HOPE HEALTH CLINIC

Final Evaluation Report, 2011‐2014

Oldham County Health Department

Baptist Health, LaGrange

Oldham County Ministerial Association

1025 E . Sanibel Way, Suite E

LaGrange, K Y 40031

October 2014

Dr.	Betty	Straub,	Ed.D.

with	Dr.	David	W.	Britt	and	

Dr.	Marion	Hambrick

University of Louisville
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FINAL EVALUATION REPORT—OCTOBER 2014

(For the program period October 2011 – September 2013)

Produced for:

Hope Health Clinic

The Foundation for a Healthy Kentucky

The Corporation for National & Community Service, Social Innovation Fund

The White House Office of Social Innovation

For more information visit: www.HopeHealthKentucky.org 
Executive Summary

Key Program Activities

Hope Health Clinic opened on the campus of Baptist Health Hospital (LaGrange, KY) in September

2011 as a free or low‐cost provider of quality health care for low‐income and uninsured residents of four

rural counties in northeastern Kentucky.  With an estimated 95% of Hope patients being employed, this

population is respectfully termed “working poor,” since the majority earn minimum wage – and often

have to choose to cover living costs rather than health care.  Staffed by mostly volunteer physicians,

specialists and community volunteers, the clinic has a considerable network of providers that see

patients through Hope referrals in their private practices, generally for a $10 fee.  A center of specialty

clinics at the University of Kentucky (well over 80 miles from most patients’ homes) provides care

primarily through a financial assistance arrangement that covers the minimum $100 fee for most Hope

patients.  Nursing students from the University of Louisville provide educational programs developed as

part of their curricula, aimed at improving nutrition and understanding of diabetes’ management, a

significant health issue for this population.

Study Design, Methods, Analysis, and Measurement

The study’s implementation design focused on a logic model that would lead to reduced costs for

providing quality healthcare to uninsured, low‐income residents, particularly patients challenged with

four chronic conditions or diseases:  cardiovascular conditions (high blood pressure, high cholesterol),

obesity, diabetes, and smoking‐related conditions.  The evaluation documented the developmental

stages of creating and sustaining a “clinic without walls” – a low‐cost/free clinic that recruited

volunteers from medical specialties and the community across four rural counties to provide services

and funding.  Social network analysis provided a quarterly assessment of the clinic’s reach and growth

among these recruited volunteer and other service providers. Qualitative and quantitative analyses

were used to identify changes in health status for a random sample of patients as recorded in their

paper medical records; detect changes in self‐efficacy for patients managing chronic diseases; gather

feedback from patients and key stakeholders; determine ongoing processes of organizational

management; and document healthcare cost reductions in non‐emergency use of the local hospital’s

Emergency Room.

Summary of Impact, Outcome, and Implementation Study Findings

As focus group participants and survey respondents confirmed consistently, patients avow that

Hope Clinic has saved their lives. More than these patients’ personal perceptions of impact, the local

hospital’s cost reductions in ER use for non‐emergencies by uninsured, low‐income patients were

reduced dramatically. A related, unexpected outcome has been the ER physicians recruiting patients for

Hope Clinic, where these patients can obtain ongoing care for their chronic conditions and diseases.

In terms of reach, Hope Clinic treated 1,916 unique patients (19.16% of the working poor in the

area) across 11,125 office visits since opening in mid‐September 2011. Additional care was provided via

1,244 referrals to specialists for a $10 fee or through financial assistance at the University of Kentucky

School of Medicine clinics.  
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Analysis of health changes for the random sample of patients (N=97) may not be a fair assessment

of total patient outcomes for the clinic. Mixed results indicate improvement and worsening of the four

chronic conditions studied, providing evidence for key stakeholders’ perceptions that patients face

overwhelming, daily challenges related to low‐income populations. These challenges may prevent the

ability for patients to focus on ceasing smoking, losing weight, eating healthier foods, making time for

frequent exercise, reducing the effects of stress, and getting adequate sleep. Hope Clinic stakeholders

express determination to discover strategies to help patients better self‐manage their chronic

conditions, perhaps through individual wellness coaching, continued educational sessions at the clinic,

and additional referrals to health departments and other service providers in the four counties served

by the clinic.
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I. Introduction to the Story of Hope Health Clinic

“Helping Other People Excel . . . HOPE”.  

This acronym for a community center in a small Indiana town succinctly imparts the story of our own

Hope Health Clinic, a patient‐centered clinic supported by the Foundation for a Healthy Kentucky and

numerous local and regional organizations. More parallels exist between these two community‐based

efforts – both organizations provide client/patient‐based centers; both inspire volunteers (from

physicians and government officials to church members and business leaders) through a simple message

of “somebody should be doing something;” (Dickey, 2014) and both focus on helping working‐poor

families who give back the blessings

they receive.

The primary goal of Hope Health

Clinic (HHC) since its inception in

September 2011 has been focused on

increasing access to quality healthcare

for uninsured, low‐income residents in

four, mostly rural Kentucky counties. This Final Evaluation Report documents evidence about the clinic

ability to provide increased access of quality healthcare for uninsured and underinsured working‐poor

residents, including outcomes at the individual and systems levels.  Underinsured is a new category

agreed upon by the Board of Directors with the advent of the Affordable Care Act and its financial

burden on Hope patients.

In a largely rural region with high rates of poverty and many individuals without health insurance,

quality affordable medical care had not been accessible to thousands that need it most. Approximatel

10,000 individuals live in poverty in the four‐county area.  Inadequate services for the uninsured in the

region causes local hospitals, Baptist Health LaGrange among them, excessive emergency room use for

non‐emergency events, particularly visits related to four preventable conditions: obesity, diabetes,

smoking/chronic obstructive pulmonary disease (COPD)—largely attributable to tobacco use, and

cardiovascular disease (CVD), particularly hypertension and hyperlipidemia.  

Oldham County is the largest and most suburban county in the service area and ranks highest in

many health status indicators and lower in terms of health risk than any other county in the

’s

y

“Someone [another patient] paid

for my fees to visit the clinic for 

one year.”

—Hope Clinic Patient from Trimble County
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Commonwealth. This reflects the relative influence of its more affluent residents, not a lack of persons

living in poverty; many large pockets of extreme poverty exists in the region, even in Oldham County. As

a relatively wealthy county, it possessed the infrastructure and provided preliminary support for the

development of a free/low‐cost clinic to serve the needy in the county, as well as for the other poorer,

more rural, adjacent counties. For neighboring Carroll, Henry and Trimble counties, these same health

status measures, patient outcomes, and quality of life lag as residents forego needed medical care due

to a lack of affordable health care options and an inability to access and pay for traditional health care.

This project harnessed additional support from a broad four‐county provider and service network to

meet HHC patients’ other human service needs.  SIF/ KHFI/Foundation support?

Building on the academic literature to address these health issues the Oldham County Department

of Health, in collaboration with Baptist Health LaGrange and the Oldham County Ministerial Association,

applied to the Foundation for a Healthy Kentucky for a 2011‐2014 Kentucky Healthy Futures Initiative

(KHFI) grant to provide initial funding for creating the Hope Health Clinic (HHC).  

Substantial research has documented health and cost benefits of providing low‐cost and accessible

primary care to uninsured community members (Rust, 2009; Roby et al., 2010; Ansell et. al., 2002), the

benefits of elements common to the patient‐centered medical home (Roby et. al., 2010; McWilliam,

2009) and the ability for volunteer providers to provide quality primary care (Dwek, 2002; Scott, Bell,

Geller & Thompson, 2000). Another large body of work has highlighted the roles of lay‐patient

navigators, community health workers, non‐clinicians and clinic volunteers to improve patient self‐

efficacy, patients’ access to services (Mishori, 2009), and sustain high‐quality care for the working poor

(Barnhill, 2001; Dohan & Schrag, 2005; Schwaderer & Itano, 2007; Smedley, Stith & Nelson, 2003).

Similarly,  information about empowerment through self‐management of chronic conditions provide a

sound rationale for Hope Advocates1 and Patient Navigator to focus on a follow‐up referral system for

educational strategies and programs that will assist patients in improving health outcomes through their

own behaviors and attitudes (Bodenheimer, Long, Homan & Grumbach, 2002). This clinic has focused on

replicating the model developed by the Church Health Center in Memphis, TN, (Hulett, 2012; Morris,

2011) which has operated successfully for 25 years, but has never been systematically evaluated for

replication.  

                                                            

1 Advocates are trained volunteers who focus primarily on holistic healthcare needs that are not apparent

symptoms of acute medical conditions, e.g., clothing, food pantries, spiritual, nutrition and exercise classes.
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This KHFI grant provided the resources needed to initially establish a full‐time, low‐cost health clinic

to provide patient‐centered primary and acute care to regional residents in need. The clinic’s embedded

patient navigation system offers clients, families, and caregivers assistance to help overcome healthcare

system barriers and facilitate timely access to quality specialty medical and other needed human and

social services (e.g., health education classes, behavioral health services, nutrition services especially for

patients with diabetes).  

By implementing these activities, we made substantial progress on the following outcomes over our

three‐year project:  

 Increased healthcare access for low‐income, uninsured, and underinsured residents with acute

and chronic conditions;  

 Increased use of holistic care services (mental health, stress management, nutrition and exercise

classes, etc.)  associated with acute disease and chronic conditions for low‐income, uninsured,

and underinsured residents;  

 Decreased healthcare costs associated with treating chronic conditions for low‐income,

uninsured, and underinsured residents and safety‐net providers through use of Hope Clinic; and

 Population‐wide improvements in morbidity and mortality.   

A visual representation of the program is presented in the logic model developed to describe the

HHC and associated outcomes, figure 1, below. Where data were available, this report documents

progress toward these outcomes during the past three years.
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Figure 1: Program Logic Model

   
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II. Evaluation Questions and Goals

The purpose of this evaluation was to provide preliminary evidence of program impact by

documenting program outputs and outcomes, and reporting these results to program staff and funders

about the implementation of this rural clinic. The first‐year evaluation documented successful

implementation and provided preliminary evidence of improved clinical outcomes among patients, and

focused on the role of a service network to support these objectives and clinic operations. This final

evaluation report documents ongoing progress and current outcomes, as the clinic continues its growth

in network outreach among providers in the four‐county area and in other parts of the states.

Evaluation questions included:  

1. To what extent did the implementation of the Hope Health Clinic adhere to its planned

implementation process?

a. How many staff members were hired? (Goal of 3)  

b. How many volunteer providers were secured at start up? (Goal of 3)

c. How many hours per week was an acute and chronic care clinic set up? (Goal is full time)

d. How many new volunteer providers were secured? (Goal is 3/month)

e. How many new volunteers and health advocates were trained per month? (Goal of all)

f. How many new network members did the marketing function secure each month?  (Goal is

one new per month)  

g. How many educational programs/month were patients engaging in?  (Goal is 1/month)

h. How many patients (n/%) received disease self‐management training where it was

indicated? (Goal is 5/month)

i. How many patients per month completed referrals?  (Goal is 3/month)

2. To what extent was  the holistic‐based Hope Health Clinic, using a patient‐centered hub for

acute and chronic care with a navigation system (Clinic Without Walls) successful in improving

health outcomes and reducing healthcare costs associated with low‐income and uninsured

residents in its four‐county service area?

a. To what extent did Hope Health Clinic increase access to clinical, social‐behavioral, material

and spiritual services, programs and activities for low‐income and uninsured residents?

b. What variations in access exist for people from different counties of residence? (Is service

access equitable for all patients in the service region?)

c. What variations in access exist for people with different chronic diseases or health statuses?

d. To what extent did provider density expand during year 1? (Are more medical and social

services providers available as part of the clinic without walls network over time?)  

e. To what extent does the location (county of residence) play a role in increasing provider

density?

f. To what extent does the chronic condition of the patient play a role in increasing provider

density?
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g. To what extent did service density expand during year 1? (To what extent and with what

frequency are HHC patients engaging in referred medical and social services?) Is this related

to medical condition, poverty status or county of residence?

h. What is the effect of a patient’s level of poverty (SES) on service density?

i. What is the effect of a patient’s county of residence on service density?

