In modern enterprises, customer data is valuable for identifying their behavioral patterns and developing marketing strategies that can align with the preferences of different customers. The objective of this research is to develop a framework that promotes the use of Kinect sensors for Big Data Analytics on customer behavior analysis. Kinect enables 3D motion capture, facial recognition and voice recognition capabilities which allow to analyze customer behaviors in various aspects. Information fusion on the network of multiple Kinect sensors can achieve enhanced insight of the customer emotion, habits and consuming tendencies. Big Data Analytic techniques such as clustering and visualization are applied on the data collected from the sensors to provide better comprehension on the customers. Prediction on how to improve the customer relationship can be made to stimulate the vendition. Finally, an experimental system is designed based on the proposed framework as an illustration of the framework implementation.
Shifting to Virtual Care in the COVID-19 Era: Analytics for Financial Success...Health Catalyst
The COVID-19 era has seen a decline in visits to ambulatory care practices by 60 percent and an estimated financial loss for primary care of over $15 billion. Shutting down elective care is financially unsustainable for health systems and for patients, who continue to need non-pandemic-related care. While virtual medicine has emerged as a viable and mutually beneficial solution for patients and providers, the shift from in-person to virtual health is logistically and financially complicated.
Processes and workflows from in-person care don’t directly translate to the virtual setting, and a financially successful shift requires deep understanding of the factors driving patient engagement and revenue in the new normal. As such, meeting patient needs and financial goals requires robust enterprisewide analytics that drill down to the provider level.
A 5-Step Guide for Successful Healthcare Data Warehouse OperationsHealth Catalyst
Starting and sustaining an enterprise data warehouse (EDW) for a sizeable healthcare organization might seem as challenging as, say, forming a new country. While it is an arduous undertaking, there are plenty who have gone before. In this article, one EDW operations manager shares five steps for success:
Start with a Leadership Commitment to Outcomes Improvement
Build the Right Team
Establish Effective Partnerships with IT
Develop Interest and Gain Buy-In
Pivot Toward Maintaining Success
Successfully implementing and sustaining EDW operations is about establishing and managing priorities and understanding the enterprise-wide implications.
Using Improvement Science in Healthcare to Create True ChangeHealth Catalyst
With improvement science combined with analytics, health systems can better understand how, as they implement new process changes, to use theory to guide their practice, and which improvement strategy will help increase the likelihood of success.
The 8-Step Improvement Model is a framework that health systems can follow to effectively apply improvement science:
Analyze the opportunity for improvement and define the problem.
Scope the opportunity and set SMART goals.
Explore root causes and set SMART process aims.
Design interventions and plan initial implementation.
Implement interventions and measure results.
Monitor, adjust, and continually learn.
Diffuse and sustain.
Communicate Quantitative and Qualitative Results.
With the right approach, an improvement team can measure the results and know if the changes they made will actually lead to the desired impact.
Solutions to the New Challenges Facing ACOs and ACO Participants from the 202...Health Catalyst
This webinar thoroughly explains four key changes in the 2021 Quality Payment Program (QPP) Final Rule: the discontinuation of the CMS Web Interface, the introduction of the Alternative Payment Model (APM) Performance Pathway (APP), the discontinuation of the APM scoring standard, and the expanded use of the APM entity submitter type. These changes create eight new challenges in quality measurement for ACOs in the Medicare Shared Savings Program (MSSP) and ACO participants in the Merit-Based Incentive Payment System (MIPS).
Darren O’Brien, Client Development Director for Regulatory Measures at Health Catalyst, and Rachel Katz, Senior Vice President of Product Development at Health Catalyst and former CEO of Able Health, analyze each change, discuss the new challenges created, and demonstrate how Health Catalyst solutions can help.
This webinar accomplishes the following:
- Analyzes the four key changes.
- Evaluates the eight new challenges.
- Creates solutions for each new challenge.
Critical Healthcare M&A Strategies: A Data-driven ApproachHealth Catalyst
Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines:
Establish the strategic objectives of the M&A with the leadership team.
Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal effort for data, tools, and talent).
Identify the near-term data strategic priorities, stakeholders, and tools.
Assess the talent and consider creating an analytics center of excellence (ACOE) to harness organizational capabilities.
Activity-Based Costing in Healthcare During COVID-19: Meeting Four Critical N...Health Catalyst
As health systems increasingly transition to a value-based care model, the financial strains and uncertainty of COVID-19 have placed more urgency on cost management. More than ever, organizations need a costing solution that helps them understand the true value of their services. With the right next-generation activity-based costing (ABC) tool, health systems can access the detailed data they need to lower the cost of care, automate costing activities, and reduce administrative costs while preparing for the mounting intricacy of the post-pandemic setting.
Activity-based costing meets healthcare’s complex COVID-19-era costing needs by addressing four big challenges:
Data management.
Scalability.
Ongoing maintenance.
Adoption.
Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it E...Health Catalyst
Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes.
This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.
Shifting to Virtual Care in the COVID-19 Era: Analytics for Financial Success...Health Catalyst
The COVID-19 era has seen a decline in visits to ambulatory care practices by 60 percent and an estimated financial loss for primary care of over $15 billion. Shutting down elective care is financially unsustainable for health systems and for patients, who continue to need non-pandemic-related care. While virtual medicine has emerged as a viable and mutually beneficial solution for patients and providers, the shift from in-person to virtual health is logistically and financially complicated.
Processes and workflows from in-person care don’t directly translate to the virtual setting, and a financially successful shift requires deep understanding of the factors driving patient engagement and revenue in the new normal. As such, meeting patient needs and financial goals requires robust enterprisewide analytics that drill down to the provider level.
A 5-Step Guide for Successful Healthcare Data Warehouse OperationsHealth Catalyst
Starting and sustaining an enterprise data warehouse (EDW) for a sizeable healthcare organization might seem as challenging as, say, forming a new country. While it is an arduous undertaking, there are plenty who have gone before. In this article, one EDW operations manager shares five steps for success:
Start with a Leadership Commitment to Outcomes Improvement
Build the Right Team
Establish Effective Partnerships with IT
Develop Interest and Gain Buy-In
Pivot Toward Maintaining Success
Successfully implementing and sustaining EDW operations is about establishing and managing priorities and understanding the enterprise-wide implications.
Using Improvement Science in Healthcare to Create True ChangeHealth Catalyst
With improvement science combined with analytics, health systems can better understand how, as they implement new process changes, to use theory to guide their practice, and which improvement strategy will help increase the likelihood of success.
The 8-Step Improvement Model is a framework that health systems can follow to effectively apply improvement science:
Analyze the opportunity for improvement and define the problem.
Scope the opportunity and set SMART goals.
Explore root causes and set SMART process aims.
Design interventions and plan initial implementation.
Implement interventions and measure results.
Monitor, adjust, and continually learn.
Diffuse and sustain.
Communicate Quantitative and Qualitative Results.
With the right approach, an improvement team can measure the results and know if the changes they made will actually lead to the desired impact.
Solutions to the New Challenges Facing ACOs and ACO Participants from the 202...Health Catalyst
This webinar thoroughly explains four key changes in the 2021 Quality Payment Program (QPP) Final Rule: the discontinuation of the CMS Web Interface, the introduction of the Alternative Payment Model (APM) Performance Pathway (APP), the discontinuation of the APM scoring standard, and the expanded use of the APM entity submitter type. These changes create eight new challenges in quality measurement for ACOs in the Medicare Shared Savings Program (MSSP) and ACO participants in the Merit-Based Incentive Payment System (MIPS).
