6
Data Integration and Health Management

Data management is a key concept in the successful implementation, conduct, and measurement of any integrated health management program. The previous chapters presented reviews of current NASA programs as well as best practices in the conduct of programs designed to maintain and improve the health of the workforce. This chapter presents an approach to world-class data management processes that give program managers ready access to common data and information related to data-driven management of a healthy workforce and workplace.

ADOPTING CHARACTERISTICS OF WORLD-CLASS PROGRAMS TO DATA MANAGEMENT

Chapter 4 presents an overview of characteristics found in world-class programs. The characteristics highlighted in that chapter clearly outline the advantages of an integrated data management strategy. The chapter also emphasizes the importance of systematic data collection, an approach that allows for data integrity and consistency.

Data that are collected on occupationally related health programs can serve a variety of purposes in an integrated data management system. First, data can be used to report statistical information on program usage and effectiveness. Such information is critical to the design and management of these programs. Second, such reports ensure that program implementation is effective and provide a measure of accountability for the program’s performance. Third, data are used for ongoing program revi-



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Integrating Employee Health: A Model Program for NASA 6 Data Integration and Health Management Data management is a key concept in the successful implementation, conduct, and measurement of any integrated health management program. The previous chapters presented reviews of current NASA programs as well as best practices in the conduct of programs designed to maintain and improve the health of the workforce. This chapter presents an approach to world-class data management processes that give program managers ready access to common data and information related to data-driven management of a healthy workforce and workplace. ADOPTING CHARACTERISTICS OF WORLD-CLASS PROGRAMS TO DATA MANAGEMENT Chapter 4 presents an overview of characteristics found in world-class programs. The characteristics highlighted in that chapter clearly outline the advantages of an integrated data management strategy. The chapter also emphasizes the importance of systematic data collection, an approach that allows for data integrity and consistency. Data that are collected on occupationally related health programs can serve a variety of purposes in an integrated data management system. First, data can be used to report statistical information on program usage and effectiveness. Such information is critical to the design and management of these programs. Second, such reports ensure that program implementation is effective and provide a measure of accountability for the program’s performance. Third, data are used for ongoing program revi-

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Integrating Employee Health: A Model Program for NASA sion. For example, baseline needs assessments can be used to identify barriers to opportunities and indicate corrective actions. Further, they allow for the quantification of results following the implementation of program changes. Finally, data are used to report program performance to aid in the generation of new hypotheses that could be tested by others in a research setting or context and, in return, may benefit the entire field of worksite health management (Edington, 2001). ORGANIZATIONAL FRAMEWORK FOR DATA MANAGEMENT The Four Faces of Measurement: An Organizing Principle An effective data management and measurement system can support organizational objectives such as Decision making, Accountability, Improvement, and Surveillance, including longitudinal analyses and knowledge discovery. A systematic approach, incorporating these “four faces of measurement” described in the quality improvement literature (Solberg, et al., 1997; Pronk, 2003a), can serve as an organizing framework for data management- and data measurement-related objectives. Such a framework adds to the realities of the business setting that demands an approach to data-driven decision-making processes. This framework also encourages managers to explicitly recognize various approaches to data collection and use, measurement, and reporting, and thus provides support for the reporting needs at the various levels within the NASA organization. A discussion of each organizational measurement aim from this framework follows. Measurement for Decision Making Measurement can be used to support decision making. To do so, leadership at appropriate levels must be aligned with its respective decision-making authority and have access to specific data analyses and information. At NASA, for example, to make decisions regarding investments in specific health-related programs, leadership at the headquarters level as well as at each individual center needs to be informed on environmental policies and risk factor prevalence statistics to be addressed; cost-related