Several additional questions, about the social network analysis, were added to complement and

better answer the evaluation questions listed above:   

3. What does a social network analysis tell us about the operations of the clinic without walls

network?

a. How does a medical service network expand from an initial baseline state?

b. How do patients with different needs and characteristics flow through a medical service

network?

c. How do different subgroups (e.g., cancer versus diabetic patients) flow through the

network, and how can network density help providers understand the network?

III. Methods

The evaluation used mixed methods and a network analysis to provide information at the clinic and

system levels, which complemented the detailed data obtained at the individual level through medical

records for a randomly selected sample. Our research study design intended to provide preliminary

levels of evidence as outlined in the guidance provided on behalf of the Corporation for National and

Community Service’s Social Innovation Fund.   

A. Data Collection

The data collection efforts focused on answering the questions posed above regarding:  program

implementation, patient reach and demographics, access to primary and referral resources, observable

health improvements, service utilization, and the development and status of a “clinic‐without‐walls”

network.

Following the implementation evaluation design in our evaluation plan, we focused on individual

(outputs, self‐efficacy) and system (outputs, ER and admissions) levels to examine preliminary

outcomes. Data sources included patients’ paper medical records, self‐efficacy forms (see Appendix E)

completed by patients, three focus groups with 23 patients (compliant, non‐compliant, and Spanish‐

speaking), quarterly program reports, hospital admission and ER records, referral logs, clinic records,
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observations, and interviews and a focus group with key staff and stakeholders.  We used these to

generate descriptive statistics for patient demographics, clinical diagnoses, health outcomes, social

services engagement, knowledge, and attitude levels.  The Social Network Analysis (SNA) (discussed

more below) examined referral documents and included a focus group with key stakeholders.

B. Description of Social Network Analysis Methodology  

An important part of this initial work is the social network analysis (SNA) we conducted to determine

the breadth and depth of outreach HHC is able to achieve. SNA was used to gain a better understanding

of the healthcare provider network and its resource exchanges, as the analytical technique was used to

explore the clinic and its network members in more detail. Data were collected from the patient

navigator’s patient referral logs, which provide information about the health services providers used and

services obtained.  

SNA employs qualitative and quantitative approaches to understand the collective network, its

members, and their relationships. This analysis includes the creation of sociograms, or visual depictions

of the networks with their members and relationships. Within a sociogram, a single point (a node)

represents a network member, and a line drawn between two nodes indicates a relationship between

two members. Sociograms display the complete collection of nodes and relationships within the

network. Researchers also can create a series of sociograms for different time periods, and these

diagrams provide insights into the network’s evolution, such as when members joined the network and

how they interacted over time (de Nooy, Mryar, & Batageli, 2005).

Analysis included three steps:  1) classifying network members into two categories (e.g., healthcare

providers, clients); 2) creating sociograms to display the social network, its members and their

relationships; and 3) using qualitative and quantitative analysis to describe the network. Sociograms

were used to display network members and their individual relationships, and provided insight into the

network’s structure with its members and their levels of connection to one another. The sociograms

were constructed at various points in time (i.e., across eleven quarters from 2012 to 2014) to explore

the development and evolution of the networks over time (de Nooy, Mrvar, & Batagelj, 2005).  

For HHC, the purpose of SNA is to evaluate whether the holistic‐based HHC—using a patient‐

centered home model and navigation system—improved health outcomes and reduced healthcare costs

for low‐income, uninsured residents in its four‐county service area.  
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The process used SNA to measure the development of and outcomes associated with HHC

healthcare delivery. HHC, with its collection of healthcare delivery providers, clinic staff, volunteers, and

patients, represents a social network, and SNA is being used to examine the network in multiple ways,

identifying:  1) which members existed within the network, 2) what relationships connected members

together, 3) what resources were exchanged through these relationships, and 4) how the resource

exchanges evolved over time.  

C. Data Analysis

Quantitative data were analyzed using SPSS and Excel to conduct measures of variability (frequency

counts, cross‐tabulations, and statistical difference in proportions test) and central tendency (means).

Excel spreadsheets were maintained to house these data collected from patient medical records,

referral logs, self‐efficacy forms, and quarterly reach reports. For each quarterly report since October

2012, self‐efficacy frequencies and means were conducted in Excel for unique patient visits and for

repeated visits. Referrals for this entire reporting period were cross‐tabbed in SPSS to discover patterns

across counties, quarters and providers. Frequency distributions were run to identify proportion of

patients by their county of residence being referred to specialists. A follow‐up crosstab explored

completed referrals versus no‐show or reschedule by county of residence. For the random sample of

102 patients, the statistical difference in proportions test was conducted for Oldham County patients,

which is the one county with a sample distribution adequate for this analysis.

Qualitative data were analyzed without software, using general theme‐search techniques, due to

the limited amount of these data collected. A qualitative comparative analysis (QCA) had been planned

for the self‐efficacy (SE) data linked to referrals, but insufficient data prevented a meaningful QCA

process, specifically because an insufficient number of referrals were related to the four conditions

followed in the evaluation, and because the variability in the administration of the SE questionnaires

unfortunately called into question the reliability and validity of the reported scores.

Gephi software was used to analyze the network and referral data; this analysis can explore a

network's diffusion process with the spread of services and other innovations; details are provided in

the Social Network Analysis section, beginning on page 35.

D. Limitations
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This design targeted a preliminary level of evidence for this project because we did not have a viable

comparison group with which to compare changes that take place with the development of this new,

unique clinic.  We do not have the resources at this point to develop a sufficiently large sample with

repeated data points to claim a higher level of evidence. Our analysis is further complicated by

substantial conditionality attributable to variations in poverty status, medical condition type, and county

of residence.   The main strategy for the evaluation has been to remain focused on the implementation

questions for the clinic’s first three years (coincides with funded project timeline) and document the

effect on inappropriate emergency room use by low‐income residents (which include an unexpected

outcome repeatedly occurring during the first three years of Hope Clinic – ER physicians refer qualifying

patients to Hope for long‐term care of chronic conditions).  

Required Changes to Original Evaluation Plan

Due to a significant barrier, we do not attempt to answer all the implementation questions,

specifically because an electronic medical records system is needed to gather and analyze the data

required. Despite this barrier, the clinic is using a no‐cost EMR system for recording general

demographics and appointments for patients.  The primary reason for delay in obtaining a system‐wide

EMR is the reality that volunteer medical providers already have a variety of EMR systems at their

individual practices. Clinic decision makers do not want to burden these already‐generous volunteers

with an additional task. A full EMR intended for a Fall 2013 implementation was not realized due to an

inability to obtain the needed hardware in each examination room.  As funds are obtained, however,

convertible tablets (able to function as a tablet or a laptop) are being provided for physicians and

specialists willing to learn a new system; for others, a medical‐educated volunteer will record physician’s

notes into the EMR.  In the meantime, the Clinic Administrator developed a continuous, near‐daily

process for collecting useful patients’ visits data for producing reports‐on‐demand, a needed resource to

provide potential funders, volunteer medical professionals, and community stakeholders.

Due to logistical constraints for clinic staff, the method for measuring short‐term outcomes for

referral strategies used by volunteers was changed from a single focus group to 20 clinic site visits to

examine and analyze patients’ medical records, make direct observations, and conduct a focus group

with the clinic administrator and three other key stakeholders (project director who is now chair of the

new non‐profit; fiscal agent for the FHK grant who is director of the local health department; and the

medical director for HHC).  
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Due to limited transportation opportunities and patients’ employment constraints (Clinic

Administrator estimates that 95% or more of HHC patients are part‐ or full‐time employed), focus

groups with patients were not feasible until gift cards for gas or groceries were provided, and the clinic

administrator willing to keep evening hours to accommodate patients’ work schedules. To obtain

additional patient feedback and input for improvement suggestions, the Patient Survey (see Appendix D)

was mailed in May 2013.  The low response rate (8%) was augmented by phone calls and three focus

groups with patients

Self‐efficacy has continued to present a challenge.  The clinic consistently collected the brief self‐

efficacy forms (six items in English, four items in Spanish—on separate forms) prior to each patient’s

visit beginning in October 2012. By January 2013, however, clerical volunteers tapered off in asking each

patient to complete the forms; we are unsure why this deviation happened, but suspect two causes:

patient complaints about its redundancy (for repeated visits) and new volunteers forgetting to include it

with the host of forms that new patients have to complete.

III. Results

During these initial years of clinic operations, the evaluation proposed to examine several short‐

term exploratory outcomes of implementation activities. Results from the clinic’s three years are

discussed regarding: 1) organizational change; 2) program reach; 3) program implementation activities;

4) the clinical experience and outcomes among a 102‐patient sample; and 5) a qualitative and

quantitative report on the social network analysis regarding referral patterns detected to date.

Organizational Change

The Oldham County Ministerial Association (OCMA) often and consistently demonstrated its

commitment to the clinic. OCMA obtained its 501(c)(3) non‐profit status specifically to develop and

operate the Hope Health Clinic. It created the dynamic vision and mission for HHC, updating them

through consensus as need is suggested by Board members. OCMA recruits most of the faith‐based

volunteers to ensure the ongoing operation of the clinic. Its Board of Directors hosted a two‐day retreat

in September 2012 to reorganize itself to better meet the clinic’s growing, complex needs. Changing

from a one‐unit group to three Councils, Board members now are grouped to focus on specific functions

required for sustaining HHC outreach and outcomes:  Operations, Resources, and Advocacy. The

reorganization was assisted in no small part by the current HHC administrator, who brought 20 years of
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experience with a church and four years’ volunteer experience with a rural clinic when hired in April

2012. A unique mix of faith ministers, medical providers, clinic staff, volunteers, and researchers

continue the clinic’s intention to sustain broad representation of support in the community. (The SNA

results below provide details about the variety of HHC supporters.) These changes allow individual

OMCA board members to focus their strengths on specific elements of clinic work where their passion

and skills are most concentrated.

A more significant organizational change occurred in Year 3:  HHC has formed its own 501(c)(3).  

Perceptions among churches in three other counties were reported by clinic volunteers, indicating that

Oldham County ministries were seeking volunteers only from its members.  As word spread about the

new non‐profit, an increase in both donations and volunteers has occurred from non‐Oldham County

churches and communities. This particular growth has been aided in some part by the continuity of the

recently retired vice‐president of the local hospital, who chairs the new organization. Her influence

among the region’s volunteer groups and funding sources is showing early signs of success.  

