Case Study
- Assess the functions of analytics as discussed by Davenport (2014).
- Davenport, T., & McNeill, D. (2014). Analytics in healthcare and the life sciences: Strategies, implementation, methods, and best practices.
- Include in your assessment:
- How analytics is organized.
- How it adds value and
- How it deals with technical challenges
- Analyze what area of the organization’s strategic goal is supported by analytics as discussed by Davenport (2014). Is it clinical, marketing, or business?
- Analyze the assigned Providers case in terms of why this case study is considered a best practice
Case Study
Chapter 19 Catholic Health Initiatives
19 Catholic Health Initiatives
Deborah Bulger and Evon Holladay
Healthcare organizations sometimes struggle with managing the volumes of data they produce—from financial, clinical, operational systems,and processes. Yet the ability to manage data and transform it into meaningful information yields significant returns to an organization’sbusiness performance. A recent report by Yonek et al. on the characteristics of high-performing healthcare organizations cited several bestpractices including:
- Establish a systemwide strategic plan with measurable goals and track progress toward achieving them with system performancedashboards.
- Create alignment across the health system with goals and incentives.
- Leverage data and measurement across the organization by, among other things, frequently sharing dashboards and nationalbenchmarks with hospital leaders and staff to identify areas in need of improvement and taking immediate actions to get back ontrack.1
To be effective, information must reach the people charged with improving performance, and must reach them in a timely and appropriatefashion. At the enterprise level, a global, measurable strategy will set direction for the organization. At an operational level, measures thatsupport financial and capacity related activities should be readily available. And for measures related to patient care activities, reportingmust reach caregivers in real-time. The challenge: How does a healthcare organization translate data into actionable information for everystakeholder across the enterprise?
About the Organization
Catholic Health Initiatives (CHI) is a national nonprofit health organization with headquarters in Denver. It is a faith-based system thatincludes 73 hospitals; more than 400 physician practices; 40 long-term care, assisted- and residential-living facilities; a home health agency;and two community health-services organizations in19 states. Together, its facilities serve more than 60 rural and urban communities andprovided nearly $500 million in community benefit in the 2010 fiscal year, including services for the poor, free clinics, education, andresearch. With approximately 70,000 employees and annual revenues of more than $8 billion, CHI ranks as the nation’s third-largest Catholichealthcare system. It is ever moving toward its vision of Catholic healthcare as a vibrant ministry, ready to provide compassionate care of thebody, mind, and spirit through the twenty-first century and beyond.
Business intelligence (BI) is a relatively new function for CHI. It is responsible for providing a historical, current, and predictive view ofbusiness operations through an enterprise data warehouse. CHI’s patient data warehouse provides information for strategic reporting andcore measures of regulatory compliance. The department is partnering with leadership across CHI to develop metric standards and definekey performance indicators and best practices benchmarks. The goal is to reduce latency in decision making by having information readilyavailable.
Current Situation
Because of the breadth and depth of services provided and geography covered, CHI represents a microcosm of the U.S. healthcare deliverysystem. The distributed nature of the organization, disparity of systems, and the sheer magnitude of data produced across the enterprise allcontribute to the complexity of data standardization. For instance, CHI uses multiple vendors across the enterprise for hospital informationand acute care billing systems, clinical decision support, compensation, revenue management, enterprise resource planning (ERP),productivity, and so on. In physician practices alone there are 14 different vendor solutions. To achieve an enterprise reporting model, CHI isleveraging commercially available tools for BI, data marts, extract/transform and load (ETL), and enterprise data warehousing.
Like Yonek et al., CHI recognizes a need for a strategic alignment of technology, information, and stewardship if the organization is to moveup the analytics maturity curve. In the book Analytics at Work: Smarter Decisions, Better Results, Davenport et al., explains how organizationscan use data and analysis to make better decisions.2 Aimed at a broad, multidisciplinary audience, it speaks to employees across theirorganizations who want to know where they stand now and what they need to do to become more analytical over time. The DELTA3 modeloutlines five key components for deploying and succeeding with analytical initiatives:
- D for accessible, high-quality data
- E for an enterpriseorientation
- L for analytical leadership
- T for strategic goals or targets
- A for analyticaltalent
It is through this model that we describe CHI’s journey toward enterprise intelligence.
