Healthcare Applications of Knowledge Discovery in Databases

Transcription

1 Healthcare Applications of Knowledge Discovery in Databases Kristin B. DeGruy, MSHS ABSTRACT Many healthcare leaders find themselves overwhelmed with data, but lack the information they need to make informed decisions. Knowledge discovery in databases (KDD) can help organizations turn their data into information. KDD is the process of finding complex patterns and relationships in data. The tools and techniques of KDD have achieved impressive results in other industries, and healthcare needs to take advantage of advances in this exciting field. Recent advances in the KDD field have brought it from the realm of research institutions and large corporations to many smaller companies. Software and hardware advances enable small organizations to tap the power of KDD using desktop PCs. KDD has been used extensively for fraud detection and focused marketing. There is a wealth of data available within the healthcare industry that would benefit from the application of KDD tools and techniques. Providers and payers have a vast quantity of data (such as, charges and claims), but no effective way to analyze the data to accurately determine relationships and trends. Organizations that take advantage of KDD techniques will find that they offer valuable assistance in the quest to lower healthcare costs while improving healthcare quality. KEYWORDS Healthcare Data Mining Knowledge Discovery Database The healthcare system in the United States is undergoing a transformation from the cottage industry to the corporate model. 1 What was once the realm of the physician and the patient is now a complex, trillion-dollar industry. 2 JOURNAL OF HEALTHCARE INFORMATION MANAGEMENT, vol. 14, no. 2, Summer 2000 Healthcare Information and Management Systems Society and Jossey-Bass Inc., Publishers 59

2 60 DeGruy Other players include employers, health insurance companies, and lawyers. In addition, the federal government and state governments are increasing their regulation and oversight of the healthcare industry. Lobbyists and special-interest groups have also become more active participants as the government involvement continues to grow. As part of this transformation, all parties are demanding greater and more detailed information on the effectiveness of the healthcare system. More and more entities place data demands on other entities, and many healthcare organizations find themselves overwhelmed with data, but lacking truly valuable information. For example, a hospital typically has detailed data about every charge entered on a patient s bill, which easily can reach hundreds of charges for only a few days stay. Each lab test, radiology procedure, medication, and so on is recorded, whether in a clinical information system or in a financial billing system. There is an enormous volume of data generated, but few tools exist in the healthcare setting to analyze the data fully to determine the best practices and the most effective treatments. 3 In general, the healthcare industry lags far behind other industries in terms of information technology expenditures. Our industry s information technology infrastructure is therefore underdeveloped in comparison. 4 This lack of information technology sophistication and some historical clinician skepticism have hindered the ability to analyze data adequately. Data are typically stored in legacy systems that were never designed to be long-term storage solutions, let alone allow for real-time analyses. 5 Historically, there has been some clinical resistance regarding the collection of data. Some clinicians believed that data collection methodologies were flawed and that the use of data would threaten their decision-making authority. 1 As healthcare continues to become more complex, the industry needs to find an effective means of evaluating its large volume of clinical, financial, demographic, and socioeconomic data. What Is KDD? Through knowledge discovery in databases (KDD), companies learn to understand the mechanisms that drive their businesses. Healthcare is not alone in its struggles with data. Other industries faced similar problems, with the volume of data exceeding their ability to properly evaluate and analyze data. 6 Just a few years ago, databases were rare and were the exclusive domain of the information technology department. However, as technology advanced, databases became easier to use, and business analysts began to create their own databases. This ever-growing volume of data needed some new techniques for interpretation and analysis. As defined by Fayyad, Piatetsky-Shapiro, and Smyth, Knowledge discovery in databases is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. 6 A seasoned analyst may be able to find relationships between two or three major variables, but

