The Impact of Patient Complexity on Health Care Utilization Erika - - PowerPoint PPT Presentation

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The Impact of Patient Complexity on Health Care Utilization Erika - - PowerPoint PPT Presentation

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The Impact of Patient Complexity on Health Care Utilization Erika Cottrell, PhD, MPP Research Investigator, OCHIN cottrelle@ochin.org September 19, 2019 Erika Cottrell Has nothing to disclose 2 Acknowledgement and Research Partners

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The Impact of Patient Complexity on Health Care Utilization

Erika Cottrell, PhD, MPP

Research Investigator, OCHIN cottrelle@ochin.org September 19, 2019

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Erika Cottrell

  • Has nothing to disclose
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Acknowledgement and Research Partners

Research reported in this work was funded through a Patient-Centered Outcomes Research Institute (PCORI) Award (HSD-1603-34987). The statements in this presentation are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee.

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A driving force for health equity

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OCHIN’s National Impact

500+ Organizations in 47 states | 10K+ Providers

The Population We Serve

5.2M Patients in the ADVANCE Research Database

39% Racially Diverse | 35% Hispanic 32% Best Served in a Language Other than English 52% Below Federal Poverty Level Chronic Conditions 55% At least one chronic condition 30% At least one MH/BH diagnosis

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Why study patient complexity?

  • Primary care clinics are increasingly

evaluated on health outcomes

  • Many risk adjustment models

account for clinical complexity

  • Despite research demonstrating

the impact of social factors, risk adjustment models typically do not account for social complexity

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Health Systems Demonstration Project

 Phase I:

  • Engaged stakeholders to identify high priority research

questions

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Health Systems Demonstration Project

 Phase I:

  • Engaged stakeholders to identify high priority research

questions

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Health Systems Demonstration Project

 Phase I:

  • Engaged stakeholders to identify high priority research

questions  Phase II:

  • Engaged stakeholders to prioritize measures
  • Partnered with OneFlorida to conduct preliminary

analysis in a safety net population

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Health Systems Demonstration Project

 Phase I:

  • Engaged stakeholders to identify high priority research

questions  Phase II:

  • Engaged stakeholders to prioritize measures
  • Partnered with OneFlorida to conduct preliminary

analysis in a safety net population  Phase III:

  • Partnered with KPNW validate and extend analysis
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Phase III: Collaboration with Kaiser Permanente Northwest

Patient Insurance Mix

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Clinical Complexity

Do social factors explain variation in clinical quality

  • utcomes above and beyond clinical complexity?

Social Complexity Quality of Care Outcome Measures

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Do social factors explain variation in clinical quality

  • utcomes above and beyond clinical complexity?

Clinical Complexity

Charlson Comorbidity Index Patient-level

Social Complexity

Social Deprivation Index Community-level

Quality of Care Outcome Measures

Diabetes control (HbA1c>9%) Patient-level

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Odds of Poor Diabetes Control: HbA1c > 9%

Model OCHIN

OR (95% CI)

KPNW

OR (95% CI)

Charlson Score: 0-1 Ref Ref Charlson Score: 2-3 1.15 (1.04, 1.27) 1.25 (1.15, 1.37) Charlson Score: 4-5 1.04 (0.93, 1.17) 1.40 (1.28, 1.53) Charlson Score: >=6 1.07 (0.95, 1.19) 1.64 (1.50, 1.79) Social Deprivation Index (Increase of 10) 1.05 (1.04, 1.07) 1.03 (1.02, 1.04) Statistically significant All models adjusted for age and sex OR = Odds Ratio CI = Confidence Interval

For KPNW patients, increasing clinical complexity is associated with poorer diabetes control. This trend is not present in the OCHIN sample.

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Odds of Poor Glucose Control: HbA1c > 9%

Model OCHIN

OR (95% CI)

KPNW

OR (95% CI)

Charlson Score: 0-1 Ref Ref Charlson Score: 2-3 1.15 (1.04, 1.27) 1.25 (1.15, 1.37) Charlson Score: 4-5 1.04 (0.93, 1.17) 1.40 (1.28, 1.53) Charlson Score: >=6 1.07 (0.95, 1.19) 1.64 (1.50, 1.79) Social Deprivation Index (Increase of 10) 1.05 (1.04, 1.07) 1.03 (1.02, 1.04) Statistically significant All models adjusted for age and sex OR = Odds Ratio CI = Confidence Interval

Increasing social complexity in the patients’ census tract is associated with poorer diabetes control in both samples. For KPNW patients, increasing clinical complexity is associated with poorer diabetes control. This trend is not present in the OCHIN sample.

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“Cold Spot” Communities (Highest Social Deprivation)

Those living in “cold spots” had 24% and 12% higher odds of poor diabetes control in the OCHIN and KPNW samples, respectively

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Stakeholder Engagement: Cross-cutting Themes from Patients, Health Systems Leaders, and Clinicians

  • Safety net clinics have very high

levels of social deprivation relative to the U.S. population

  • Providers from these clinics may

benefit from having their performance metrics adjusted to account for the social complexity

  • f their patients
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Potential Clinical and Policy Implications

  • Community-level data can help us understand the social

complexity of different populations

  • Findings highlight the need to consider social factors in the

developing performance metrics and how we pay providers

  • Risk-adjustment models incorporating social complexity would

especially benefit providers serving more socially-complex groups

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Thank You!

Erika Cottrell

Research Investigator, OCHIN cottrelle@ochin.org