Data-Driven Process Improvement Raises Patient Safety for Highest-Risk Medication

Intravenous (IV) heparin is widely used to prevent thrombosis in a variety of clinical settings, yet it is considered one of the highest-risk medications used in the inpatient setting because of the potential for dosing errors. Allina Health identified multiple IV heparin protocols among its hospitals, a variation that increased the risk of errors. Standard practices that addressed patients’ clinical needs in a disease-specific way were lacking. Over the course of 1.5 years, more than 9,000 patients at Allina Health had an IV heparin protocol ordered, so IV heparin safety was of utmost concern.

To address this quality issue and improve clinical value, Allina Health created a systemwide interdisciplinary team to standardize IV heparin therapeutic guidelines and monitor the impact of the standard guideline on patient outcomes. Allina Health engaged multiple physician stakeholder groups to review proposed protocols and provide critical feedback to help ensure the best possible patient care and safety. To effectively monitor IV heparin outcomes, patient safety, and the impact of the new, standard guidelines and protocols, Allina Health developed an anticoagulation safety analytics application, using the Health Catalyst Analytics Platform, including the Late-Binding™ Data Warehouse and broad suite of analytics applications. These outcomes improvement efforts resulted in:

  • A seven percent relative improvement in the percentage of patients therapeutic within 24 hours of protocol initiation.
  • Paring 20+ site-based documents (e.g., policies, protocols, and order sets) to one systemwide guideline and four systemwide protocols.
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Faster Data Acquisition Delivers Speedy Time to Value

Effective data integration enables high value through more strategic, data-driven decision-making, while faster data acquisition feeds and speeds up the process. Orlando Health, one of Florida’s most comprehensive private, not-for-profit healthcare networks, recognized the need for effective data integration to successfully manage to the organization’s changing business needs. The health system needed the ability to rapidly acquire and link disparate healthcare data sources in various ways in order to answer clinical and business questions.

Leaders at Orlando Health needed a data warehouse that better met their needs. They determined that switching from an early binding data process to a late-binding process would provide greater flexibility and expand their access to critical data, with shorter data acquisition times.

With the new EDW, Orlando Health achieved the following efficiencies:

  • 245 fewer days and 1.0 less full time employee (FTE) needed to integrate encounter billing summary system data.
  • 56 fewer days and 0.4 less FTE needed to integrate Infection control system data.
  • 99 percent reduction (90 days saved) in the amount of time needed to implement system enhancements.
  • 98 percent reduction in the work hours needed to incorporate system enhancements.
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Data-Driven Clinical Documentation Improvement Program Increases Revenue and Improves Accuracy of Risk Adjusted Quality Metrics

Allina Health, an integrated delivery system throughout Minnesota and western Wisconsin, has long understood the value of clinical documentation improvement (CDI), and its growing importance in recent years. With the implementation of ICD-10, the specificity needed for accurate coding has increased, and reimbursement shifts have occurred as well, creating sizeable payment disparity for some clinical conditions. Leaders at Allina wanted to understand where their CDI program would have the greatest return on investment. However, data from the EHR was not sufficient to inform their strategy. CDI specialists still lacked the ability to perform a comprehensive assessment of the accuracy of clinical documentation, and were unable to confidently target improvement efforts in areas that would generate the greatest return on investment. To take a more data-driven approach, team members leveraged the Health Catalyst Analytics Platform, including their Late-Binding™ Data Warehouse and broad suite of analytics applications to develop a CDI analytics application. With the application, the team identified opportunities and thoroughly vetted them, before collaborating with physicians and service line leaders to educate providers on documentation improvements.

They achieved the following results:

  • 12.1 percent improvement in CV surgical cardiology CC/MCC capture rate.
  • 6.3 percent increase in medical cardiology CC/MCC capture rate.
  • Increased accuracy in publically reported risk adjusted quality metrics
  • Revenue capture improvement across the system – resulting in millions of dollars of additional reimbursements.
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Using Value to Prioritize and Guide Analytics Investments

With the advent of analytics, hospitals have new access to high quality, reliable data. In turn, this can fuel any number of outcomes improvement projects, but hospitals have finite resources to expend on these initiatives. A process is needed to identify which ones will deliver the highest value and best align with the hospital’s overarching priorities.