Darren O’Brien, Client Development Director for Regulatory Measures at Health Catalyst, and Rachel Katz, Senior Vice President of Product Development at Health Catalyst and former CEO of Able Health, analyze each change, discuss the new challenges created, and demonstrate how Health Catalyst solutions can help.
This webinar accomplishes the following:
- Analyzes the four key changes.
- Evaluates the eight new challenges.
- Creates solutions for each new challenge.
Critical Healthcare M&A Strategies: A Data-driven ApproachHealth Catalyst
Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines:
Establish the strategic objectives of the M&A with the leadership team.
Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal effort for data, tools, and talent).
Identify the near-term data strategic priorities, stakeholders, and tools.
Assess the talent and consider creating an analytics center of excellence (ACOE) to harness organizational capabilities.
Activity-Based Costing in Healthcare During COVID-19: Meeting Four Critical N...Health Catalyst
As health systems increasingly transition to a value-based care model, the financial strains and uncertainty of COVID-19 have placed more urgency on cost management. More than ever, organizations need a costing solution that helps them understand the true value of their services. With the right next-generation activity-based costing (ABC) tool, health systems can access the detailed data they need to lower the cost of care, automate costing activities, and reduce administrative costs while preparing for the mounting intricacy of the post-pandemic setting.
Activity-based costing meets healthcare’s complex COVID-19-era costing needs by addressing four big challenges:
Data management.
Scalability.
Ongoing maintenance.
Adoption.
Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it E...Health Catalyst
Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes.
This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.
How to Run Your Healthcare Analytics Operation Like a BusinessHealth Catalyst
A robust data analytics operation is necessary for healthcare systems’ survival. Just like any business, the analytics enterprise needs to be well managed using the principles of successful business operations.
This article walks through how to run an analytics operation like a business using the following five-question framework:
Who does the analytics team serve and what are those customers trying to do?
What services does the analytics team provide to help customers accomplish their goals?
How does the analytics team know they’re doing a great job and how do they communicate that effectively to the leadership team?
What is the most efficient way to provide analytics services?
What is the most effective way to organize?
EHR Integration: Achieving this Digital Health ImperativeHealth Catalyst
As the digital trajectory of healthcare rises, health systems have an array of new resources available to make more effective and timely care decisions. However, to use these data analytics, machine learning, predictive analytics, and wellness applications to gain real-time, data-driven insight at the point of care, health systems must fully integrate the tools with their EHRs. Integration brings technical and administrative challenges, requiring organizations to coordinate around standards, administrative processes, regulatory principles, and functional integration, as well as develop compelling integration use cases that drive demand. When realized, full EHR integration will allow clinicians to leverage data from across the continuum of care (from health plan to patient-generated data) to improve patient diagnosis and treatment.
Optimize Your Labor Management with Health Catalyst PowerLabor™Health Catalyst
To cut costs, healthcare leaders are looking at their greatest operating expense—labor management. However, with outdated labor management systems, decision makers rely on retrospective, incomplete data to forecast staffing volumes and patient support needs. Limited workforce insight can result in misaligned staffing or worse, jeopardizing patient care due to lack of labor support. With the Health Catalyst PowerLabor™ application, part of the Financial Empowerment Suite™, decision makers have access to a comprehensive view of labor data by organization, department, team, and job role. Timely insight into current and future hospital needs allows leaders to staff to patient volume, control escalating labor expenses, and ensure optimal resources for excellent patient care.
Four Essential Ways Control Charts Guide Healthcare ImprovementHealth Catalyst
Control charts are a critical asset to any health system seeking effective, sustainable improvement. With a simple three-line format, control charts show process change over time, including the average of the data, upper control limit, and lower control limit. This insight helps improvement teams monitor projects, understand opportunities and the impact of initiatives, and sustain improved processes.
Also known as Shewhart charts or statistical process control charts, control charts drive effective improvement by addressing three fundamental questions:
1. What is the goal of the improvement project?
2. How will the organization know that a change is an improvement?
3. What change can the organization make that will result in improvement?
Three Must-Haves for a Successful Healthcare Data StrategyHealth Catalyst
Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy.
To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:
Best practices to identify target behaviors and practices.
Analytics to accelerate improvement and identify gaps between best practices and analytic results.
Adoption processes to outline the path to transformation.
The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentia...Health Catalyst
Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience.
These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors:
Vendor’s expertise and exclusive focus on healthcare.
Machine learning model’s access to extensive data sources.
Machine learning model’s ease of implementation.
Machine learning model’s interpretability and buy-in.
Machine learning model’s conformance with privacy standards.
These five factors separate the high-value vendors and models from the crowd, so healthcare systems can quickly implement machine learning and start seeing improvement results.
Five Practical Steps Towards Healthcare Data GovernanceHealth Catalyst
Health systems increasingly recognize data as one of their top strategic assets, but how many organization have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data.
By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset:
Identify the organizational priorities.
Identify the data governance priorities.
Identify and recruit the early adopters.
Identify the scope of the opportunity appropriately.
Enable early adopters to become enterprise data governance leaders and mentors.
Four Strategies Drive High-Value Healthcare Analytics for COVID-19 RecoveryHealth Catalyst
COVID-19 response and recovery is pushing healthcare to operate at an unprecedented level. To meet these demands and continue to improve outcomes and lower costs, healthcare analytics must perform more actionably and with broader organizational impact than ever. Health systems can follow four strategies to produce high-value analytics to withstand the pandemic and make healthcare better in the long term:
Minimize benchmarking.
Outsource regulatory reporting.
Grow risk-based stratification capabilities.
Run activity-based costing plus at-risk contracting.
Platforms and Partnerships: The Building Blocks for Digital InnovationHealth Catalyst
Virtually all service-oriented industries have experienced massive disruption and transformation, resulting from the confluence of digital, mobile, cloud, data, and consumerization. And then there’s healthcare…
In this webinar Ryan Smith, executive advisor at Health Catalyst, shares practical insights gained from his combined 25 years of IT and digital leadership roles at Banner Health and Intermountain Healthcare. He explores why our industry is struggling to provide the tools and self-service experiences that patients and consumers have come to expect in every other aspect of their lives. To attract and retain patients and members, healthcare organizations need to “shift gears” and go on the digital offensive to sustain brand loyalty; however, decades of siloed, monolithic approaches to implementing technology and managing data continue to hamper industry progress.
During this session, Ryan shares his approach for building business support to enable digital transformation.
By viewing this webinar, you will learn key digitization concepts:
- How to conceptualize a digital enablement framework.
- Ten strategic guiding principles for technology leaders.
- Why it’s vital to create business-driven technology governance.
- Why building strategic vendor partnerships really matters.
- How to apply case studies to bolster digital investments.
Six Proven Methods to Combat COVID-19 with Real-World AnalyticsHealth Catalyst
As data in healthcare becomes more available than ever before, so does the need to apply that data to the unique challenges facing health systems, especially in a pandemic. Even with massive amounts of data, health systems still struggle to move data from spreadsheets to drive change in a clinical setting.
