The goal of this study is to evaluate system related process efficiencies in a routine task of central cancer registries, case consolidation. Seventy-two de-identified cancer abstracts from the 4 major incident sites (breast, prostate, lung and colon) will be consolidated by 6 registrars across three data management systems. All consolidation activities will be timed. Post-consolidation interviews with the 6 registrars and a separate interview session with SEER registry staff will be conducted to highlight known efficiencies in the consolidation process across data management systems. Recommendations will be made to optimize data management system design and improve operational efficiencies. System development for data management applications often places greater emphasis on achieving the desired operational outcome with relatively less attention paid to the efficiency with which the outcome is achieved. Process efficiencies in system development and the resulting time saved through changes in ?process only? could be substantial in terms of operational costs when aggregated over all cases processed through a registry in a given year. This study will focus on evaluating and improving process efficiencies in the specific task of case consolidation. Case consolidation is one of the primary activities performed by all SEER registries. The consolidation process involves merging multiple cancer abstracts pertaining to the same case into a single ?consolidated? record that best describes the cancer patient?s diagnosis, stage and treatment. While the consolidation concept is consistent across all SEER registries, the manner in which this process is conducted may vary. Developers of cancer registry software generally do not implement a formal process evaluation as part of system development. System development typically places greater emphasis on achieving the desired operational outcome with relatively less attention paid to the efficiency with which the outcome is achieved. As such, the efficiency of conducting any individual registry process within a single data management system could vary greatly when compared to other systems performing the exact same task. Process efficiency leads to increased data quality and productivity while reducing overall operational costs. A variety of formal methodologies exist for evaluating a process within an organization with a focus on improving quality and productivity. The underlying key to these methodologies is to let the data drive continual improvement of the process at hand. Almost every process has some degree of waste associated with it. In the context of process evaluation, waste is defined as any activity that requires resources but does not provide value. It is often described as non-value added activity and would include things such as waiting time, processing errors, and overproduction. Through the elimination of waste, one can begin to maximize process efficiency. Two of the most common process evaluation methodologies in use today are Six Sigma and Lean. Six Sigma is a data driven approach to continually improve process quality and productivity while reducing operating costs. It strives to drive out inefficiencies, reduce process variation, and minimize outputs that don?t meet desired standards. Lean, on the other hand, focuses on maximizing the speed (efficiency) of a process by reducing waste. It strives to improve process flow, remove costs of unnecessary complexity and separate value added from non-value added activities. In an effort to obtain the best of both approaches, these two process improvement methodologies have been combined to form Lean Six Sigma. Lean Six Sigma provides a methodology for improved process quality and productivity with a focus on maximizing speed through reducing waste. A step-by-step description of the Lean Six Sigma approach can be presented as follows: 1. DEFINE opportunities ? In this first step, the goal is to identify opportunities for improvement and the benefits of improvement. This requires a prioritization of effort in terms of desired goals of the organization and the impact of improvements to each potential process. Boundaries for the process to be evaluated must be set, the data to be collected must be clearly defined, metrics must developed for future review, and high level map of the process should be drawn. 2. MEASURE performance ? In this second step, the process is first observed in detail and then mapped or recorded in detail. A data collection plan must be developed to collect the baseline data for the process. This step provides a measure of the current performance level through an evaluation of the current system. 3. ANALYZE opportunity ? The third step allows a review of the performance of the current process and helps to identify the key drivers of performance. During this phase, the main goals are to identify problem areas, target process steps with delays (waste), and focus efforts for improvement. 4. IMPROVE performance ? The forth step of the Lean Six Sigma approach is where corrective action is taken. This step generally begins by using the data that has been collected to drive decisions. Focus should be targeted on the areas of the process where the data indicates there are problems. Best practices should be reviewed and possible solutions brainstormed. Steps that hinder productivity through waste should be eliminated. Solutions to automate and streamline the processes should be implemented. 5. CONTROL performance ? The fifth and final step addresses the issue of how to maintain performance gains. The new improved process is documented and mapped. It is at this point that potential cost savings should be assigned to the improved process if possible. Staff must be trained and key process metrics must be continuously tracked and reported. Numerous data management systems exist at the central cancer registry level. Both the National Cancer Institute?s SEER Program and the Centers for Disease Control and Prevention?s NPCR Program have developed systems for use by member registries. Many individual states, including Georgia, have also developed their own systems for processing and managing data. Selected data management systems may facilitate more efficient processing of case consolidation by their design alone, holding other variables constant. Utilizing information collected with a formal evaluation technique, decisions and recommendations can be made that will be guided by fact and data (i.e. data-driven decisions). These recommendations should help reduce the procedural customization necessary to complete SEER contract requirements of case consolidation by promoting the standardization of efficient processes.
