AD Final Report

Ambassador Database - Summer Intern Project July 31, 2015

Ambassador Database ReportBy Kyle Parker & Jerrod Castleberry (2015 AOA Summer Interns)

Department of Research & Development

Introduction

For this project, current AOA staff and constituents provided recommendations of

individuals who could serve as potential AOA ambassadors. Unfortunately, the majority

of the information received were names. Our task for the summer was to identify as

much information as possible about each recommendation and enter it into the system.

This was an important task because including as much information as possible about

each ambassador is essential to creating a database that will help identify the most

appropriate individuals to represent the AOA at various events.

Structure of Database

Our first task involved revising the “Ambassador Database.” The current system

contains 23 category descriptions for each individual. The categories are: AOA number,

last name, first name, full name, degree(s), certification(s), osteopathic affiliation(s),

position in affiliations, other affiliation(s), type of training program, college of osteopathic

medicine from which they graduated, specialty, subspecialty, birthdate, ethnicity,

gender, city and state where they are practicing, extra notes/comments, curriculum

vitae, interest(s), and current employment.

Data Collection

In order to gather information on physicians, we accessed several websites. The

primary websites that were used were:1


www.healthgrades.com

www.vitals.com

http://health.usnews.com/

doctors/

http://doctor.webmd.com

On these various websites, we would type in a specific physician’s name from the

database and, generally, I was able to obtain most of their information. It was more

challenging, however, to find their ethnicity, birthdate, training program type, and

curricula vitae. Thus, concerning birthdate and training program type, we utilized

software that includes an AOA database containing pertinent information of all AOA

members and some non-members, called IMIS where this information was available most

of the time. In regards to ethnicity, we searched other websites to find pictures,

specifically, we conducted google searches looking for pictures and visiting webpages of

the individual we were trying to identify. To make sure we found the correct individual,

we checked to see if their degree(s) and certification(s) properly lined up. Finally, in

order to obtain any curricula vitae, we found the most effective method was to search by

name and include the words “cv” or “pdf”.

The database includes individuals who are not physicians. For these people, we

simply typed in their name on google and reviewed as many pages as possible until we

collected the information needed. For this group of “non-physicians” it was a tremendous

challenge finding their birthdate on the internet, therefore, we used IMIS which typically

included it.

Assessment of Content of Database

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As of July 29, 2015, there are a total of 300 names in the Ambassador Database.

Over two-thirds of the individuals represented are male and over 80 percent of the

names are current AOA members (See Tables 1 & 2, respectively).

Using the birthdates of each individual in the database, we calculated their ages

and placed them into 6 categories (See Table 3). This table provides the distribution of

ages by count and overall percentage.

Table 3. Age DistributionAge Range Count Percent26-35 28 9.33%36-45 57 19.00%46-55 52 17.33%56-65 71 23.67%65+ 42 14.00%Unknown 50 16.67%Grand Total 300 100.00%

Geographically, the current database population is nicely disbursed across the US.

Figures 1 and 2 depict the distribution of the ambassadors by region and state,

respectively.

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Table 2. MembershipStatus CountMember 245Non Member 55Grand Total 300

Table 1. GenderGender CountFemale 99Male 201Grand Total 300


16.67%

25.67%

32.00%

25.67%

Figure 1. Region Breakdown for Database

WestNortheastMidwestSouth

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Figure 2. Geographic Distribution by State


*Any state missing a number currently has zero names represented.

There were a total of 33 specialties represented in the database. Table 4

summarizes these specialties.

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Table 4. SpecialtiesAcupuncture Diagnostic

RadiologyNephrology Psychiatry

Allergy & Immunology

Emergency Medicine

Neuromusculoskeletal Medicine

Pulmonology

Anatomy Family Medicine Nutritional Biochemistry

Radiology

Anesthesiology General Surgery OB/GYN RheumatologyChild & Adolescent Psychiatry

Geriatric Medicine

OMM/OMT Sports Medicine

Clinical Chemistry,

Geriatric Psychiatry

Orthopedic Surgery

State/Specialty Executive

Colon & Rectal Surgery

Hospice Care Pathology

Critical Care Medicine

Internal Medicine Pediatrics

Dermatology Neonatology Physical Medicine/ Rehabilitation

Table 5 provides the Top 8 specialties. The table gives both the total number for

the specialty and the percentage is makes up of all specialties. There were a total of 293

names with specialties. It is important to note that many individuals included in the

Database had more than one specialty. To ensure that the end user is able to consider

all the possible candidates for each specialty, we have captured all of the specialties

which are searchable through different filters. For example, a person who has Family

Medicine and OMM listed as specialties will be represented in both categories.