These six methods allow health systems to transform data into real-world analytics, going beyond basic data usage and maximizing actionable insight:
1. Create effective information displays.
2. Add context to data.
3. Ensure data processes are sustainable.
4. Certify data quality.
5. Provide systemwide access to data.
6. Refine the approach to knowledge management.
Advancing data use in healthcare with real-world analytics arms health systems with effective tools to combat COVID-19 and continue delivering quality care driven by comprehensive, actionable insight.
Population Health Success: Three Ways to Leverage DataHealth Catalyst
As the healthcare industry continues to focus on value, rather than volume, health systems are faced with delivering quality care to large populations with limited resources. To implement population health initiatives and deliver results, it is critical that care teams build population health strategies on actionable, up-to-date data. Health systems can better leverage data within population health and drive long-lasting change by implementing three small changes:
Increase team members’ access to data.
Support widespread data utilization.
Implement one source of data truth.
Access to accurate, reliable data boosts population health efforts while maintaining cost and improving outcomes. With actionable analytics providing insight and guiding decisions, population health teams can drive real change within their patient populations.
The Top Seven Healthcare Outcome Measures and Three Measurement EssentialsHealth Catalyst
Healthcare outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this article adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples. The top seven categories of outcome measures are:
Mortality
Readmissions
Safety of care
Effectiveness of care
Patient experience
Timeliness of care
Efficient use of medical imaging
CMS used these seven outcome measures to calculate overall hospital quality and arrive at its 2018 hospital star ratings. This article also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement.
This report analyzes the most recent financials and market updates for five major IT services firms (Wipro, Infosys, TCS, Cognizant, and HCL - sometimes referred to as the WITCH group), along with their market announcements during the 2nd quarter of 2016. The report also contains market updates for the last quarter on a number of other companies with a healthcare focus.
Data Visualization Dashboards: Three Ways to Maximize DataHealth Catalyst
With an unpredictable future due to COVID-19, health systems must leverage data to drive decision making at every organizational level. Data visualization dashboards allow health systems to optimize their data and create a data-driven culture by displaying large, real-time data sets in an easy-to-understand dashboard.
Health systems that rely on dashboard reporting maximize their data in three important ways:
Time to value. Decision makers do not have time to wait for manually-created reports; dashboards quickly convey information so leaders can make swift decisions.
Data democratization. Leveraging a central source of truth, dashboards allow leaders at every level to access the most updated, accurate data.
Digestible data. Analysts can configure dashboards to highlight important figures and trends, so high-level leaders can understand complex data without diving into spreadsheets.
Advancing Health Equity: A Data-Driven Approach Closes the Gap Between Intent...Health Catalyst
Improving health equity is gaining traction as a healthcare delivery imperative. Yet, while equity is indivisible from healthcare quality, many initiatives targeting disparities fall short. Organizations too often rely solely on leader and stakeholder passion and perseverance without sufficiently leveraging data and analytics to understand, measure, and support equity improvement efforts. It’s time for the industry to pursue equitable care with the same resources it uses in other key dimensions, such as safety and efficacy—by leveraging data. A data-driven approach to equity opens health system’s most advanced predictive resources to equity efforts, thereby driving massive, measurable, data-informed improvement that benefits all.
Harnessing the Power of Healthcare Data: Are We There YetHealth Catalyst
What can healthcare learn from Formula One racing? According to Dr. Sadiqa Mahmood, SVP of medical affairs and life sciences for Health Catalyst, race support teams leverage about 30TB of baseline data to create a digital twin of the car, track, and racer for simulation models that drive decisions at each race. Applied in the healthcare setting, a digital twin can help clinicians better understand each patient and their health conditions and circumstances in real time and make comprehensive, informed care decisions. But for the healthcare digital twin to happen, the industry must move away from data silos and towards a digital learning healthcare ecosystem.
Putting Patients Back at the Center of Healthcare: How CMS Measures Prioritiz...Health Catalyst
Today’s healthcare encounters are too often marked by more clinician screen time than patient-clinician engagement. Increasing regulatory reporting burdens are diverting clinician attention from their true priority—the patient. To put patients back at the center of care, CMS introduced its Meaningful Measures framework in 2017. The initiative identifies the highest priorities for quality measurement and improvement, with the goal of aligning measures with CMS strategic goals, including the following:
Empowering patients and clinicians to make decisions about their healthcare.
Supporting innovative approaches to improve quality, safety, accessibility, and affordability.
Predicting Denials to Improve the Healthcare Revenue Cycle and Maximize Opera...Health Catalyst
Healthcare financial leaders are constantly brainstorming ways to increase operating margins through better revenue cycle performance. These efforts often lead revenue cycle leaders to denied claims—when a payer doesn’t reimburse a health system for a service rendered. Although denials are a common reason for lost revenue, experts deem nearly 90 percent avoidable.
Effective denials management starts with prevention. Organizations can use revenue cycle performance data, combined with artificial intelligence, to predict areas within each claim’s lifecycle that are likely to result in a denial. With denial insight, health systems can optimize revenue cycle processes to prevent denials and increase operating margins.
Interoperability in Healthcare Data: A Life-Saving AdvantageHealth Catalyst
When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources—such as health information exchanges (HIEs) and patient-generated and -reported data—round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices.
To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.
How to Run Your Healthcare Analytics Operation Like a BusinessHealth Catalyst
A robust data analytics operation is necessary for healthcare systems’ survival. Just like any business, the analytics enterprise needs to be well managed using the principles of successful business operations.
This article walks through how to run an analytics operation like a business using the following five-question framework:
Who does the analytics team serve and what are those customers trying to do?
What services does the analytics team provide to help customers accomplish their goals?
How does the analytics team know they’re doing a great job and how do they communicate that effectively to the leadership team?
What is the most efficient way to provide analytics services?
What is the most effective way to organize?
EHR Integration: Achieving this Digital Health ImperativeHealth Catalyst
As the digital trajectory of healthcare rises, health systems have an array of new resources available to make more effective and timely care decisions. However, to use these data analytics, machine learning, predictive analytics, and wellness applications to gain real-time, data-driven insight at the point of care, health systems must fully integrate the tools with their EHRs. Integration brings technical and administrative challenges, requiring organizations to coordinate around standards, administrative processes, regulatory principles, and functional integration, as well as develop compelling integration use cases that drive demand. When realized, full EHR integration will allow clinicians to leverage data from across the continuum of care (from health plan to patient-generated data) to improve patient diagnosis and treatment.
Optimize Your Labor Management with Health Catalyst PowerLabor™Health Catalyst
To cut costs, healthcare leaders are looking at their greatest operating expense—labor management. However, with outdated labor management systems, decision makers rely on retrospective, incomplete data to forecast staffing volumes and patient support needs. Limited workforce insight can result in misaligned staffing or worse, jeopardizing patient care due to lack of labor support. With the Health Catalyst PowerLabor™ application, part of the Financial Empowerment Suite™, decision makers have access to a comprehensive view of labor data by organization, department, team, and job role. Timely insight into current and future hospital needs allows leaders to staff to patient volume, control escalating labor expenses, and ensure optimal resources for excellent patient care.
