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Designing Hospital Quality Function Around the Value Chain

Publicly reported measures of healthcare quality includes the Hospital Safety Score Grades which award a letter grade representing performance for 30 evidence-based measures of patient safety. An “A” represents the best Hospital Safety Score, followed in order by “B,” “C,” “D,” and “F.” In the fall of 2014, Piedmont’s Hospital Safety Score Grade for its five hospitals included four “C’s” and a “D.” This demonstrated a need to change its approach to quality improvement and ensure proper resources were allocated and aligned with the value chain, enabling it to efficiently conduct surveillance activities, perform analysis, and facilitate sustained outcomes improvement.

To increase capacity for performing more value-added work, Piedmont leveraged its analytics platform to automate surveillance activities and monitor the effectiveness of quality improvement efforts. These tools helped Piedmont redesign its quality improvement efforts, resulting in a:

  • 35 percent relative reduction in healthcare facility acquired infections per patient day.
  • 50 percent reduction in the time required for peer review.
  • 50 percent reduction in the time to implement improvement projects.
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Improving Clinical Processes and Effectiveness of Care through Creation of a Disease-Specific Registry

Multiple Sclerosis (MS) is a disease that affects the central nervous system of about 400,000 people in the United States. With no known cure, current treatment for MS is to slow disease progression, manage symptoms and maintain the patient’s quality of life. Effective treatment of MS requires detailed patient information be readily available.

To better monitor disease progression and long-term patient outcomes, clinicians with OSF HealthCare Illinois Neurological Institute collaborated with researchers at the University of Illinois College of Medicine Peoria (UICOMP) to build a customized database.

The customized MS flowsheet registry resulted in several benefits, including:

  • 20.9 minute reduction (per patient) physician time spent searching for data.
  • 2.2 minute reduction (per patient) support staff time spent searching for data.
  • 300 percent increase in investigator initiated studies.

The success of the customized database suggests possible expansion may improve outcomes in other chronic or specialty care patient populations.

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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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