Getting Started: Essential Elements for the Successful Use of Data
1. Getting Corporate Buy-In
Creating a data repository is a resource-intensive undertaking that can furnish a company with an important, strategic tool to shape value-based approaches for the health and well being of its employees and the business as a whole. Nevertheless, like most business-related initiatives, the first activity is ensure that senior management fully understands and supports the project. Be clear that “data warehousing” denotes the data repository used by a human resources and Benefits department, which could consist of nothing more than medical and pharmacy data or be much more comprehensive, including hundreds of data elements with analytical and reporting capabilities. Issues to be addressed with the senior team included desired financial, clinical or programmatic outcomes and the anticipated timing of such outcomes. Employer innovators repeatedly underscore that this is an important first step as there are both financial and organizational implications to data collection and analysis.
Management buy-in should be broad based to stave off the possibility of a single corporate sponsor/advocate leaving the company thereby endangering the continuation of the project.
It is also important to build corporate agreement among all departments and divisions. Data is often collected and analyzed in various parts within the organization. Compiling such data, may lead to turf battles with some feeling like they are being invaded or losing power. One of the best ways to address these issues is to have a “corporate data summit,” convening all of the people involved in your organization and laying out the project objectives and individuals’ responsibilities. If everyone can focus on the overall mission, the internal data collection process will go much more smoothly and the team can build a united front for vendor interaction.
2. Establishing Partnerships
Once corporate agreement has been reached, decisions have to be made regarding necessary partnerships with other organizations—such as employer coalitions, the companies’ health care vendors and data aggregation and analysis organizations. There are times when employers may choose to partner with coalitions, which can be advantageous cost-wise as well as providing an opportunity to delve more deeply into the health-related data. Once the partner organizations are identified. It is time to call a data summit.
3. Goal Setting and Expectations
After the commitment has been made to move forward on a data integration project, it is important for all parties to agree on the project goals and the anticipated timeframe in which they can be achieved. Senior management must also understand and support the objectives as the goals of data warehouse projects can vary greatly.
- Intelligent health vendor negotiations
- Addressing the overuse, underuse and misuse of health services
- Quality improvement initiatives
- Cost control initiatives
One of the greatest risks associated with data warehousing are unrealistic goals being set and the project being seen as a failure because of lofty expectations. Therefore, establish goals that align with the available data. These goals include both the outcomes and the timing for these outcomes. It is unlikely any significant outcomes will take a number of years to achieve.
4. Identifying Data Sources
More organizations have cost-associated data (obtained through medical claims) than they do outcomes-based data. This is most likely due to fact that, until recently, outcomes were not accurately tracked. Even so, it is often difficult to collect this information as it is sitting in the medical record and not easily identified. Accurate and secure data is necessary to shape benefit design solutions that address significant out of pocket costs as well as the ensuing clinical and financial implications that arise due to this barrier.
In order to best assess the data needs for effective plan design, it is important that the team determine in advance what problem areas they seek to address through Value-Based Benefit Design. Employers have identified goals such as improving workforce productivity, decreasing health care costs and improving Medication adherence. Successfully addressing any of these issues requires baseline data to see where the targeted population stands prior to the initiation of the new plan design. Data will be vital to every step of the design and implementation of Value-Based Benefit Design – from identifying the target population to determining the impact once the plan is active. A variety of data points, both qualitative and quantitative, regarding the targeted population can be collected and utilized.
In order to do a narrow, basic analysis, it is necessary to have components of medical and pharmacy data as well as basic demographic data at the (de-identified) consumer/patient level. An organization that gleans more data will have a greater the opportunity to cross-reference it. To do more intricate analysis, it is best to have the data coded by diagnosis, primary provider, treatment provider, the treatment provided, disease burden, and costs. This helps employers understand the cost implications for how conditions are treated on an individual (de-identified) level and by whom.