Four Essential Ways Control Charts Guide Healthcare ImprovementHealth Catalyst
Control charts are a critical asset to any health system seeking effective, sustainable improvement. With a simple three-line format, control charts show process change over time, including the average of the data, upper control limit, and lower control limit. This insight helps improvement teams monitor projects, understand opportunities and the impact of initiatives, and sustain improved processes.
Also known as Shewhart charts or statistical process control charts, control charts drive effective improvement by addressing three fundamental questions:
1. What is the goal of the improvement project?
2. How will the organization know that a change is an improvement?
3. What change can the organization make that will result in improvement?
Three Must-Haves for a Successful Healthcare Data StrategyHealth Catalyst
Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy.
To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:
Best practices to identify target behaviors and practices.
Analytics to accelerate improvement and identify gaps between best practices and analytic results.
Adoption processes to outline the path to transformation.
The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentia...Health Catalyst
Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience.
These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors:
Vendor’s expertise and exclusive focus on healthcare.
Machine learning model’s access to extensive data sources.
Machine learning model’s ease of implementation.
Machine learning model’s interpretability and buy-in.
Machine learning model’s conformance with privacy standards.
These five factors separate the high-value vendors and models from the crowd, so healthcare systems can quickly implement machine learning and start seeing improvement results.
Five Practical Steps Towards Healthcare Data GovernanceHealth Catalyst
Health systems increasingly recognize data as one of their top strategic assets, but how many organization have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data.
By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset:
Identify the organizational priorities.
Identify the data governance priorities.
Identify and recruit the early adopters.
Identify the scope of the opportunity appropriately.
Enable early adopters to become enterprise data governance leaders and mentors.
Four Strategies Drive High-Value Healthcare Analytics for COVID-19 RecoveryHealth Catalyst
COVID-19 response and recovery is pushing healthcare to operate at an unprecedented level. To meet these demands and continue to improve outcomes and lower costs, healthcare analytics must perform more actionably and with broader organizational impact than ever. Health systems can follow four strategies to produce high-value analytics to withstand the pandemic and make healthcare better in the long term:
Minimize benchmarking.
Outsource regulatory reporting.
Grow risk-based stratification capabilities.
Run activity-based costing plus at-risk contracting.
Platforms and Partnerships: The Building Blocks for Digital InnovationHealth Catalyst
Virtually all service-oriented industries have experienced massive disruption and transformation, resulting from the confluence of digital, mobile, cloud, data, and consumerization. And then there’s healthcare…
In this webinar Ryan Smith, executive advisor at Health Catalyst, shares practical insights gained from his combined 25 years of IT and digital leadership roles at Banner Health and Intermountain Healthcare. He explores why our industry is struggling to provide the tools and self-service experiences that patients and consumers have come to expect in every other aspect of their lives. To attract and retain patients and members, healthcare organizations need to “shift gears” and go on the digital offensive to sustain brand loyalty; however, decades of siloed, monolithic approaches to implementing technology and managing data continue to hamper industry progress.
During this session, Ryan shares his approach for building business support to enable digital transformation.
By viewing this webinar, you will learn key digitization concepts:
- How to conceptualize a digital enablement framework.
- Ten strategic guiding principles for technology leaders.
- Why it’s vital to create business-driven technology governance.
- Why building strategic vendor partnerships really matters.
- How to apply case studies to bolster digital investments.
Six Proven Methods to Combat COVID-19 with Real-World AnalyticsHealth Catalyst
As data in healthcare becomes more available than ever before, so does the need to apply that data to the unique challenges facing health systems, especially in a pandemic. Even with massive amounts of data, health systems still struggle to move data from spreadsheets to drive change in a clinical setting.
These six methods allow health systems to transform data into real-world analytics, going beyond basic data usage and maximizing actionable insight:
1. Create effective information displays.
2. Add context to data.
3. Ensure data processes are sustainable.
4. Certify data quality.
5. Provide systemwide access to data.
6. Refine the approach to knowledge management.
Advancing data use in healthcare with real-world analytics arms health systems with effective tools to combat COVID-19 and continue delivering quality care driven by comprehensive, actionable insight.
Population Health Success: Three Ways to Leverage DataHealth Catalyst
As the healthcare industry continues to focus on value, rather than volume, health systems are faced with delivering quality care to large populations with limited resources. To implement population health initiatives and deliver results, it is critical that care teams build population health strategies on actionable, up-to-date data. Health systems can better leverage data within population health and drive long-lasting change by implementing three small changes:
Increase team members’ access to data.
Support widespread data utilization.
Implement one source of data truth.
Access to accurate, reliable data boosts population health efforts while maintaining cost and improving outcomes. With actionable analytics providing insight and guiding decisions, population health teams can drive real change within their patient populations.
The Top Seven Healthcare Outcome Measures and Three Measurement EssentialsHealth Catalyst
Healthcare outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this article adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples. The top seven categories of outcome measures are:
Mortality
Readmissions
Safety of care
Effectiveness of care
Patient experience
Timeliness of care
Efficient use of medical imaging
CMS used these seven outcome measures to calculate overall hospital quality and arrive at its 2018 hospital star ratings. This article also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement.
This report analyzes the most recent financials and market updates for five major IT services firms (Wipro, Infosys, TCS, Cognizant, and HCL - sometimes referred to as the WITCH group), along with their market announcements during the 2nd quarter of 2016. The report also contains market updates for the last quarter on a number of other companies with a healthcare focus.
Data Visualization Dashboards: Three Ways to Maximize DataHealth Catalyst
With an unpredictable future due to COVID-19, health systems must leverage data to drive decision making at every organizational level. Data visualization dashboards allow health systems to optimize their data and create a data-driven culture by displaying large, real-time data sets in an easy-to-understand dashboard.
Health systems that rely on dashboard reporting maximize their data in three important ways:
Time to value. Decision makers do not have time to wait for manually-created reports; dashboards quickly convey information so leaders can make swift decisions.
Data democratization. Leveraging a central source of truth, dashboards allow leaders at every level to access the most updated, accurate data.
Digestible data. Analysts can configure dashboards to highlight important figures and trends, so high-level leaders can understand complex data without diving into spreadsheets.
Advancing Health Equity: A Data-Driven Approach Closes the Gap Between Intent...Health Catalyst
Improving health equity is gaining traction as a healthcare delivery imperative. Yet, while equity is indivisible from healthcare quality, many initiatives targeting disparities fall short. Organizations too often rely solely on leader and stakeholder passion and perseverance without sufficiently leveraging data and analytics to understand, measure, and support equity improvement efforts. It’s time for the industry to pursue equitable care with the same resources it uses in other key dimensions, such as safety and efficacy—by leveraging data. A data-driven approach to equity opens health system’s most advanced predictive resources to equity efforts, thereby driving massive, measurable, data-informed improvement that benefits all.
Harnessing the Power of Healthcare Data: Are We There YetHealth Catalyst
What can healthcare learn from Formula One racing? According to Dr. Sadiqa Mahmood, SVP of medical affairs and life sciences for Health Catalyst, race support teams leverage about 30TB of baseline data to create a digital twin of the car, track, and racer for simulation models that drive decisions at each race. Applied in the healthcare setting, a digital twin can help clinicians better understand each patient and their health conditions and circumstances in real time and make comprehensive, informed care decisions. But for the healthcare digital twin to happen, the industry must move away from data silos and towards a digital learning healthcare ecosystem.