3 Healthcare Applications of Knowledge Discovery in Databases 61 would not be able to find more subtle underlying relationships that may be present in multiple variables. Understanding the relationships is key to building a more successful business. Simply put, there is money to be found in data when it is properly leveraged. More traditional query tools require the user to make many assumptions. For example, a user may use a query tool to ask the question, What is the link between cholesterol and heart disease? By running this query, the analyst assumes that there is a relationship between cholesterol and heart disease. The power of KDD is that it will search the dataset for all relationships, including those that may not have occurred to the analyst. With large datasets, there may be many variables interacting with one another in very subtle ways. KDD can help find the hidden relationships and patterns within data. 7 Using manual tools to examine large databases, which now easily range from gigabyte to terabyte size for many organizations, is like looking for the proverbial needle in the haystack. KDD tools offer the ability to find that needle, that one small piece of knowledge that may make the difference in how a business operates. A KDD process automatically examines data to determine the types of relationships, using a variety of techniques from statistical, artificial-intelligence, or machine-learning algorithms. The tool may determine which relationships are significant, but prior knowledge on the part of the domain expert is required to determine if the relationship is truly significant, given the business considerations and implications of the problem at hand. 6 There is some discussion in the information technology community regarding the differences between KDD, data mining, and on-line analytical processing (OLAP). For the purposes of this article, data mining and OLAP are considered tools of the overall KDD process. 8 It is important to note that KDD is not a new field of study. Large corporations have used it for many years. However, technological advances in recent years have made KDD accessible to smaller companies. In the past, the computing power required was so great that only research institutions and large corporations had the finances to acquire access. With today s faster, more powerful computers, KDD can be run from desktop PCs. 7 KDD software programs use sophisticated statistical and computer science techniques to uncover the hidden relationships in datasets. These fields have made substantial progress in developing fast useful algorithms for predictive and descriptive purposes. Software advances have also created more usable packages. The KDD end-user no longer must be a statistician, epidemiologist, or computer scientist; he or she can be a business analyst with a basic understanding of statistical concepts. Other industries have had substantial success converting data into useful information by using the tools and techniques of KDD. Healthcare analysts can learn from the KDD successes of other industries and apply this process to the problems of the healthcare industry. There is a variety of healthcare data that lends itself well to the tools and techniques of KDD. Hospital and provider charges, as well as insurance company claims, are some obvious places to start.

4 62 DeGruy Physicians can also benefit from KDD analysis of data to improve patient outcomes. Pharmaceutical and medical research companies can evaluate their data via KDD. A data-mining consortium, the Data Mining Group, announced in July, 1999 that they had developed the first version of an open standard for datamining products. This allows users to access different vendors products on the same model. A user can develop a model on system A and then analyze it using system B. 9 This exciting development will give companies greater flexibility in implementing KDD solutions. Industry Success A variety of industries have had tremendous success implementing KDD solutions. KDD s flexibility allows it to be well used in a number of different situations. In 1999, 20 percent of companies expect to be able to increase their revenues based on customer data, and 16 percent expect to cut their expenses. By the year 2001, an astonishing 74 percent of companies believe they will be able to mine customer data effectively enough to increase their revenues. 10 KDD techniques have proven to be especially valuable in the areas of fraud detection, marketing, and customer retention. Fraud Detection. Credit card companies and telecommunication companies are two examples of industries that have used KDD techniques to help detect fraud. Both keep detailed records of each customer s individual transactions. This is an enormous volume of data because these companies have millions of customers with millions of transactions each year. They have determined, for each customer, what constitutes normal use patterns. If an unusual transaction occurs, the system flags it, and an analyst may contact the customer to verify the activity. For example, if a calling card customer normally makes short, domestic telephone calls, the use of the card for several extended international telephone calls would be flagged. The telecommunications company may then contact the customer to verify that he or she made the telephone call. 7 A consumer may receive a similar call from a credit card company if his or her account suddenly shows multiple purchases of expensive electronics in a short period. These companies have learned that it is crucial to stop fraudulent activity as soon as possible. In the past, consumers detected fraudulent activity only weeks later when their bills arrived. The fraudulent purchases or telephone calls could easily reach thousands of dollars by that time. Consumers are only liable for $50 of fraudulent use, so the companies absorb the remaining loss. The sooner the company can detect the fraud, the less it costs. Fraud detection also improves customer perceptions of the company s performance. Marketing. Using KDD techniques, retailers can more accurately aim their marketing campaigns toward new and existing customers. KDD is quite useful for determining the characteristics of customers who are likely to make

