juan ignacio barrientos sajid islam alisha manzo forecasting patient turnaround times at concentra...

JUAN IGNACIO BARRIENTOSSAJID ISLAM

ALISHA MANZO

Forecasting Patient Turnaround Times at Concentra Centers

Summary

Forecasting Difficulties at Concentra Medical Services High levels of uncertainty High variability over many dimensions

Data Inputs Center number, date Patient flow by service type, average time

Methodology Linear Regression analysis Qualitative Analysis

Analysis of Findings

COMPANY BACKGROUNDDEFINING THE PROJECT

SCOPEOBJECTIVES

The Project

Concentra Background

National outpatient healthcare company based in Addison, TX

300 medical centers located in 90 cities and 40 states

Their management consists of primary care physicians and physical therapists These include injuries, illnesses, disease prevention,

physical exams, and drug testing.

Defining the Project

Goals: Model to provide accurate turnaround time for

patients visiting the Dallas area clinics.

Why? Reliable turnaround times set expectations and

empower the customer to know what to do with their time.

Customer satisfaction: we want to provide patients with the best experience when visiting Concentra clinics.

Scope

Focus on Dallas, TX centers Stemmons – 118 patients/day Live Oak – 87 visits/day Redbird – 41 visits/day

Analysis six services: Initial Visit Recheck Visit Therapy Specialist Visit Physical Drug Screen

Objectives

Minimize inaccuracies and fluctuations with projected waiting times.

Reliable forecasting.

Build a functional application for forecasting.

DATA ANALYSISLINEAR REGRESSION

EQUATIONSBUILDING THE APPLICATION

MODEL LIMITATIONS

Methodology

Center Center ID

Initial Visit Count

Avg. Time (Min)

Recheck Visit Count

Avg. Time (Min)

Therapy Visit

Count

Avg. Time (Min)

Specialist Visit Count

Avg. Time (Min)

Physicals Visit Count

Avg. Time (Min)

Drug Screen

Visit Count

Avg. Time (Min)

Total Visit

Date

CMC - DFW Stemmons

4520 4 97 5 47 0 0 0 0 3 71 4 32 2001/02/10

CMC - DFW Stemmons

4520 16 110 30 68 12 48 10 76 17 96 44 40 13401/04/10

CMC - DFW Stemmons

4520 12 77 36 69 8 44 24 85 13 55 41 30 137

01/05/10CMC - DFW Stemmons

4520 9 74 13 53 9 48 0 0 12 57 32 31 76


4520 6 98 25 62 7 42 13 52 15 94 36 37 105


4520 8 62 22 58 9 55 8 63 17 72 26 33 91


4520 6 98 25 62 7 42 13 52 15 94 36 37 105


4520 8 89 4 43 0 0 0 0 6 79 10 39 33


4520 8 64 27 56 11 48 12 70 16 69 39 29 117

01/11/10

Data Analysis

Organize data by date and center.

Remove outliers from data set. Quartile method. Perform more accurate statistical analysis. Final regression line more accurately represents the

sample population.

Perform linear regression analysis.

Linear Regression

Plot data points on an X,Y scatter plot X = independent variable. Number of patients for any

given day. Y = dependent variable. Turnaround time.

Find best fit line for all data points.

Find equation of the line for future forecasting.

Example of Linear Regression

0 5 10 15 20 250

20

40

60

80

100

120

140

160

180

f(x) = 0.0211948170345283 x³ − 0.82209188136947 x² + 10.8513792468371 x + 47.4466081598257

Stemmons - Initial Visit

Final Set of Equations

4520: CMC-DFW Stemmons

Procedure EquationInitial Visit y = 0.0118x3 - 0.49062 + 7.2148x + 59.749Recheck y = 0.0003x3 - 0.0324x2 + 1.3752x + 41.52Therapy y = 0.0022x3 - 0.0577x2 + 0.5866x + 55.494

Specialist y = 0.001x3 - 0.0273x2 + 0.5015x + 65.984Physical y = 0.0006x3 - 0.0576x2 + 2.5884x + 57.278

Drug Screen y = -0.0001x3 + 0.0169x2 - 0.5017x + 42.425

4530: CMC-DFW Live Oak (Baylor)

Procedure EquationInitial Visit y = 0.1693x3 - 2.8487x2 + 16.857x + 79.143Recheck y = 0.0233x3 - 0.8719x2 + 9.8417x + 27.593Therapy y = 0.0237x3 - 0.3802x2 + 0.7053x + 75.452Physical y = -0.0241x3 + 0.7101x2 - 4.257x + 104.93

Drug Screen y = 0.0075x3 - 0.2538x2 + 2.737x + 35.611

Final Set of Equations

4535: CMC-DFW Redbird

Procedure EquationInitial Visit y = -0.05x3 + 1.177x2 - 3.9062x + 94.217Recheck y = 0.0035x3 - 0.2062x2 + 4.1652x + 40.239Therapy y = -0.0062x3 + 0.1182x2 + 0.5925x + 50.986

Specialist y = 0.0031x3 + 0.1305x2 - 3.6216x + 96.074Physical y = -0.0027x3 + 0.1938x2 - 2.6001x + 95.23

Drug Screen y = -0.0001x3 + 0.0165x2 - 0.0009x + 33.908

Snapshot of the Application

Model Limitations

Incomplete data. Correlation between number of patients and total

turnaround time is small. Other factors are equally important.

Data available only encompassed on year. Hard to find repetitive patterns that come with

seasonal changes.

Model is limited to range of patients that each center was exposed to.

MANAGERIAL INTERPRETATIONRECOMMENDATIONS

Q&A

Conclusion

Managerial Interpretation

We can draw conclusions that patient turnaround times does to some extent depend on the number of patients in the clinic.

With respect to the data, our model is the best way to quantitatively predict turnaround time.

Data that would be useful would include: staff available on any given day, center capacity, check-in time, total procedure time, and check-out time.

Recommendations

Generate a greater range of data Arrival Rate Identify factors contributing to variation in procedure

time Procedure time vs. waiting time Gather patient information prior to quoting

turnaround time

Staff evaluation Staffing levels at various points in the day Skill level of staff on hand – equal? Leveling of staff based on demand forecast per service

type

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