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Integrating Employee Health: A Model Program for NASA data that will support the business case; and readiness of the workforce and management teams to participate and support the proposed programs. For management to make decisions in a timely manner, the data to be considered should be valid and reliable and may need to include projections based on data-driven assumptions (e.g., as related to projected return on investment) and should not be overwhelming. Moreover, there must be reasonable assurance to management that the data collected are representative, accurate, and reliable, thereby supporting their use for decision making that prompts action. Measurement for Accountability Measurement can also be used for accountability. One method of accountability for achievement of program objectives is periodic reporting of a set of measures created a priori. Moreover, program staff may assume accountability in a very proactive manner when the measures against which they are held accountable are known in advance. These measures may include process measures, but for the purposes of accountability, most of them will be outcomes or results-type measures. They are also useful for monitoring overall program performance. Measurement accountability should be reported openly, however, so that it can be used for performance comparison. Assurance that the measures used for accountability are accurate and valid requires a focus on a few vital measures. In addition, the measurement process may need to include external staff or independent audits and be appropriately adjusted for validity. Furthermore, the creation of the measures themselves should be done in a collaborative manner so that agreement exists on the measures themselves. Measurement for Improvement The Plan-Do-Study-Act (PDSA) cycle is a good example of a measurement for improvement strategy (Langley et al., 1996). This approach includes data collection and measurement that identifies potential problems, barriers, or opportunities for improvement; facilitates the implementation of improvement initiatives; and follows with data collection to measure the improvement or change that has taken place. Following such an approach, measures and data used for improvement should be simple, easy to implement, and collected and reported in effective and efficient time frames. In addition, all data analyses should be capable of specific as well as centralized analyses.

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Integrating Employee Health: A Model Program for NASA Measurement for Surveillance, Longitudinal Analyses, and Knowledge Discovery Measurement for surveillance, longitudinal analysis, and knowledge discovery supports the need for uncovering unanticipated problems related to integrated health programs; allows for retrospective analyses of data that may provide critical insights into specific health-related questions or issues; and provides an opportunity to position an organization or agency as a leader in the field of integrated health management. Ongoing surveillance methodology is an important component of data-driven management of health-related trends. In addition, this approach supports the need to more fully understand what may explain underlying drivers of trends in health management or decline in health status. The objectives and complexity of such analyses and data programming practices require well-trained expertise; however, it also provides opportunities for the generation of a research agenda and new knowledge. In the case of NASA, application of such an agenda will enable a meaningful contribution of new knowledge to the field of health management. Who, Why, What, When, How? Effective data management and data mining require integration and consistency in data collection. In addition, maintaining the confidentiality and security of the data collected requires sustainable diligence. The result of this strategy is a more valuable database that can increase in value over time. The overarching goal of this framework approach to data management (described above) is to drive collection of universal and reliable data that will satisfy common program goals and ensure that information obtained is meaningful to all participants. In Table 6-1, rows labeled “who, why, what, when, and how” are specific to NASA’s needs for data integration and health management across the four faces of measurement. Specific attention is given to the respective programmatic needs of NASA centers as well as the role of Headquarters as a leader and coordinator in driving health-related goals and objectives. A DATA MANAGEMENT SYSTEM FOR AN EFFECTIVE INTEGRATED HEALTH MANAGEMENT PROGRAM A data-driven approach is the core technology needed to implement an integrated and sustainable health management program to achieve world-class status. Figure 6-1 illustrates a systems approach that represents a comprehensive data management strategy. The figure denotes four

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Integrating Employee Health: A Model Program for NASA TABLE 6-1 Framework for Data Management   Decision-making Accountability Who? Audience (For whom?)   NASA Top Management HQ Director Team Center Director Team   NASA Top Management HQ Director of OHS Center Director Why? Purpose   Understanding of cost-benefit Prioritization Understanding of need and demand   Demonstrate performance Comparison Reassurance Identify need for change What? Scope   Specific to HQ Specific to individual centers   Specific to NASA Specific to HQ Specific to individual centers Measures   Few Reasonable, reliable and valid Projected estimates with acceptable assumptions   Very few Accurate, reliable and valid Time Period Intermediate, past, projected (estimated) Long, past Confounders Consider, but rarely measure Describe and try to measure When? Timing and timeliness   Annual Periodic based on schedule In time to inform budget-related decisions   Annual Insync with comparison units and centers How? Data collection staff Internal with external expertise as required Internal with external expertise as required Sample sizes Large or specific to identified population Large Data collection process Sufficiently complex to assurance acceptable level of accuracy, reliability and validity to take action   Complex Requires moderate effort and cost Confidentiality and Privacy   Very high De-identified data only Comply to HIPAA and ADA regulations   None for purposes of comparison—the goal is exposure and transparency De-identified data only Comply to HIPAA and ADA regulations

