MINING YOUR DATA FOR HEALTH CARE QUALITY IMPROVEMENT

Quality improvement in the health care industry can best be defined by examining the driving forces that are effecting the industry. The evolution of the health care in this country is driven by a single purpose. How can health care organizations lower cost, raise quality, and still remain competitive? The implementation of patient-focused quality improvement programs are at or near the top of the list for both information technology (IT) and business unit managers alike in their efforts to lower cost. For IT managers, the key benefit SAS software brings to the table an environment of seamless integration of tools, data, and solutions across the enterprise. Data is at the heart of any patient-focused qua lity improvement program. The SAS System's access methods and data warehousing capabilities give IT managers what they need to implement the infrastructure necessary for a successful quality improvement and data mining solution. For the business unit managers interested in quality improvement, the key benefit of the SAS System is its depth and breath of end-user analytical and decision support tools. Numerous health care organizations are using the SAS System's industry-standard quality improvement tools to raise the quality and efficacy of health-related products and services. In this paper, we address how SAS software data mining technology can be utilized as a solution for improving the quality of care as well as help control spiraling costs in the health care industry. More specifically, we will review some of the driving forces in the market today and discuss the SAS software methodology and a pproach for data mining. Finally, you will see how this technology can be applied to help solve critical business solutions within the health care industry. HEALTH CARE TRANSFORMATION The once strong national legislation reform movement has created a firestorm within the industry. This has put health care into the hearts and minds of the public. Contrary to popular belief, the national health care reform effort didn't create this change in the industry; it has only served to speed up the process. The industry is reforming itself with or without national legislation. Reform efforts have grown out of the industry's need to control spiraling costs and inefficiencies. Today, real reform manifests itself in the form of market pressures. In other words, the industry is changing itself; no more of the status quo. The traditional, not-for-profit health care industry is now learning how to run health care like a business. Over the last two decades, most American industries have had to learn the value of cost control and higher quality through pressures created by foreign competition and investment. The health care industry is now being forced to learn the same lessons. In addition to market pressures reform, many states are in the process of enacting their own health care legislation—involving everything from legislation that supports the growth of managed care, to the shifting of Medicaid. States have now gained federal approval to put their billion-dollar Medicaid programs into the hands of managed care organizations. Many feel this could be problematic. A lack of a national coherent regulatory framework could lead to what Dr. David Lawrence, CEO and chairman of the board for Kaiser Foundation Health Plan called a "rapid nonlinear discontinuous change" in the industry. Some agree that this lack of a coherent framework is a model for failure and potential collapse, while others in the industry would wholeheartedly disagree. Within this complex set of circumstances, the great challenge is how to lower the cost of health care while improving the quality and accessibility of that care. THE EXPLOSIVE AVAILABILITY OF DATA AND TECHNOLOGY Over the past decade or so, businesses have amassed exponentially growing amounts of data in large databases. These stockpiles mainly contain customer or patient data, the unique "gold" the competition lacks. But the data's hidden value, the potential to predict business trends and customer behavior, has largely gone untapped. To collect and store large amounts of data is a waste of resources. Unless the data is used productively, the organization is sitting on an unexplainable overhead. It becomes an untapped resource costing the organization money. This is especially apparent within the health care industry. As the number of new alliances, consolidations, buy-outs, mergers, and acquisitions continues to grow, the industry will further find itself in a state of data confusion. With it comes a proliferation of non-integrated, stand-alone systems with useless collections of data. To convert this potential value into strategic business information, many companies are turning to data mining, an emerging technology based on a new generation of hardware and software. Data mining combines techniques including statistical analysis, visualization, induction, and neural networks to explore large amounts of data and discover relationships and patterns that shed light on business problems. In turn, companies can use these findings for more profitable, proactive decision making and competitive advantage. Data mining was designed for exploiting massive amounts of data. This process can be more efficient if you first define what the business problem is, and then determine the amount of data you will need to solve the problem. By taking this "bottom up" approach to data mining and involving upper management in the understanding of business problems and the potential return on investment (ROI), the process will be much more acceptable and the goals attainable. SOLVING BUSINESS PROBLEMS The SAS data mining solution has been used in health care to overcome a wide range of business issues and problems. Some of these include: • Segmenting customers/patients accurately into groups with similar health patterns. • Rapidly identifying who are the most profitable customers and the underlying reasons. • Understanding why customers leave for competitors (attrition, churn analysis). • Planning for effective information systems management. • Preparing for demand of resources. • Anticipating customers’/patient’s future actions, given their history and characteristics. • Predicting medical diagnosis. • Forecasting treatment costs. • Predicting length of stay in a hospital. • Identifying medical procedure expenditures and utilization by analyzing claims and point-of-care data. • Predicting total cost of patient care. Once the business problems have been defined and agreed upon, the next logical step is to determine the type and amount of data that will be necessary for making business decisions. As a precursor to data mining, a data warehouse strategy and implementation is suggested. Integration with SAS software gives the SAS data mining solution several distinguishing characteristics which allow faster, easier and more accurate conversion of data into knowledge useful to decision makers. Data diversity: The SAS data mining solution is designed to accept a wider range of data formats than any other data mining product currently on the market. It will accept data from relational and hierarchical databases, flat files, and other data formats, and it will accept this data from all major hardware platforms. Distributed client/server: The SAS data mining solution supports both the “data server” model of client/server computing, in which data located on a remote machine can be accessed, and the “compute server” model, which allows data to be processed on a remote server and then forwarded to a client. This is particularly well suited to analytical tasks involving large volumes of data that require superior processing capabilities. Consistent implementation on multiple platforms: The SAS data mining solutions are fully integrated with SAS Software and give users the flexibility to use their platforms of choice, ranging form desktop machines to powerful servers. Integrated data management: SAS software’s data management facilities guarantee data integrity without the need for re-keying or additional validation of data. No other data mining solution includes seamless integration with such a comprehensive range of data management functionality. Once the business objectives and data issues have been resolved, the methodology and approach to data mining can begin. THE SEMMA METHODOLOGY The methodology and a pproach that SAS Ins titute proposes is referred to as SEMMA, for Sample, Explore, Modify, Model, and Assess . Beginning with a statistically representative sample of data, users can apply exploratory statistical and visualization techniques, select and transform the most significant predictive variables, model the variables to predict outcomes, and affirm the model’s accuracy. Sample: The first step is to extract a portion of a large data set big enough to contain the significant information yet small enough to manipulate quickly. Explore: This phase involves searching speculatively for unanticipated trends and anomalies so as to gain understanding and ideas. This can reveal which subset of attributes will be the most productive to work with the modeling phase. Data visualization delivers intuitive tools for business professionals, while statistical techniques offer added detail for specialist. Modify: The insights that are gained from the exploration phase enable knowledge workers to group the most productive subsets and clusters of data together for further analysis and exploration. Model: This process involves searching automatically for a variable combination that reliably predicts a desired outcome. Data mining techniques such as neural networks, tree-based models, and traditional statistical techniques can help reveal patterns in the data and provide a best-fitting predictive model. Assess: During this evaluation process, assessment of the results gained from modeling provides indications as to which results should be conveyed