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.
Multiple Data Source Integration
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.
Growth in the government payer mix and an increased cost burden to the commercial population, decreases in the private payer population, and programs like the Medicare Shared Services Program, have caused joint ventures, partnerships, and co-branding efforts, better known as at-risk contracts, between payers and providers to increase.
Allina Health has three Integrated Health Partnership (IHP) contracts, an accountable care model that incentivizes healthcare providers to take on more financial accountability for the cost of care for Medicaid patients, which cover approximately 90,000 members. To achieve success in its IHP contracts, and avoid losses, Allina Health needed to reduce healthcare costs while improving patient outcomes and experience.
Allina Health has integrated several data sources, including claims and developed the infrastructure required to perform opportunity analysis. Using data and analytics for opportunity analysis has given Allina Health insight into its IHP patient population, supporting the development of interventions to decrease the total cost of care and improve outcomes.
Research shows that despite an increase in the number of improvements in clinical, cost, and operational outcomes, there is a lack of sustained improvements. Some of the key challenges can be access to the data and analytics, and adherence to data-driven clinical standards, things the Allina Health Spine Clinical Service Line (CSL) clinical leadership team experienced.
By providing widespread access to the data and analytics, the Spine CSL at Allina Health has been able to continue its reduction in LOS and further improve its reduction in complications, all while increasing cost savings and achieving pay-for-performance incentives.
$1 million in pay-for-performance incentives received.
More than $2 million in supply chain savings, a result of data-driven clinical standardization.
31 percent of expected complications avoided.
22 percent relative reduction in surgical site infections.
Healthcare reimbursement continues to shift away from fee-for-service reimbursement models to value-based, risk-sharing agreements. This shift has resulted in organizations revising compensation strategies to engage physicians in value-based compensation arrangements. An effective value-based physician compensation plan is critically important, particularly in competitive environments where organizations must optimize the ability to recruit and retain highly skilled providers. One commonly used physician compensation approach includes a base salary and productivity incentives, coupled with additional compensation opportunities for achieving quality and service goals. The physician compensation package at John Muir Health is not only competitive, it is also complex, but the support process was burdensome, inefficient, and lacked transparency.
John Muir Health developed a plan to leverage the Health Catalyst® Analytics Platform, including the Late-Binding™ Data Warehouse and broad suite of analytics applications, to develop an automated process for physician compensation. The plan created efficiencies in time and effort across multiple domains and produced software to automate future work. The benefits included:
Saving 1,560 hours of time required to produce the data necessary to calculate physician compensation.
Successfully integrating more than ten different compensation models and 20 different data elements for more than 300 different providers into the physician compensation analytic application, automating the process.
Mixed reviews of the effectiveness of pay-for-performance programs leave hospitals wondering how to affect meaningful change in patient care and outcomes. However, MultiCare’s experience with focused improvement efforts supported by analytics for pneumonia, sepsis, and women’s care showed that better data consistently leads to better patient outcomes.
Committed to improving population health, and informed by their experience as well as national trends and outcomes, MultiCare formed a new partnership with Health Catalyst, a next-generation data, analytics, and decision support company. The shared risk partnership generated an improvement framework and governance structure formed around a Shared Governance Committee which is responsible for prioritizing, resourcing, and aligning improvement initiatives across MultiCare. The committee and the projects it ultimately approves are informed by data-driven opportunity analysis and ongoing analytics support. This partnership and structure have achieved the following:
Strategic alignment of outcomes goals across the organization.
Established an Analytics Center of Excellence.
Integrated financial data into outcomes improvement initiatives.
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.
Clinical variation can be frustrating for patients and their families, often leaving the impression that healthcare team members are not on the same page and don’t agree on the plan for the patient’s diagnosis or treatment. It is also costly—the Institute of Medicine estimates that $265 billion (30 percent) of healthcare spending is waste that directly results from clinical variation.
To reduce unwanted variation, Texas Children’s Hospital invested considerable resources to develop clinical standards tools, including evidence-based order sets; however, demonstrating the effectiveness and utilization of those guidelines, pathways, and order sets had been daunting. To that end, Texas Children’s deployed an analytics platform from Health Catalyst to aggregate and analyze the data needed to perform both of these critical functions.
$2,401 reduction in cost per patient with order set utilization, and an 8.4-day difference in average length of stay (LOS).
$15 million reduction in total direct variable costs in Fiscal Year 2015, $32 million anticipated reduction in Fiscal Year 2016 at the current order set usage rate, and a potential $64 million annual reduction with a hypothetical 80 percent order set usage rate.
1,629 percent return on investment (ROI).
A hospital’s core mission is to provide the best care possible. To continue to do so, however, hospitals must be paid promptly. Discharged not final billed (DNFB) cases—where bills remain incomplete due to coding or documentation gaps—represent an ongoing challenge for hospitals around the country.
Thibodaux Regional Medical Center, like other hospitals, faces a myriad of new government regulations that have made hospital bill collection efforts more onerous. Its leaders recognized their inadequate manual DNFB process left hospital staff overburdened and put at risk the necessary cash flow to best serve patients.