Program Reach and Demographics

The data indicate that Hope Health Clinic increased access to clinical visits, diabetic and other

educational programs, and healthy eating activities for low‐income and uninsured residents in the

service region. While the previous clinic provided very limited service access (2‐4hours/week) and was

able to provide care for 719 patients over its five years of operation, Hope Clinic established full‐time

clinic hours (40+ hours/week), and treated 1,916 unique patients—19.16% of the working‐poor

population in the four‐county service area—in 11,125 office visits since opening in 2011. Figure 2

illustrates HHC’s reach across the clinic’s first three years of service.  

   
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Figure 2: Hope Clinic Reach –October 2011‐September 2014

0

200

400

600

800

1000

1200

1400

1600

Hope Clinic Reach, 2011‐2014

New Patients

Unique Patients

Office Visits

As the chart above illustrates, the clinic attracted a growing pool of new patients up to the quarter

ending September 2013 (between 137 and 279 per quarter, peaking during April‐June 2012, with an

average of 189 new patients per quarter). One indicator that the clinic is meeting and serving the pent‐

up demand for services, the average number of new patients in Year 1 was 224 per quarter. In Year 2,

this number dropped by one third to 154 per/quarter, a number believed to be sustainable. Beginning

in the last quarter of 2012, in addition to tracking new patients discovering and visiting the clinic (a

proxy for improved access and successful outreach/marketing), we began to track the totals of new and

returning patients (“unique patients”) by quarter. Just as new patient numbers stabilized and dropped,

the census showed an increase in unique patients over time from 422 in Q4 2012, to 523 in Q1 2013,

560 in Q3, and also peaking in Q4 2013 at 566. The trend for increasing office visits escalated very

aggressively up to September 2013, peaking at 1,413 office visits, which is unsustainable, but leveled off

and began to decline to 769 visits by Q4 2014. As word of mouth spread, the clinic saw an increase in

patients from the most distant county (Carroll, from 5% in Year 1 to 11% in Year 3), but distance remains

a barrier that will limit this trend. Oldham County saw a decrease in percentage of patients, from 62% in

Year 1 to 54% in Year 2 and 56% in Year 3.  Trimble County’s percentage also decreased over time, from

13 % in Year 1, to 11% in Year 2 and 10% in Year 3.  Henry County, closest of the three rural counties to

the physical location of the clinic in Oldham, peaked in Year 2 at 23% of patients, with a 19% proportion

in both Years 1 and 3.  
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Hope patients come from throughout the four‐county region—Oldham, Trimble, Carroll and

Henry—targeted by the clinic, with the largest numbers coming from the host county, Oldham (see

figure 3), and patients coming in numbers related to the relative distance from the clinic, once again

reaffirming the concept of distance as a barrier the working poor face in accessing health care services.

Figure 3: Distribution of Hope Patients by County

of Residence

     The population accessing services is among the

poorest in the counties. The clinic continues to

serve uninsured and underinsured patients, but to

assure we were providing clinic access to our

intended —the most needy —patient population,

we began collecting annual household income on

the patient intake form.  Figure 4 provides results over the three‐year project, indicating that 72% of

patients providing these data reported income at 100% or less than the Federal Poverty Level (FPL)

during the first half of this project, with that level increasing to 78% over the last 18 months; these rates

exceed the average poverty rates in the four‐county service region by at least three, and as many as ten

times. By contrast, only 1% of Hope’s patients claim an income greater than 200% of FPL. Figures 4

illustrates the distribution, by quarter and cumulatively, of new HHC patients by income as a percentage

of FPL.

58%

12%

8%

21%

2%

Oldham

Trimble

Carroll

Henry

Jefferson
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Figure 4: Poverty Among Hope Patients

0%

20%

40%

60%

80%

100%

120%

Poverty Status: HHC Patients, by % of FPL

151% ‐ 200%

101% ‐ 150%

100% or less

To summarize other demographics, slight changes in patients’ descriptions remained relatively

stable over the three years of the project, with 64% females and 36% males, 99% between the ages of

19‐64, and 15% identifying themselves as Hispanic ethnicity. Regionally, Hispanics constitute only 3% of

the total population. The clinic population, in terms of traditional U.S. markers of poor health care

access (lack of insurance, low income and Hispanic ethnicity) compared to county means is depicted in

Figure 5.  Comprehensive demographics for HHC patients by quarter are provided in Appendix A.

Figure 5: Access and Disparities: Hope Clinic and Comparisons

  

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Uninsured Annual Income <

100% FPL

Hispanic Ethnicity

Hope Clinic Patients

Oldham County

Carroll County

Trimble County

Henry County

Kentucky
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In addition to access to Hope Clinic’s services, referrals extend access beyond the acute care and

chronic disease management it provides to include medical specialty care at the University of Kentucky

clinics and at specialists’ offices located on Baptist Health LaGrange’s campus (adjacent to the clinic).

Between October 2011 and September 2014, patients received 1,244 referrals for outside care. Figure 6

provides referral volume of the clinic over its three‐year period, and includes the two most prevalent

referral targets (University of Kentucky Specialty Clinics and Hope Health Care Network Specialists).

“Other” refers to all other referrals made: the health departments in each of the four counties, dentists,

counseling, University of Louisville neurology, Norton oncology, and the Lyons Eye Institute. Details

about the referral options are presented later in this report among the results of the social network

analysis, found in the appendix. As the network and relationships with referral resources developed,

referrals increased. Quarterly referral volume increased threefold from the early months of clinic

operations when the clinic averaged about 70 per quarter, to the 213 in the quarter January‐March

2013.  Referrals peaked in the next quarter, April‐June 2013. During the following quarter, patients

began to express concerns and fears about the coming rollout of the Affordable Care Act (ACA) and its

effects on their ability to continue as a Hope patient.  These concerns and fears manifested in decreases

in patient visits and related referrals across quarters during the last 18 months. More about the impact

of ACA impact follows in the Discussion section.

Most of the referrals (84%) don’t appear to be related to the four conditions being followed by this

study, but HHC determinedly meets the acute needs of patients by providing needed access to primary

and specialty care.
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Figure 6: Hope Clinic Patient Referrals, 2011 ‐ 2014

0

50

100

150

200

250

Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

HHC Patient Referrals

HHC Network

UK

Other

Total

Though referrals to the specialty clinics at the University of Kentucky’s School of Medicine (UKSOM)

during the early months constituted the majority, at just over one‐third of the total referrals, the

network of other specialists grew rapidly and engaged more frequently with Hope patients (nearly 90%

located in Oldham County), peaking at 65% of 215 referrals in Quarter 7 (April‐June 2013).  By the final

quarter of the three‐year project, total referrals decreased to 38 of which 61% were the network

specialists.  Transportation for the 150‐mile trip to UKSOM has been a considerable factor for most

patients, as are the qualifying financial forms to cover the $100 fee and the increased availability of local

specialists to meet acute and chronic needs of Hope patients.

Qualitative data collected from staff interviews and evaluator’s observations originally revealed that

although the clinic administrator developed clear clinical protocols to govern making and monitoring

compliance of patient referrals, these data are not consistently available.  The nurse calls and makes the

referral appointments for the patients, and then enters them into the clinic log so clinic and volunteer

staff can make reminder calls to patients of their referral appointments and, ideally, confirm it was

completed. However, confirmation of completed referral is not recorded to date, due to lack of time to

make the calls. The logs do have notations for a no‐show or when a patient has to be rescheduled,

providing our rationale that no notation assumes a completed referral has occurred. Because acute care

is still the priority for most patients, meeting their immediate medical needs has been the focus for

85.2% of referrals. However, the Board passed a proposal from the Operations Council at its Fall Retreat
20 | Page

2013 that addressed follow‐up on all referrals. Since that decision, volunteers assist the nurse on a near‐ 

daily basis in making follow‐up calls to ensure patients are reminded of referrals.  

The evaluation also proposed to illustrate variations in access for people from different counties of

residence (i.e., is service access equitable for all patients in the service region?) and variations in access

for people with different chronic diseases or health status.  The evaluation does not yet have sufficient

data to assess this variation. However, our qualitative analysis from interviews with the Clinic

Administrator and two primary volunteers cite transportation as a persistent problem that affects

service access for the remote counties, a factor for providing healthcare equitably across the service

region. Because of this issue, clinic leaders continue to consider seeking funding for an expansion

satellite office in either Trimble or Carroll County.

Implementation Fidelity

Implementation fidelity to the anticipated process for serving patients has been achieved for the

first two sections (Section A & Section 2) of the detailed flow chart presented in figure 7.  

Section 3 has been implemented with a minor change. The “Advocate Referral” primarily is carried

out by the nurse/patient navigator, rather than the trained group of Health Advocates. Originally

anticipated to be a highly interactive group with the patients, the Health Advocates had minimal face‐to‐

face contact with the patients, making phone calls instead to assess needs.  
21 | Page

For this patient population, a stranger’s voice on the phone was not trusted, representing an

inconsistency with the hands‐on respectful

treatment patients receive at the clinic.

Frustration with the advocates by the

patients led to a reassessment of how this

important service could be provided. At a Fall

2013 Operations Council meeting, a proposal

was developed to launch a Patient Advocacy

Project with advocates who will serve in a

various capacities. Although the Board

approved the multiple strategies proposed,

volunteer staff needed for implementation

has been difficult to develop to date.

However, qualitative analysis on the face of

the proposal indicates that significant fidelity

with Section 3 will be accomplished, if

volunteer staffing can be recruited.

In the next section, table 1 provides

details for answers to the implementation

fidelity evaluation sub‐questions regarding

program outputs over the three years of the

study.

New

Patient?

Y

N NP or

Vol Doc

Specialist or

Adv. testing

Needed?

Y

N

Ordered

Referral

Follow

up?

Advocate Y

Referral N

Follow up?

N

Follow up?

File Devel‐ Y

opment

N

Routine

Testing

Needed?

Y

Clinic

Lab

N

Section A. Flow Chart Overview

Specialist or

Adv. testing

Needed?

Y

Financial

Counseling

Qualified? Y

N

Ordered

Referral

Section 2. Detail for Patient Qualification

Advocate

Referral

Health Related

Needs?

Non‐Health

Related Needs?

N

N Y

Y

Out of system

Prevention

Counseling

and referral:

Smoking

Diabetes

Mental/emotional

Nutrition/exercise

Spiritual

Financial assistance

Child care

Housing

KPAP

Spiritual

Section 3. Detail for Advocacy Referral Process

ons.
22 | Page

Table 1: Implementation Fidelity: Evaluation Outputs Sub‐questions, October 2011–September 2014.

Implementation

Question

Goals Actual

Three

Years

for Goal

reached

Notes

a. Number of staff

hired

b. Number of volunteer

providers secured at

start up

c. Number of hours per

week clinic serves

patients

d. Number of new

volunteer providers

secured per Qtr in Y1

e. Number of new

volunteers and health

advocates trained per

month

f. Number of new

network members

secured by marketing

function each month   

g. Number of

educational programs

engaging patients  

h. Number of patients

who received disease

self‐management

training where it was

indicated  

i. Number of patients

who completed

referrals

3

3

40

3/Qtr in

Y1

100%

20  

1/mo by

end of Y1

(total

goal 12)

5/mo

99

(3/mo x

33 mos)

4

6

42.5 on

average

28+

100%

80

33

25x10  

=250 (not

unique

patients)

1,242

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4 staff use 2.5 FTE. See discussion

4 providers continue to support Hope

clinic, who also volunteered at the

previous clinic; 2 additional providers

serve weekly at the clinic

38 hours clinic plus 4.5 hours mental

health counseling, unanticipated at

the inception of the clinic

12 new providers by end of year 1

deemed necessary for clinic

operations; 28+ doesn’t include

UKSOM specialty clinic providers

All volunteers are trained within a

month of volunteering

Increase from 52 members during first

18 months, a 54% increase during the

final 18 months.