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Integrating Employee Health: A Model Program for NASA Improvement Surveillance, Longitudinal Analysis, and Knowledge Discovery   Program staff Center leadership HQ leadership Program leadership at every level   Understanding of process and user groups or employees Baseline assessment Evaluation of changes   Notice patterns and trends Explain areas of concern Monitor population health statistics of special interest Specific to individual centers Universally applicable to all levels of NASA   Few Easy to collect Simple Approximate   Many Complex collection Precise, reliable, and valid Short, current Long, past Consider, but rarely measure Measure and/or control   Ongoing Insync with improvement cycles Insync with program implementation efforts   Ongoing Specific to issue or topic needing to be examined Internal Internal if expertise is available; otherwise external Small Large   Simple Minimum cost required Integrated with program implementation Usually repeated frequently in order to create sun charts Highly complex   Very high De-identified data only Comply to HIPAA and ADA regulations   High De-identified data only Comply to HIPAA and ADA regulations

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Integrating Employee Health: A Model Program for NASA FIGURE 6-1 A systems approach to data management for NASA.

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Integrating Employee Health: A Model Program for NASA levels of nested data domains. Each of the nested levels allows for analyses that are supportive of each of the columns in Table 6-1. This type of data management system will enhance development of an information database that can be directly tied to mission-critical goals and objectives. Such an approach specifically addresses the needs for data collection and management, quality assurance and standardization, data protocols and standards, data elements and priorities, scorecards, stratification, and benchmarking and best practices. Whereas the nested levels of this approach to data management allow for vertical integration, there are two additional features illustrated in the figure that are important to recognize as well. First, horizontal integration is also supported by this approach because it allows data to be compared between individuals, groups, and organizational centers. It may even support the comparison of an agency-specific set of measures to external organizations. Second, this type of data management system supports collection of longitudinal data for comparison measures collected at any of the levels. Data Collection and Management Effective data management begins at the data collection stage. Having a common set of core metrics throughout an organization ensures that the primary goals of the organization are addressed. Applied to NASA, the system illustrated above allows for and encourages each center to have its own unique priorities and data needs. Quality Assurance and Standardization Good quality assurance requires the creation of a common and preferred set of processes and protocols that can be followed in selecting data collection tools. Following such common and preferred processes and protocols provides an opportunity to report data in a standardized manner and ensures consistency in data integrity, collection, and management processes. Data Management, Protocols and Data Standards and Elements Core data elements also need to be standardized to generate meaningful metrics comparison. This requires the ability to designate the additional data elements that will be needed for effective programming and continuous improvement in a unique location or within a unique mission.

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Integrating Employee Health: A Model Program for NASA Data Protocols for Consistency Consistency in data collection is a critical component of quality metrics. Achieving consistency requires communication and agreement on core data measures throughout an organization. In cases such as NASA, where a decentralized system is in place, collecting additional but similar data requires standardization of protocols. Data Warehousing The establishment of data warehouses to store data on health, safety, and productivity management is a trend in data management. Data warehouses are generally designed to organize existing databases to provide the organization with common metrics across multiple employee benefit plans (Goetzel et al., 2004). In the case of NASA, as a component of the central system, each center would maintain its own data warehouse, containing its own data, with physical servers located either in one central location or at each of the centers. This would allow for local access and use of data but would not compromise the overall central warehousing and analyses of NASA-wide data. The primary criterion is that the individual data tables are accessible across centers, according to the level of agreed data sharing. Technical Standards Common protocol and technical standards are a recurrent theme throughout the establishment and implementation of a data-driven system. Data need to be standardized, and computer hardware and software need to be standardized. For effective data-sharing and report generation, each data table and aggregation rules are uniform across work sites in decentralized organizations. In addition, special attention to security and confidentiality needs to be in place as a component of an organization’s standard operating procedures. Data Capability Maximizing an organization’s data capability requires placing computer hardware and software as well as data collection and input into the data warehouse. The multiple functional and relational databases that are created by accessing the data warehouse require similar analytical characteristics for uniform report generation.

