Anticipate Data Challenges and Opportunities
What does it mean to anticipate data challenges and opportunities, and why is it important?
Data is essential for implementing every component of the Roadmap, including identifying disparities, conducting root cause analyses, designing and implementing care delivery and payment models, tracking progress, and refining health equity efforts—but the following common challenges can undermine your initiative:
- Low quality data
- Unanticipated and time-consuming roadblocks to sharing data across organizations
- Inconsistent and shifting definitions of data meaning within and across organizations
- Underestimating the level of resources and amount of time needed to access and analyze data
- Misalignment on success metrics among stakeholders
Addressing these proactively positions your team to meet its data needs in a timely manner. Build data considerations into your initiative from the start. If your team struggles to measure impact or encounters unexpected data discrepancies, pause and refine your data strategy to stay aligned with your equity goals.
The chart below details the steps as well as the approximate number of meetings for which to plan, however, estimates will vary from team to team.
| Key Activities | Time Estimate: ~ 14+ Hours |
|---|---|
| Identify new and existing sources of data | 2-4 one-hour meetings |
| Partner with stakeholders to collect and interpret data | variable based on previously established relationships |
| Develop plans for review and analysis of data | 4-6 one-hour mtgs |
| Discuss challenges with data | 4-8 one-hour mtgs |
| Revise plans for data review, sharing analysis, and meaning-making based on learnings | 4-6 one-hour mtgs |
How should I anticipate data challenges and opportunities?
Begin with the Anticipate Data Needs and Opportunities presentation, below. It explains the connection between health equity and quality improvement through data and introduces high-level considerations for choosing a health equity focus and applying an intersectional lens. Then, work through the resources that follow.
Resources for Anticipating Data Challenges and Opportunities
Anticipate Data Needs and Opportunities (Presentation)
A foundational presentation connecting health equity to quality improvement, covering how to choose a health equity focus, apply an intersectional lens, partner with community stakeholders to collect and interpret data, and develop concrete data action items and next steps.
Anticipating Data Needs and Opportunities (Tool)
A step-by-step planning tool covering opportunities to strengthen partnerships when collecting and analyzing data, data source identification, assessing data quality, earning buy-in from key stakeholders responsible for data management, data sharing, and data analysis
Consideration for Accessing, Collecting, and Sharing Data (Tool)
Helps your team answer three overarching questions, with an emphasis on accessing, collecting, and sharing data:
- Whose buy-in do you need to sustainably scale-up a successful initiative?
- What data do you need to collect to address all stakeholder interests?
- How will you use that data during various stages of your initiative?
Designing and Implementing Integrated Care and Payment Transformation Initiatives to Advance Health Equity: Lessons Learned from Three Pioneering Health Care Provider and Health Plan Partnerships
Sometimes it is helpful to learn from the experiences of others. This report presents case studies of care and payment transformation models designed and implemented by three pairs of health care provider and health plan partnerships to advance health equity. See page 37 for important lessons that they learned about anticipating data challenges.
Assessing Data Challenges and Opportunities
This resource will help you assess and address the following common data-related challenges:
- Earning and benefitting from timely key stakeholder buy-in
- Initiative sustainability
- Data quality
- Data collection, management and analysis
- Data-related risks of care and payment models
| Topic | Next Steps |
|---|---|
| Earning and Benefitting from Timely Key Stakeholder Buy-in Front-line finance, billing, information technology, and data analytics team members are often neglected when health equity initiative team members make key data-related decisions. It can lead to unexpected delays and resource shortages when the team seeks data sets and analytic services to inform and implement the health equity initiative. | Instructions: Review and discuss the following prompts and use the team’s responses to identify next steps. 1. Are front-line, finance, and billing colleagues active members of your team? (For multi-organization collaboratives: Do you have representatives from each partner organization?) * What are their roles in the organization? > Are they involved in decision making for your health equity initiative — especially decisions that might impact their work load? * What should be their role on your team? > What is it currently? > What will it be in the next 6-12 months? 2. Are IT and data analytics colleagues active members of your team? For multi-organization collaboratives: Do you have representatives from each partner organization? * What are their roles in the organization? > Are they involved in decision making for your health equity initiative — especially decisions that might impact their work load?? * What should be their role on your team? > What is it currently? > What will it be in the next 6-12 months? |