To balance the demand for analytics support of improvement projects Mission Health designed a prioritization tool that has helped them identify the right projects to approve–while keeping stakeholders more engaged than ever in improving outcomes for patients.

To date, 80 percent of 55 approved projects have met or exceeded their initial targets. Actual realized targets include:

  • 32 percent reduction in sepsis mortality
  • 20 percent improved compliance with the sepsis care process
  • 7 percent reduction in LOS for bowel surgery patients
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Turning Data from Five Different EHR Vendors into Actionable Insights

When healthcare information systems don’t talk to each other, countless inefficiencies and patient safety issues may arise.

Community Health Network (CHNw) believes in delivering outstanding care to every patient. In order to minimize patient safety risks and inefficiencies resulting from using different EHRs, CHNw embarked on a journey to integrate its healthcare information technologies. After implementing a Late-Binding™ Data Warehouse from Health Catalyst that integrates all key data sources, CHNw now has a consistent and comprehensive perspective for multiple patient encounters across the enterprise. It has achieved the following results:

  • Data from multiple EHR vendors, including four inpatient EHRs and two ambulatory EHRs, plus five transactional systems—HR, patient experience, patient safety, finance, and supply chain— were integrated within 12 months.
  • More than 55,000 data elements and over 18 billion rows of data were incorporated.
  • Patient-to-patient matching was implemented for over one million patients across the four inpatient EHRs. This is vital for managing patient populations.
  • Operational efficiency was improved by 70 percent, with data architects spending an estimated 15 percent of time supporting interfaces compared to an estimated 40-50 percent before the integration. In one example, CHNw linked its ERP/costing system to the EDW’s EHR source marts with just a single interface; previously, this would have required building separate interfaces for all six EHRs.
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Reduce Readmissions with Predictive Analytics and Process Redesign

With nearly 20 percent of elderly patients released from a hospital being readmitted within 30 days, Allina Health is focused on providing patients optimum care and support post discharge to minimize readmissions. Focusing on 30-day potentially preventable readmissions (PPRs) as its global outcome measurement, Allina Health used key clinical variables to derive the clinical relationships between hospitalizations that determine PPRs. It further built analytic capabilities to identify opportunities for improvement in care management and to test quality improvement ideas.

Allina Health’s multipronged solution included redesigning care management processes, implementing predictive analytics to identify at-risk patients, using analytics to measure the impact of its interventions, and educating patients, families, and clinicians.

These efforts are driving measurable improvements including: 10.3 percent overall reduction in PPRs, 27 percent reduction in PPRs for patients with clinic follow-up within 5 days, and $3.7 million reduction in variable costs due to avoided readmissions.

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Sepsis: The Impact of Timely, Trustworthy Data and a Systemwide Approach to Care Improvement

For patients with the severest form of sepsis, the chance of survival decreases by 7.6 percent for every hour that antimicrobial treatment is delayed. Coordinated team work and the speed with which recognition, diagnosis, and treatment of sepsis occur are critical. Health systems across the country have discovered that by successfully engaging clinicians in driving and maintaining best practice interventions they are able to save lives and improve patient outcomes. At Piedmont Healthcare, the work of educating clinicians on the importance of following sepsis care best practices had been done. The missing pieces were a well-resourced, systemwide improvement team to improve sepsis care, and a concise way to view and give timely feedback on performance based on accurate, trusted data. To fill in these missing pieces, Piedmont created a cross-representative sepsis improvement team and enabled tracking for compliance to best practices with an analytics application from Health Catalyst. Within just three months of deploying the Sepsis Improvement Application, Piedmont has accomplished significant improvements in efficiency—and completely won trust in the data. Piedmont has already identified early indications of patient outcome improvements. Initial achievements of its sepsis improvement team include deploying systemwide visibility into sepsis care performance and best practices compliance, improved acknowledgement of first alert by 19 percent across the system, and a reduction in manual data collection by 97 percent.

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Patient Identification and Matching—An Essential Element of Using an Enterprise Data Warehouse to Manage Population Health

In a healthcare industry transitioning to value-based reimbursement and population health management (PHM), matching patients accurately to their care events across multiple sites of care and sources of information is becoming ever more important. Being able to accurately track utilization of services for a particular patient, patient population, or provider is fundamental to the strategies underlying effective population health management. Partners HealthCare developed an effective patient matching solution for more than 10.5 million patients achieving a 20 percent improvement in patient matching accuracy and a 96-99 percent high-risk patient matching rate. This has allowed the organization to accurately “flag” high risk patient populations and better manage risk under risk-based contracts.

