The Top Seven Quick Wins You Get with a Healthcare Data Warehouse
Healthcare is in a complicated, transitional state. As the industry shifts from fee-for-service to value-based care, some health systems are struggling to adapt while others are thriving. Systems that understand the critical role of healthcare analytics in outcomes improvement are leading the industrywide transition to value-based care; they are continuing to raise the outcomes improvement bar. Some of these thriving health systems have implemented healthcare data warehouses to achieve critical reporting and analytics efficiencies—quick wins that have placed them ahead of the curve. This executive report profiles the top seven quick wins, explains how health systems have achieved them, and makes the case for leveraging a healthcare data warehouse to improve outcomes and thrive amidst the transition to value-based care.
Quick Win #1: Provides Significantly Faster Access to Data
Given the common health system complaint, “I don’t have the data I need,” faster access is a quick win when it comes to outcomes improvement work. Limited or a complete lack of access to data is an industry wide problem that happens for a variety of reasons. For example, health system finance departments are often unwilling to share data with stakeholders because they don’t think stakeholders will understand it (or its confidential nature).
Implementing an Enterprise Data Warehouse (EDW) and corresponding data governance ensures the right people have quick and easy access to the right data. It also provides critical data literacy support. The EDW protects source systems while providing fast access to the appropriate people—granting stakeholders access to only the data they need—nothing more, nothing less. For example, at Crystal Run Healthcare, the data warehouse provided clinicians with self-service analytics. Rather than relying on the BI team for a report, clinicians access robust data visualizations on their own, enabling them to drill down into and filter data based on their specific information needs.
Unlike other data warehouse approaches that waste time and resources developing massive data models and mapping them to every source at the onset, the Health Catalyst EDW™ maximizes the speed of data access with its Late-Binding approach. Rather than having to develop the entire data model up front before knowing all the use cases for the data, Health Catalyst’s Late-Binding™ approach enables health systems to bind data late in the process—just in time to solve an actual clinical or business problem. This approach helps systems overcome the challenge of the healthcare data variability that results from a plethora of diverse IT solutions.
Several health systems experienced faster data access as a result of implementing Health Catalyst’s EDW:
- Crystal Run Healthcare experienced a 99 percent improvement in time to access, empowering it to answer clinical and operational questions up to 98 percent faster.
- OSF HealthCare delivered quick, self-service access to data by creating strategic dashboards that run on the EDW, resulting in an 80 percent reduction in customized report requests.
- MultiCare, featured in the success story below, used a Health Catalyst pneumonia application to reduce the time spent accessing data, culling data, and delivering reports.
Data Access Success Story: One Healthcare System’s Effective Strategy to Improve Pneumonia Outcomes
MultiCare’s biggest challenge was its limited access to data. Its existing process for obtaining information to support improvement efforts required submitting a request to the information intelligence department for a custom report that would need to be checked, adjusted, and vetted before the information could be used. For each request, resources had to be prioritized and allocated—and the data validated. It took between two days to a month to get the information needed to support improvements. Preparing for a clinic visit, for example, required primary care providers to review multiple sources of data to gather the information needed to appropriately treat pneumonia patients.
MultiCare deployed a Health Catalyst analytics application to provide near real-time feedback on compliance and performance while offering a single view of patient-specific data across multiple visits and care settings. Health Catalyst’s application created a single view across medication and readmission histories, enabling MultiCare to show information from multiple sources and care locations on one screen. What would have normally taken multiple chart reviews was visible in one place.
The Key Result
The application reduced the wait time for information by 75-100 percent.
Quick Win #2: Improves Data-Driven Decision Making
Rather than having to get data from a variety of sources, health system staff can turn to the EDW as their single source of integrated truth. Simpler, faster access to high quality data inevitably improves decision making capacity. The multidisciplinary teams that drive outcomes improvement rely on the EDW for efficient, data-driven decision making. Given the diversity of expertise represented on these multidisciplinary teams, the EDW is essential for streamlining meetings with displays of validated, reliable information that’s easy to access. These teams can’t drive improvements without the accurate data they need to plan. The EDW gives teams the ability to query, question, and understand—three essentials for improving outcomes.
Data-Driven Decision Making Success Story: Managing Half a Million Risk-Contracted Lives: Partners HealthCare Population Health Strategy
Partners HealthCare realized it needed the ability to efficiently integrate and analyze multiple data sources (clinical, claims, and financial data) to effectively manage cost and risk. Its existing analytics environment was fragmented into three separate data warehouses and numerous smaller repositories, making it difficult to obtain the integrated views required for effective risk and cost management. As continuous cost and quality improvement throughout the system became essential to survival in an increasingly value-based industry, Partners recognized a clear need to shift to a data-driven decision making culture, especially among clinicians. This shift required a readily accessible and trustworthy source of integrated analytic information—an EDW.