During first 18 months, 10 educational

programs by nursing students

engaged 25‐30 patients per session at

HHC, plus 23 patients engaged in

smoking cessation; for last 18 months,

see discussion.

Educational programs are related to

self‐management of the four

conditions we are following; an EMR

would identify “where it was

indicated” with precision

Referral log indicates 1,244 referrals

made from 1/12‐9/14 (33 months)

Clinical Experience & Outcomes of the Patient Sample

The program logic model presented earlier includes short‐term implementation outcomes, and

future patient outcomes we are beginning to track. Ultimately, the project proposed to improve the
23 | Page

health of those living with chronic illness through a holistic delivery of services to Hope patients. To

assess patient outcomes, the evaluation team created a sample of patients with at least one of the fo

chronic diseases listed. The evaluators hoped that by selecting a population whose disease(s) require

regular clinical monitoring, the sample would provide the opportunity to follow these patients over

time.  Although this sample lacked a comparison group, we hoped to see changes within the populatio

that suggests an effect of the clinic to improve health status and change health behaviors, including

disease self‐management.

The evaluation team drew a random sample of 102 clinic patients from a sub‐population of clinic

patients with one or more diagnoses of the conditions of interest (CVD, obesity, tobacco use disorder

[smoking] and Type II diabetes). Of the original 102 selected, 98 active patients remained during the fi

18 months of the clinic. During the last 18 months, we confirmed that three patients obtained insuran

and are required to obtain care elsewhere, one patient was dismissed from the clinic, and five patient

files could not be located for data follow up or no evidence of study criteria could be found in the file

(though patient qualified during the October 2012 random draw). We kept the 102 in the sample as a

way of obtaining additional patient perspectives into Hope’s effectiveness, either through phone calls,

mailed surveys, or focus groups. In the random drawing process, we replaced 18 cases to replace

ineligible patients who did not meet the selection criterion.   

While the sample size for some counties in the sample distribution are too small to employ a

statistical difference in proportions test, the distribution across Oldham County is not significantly

different in proportion to total of Oldham County patients seen in the clinic at a 95% confidence level

(58.8% in both cases). Gender proportion differences between the two groups are not statistically

significant at a 95% confidence level. This is not unexpected; both categories were apportioned

deliberately to resemble the patient population.

ur

n

rst

ce

Table 2: Distributions between Patient Sample and Total Patient

Population at the time of Random Draw in October 2012

Gender Sample (n=102) Population (N=1336)

Male 42.6% 34.1%

Female 57.4% 65.2%

Missing 0.0% 0.7%

County of Residence

Oldham 58.8% 58.8%

Carroll 9.8% 6.7%

Trimble 11.8% 12.4%

Henry 17.6% 20.9%

Other 0.0% 1.3%
24 | Page

Medical records for this group in October 2012 indicate that 41 patients were diagnosed with only

one of the four conditions (see breakdown for distribution across cardiovascular disease [CVD], obesity,

smoking [tobacco use disorder], and Type 2 Diabetes [DM]). The remaining 61 patients’ records indicate

co‐morbidity with at least one of the other three conditions. More patients have multiple morbidities

than those patients that have only one.  Figure 8 provides diagnoses in September 2014; tables 3 and 4

provide an in‐depth look at co‐morbidities for each of the four conditions.  

Figure 8.  Random Sample of Patients by Diagnoses of Four Conditions, October 2012

65

53

68

29

Hope Random Sample (N=102)

Diagnoses

Overweight

Smoking

Cardiovascular

Diabetes

Results from the survey of May 2013 to better understand how patients manage co‐morbidities

achieved a very low response rate (N=9, 8.8%). Follow‐up phone interviews (N=10; details in later

discussion) were conducted using the same items (see Patient Survey in Appendix C).  Data from the

sample in table 3 give an illuminating snapshot of the disease burden of this study group; table 4

provides an updated view of these same patients, with an increase in co‐morbidities that highlights the

difficulty patients have in managing chronic conditions while dealing with overwhelming daily life issues.

Table 3: Chronic Disease (CD) Burden (four types) among the Patient Sample by Morbidity, October

2012.

   Diabetes Obesity Smoke CVD ALL

CVD only    20 20

CVD + Obese    18    18 18

CVD + Obese + Smoke    12 12 12 12

Smoke only       11    11

CVD + Obese + DM 11 11    11 11

Obese only    8       8

CVD + Smoke    7 7 7
25 | Page

Obese + Smoke    5 5    5

DM only

CVD + DM

2

2

   

  

   

   2

2

2

DM + Smoke    2    2

CVD + Smoke + Obese + DM 2 2 2 2 2

DM + Obese 1 1    1

CVD + DM + Smoke 1    1 1 1

Total 19 57 40 73 102

Total 1 CD 2 8 11 20 41

Total >1 CD 17 49 29 53 61

% comorbidity among

patients with each diagnosis

89% 86% 73% 73% NA

Table

2014.

4. Chronic Disease (CD) Burden (four types) among the Patient Sample by Morbidity, September

   Diabetes Obesity Smoke CVD ALL

CVD only   

CVD + Obese   

CVD + Obese + Smoke   

Smoke only   

CVD + Obese + DM

Obese   

CVD + Smoke   

Obese + Smoke   

DM

CVD + DM

DM + Smoke   

CVD + Smoke + Obese + DM

DM + Obese

CVD + DM + Smoke

Obese + Smoke + DM

Total

Total 1 CD

Total >1 CD

% comorbidity among

patients with each diagnosis

16

1

2

12

0

1

2

34

1

33

97%

17

12

16

6

3

12

0

2

68

6

62

91%

12

8

10

3

0

12

1

2

48

8

40

83%

6

17

12

16

10

2

12

1

  

76

6

73

96%

6

17

12

8

16

6

10

3

1

2

0

12

0

1

2

96

21

75

NA

The most prevalent disease among this group of four for the sample continues to be cardiovascular

disease, primarily hypertension and hyperlipidemia.  In October 2012, nearly three‐fourths of the

sample had a diagnosis of CVD (73/102), and among those, 53 cases co‐occurred with at least one of the
26 | Page

comorbidities we are tracking.  By September 2014, 78.4% of the remaining sample (76/97) were

diagnosed with CVD.  (Sample decreased by five patients whose files could not be found or data were

inadequate for inclusion.)  However, while only one patient in the sample had a simple diagnosis of

diabetes, diabetes almost always co‐occurred with other diseases; the remaining patients with diabetes

had at least one on the other condition. In a majority of the diabetes cases, it co‐occurred with both CVD

and obesity together (16/34, 47%). Obesity co‐occurred with other diseases 91% of the time, and

tobacco use and CVD both co‐occurred with other diseases in this sample. These data underscore the

complexity of managing this highly co‐morbid and multiply diagnosed population.   

It is important to note that this was a sample of persons with at least one of the four conditions of

interest. It is not a random sample of the clinic population and likely may not reflect the chronic disease

burden of the total clinic population, though providers and the clinic administrator anecdotally suggest

that these four conditions are prevalent in most patients. Given the resources available and the medical

record system employed by the clinic, a thorough assessment of disease burden in the patient

population is not possible at this time.  If the clinic implements an electronic medical record, future

evaluation efforts will be able to assess disease burden in the patient population and make such a

comparison.

Patient demographics for the random sample are presented in figure 9.  Five patients (two men and

two women from Oldham County, one man from Carroll County) were excluded in analyses because

their files or data were unable to be found after October 2012); or only a payment was found in the

previous clinic’s file for one patient, though payment was made on 9‐27‐11 (during early start‐up of

clinic, many well‐meaning volunteers had not been trained yet and may account for this loss).

   
27 | Page

Figure 9. Random Sample Demographics

Random Sample Demographics, N=102

SPANISH‐Speaking 10

MALES 43

FEMALES 59

TRIMBLE CO 11

HENRY CO 18

CARROLL CO 9

OLDHAM CO 64

0 10 20 30 40 50 60 70

Patient visits varied among the random group; see figure 10. Because 16.5% (16 of 97) of these

patients had only one visit recorded in their medical files, we were unable to make any determination of

change. Table 5 displays patient visits in the random group by gender and county. Four patients made

no visits that were recorded in their files. Though women represent 59% of the random group, they

made 65% (522/799) of the visits. Number of Patients, N=97

Figure 10. Patient Visits for Random Sample, September 2011‐September 2014

Patient Visits for Random Sample

30

24 25 23 22

20 16

15 11

10

5 1

0

1 visit 2‐5 visits 6‐10 visits 11‐15 16‐20 21 visits

visits visits

   
28 | Page

Table 5. Random Sample Patient Visits by Gender and County

Gender Carroll Henry Oldham Trimble   Totals

Female 46 46 367 63 522

Male 16 52 169 40 277

Totals 62 98 536 103 799

Findings did provide evidence of nearly half (47/97, 48%) of these patients had some success in

better managing their conditions (see figures 11 & 12).  Using objective cut‐off data (e.g., blood

pressure, BMI, A1c level with blood glucose readings), provider notes in medical files, and general

categories for outcomes, we found some improvement due to: 1) compliance with treatment plans (i.e.,

taking medications as prescribed; coded IMP‐TX) and 2) improvements in lifestyle (e.g., losing weight,

reducing or ceasing smoking; coded IMP‐LIFE).  We also found conditions that were nearly the same

across individual patient’s visits (coded SAME).  Some patients declined in one or more conditions due to

provider’s notation of “noncompliant” (coded WORSE‐NON) or worsened due to lifestyle contexts (e.g.,

gained weight, increased smoking, coded WORSE‐LIFE).  Totals in figure 14 do not add to N=97 because

patients may have improved on one condition and grew worse on another or others.  

Figure 11. General Categories of Changes in Health Status for Random Sample, N=97

Patient Health Changes

NO INFO,

23, 14% IMP TX,

35, 22%

SAME, IMP LIFE,

49, 31% 17, 11%

WORSE WORSE

LIFE, 22, NON, 12,

14% 8%

Table 6 illustrates this phenomenon with more detail among genders. The categories of change in

the four health conditions are 1) Better, 2) Same, 3) Better & Worse, 4) Better & Same, 5) Same &

Worse, 6) Better/Worse/Same, 7) Worse, and 8) NoInfo (not enough information to detect a change).  

More women (12% of females versus 2% of males) improved some component of the four conditions

over the three years. Conversely, 14% of men (versus one female) tended to decrease or do worse
29 | Page

among the measured components of health status. The percentage of each gender whose health status

remained about the same was equal at 19%. A couple examples may clarify these mixed results. Patient

#101 rated Better & Worse because patient was able to control high blood pressure (BP) with

medication compliance, but was unable to lose weight, going from overweight to obese from January

2012 to August 2014 (20 visits). Patient #94 rated a Better & Same, also maintaining BP control through

meds, but had weight fluctuations from September 2012 to September 2014 through 13 visits, and

made attempts to quit smoking but hasn’t quit to date (e.g., quit for a month using a patch, smoked

half‐pack, smoke 2‐3 cigarettes per day).  Though patients improved in one measure, they were unable

to improve on another.