5 Healthcare Applications of Knowledge Discovery in Databases 63 purchases. This ability to predict customer behavior can help make direct-mail marketing campaigns more effective. Merchants often can send fewer catalogs, which reduces expenses, but increase their revenue by targeting customers who are more likely to make purchases. Retailers such as Wal-Mart or grocery store chains have an enormous volume of data, due to their checkout scanners. These scanners record every item purchased by every customer at every store across the nation. These data allow them to determine which combinations of products consumers typically buy together. Using this information, they can place items in particular stores, or even at locations within a store, to maximize purchases. They can even get detailed information about which side of the store is more beneficial for certain products and which products are best grouped together or kept far apart in the store. These data are also quite useful for tracking the impact of sales and promotions on consumers purchases. Another example is Amazon.com s use of purchase circles. Amazon.com tracks customer purchases by various characteristics, such as region of the country, employer, and other books purchased. 11 Shoppers can view the purchase circles for suggestions or ideas. Once a shopper selects a book, Amazon.com recommends automatically several other books, based on purchases by other shoppers who also purchased the selected book. Due to privacy concerns, Amazon.com will change its practices to allow customers to opt-out of having their purchases disclosed as part of a purchase circle. 12 Customer Retention. Although it is a well-known tenet that it is less expensive to keep an existing customer than to attract a new customer, many do not realize the magnitude of the relationship between profitability and customer retention. Studies reveal that reducing customer attrition by 5 percent can yield remarkable percent increases in profitability. It varies depending on the industry in question. Software companies that reduce attrition by 5 percent can increase profitability by approximately 30 percent, whereas auto, home, and life insurers show profitability increases near 90 percent. The banking, credit, publishing, and industrial segments fall somewhere in the middle of the range. 13 KDD is effective at identifying the characteristics that make a customer loyal or disloyal. By studying past customer histories, companies can predict which customers are most likely to stay or leave an organization. Extra attention can then be paid to the categories of customers who are likely to leave. A little effort to keep a customer is a worthwhile endeavor due to its large impact on profitability. KDD Development Process KDD is an iterative or cyclic process that involves a number of stages. Although the specific techniques may vary from project to project, the basic process is the same for all KDD problems. The steps are problem definition, data preparation, model building, model deployment, and model evaluation.