Putting Patients Back at the Center of Healthcare: How CMS Measures Prioritiz...Health Catalyst
Today’s healthcare encounters are too often marked by more clinician screen time than patient-clinician engagement. Increasing regulatory reporting burdens are diverting clinician attention from their true priority—the patient. To put patients back at the center of care, CMS introduced its Meaningful Measures framework in 2017. The initiative identifies the highest priorities for quality measurement and improvement, with the goal of aligning measures with CMS strategic goals, including the following:
Empowering patients and clinicians to make decisions about their healthcare.
Supporting innovative approaches to improve quality, safety, accessibility, and affordability.
Predicting Denials to Improve the Healthcare Revenue Cycle and Maximize Opera...Health Catalyst
Healthcare financial leaders are constantly brainstorming ways to increase operating margins through better revenue cycle performance. These efforts often lead revenue cycle leaders to denied claims—when a payer doesn’t reimburse a health system for a service rendered. Although denials are a common reason for lost revenue, experts deem nearly 90 percent avoidable.
Effective denials management starts with prevention. Organizations can use revenue cycle performance data, combined with artificial intelligence, to predict areas within each claim’s lifecycle that are likely to result in a denial. With denial insight, health systems can optimize revenue cycle processes to prevent denials and increase operating margins.
Interoperability in Healthcare Data: A Life-Saving AdvantageHealth Catalyst
When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources—such as health information exchanges (HIEs) and patient-generated and -reported data—round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices.
To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.
Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion...IJECEIAES
In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact.
Mining the Web Data for Classifying and Predicting Users’ RequestsIJECEIAES
Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client‟s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands.
Data-enhanced customer experience: a recipe for happy and loyal customers. Perception defines quality, and it’s time to turn user data into a competitive advantage in enhancing the customer experience.
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
Learn how financial institutions are betting on the Big Data and Artificial Intelligence through APIs that help banks to define products, segmenting customers and detect possible fraud. Throughout this ebook we offer a review of the APIs bank data aggregation. More information in http://bbva.info/2t1NEv7
Consumer analytics is the process businesses adopt to capture and analyze customer data to make better business decisions via predictive analytics. It is a method of turning data into deep insights to predict customer behavior. It may also be regarded as the process by which data can be turned into predictive insights to develop new products, new ways to package existing products, acquire new customers, retain old customers, and enhance customer loyalty. It helps businesses break big problems into manageable answers. This paper is a primer on consumer analytics. Matthew N. O. Sadiku | Sunday S. Adekunte | Sarhan M. Musa "Consumer Analytics: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33511.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/33511/consumer-analytics-a-primer/matthew-n-o-sadiku
E-Commerce Mobile Marketing Model Resolving Users Acceptance Criteria IJMIT JOURNAL
The growth of e-commerce and mobile services has been creating business opportunities. Among these,
mobile marketing is predicted to be a new trend of marketing. As mobile devices are personal tools such
that the services ought to be unique, in the last few years, researches have been conducted studies related
to the user acceptance of mobile services and produced results. This research aims to develop a model of
mobile e-commerce mobile marketing system, in the form of computer-based information system (CBIS)
that addresses the recommendations or criteria formulated by researchers. In this paper, the criteria
formulated by researches are presented then each of the criteria is resolved and translated into mobile
services. A model of CBIS, which is an integration of a website and a mobile application, is designed to
materialize the mobile services. The model is presented in the form of business model, system procedures,
network topology, software model of the website and mobile application, and database models.
Unveiling the Power of Data Analytics.pdfJyoti Sharma
In today's digitally-driven world, data is more than just numbers and statistics – it's the fuel that powers informed decision-making and propels businesses to new heights. Enter data analytics, a dynamic field that extracts meaningful insights from raw data, enabling organizations to optimize processes, enhance customer experiences, and drive innovation. In this blog, we delve into the realm of data analytics, exploring its significance, methodologies, and real-world applications.
IMPLEMENTATION OF A DECISION SUPPORT SYSTEM AND BUSINESS INTELLIGENCE ALGORIT...ijaia
Data processing is crucial in the insurance industry, due to the important information that is contained in
the data. Business Intelligence (BI) allows to better manage the various activities as for companies
working in the insurance sector. Business Intelligence based on the Decision Support System (DSS), makes
it possible to improve the efficiency of decisions and processes, by improving them to the individual
characteristics of the agents. In this direction, Key Performance Indicators (KPIs) are valid tools that help
insurance companies to understand the current market and to anticipate future trends. The purpose of the
present paper is to discuss a case study, which was developed within the research project "DSS / BI
HUMAN RESOURCES", related to the implementation of an intelligent platform for the automated
management of agents' activities. The platform includes BI, DSS, and KPIs. Specifically, the platform
integrates Data Mining (DM) algorithms for agent scoring, K-means algorithms for customer clustering,
and a Long Short-Term Memory (LSTM) artificial neural network for the prediction of agents KPIs. The
LSTM model is validated by the Artificial Records (AR) approach, which allows to feed the training dataset
in data-poor situations as in many practical cases using Artificial Intelligence (AI) algorithms. Using the
LSTM-AR method, an analysis of the performance of the artificial neural network is carried out by
changing the number of records in the dataset. More precisely, as the number of records increases, the
accuracy increases up to a value equal to 0.9987.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Overview on Edible Vaccine: Pros & Cons with Mechanism
Big Data Analytics on Customer Behaviors with Kinect Sensor Network
1. Hongye Zhong & Jitian Xiao
International Journal of Human Computer Interaction (IJHCI), Volume (6) : Issue (2) : 2015 36
Big Data Analytics on Customer Behaviors with Kinect Sensor
Network
Hongye Zhong hzhong@our.ecu.edu.au
School of Computer and Security Science
Edith Cowan University
WA, 6050, Australia
Jitian Xiao j.xiao@ecu.edu.au
School of Computer and Security Science
Edith Cowan University
WA, 6050, Australia
Abstract
In modern enterprises, customer data is valuable for identifying their behavioral patterns and
developing marketing strategies that can align with the preferences of different customers. The
objective of this research is to develop a framework that promotes the use of Kinect sensors for
Big Data Analytics on customer behavior analysis. Kinect enables 3D motion capture, facial
recognition and voice recognition capabilities which allow to analyze customer behaviors in
various aspects. Information fusion on the network of multiple Kinect sensors can achieve
enhanced insight of the customer emotion, habits and consuming tendencies. Big Data Analytic
techniques such as clustering and visualization are applied on the data collected from the
sensors to provide better comprehension on the customers. Prediction on how to improve the
customer relationship can be made to stimulate the vendition. Finally, an experimental system is
designed based on the proposed framework as an illustration of the framework implementation.
Keywords: Big Data, Kinect, Customer Behavior Analysis, Human Computer Interaction.
1. INTRODUCTION
With the increasingly market competition fierce, the core of competition has been shifting from the
products to the customers. Customer Relationship Management (CRM) has become an emerging
topic for enterprises, which encourage relationship enhancement, sales force automation and
opportunity prediction that require the marketing to be of higher technology and systemization.
