplish business measures online
https://www.slideshare.net/EricBruggeman1/using-an-ocp-to-accomplish-business-measures-online-126064387/edit?src=mypage
Data drift and machine learning
This document discusses machine learning methods and analysis. It provides an overview of machine learning, including that it allows computer programs to teach themselves from new data. The main machine learning techniques are described as supervised learning, unsupervised learning, and reinforcement learning. Popular applications of these techniques are also listed. The document then outlines the typical steps involved in applying machine learning, including data curation, processing, resampling, variable selection, building a predictive model, and generating predictions. It stresses that while data is important, the right analysis is also needed to apply machine learning effectively. The document concludes by discussing issues like data drift and how to implement validation and quality checks to safeguard automated predictions from such problems.
httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docx
http://home.ubalt.edu/ntsbarsh/business-stat/opre/partIX.htm
Tools for Decision Analysis: Analysis of Risky Decisions
If you will begin with certainties, you shall end in doubts, but if you will content to begin with doubts, you shall end in almost certainties. -- Francis Bacon
Making decisions is certainly the most important task of a manager and it is often a very difficult one. This site offers a decision making procedure for solving complex problems step by step.It presents the decision-analysis process for both public and private decision-making, using different decision criteria, different types of information, and information of varying quality. It describes the elements in the analysis of decision alternatives and choices, as well as the goals and objectives that guide decision-making. The key issues related to a decision-maker's preferences regarding alternatives, criteria for choice, and choice modes, together with the risk assessment tools are also presented.
Professor Hossein Arsham
MENU
1. Introduction & Summary
2. Probabilistic Modeling: From Data to a Decisive Knowledge
3. Decision Analysis: Making Justifiable, Defensible Decisions
4. Elements of Decision Analysis Models
5. Decision Making Under Pure Uncertainty: Materials are presented in the context of Financial Portfolio Selections.
6. Limitations of Decision Making under Pure Uncertainty
7. Coping with Uncertainties
8. Decision Making Under Risk: Presentation is in the context of Financial Portfolio Selections under risk.
9. Making a Better Decision by Buying Reliable Information: Applications are drawn from Marketing a New Product.
10. Decision Tree and Influence Diagram
11. Why Managers Seek the Advice From Consulting Firms
12. Revising Your Expectation and its Risk
13. Determination of the Decision-Maker's Utility
14. Utility Function Representations with Applications
15. A Classification of Decision Maker's Relative Attitudes Toward Risk and Its Impact
16. The Discovery and Management of Losses
17. Risk: The Four Letters Word
18. Decision's Factors-Prioritization & Stability Analysis
19. Optimal Decision Making Process
20. JavaScript E-labs Learning Objects
21. A Critical Panoramic View of Classical Decision Analysis
22. Exercise Your Knowledge to Enhance What You Have Learned (PDF)
23. Appendex: A Collection of Keywords and Phrases
Companion Sites:
· Business Statistics
· Success Science
· Leadership Decision Making
· Linear Programming (LP) and Goal-Seeking Strategy
· Linear Optimization Software to Download
· Artificial-variable Free LP
Solution
Algorithms
· Integer Optimization and the Network Models
· Tools for LP Modeling Validation
· The Classical Simplex Method
· Zero-Sum Games with Applications
· Computer-assisted Learning Concepts and Techniques
· Linear Algebra and LP Connections
· From Linear to Nonlinear Optimization with Business Applications
· Construction of the Sensitivity Region for LP Models
· Zero Sagas in Four Dimensions
· Systems Simulation
· B.
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Standards
- 1. The Immutable Principles of Quality and Accountability Get All You Can from All You’ve Got
- 2. Responsibility Someone is ultimately responsible for the condition of any given resource.
- 3. Opportunity to Affect Others have the opportunity to affect the condition of the resource.
- 4. Processes Processes used have the ability to affect the condition of a resource.
- 5. Standards Standards define processes.
- 6. Assurance Standards provide assurances regarding the condition of a resource.
- 7. Except Standards which are not followed provide no assurances.
- 14. Avoid Tripping
- 15. Worse Things Than Tripping
- 16. Or In a hurry… Unnecessary risk
- 21. While I am busy with little things, I am not required to do greater things. St. Francis de Sales
- 22. WhiteLake Data Management Michael P. Meier http://www.m2dxtx.com http://bi-keep-it-simple.blogspot.com m pmeier @hotmail.com (507) 273-9742 (cell)