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Using Billing Data To Understand System Flow

Using Billing Data To Understand System Flow. A Decision Points Framework Nathaniel Israel, PhD SFDPH CBHS CYF-SOC. Containers. Conference design Didactic Collaborative Rapid Action Cycle (PDSA) Data Systems Framework / Mindset Billing, Clinical Data, Rapprochment

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Using Billing Data To Understand System Flow

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  1. Using Billing Data To Understand System Flow A Decision Points Framework Nathaniel Israel, PhD SFDPH CBHS CYF-SOC

  2. Containers • Conference design • Didactic • Collaborative • Rapid Action Cycle (PDSA) • Data Systems Framework / Mindset • Billing, Clinical Data, Rapprochment • First Step: Billing Data

  3. Values • Grounded in improving well-being of children and families in our communities • Begin with data everyone has available • Build using low-cost, replicable frameworks • Only analyze data that inform client and policy decisions • Set the stage for “Why” data: seeking more complete stories • Analyze data for communication at all levels

  4. Container: Billing and Clinical Data • Billing Data • “What” of how clients move through the system • Proxies for effectiveness / outcomes • Clinical Data • “Why”: tells us whether that movement was clinically indicated or …not • More direct measurement of clinical / functional progress

  5. Billing Data: Goals • Understand how clients move through episodes of care in any behavioral health system (system flow) • Understand key decision points that replicate across levels of care • Understand the limitations of these data for decision-making • Identify the points at which our behavior change has the most impact for children and youth

  6. Container: Billing Data • Episode • Client • Clients of a Clinician / LPHA • Clients within a Program / RU • Clients within an Agency • Clients within a Level of Care • Clients within a System …..offers comparisons / aggregation at each level

  7. Framework • All episodes of care have points at which client and provider behavior is critical to client outcomes: • Access to services  • Engagement / dropout  • Service delivery  • Linkage…..(  )

  8. Billing data tell us what? • Generally seen as proxies for service processes • Access: • How many clients enter at each level of care? • Are there disparities in access (ethnicity, age)? • Engagement / Dropout: • Who is engaged? Who may require more intensive engagement? • Service Duration / Intensity: • Is this a normative dose (intensity, length) of treatment? • Linkages: • Where do children / youth “slip through the cracks”? • Who transitions to more, less intensive care?

  9. Defining AESL • Access definition • Number in, number out, number retained • Engagement definition • Number of services after opening (variable length- 21 vs 42 days) • Service characteristics • Length, intensity of treatment • Linkage parameters • Time to next service; fail-up

  10. Billing data by LOC

  11. Knotty Problems • Engagement: • Continuous vs discrete. How much time does one have to engage? • Services: • Metric(s) for aggregating different types of services. Methods for aggregating over time. • Linkages: • What linkages matter most? How long do we look for a link? • Outcomes: • Proxies only. “Good” vs “bad” movement. What does disappearing mean?

  12. Benchmarking • What points of reference are used to decide when to dive in? • Aggregate performance and extreme scores (Graphman and Lyons) • Guidelines from the empirical literature (Daleiden) • Statistical vs practical criteria (Guerin)

  13. Early Rich Points • Access: • Whites twice as likely to use Crisis Services • Pacific Islanders, AA 4-5x as likely as Asians to use JJ-MH • Engagement: • Intensive Outpatient services, 20% of subset get no services within 21 days of opening • Services: • LOS differs 5-fold by Day Tx provider • Linkages: • Time to next service post-hospitalization

  14. Action Planning • Reporting out these data requires a common mindset • ‘What’ data often lead to ‘Why’ questions • These data only hint at reasons for actions or outcomes • Going to the next level often requires careful attention to stories and / or ‘Why’ data • Danger in considering data as truth

  15. Other Anticipated Benefits • Knowing flow helps us manage flow • Begin to think ‘upstream’ • Orients system to decisions from the POV of the child / youth • Identifies clients who might otherwise fall through the cracks of the system • Provides means for understanding providers’ performance that goes beyond mere productivity

  16. Next Steps • Benchmarks • Empirical Literature • Multi-county / statewide comparisons • Simulations: “what now” and “what if” data • Clarifying methods for uncovering “the rest of the story” • Uncovering rich points: data for dialogue

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