Learn how The Queen’s Health Systems (QHS) and The Queen’s Clinically Integrated Physician Network (QCIPN) adopted a Rapid Response Analytics Solution, enabling the accelerated implementation of custom algorithms to better identify patients.
Data Warehouse & Analytics Platform
Read how The University of Kansas Health System embraced the implementation of an advanced analytics team to help the healthcare system unleash the data capabilities needed to become a data-driven organization.
On an annual basis, ACOs are required to accurately report data that is used to assess quality performance which is also linked to eligibility to share in any savings generated. Read how Mission Health implemented a proactive approach to measure and evaluate performance, including widespread adoption of analytics and shared responsibility for ACO measure performance, enabling the organization to sustain and further improve its performance across multiple ACO measures.
The need for seamless reporting, advanced visualizations, and end-user self-service models is critical to inform decision making. Learn how Orlando Health leveraged its analytics platform and applications to provide users with a data model that could enable a single source of truth for data-driven decision making.
Changing payment models are putting pressure on clinicians to have instant access to actionable information about their patients, their performance, and their potential to close gaps in care. Read how Orlando Health recognized the value of immediate access to adaptive, integrated data, giving users access to rapidly deployed data in consumable, actionable visualizations.
With the current state of uncertainty facing healthcare organizations, survival requires unprecedented agility when it comes to acquiring and responding to meaningful, strategic information. After adopting the Health Catalyst Analytics Platform, including the Late-Binding™ Data Warehouse and broad suite of analytics applications, Partners HealthCare promoted a philosophy of expanded access to the enterprise data warehouse (EDW) to increase adoption and self-service analytics to improve patient care and outcomes.
Partners needed widespread adoption of the EDW so that information could be meaningfully incorporated into strategic, clinical and operational decision making to support patient care. This meant that users who had a legitimate need to access data to support their job function were encouraged to seek access to the EDW. The organization continues to focus on further increasing the effectiveness of this strategy by ensuring that users have the means to acquire the skills, knowledge, and support they need to effectively use data stored in the EDW.
243 percent increase in user base—achieved over a two-year period (700+ unique users).
More data available to a broader audience than ever before.
Physician time to access data reduced from weeks to clicks.
87 percent of user community satisfied with the effectiveness of communication provided to support their use of the EDW.
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.
The U.S. healthcare system is the most expensive in the world, but data consistently shows the U.S. underperforming relative to other countries on most dimensions of performance. The Centers for Medicare & Medicaid Services’ (CMS’s) accountable care organization (ACO) model is aimed at addressing that issue by offering financial incentives for providers to improve the health of populations and reduce costs through greater efficiencies and a focus on preventive care.
Mission Health formed a Medicare Shared Savings Program (MSSP) ACO called Mission Health Partners (MHP), which is responsible for 40,000 patient lives. MHP knew that its manual approach to data collection and reporting would not be sufficient for the required ACO quality metrics. By leveraging a previously implemented enterprise data warehouse platform and implementing an ACO MSSP analytics application, MHP was able to automate the processes of data-gathering and analysis and align the data with ACO quality reporting measures. The visibility and transparency of near real-time, online performance data coupled with focused process improvement has resulted in subsequent improvement in all 33 of the ACO performance metrics. Specifically, improvements have included:
9.6 percent increase in compliance over all reported ACO metrics, with 23,000 more patients receiving recommended treatment or screenings.
98.9 percent of eligible patients received screenings for clinical depression and follow up.
40 percent increase in number of patients receiving any cancer screening; 46 percent improvement in the number of patients receiving colorectal cancer screening.
456 percent increase in the number of patients getting fall risk screening.
The consequences of poor-quality surgical care are significant for both hospitals and patients. Consider the following: One in four patients having a colon re-section is readmitted within 90 days, costing U.S. healthcare approximately $300 million a year and negatively affecting the lives of tens of thousands of patients and their families.
In 2013, Mission Health, North Carolina’s sixth-largest health system, identified opportunities to improve clinical outcomes for its bowel surgery patients. With a vision of achieving the best outcome for each patient, Mission set goals to reduce length of stay (LOS), decrease readmissions, and reduce surgical site infections (SSIs) for its bowel surgery patients.
Mission recognized that care process models (CPMs) were key to making it easier for clinicians to deliver the best care to patients by doing the right thing consistently. The health system therefore organized a multidisciplinary improvement team charged with developing and implementing an evidence-based CPM for bowel surgery. In support of this effort, Mission leveraged technology and analytics to encourage clinician adoption of the CPM and to deliver performance insights.
Through these efforts, Mission has achieved impressive improvements in bowel surgery care:
92 percent reduction in colorectal surgery SSI rates
28.5 percent reduction in mortality
10.6 percent reduction in 30-day readmissions
4.4 percent reduction in LOS
8.5 percent reduction in cost per case
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.
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.
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.
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.
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.
Improving Healthcare Performance through Analytics and Cultural Transformation: One Healthcare Organization’s Journey
OSF HealthCare, a pioneer accountable care organization (ACO), was looking to deliver superior clinical outcomes, improve the patient experience, and enhance the affordability and sustainability of its services. OSF’s leaders recognized that to effectively achieve these goals, they needed to reinvent the organization’s performance improvement measurement and reporting system. In addition to deploying new analytics technology, OSF knew they needed to drive a cultural shift throughout the organization to embrace a data-empowered system. By engaging leadership, aligning the initiative with business strategies, and building data-driven clinical and operational improvement teams, OSF was able to save $9-12 million over three years—through both process improvement and cost avoidance. OSF also drove clinical performance improvements in key areas including heart failure and palliative care.
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.
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.
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.
Clinical data can be difficult to extract from an EMR, particularly when you want to combine it with other data and use it in a timely manner. But when Texas Children’s Hospital adopted an enterprise data warehouse, they found they were able to extract the data and create near real-time reports that came with an added bonus: a 67% cost savings.
While the staff at Texas Children’s Hospital hoped that EHRs would be the data goldmine they were hoping for, shortly after implementation, they discovered they needed something more. The answer? A data-driven clinical culture coupled with an enterprise data warehouse.
Former Stanford CIO Carolyn Byerly talks about Stanford’s journey to build a centralized data warehouse. She shares how they launched a data warehouse in a matter of months and why it was one of the best decisions of her career. Learn more about the technologies and methodologies that are transforming healthcare and driving improvement outcomes.