Table 6. Changes in Health Status among Four Conditions for Random Selection of Patients, N=102

     MALES    FEMALES   

Better 0.05 2 7 0.12

Same 0.19 8 11 0.19

Better, Worse 0.05 2 9 0.15

Better, Same 0.19 8 12 0.20

Same, Worse 0.00 0 5 0.08

Better, Worse, Same 0.07 3 4 0.07

Worse 0.14 6 1 0.02

No Info 0.33 14 10 0.17

totals    43 59   

Figure 12 suggests that the number of visits by patients in the random group may have effected

changes in health status. Among female in the first three categories that indicate some improvement,

more visits appear linked to an improvement in one or more conditions, even with some function of

health status remaining the same or worsening.

   
30 | Page

Figure 12. Random‐Sample Patient Visits with Health Status Change by Gender  

0

20

40

60

80

100

120

140

Number of Visits

Number of Visits and Health Status Change

Females

Males

Figure 13 provides a glance at positive movement among the random sample regarding smoking.  

Fifty‐three patients were smokers at some point during the three years, with five patients succeeding in

cessation; hence, the total number of patients in this analysis differs from the count in diagnoses charts

above.  This analysis indicates that eight of the patients were not smokers or their records did not

indicate smoking in October 2012. While 15% (8/53) made attempts or quit smoking, 85% (45/53)

continued to smoking. Patients who continued to smoke include the four patients diagnosed with COPD,

illustrating again the difficulty this population has in self‐management of chronic conditions, as does the

depiction in figure 14 regarding weight problems.  This group includes overweight notation in the

medical file (not just obesity), where the provider indicate weight loss would be helpful.  Key

stakeholders and providers indicated that they believe they are making progress across chronic

conditions of this study except for obesity.  Figure 14 somewhat confirms their concern, at least for this

small group.

   
31 | Page

Figure 13.  Smoking among Random‐Sample Patients, with Attempts and Success of Cessation, N=53

of 102 (52%)

Figure 14.  Number of Random‐Sample Patients with Weight Problems, N=65 of 102 (64%)

41

5

3 4

Hope Random Sample, Smoking Dx,

N=53

SMKG

SMKG QUIT

TRY TO QUIT

COPD

7

40

18

Hope Random Sample, Weight Dx,

N=65

OVERWT

OBESE

MORBID OBESE

Cardiovascular disease (CVD)—primarily hypertension (HTN) and hyperlipidemia (HLD)—was

diagnosed slightly more frequently (67%) than obesity (64%) among the random sample of patients; the

other conditions were diagnosed at 52% for COPD/smoking and 28% for diabetes and pre‐diabetes.

Figure 15 visualizes proportion of the 68 patients with CVD by HTN, HLD, or both conditions.

   
32 | Page

Figure 15. Cardiovascular Disease among Random‐Sample Patients, N=68 of 102 (67%)

35

7

26

Hope Random Sample, CVD Diagnoses, N=68

Hypertension

Hyperlipidemia

Both HTN & HLD

Self‐Efficacy

At the study’s inception, the evaluation team sought a measurement that would assist providers in

understanding how well their patients might be managing the four conditions targeted by this project.

The English version has six items (see Appendix E) and the Spanish version has four items. However,

measuring self‐efficacy (SC) never found a stronghold among providers, staff or patients.  Patients

complained about its repetitiveness (they originally were asked to complete the form prior to each visit);

volunteer staff didn’t always remember to include it in with the many forms new patients had to

complete; and providers have been somewhat unable to focus on self‐management of chronic disease

due to immediate needs of patients’ acute conditions.  The following observations, therefore, are

provided for Hope Health Clinic stakeholders to consider for SE’s future potential.

From October 2012 through September 2014, approximately 1,157 SE forms were completed.  

These forms were recorded by patient per visit, thus illustrated in figure 20 by the patient’s first visit,

second visit, etc., up to one patient who completed the form before 15 visits.  We provided SE mean

scores per number of visits on each quarterly report since January 2013, which depicted a mixed

outcome in terms of how self‐efficacious patients perceived themselves – some patients indicated

better, then worse, then better‐again means; others didn’t change from visit to visit; still others

reported worse scores from visit to visit.
33 | Page

Figure 16. Number of Visits for which Hope Patients Completed Self‐Efficacy Forms

709

220

102

51 28 15 13 8 5 111111

0

100

200

300

400

500

600

700

800

Number of Visits

Self‐Efficacy Forms Completed by Hope

Patients

Among the random sample of patients, 51 patients (52.6%, 51/97) completed SE forms for one visit

(N=51 ) to 15 visits (N=1).  Of these 51 patients, 27 of them completed the form on a second visit; 10 on

a third visit; five on a fourth visit; and one patient (#41) completed the SE form prior to 15 visits (from

November 2011 to August 2014).  Patient #41 was diagnosed with all four conditions, had Rx‐controlled

hypertension, remained the same level of morbid obesity, made multiple attempts to quit smoking, and

fluctuated on A1c levels.  SE levels also fluctuated, with a low mean of 6.0 (out of a possible 10.0) at a

time when the patient’s file reflected increasing levels of A1c but decreases in smoking. By August 2014,

Patient #41 scored a 9.0 mean on self‐efficacy, with a corresponding maintenance of improved blood

pressure but no change in smoking or weight.  If the clinic chooses to continue using the SE

measurement, the random sample’s 51 patients provide evidence for making self‐efficacy a focus of its

educational programs, both for patients and for providers.  As stakeholders continue to monitor Hope

patients, and continue to gather longitudinal data through repeat visits, the clinic decision makers will

be able to make better assessments of patients’ intentions or perceptions they can maintain

improvements in chronic diseases and conditions.

A Story of Three Patients

To gain increased understanding of the struggle Hope patients experience as they work with a host

of providers and specialists to improve their level of health and ultimately self‐manage chronic
34 | Page

conditions, a description of three patients from the random group may offer insight.  Kate (not her real

name) is 45, diagnosed in July 2012 with CVD (hypertension and hyperlipidemia) and, with a BMI of 31,

is considered obese because she has an obesity‐related condition (Highland Bariatric Surgery Center,

2014). Before her fourth visit, she scored a mean of 5.0 (out of possible 10) on the self‐efficacy scale,

indicating a lack of confidence in being able to manage her two chronic conditions. Her individual scores

for the two forms she completed (July 2012 and September 2013) were:

Confident in preventing disease‐related fatigue from interfering with things you want to do:  4.5 and 4.0

Confident in keeping physical discomfort or pain from interfering with things you want to do:  5.5 and 4.0

Confident in preventing disease‐related emotional distress from interfering:  4.5 and 4.0

Confident in keeping other symptoms from interfering with things you want to do:  5.5 and 5.0

Confident you can do tasks and activities needed to manage your health condition:  5.5 and 4.0

Confident you can do more than taking medication to reduce illness affecting everyday life:  4.5 and 5.0

She was able to quit smoking just prior to becoming a Hope patient and has maintained acceptable

levels for both HTN and HLD with medications. Kate’s weight yo‐yos, however, and is inching up, with a

current BMI of 32. She has taken advantage of dental referrals, as the provider’s strategy in improving

her oral health and reducing the number of visits to treat sinus or other possibly oral‐health related

infections. Over the course of 13 visits, she completed the self‐efficacy form again in September 2013,

resulting in a lower mean of 4.5, perhaps a call for help with her weight problem. She praises Hope Clinic

in glowing terms for assistance in “getting a handle on two big concerns: my high cholesterol and blood

pressure. They’ve probably saved my life.”

Jim also has problems with his weight, reaching a diagnosis of morbid obesity with a BMI of 40 by

October 2013. With hypertension and diabetes, his health status has improved for both conditions, but

he exercises little and experiences multiple infections and skin problems. Jim has been faithful about

following up on referrals to specialists in cardiology, endocrinology, and spine imaging.  For self‐efficacy,

Jim reported the following on two out of the eight visits recorded in his file:

Confident in preventing disease‐related fatigue from interfering with things you want to do:  10 and 10

Confident in keeping physical discomfort or pain from interfering with things you want to do:  7 and 10

Confident in preventing disease‐related emotional distress from interfering:  6 and 10

Confident in keeping other symptoms from interfering with things you want to do:  8 and 1

Confident you can do tasks and activities needed to manage your health condition:  7 and 10

Confident you can do more than taking medication to reduce illness affecting everyday life:  6 and 10

Jim scored means of 7.33 (November 2012) and 8.5 (May 2013) – for the score of 1 on the fourth item,

Jim wrote in “No one can” keep other symptoms from interfering with things you want to do, though he

scored an 8 on the item six months earlier).  In May 2013, Jim had a corresponding drop in A1c level and
35 | Page

blood pressure. He believes Hope Clinic “has made a big difference in my life. I can’t get over how much

respect everybody shows each other and me.”  With a higher level of self‐efficacy than Kate, he may be

more receptive to small steps he could take to reduce his unhealthy weight.

Ellie, on the other hand, had a drop in her self‐efficacy between December 2012 and January 2014:

Confident in preventing disease‐related fatigue from interfering with things you want to do:  4 and 2

Confident in keeping physical discomfort or pain from interfering with things you want to do:  5 and 1

Confident in preventing disease‐related emotional distress from interfering:  8 and 3

Confident in keeping other symptoms from interfering with things you want to do:  4 and 3

Confident you can do tasks and activities needed to manage your health condition:  3 and 4

Confident you can do more than taking medication to reduce illness affecting everyday life:  3 and 2  

Rating a mean of 4.5 at a time when she was smoking more, having GI problems added to her diabetes,

and weight approaching obesity (BMI of 29), her confidence in managing chronic diseases dropped to a

mean of 2.5.  She has visited Hope 14 times with multiple symptoms that are recorded by her providers

as being related to her diagnoses: muscle pains, skin problems, sinus and ear infections, and possible

rheumatoid arthritis.  She has no means of getting to the UK clinic that could treat RA, and refuses to sit

in smoking cessation classes at the health department.  She did follow up on the referral to get a

nicotine patch at the health department and is “down to just a pack a day. I tell you, these people are

great. I thank my lucky stars the day Hope Clinic opened to help people like me.”

Additional Patient Feedback

To obtain more of the story of Hope Health Clinic, we sought feedback from patients, first through a

survey, followed by focus groups, and finally phone calls. As mentioned previously, the response rate for

the mailed Patient Survey was very low, 8.8%.  To learn more from these patients, the evaluation team

attempted to call the remaining 93 patients.  We made ten successful calls; 57 phone numbers were

disconnected; 26 patients declined to answer questions.   After consulting with the clinic administrator

and grant’s fiscal agent during a site visit by the Foundation for a Healthy Kentucky, we decided to

conduct three focus groups of patients who were not in the randomly selected group. To accomplish this

task, the clinic administrator agreed to open the clinic from 6:00‐7:00 PM on nights when the clinic was

usually closed; provide an incentive for participation; and selected and recruited patients (in

consultation with the nurse and medical providers) for three groups:  compliant (N=7), non‐compliant

(N=8), and Spanish‐speaking (N=8) (total N=23).  For the non‐compliant patients who could not get to

the focus group, we conducted telephone interviews.  For the protocol, we used the Patient Survey for
36 | Page

the content of our discussion in order to obtain consistent information across the focus groups and the

mailed survey respondents, with the addition of one final question:  “What would you do differently at

Hope Clinic if you were the boss?”   