In addition to Health Catalyst’s EDW, Partners deployed three advanced analytics applications that, in combination, helped it understand its overall business trajectory, assess performance, manage utilization, and control costs.
The Key Result
The single repository of clinical, operational, financial, and claims data—the EDW—aggregated data from different source systems to create a consistent view of data collected across the system, enabling informed, data-driven decision making and performance analysis.
Quick Win #3: Enables a Data-Driven Culture
Health systems that implement the Health Catalyst EDW lay the foundation for a data-driven culture by aligning health system staff to a single source of integrated truth. When everyone uses the same system, the organization shifts from instinct-driven decision making to fact-based decision making. In one assumptive, instinct-driven example, a health system thought one of its physicians was keeping patients longer than necessary and, therefore, costing the system more money. But the system’s EDW revealed that while the physician’s patients were staying longer, their readmission rates were lower, saving the system money. A data-driven culture helps systems and their people transition from instinct-based assumptions to data-driven decisions.
Data-Driven Culture Success Story: Improving Healthcare Performance through Analytics and Cultural Transformation: One Healthcare Organization’s Journey
OSF HealthCare needed to deliver superior clinical outcomes, improve the patient experience, and enhance the affordability and sustainability of its services. OSF already had two unsuccessful attempts at implementing an EDW under its belt—failures largely attributed to treating the development of the EDW as a siloed IT project. This history meant that leaders would have to overcome skepticism in the organization about the effectiveness of EDWs.
Rather than just deploying new analytics technology, OSF needed to drive a cultural shift throughout the system to embrace becoming a data-driven system. OSF created a data-empowered culture that enlists leadership support and identifies owners of each initiative to ensure accountability and shared goals.
The Key Result
Within a few months of launching the EDW initiative, OSF began to see value. It aligns its priorities, quality measures, and action plans with specific improvement goals. It ties data to specific interventions, which allows stakeholders throughout the organization to see the value and clinical impact of quality improvement. As individual teams implement best practices and drive progress, they share those practices throughout the organization to drive performance and sustain its data-driven culture.
Quick Win #4: Provides World Class Report Automation
Health Catalyst’s EDW eliminates the manual data-gathering process for reporting by automating data distribution. It empowers humans to do what they do best: analyze and interpret the data (and make decisions using it). The EDW enables health systems to use resources wisely by reallocating staff from manual reporting roles to analytical ones. For example, one hospital had clinicians scouring lab culture results for infections. By implementing an EDW to automate reports indicating problematic results, the hospital shifted from 3 FTEs down to one, thus being able to re-allocate resources to other projects.
Report Automation Success Story #1: Improving Healthcare Provider Productivity with Advanced Analytics
As part of Texas Children’s Hospital’s effort to assess provider productivity, practice managers were required to access multiple databases on an ongoing basis to gather data. The effort was resource intensive and time consuming, and the resulting reports were inconsistent. Each report was unique to the practice administrator, revenue cycle manager or data analyst that produced it. This complicated, time-consuming, inconsistent process made it difficult or impossible for leadership to understand where physicians’ productivity and associated compensation stood compared to their peers.
Texas Children’s implemented the Health Catalyst EDW and healthcare analytics applications designed to measure provider productivity.
The Key Result
Texas Children’s experienced significant improvements in acquiring timely, actionable information required for physician engagement and data-driven decisions. It automated what once was a month-long manual process that involved dozens of practice managers, data analysts, and revenue cycle managers gathering, validating, integrating, and analyzing data. This manual work has been reduced to just a few hours per month—representing a greater than 90 percent improvement.
Report Automation Success Story #2: How to Reduce Preventable Healthcare Associated Conditions in Children Using Best Practice Bundles and Analytics
Although Texas Children’s Hospital made great strides in developing and implementing best-practice bundles, tracking providers’ compliance with these bundles presented a significant challenge. Collecting data to inform both the Hospital Acquired Conditions (HAC) rates and bundle compliance rates was a time-consuming process. Lack of timely data and outdated software were problematic.
Texas Children’s implemented Health Catalyst’s EDW to aggregate clinical, financial, and operational data to create a consistent view of this data—a single source of integrated truth to inform decisions. On top of the EDW, it implemented an advanced analytics application from Health Catalyst to serve as its infectious disease surveillance system. The team captured confirmed infection cases in the EDW platform, enabling them to phase out the use of their antiquated infection surveillance system.
The Key Result
With data collection and distribution automated through the EDW, Texas Children’s dramatically reduced the amount of time spent gathering data and calculating infection rates. The team has achieved a 75 percent decrease in manual chart reviews.
