Three Times a Week:Mapping the Transportation of Dialysis PatientsDayton, OhioAmbreen HasanResearch Analyst

Lakeland Community College

2013 Ohio GIS ConferenceSeptember 11 - 13, 2013 | Columbus Marriott Northwest | Dublin, Ohio

Langdon SandersGIS Technician

City of Kettering, OH

Three Times a Week:

Mapping the Transportation of Dialysis Patients in the Greater Dayton Area

Ambreen Hasan and Langdon SandersSponsored 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 TablePickup/Dropoff, Provider, Trip_ID . . .

Geocode Origin & Destination Addresses

Mapping Analysis via unique TRIP IDs

Direct Observation Findings

Personal Vehicle

RTA (Project mobility)

Ambulette (E.M.T., America,

Medcorps)

GreeneCATS Taxi/Van

51%

10%16%

1%

22%

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

White32%Black / Afro-

American62%

Asian 4%Other 2%

Race

drove self / family

Proj. Mob RTA

Reg. RTA bus

Ambulette

TaxiVan paid by Med/Ins

Senior Center

29%20%

2%6%

41%2%

How Patients Travel to Dialysis

Dr. Rec Convenient Dist. Customer Serv.

74%53%

16%

Top 3 Reasons for Choosing the Current Center

Geocoded points by transit provider Where are the Patients?

Created a density surface using Spatial AnalystNotes:• 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

Greene CATS (n=17)

MCPT (n=51)

Anton's Transportation (n=30)

Greater Dayton RTA (n=111)

88%

86%

53%

45%

12%

14%

47%

55%

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 HasanResearch AnalystLakeland Community CollegeAmbreen.Hasan@wright.edu

Langdon SandersGIS TechnicianCity of KetteringLangdon.Sanders@ketteringoh.org(937)296-3209

Thank you.