5. Vendor Summit and the Collection of Data
At the beginning of the project it is important to convene health vendors and other business associates that are storing needed data in order to discuss the project and create an agreed-upon data infrastructure. This “data summit” should include IT and data analytics representatives as both will be necessary to bring the data together in an integrated and usable fashion.
The health plan, which is the direct connection to the patient population, should be an active leader of the data summit. It is generally recommended that one person take the lead on the data requirements. This person should not necessarily be the only one addressing data collection and analysis but he or she will need to be the leader in this area. Identifying a team leader will establish clear responsibility for this essential function and present a single point of contact for all vendors and business associates supplying data.
A primary goal of this data summit is to document agreement among all parties of the specific elements and quantity of data needed to both implement new designs and longitudinally measure results. Participants/organizations must understand the goal of addressing health and economic value through implementation of Value-Based Benefit Design. Engaging each of these data holders at once will maximize the chances of receiving the data in a useable format. A finite timeline must be in place to ensure achievable goals can be attained.
Coming to a common agreement on data element definitions and values is a second goal of the summit. Differing criteria, specifications, collection methodologies and formatting among data holders may make the integration – and therefore the usability – of the data a challenge. A single set of data requirements by source should be set and followed by all contributors. One data notebook with interoperability rule sets and a data dictionary should be created for documentation purposes. It will also be helpful for new members of the data team that were not involved with the initial summit.
The final goal of the data summit is to set a data “swap schedule.” This will give all participants a thorough understanding of the time requirements associated with the data downloads. Keep in mind data sharing will not occur on a one-time basis. It will be a periodic occurrence. Although data is necessary at the onset of the project in order to understand the payer’s population, ongoing data is necessary to track changes in the population and to evaluate the results of the value based plan design on desired outcomes. The schedule needs to address the “how” part of data transmission. For example, it will have to be determined if all parties have the ability to electronically push the data to the integrating organization or if some groups will need the integrator to pull the data in. Additionally, some providers may require a more rudimentary process for sharing data, such as data disks. Once a process is agreed upon, all rules and schedules should be a part of the final data summit sign-off document.
Alternative Data Collection: Employee Survey Participation
Acquiring needed data is not easy. Information such as accurate lab values, reasons for missing work and non-adherence to treatment cannot necessarily be derived from quantitative data. For example, many employers use paid time off (PTO), meaning there is no longer a separate pool of “sick” time off. Medication non-adherence could be driven by high out-of-pocket costs or by a patient simply not returning to the doctor to obtain a new prescription. Accurate lab values may also be difficult to obtain. In these and other cases, it may be necessary to rely on self-reported data.
Given these and other challenges, survey data can be the most efficient way of gathering information needed to establish a baseline among the targeted population and measure impact of plan design changes. A comprehensive health assessment completed by the employee or dependent when they begin participation in the plan can provide essential data to measure changes in health outcomes.
One commonly utilized type of survey data is the Health Risk Assessment (HRA). This can be done with or without basic biometric testing. Do not discount patient self-reported data. This data can be helpful in a number of ways. The use of HRA data helps to identify preventable health risk often earlier than seen in medical claims. With preventable risk being a major contributor to continuing rise in overall costs of health care as condition precursors and many of these risks being considered lifestyle related are therefore modifiable through behavior modification of the health care consumer. This information becomes invaluable in allowing a company to focus their wellness efforts in a way that is personalized to both the company’s and the individual’s needs.
Historically, cost was a barrier for many companies hoping to utilize HRAs. This was especially true for small companies. More recently the costs of doing an HRA have gone down significantly and have therefore broadened this use of this form of employee survey. As a result, more companies with fewer than 200 employees are now using HRAs.
Unfortunately, the issue of participation in HRA or biometric screening remains. We are seeing a movement towards incentives/disincentives in order to get greater participation as employers are recognizing the value of this data. In some cases, employers are encouraging all employees to complete an HRA whether they receive health Benefits or not as they may require Benefits in future as well as the potential impact on workplace Productivity.