Data
Data are the foundation for analytics, and CHI recognizes that managing data across the enterprise requires discipline. The organization hasidentified three critical steps to establishing an enterprise data model.
Selection of the Standard
At CHI, the lack of consistent definitions has, at times, led to false assumptions about an individual organization’s performance, creatingbarriers to an enterprise approach. To first agree on relatively simple definitions, such as whether to include day surgery or lab visits involume measures, makes it easier to tackle more complex definitions such as adjusted patient days. CHI is designing this model operationallythrough its governance structure (discussed later in thischapter) and has started the standardization process with acute care, to be followedwith physician practices and home care.
Of particular importance are the partnerships CHI has developed with its software vendors to standardize naming conventions within theirproducts. This helps to ensure definitions are consistent even outside the enterprise data warehouse.
Implementation
Once standards are determined, the organization needs to ensure that they are implemented. CHI plans to use data governance tounderstand business requirements, design data definitions, develop and test metrics, and ensure effective implementation.
A change control process that was started in BI will be adopted across all CHI reporting systems. This process begins with a gap analysis toevaluate new data definitions or changes to calculations and determines when those changes will be activated. To ensure accountability, CHIuses a RACI4 matrix to assign people who are responsible, accountable, consulted, and informed for activities and decisions that impactimplementation.
Ongoing Monitoring
It is critical to monitor data continuously to ensure its integrity, identify new measures, and assess the information behaviors of people whogather and use it. Administrative data are highly standardized, but as new metrics demand a higher level of data accuracy, it is necessary totrain people to collect data correctly. One example at CHI is the capture of patient race and ethnicity data. These are key data elements usedfor monitoring underserved populations and mitigating disparities in care, a critical component of health reform. There are nationalstandards for this—no need to recreate the process—but the data are not always captured accurately upon admission. A high percentage ofpatient records at CHI were listed as “unable to determine.” Rather than take a punitive approach, the organization chose to address theissue as a function of behavior and ongoing process. Once admission personnel understood how these data were used and the importanceto patient care, capture rates improved.
Data Management in Action
An example of a decision that impacts implementation is determining whether data must go to the warehouse for furthernormalization, aggregation, or modeling, or if they can be viewed directly through BI tools in transaction systems or otherreporting systems like cost accounting. The decision is based on the frequency of data needs (e.g., real-time patient censussupported by a single variable) and the complexity of analysis, such as projected payments requiring statistical modelsinherent in the data warehouse.
Enterprise
CHI aggregates financial and operational data supplied by each entity. However, it has not historically provided an enterprise reportingmethodology with a standard taxonomy for comparing key business practices. As the healthcare delivery model becomes more disparate—acute, ambulatory, home care, long-term care—it creates some interesting challenges for comparisons at an enterprise level. For example,the acceptable operating margin may be very different for long-term care than acute care, so it is important to understand the reason for thedifference and recognize the unique contributions of each care setting. CHI’s objective is to view the two holistically, align around a model ofshared accountability, and recognize that long-term care is an equal partner in the delivery model. As long-term care centers are added tothe organization—40 centers at this writing—they are treated as part of the enterprise rather than independent entities. CHI’s mission is toachieve the best possible care for the community by ensuring appropriate handoffs and measuring effectiveness across the continuum.
Leadership
Becoming accountable for the care of a broad and diverse patient population means that strategic decisions must be made based on reliabledata. CHI has structured its information management model to support an enterprise strategy for decision making (see Figure 19.1).
Figure 19.1 Simplified graphic of Catholic Health Initiatives data governance data structure
In this model, senior leaders drive a top down directive for corporate alignment of strategic metrics with measurable action plans.
At an operational level, the information management council is a multidisciplinary group of individuals selected by their senior operationalleaders, representing all regions of CHI. The objective of this group is to define priority solutions that will provide stakeholders with timelydata so they can make better decisions based on information rather than anecdote.
CHI’s data governance structure is composed of functional vice presidents from across the enterprise. Their goal is to create accountabilityfor standardized information that aligns with functional and operational priorities and external best practices. In addition, smallerworkgroups aimed at specific business issues are designed to create an environment where participants can build relationships, havedifficult conversations, and solve problems.
