• Data Strategy & Leadership 

    • 1. Leaders effectively communicate and model the importance of data to the organization's success.

    • 2. Our organization leadership ties data to business strategies. For example, leadership has ensured that there is already in place or that there is a plan for: A library of queries, best practices, and input metrics with regular reporting that measures program success.

    • 3. Investments in data management and analytics are prioritized.

    • 4. Leadership knows which roles and what data assets to onboard as the organization matures. They have a plan, roadmap, and grasp of which users should be onboarded first (e.g., SMEs).

    • 5. There is a respected leader within the organization who is recognized as the data authority.

    • 6. Leadership team effectively champions data-related initiatives and projects so that staff are not overwhelmed with repetitive data-related tasks.

    • Data Accessibility 

    • 7. The data that is available to users is in a format that they can use and understand.

    • 8. We have trusted, well-curated data available for staff to support self-service.

    • 9. There are processes in place for staff to request justified access to data support their roles.

    • 10. Staff can find and use data easily.

    • 11. Timely data is readily available within their workflow among their other tasks throughout the day.

    • 12. There are processes in place for staff to request justified access to data to support their roles.

    • 13. Data and relevant analytics are easily accessible to authorized staff members.

    • 14. We have intuitive (Google-like) search tools to quickly find and preview relevant data.

    • 15. Staff members receive ongoing training and support to effectively utilize the technologies and tools for data access and analysis? For example: there are regular live trainings or there are on demand training videos or training technology platforms.

    • 16. We have a set of named and identified Power Users for each dataset.

      Definition: A "power user" of data is someone who has advanced skills in analyzing and interpreting large sets of data to generate insights and make informed decisions.

    • 17. Our organization has established channels for sharing and communicating data to all stakeholders, internal and external. For example, there is a metrics dashboard that staff can access at any time to monitor ongoing key metrics for the organization (e.g., number of patients served this month).

    • Data Governance 

    • 18. There are logical and clearly defined data models that users can understand and utilize effectively.

      Definition: A data model is a way to organize and structure data, defining how different pieces of information relate to each other. It helps in understanding the data's meaning and helps in designing databases and workflows for real-time insights.

    • 19. Staff have tools to address quality, classify data, and implement data policies.

    • 20. Data quality is actively monitored and maintained within the organization. For example, the count of records added to a database is monitored on a regular basis to ensure that data is consistently captured.

    • 21. There is a clear accountability structure for data assets, their use, and related decisions and actions. For example, someone owns and maintains a data dictionary or information glossary.

    • Data Intelligence 

    • 22. We have integrated measures of Social Drivers of Health in our care and population health management initiatives?

      Definition: Social Drivers/Determinants of Health. These are the conditions in which people are born, grow, live, work, and age that affect their health, functioning, and quality of life. They can include factors like socioeconomic status, education, neighborhood and physical environment, employment, and social support networks. These factors can impact access to healthcare and health outcomes.

    • 23. We have documented workflows and logic models in support of our health plan related business.

      Definition: Documented workflows refer to the step-by-step processes and procedures that are recorded and clearly defined for a particular task, function, or business area. In the context of a health plan related business, documented workflows would outline the systematic sequence of activities required to carry out various operations, such as claims processing, member enrollment, or care management. Logic models are visual representations or diagrams that illustrate the relationships between inputs, processes, outputs, and outcomes of a program or initiative within the health plan. They help to outline the underlying logic and expected results of specific activities, services, interventions, or policies.

    • 24. We have implemented key performance indicators that are important to health plans, such as HEDIS or NCQA measures?

      Definition: HEDIS stands for Healthcare Effectiveness Data and Information Set. It is a widely used set of performance measures in the healthcare industry to assess the quality of care provided to patients. HEDIS measures are developed and maintained by the National Committee for Quality Assurance (NCQA).

    • 25. We are able to measure meaningful patient engagement (e.g., no-shows, early drop-outs, loss to follow up) across our patient populations?

    • 26. We have efficient processes in place to capture and report care quality and care cost for our client population?

    • 27. We know how to stratify our population by risk indicators?

      Definition: Risk stratification refers to the process of categorizing a population based on their individual risk indicators for certain outcomes, such as disease, hospitalization, or the likelihood of responding to an intervention. This allows for targeted interventions and resource allocation based on the level of risk associated with each group.

    • 28. We have implemented predictive and/or prescriptive analytics to forecast outcomes or suggest next best actions.

      Definition: Prescriptive analytics involves using data and analytics to recommend the best course of action to achieve a specific outcome. It suggests specific actions to optimize a decision based on the predictions it makes.
      Predictive analytics involves using data and statistical algorithms to predict future outcomes based on historical data and analytics. It uses patterns in the data to forecast potential future events.

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    • Quality Assurance 

    • 30. The data is be trusted by users.

    • Data Literacy 

    • 31. Staff in our organization understand the value and potential of data in improving patient care and outcomes.

    • 32. Data-related terminology and concepts are used frequently and are well understood throughout the organization.

    • 33. Staff actively seek opportunities to apply data-driven insights in their decision-making processes, and understand the impact of data on organizational strategies and goals.

    • 34. Even business users who are not data experts use data with minimal support.

    • 35. Staff can access a business glossary that aligns key business and data terms, which supports organization-wide consistent data terminology and frameworks.

    • 36. The agency has access to internal or external data user groups (e.g., meetings, conferences, data-oriented channels)

    • 37. Staff receive adequate training and support to enhance their ability to use and understand data.

    • 38. Staff are equipped with the necessary tools and resources to effectively analyze and interpret data.

    • 39. There are documents on datasets (e.g., query, dashboard, data report) that are used and linked to other related datasets so newcomers can quickly get up to speed.

    • 40. People can easily discover column meanings, accepted values, and value frequencies when searching data.

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