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Integrating Employee Health: A Model Program for NASA Eligibility Rules Avoiding potential problems when unifying a data management system is important; thus, each individual in the system is coded to his or her status within an organization. In that way, analyses and reports can be created using the appropriate employee codes that protect the privacy of individuals. In the case of NASA, each individual center would have to manage its own program eligibility requirements because of differences in contract agreements. Data Elements and Priorities Health Behaviors and Other Personal Risk Factors Health behavior data are considered core measures for programs designed to influence the overall health of an individual. Employee data records that capture this information include short- and long-term disability and other disability metrics such as EMPAQ measures (see Box 6-1 and http://www.empaq.org/empaq/), Family Medical Leave, and Worker Compensation data. Additionally, data collected through the use of a self-reported Health Risk Appraisal (HRA) tool are often supplemented with biometric measurements. Self-reported health risks and behaviors are among the type of data collected and may include questions BOX 6-1 Employer Measures of Productivity, Absence, and Quality In 2003,The Washington Business Group on Health (WBGH), now the National Business Group on Health, established a council of employer members to address issues related to disability, absence, and health-related productivity. This group became the Council on Employee Health and Productivity (CEHP). An important goal of this group was to develop tools to support innovative ways to improve absence and productivity management. The product of CEHP’s efforts is a document known as EMPAQ: Employer Measures of Productivity, Absence, and Quality. EMPAQ provides a methodology and set of standardized metrics for employers to accurately measure program outcomes, participate in meaningful benchmarking, evaluate vendor performance, and identify best-in-class organizations and practices. In a complex arena that includes many different stakeholders, EMPAQ provides a common lexicon and platform for consistent and rigorous measurement.

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Integrating Employee Health: A Model Program for NASA on body weight and height, physical activity levels, tobacco use, safety habits, dietary habits, use of preventive services, and psychological perceptions of health, stress, and life and job satisfaction (see Chapter 5 for detailed discussion of HRAs). Medical and Pharmacy Utilization Privacy and liability issues can pose data collection barriers to an organization by limiting access to medical and pharmacy utilization data from its employees. However, because these are the most frequently used outcome measures to demonstrate success of occupational health management programs, they are important metrics to collect. This problem can be overcome by the use of proxies for medical or pharmacy claims. For example, questions that are frequently integrated into self-reported HRAs, which can be used as proxies, include the frequency of medical visits, utilization of emergency room services, and length of stay in the hospital during the past year. Productivity Indicators Productivity measures are time-away-from-work measures, such as absence days, short-term disability days, and workers’ compensation days, that are often common throughout an organization. Self-reporting surveys have been developed that collect data related to productivity, such as presenteeism, which is an indicator of on-the-job productivity (see Chapter 5). In addition, questions related to productivity indicators, such as presenteeism, are often included in an HRA. They can also be used as stand-alone questionnaires. Samples of these surveys can be found in the “Gold Book” distributed by the Academy for Health and Productivity Management (IHPM, 2001). Quality-of-Life Indicators Quality-of-life measures are frequently considered core to the central objective of an organization like NASA. These measures are a function of an individual’s perception of their physical, mental, and emotional health and are critical to how they perceive their overall life and job satisfaction. Quality-of-life indicator data are often collected as part of a self-reported HRA. Examples of functional questions that provide an additional dimension to quality-of-life indicators that may appear on the HRA include an employee’s perception of how easy it is to do daily tasks.