Through obtaining responses to the Patient Survey items, two focus groups lasted from 60 to 75

minutes, the non‐compliant group ran 25 minutes, and the phone interviews each lasted 10‐15 minutes.  

Overwhelmingly, patient feedback from the focus groups, the mailed survey, and the phone interviews

was very positive, offering comments that credited the clinic with “saving my life” (N=5) and “helping

me figure out what’s wrong with my health” (N=12) to being treated with respect that they had not

experienced in any other medical setting (N=15).  On the other end, only one mailed survey and one

phone respondent provided negative feedback; the mailed survey respondent indicated that the clinic

“did nothing right” and the phone respondent said, “If I had wanted a sermon, I would have gone to

church.”   

When asked what other services Hope Clinic could provide, the majority (61%) responded that

nothing else was needed.  Of the other respondents, services for a dentist at the clinic dominated the

answers, with child care requested by seven patients.  When asked what changes Hope would make “if

you were boss,” all respondents replied positively, very similarly to these examples: “They do such a

wonderful job, I wouldn’t change a thing” and “They do a wonderful job with the resources they have.”

Social Network Analysis

Social network analysis was used to examine the Hope Health Clinic (HHC) healthcare provider social

network. This analytical technique was used to explore the evolution of the HHC service delivery

process, examining the network members and their exchanged resources within the social network.  

As mentioned previously, the purpose of this study was to evaluate whether the holistic‐based HHC,

using a medical home model and navigation system, improved health outcomes and reduced healthcare

costs for low‐income, uninsured residents in its four‐county service area. The study addressed the

following research questions:

RQ1: How does a medical service network expand from an initial baseline state?

RQ2: How do patients with different needs and characteristics flow through a medical service

network?
37 | Page

RQ3: How do different subgroups (e.g., cancer versus diabetic patients) flow through the network,

and how can network density help service providers understand the network?

The study used social network analysis to measure the development of and outcomes associated

with HHC healthcare delivery. HHC, with its collection of healthcare delivery providers, clinic staff,

volunteers, and patients, represents a social network, and social network analysis was used to examine

the network in multiple ways: identifying (a) which members existed within the network, (b) what

relationships connected members together, (c) what resources were exchanged through these

relationships, and (d) how the resource exchanges evolved over time.  Social network analysis was used

to gain a better understanding of the healthcare provider network and its resource exchanges, as the

analytical technique was used to explore the clinic and its network members in more detail.  

Data were collected from the HHC’s Health Navigator patient referral logs, which provide

information about the health services providers used and services obtained. The patient referral logs

spanned two time periods: (a) Period 1 occurred from January 2012 to March 2013, and (b) Period 2

occurred from April 2013 to September 2014. These data provide information about the number of

members joining the network, what relationships develop among them, and how they use relationships

to share resources and provide services. This can be quantified via network density, or the ratio

between the actual number of relationships within the network versus the maximum number of

possible relationships within the network. Density values range from zero to one, where .000 indicates

no relationships among network members and 1.000 reveals all members share direct connections to

one another. Higher density values indicate information and resources can flow more quickly and

efficiently through the network, while lower values indicate a slower and less efficient flow of resources

(de Nooy et al., 2005). In this study, density values along with the number of nodes and relationships

were examined to track the evolution of the HHC social network over time. Our specific results follow by

each research question we posed.

RQ1: How does a medical service network expand from an initial baseline state?  

The following quantitative data and sociograms provide details about the social network as it

evolved over time.  

Period 1 ‐ Quarter 1 2012 ‐ Quarter 1 2013, Five Quarters of Referrals
38 | Page

The data provide evidence of the HHC provider network expansion over time as the number of new

and existing clients used services across the network. Looking at the data by provider type reveals that

the Local Network Specialists and UK Clinic offered the most services over the five quarters. As a

baseline state, the first quarter of 2012 reveals the social network contained 80 members with six

provider categories: (a) BHNE Clinic, (b) HD, (c) Local Network Specialist, (d) UK Clinic, (e) UofL, and (f)

Other (see table 7), which shared 71 relationships, and each relationship was defined as a single

interaction between a client and a network provider.  Growth continued thusly:

First quarter    80 network members    71 relationships

Second quarter    75 network members    71 relationships

Third quarter    77 network members    69 relationships

Fourth quarter    111 network members    136 relationships

Fifth quarter    140 network members    213 relationships

Figure 17 provides a visual of the above‐described growth from January 2012 – September 2014.

Figure 17. HHC Network Relationships by Quarter and Cumulatively

0

200

400

600

800

1,000

1,200

1,400

HHC Relationships, Quarterly and Cumulative

Relationships

- Quarterly

Relationships

- Cumulative
Figure18. Overall HHC Social Network for Period 1

Next, sociograms were used to display the network relationships visually among the clients and their

individual relationships with network providers. To protect the privacy of clients, each individual was

given a random identification number, as this would allow clients to be depicted in the social network

without revealing their identities. Each client and network provider was displayed as a node, or circle,

and the lines connecting two nodes represented the shared relationship between the client and the

provider. The series of sociograms shows the evolution of the network over time, which are provided in

the complete Social Network Analysis in the appendix.  Here, figure 18 is the sociogram for the first

period of analysis. The sociogram pattern most evident during this first period of analysis was the star

pattern, where the network provider was located at the center of a circular figure with clients attached

to the provider.  
1 | Page

Overall, this first period demonstrated cohesiveness across the network with a sizeable number of

network members and relationships, which evolved over five quarters, resulting in multiple interactions

among the providers and clients. The continued network growth highlights the potential benefits of

bringing together network providers to offer a myriad of services to potential clients through

collaborations among Local Network Specialists with the UK Clinic and BHNE Clinic, as patients were

referred for an array of services from these providers.  Because the network as a whole grew, the result

reflects the desired strategic outcomes of the HHC stakeholders.

Period 2 ‐ Quarter 2 2013 ‐ Quarter 3 2014, Six Quarters of Referrals

A different pattern emerged during the second period of analysis.  With the same six provider

categories, network dynamics were:

First quarter    156 network members    262 relationships

Second quarter    87 network members    130 relationships

Third quarter    69 network members    102 relationships

Fourth quarter    72 network members    93 relationships

Fifth quarter    50 network members    56 relationships

Sixth quarter    35 network members    35 relationships

In comparison to the previous period, the data from the current period outlines the HHC provider

network’s contraction across the quarter as a more limited number of individuals took part in the

network’s services in comparison to the previous quarters. The network’s growth peaked in the second

quarter of 2013, attaining the largest number of individuals and relationships over Periods 1 and 2.

However, the number of members and relationships within each quarter declined as the year

progressed, and this tapering off continued into 2014. These results parallel the decrease in new and

continuing patients seen at the clinic.  The sociogram depicted in figure 19 illustrates the peak of

network members and relationships.

   
2 | Page

Figure 19. HHC Social Network in Q2 2013

The final sociogram seen in figure 20 depicts the entire network at the end of Period 2. Similar to the

final sociogram for Period 1, the final network revealed a relatively high level of cohesiveness among the

network members and relationships. The number of new patients and relationships tapered off across

the individual quarters during this period, yet the network as a whole remained stable and

comprehensive. The sociogram highlights that patients continued to take advantage of the network

providers and services. Assuming the growth trends remain the same, one would expect the network

structure to exhibit a similar structure going forward.  

   
3 | Page

Figure 20. Overall HHC Social Network for Period 2

The remaining research questions intended for SNA to address proved challenging to answer in

detail.

RQ2: How do patients with different needs and characteristics flow through a medical service network?

RQ3: How do different subgroups (e.g., cancer versus diabetic patients) flow through the network, and

how can network density help providers understand the network?

The sociograms and quantitative data provide somewhat limited insights into research questions 2

and 3 as they relate to specific client needs and characteristics, as well as how they might be served or

flow through this social network.  
4 | Page

From focus group results with key stakeholders (N=5), several factors were identified as influencing

the network’s expansion and contractions:  1) mistrust of patients to go beyond the walls of the clinic; 2)

effect of the Affordable Care Act (ACA) on patients; and 3) extended time for exploration of other

needed services, particularly dental and mental health services.  “Mistrust of patients” was defined as

the reluctance of patients to complete referrals outside the clinic walls or the immediate area of the

local hospital and its offices of specialists.  If patients had to travel outside their own communities,

transportation was frequently mentioned as a reason not to go, particularly if the referrals were non‐

medical related, e.g., exercise classes, smoking cessation programs.  

ACA was viewed, and still is, as a source of confusion and excessive cost by most Hope patients who

discuss it with volunteers, staff, and providers. HHC’s medical and administrative staff members

attended multiple informational sessions locally and statewide to be able to explain its implications,

answer patients’ questions, and allay patients’ fears. Because visits dropped sharply in the first quarters

during ACA implementation in Kentucky, we knew some patients obtained insurance and found primary

care physicians.  However, we know some patients stayed away because they assumed the law required

insurance, and HHC since its beginning didn’t allow insurance.  At the Fall Retreat in September 2013,

the HHC Board of Directors decided to accept underinsured patients, after legal counsel reported that

this acceptance would not change any part of its non‐profit status.  Once this word got out, patient visits

began to increase gradually during subsequent quarters.

Extended time for seeking providers of other needed services had the apparent effect of diminishing

the rapid network expansion of Period 1.  The network expands through recruitment of additional

providers – both medical and social services.  Recruitment primarily has been focused on identifying

dentists who can provide much‐needed care, particularly for patients with diabetes or long‐time tobacco

abusers, among others; and for mental health professionals who could meet an unanticipated need by

HHC’s vision:  patients with overwhelming issues of depression, anxiety, panic, and other distressing

conditions brought on by poverty and co‐morbidities.

Discussion and Implications of SNA in Understanding Hope Clinic Growth.  Overall, the data

provide insights into the HHC social network from a baseline perspective beg inning in the first quarter

of 2012 and how it continued to evolve over a time period of 11 quarters. The sociograms and

quantitative data offer clear evidence of the increase in network members and relationships, which

allowed the network to grow over time. Although the number of new members and relationships

tapered during this time, the network maintained cohesion and stability. The data also provided insights
5 | Page

as to which providers‐‐in this case, BHNE Clinic, Local Network Specialists, and UK Clinic‐‐offered the

most services to clients within the network. The network’s evolution reveals an opportunity for

continued growth as more clients enter the network and take advantage of the myriad of services.

Additionally, an opportunity exists for providers to collaborate more with one another as they provide a

comprehensive menu of services to their clients over time. 

Social network analysis, we believe, examined the Hope Health Clinic (HHC) healthcare provider

network in enough detail to better understand the evolution of HHC service delivery, examining the

network members and their exchanged resources within the social network. Applying the principles of

the socio‐ecological model, we understand that multiple, nested systems (also called layers of

environment) influence the individual (Bronfenbrenner, 1979). Hope Health Clinic continues to seek to

deliberately engage and integrate these systems which make up the social network in and around our

four counties, to ensure the working‐poor population in this rural region obtains the help needed as a

means to improve the four chronic conditions challenging many Hope patients. Figure 21 presents the

model we envisioned for Hope Clinic, a causal diagram of how these cross‐level environments have been

constructed and implemented, with their resulting longitudinal impact on individual health and system

costs.  