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Partners’ Enterprise Data Warehouse: Focus on Service and Value

As the healthcare industry rapidly evolves, implementing an enterprise data warehouse has become essential both for population health management and economic survival. While this requires building analytics competency across the enterprise, once adopted, the benefits are abundant—from improved patient outcomes to reduced waste and costs. To rapidly gain value from this platform, healthcare organizations should follow an implementation strategy that, before anything else, identifies the problems analytics is intended to solve. It should also place as much emphasis on people and processes as it does technology. Partners HealthCare is an example of how implementing a data warehouse can quickly leverage analytics across the enterprise to achieve value with high end-user engagement and satisfaction.

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The Enterprise Data Warehouse (EDW): Creating the Foundation for Effective Healthcare Improvement Analytics

Population health management and value-based care has arrived. However, many healthcare organizations don’t have a single source of truth for their data, nor can they easily access their information. In the absence of integrated data visibility, many hospitals are relying on manual workarounds that can take months, and sometimes even years to implement—and in the end, may still fall short of delivering the level of insight needed. Learn how Partners HealthCare consolidated its disparate data warehouses, incorporating more than 27,000 data elements from multiple sources systems—and implemented on time and on budget. Partners’ enterprise data warehouse now serves as the analytics foundation for its overall value strategy.

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Revenue Cycle Management: Analytic Driven Insights and Efficiencies

To run efficiently and use the money they earn to improve the health of a community, healthcare institutions must manage their revenue cycle well. Crystal Run Healthcare, one of the fastest growing multi-specialty group practices in the country, anda physician-led accountable care organization (ACO), is committed to ensuring that the dollars it earns serve its patient population and are not wasted on inefficient processes. To that end, Crystal Run recognized that to minimize manual reporting and make quick, well-informed decisions related to revenue cycle management, they needed to employ analytics. With the implementation of an advanced analytics application, on top of their EDW platform, this ACO now accesses data up to 99% more quickly, has reduced staff time to identify variance root causes by 97%, and is actively identifying financial management improvement opportunities.

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Operating Room Excellence: How One Hospital System is Driving Improvements with the Use of Advanced Analytics

Mission Health in North Carolina has always been dedicated to expanding access to care. To preserve this commitment in an era of declining reimbursement rates, Mission needed better access to data for quick and flexible decision-making. As at most hospitals, operating rooms are Mission’s biggest revenue generator, but they also represent a significant cost center. So, leveraging their new analytics capabilities to drive operational improvements across their system of operating rooms was a strategic opportunity. Mission now has improved ability to drive care and operational improvements with integrated data and analytic tools like their OR Dashboard—resulting in dramatic improvements including a 20% increase in first-case on-time surgical starts.

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Automating the Executive Healthcare Dashboard: Spend Less Time Collecting and Validating KPI Data

Healthcare executives rely increasingly on executive healthcare dashboards to provide a snapshot of their organization’s performance measured against established monthly and yearly key process indicator (KPI) targets. However, collecting and aggregating the needed data to create the dashboard can be a very time-intensive process and many organizations are using Excel spreadsheets to “cobble together” these dashboards from a variety of sources. Learn how this organization is leveraging a healthcare enterprise data warehouse (EDW) and analytics technology to automate and improve the dashboarding process.

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How to Integrate an EHR into a Healthcare Enterprise Data Warehouse in Just 77 Days

Integrating EHR data into a healthcare enterprise data warehouse (EDW) can take years, depending on the EDW platform and data model. Crystal Run — a physician-owned medical group in New York with more than 300 physicians in 40 medical specialties — couldn’t wait that long. They need a solution that could integrate their EHR data in a matter of months, not years. Using a late-binding model, Crystal Run was able to integrate their EHR data in just 77 days, with easy-to-use tools for data acquisition and storage and metadata management. 

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To Build or To Buy a Healthcare Enterprise Data Warehouse? A Health System Experience

Many healthcare organizations are facing the decision to buy or build an enterprise data warehouse (EDW). Their home grown solution can’t scale to meet their growing healthcare analytics needs for population health and accountable care organizations. But, how do you they make the decision to buy or build. Learn how Crystal Run Healthcare, a physician-owned medical group in New York with more than 300 physicians in 40 medical specialties — made the decision to set aside its legacy EDW in favor of buying the Health Catalyst Late-Binding™ Data Warehouse and launched a scalable, cost-effective and platform in 54 days.