Analysis of different consumer behaviors and clustering customers into different characteristic
groups will contribute to the development of efficient marketing strategies to enhance customer
satisfaction and loyalty [1]. Especially in the dealer stores of Toyota, customer-centric operations
are highly valued. It is advocated that the behaviors and characteristics of visiting customers
should be tracked, managed and analyzed. The results can then be utilized to provide better
services and information to the customers.
As a recent evolution of sensing technology, Microsoft introduced Kinect as a cost-efficient device
for RGB-D mapping, motion tracking, facial recognition and voice recognition. Kinect has been
widely used in interactive gaming that allows the players to control the objects in the games by
gestural or vocal commands. Because of its economical price and flexible functionality,
enterprises also start to adopt Kinect as a production component in different environments, for
example, in facial authentication systems and for robotic controls [2]. For its versatility, we
propose to use Kinect to collect various aspects of customer behavior for analysis.
However there are some known limitations with Kinect. The racemization range of a Kinect device
is approximately 0.7 to 6 meters, and it is capable of simultaneously tracking up to six people [3].
2. Hongye Zhong & Jitian Xiao
International Journal of Human Computer Interaction (IJHCI), Volume (6) : Issue (2) : 2015 37
For enterprise scenarios such as in a retail store, one Kinect can hardly cover all monitor areas.
We need to form a sensor network by connecting multiple Kinect devices to collect all the
essential information within the specific scene and merging the multiple sources of data by
utilizing Information Fusion techniques.
The data collected from the sensor network per day can be in a very large volume, which
additionally needs to be combined with data from other sources for analysis. To process the data
of various sources and multiple forms, the techniques of Big Data Analytics (BDA) are required to
comprehend the large amount of data and extract indicative information to perform business
estimation and support decision makings [4]. Through the analysis on the data related to
customer behaviors, we expect to have better understand of them, so that we can serve them in a
manner they feel more comfortable, or we can find a better way to persuade them to consume
our goods.
2. PRELIMINARY METHODOLOGIES AND INSUFFICIENCIES
Various studies have been presented on Consumer Behavior Analysis (CBA) to create a more
effective tool for understanding consumer behaviors. Through selection of appropriate techniques
to collect, analyze and understand the data related to consumers, enterprises can improve their
marketing strategies by utilization of affecting factors such as consumer psychology influenced by
the environment, and consuming motivation and decision tendencies differing between products
[5].
A fundamental aim of CBA is to develop a radical behaviorist framework that suits the
interpretation and prediction of human economic behavior in common occasions. The framework
is denoted as Summative Behavioral Perspective Model [6] (see Fig. 1). This model assumes
consumer choice as a point where the experience of consumption meets a new opportunity to
consume. Such point is the joint production of the learning history of the consumer meeting the
current consumer behavior setting.
FIGURE 1: Summative Behavioral Perspective Model.
The Consumer Behavior Setting (CBS) comprises the antecedent stimulus conditions that certain
setting elements encourage or inhibit the behavior predicted to occur in such contexts. The
Consumption Learning History (CLH) is the past choices of the consumers that are believed to be
influential on the present consumer behaviors. In addition, current choice is also affected by the
reinforcing and punishing consequences. Utilitarian Reinforcement is the functional outcomes of
behavior that consists of direct usable, economic and technical benefits of owning and consuming
a product or service. Informational Reinforcement is the symbolic outcomes that stems from the
prestige or status and the self-esteem generated by ownership and consumption such as
performance feedbacks to the users. The corresponding punishing consequences are the
3. Hongye Zhong & Jitian Xiao
International Journal of Human Computer Interaction (IJHCI), Volume (6) : Issue (2) : 2015 38
aversive costs of purchase and consumption. All these factors of the developed model can be
used to analyze the consumer situation in terms of their demands, intention and psychological
condition. Therefore, whenever we need to estimate the consumer situation, we need to combine
the information of CBS, CLH and other influential factors to deduct a conclusion. This process
can be presented as Consumer Behavior Analysis Flow [5] (see Fig. 2).
FIGURE 2: Consumer Behavior Analysis Flow.
The analysis flow starts from recognizing the problem. This is to set the main goal of the analysis.
A problem can be to enhance customer relationship or to make a marketing strategy. Then we
need to determine what information is relevant for solving the problem. Such relevant information
is searched out from the databases of the historic records. In combination with the contextual
information, the extracted data is analyzed. The analysis result leads to a perdition about the
present or future situation of the consumer that are utilized for decision makings. After the
execution of the decision, the subsequent effects should be observed and recorded to verify the
prediction and for future analysis.
FIGURE 3: Toyota eCRB Project Overview.
4. Hongye Zhong & Jitian Xiao
International Journal of Human Computer Interaction (IJHCI), Volume (6) : Issue (2) : 2015 39
Toyota has been implementing this flow in its eCRB (Evolutionary Customer Relationship
Building) project (see Fig. 3). The project is attempting to integrate customers, dealers and
distributors with multiple inter-related systems to keep track of the customer information. In
addition, it keeps the customers informed about new products and the status of their vehicle that
has been sent for services via multiple channels in terms of SMS and email etc.
A core task of eCRB is to mark down the information of customers who walk into the dealer store
and the information will be utilized for follow-up and further analysis. Currently, sales consultants
go and talk with the visiting customers and use an iPad app called i-CROP to record the valuable
information acquired from the conversations (see Fig. 4), for example ages, interests and
contacts are logged. However, many customers are impatient to be questioned by the
consultants, especially when seeing the consultant taking notes during the conversations.
Further, the notes taken by the consultants are inconsistent and unsuitable for systematic and
comprehensive analysis.
FIGURE 4: Customer Records in i-CROP Client.
The growing demand of strategic planning entails a more automated and systematic approach to
keep track of visiting customers. More aspects of their behaviors should be marked down for
comprehensive and systematic analysis. Meanwhile, innovative measures can be introduced into
sensing their behaviors so that the customers will not feel unhappy when they find themselves
are being monitored.
Worcester Polytechnic Institute designed an automated system using Kinect devices for remote
control and user identification in a smart home environment [7]. In the project, the vocal
recognition function of Kinect enables the remote control of the appliances, by calling vocal
commands, within a smart house. The facial recognition function is used to replace the traditional
door keys to lock or unlock the house doors. Motion tracking function is used to monitor the
walking activities of people in the house. However it lacks of details of analytic processes and
algorithms that may apply to analyze the data collected from the Kinect sensors. Although the
project arouses researchers’ concern about using of Kinect for human motion tracking, it is
illuminative for enhancing the flexibility and operation efficiency of Toyota’s eCRB system.
3. THE PROPOSED FRAMEWORK
In order to achieve more comprehensive analysis, the analytic system should be implemented
with several distinguished sensing approaches, so that the consumer behaviors can be observed
with different aspects. The diversified data sources are believed to provide more accurate analytic
5. Hongye Zhong & Jitian Xiao
International Journal of Human Computer Interaction (IJHCI), Volume (6) : Issue (2) : 2015 40
results overall. Kinect is capable of motion and voice capture, and Kinect v2 even has the ability
to detect the pulse of human beings [8]. These can assist to measure both the external (e.g. facial
expressions) and internal behaviors (e.g. emotions) of the consumers. In addition, to increase the
accuracy, the analytic system should not only implement passive observations, but also actively
trigger some interactions with the customers and watch the non-regulatory feedings.