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Integrating Employee Health: A Model Program for NASA Environmental Policies and Factors Health professionals now recognize that environmental policies and factors at the workplace are important determinants of overall health (Golaszewski and Fisher, 2002; Golaszewski et al., 2003). The autonomy available at the worksite and how one interacts with others likely affects one’s health and productivity. Simple things, such as opportunities for flexible scheduling, healthy choices in vending and cafeterias, stairway alternatives to elevators, ergonomic and safety considerations at the individual’s workstation, and so on all contribute to a positive work environment (see discussion in Chapter 5). In addition, this is an area of overlap with effective leadership, management, and supervisor training. Program Participation The number and range of employees who participate often determine the success of a health management program. These data are necessary to assess the effect of participation and changes in health status and environmental policies. Data on program participation, however, requires that extensive records be kept on program participants and date of participation. Scorecard Report Management The data collection and management framework presented above allows for the initiation of a “Scorecard Report Management” approach that may facilitate an ongoing method for health improvement. This approach uses scorecards as a tool to monitor health status and health improvement progress of employees. Based on characteristics presented in Table 6-1, the measures reported in scorecards are considered to be measures of accountability. Measures of this type can be an important source of information on the health status of individual employees. Scorecards are advantageous because they accommodate unique metrics designed for specific areas within an organization as well as organization-wide metrics. The scorecard management approach is not, however, intended for interorganizational comparisons, although it can be used to compare performance in an ongoing context and provide an opportunity for sharing information from successful initiatives within an organization.

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Integrating Employee Health: A Model Program for NASA Stratification Identification of Targeted At-Risk Populations Loss of productivity because of absence and disability can account for up to 29 percent of health- and productivity-related expenditures (Goetzel et al., 2003). Specific subsets of the workforce population that can be considered at risk may include, but are not limited to, those who: Have specific chronic conditions (e.g., diabetes, heart disease, hypertension, asthma) Smoke Are obese Have applied for gastric bypass surgery Are at high risk for diabetes Are identified as having more than three modifiable risk factors out of the top four actual causes of mortality Databases can be used to collect information on high-risk health conditions and identify at-risk employees in the workforce so that they can be channeled into appropriate programs. Predictive Modeling There is ample evidence that identification and intervention of at-risk populations by single risk factors may not be the most effective way of improving the health status of either individuals or populations. Recently, data-driven analytical techniques have been developed to identify clusters of risks that could lead to disease or loss of productivity (personally as well as on the job). The most celebrated cluster is metabolic syndrome, a cluster of five risk factors, the presence of any three of which puts one at higher risk for diabetes, heart disease, and a variety of other metabolic diseases (NIH, 2001; Ford et al., 2002; Pearson et al., 2003). Identification of Targeted Low-Risk Populations The objective of including low-risk populations in data stratification is to maintain that population in the low-risk category (Edington, 2001; Musich et al., 2003). Whereas most traditional health promotion programs target populations defined as at-risk for a single risk factor (smoker, high blood pressure, high cholesterol, overweight and obese, etc.), a more comprehensive health management program considers the total population, which includes the low-risk population as well. An important distinction

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Integrating Employee Health: A Model Program for NASA between traditional and comprehensive programs is that resources in comprehensive programs are targeted to provide for a healthy worksite that includes environmental concerns, convenient physical activity options, and healthy food choices in cafeterias and in vending machines (see Chapter 5). Benchmarking and Best Practices Any field of care, including health management, uses benchmarking and best practices to measure effectiveness, ensure use of common metrics, and achieve consistent high-quality programs. Benchmarking and best practices—concepts that arose out of the quality movements—have been implemented in a wide variety of situations (see Chapter 4). Best practices is a term applied to those protocols that are considered the most effective, either in a given situation or with individuals that share common health characteristics. In the case of health management, for example, benchmarking is used to make comparisons between populations of employees. Both of these concepts are considered essential components of world-class programs. A recent benchmarking study found that achieving “best practice” levels of performance in health and productivity management would help companies realize savings of as much as $2,562 per employee—a 26 percent reduction in the overall $9,992 per employee costs distributed among group health, turnover, absenteeism, disability, and workers’ compensation programs (Goetzel et al., 2001). As indicated above, the use of benchmarking and best practices allows an employer to use resources wisely and make comparative measures of effectiveness. This mode of operation also ensures that individual participants are receiving the best possible care and attention. Regulatory Requirements Federal agencies such as NASA must be constantly vigilant about regulatory requirements, protection of personal health information, and quality assurance guidelines. Having common metrics and protocols increases the likelihood that guidelines will be implemented and followed as outlined in the Health Insurance Portability and Accountability Act of 1996 (HIPAA). The Centers for Medicare and Medicaid Services (CMS) are responsible for the implementation of HIPAA. These provisions include Title I (1996), which protects health insurance coverage for workers and their families when they change or lose their jobs, and Title II, the Administrative Simplification provisions, which require that the U.S. Department of Health and Human Services (USDHHS) establish national