Figure 21: Pathways of Increasing HHC Capacity
6 | Page

Moving from left to right in Figure 35, the ongoing task of creating and sustaining this clinic is

graphically represented by the multiple small lines emanating from HHC Context Development. Based

on Hope Clinic’s vision, the network was designed around the model that depicts the multiple needed

components and connections that have been receiving attention during the clinic’s first three years, the

developmental and implementation phases. From the initial HHC Context Development, Hope Clinic has

documented the following:  1) opened a medical office; 2) established a Board of Directors to oversee

this non‐profit entity with IRS designation 501(c)(3) and replaced it with a non‐profit in the name of

Hope Health Clinic;  3) recruited a staff with clinical and non‐clinical volunteers that include its Medical

Director, six other physicians and a nurse practitioner, and community volunteers; 4) recruited and hired

four paid positions: two half‐time Nurse Practitioners, one half‐time nurse/patient navigator, and the

full‐time clinic administrator; 5) developed numerous local funding sources (a generous mix of public

and private partners) and the Foundation for a Healthy Kentucky; and 6) retained the services of a

skilled evaluation team from the University of Louisville who provided data collection, analyses and

feedback mechanisms for improvement in services and outreach.  From an organizations/systems

baseline of zero, this human and operational resource implementation phase has laid the foundation for

ongoing increases in HHC Capacity:  

1. The detailed coordination of schedules and protocols across a brigade of volunteers at the

clinic site and across four counties;  

2. the ongoing development and maintenance of a service network broad enough to provide

high‐quality, holistic healthcare for an average of 3,000 patient visits annually; and

3. the marketing and outreach necessary to attract sustained funding and increased awareness

of services for the targeted population.  

Cost Reductions for Hospital Emergency Room Usage

As the clinic moved through its first three years, Hope stakeholders continued to provide resources

to help patients, who are expected to increase self‐efficacy managing their health problems through

Hope Clinic’s holistic approach to wellness. At more distal (impact) outcomes, we have found parallel

changes in cost reduction at the system/clinic level and health improvements for the four major

conditions under investigation. Ultimately we have measured these changes using available community‐

level and clinic/hospital provider secondary data sources.  
7 | Page

Hospital emergency room data from Baptist Health LaGrange provide evidence of this reduced cost.  

Though data categories changed from Year 1 of the study to its current quarterly summaries, tables 7 –

10 provide periodic snapshots of the reduced levels of ER use for non‐emergencies, depicted here as ER

Level 1 (table 7) and ER Level 0 and Level 1 (tables 8‐10).  Looking at Total ER Visits under the Hope Clinic

Patients column in table 7, Hope patients represented a fraction of all ER visits, from .007% in Quarter 1

of 2011 to .017% in Quarter 4 of 2012 (Year 1 of our study). These general ER data weren’t as

meaningful for clinic stakeholders, which resulted in specifying data at ER Level breakdowns.  

Subsequently, table 8 indicates 8.29% of Hope patients from April 2013 to June 2013 used ER services,

though none of these visits were for non‐emergencies.  The same quarter a year later, the percentage of

Hope patients dropped to 5.39%, again without any non‐emergencies.  Midway between these two

quarters, 7.04% of ER use was by Hope patients, with only one visit for a non‐emergency.  For all the

dedicated community volunteers, medical providers, and funders across the four‐county area, these

data are robust pieces of evidence of realizing a primary goal for Hope Clinic.

Table 7.  Hospital ER Visits by Hope Clinic Patients, Year 1 of Study

Month/Quarter ER Level 1 –

non‐

emergency

Total other

ER visits,

emergencies

Inpatient

Admits

All ER

Visits

% of all ER

visits by Hope

patients  

Oct 2011 13 6 0 1,187 0.0051

Nov 2011 30 9 1 1,219 0.0074

Dec 2011 26 11 2 1,144 0.0096

Quarter 1

subtotals

69 26 3 3,550 0.0073

Jan 2012 74 12 0 1,316 0.0091

Feb 2012 88 14 1 1,191 0.0118

Mar 2012 73 12 0 1,306 0.0092

Quarter 2

subtotals 235 38 1 3,813 0.0100

Apr 2012 79 23 2 1,254 0.0183

May 2012 75 16 1 1,413 0.0113

June 2012 54 32 6 1,203 0.0266

Quarter 3

subtotals 208 71 9 3,870 0.0183

July 2012 49 24 5 1,306 0.0184

Aug 2012 46 22 1 1,304 0.0169

Sep 2012 45 20 0 1,220 0.0164

Quarter 4

subtotals 140 66 6 3,830 0.0172
8 | Page

Table 8. Comparison of Hospital ER Visits by Hope Clinic Patients, Year 2, Qtr 3

 4/1/2013

 to 06/30/2013

Self Pays HHC pts 

 

3 

17 

7 

0 

55 3 

515 40 

198 21 

24 4 

1 Qtr: .0829 

870255 TRIAGE ONLY NEW PATIENT (ED) 

870259 TRIAGE ONLY ESTAB PATIENT (ED) 

870261 ER LEVEL 0 (ED) 

870247 ER LEVEL I (ED) 

870248 ER LEVEL II (ED) 

870249 ER LEVEL III (ED) 

870250 ER LEVEL IV (ED) 

870251 ER LEVEL V (ED) 

870252 ER LEVEL VI INITIAL 74 MIN 

Table 9. Comparison of Hospital ER Visits by Hope Clinic Patients, Year 3, Qtr 1

10/1/2013 

to 12/31/13 

All 

Self Pays HHC pts. 

 

870255 TRIAGE ONLY NEW PATIENT (ED) 2 

870259 TRIAGE ONLY ESTAB PATIENT (ED) 7 

870261 ER LEVEL 0 (ED) 3 1 

%870247 ER LEVEL I (ED) 1 

870248 ER LEVEL II (ED) 40 3 

870249 ER LEVEL III (ED) 362 22 

870250 ER LEVEL IV (ED) 163 15 

870251 ER LEVEL V (ED) 19 1 

870252 ER LEVEL VI INITIAL 74 MIN Qtr: .0704 

Table 10. Comparison of Hospital ER Visits by Hope Clinic Patients, Year 3, Qtr 3

4/1/2014 

to 06/30/2014 

All 

Self Pays HHC pts 

 

8 

 

9 

870255 TRIAGE ONLY NEW PATIENT (ED) 

870259 TRIAGE ONLY ESTAB PATIENT (ED) 

870261 ER LEVEL 0 (ED) 
9 | Page

870247 ER LEVEL I (ED) 0 

870248 ER LEVEL II (ED) 15 2 

870249 ER LEVEL III (ED) 132 5 

870250 ER LEVEL IV (ED) 33 4 

870251 ER LEVEL V (ED) 7 

870252 ER LEVEL VI INITIAL 74 MIN Qtr: .0539 

The social analysis exploration has aided in evaluating the consequences of the community health

clinic and its intended network of providers for increasing access to quality healthcare, examining the

service density of care among low‐income and uninsured patients with chronic conditions, and reducing

the costs of care, especially as it shows up in a reduction of non‐emergency ER care. Information

stemming from these data help make sense of what is going on in the social network as these services

are provided. Instituting such a feedback mechanism helps the people shaping the network to make

more informed decisions.

The workforce continues to grow to meet the number of patients. Medical providers (volunteers)

who see patients regularly at the clinic have increased from six on opening day to eight as of September

2014.  Sixty volunteer medical providers and nine other service providers (see appendix C) represent a

consistent growth of provider network density across quarterly calculations. Specialties and other

services include: addiction treatment, anger management, counselors, a social worker, clinical

psychologist, therapist, dentist, audiologist, and pastoral counseling. Service density also continues to

increase, with 1,244 referrals made over the three‐year period. (We defined “service density” as number

of completed referrals by patients in the provider network.) Coupled with provider network density,

level of saturation has been expressed quantitatively.  We have been unable to confirm referral

completions with a high degree of certainty, but continue to assert completed referrals, if a notation in

the referral log does not indicate “no show” or “rescheduled.”  The number of community volunteers

(nursing, clerical, cleaning, landscaping) has grown to 45, with detailed training provided within the first

30 days for volunteers who work directly with patients or their medical records. The enthusiasm for its

mission and respectful care of patients are palatable among the busy volunteers on any given day at the

clinic. Typical patient comments include ones similar to a statement made during a recent focus group:

“I’ve never been treated respectfully at any medical office before coming to Hope Clinic.”

   
10 | Page

Table 11. Referrals through September  2014, Coded by Provider Type  

Provider Freq. %

Oldham Co Health Department (smoking cessation, diabetes educ.,

gynecology)

UK Clinics – various specialties incl. urology, pain, hepatitis, hand surgery

Local network specialists incl. cardiology, dermatology, dentist

Hospital for test (separate category through 4‐1‐13)

Health Advocate

Baptist Health LaGrange, clinics, tests, etc. at hospital

UofL  

Health departments in Henry, Trimble & Carroll counties

HHC – for mental health counselors who meet clients at HHC

Mercy Clinic, Shelbyville

Blue Surgery Clinic

Social services

PHHC – same as HHC on next line

Other: Lyons Eye Institute, Norton Oncology

Missing ID or unknown

Total

115

367

319

92   

3

266

4

13

2

12

1

12

1

3

34

1244

9.24

29.5

25.64

7.4

  0.24

21.38

0.32

1.05

0.16

0.96

0.08

0.96

0.08

0.24

2.73

100.0

IV. Discussion

The results obtained over the course of the three‐year project indicate evidence of the, in the words

suggested by several patients and volunteers, “miracles” occurring at Hope Health Clinic. The primary

goal has been achieved: low‐income, working poor residents of four rural counties now have access to

quality health care.  An important objective was met:  According to the data, hospital emergency room

use by Hope patients is now limited to authentic emergencies, resulting in decreased costs for primary

care and lower hospital admission rates for chronic conditions among the area’s uninsured,

underinsured, and low‐income residents. An unexpected outcome likely is partially responsible for this

achievement:  ER physicians are regularly overheard referring qualified patients to Hope Clinic for

primary care.

The social network analysis has confirmed the stability of sustained growth in Hope reaching beyond

its walls to meet patients’ health needs. The clinic administrator affirms that patients’ concern about

ACA caused a drop in visits and referrals, but the SNA shows a robust, continuing relationship

development among the numerous network members that will sustain quality care well into the

foreseeable future. Medical providers speak openly of recruiting colleagues to continue the growth of
11 | Page

volunteers across specialties. The annual appreciation dinner is bolstered with financial reports and

patient testimonials that physicians use to convince others to contribute time or other resources.

Direct observations at the clinic while gathering data confirmed repeated comments from patients

over the three years: respectful treatment is universal among providers, volunteers, and the patients

themselves. Patients are giving back. One woman and her children weekly brought in vegetables from

their garden as a means of paying back the generosity of care she experiences at Hope. It was a

bountiful display from early spring to late summer that encouraged the patients to partake of healthier

choices.

Assessing patient outcomes was the most challenging element to evaluate.  Lack of an electronic

medical records system required reliance on paper records that could not always be located, and

rethinking best ways to objectively gather as much medical and qualitative data as possible to answer

the posed research questions from the SEP.  Some answers have been unobtainable; we cannot

determine specifics about whether county of residence or type of chronic condition affects patient

outcomes.  