Table 5. Top 8 Specialties

Specialty TotalPercentag

eFamily Medicine/Practice 108 36.86%Internal Medicine 34 11.60%Emergency 16 5.46%

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MedicinePediatrics 20 6.82%OMM/OMT 15 5.12%Surgery 15 5.12%Physical Medicine 11 3.75%Neurology 10 3.41%

In addition to recording a person’s specialty(ies), we have also incorporated

interest areas. An interest can consist of a topic or idea that is like a sub-category of a

specialty. For example, a person can have a specialty in Pediatrics, which they are

licensed in, while having an interest in Obesity. Interests can be grouped with many

specialties. Chart 3 contains 21 areas of interest and a count of how many people in the

database represent the interest area. Since there are a great number of interests

possible, this chart only highlights a few.

Obesity

Diabete

s

Health

Dispari

ties

Pain M

anag

emen

t

Mental

HealthCan

cer

Heart D

isease

Dermato

logy

Orthop

aedics

Immuniza

tion

Women

's Hea

lth

Health

care P

aymen

t

Alzheim

er's

Infectio

us Dise

ases

Pulm

onary

Issues

Neurol

ogica

l Dise

aseStro

ke

Patie

nt Safe

ty

Quality

Rectum/Colo

n

Tech

nology/

Electr

onic

05

10152025303540

12

26

3

15 1511

1710

34

310

16

1619

74 3 1

61

Figure 3. Interest Areas

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These next two tables contain information on medical school and residency

training for the ambassadors. Table 6 shows the breakdown of the colleges of

osteopathic medicine (COM) the ambassadors attended. If the ambassador did not

attend a COM, they are counted in the number for “Not Applicable” (N/A). Table 7

reflects the type of residency program from which the ambassadors received their

training. The Unknown category represents the 44 ambassadors for whom we did not

have any information regarding their training. For those that are not osteopathic

physicians, but are in the Database, we have noted them in the category labeled “N/A.”

Table 6. Colleges of Osteopathic MedicineCOM CountATSU-KCOM 29AZCOM 4CCOM 17COMP 8DMU-COM 18KCUMB-COM 25KYCOM 3LECOM 5LMU-DCOM 2MSUCOM 19MSUCOM 3N/A 52NSU-COM 9NYITCOM 18OSU-COM 9OU-HCOM 15PCOM 26PNWU-COM 1RowanSOM 12TCOM 5TouroCOM 1TUCOM 3TUNCOM 1UNE-COM 8

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Table 7. Training ProgramsProgram Type TotalAllopathic 46Osteopathic 133Dual Training 15Military 7Unknown 44N/A 55Grand Total 300


VCOM 2WVSOM 5Grand Total 300

Challenges and Next Steps

Although we were able to gather information on the existing 150 ambassadors

included in the database and add an additional 150, we still faced many constraints in

our data collection. For example, we were challenged in collecting information on

training program type, birthdate, and ethnicity. These are the three columns in the data

system with the most missing information. Besides using the internet, we also tried to

leverage AOA’s IMIS system. We thought it would be helpful in finding information

regarding birthdates and residency training. Unfortunately, IMIS lacked some of this

information. For birthdates, if the birthdate of an individual was not included on IMIS, it

was nearly impossible to find that information or even the age of an individual on the

internet.

While pictures were plentiful on the internet, it was difficult to actually identify the

ethnicity of the individual with absolute certainty. Thus, the current database has limited

information on ethnicity. We would recommend a person more familiar with the

individuals on the database complete the ethnicity component or to simply ask the

ambassadors to complete a form that includes this information.

Also, during the project, we were asked to add additional categories which

required us to go back and add the missing information for those individuals already in

the system. Some of this information still needs to be entered. For example, the

“Employment” category is not complete for the majority of the individuals in the system.

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This was one of the categories that we were asked to be added well after most of the

names were already entered into the database. Thus, this category would be completed

by simply searching the individuals on the internet to find their current employment.

One other challenge of this database project was that we were note able to enter

all the names that should be included in the database. Our internship was only seven

weeks, and therefore, our time was limited in how many names we could receive and

subsequently store into the database. We did not receive responses from all the

specialty colleges and state affiliates regarding people they would recommend. Alexis

Curtis is still collecting that information. In addition, we were not able to review the AOA

CEO’s Linked In page to obtain the names of individuals who have connected with her

over the past few years and would be appropriate for the database. By addressing this

and the other issues/challenges, the database will be more robust.

In an effort to highlight information that is missing, we developed a coding system.

Any information that could not be found through either the internet or IMIS was shaded

purple and any individuals who were not in IMIS were shaded green.

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