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Integrating Employee Health: A Model Program for NASA standards for electronic health care transactions and national identifiers for providers, health plans, and employers. It also addresses the security and privacy of health data. It is essential to adhere to personal data protection as provided for in HIPAA as databases are built, maintained, and shared within an agency. Interaction of Data Sets Data collection and management processes occur at multiple levels within any large organization, including NASA. Further, these processes occur at the individual, departmental, center, and multicenter levels, up to the level of Headquarters. Given such circumstances, multiple levels of data can also interact with each other. It is at those places of interaction that the need to ensure protection of privacy, anonymity, and confidentiality arises. Figure 6-2 presents an example of how worksite health promotion data at the personal and group level may interact (Pronk, 2003b). In this figure, a worksite would consider how to ensure protection of personal health information and decide on the type and importance of data used for sharing across multiple groups. For example, whereas personal information that allows an individual to be identified remains in the domain of personal health management, some of those data are needed to connect information among the various data sets to the correct person. Importantly, this type of data should not be shared while identifiers are attached, although it may be reported in aggregate format. FINDINGS The committee gathered evidence for NASA’s investment in the health of its workforce, both civil servants and contract employees, from information and data provided by the agency, invited presentations, and site visits to selected NASA centers. Although the overall goal of occupational health-related programs—to improve and maintain the health status of the workforce—was clear, evidence for consistency in program application, implementation, and evaluation between centers was less apparent, and evidence showing that the goals and objectives of the various centers are coordinated was not found. The committee was particularly concerned about organizational structure, eligibility rules, programs, and evaluation measures, including: Overview of practices by site; Addressing issues of intra- and intercenter consistency;

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Integrating Employee Health: A Model Program for NASA FIGURE 6-2 Interaction of personal and group information factors. SOURCE: American College of Sports Medicine (ACSM). Headquarters oversight of data practices; Current activities around benchmarking, quality assurance, common protocols, and so forth; Materials available to the workforce; and NASA-generated research and reports. Data Collection and Management Site visit observations of specific occupational health programs at NASA, findings from the literature, and comparisons with “best practice” models described in Chapter 4 further indicated that there was a

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Integrating Employee Health: A Model Program for NASA need for data-driven integration and health management capabilities across NASA and within its centers, so that a truly integrated health management program can be implemented. Consistent with many organizations that follow a traditional approach, occupational health programs and initiatives at NASA tend to be program specific, and although readily available to the centers, the selection of such programs and initiatives does not appear to be based on health-related, employee-based, NASA-wide data- or center-specific analyses. For example, whereas the HERO analysis (Wasserman et al., 2000), conducted for NASA, was informative and may be regarded as an important contribution to the field, the data were not derived from NASA employees or center-related observations, and thus may not reflect a NASA-based assessment of need. The health improvement data at NASA that are available for program planning, prioritization, and resource allocation tend to be fragmented and sparse. Therefore, because NASA is interested in moving forward as a world-class integrated health program, it is imperative that a robust, agency-wide, center-specific, and employee-centered data management system be implemented. Closing the Gap Between Traditional and World-Class, Integrated Health Programs Although the NASA health program has a long and distinguished history, the current traditional approach to occupational health care does not meet the needs of a world-class program. Data management agency-wide as well as at the level of the individual centers is an important area for improvement. The descriptions in Table 6-1, when compared to the committee site visit observations at NASA centers, illustrate an immediate need to create a comprehensive data management system that will support data-driven decision making and information for program improvement, assessing accountability, and long-term observations and knowledge discovery. The committee determined that primary gaps are most likely to occur in the following three areas: Common metrics; Common procedures and quality assurance; and Clear eligibility guidelines. Addressing and filling these gaps will be an important step toward crafting a comprehensive data management system.

