Increasing the number of consumers accessing a clinical service, program, health plan, or healthcare service is growth. Profitable growth is derived from understanding the market – key drivers, trends, future forecasts, and revenue stream. Resource allocation is guided by the industry and organizational financial model. The unique challenge for leadership and governance’s to secure strategic and profitable growth are competing financial models: episodic care fee for service, capitation, bundled payment, limited to full global budgeting. Decision support and business intelligence tools provide historical and current snapshots of a healthcare enterprises volume and revenue. A best practice response to this knowledge gap is development of an enterprise specific data warehouse, enabling efficient access to business analytics and information in a self-service model. The result is information set that is relevant to the business problem or market opportunity, offers clear visualization of the analytics, and information required for building, testing, and measuring predictive models.
Results Summary
The ability to respond and dominate a healthcare market when the industry is in a consistent state of transformation requires clarity, unity, and agility. Achieving profitable growth can be achieved by increasing volume in a Fee for Service (FFS) model or population health management under global pricing. A health system’s journey toward greater integration of clinical and operational integration demand decision making that exceeds the capacity of decision support systems, business intelligence models, and in-house data integration and analysis. The information required to understand trends in care and utilization, forecast future demand, and analyze the outcome of patient transitions within a system of care, is derived from harness thousands of data elements. Once collected, easily and rapidly accessing this massive wealth of data and information, understanding the information, and creating actionable strategies based on key learning’s is a formidable task – especially without adding FTEs.
Integration of these data elements into an analytic framework supporting decision-making is identified by industry leaders across the spectrum as its primary challenge. Short of these tools, decision-making is based on gut level perceptions, beliefs, or insufficient input.
Competing financial models – profitable growth derived from increasing volume in a fee for service model versus profitable growth from managing populations and moving care delivery to the most appropriate sites within a broad system of care, immediately expands the scope and volume of data elements needed. The reality for most healthcare systems is the integration of these data elements is often impeded by a series of different decision support and financial data systems. Resource allocation decisions (i.e. physician engagement strategies, physician employment, deployment of ambulatory sites of care, integration of home care services) are typically hamstrung because the information available lacks focus, reliability, or testing. This is due, in part, to:
- Silo business unit patient and financial data;
- A collection of individual databases with singular purpose and data elements incapable of sharing information;
- Access to historical volume, financial, and clinical data driving automated reporting; alerts based on current year projected values;
- Standardized reporting; accessing relevant data often requires IT skills and competencies supported by specially trained and designated staff existing behind the scenes.
Healthcare historically has been a late adopter of business analytic frameworks and the active engagement of predictive modeling as standard strategy development process. Two exceptions are evidenced based medicine and actuarial research by health plans and payors. Early adopters have elevated business analytics to a distinctive, competitive advantage; decision-making and strategy development is characterized by:
- Focused, relevant data;
- System-wide single source of truth — business and clinical analytics resulting from the integration of enterprise-wide data elements, external data sources, benchmark and forecasts;
- Analysis of patient-centric information (volume, leakage, effectiveness, utilization, future resource investment) for all sites of care a patient transitions through within a broadly defined system of care;
- Easily accessible and understandable information; and
- Information accessed in real-time for rapid cycle decision-making.
Background
Accountable care – its delivery, process, resource allocation, and outcomes, requires an in-depth understanding of organizational, market, and industry trends. Operational Integration and alignment in response to financial imperatives, along with providers accepting increased risk has pushed the enterprise sphere of influence beyond hospital based inpatient and outpatient services to include a spectrum of financial and delivery models. Enterprise-wide strategy and business development is a paradigm shift within healthcare. Decentralized business units and service lines have evolved as intrapreneurial business operations; barriers to data sharing have operationally and organizationally been constructed in response to regulatory and legal requirements characteristic of a disjointed system of care.
Decentralization of business intelligence and business analytics has created a cadre of independent analysts, assigned by business unit, to provide metrics and measurement. Decision support systems provide automated historical analysis of volume and financial data. In an effort to be responsive to business planning, budgeting, or strategic planning requests, manual spreadsheet analysis and accessing state data, market demographics, and enterprise patient data requires a series of processes and original sources. The result is a preponderance of time devoted to data acquisition, incongruence of data sources, and limited metrics accessible for the enterprise as a whole.
The organizational barriers resulting in staff time devoted to hunting and gathering of data and resources rather than problem scoping, analysis, and strategy development is to move business analytics to the forefront of the enterprise. The individual databases supported by business unit’s analysts lack the power to create a single source of truth for the enterprise or integrate the internal and external data elements required for data mining, trend identification, and predictive modeling.
An investment in technology and staff competencies required to exploit analytics for strategic and profitable growth is based on organizational strategies including, but not limited to:
- Increase the speed, efficiency, and outcomes of decision-making enterprise-wide.
- “Should employment be extended to a specific physician or practice?”
- “What is the impact on volume, revenue, and market penetration is we deploy an ambulatory facility at …?”
- “When accepting increased risk, is there sufficient volume to warrant adding neurosurgical procedures at hospital …?”
- Establish a single source of truth.
- “Is spine revenue included in this figure or is it identified in a different service line?”
- “If we look at all of our network’s assets in these two counties, what is the total impact – volume, revenue, downstream revenue, that we received in CY2010 based on our investment of $…..?”
- Enhance business performance and operational performance.
- “Which business units did patient X interact with beginning with the PCP and diagnoses of hypertension until today?”
- “What is the estimated impact of establishing an ambulatory site including a testing center at location Y on current operations?”
- “What are the most effective patient activation strategies resulting in a 12% reduction in CHF readmission?”
- Targeted patient acquisition and retention
- “What clinical services should be added to the cardiology service line resulting in a 3% inpatient market share change in our PSA?”
- “We deployed Dr. Y a year ago. Her schedule is not filled; do we know if she is gaining any market share within the north side of the city?”
- Pricing Optimization
- “What is the optimum price that we can establish for the new vein stripping program at …?
The objective of enhancing strategic planning resources is to gain and disseminate insights into market opportunity, strategy, and accessing analytics as efficiently and effectively as possible. This requires cultural preparedness and a technology platform aligned with organizational aspirations. In terms of cultural acceptance and preparedness, best practice organizational characteristics include a commitment to fact-based decision-making; understanding and engagement in predictive modeling as part of the decision-making process; support for sharing proprietary analytics and knowledge between business units; and managing for outcomes – financial, behavioral, procedure or process.
The technology solution to support an integrated strategic planning process begins with an extensible data model. An extensible data model enables the integration of a spectrum of data sources; essential future-proofing the data warehouse. The system also needs to enable the user the ability to build project-specific analytic sets and definitions (i.e. product bundled service lines, procedure code sets, unique geographic regions). By using an extensible data model, a user is able to upload any data set using customized data mapping.
The outcome of building a data warehouse enables the integration of strategic planning tools aligned with the strategic direction of the organization.
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