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The Fastest Way to Integrate Source Marts into a Healthcare Data Warehouse

Making sure data from source systems is moved quickly, accurately and consistently into an enterprise data warehouse (EDW) is an important task for Information Systems (IS) departments. Indiana University (IU) Health IS was tasked with increasing the value decision-makers get from their health system’s data – and doing it with fewer resources. Using the Health Catalyst Source Mart Designer IU Health achieved: a) 75 percent faster design and development of its’ Source Marts, b) well-structured data fields within their EDW, c) autonomy of data architects while ensuring enterprise supportability, and d) improved analysis through the use of meta data.

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Streamlining Radiology Operations and Care Delivery through Analytics

Texas Children’s Hospital radiology practice administrators used to dedicate several hours each week performing manual report reviews for weekly reporting — interfering with time they could be spending on streamlining their operations and delivering patient care. They are improving their operations and increasing patient satisfaction through health analytics while reducing costs by an estimated $400K. Texas Children’s established an analytics platform that enables near-real time reporting to track key performance measures such as average procedure duration, results turnaround time to providers, anesthesia utilization, patient flow cycle times and more.

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Indiana University Health – A Cerner data warehouse in 90 days

Digitizing healthcare comes with its own set of problems — including how to use all the raw data created and turn it into something meaningful that results in improvements in quality and cost of care. Indiana University Health found a solution that integrated with their Cerner EHR. And the best part? From start to finish, it took just 90 days.

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Other Content in Analytics

Posts

How to Use Text Analytics in Healthcare to Improve Outcomes—Why You Need More than NLP

Given the fact that up to 80 percent of clinical data is stored in unstructured text, healthcare organizations need to harness the power of text analytics. But, surprisingly, less than five percent of health systems use it due to resource limitations and the complexity of text analytics.

But given the industry’s necessity to use text analytics to create precise patient registries, enhance their understanding of high-risk patient populations, and improve outcomes, this executive report explains why systems must start using it—and explains how to get started.

Health systems can start using text analytics to improve outcomes by focusing on four key components:

  1. Optimize text search (display, medical terminologies, and context).
  2. Enhance context and extract values with an NLP pipeline.
  3. Always validate the algorithm.
  4. Focus on interoperability and integration using a Late-Binding approach.

This broad approach with position health systems for clinical and financial success.

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The Best Way to Maximize Healthcare Analytics ROI

When it comes to maximizing analytics ROI in a healthcare organization, the more domains, the merrier. Texas Children’s Hospital started their outcomes improvement journey by using an EDW and analytics to improve a single process of care. It quickly realized the potential for more savings and improvement by applying analytics to additional domains, including:

  • Analytics efficiencies
  • Operations/Finance
  • Organization-wide clinical improvement

The competencies required to launch and sustain such an organizational sea change are all part of a single, defining characteristic: the data-driven culture. This allows fulfillment of the analytics strategy, ensures data quality and governance, encourages data and analytics literacy, standardizes data definitions, and opens access to data from multiple sources.

This article highlights the specifics of how Texas Children’s has evolved into an outcomes improvement leader, with stories about its successes in multiple domains.

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Hadoop in Healthcare: Getting More from Analytics

Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight.
 

Specific use cases where Hadoop adds value data strategy include:

  1. Archiving
  2. Streaming
  3. Machine learning
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Understanding Risk Stratification, Comorbidities, and the Future of Healthcare

Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.

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Questions You Should Ask When Selecting a Healthcare Analytics Platform

As vice president of technology for a healthcare IT company, I’m often asked what should be considered when selecting a solution for healthcare analytics. Healthcare organizations have many choices when selecting a healthcare data warehouse and analytics platforms. I advise them to consider the following fundamental criteria: 1) time-to-value (measured in months, not years), 2) experience as a predictor of future success, and 3) extensibility to meet your needs.

