Three Times a Week: Mapping the Transportation of Dialysis Patients Dayton, Ohio

Ambreen Hasan

Research Analyst

Lakeland Community College

2013 Ohio GIS Conference

September 11 - 13, 2013 | Columbus Marriott Northwest | Dublin, Ohio

Langdon Sanders

GIS Technician

City of Kettering, OH

Three Times a Week:

Mapping the Transportation of Dialysis Patients in the Greater Dayton Area

Ambreen Hasan and Langdon Sanders

Sponsored by

Ohio GIS Conference, Sept. 13, 2013

• Examine current transportation system for dialysis patients in Montgomery, Miami & Greene co.

• Inform Transit, Medical, and Public communities

– Identify Target Areas, Issues & Challenges

Towards Improving Service, Reducing Cost

Purpose

Research Questions • Where are the patients?

– “Hot Spots” and rideshare possibilities

• How do they travel to dialysis? – Field Observation, Patient Survey

• Are they going to the closest center?

Raw Data to Master Table

Pickup/Dropoff, Provider, Trip_ID . . .

Geocode Origin & Destination Addresses

Mapping Analysis

via unique TRIP IDs

Direct Observation Findings

51%

10% 16%

1%

22%

PersonalVehicle

RTA (Projectmobility)

Ambulette(E.M.T.,

America,Medcorps)

GreeneCATS Taxi/Van

How People Travel to Dialysis (n=77)

*Observations done at the two centers located in Dayton *Data includes both arriving and departing vehicles from the center

Selected Survey Results

White 32%

Black / Afro-

Ameri…

Asian 4% Other 2%

Race

29%

20%

2%

6%

41%

2%

drove self / family

Proj. Mob RTA

Reg. RTA bus

Ambulette

TaxiVan paid by Med/Ins

Senior Center

How Patients Travel to Dialysis

74% 53%

16%

Dr. Rec Convenient Dist. Customer Serv.

Top 3 Reasons for Choosing the Current Center

Geocoded points by transit provider

Where are the Patients?

Created a density surface using Spatial Analyst

Notes:

• We used kernel density

• Pick a search radius

– play with results

• Cell size det. ‘smoothness’

• Lowest color empty

Where are the ‘hotspots’?

Areas with high percentages of households without a vehicle

Accessibility to Personal Transport

Density of Patients & Public Bus Routes

Accessibility to Public Transport

• Geocoded PickUp & DropOff Addresses, dialysis centers

• XY to Line tool

– Org. Dest.

– Kept TRIP_ID

• Distance Traveled

• Nearest Center

• Ratio of distance

– Actual / Closest

• Results – 2/ 2 = 1.000

– Or 2.5/ 2 = 1.25

Travel Efficiency: Closest Dialysis?

• 40% not going to nearest center – Weighted by GDRTA with 111 trips

88%

86%

53%

45%

12%

14%

47%

55%

Greene CATS (n=17)

MCPT (n=51)

Anton's Transportation(n=30)

Greater Dayton RTA(n=111)

Percent Travelling to Nearest Center by Provider (n=210)

Yes No

Nearest Center Results

RideShare Analysis: Day & Provider

RideShare Analysis: Day & Provider

RideShare Analysis: Day & Provider

RideShare Analysis: Day & Provider

Where do we go from here?

Patients

• Educate & empower of options available (such as where other centers are located & rideshare opportunities)

Transit Providers

• Further study

• Show mismatch of service duplication and inefficiencies

• Avoid trip duplication

• Promote rideshare

• Talk with other providers

Medical Community

• Work with patients to ID willingness to change

• Explain dialysis center assignment process - include transportation in decision

• ID where changes can/cannot be made

Implications for Decision Makers

• Only part of the system – Data from RTA, Anton’s, Greene CATS, MCPT and

Fairborn Sen. Center.

• Straight lines distance used instead of actual distance. – Easy, does not require network

• Different date ranges of data.

• Surveyed & Observed only two centers – both in Montgomery County

Limitations & Thoughts for Future

Ambreen Hasan

Research Analyst

Lakeland Community College

Ambreen.Hasan@wright.edu

Langdon Sanders

GIS Technician

City of Kettering

Langdon.Sanders@ketteringoh.org (937)296-3209

Thank you.