State of the ClinicBelleville Family Health Center

September 5, 2013

Many thanks to Wen-Jan Tuan for assistance with data gathering for this talk

Please complete the required IPHIT participant form for our HRSA grant.

Please complete the evaluation form at the end or email me any feedback you have about this presentation so we can continue to improve upon it.

Thanks – Jen and The Integrating Public Health Inquiry and Transformation (IPHIT) team

Introductory comments

Review Belleville Clinic demographics, geo-maps and quality metrics

Share the state of clinic finances Look at different ways in which data can be

queried and presented Explore examples of how to data can inform

future investigations and interventions

Objectives

As you look at this data, formulate your own questions: How was this data extracted? Do we think it is accurate? What else do you want to know? What seems to be missing? What assumptions are we making?

Goals

As of June 1, 2013 5,633 patients were assigned to a PCP at Belleville clinic and have had some contact with UW Health in the past 3 years. From this we have panel-based data.

From 7/1/12 to 6/30/13 (FY13) 4,633 patients were actually seen at Belleville at least once during this year. From this we have service-based data.

Our patients

• Panel: patients assigned

to a PCP at BV on June 1 2013 and contact with UW in the past 3 yrs

• Service: patients seen at BV during 7/1/12-6/30/13

• Not BV assigned: patients not assigned to a PCP at BV

Panel

Service

Not Belleville assigned

This is how it looks:

Panel vs Visit Data

Which questions are best answered with panel data?

Which questions are best answered with visit data?

FY 2013 (July 1, 2012 – June 30 2013):

◦ There were 19,425 visits – includes faculty, residents, nurses, lab and x-ray.

Visit data

Physician visits month by month 2011-2013

RVUs month by month 2011-2013

Nurse visits month by month 2011-2013

Lab visits month by month 2011-2013

DEMOGRAPHICS

5.70%

7.50%

8.00%

21.60%

21.80%

21.80%

10.10%

3.50%

Age distribution of Belleville panel June 2013

age 0-5age 6-11age 12-17age 18-34age 35-49age 50-64age 65-79age 80 +

Sex distribution of Belleville Panel June 2013

Female 50.6%Male 49.4%

Belleville Patients Count of Patients by Census Tract

1,600

1

What do you think our top 5 patient issues are?

Moving on to diagnoses…

Condition # of Patients %Patients %Female

Obesity 1,469 30.4% 49.4%

Hyperlipidemia 1,203 21.4% 46.1%

Hypertension 1,123 19.9% 48.5%

Depression 830 14.7% 69.9%

Smoking 776 13.8% 43.9%

Top 5 issues for Belleville panel members

Condition # of patients % patients % Female

Obesity 1318 31.9% 51.1%

Hyperlipidemia

1146 24.7% 47.6%

Hypertension 1110 24% 48.7%

Opioid 715 15.4% 56.9%

Depression 658 14.2% 72.8%

Top 5 visit diagnoses Belleville FY 2013

Condition # of Patients

on panel

%Patients in panel

# of patients seen in the past year with this diagnosis

% of patient visits with this diagnosis

 

Obesity 1,469 30.4% 1318 31.9%  

Hyperlipidemia 1,203 21.4% 1146 24.7%  

Hypertension 1,123 19.9% 1110 24%  

Depression 830 14.7% 658 14.2%  

Smoking 776 13.8% 569 12.3%  

Opioid 695 11% 715 15.4%  

Anxiety Disorder

597 9.5% 440 9.5%  

Chronic Back Pain

556 8.8% 471 10.2%  

Asthma 406 6.4% 348 7.5%  

Osteoarthritis 374 5.9% 348 7.5%  

Diabetes 349 6.2% 362 7.8%  

A comparison of panel diagnoses to visit diagnoses

What are the questions that come to mind for you?

What would you like to know more about? How could we query this data?

LOTS to think about here…

Let’s choose our top diagnosis of obesity as an example of how we can break this down into more detail.

How would you identify our obese patients in Epic?◦ Problem list?◦ Billing diagnosis code?◦ BMI from vital signs?

Where do we begin – defining and identifying obesity in our patient population

BMI>=30 as recorded in the vital signs

How obesity was defined for this report

BMI>=30 as recorded in the vital signs

Any problems with this?

How obesity was defined for this report

BMI>=30 as recorded in the vital signs

Any problems with this?

No differentiation made for age of the patient

- misleading data for children in this report: under-reporting

How obesity was defined for this report

ages N % of patients in this age range with obesity

Odds ratio*

*N not large enough for this to be reliable

18-34 243 25.9% 1.0

35-49 428 41.3% 2.05 (1.69-2.49)

50-64 487 43.3% 2.13 (1.76-2.58)

65-79 266 48.3% 2.47 (1.50-4.04)

80+ 49 31.4% 1.22 (0.66-2.25)

Age breakdown for obesity

Belleville Obese Patients Count of Patients by Census Tract

200

1

Show me the money…

2013 “The work we do” “The money we get” “Money/Work”

Payor Charges % of Chgs Payments % of Pays Collection %

GHC $656,965 12% $464,603 17% 71%

Medicaid $455,227 9% $118,352 4% 26%

Medicare $1,413,597 27% $460,728 17% 33%

Physicians Plus $509,512 10% $181,871 7% 36%

Unity $775,989 15% $439,378 16% 57%

Workers Comp $29,605 1% $24,938 1% 84%

All others & self pay $1,418,850 27% $1,009,346 37% 71%

TOTAL $5,259,745 100% $2,699,216 100% 51%

2013 Payor mix

2013 Belleville payor data

GHC 12%

Medicaid 9%

Medicare 27%

Physicians Plus 10%

Unity 15%

Workers Comp <1%

All others & self pay

27%

2013 Percent of Charges

GHC 17%

Medicaid 4%

Medicare 17%

Physicians Plus 7%Unity 16%

Workers Comp 1%

All others & self pay

37%

2013 Percent of Payments