Utilizing patient-cost data to focus quality improvement efforts in a residency clinic AMANDA WEINMANN, RESIDENT 6 DEC 2014 CHRIS FALLERT, FACULTY

Disclosures I have no actual or potential conflicts of interest in relation to this presentation. This work has not been supported by any grants or funds.

Starting Point Simple data analysis of a clinic’s patient cost data Resident-level 2 weeks

Starting Point Simple data analysis of a clinic’s patient cost data 2411 patients 67 data points each

Starting Point Simple data analysis of a clinic’s patient cost data Resident-level2411 patients 2 weeks67 data points each What can we hope to accomplish?

Our Clinic University of MN – St. Joseph’s Program Bethesda Clinic ◦ St. Paul, MN in “Frogtown” neighborhood ◦ Urban, underserved population ◦ Healthcare Home Certified ◦ Ramsey County refugee health

Our Clinic University of MN – St. Joseph’s Program Bethesda Clinic ◦ St. Paul, MN in “Frogtown” neighborhood ◦ Urban, underserved population ◦ Healthcare Home Certified ◦ Ramsey County refugee health

Starting Point Simple data analysis of a clinic’s patient cost data Resident-level 2 weeks What can we hope to accomplish? Focus quality improvement efforts

Goal Current focus MN community measures o diabetes o asthma o depression o vascular disease o cancer screening Potential focus High cost High freq Cost of disease Frequency of disease

Methods: Outline Population: Higher cost patients 1. Divide patients into two cohorts based on prior cost 2. Match patients from higher cost cohort with those in lower cost cohort 3. Compare the cost and frequency of various chronic diseases between cohorts High cost High freq Cost of disease Frequency of disease

1. Divide patients into cost cohorts 2411 patients ◦ Exclude patients who have high cost due to uncontrollable factors (dialysis, home nursing, and transplant) 2285 patients left ◦ Top 20% = “high cost” cohort 457 “high cost” patients Source: AHRQ Medical Expenditure Panel Survey. “The concentration and persistence in the level of health expenditures over time: Estimates for the U.S. Population 2008 – 2009.” 2012.

2. Match patients Known confounders: age, race/ethnicity, sex, health status, and insurance coverage Match for age, interpreter needed, and gender ◦ 457 “high cost” patients ◦ 457 age, interpreter, and gender matched non-high cost patients Source: AHRQ Medical Expenditure Panel Survey. “The concentration and persistence in the level of health expenditures over time: Estimates for the U.S. Population 2008 – 2009.” Source: Ho, Daniel E., et al. "Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference." Political analysis 15.3 (2007):

2. Match patients VariableHigh-cost cohortNon-high cost cohortp value Age44.5 ± ± Gender57% F, 43% M61% F, 39% M0.16 Interpreter74% N, 26% Y72% N, 28% Y0.50 Cost$25,514$1,915< 0.01

3. Compare disease cost and frequency Bipolar disorderHIVOsteoporosis Chronic renal failureHypertensionParkinson’s CHFHyperlipidemiaPersistent asthma COPDHypothyroidismRheumatoid arthritis DepressionIschemic Heart Disease Schizophrenia DiabetesLow Back PainSeizure disorder GlaucomaMacular degenerationTransplant

3. Compare disease cost and frequency CDC Epi Info 7 Training resources (including tutorials, user guide, and videos) available at web site o Import data via forms, Excel, Access, SQL o Export to Excel, Word, HTML

3. Compare disease cost and frequency Visual dashboard Analysis gadgets ◦ Featured: line list, frequency, MxN (2x2) tables, means ◦ Others: word cloud, matched pair case-control, charts (8 types), advanced statistics (5 types)

Line list

Frequency

MxN (2x2) table: compare frequencies

ChartMeans

Means

3. Compare disease cost and frequency Disease frequency Example: Bipolar disorder RR = 1.95 (1.75 – 2.16)

3. Compare disease cost and frequency Disease cost Example: Bipolar disorder Diff = $23,194 (baseline $23,599) Avg cost Bipolar in Top 20%: $26,113 Avg cost Bipolar outside Top 20%: $2,919

3. Compare disease cost and frequency Bipolar disorder RR = 1.95 (1.75 – 2.16) Diff = $23,194 [Overall diff. ($23,599)] [RR = 1]  RELATIVE RISK  COST COMPARED TO MATCHED COHORT 

Conclusions Current focus MN community measures o diabetes o asthma o depression o vascular disease o cancer screening Potential focus For our clinic: o CHF o COPD o chronic renal failure Do: Guidelines Check: Measurable

Conclusions Quick clinic-level analysis can be performed by family medicine clinics with free, user-friendly tools. Future efforts: o Incorporate our clinic-specific results into QI practice. o Trend results over time.

How do other clinics involve residents in quality- and process-improvement efforts? How do other clinics prioritize QI projects? Discussion