FIGURE 5: Kinect Behavior Analytic Framework.
Based on the above concerns, we propose a design namely Kinect Behavior Analytic Framework
(KBAF, Fig. 5). It is consisted of eight core modules: Motion Observer, Vocal Recorder, Pulse
Monitor, Interaction Center, Data Fuser, Learning History Repository, Big Data Analyzer, and
SOA Provider. In small environments, a single Kinect sensor might be able to competent for all
sensing tasks. In large environments, one Kinect device might not be able to cover all monitor
areas, whereas a sensor network is required to connect multiple Kinect devices. The sensing
modules can be implemented separately. Each Kinect sensor is in charge of monitoring certain
locations or aspects of the customers.
Motion Observer is a sensing module that uses Kinect for body tracking and facial recognition.
Body tracking enables to count the visitor numbers and monitor the reactions of the customers
towards different products. Facial recognition can analyze the ages and genders of the customer,
and allows to observe the facial expressions of customer under different contexts which can be
used to infer the emotions and the interests of a customer. It can also recognize the revisits of
customers. Higher revisit count might signals higher purchase tendency of the customers.
Vocal Recorder is a sensing module that uses Kinect for capturing voice. As facial recognition
with Kinect has restricted precision, assistance of vocal recognition can increase the accuracy of
customer recognition. On the other hand, the captured vocal data can be used to detect the
emotion status of the customer by analyzing the vocal patterns [9], so that we can determine the
communication strategy toward specific customers.
Pulse Monitor is a sensing module that detects the heartbeats of the customer. Pulse detection is
a new function added to Kinect v2 [8]. According to past research, emotion can be recognized
from the pulse waveform [10]. With the pulse information, we can trace the emotional change of
customers. Based on this information, we may get a better understanding of which products are
more interested to which customers, and apply corresponding sales strategies based on the
differences of interests.
Interaction Center is a module that intentionally triggers events that allow the customers to
participate and response. During the participation, the reactions and feedbacks of the customers
6. Hongye Zhong & Jitian Xiao
International Journal of Human Computer Interaction (IJHCI), Volume (6) : Issue (2) : 2015 41
are observed. Customers evoke behaviors related with the established events. Through
examining the influences on the customer behaviors related to the specific events, the reinforcing
or punishing effects of the environmental conditions can be learned [6]. The reinforcing and
punishing variables are used to enhance accuracy of the consumer behavior prediction.
Data Fuser is a data process module that transforms raw data into analyzable data for future
archival or analytical purposes. The original data is in multiple forms in terms of video, image and
voice data etc. The raw data is unsuitable for comparison and statistical analysis, and the
computation is inefficient and inaccurate. Directly keeping the video and voice data also
consumes huge storage space. In addition, in some environments multiple Kinect devices are
used to enlarge the monitor areas or increase the capture quality. Multi-sensor integration is
necessary to enhance the synergistic effect of the monitoring by better robustness and noise
suppression. Therefore, normalizing the raw data into a consistent format and endowing with
proper metadata is essential for successful analytics.
Learning History Repository is a database modeled for flexible storage and retrieval of data. It
stores the monitor records and corresponding metadata about the visited customers and
environmental information about the visits. Although the data sourced from different sensors have
been refined by the Data Fuser, the contents and formats are not all textually similar or
meaningful. NoSQL databases are encouraged to be implemented to support flexible and efficient
processes on the unstructured data [11].
Big Data Analyzer is the module that performs analytic processes in terms of statistical analysis
and visualization on the archived data in the repository. Some enterprises might select to
implement this module with commercial analytic software (e.g. SPSS, SAS or Stata). Some
others might consider to develop in-house analyzers with dedicated analytic algorithms and
analytic frameworks including Python, R and Mahout. Especially R is favored by large enterprises
as it comes with a sum of built-in statistical functions and powerful drawing facilities. Moreover, it
can work together with a popular distributed computing framework called Hadoop. HDFS
(Hadoop Distributed File Systems) and MapReduce are the most vital functionalities with the
Hadoop. For larger storage space or redundant storage strategy, HDFS is usually used to share
data between different modules. MapReduce is a data optimizing component that are helpful for
abstracting large volume of data and providing efficient job control and task management to
enhance analysis performance [4].
SOA Provider is the module that converts the past analytic results into web services that can be
retrieved by other systems. SOA (Service-oriented Architecture) is a design style for system
development based on loosely coupled, coarse-grained and autonomous components. In large
information systems of enterprise, SOA encourages to encapsulate complex business logics into
services that can be distributed as flexibly consumable components. Request/Reaction Pattern of
SOA should be implemented to allow asynchronous communication when the results are being
retrieved [12].
4. EXPERIMENT DESIGN AND EVALUATION
KBAF can be implemented in many different environments with different sensor fusion and
analytic algorithms. Toyota is developing an experimental project called iDealer that has been
designed based on the KBAF (see Fig. 6). The main aim of this project is to establish a serial of
facilities to capture and analyze the behaviors of visiting customers and determine corresponding
marketing strategies catering for different customers. iDealer project is mainly consisted of two
sub-systems: Extroverted System and Analytic System.
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FIGURE 6: iDealer Project Overview.
Extroverted System works at the front end and directly interacts with the customers. It also uses
Kinects sensors and touchable devices to capture the actions and reactions of the customers. As
Kinect SDK is required to be installed on Windows platforms, we use Windows Server 2012 as
the operating system for management of the sensor network. Analytic System works behind and
in charge of the analytic processes. It is tasked to store and analyze the data collected from the
Extroverted System. As Linux platforms have more powerful statistic tools and higher flexibility for
analytic algorithm implementation, we choose Ubuntu as its operating system.
Interaction Center is the brain of the Extroverted System. It manages media and interactive
components presented to the customers. In a Toyota dealer store, there are several large-screen
displays playing various advertising video clips such as car introduction and car news (see Fig.
7). While the customers are watching such videos, their reactions are monitored by Kinect. From
the sensed data collected, we could understand the emotional changes of customer when playing
different video, so that we can adapt the marketing strategy accordingly. For example, if
customers feel disappointed when watching the video of Highlander, we can offer more discounts
on Highlander to stimulate its sales. More directly, Interaction Center can receive feedbacks from
the interactive applications when the customers are playing with the applications. These
applications include multi-media car type information panel and car racing games etc. The actions
taken by customers on the applications provides many clues of personal information about the
customers themselves such as their favored car and favored colors. For instance, a customer
click Camry for many times, we might infer that he/she is thinking about buying a Camry.
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FIGURE 7: Interactive Components and Behavior Recognition.
Kinect can be used on more aspects. For example, we can place a sensor facing the front gate,
then the faces of most customers can be captured. As Kinect can be used for face recognition,
we can use it to count the number of visitors or to identify the revisit customers. The facial
captures are 3d models composed of polygonal meshes and texture images. They are exported
as FBX files which can be stored in databases and used for identifying revisit customers. We
compare both the meshes and images for face recognition to enhance the accuracy.