6 64 DeGruy Problem Definition. Although KDD does reduce dependence on the person creating the model, it is still quite possible to make some critical errors during the problem-definition stage. It is important that the problem be defined with clear objectives. The modeler should be familiar with the industry and specific company in question to minimize the likelihood of misinterpreting the problem. This business knowledge is crucial for a successful KDD implementation. 13 There are two basic categories of models: predictive and descriptive. Predictive models attempt to anticipate likely outcomes. Descriptive models search for patterns in a dataset, and these patterns may be found in another dataset. 13 Most KDD problems tend to be more predictive in nature which patients will develop which disease, which customer will default on their mortgage, and what the stock price of company XYZ will be in three weeks. Data Preparation. By far, the KDD analyst spends the bulk of his or her time in the data-preparation stage. Depending on the quality of the data, an analyst may spend percent of the total project time working on data preparation. 13,14 The total time depends on the quality of the initial data. It is important to note that a data warehouse is not required to do KDD. However, data warehouses do make it easier to implement a KDD solution because the data has already gone through a cleansing and standardizing process. 7 The first step of data preparation is to gather the necessary data elements. After an initial review of data quality, data cleaning can begin. The data may require some transformations, whether combining the data elements into various categories (such as grouping patients into the categories by age, 0 1, 2 17, 18 64, and 65 ) or calculating new values based on other data elements. The final step is to select a dataset from the cleaned and transformed data. 7 It is not necessary to mine an entire set of data to gain benefit. Often a smaller, randomly selected sample works better. It is easier and faster to develop a model with a smaller dataset. An analyst may partition the entire dataset into a number of smaller datasets. The analyst then could use several of the datasets to develop the model, several to test the model, and several to deploy the model. 15 Model Building. There are a number of algorithms implemented by a number of vendors. One method may be more appropriate than another, depending on the type of data and the goal of the project. Each algorithm has its own unique advantages and disadvantages, some of which are discussed later in more detail. One important consideration is the level of noise present in the data. There is no such thing as perfectly clean and accurate data. All data have some inherent level of errors, or noise. Commercially available KDD packages allow the end-user to specify acceptable limits for noise in the model. 7 Model Deployment. After the model is built, it is ready to be deployed. At this stage, once a comfort level has been reached with the success of the

7 Healthcare Applications of Knowledge Discovery in Databases 65 model, business decisions can be made. For example, a prediction that a patient is at increased risk for postsurgical infection may cause a special protocol to be followed to help reduce the possibility of infection. Model Evaluation. Things change over time, so the model effectiveness must be reevaluated periodically. Minor modifications may be needed from time to time. Eventually, it is likely that the model will have to be replaced. If medical research identifies new risk factors for a disease, for example, a model that predicts a patient s predisposition to that disease will need evaluation for its continued effectiveness. The KDD process starts again, with the problemdefinition stage. Although the problem may stay the same, it is important to revisit the first stage to ensure that the problem is fully reconsidered. KDD Techniques There are a variety of KDD techniques available, all with pros and cons, depending on the business problem at hand and the data available for analysis. Most are based on statistical or computer science algorithms. Some of the more common techniques include traditional statistics, neural nets, and decision trees. The traditional statistical methods include techniques such as logistic regression or discriminant analysis. Statistical regression may well be the most common tool used. These algorithms are quite useful and relatively easy to use and understand. 13 Neural nets are an outgrowth of artificial intelligence. These models attempt to mimic the human brain and learn the patterns of a dataset in order to make predictions about other datasets. Neural-net algorithms work well for predicting complicated outcomes. Some applications using neural nets have predicted stock performance and the trends of other financial markets. 7,13 Decision trees are another common technique of KDD. There are many different approaches and algorithms available in the decision-tree category. All have the similarity that they establish a set of rules to classify data. At each node, a decision must be made. Based on the decision that is made, a distinct branch of the tree is followed. A common statistical decision tree is the CART (classification and regression tree) method. CART requires that each node, except the bottom-most nodes, have exactly two branches. 7 One benefit of decision trees is that the rules generated are easy for humans to understand. These rules can be stated in the form of familiar IF...THEN...ELSE statements. 16 Applications in the Healthcare Industry Although KDD is not well known in the healthcare industry, a number of organizations have implemented KDD successfully. They have discovered that the tremendous power of KDD is as applicable to the healthcare industry as it is to other industries.