Quick Win #5: Significantly Improves Data Quality and Accuracy
Health Catalyst’s EDW improves data quality and accuracy, partly by revealing gaps and discrepancies. Other EDW-generated quick wins work in tandem to improve data quality. For example, world class report automation improves data quality by reducing human error that stems from manual reporting (e.g., missing information). EDWs expose data-related errors; a critical first step in health systems developing processes to minimize mistakes.
Data Quality Success Story #1: Patient Identification and Matching—An Essential Element of Using an Enterprise Data Warehouse to Manage Population Health
In keeping with its commitment to continuous improvement, Partners HealthCare was determined to improve the accuracy of matching its patient records to patient records, and provider records to provider records.
Partners started down the path of building a robust, data-rich analytic environment based on four components: an EDW, user-friendly analytic tools, services and education, and a strong governance structure. Each component had an element that was dependent on or impacted by the accurate matching of records to build a reliable source of information. Accurate identification and matching increased trust in the data among a wide range of users. A strong governance structure reinforced trust in the data and established confidence in the data matching to help support data-driven decisions.
The Key Result
Accurate matching allowed Partners to connect more than 10.5 million patients across sources and facilities. Partners estimates a 10-20 percent improvement in the patient identification accuracy and matching. For high-risk patients, Partners achieved patient identification and matching rates as high as 96-99 percent.
Data Quality Success Story #2: Improving Healthcare Data Quality to Drive Lower C-Section Rates
Cesarean deliveries have become one of the most common surgical procedures performed in the United States each year. Many systems are working to reduce this rate for clinical, financial, and regulatory reasons. One healthcare system’s Women & Children’s division developed an initiative to drive higher-quality, lower-cost obstetrical care by measuring and reducing unnecessary cesarean deliveries.
Collaborating with Health Catalyst, the system’s business intelligence team created an EDW to enable an enterprise-wide, consistent view of data combined from the EMR and financial, patient satisfaction, and other systems. With combined clinical and financial data on hand in the EDW, the business intelligence and clinical teams could identify the care improvement opportunities that would have the greatest impact on cost and quality. Health Catalyst’s Key Process Analysis (KPA) Application analyzed EDW data to pinpoint variability in care and areas of high resource consumption throughout the organization.
The Health Catalyst EDW and its automated processes was the solution the team needed to scale its efforts and branch out from its success of lowering the elective C-section delivery rate. The multi-disciplinary workgroup began to notice gaps in the data that indicated problems with its EMR data capture processes — a common discovery in many analytics projects. Some data was missing; other data was inconsistent. Because these documentation gaps affected the ability to measure outcomes, they became an important focus of the workgroup team.
The Key Result
This commitment to analyzing data issues has given the team the opportunity to work with clinicians and the EMR optimization team to better align documentation to clinician workflows and to teach the importance of accurate and meaningful records. As a result, the health system has made strides in improving their documentation. For example, by creating a report that nurse managers can run to see how many charts don’t have the “marked as delivered” field checked and subsequently educating staff, the team has improved capture of this important data point.
Quick Win #6: Provides Significantly Faster Product Implementation
Achieving data-driven improvements doesn’t have to take years. In just a few months or less, health systems implement Health Catalyst solutions with impressive results.
Product Implementation Success Story #1: How to Integrate an EHR into a Healthcare Enterprise Data Warehouse in Just 77 Days
Crystal Run quickly came to the realization that its transactional EHR system was not architected to perform the sophisticated analytics the organization needed to deliver cost-effective and quality patient care. It determined that to perform analytics successfully it needed an EDW. It built its own EDW, which met the group’s analytics needs for years. But as regulatory and reporting requirements matured Crystal Run found that its homegrown EDW was too difficult to maintain and couldn’t keep up with user demand for data, insight, and reports.
Crystal Run transitioned to an EDW from Health Catalyst. It knew that Health Catalyst was managed by a group of healthcare veterans who had spent decades developing data warehousing and quality improvement models that had proved successful in real-world implementations. One characteristic that particularly appealed to Crystal Run was Health Catalyst’s proven record of integrating data from several of the top EHR vendors into its EDW.
Health Catalyst streamlined and simplified Crystal Run’s process for bringing data into the EDW. Unlike the labor-intensive, manual processes Crystal Run had relied on, Health Catalyst’s data acquisition and storage processes were largely automated. One of the most important factors in simplifying the process was Health Catalyst’s Source Mart Designer tool. This tool significantly automates the process of integrating data from source systems into the EDW’s source marts.
Health Catalyst and Crystal Run successfully deployed a scalable, adaptive EDW to replace its homegrown EDW. The new EDW’s late-binding architecture gives Crystal Run maximum flexibility for using data to tackle a wide variety of use cases as the need arises. Rather than having to establish an enterprise-wide data model up front before knowing what all the use cases for the data will be, Crystal Run can bind the data late in the process to solve actual clinical or business problems as they arise. The EDW adapts to rapidly changing vocabularies, standards, and new healthcare analytics use cases.