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Integrating Employee Health: A Model Program for NASA Benchmarking at NASA Observations by site visit teams to selected NASA centers (see Chapter 2) also indicated that the occupational health programs in place were not consistently using effective benchmarking. In all too many cases, local preferences dominated program selection and use. This may not be undesirable, because there are many dedicated professionals conducting the programs to meet the unique needs of the various centers. However, it does indicate a need for more consistent and uniform metrics to maintain and improve the quality of NASA’s occupational health programs. RECOMMENDATIONS As discussed above, NASA can, through intra-agency cooperation and data sharing, become a world-class best practice organization through both center-specific as well as NASA-wide improvements in strategies and tactics for collection and management of employee health data. The core recommendation (see Chapter 3), that NASA adopt a mission-driven vision for an integrated health management program, requires the establishment of an agency-wide system for data collection to serve as a database that can be readily accessed and used in program planning, evaluation, decision support, and knowledge discovery. NASA should implement a systems-based approach to data management that includes the following components: Data collection, management, and reporting according to agreed-on protocols and standards; Consistent data practices across all NASA centers; and Longitudinal tracking of data across all centers and the agency as a whole. NASA should adopt a framework (see Figure 6-1) for measurement that will allow the agency direct access to data collected for the purposes of decision making, accountability, improvement, surveillance, longitudinal analyses, and knowledge discovery. NASA should create and initiate a data-management collaborative that includes representatives from all centers as well as Headquarters who are trained and well informed about measurement and evaluation. At a minimum, the objectives of the collaborative would include Generation and ongoing monitoring of performance data measures;

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Integrating Employee Health: A Model Program for NASA Initiation of a data-driven exchange of improvement strategies and tactics for practitioners at the centers; Provision of input and feedback to center- and agency-specific health initiatives; and Provision of specific recommendations for data management-related resource needs, training, and integration. NASA should establish agency-wide data architecture and technology, that may or may not include a comprehensive electronic medical record, to support its operational goals. Clarification of occupationally-related, compared to general, health promotion and disease prevention and management data requirements is an essential step in defining agency-wide technology solutions. NASA should use the opportunity of building such new programmatic endeavors to contribute to knowledge about program effectiveness, cost benefits arising from these programs, and factors that can contribute to the success of these programs. Implementation of a standardized methodology using NASA’s full cost accounting approach for a health and productivity element (as discussed in Chapter 4) would greatly assist in this regard. In this way, NASA’s experiences can help to inform the directions taken by other worksites. Specifically, the Committee recommends that NASA consider research in program outcomes (including improved health outcomes for workers and overall cost savings), factors that contribute to program success (e.g., as measured by employee participation rates or behavior change), barriers and facilitators that contribute to worker participation in programs—and how these barriers and facilitators differ by type of worker, center, and other factors—and factors that contribute to each center’s ability to initiate, implement, and sustain integrated health programs. SUMMARY In an organization like NASA, where measurement and evaluation is a cultural norm, a data-driven decision-making system is a prerequisite for success. However, observations made by the committee at site visits to selected NASA centers indicate that this type of system is not uniformly in place across the agency, or in complete form at the observed centers. As a process of moving its health management programs toward a world-class standard, it is imperative that NASA institutes a common operating measurement system throughout the agency that includes each of its cen-

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Integrating Employee Health: A Model Program for NASA Wasserman J, Whitmer RW, Bazzarre TL, Kennedy ST, Merrick N, Goetzel RZ, Dunn RL, Ozminkowski RJ, and the Health Enhancement Research Organization (HERO) Research Committee (Anderson D, Caputo N, Dundon M, Greenlaw R, Howze E, Lynch W, Pronk NP). 2000. Gender-specific effects of modifiable health risk factors on coronary heart disease and related expenditures. Journal of Occupational and Environmental Medicine 42(11):1060–1069. Websites: www.empaq.org/empaq/ www.ihpm.org/publications/assessment.html