8 66 DeGruy One health maintenance organization (HMO) used KDD techniques and historical data from other patients to determine which of its enrollees may be at risk for certain diseases. Targeted intervention for these enrollees and diseases makes sense; it keeps the enrollee healthier and lowers the provider s cost of treatment. 17 KDD s ability to search for patterns and relationships, particularly those not readily apparent, helps to identify patients at risk. As the volume of collected data continues to grow, using KDD s automated tools for discovery becomes more and more helpful. 5 When researchers investigate the records of patients with a particular disease to determine if there are any risk factors in their histories that could help predict the occurrence of the disease in other patients, they may identify a new risk factor could help detect the disease sooner in other patients and allow for more timely intervention. 18 KDD has proved as successful at identifying fraud in the healthcare industry as it has in other industries. Several insurance companies use KDD techniques to sift through their claims, seeking to identify fraudulent providers. As seen by Department of Justice inquiries in the 1990s, there is a great deal of fraud in healthcare. KDD can help identify the fraud, which frees more dollars to be used for legitimate healthcare activities. Details about specific fraud applications are difficult to find because most organizations regard their frauddetection activities as confidential and proprietary. 6 The New York State Workers Compensation Board used decision trees to streamline the processing of workers compensation claims. New York had a backlog of approximately 3 hundred thousand cases, and state law mandated the creation of a fast-track processing procedure. The expedited processing was for those claims likely to have paid benefits for eight weeks or less. A decisiontree algorithm, probabilistic inductive learning, was used to predict which cases would fall in the eight-week limit. 16 Many providers are migrating toward the use of computer-based patient records (CPRs). CPRs store a large quantity of patient data on test results, medications, prior diagnoses, and other medical history. This is a valuable source of information that could be better used by employing KDD techniques. Several examples include identifying patients who should receive flu shots, identifying patients who should enroll in a disease management program, and identifying patients who are not in compliance with a treatment plan. Finding themselves in an increasingly competitive market, many healthcare organizations are now employing sophisticated marketing efforts. KDD can help in this arena in ways similar to other industries. Organizations can use their data to identify those most likely to use their facilities and the most effective marketing activities to reach those individuals. Some organizations have targeted women because they make the majority of healthcare decisions for their families. KDD analysis could help determine whether this is a successful marketing strategy. Tracking referral patterns, based on a variety of factors such as physical proximity, risk-sharing arrangements, or disease, may reveal previously unknown relationships. Adding geographical data to already

9 Healthcare Applications of Knowledge Discovery in Databases 67 collected patient data may help reveal where a new facility should be built or if an area of the community is over- or underserved by a particular physician specialty. Similarly, an organization could determine how the closure of a facility would affect the surrounding population. 19 Conclusion KDD is a rapidly evolving field, and an ever-increasing number of commercial applications are available. Prices may start as low as $1,000 and reach $150,000 or more, depending on the product selected. 20 Products range from stand-alone PC-based applications to more complex client-server architectures. Some have GUI front-ends, whereas others require use of scripting languages. A healthcare organization wishing to implement KDD needs a skilled employee who understands the organization s data, the healthcare industry, the KDD software chosen, and modeling principles. Management engineers, business users with good analytical skills, and information systems analysts are good candidates for using KDD products. Implementation times vary considerably, depending on the product chosen and the cleanliness of the organization s data. 15 Those organizations with data warehouses may have shorter implementation periods because their data has already been somewhat scrubbed and cleansed. Some simpler products may be implemented almost immediately; more complex software products may require several months before they are operational. 21 After identifying a small subset of products that meet your needs, ask the vendors to allow you to use their products for a pilot project. Only after actually using a product can you determine if it fully meets your needs. 15 Before implementing a KDD solution, it is important to realize that KDD is not a silver bullet, magic wand, or the solution to all of your problems. KDD does not replace the business analyst or statistician, but instead it is a process that allows them to do their jobs more effectively. Data can be a competitive advantage when leveraged properly, and KDD is the tool to help you do so. Some individuals and privacy organizations are concerned about KDD s ability to unravel the secrets of our lives. The possibility of combining health data, credit data, banking data, and so on has raised concerns. The increasing use of the Internet is also contributing to privacy concerns. For example, if someone visits a disease-specific website and asks for more information, this could someday be passed along to her or his insurer from a data bank. 22 Healthcare is well equipped to handle any privacy concerns regarding the use of KDD. There are already well-established confidentiality standards in the industry to protect patients privacy. The Health Insurance Portability and Accountability Act (HIPAA) of 1996 also addresses many confidentiality concerns regarding electronic data. Both providers and payers have a tremendous volume of data in their systems that is not thoroughly analyzed or understood. KDD is very effective at