The hospital automated and streamlined this process to relieve the burden on physicians, provide an integrated view of data, optimize visibility and workflow, and reduce the need to “downcode” reimbursements due to missing documentation. The hospital leveraged analytics to provide actionable feedback to continuously improve the process.
Thibodaux has already achieved significant improvements to cash flow and operational efficiency:
44.4 percent improvement in delinquency rate
8.2 days reduction in A/R days
70.5 percent decrease in the number of billhold accounts outstanding
50 percent decrease in physician portion of DNFB dollars
97 percent improvement in operational efficiency
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.
U.S. healthcare is shifting from procedure and visit approaches to a longitudinal view of patient care. The Centers for Medicare & Medicaid Services (CMS) is supporting this change with their “Bundled Payments for Care Improvement Initiative.” Under the initiative, healthcare organizations enter into payments arrangements with financial and performance accountability for 48 episodes of care. This requires health organizations to integrate data from a combination of sources in order to identify the bundles with the highest costs and the sources of variation. Learn how Partners HealthCare, an Integrated Healthcare Delivery System and ACO, successfully integrated hospital, provider, and claims information for the first time—and how they can now easily evaluate and compare clinical and financial performance for the 48 CMS episodes of care.
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.
Effective Healthcare Data Governance: How One Hospital System is Managing its Data Assets to Improve Outcomes
As healthcare invests in analytics to meet the IHI Triple Aim, data has become its most valuable asset—and one of the most challenging to manage. Healthcare organizations must integrate data from a complex array of internal and external sources. To establish a single source of truth, The University of Kansas Hospital deployed an enterprise data warehouse (EDW). However, they quickly realized that without an effective data governance program clinicians and operational leaders would not trust the data. Led by senior leadership commitment, The University of Kansas Hospital established processes to define data, assign data ownership and identify and resolve data quality issues. They also have 70+ standardized enterprise data definition approvals planned for completion in the first year and have created a multi-year data governance roadmap to ensure a sustained focus on data quality and accessibility.
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.
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.
The need to effectively manage the health of populations is largely driven by the fact that 5 percent of the population accounts for 50 percent of healthcare costs. Being able to identify these patients, provide high-quality care and reduce their utilization is a pressing goal for many of today’s primary care providers (PCPs). Learn how this healthcare organization used a healthcare enterprise data warehouse and analytics to better manage their individual patients and patient population, integrate regulatory and performance reporting, and allow PCPs to spend more time with patients and less time collecting data.
How to Integrate Patient Satisfaction Data to Deliver Quality Healthcare and Improve Operational Efficiency
Patient Satisfaction Explorer has enabled Texas Children’s Hospital to trend key performance indicators for patient satisfaction by service unit. Clinicians, executives, and operational leaders can easily drill down into the granular data to analyze patient satisfaction in more detail, including individual survey responses. Their current results include: improved operational efficiencies with integrated data and analytics – which is helping the healthcare system achieve its system-wide initiative to eliminate the use of vendor portals; increased system-side transparency across 3 campuses, 28 survey units and 145 locations with all levels of the organization having full transparency into how their performance is being measured; full empowerment to discover how to improve their patients’ experience, integrated patient satisfaction data into quality improvement initiatives; reduced custom reporting requests by 15%.
Quality improvement in healthcare is essential for healthcare organizations as they transition to value-based care. Including palliative care in the planning and implementation of value-based care initiatives is more important than ever—especially for accountable care organizations (ACOs). This case study reviews the OSF Healthcare community-wide palliative care program and examines their key results: a) completion of 4300 advance care plans and engagement of more than 980 physician and community facilitators; b) leveraged a healthcare enterprise data warehouse (EDW) in a heterogeneous EHR environment; c) enabled data transparency at all levels through reporting and visualizations.
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.
Spinal problems are a common issue with a profound impact on healthcare costs. Faced with the high cost of surgical spine care in an industry transitioning to value-based payments, health systems need analytic solutions to evaluate the effectiveness of surgical interventions. Read how this medical center: 1) built a Spine Registry, 2) drove patient engagement through patient portal usage, 3) integrated data from a multitude of quality of life surveys, and 4) developed a healthcare analytics platform to measure spinal surgery outcomes.
In a recent report, the Institute of Medicine (IOM) declared that the cancer care delivery system is in crisis—amplified by the complexity of cancer care and historical limitations in quality-improvement tools.
As a result of an aging population, the IOM predicts a 30 percent increase in cancer survivors by 2022 and a 45 percent increase in cancer incidence by 2030. Parallel to this increase in incidence is a trend toward increasing costs. In 2010, $125 billion was spent on cancer care compared to $72 billion in 2004. In fact, the cost of cancer is expected to reach $173 billion by 2020—a 39 percent increase in just seven years.
One renowned health system recently implemented a solution to mitigate this crisis: a high-quality cancer care delivery system based on healthcare analytics and business intelligence. The health system has implemented a framework for data-driven clinical improvement.