As the faces are captured in slightly varied distances and angles, the coordinates of mesh have
to be normalized to be comparable with scaling and rotations. The vector normalizations can be
expressed in the following equation:
where PN denotes normalized deformed textured model, PD the deformed 3D model coordinates,
the rotation matrix, and the mean coordinates of all the deformed models [13]. is
calculated by applying the combination of necessary rotation of difference axes, which can be
express as . The 3D decimal coordinates can then be projected into 2D integer
coordinates for simpler comparison. The texture images of faces can be compared by using
modified PCA (Principal Component Analysis) [14]. We project image A onto n-D unitary column
vector by applying the transformation: . Then we find out the image covariance matrix:
where i = (1,2,3, …N,) is the number of training samples and denotes the average image
matrix. If a given image A has optimal projection vectors X1, X2, …, Xd, we have
. Suppose projection vectors and , the
reconstructed depth map of the image can be obtained by:
If d=N in the reconstructed image , the image will be the same as the original image A, i.e.
.
However, Kinect has limited recognition distance. To achieve different monitor functions and
cover more areas, multiple Kinect devices are required to work simultaneously. How to efficiently
assembly the data from multiple sources of sensor arouses a problem of Information Fusion. One
of the key tasks of Data Fuser is to resolve this problem.
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FIGURE 8: The Revised JDL Model.
According to JDL (Joint Directors of Laboratories) model (see Fig. 8), the activities of data fusion
can be categorized into four levels in accordance with their functional sequences in an intelligent
system, namely Object Assessment, Situation Assessment, Impact Assessment and Process
Refinement [15]. Data Fuser sits at the level 1 and level 2 of the JDL model that it fuse the status
information of customers and the underlying environments from multiple sources of sensor into
normalized data suitable for query and analysis.
In the level 1 fusion, metrics are applied on various types of original data. For example, voice
records of customer are categorized according to their sample shapes of sound waves. Emotional
patterns of the voices can be identified and documented with ordinal notations. For instance, 0
stands for normal emotion, -2 stands for disappointment, and 3 stands for happiness etc.
Similarly, facial images and pulse records can also be transformed into ordinal or numerical data.
In addition, metadata is about attached with the transformed data such as the sensor number of
the data source and capture dates of the data etc. The metadata provides contextual information
related to the measurement results. Then the normalized data becomes ready for mathematically
computation. In the level 2 fusion, based on the environmental and time relations of interest, the
normalized data is aggregated into a representation that are suitable for further analysis.
After normalization and aggregation data is stored in a Fusion Database. Akin to Fusion
Database, we create a Data Repository for efficient data storage and retrieval in the Analytic
System of iDealer. It is implemented with CouchDB which is a NoSQL database relying on a
different approach rather than relational data management structure, storage and retrieval
methods. The motivation of using CouchDB is for the data storage of unstructured formats and
availability for agile query with RESTful http requests which can enhance the efficiency of BDA
processes. Data Fuser can append a new entry into the CouchDB by issuing PUT and POST
requests (see Fig. 9), and such entry can be retrieved via http GET requests.
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FIGURE 9: CouchDB Data Record.
Moreover, with CouchDB-Python library, we are able to perform database management and
advanced mapping between JSON documents and Python objects. It simplifies processes for
retrieving the data into Python environment and performing statistical analysis on the data
objects. Besides the strong CouchDB support, various Python libraries for statistics and analytics
are available. In addition, it is made with powerful data manipulation and algorithm
implementation capacity, which renders Python to be an excellent choice for building data-centric
applications.
The data analytic module in our project is called Data Analyzer, which is built on Python
environment with several statistical frameworks, mainly including Pandas, Scikit and Matplotlib.
Pandas provides rich data structures and handy functions to manipulate structured data such as
to reshape, slice, aggregate and filter datasets. Scikit is a machine learning library which features
various mathematical functions in terms of classification, regression and clustering analysis. It
provides built-in API for applying large number of analytic tools such as Vector Machines, Logistic
Regression, Naive Bayes, Random Forests, Gradient Boosting, K-means and DBSCAN.
Matplotlib is a graph drawing framework that can be used to plot numeric data [16].
Data Analyzer provides level 3 and level 4 fusion of the JDL model. A number of data analytics
are performed in accordance with business requirements of the dealer store. For example,
customers are clustered into several categories (see Fig. 10), so that different marketing
strategies are applied accordingly. We use Indicators of Collective Behavior (ICB) algorithm to
identify maneuvering entities of akin actions [15]. Assuming totally N customers have been
tracked, the edge weights (EW) graph of these entities can be represented as:
where denotes the distance between entities i and j, and are the differences in the
speed and heading of the entities, and and are the constant determinants of emphasis to
separation, speed, and heading criteria. The nodes of EW graph is then allocated into candidate
clusters by repeating application of Parzen window estimator [17]:
where denotes the distance from to its kth nearest neighbor, K is the kernel function to
compute similarities, and the output probability dense are used for searching clusters.
Another example is to establish regression analysis on the customer counts. We use Gaussian
Processes to solve this regression problem [18], in which the distribution can be expressed as:
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where and are the mean and covariance of the distribution. We can apply promotion
strategies on business downturn or reinforce sale consultants in peak seasons in accordance with
the prediction based on the visiting customer distribution (e.g., Fig.10 shows a single month
prediction of customer numbers).
FIGURE 10: Analytics with Customer Data.
The analytic results are forwarded to the Service Provider, a web component that can convert the
analytic results to web services available to other modules or other systems. It is implemented by
two python toolkits, namely Webpy and Soaplib. Webpy is a simple and powerful web
development framework that can easily interact with other python libraries. Soaplib is a python
library that can publish SOAP web services using WSDL standard, and answering SOAP
requests. Many enterprises implement well encapsulated web services to communicate between
systems to reduce the complexity of information systems and increase the integration. For
example, Toyota is constructing TSB (Toyota Service Bus) to enable the sharing of data and
systems globally. The updated results are periodically retrieved by Message Dispatcher in the
Extroverted System, and forwarded to sales consultants to follow-up. Many customers have left
their contact methods in our databases. When the sales consultants receive the analytic reports
on their smart phones, they might give phone calls to the high profile customers, give them more
descriptions about their favored cars, or even offer the customers special discounts to persuade
them. Additionally, based on the analytic results, Message Dispatcher can automatically send
promotion email to the customers to inform them about the products or services they might be
interested. For the customers whose contact information is not in the databases, they will be
attended by the sales consultants next time when they visit the dealer store.
5. CONCLUSION AND FUTURE RESEARCH
This research explores the potential value for utilizing the concepts of big data analytics and
information fusion with Kinect sensors for customer behavior analysis. Kinect sensors provide
capacities of voice recognition, motion tracking and facial recognition that allows us monitor
customer behaviors in multiple aspects. Information fusion is useful when multiple sensors are
utilized simultaneously to enlarge the monitor area or enhance capture efficiency. It can assemble
the information from multiple sources to a more analyzable form that enhances the accuracy of
the analytic results and the efficiency of analysis process. In addition, the data repository also
enables the Big Data Analytics to be performed on the sensor records to probe for deeper and
more ensconced information about the customer values. The important incentive for customer
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behavior analytics for enterprises is to use the analytic reports to develop marketing strategies
that can effectively enhance customer relationship and promote their products.
For future research directions, we consider to use the developed system to make more complex
predictions for product promotion in Toyota retail stores, e.g. the demand per vehicle model per
season, and to develop approaches to evaluate and improve the prediction accuracy.
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