10 68 DeGruy working with a large volume of data and determining meaningful patterns. In addition, once these patterns have been identified, organizations can implement strategic solutions specifically designed to address particular problems. As the costs of healthcare continue to rise in the United States, it is even more critical for organizations to get a good handle on their expenses and ensure the maximum benefit-cost ratio possible. Both providers and payers should take advantage of the opportunities presented by the KDD field. They will find that KDD tools and techniques offer valuable assistance in the quest to lower healthcare costs while improving healthcare quality. References 1. Krieger, L. Data Collection and Utilization: Bringing Physicians on Board. Healthcare Financial Management, 1999, 53(5), National Healthcare Expenditures. Modern Healthcare, 1999 (suppl), Hawkins, H. H., Hankins, R. W., and Johnson, E. A Computerized Physician Order Entry System for the Promotion of Ordering Compliance and Appropriate Test Utilization. Journal of Healthcare Information Management, 1999, 13(3), PricewaterhouseCoopers introduces integrated CRM suite for health care. Data Mining News, Accessed Sept. 15, Marietti, C. The Data Warehouse: New Uses for Old Data. Healthcare Informatics, 1997, Accessed June 29, Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, S. From Data Mining to Knowledge Discovery: An Overview. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (eds.), Advances in Knowledge Discovery and Data Mining. Menlo Park, Calif.: AAAI Press, 1996, Groth, R. Data Mining: A Hands-On Approach for Business Professionals. Upper Saddle River, NJ: Prentice Hall, Uthurusamy, R. From Data Mining to Knowledge Discovery: Current Challenges and Future Directions. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (eds.). Advances in Knowledge Discovery and Data Mining. Menlo Park, Calif.: AAAI Press, Data Mining Consortium Develops Predictive Modeling Open Standard. Knowledge Discovery Nuggets, July 23, 1999, 99(15) p The Information Gold Mine. Business Week E.Biz, July 26, 1999, pp. EB 16 EB Amazon.com Launches Purchase Circles. Knowledge Discovery Nuggets, Aug. 22, 1999, 99(17), p Amazon Allows Opt-Out from Purchase Circles Policy. Knowledge Discovery Nuggets, September 7, 1999, 99(18), p Edelstein, H. Data Mining: Myth and Reality. GIGA Information Group Telepresentation. June 30, Chapman, P., Clinton, J., Khabaza, T., Reinartz, T., and Wirth, R. The CRISP-DM Process Model. CRISP-DM Consortium, 1998, Introduction to Data Mining and Knowledge Discovery, Two Crows Corporation. 2nd ed. Potomac, Md.: Two Crows Corporation, 1998, Arunasalam, R. G., Richie, J. T., Égan, W., Gür-Ali, Ö., and Wallace, W. Reengineering Claims Processing Using Probabilistic Inductive Learning. IEEE Transactions on Engineering Management, 1999, 46, Knowledge Discovery Understand the Patterns in Your Business and Discover the Value in Your Data. Accessed Sept. 15, 1999.

11 Healthcare Applications of Knowledge Discovery in Databases Forslund, D., Kilman, D. The Virtual Patient Record: A Key to Distributed Healthcare and Telemedicine. Los Alamos National Laboratory. Feb. 29, 1996, TeleMed/ Papers/virtual.html. Accessed June 29, Geographical Mapping and Analysis in Health Care Bardell, M. , Jan. 17, Knowledge Discovery Solutions: Approach. business/approach.html. Accessed January 13, Neuborne, E. Mining Info What s in It for Me? Business Week E.Biz. July 26, 1999, p. EB 12. About the Author Kristin B. DeGruy, MSHS, is regional finance manager at Humana Inc.