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Analyst Reviews & Awards

Health Catalyst awarded “Best in Show: Best Problem-Solver”

Health Catalyst awarded “Best in Show: Best Problem-Solver” and recognized with top honors in the “Data Analytics” category by this year’s Fierce Innovation Awards: Healthcare Edition, an awards program with winners selected by a panel of CIOs including from renowned U.S. hospitals and healthcare systems, including Mayo Clinic, Boston Children’s Hospital, Memorial Sloan-Kettering Cancer Center, University of Michigan Hospitals and Health Centers and Beth Israel Deaconess Medical Center.

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Health Catalyst awarded “Best in Show: Best Problem-Solver”

Health Catalyst awarded “Best in Show: Best Problem-Solver” and recognized with top honors in the “Data Analytics” category by this year’s Fierce Innovation Awards: Healthcare Edition, an awards program with winners selected by a panel of CIOs including from renowned U.S. hospitals and healthcare systems, including Mayo Clinic, Boston Children’s Hospital, Memorial Sloan-Kettering Cancer Center, University of Michigan Hospitals and Health Centers and Beth Israel Deaconess Medical Center.

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Webinars

Powering Medical Research With Data: The Research Analytics Adoption Model (Webinar)

Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.

Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.

Content presented and discussed:

  • A summary of some of the challenges in using data and analytics for research
  • A research analytics adoption framework for all organizations interested in using clinical data for research
  • What is needed from a workflow and organizational perspective to power research with data

We hope you enjoy.

 

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Microsoft: The Waking Giant in Healthcare Analytics and Big Data (Webinar)

In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.

Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.

In this context, Dale will talk about:

  • His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
  • A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
  • The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
  • Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
  • The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
  • Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
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Preparing for the Coming Change: An Overview of the Healthcare Analytics Market

Jim Adams, Executive Director, The Advisory Board, will discuss the two market forces in particular, population health management and the retail revolution, that are driving the need for new applications of analytics and business intelligence (BI).

Attendees will learn:

  • The role of analytics in population health and the growing retail market
  • The key challenges provider organizations are facing in developing analytics capabilities
  • The pros and cons of the core strategies providers are utilizing to develop analytics capabilities and the vendors that map to those strategies 

Bring your most pressing healthcare problems and spend an hour listening to one of the most seasoned industry analysts talking through the top forces shifting the landscape of the healthcare market in 2015.

We hope you’ll come away with some insight and refined thinking about solutions that will drive your work forward. Please do join us.

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Precise Patient Registries: The Foundation for Clinical Research & Population Health Management

Please join Dale Sanders as he shares his experience in developing disease registries, the history of patient registries, and the current design patterns in data engineering to create highly precise registries to support clinical research and population health management.

Attendees will learn:

  • How the definition of the term “patient registry” has evolved from being associated with a federal- or state-mandated reporting requirement to a hospital or health system’s own population of patients, including device registries, drug registries, and procedure registries.
  • Why engaging certain populations via group registries allows them to better understand their conditions and reach out for support from others who share their condition.
  • Several untapped benefits of registries for disease and quality management.
  • When to utilize patient registries to guide decision-making and drive change, especially at the point of care.
  • Which of the critical steps to building a disease registry is most important.
  • The keys to winning organizational support in order to implement a successful registry initiative.
  • Precise patient registries play a significant role in the management of a broad variety of healthcare processes, including chronic diseases and conditions, as well as clinical research.

Understanding how registries are currently built vs. how they should be built is critical to the future of healthcare outcomes improvement, cost reduction, and translational research.

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Breaking All the Rules: What the Leading Health Systems Do Differently with Analytics and Data Warehousing

Voluntarily or not, we are entering the Age of Analytics in healthcare. As the healthcare industry emerges from the deployment of EMR’s and health information exchanges, enterprise data warehouses represent the next significant opportunity in information technology.

However, the meaningful use of an enterprise data warehouse is much more difficult to achieve than the meaningful use of an EMR.  There are scant few organizations in healthcare that have achieved excellence in the “meaningful use” of an enterprise data warehouse.

Fortunate to see both failings and successes, Dale Sanders has spent the last 18 years analyzing the characteristics of healthcare analytics and data warehousing leadership. Join him as he shares his observations and lessons to help you and your organization become one of the success stories.

Attendees will learn:

  • Why C-level involvement is important, but not a guarantee of success, and can sometimes be a hindrance
  • The pivotal characteristics of culture, strategy, and execution that are critical to data warehousing and analytics success
  • How to balance tactical analytic victories without sacrificing strategic adaptability and scalability
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