| Initiative Sustainability The key stakeholders needed to launch an initiative may be different than the key stakeholders needed to sustain it over the long-term. | Has your team identified the key stakeholders who hold the decision-making authority and power to ensure that your initiative has what it needs to sustain it over the long-term? * If yes, has your team asked them what data or metrics they would need to see that would convince them to sustain their support? > If yes, is your initiative tracking those metrics? > If not, what is necessary to begin tracking those metrics? * If not, how can your team identify the right stakeholders? Is there a clear line between your initiative’s logic model, the data plan, and the data your key stakeholders need to see? If a key stakeholder asked for a progress report today could your team produce it? |
| Data Quality Which metrics and data fields are needed to implement the care transformation model? Don’t forget to consider metrics and data fields to trigger care pathways, referrals and specific interventions. | 1. Are metrics being captured in a structured, query-able format? * How much of the data is complete, missing, or declined? > How was the data assessed (e.g., formal audit, team member impressions or guesses)? – How confident is the team regarding the assessment’s accuracy? > Can you distinguish between “missing” and “declined” data fields? * Which data fields have been cross-validated against a secondary source (e.g., claims, electronic health record)? * Are data fields entered consistently across sites, providers, and time periods? * How are data quality issues communicated back to the staff members who enter the data? * Is there a process for correcting data errors? 2. Which metrics are needed to implement the payment model? (Note: Don’t forget to consider fields and metrics that affect reimbursement, risk adjustment, quality bonus incentive payments, etc.) * Are billing and coding staff members aware of which fields have payment implications (including quality incentives)? * Is there enough sufficient documentation to support the payment model? * Are there gaps in the data tied to specific payers, service lines, or time periods? * Is there a need to account for care received that does not appear in your data systems (e.g., outside of partner organizations? * Have you reconciled encounter data with claims data to check for discrepancies? * Have you confirmed that diagnostic and procedure codes are applied consistently across providers? * Are there strategies in place to counteract incomplete or inaccurate payment data? > Ex.: Regular claim rejection or denial reviews that feed back into documentation practices > Ex.: Processes to capture and reconcile out-of-network or out-of-system utilization > Ex.: Delegation of data reconciliation processes to the clinical or financial systems 3. Does the team run test reports to confirm all needed data can be entered, accessed, and analyzed prior to launch of the care or payment model? * Do those test reports include end-to-end testing from data entry to final report output? 4. Is there a system in place to regularly audit all data reports? |
| Identifying Opportunities to Improve Data Collection, Management, and Analysis | Instructions: Review and discuss the following prompts and use the team’s responses to identify next steps. Challenges * What data-related challenges has your team encountered? (Ex.: difficulties accessing data, sharing data within and across organizations, and/or analyzing and interpreting data) * Are there metrics that your team has to track manually because data collection processes are not automatically captured? * Have data gaps ever delayed a decision or forced your team to move forward with incomplete information? Potential Solutions * Can your team find a workaround for any challenges? Can that workaround be turned into a more durable solution? * Can you find the right person(s) to lead a response to the data challenge(s)? * Do you have contacts at peer-organizations that can provide advice or recommendations? * Is there a new key stakeholder that you need to bring to the team on a temporary basis with the decision-making authority and power to clear the roadblocks? |
DATA-RELATED RISKS OF CARE AND PAYMENT MODELS | Instructions: Review and discuss the following prompts and use what is uncovered to identify next steps. 1. How might patients’ shifting payer status affect claims data continuity? * What impact do changes in healthcare payers have on your ability to measure outcomes over time? > Has your team established a way to track patients longitudinally when their insurance changes? 2. How might boundary crossing (i.e., care received outside of assigned region or across systems) affect data integrity, especially for quality measures and incentive tracking? * Do you know how often patients receive care outside your system or ACO boundary? * How do you attribute outcomes for patients who receive care across multiple systems? 3. How might a lack of referral follow-up undermine assumptions about the care or payment model? * Does your team track whether referrals are completed? * Are there patient populations with systemically lower referral follow-up rates? 4. How might a lack of inter-agency data sharing undermine assumptions about the care or payment model? * Are there partner agencies (e.g., social services, housing, behavioral health) whose data you need but cannot access? * What decisions does your team make with incomplete cross-agency information? |
About the Roadmap Goal and Objective Setting Tool
About the Roadmap Goal and Objective Setting Tool
Use the Roadmap Goal and Objective Setting tool to facilitate and document the development, implementation, and evaluation phases of your health equity initiative. It will help your team realize your vision to reduce and eliminate health and healthcare inequities by providing a centralized resource to:
- establish process goals that align with each Roadmap component;
- document task status, identify project champions, and maintain detailed notes;
- monitor progress across multiple Roadmap components simultaneously; and
- promote consistent team communication, accountability, and progress.