of commercial off -the-shelf (COTS) and software-as-a-service (SaaS) off erings available today.
Consolidating and integrating pa-
tient, services, claims and cost data in a single data warehouse for the multitude of source systems provides the necessary centralized hub for both reporting and analysis. However, any healthcare or- ganization that has attempted to build a data warehouse from the ground up knows that one of the most complicated, time-consuming and expensive parts is the extraction and integration and on- going maintenance of clean, complete data for analysis. Today, healthcare organizations have new options with COTS models and data integration infrastructures built specifically for the industry, which can be tailored to organization-specifi c requirements. T is approach saves both time and money, due to the collective expertise of the vendors developing these solutions. Ideally, the warehouse data model should be structured under a relational third normal form (3NF) model, with marts created as star/snowfl ake schema for optimum performance and ease of use. Near-real-time extract, transform and load (ETL) can be used for more rapid and optimized run times when business requirements are well defi ned. The framework must be able to handle many diff erent sources of data and varying levels of quality and com- pleteness and also be able to connect the two. For example, the system may have a patient record without any encounter history and an encounter history with- out a patient. It must be able to bring this data together and handle the normal case of these coming in at two diff erent times from diff erent systems. It is also imperative to capture and le-
verage data at diff erent levels of granular- ity. Today, many clinical and operational source systems enable providers to cap- ture data at levels never before possible. For example, new wireless capabilities allow organizations to track vitals during a procedure, or over a long period in the outpatient setting, for chronic disease
management. To enable predictive cost- quality analysis, providers also should reliably capture the following: detailed clinical data; clinician, support staff and administrative salary and benefi t data; billing and administrative costs; equip- ment, supplies and medications lever- aged per case; and even square-footage costs involved in care delivery. T is more granular data, which is important to the activity tracking and activity-based costing that many organizations seek, will likely help to drive more accurate and insightful analysis of true costs, how and where costs are expended, and their relationship to outcomes. T e data warehouse environment also must be extensible. Healthcare organizations are very dynamic, and so is the data they collect. For example, a provider may feel that it is important to begin to record data on the arm used to capture blood pressure readings. Tra- ditionally, an addition like this would require a change to the data model and ETL infrastructure, which can divert considerable time and resources from other priorities. As the range and type of data captured grows, so does the maintenance burden. Extensible solu- tions enable organizations to accom- modate new data categories without the need to completely rework the data model. A fi nal consideration when creating a viable data warehouse is whether the environment will become more cost eff ective to operate and maintain as it grows, thereby encouraging organiza- tional stakeholders to centralize infor- mation. An enterprise data warehouse asset would support clinical, fi nancial, operational and research uses. Focusing on an initial set of business needs with a central asset keeps scope reasonable and allows for a quick time to value. T e timeframes rival other mart-focused approaches, but you gain the advantages of an enterprise warehouse.
Next stop: data analysis T e data warehouse alone cannot deliver the insight that ACOs require.
HEALTH MANAGEMENT TECHNOLOGY
Analytics brings it all together, making sense of data in a way that is both mean- ingful to business users and actionable to care providers. It is no longer enough to simply measure and understand data. Prudent organizations should be able to leverage data – in essence creating a closed-loop system – to facilitate consis- tent improvement. Once again, options abound, in- cluding patient, physician, costing and allocation, planning, supply chain and operating room analytics solutions. When choosing a solution, healthcare organizations should keep in mind several important criteria, including scalability (as data volumes increase dra- matically), fl exibility (to accommodate changing requirements and the need for new metrics), as well as the ability to easily model multiple scenarios for detailed analysis and insight into numer- ous “what-if” scenarios.
Considerations should include an enterprise-wide hospital tool that consolidates clinical, financial and operational data in one reporting and analytical view; surveillance reporting to improve patient outcomes before discharge; predefi ned indicators and measures for retrospective analysis; data extraction and online analytics processing designed to specifically promote data integrity – with no ad- ditional data transfer interfaces re- quired – resulting in lower total cost of ownership; a healthcare-specific business intelligence tool; and an ad- vanced Web-based portal with a shared dashboard solution. Achievement in the era of ACOs
will require an effi cient alignment of internal resources to streamline clini- cal, fi nancial and operational informa- tion to more eff ectively manage costs across the continuum of care. Setting the foundation for accountability, and ensuring the appropriate IT infra- structure is in place to deliver insights when and where needed, can drive in- novation, and provide for achievement and profi tability for forward-looking organizations.