Target or Goal
Standardized data definitions allow for meaningful comparisons. CHI is large enough to benchmark results both internally and externally.This allows the organization to define current achievement and set aspirational goals. CHI’s enterprise measurement dashboard iscompletely transparent, allowing each facility to view every other facility’s results. These open comparisons enable organizations to see whois doing better on various metrics and to share the possibilities. It is acceptable for entities to be different as long as they explore thosedifferences and are driven to improve. BI’s goal is to create energy and power by providing information that facilitates learning.
To drive that energy, CHI is shifting the analytics paradigm to include more exploratory analytics that empower local organizations to ask andanswer difficult questions about their results. CHI recognizes two important aspects of this applied learning model:
- Access to truly comparative information—Organizations can “drill” to comparisons on the dashboard at the market level andacross all facilities with confidence in the results.
- Collaboration—At a functional level, organizations know each other and ask questions to understand the results and create sharedpractices. This approach creates an internal consultancy of shared practices based on long-term relationships that can be leveraged asneeded.
By comparing outside its own organization, each entity can leverage the value of the collective enterprise knowledge.
Analysts
Analytical talent is necessary to “connect the dots” from data source to end results to provide critical insights. Technology enables analysis,but it is human capital that most benefits organizations that compete based on analytics. Three levels of analysts are described by Davenportet al.5
- Senior management sets the tone for analytical culture and makes decisions.
- Professional analysts gather and analyze data and report results to decision makers.
- Analytics “amateurs” use the outputs to perform their jobs.
This is an area where CHI, like many healthcare organizations, continues to learn. At this point in the journey, CHI is on the cusp of Stage 3—still dependent on localized analytics but aspiring to become an analytical organization (see Figure 19.2) with leaders who are setting theanalytics tone of the organization. The next steps are to start aligning analysts—both professional and “amateur”—around a commonunderstanding of data and measurement and to “get everyone out of their silos.” As these capabilities mature, analysts will help build aframework of trust in both the data and shared practices that drive improvement.
Figure 19.2 Five levels of analytics capability
Conclusion
The focus on coordination of care as an industry driver, the expansion of the care delivery model outside acute care, and pivotal leadershipchanges have created the “perfect storm” for CHI. As it moves into the next phase of analytics maturity, CHI will plan for additionalmilestones:
- Evaluate the concept of incentives tied to improving performance by measuring the “return on information” investment.
- Develop enterprise intelligence solutions that provide data in a more real-time manner. Ideally every stakeholder should have acommon set of standardized information at her fingertips.
- Define an analytics roadmap. CHI has engaged consultants to help the organization accelerate deployment of high-value analytics.
- Find those true areas that need to improve by deploying more advanced analytics.
- Leverage meaningful use requirements as a baseline to create the next level of learning.
CHI views this process as a journey that promises to weave information into its business practices and deliver against predictable milestonesin the future. The organization has made significant progress in the last 14 months as its culture has shifted to support an enterprise model,but there is still much work to do.
Notes
- 1.Yonek, S. Hines, and M. Joshi, A Guide to Achieving High Performance in Multi-Hospital Health Systems, Health Research & EducationalTrust, March 2010, http://www.commonwealthfund.org/Content/Publications/Fund-Reports/2010/Mar/A-Guide-to-Achieving-High-Performance-in-MultiHospital-Health-Systems.aspx?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+TheCommonwealthFund+%28The+Commonwealth+Fund%29.
- 2.Thomas H. Davenport, Jeanne G. Harris, and Robert F. Morison, Analytics at Work: Smarter Decisions, Better Results(Harvard BusinessSchool Press, 2010).
- 3.Davenport et al. DELTA is also the Greek letter that signifies “change” in an equation.
- 4.RACI is an acronym that stands for responsible, accountable, consulted, and informed and deploys a matrix to assign. There are manyreferences to RACI. This is one of them: http://www.projectsmart.co.uk/how-to-do-raci-charting-and-analysis.html.
- 5.Thomas Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning(Harvard Business School Press, 2007).
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