When developing and administering a survey whether it be an HRA, a quality of life assessment, a workplace Productivity survey, or another type of survey, it is best to partner with a survey data specialist if a customized survey is needed. This will help ensure compliance with privacy regulations. Otherwise, it is always preferable to use an existing tool to measure change. (For more information on HRAs, please see “Getting Started” section of the Wellness and Health Promotion chapter.)
Data can be time consuming and expensive to collect, and because of this many employers and coalitions may decide to only utilize easily accessible data. However partial data may be misleading. An example of this is if an employer were to focus on the five most costly conditions, such cancer and coronary artery disease, and addressing these conditions would also significantly affect Productivity. This problem with this approach is that the five most costly conditions in terms of Productivity include depression, obesity, arthritis, and back pain rather than those such as cancer and heart disease.
Some of the barriers, such as obtaining data from the various health care vendors with different collection methodologies, formatting, and patient identifiers, can be addressed through the use of the data summit. Others, such as privacy issues, often are outside of the control of the employer but still have to be addressed. Careful analysis, planning and implementation by the employer and/or coalition can overcome these and other barriers, leading to successful utilization of data.
6. Data Interpretation and Decision-making
Data supports employers in their understanding of where they sit today, providing baselines prior to implementing any new initiatives and/or plan designs, and in evaluation of whether these changes are reaching the goals that they are hoping to achieve.
While the collection of good data is important, equally significant is the interpretation of the data. This is a critical step to ensuring that the benefit design model truly meets the needs of the intended population. It is important not to simply look at averages when it comes to data. Utilizing only averages in health care data is similar to focusing on the average temperature in your region for the year: you overlook or negate the effect of days that are below freezing or sweltering hot by looking only at average temperature for the year.
Similar to the case of data collection, data analysis becomes more complex throughout time. Initially most organizations utilize data to analyze cost information. As more data becomes available and the organization becomes more agile with the analysis, focus often shifts to questions regarding quality of care. If data is used properly it can be effective for tracking trends, predicting future risk, cutting costs, auditing negotiated costs, identifying fraud, improving quality and increasing Productivity within the analyzed population. Data can be the foundation of purchasing decisions, plan design modeling and good business decisions affecting the health Benefits of employers and coalitions alike.
The use of data can have greater implications beyond those to the employer alone. As employers utilize quality data to help choose a health plan and their other health care vendors, they are indirectly moving the health insurance industry in their region to improve the quality of care that they are delivering.
Information that can be achieved from a data warehouse includes:
In addition to looking at one’s data year over year, employers will often look to benchmark their data against other’s data. A benchmarking activity can be done through their data integration vendor, where the vendor utilizes their aggregated client data as the benchmark, it can be done in conjunction with employer coalition with data from other members of the coalition or it can be done with nationally available data. There is a significant opportunity to utilize benchmark data as a comparator for baseline data in order to help identify potential areas of improvement as well as to be utilized in the goal setting of an associated quality improvement project. Benchmarking activities can help employer’s more fully understand the cost implications related to chronic disease. It can be a tool in Predictive modeling on both a corporate/aggregate level as well as on the individual level, forecasting financial and clinical implications of employees’ state of health and long-term consequences. While helpful in analyzing the problem, benchmarking activities can be equally beneficial to employers who want to model the proposed Value-Based Benefit Design solutions and the outcomes of the programs they intend to initiate.
Most employers begin the data aggregation process slowly collecting and analyzing a few key data elements. These elements usually include medical and pharmacy data along with basic demographic data. It is common that once the organization becomes more proficient with the use of data that they then look to add additional data elements and to broaden their analysis capabilities. Some employers such as JP Morgan Chase and Gulfstream have gone on to more complicated data collection and analysis such work place Productivity and physician quality activities. It is important that organizations do not try and do too much and go to fast as this can complicate issues to the point of failure.
 American College of Occupational and Environmental Medicine, “How Companies Consider Value in Health Policy and Design: Results of the Survey of Employer Decision-Making for Health and Productivity,” 2004, http://www.acoem.org/publication.aspx.