The Key Result
Crystal Run’s new EDW didn’t take long to implement and delivered a rapid time-to-value. The team completed integration of this data into the EHR source mart in just 77 days—a process that could have taken several years using a manual process or a traditional, early-binding data architecture that would have required modeling all data relationships up front.
Product Implementation Success Story #2: How to Improve Clinical Quality Improvement with an EDW
To succeed under a value-based system, Texas Children’s leaders knew they needed the ability to analyze and better manage specific populations of patients; especially the most-costly patients with chronic problems. They also knew they needed to identify areas of inefficiency and waste in their care programs, but lacked the hard data to pinpoint the suspected problems and to uncover hidden inefficiencies and safety issues.
Texas Children’s launched an overall quality and safety strategy. Leaders of quality, clinical, and IT departments knew the solution was to nurture a truly data-driven clinical culture and develop an EDW to help meet the expectations of the clinicians. Texas Children’s worked with Health Catalyst to implement a clinical and analytic framework:
- Implementation of an adaptive data warehouse platform and advanced analytics to collect data from systems inside and outside the system, allowing users to report on a variety of short-term operational and clinical metrics.
- Development of permanent, integrated teams of clinicians, technologists, analysts, and quality personnel to identify areas for improvement in care processes and build evidence-based care guidelines into the care delivery workflow.
- Implementation of a measurement system infrastructure to better track and interpret iterative improvement—a tactic that Texas Children’s found critical to sustain improvements.
The Key Result
Implementation of the Health Catalyst EDW was completed in just three months—a “phenomenally fast time,” according to Texas Children’s Hospital Director of Quality and Clinical Systems Integration.
Quick Win #7: Improves Data Categorization and Organization
The Health Catalyst EDW improves health systems’ ability to effectively categorize and organize data to better manage costs and thrive in a value-based care environment.
Data Categorization Success Story: How Partners HealthCare is Managing Costs in the Emerging At-Risk Environment
In 2011, Partners signed accountable care contracts within all major payer categories, placing a significant fraction of revenue in risk-based arrangements. This new reimbursement model presented Partners with the challenge of holding increases in total medical expenses below the national average. To meet this and other objectives in the new value-based landscape, Partners sought to implement an analytic environment that would manage risk and optimize value for high-need populations.
Partners was also hindered by an IT infrastructure with limited interoperability and separate data warehouses for clinical, financial, and claims data. Without a foundation in place to house and integrate data from multiple sources, analytic activities at Partners were historically done manually—a time intensive process prone to error—or not at all. Partners recognized the need for more effective analytics capabilities to manage its risk-based contracts, especially to identify cost reduction and care improvement opportunities.
To enable the analysis of current performance and drive clinical transformation, Partners implemented an EDW from Health Catalyst, which aggregated clinical, financial, operational, claims, and other data to create consistent views of the data to inform decisions for providers and managers alike. As the EDW was implemented, Partners and Health Catalyst developed analytic applications to enable Partners’ leaders to make informed decisions in the new era of value-based care (including applications to effectively manage episodes of care and populations within at-risk contracts).
The Key Result
Partners now has a customized service grouper, mutually exclusive clinical grouper, and Practice/RSO grouper to drive managerial action. Partners developed a customized service grouper with six top-level groups: inpatient, post-acute, imaging, lab/pathology, outpatient, and pharmacy. More than 20 clinical categories were also developed, allowing Partners to attribute total medical expenses to specific categories and conditions without overlap. The categories are additive (either by identifying a primary condition if done on a patient level or using claim level diagnosis). By making these views mutually exclusive, managers are assured that expenses are accurately attributed to an individual patient or provider—key to avoiding double counting of costs, revenue, and volume.
Leverage a Healthcare Data Warehouse for Quick Wins and Long-Term Outcomes Improvement
The seven reporting and analytics quick wins health systems get when they implement healthcare data warehouses puts them on the fast track for long-term outcomes improvement. The EDW serves as the data-driven backbone of an industry moving from volume to value—a flexible foundation that allows for an unparalleled capacity to rapidly adapt to the plethora of changes health systems face every day (care delivery model, financial, insurance, and risk changes).
The predictive power of EDWs enables systems to adapt and model the information they’ve gathered to analyze what-if scenarios as they diligently plan for unexpected changes. Although change is a healthy sign of progress, it can put a tremendous amount of stress on healthcare organizations. An EDW alleviates this stress by becoming the organization’s single source of truth that can be implemented quickly—a source of integrated truth that simplifies access to high quality data, reduces manual reporting through automation, facilitates data-driven decision making through easy-to-access visualizations, and, ultimately, empowers the data-driven culture required to successfully transition to value-based care.
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