Using the general categories we constructed as a measurement of change in chronic conditions for

the random sample, we confirm perceptions of providers and clinic staff that the clinic has mixed results

in helping patients manage their health. Patients and providers resoundingly agree that acute conditions

are treated immediately, and staff finds a way to eliminate barriers to provide needed services. It

remains unclear whether self‐efficacy measurement will assist providers and staff in helping patients

better manage their chronic conditions. The measure provides a snapshot of the patient’s perception of

being able to make a difference in their lives without sole reliance on medications. Patients expressed

frustration in phone calls about not understanding the need to answer the six items on the SE form.

Limited resources—time, volunteers to assist with educational programs and activities, space—were

mentioned frequently, informally and formally, as the greatest challenge to serve more patients with

more services.  Staff supplants these limitations with creative solutions that chip away at the

overwhelming odds faced by Hope patients:  recruiting nursing students to develop and deliver diabetes

educational sessions in a tent erected on the parking lot to accommodate the large number of patients

willing to find out more.  
12 | Page

V. Lessons Learned and Recommendations

Patients express hope that they will get better: “Hope Clinic showed me what to do for all the things

going wrong with my health. I’ve never seen any clinic like this,” said a Spanish‐speaking patient.  A

patient with chronic depression expressed, “I’m confident they’re here to help, without being

judgmental, just focusing on let’s get this problem resolved.”  

The clinic could not be better named. The critical lesson learned for Hope Health Clinic is that

fulfillment is achieved by the persistent commitment for responding to expressed patients’ needs. The

ears of the clinic administrator, the Board, and key volunteers are ever vigilant to pick up on these

expressions, often not stated by the patient in need. Impromptu discussions occur on the spot as these

signs of need are identified, often with the clinic administrator knowing someone in her broad network

who can be called upon to lead the solution. One example is the need for finding ways for poor patients

to access costly medications. Inquiries resulted in obtaining delivery of free drugs on a regular basis that

can be dispensed at the clinic (approximate value over the three years exceeded $2 million, clearly an

outcome of high interest to patients and providers). These medications are in addition to the formal

KPAP system for which most low‐income patients qualify, resulting in very low cost for life‐saving and

chronic pain‐reducing medications.

A sad lesson for staff and providers has been the inability for patients to reduce levels of obesity; the

data from the random sample suggest that weight gain is the norm. Decision makers are seeking

resources such as volunteer wellness coaches to work one‐on‐one with patients to help them with

intractable weight problems. A first recommendation would be for the Operations Council to devise

strategies for weight reduction, especially among the patients diagnosed as morbidly obese.  Focus

group participants suggested exercise classes of some kind be held at the clinic; where, in what space, is

the challenge.  Nursing students designed a walking map around the hospital and clinic grounds. Finding

ways to get patients on the walkway would be a small step toward better health.

Although self‐efficacy assessment forms are no longer being obtained from patients, better self‐

management of chronic conditions remains an important goal for Hope patients. Medical providers may

need a refresher on the importance of self‐efficacy in patients reaching effective management of

chronic conditions.  Without their commitment to assisting patients with self‐efficacy, providers will

have to rely on acute measures to solve chronic conditions. Self‐efficacy strategies could be a useful
13 | Page

mechanism for patients to help themselves back to better health without overburdening this essential

health care resource.  

Though Hope has a robust network of specialists, it has not become a clinic without walls as

originally conceived. Transportation is still frequently cited as a problem for making visits and

completing referrals, indicating that patients need to find rides and have asked for a van or bus service

to be considered in the future. If such a service could be provided, more referrals for the area’s smoking

cessation, exercise and nutrition activities, and other needed social services would give patients a

greater chance of becoming healthier. The advent of the new non‐profit status for Hope Health Clinic

may provide another phalanx of volunteers from churches across the other three counties that can

devise ways to get patients to the help they need.  According to an interview with five key stakeholders,

the original agency who oversaw the development and implementation of Hope Clinic—the Oldham

County Ministerial Association—has been perceived by the faith community in the other participating

counties as focusing only on Oldham County churches and, therefore, Oldham County residents. The

perception limited participation among much‐needed volunteers from Trimble, Henry and Carroll

counties – and monetary support from its churches and other organizations.  Since Hope Clinic is now its

own non‐profit, small increases in support and volunteers have begun since July 2014.

Inspiration meets the patient when walking into Hope Health Clinic. Greetings include positive

banners that address the clinic’s mission: To be of help and of hope; patients’ photos with personal

testimonials about their experiences; and smiling volunteers who speak softly and exude a respectful

demeanor for every patient. The data document that HHC efforts extend beyond its walls, though

efforts toward sustainability must continue to reach a broader representation in three of the four

counties (Henry, Trimble and Carroll), to prevent Oldham County residents getting frustrated in aiding

patients from beyond its borders. Key leaders believe that establishing a satellite in Trimble or Carroll

will draw support for a more local site, and efforts continue to seek funding for expansion.

Expansion is underway, fortunately, on the grounds itself.  Baptist Health LaGrange has donated

additional space adjacent to the current clinic that is planned for dental services and space for

educational classes. A continuous flow of nursing students provide much‐needed support for diabetes

education and weight management through improved nutrition and exercise encouragement. An intern

fluent in Spanish supplants two volunteer interpreters to assist the growing number of Spanish‐speaking

patients.
14 | Page

The current clinic administrator provides the kind of leadership and management suited to the

clinic’s mission and needs: she has a background that includes 20+ years of experience working in a

ministry environment and four years at a rural health clinic, shown to be essential to the clinic’s growth

and reach. The “patient navigator” role continues to be shared by the half‐time nurse and two key

volunteers who make appointments for provider referrals. These staff members make calls to encourage

patients’ referral completions and address the variety of patients’ needs. Although Health Advocates

were trained during Year 1, they lacked exposure to patients at the clinic and primarily used phone calls

to connect with patients. The patients expressed a lack of trust with these well‐meaning advocates,

since they had never met in person.  Plans to have advocates at the clinic will meet this disparity, though

the number of volunteers needed for this ambitious strategy is still being recruited.

HHC remains open more than 40 hours per week. Meeting patients’ needs is the compelling

rationale for staying open longer than posted hours, primarily to accommodate patients’ work

schedules. This clinic’s paid staff and volunteers are synchronized to a high degree in following the same

mission: hear about a need, find a solution. As patients express concerns, the clinic administrator shows

determined leadership in listening carefully to staff and volunteers to find feasible, sustainable

responses. The example of educational sessions for 25‐30 patients at a time illustrates this achievement:

No room exists in the clinic building for such a large group of patients, so nursing students worked with

the clinic administrator to use a large meeting tent in the parking lot for the sessions. The collaboration

among volunteers, staff and community members creates this seamless integration of identifiable and

creative resources with patients’ needs.

A trained referral specialist is envisioned for the volunteer staff in the future; as discussed in

previous reports, the nurse hired as the patient navigator primarily focuses on triage and making

medical referrals—clearly identified as best uses for her skills. Health Advocates trained to provide

referral information have not been viable in current form (few are found in referral logs, less than 1%);

rationale provided earlier likely explains this gap. When advocates begin to work with patients at the

clinic, different outcomes are expected.  As mentioned, a formal proposal has been developed by the

Operations Council that provides a three‐tiered strategy for ensuring advocacy becomes a primary focus

for the clinic. Finding time and the volunteers to implement this project are the current challenges.

   
15 | Page

References

Ansell, D., Schiff, R., Goldberg, D., Furumoto‐Dawson, A., Dick, S., & Peterson, C. (2002). Primary care

access decreases nonurgent hospital visits for indigent diabetics. J Health Care Poor Underserved, 13(2),

171‐183.   

Barnhill, K.E., Beitsch, L.M., & Brooks, R.G. (2001). Improving access to care for the uninsured: State‐

supported volunteerism as a successful component.  Arch Internal Med, 161, 2177‐2181.

Bodenheimer, T., Long, K., Homan, H., & Grumbach, K. (2002) Patient self‐management of chronic

disease in primary care.  JAMA, 288(19), 2469‐2475.

Bronfenbrenner, U. (1979). The Ecology of Human Development: Experiments by Nature and Design.

Cambridge, MA: Harvard University Press.

deNooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. New

York: Cambridge University Press.

Dickey, K (2014). Family’s answer to giving: HOPE. Courier‐Journal, October 27, A8

Dohan, D., & Schrag, D. (2005). Using navigators to improve care of underserved patients. Cancer,

104(4), 848‐855.

Dwek, J. (2002). Volunteerism in the care of the uninsured. Archives of Internal Med, 162(8), Editor’s

correspondence.

   

Fredericks, K. A. (2006). Network analysis of a demonstration program for the developmentally

disabled. In M. M. Durland & K. A. Fredericks (Eds.), Social Network Analysis in Program Evaluation.

Danvers, MA: Wiley Periodicals, Inc.

Gephi. (2014). Features. Retrieved from http://gephi.github.io/features/

Hambrick, M. E. (2012). Six degrees of information: Using social network analysis to explore the

spread of information within sport social networks. International Journal of Sport Communication, 5,

16‐34.

Hambrick, M. E., & Sanderson, J. (2013). Gaining primacy in the digital network: Using social network

analysis to examine sports journalists’ coverage of the Penn State football scandal via Twitter. Journal

of Sports Media, 8, 1‐18.

Highland Hospital Bariatric Surgery Center (2014). Online medical site.  Retrieved from

http://www.urmc.rochester.edu/highland/departments‐centers/bariatrics/right‐for‐you/morbid‐ 

obesity.aspx

Hulett, J. (2012). Church health center: Transforming healthcare. Online articles at

http://churchhealthcenter.wordpress.com/
16 | Page

Kochan, S., & Teddlie, C. (2006). An evaluation of communication among high school faculty using

network analysis. In M. M. Durland & K. A. Fredericks (Eds.), Social Network Analysis in Program

Evaluation. Danvers, MA: Wiley Periodicals, Inc.

McWilliam, C. (2009). Patients, persons or partners? Involving those with chronic disease in their

care. Chronic Illness, 5(4), 277‐292.

   

Mishori, R. (2009). Patient navigators guide us through the medical maze. Parade: Wellness: Stay

Healthy.  Online at: http://www.parade.com/health/2009/03/patient‐navigator.html   

Morris, S. (2011). Health care you can live with. Uhrichsville, OH: Barbour Publishing, Inc.

Roby, D.H., Pourat, N., Pirritano, M.J., Vrungos, S.M., Dajee, H., Castillo, D., & Kominski, G.F. (2010).

Impact of patient‐centered medical home assignment on emergency room visits among uninsured

patients in a county health system. Medical Care Research and Review, 67(4), 412‐430.

Rust, G., Baltrus, P., Ye, J., Daniels, E., Quarshie, A., Boumbulian, P., & Strothers, H. (2009).

Presence of a community health center and uninsured emergency department visit rates in rural

counties. J Rural Health, 25(1), 8‐16.

Scott, H.D., Bell, J., Geller, S., & Thomas, M. (2000). Physicians helping the underserved: The reach

out program.  JAMA, 283(1), 99‐104.

Schwaderer & Itano, (2007). Bridging the healthcare divide with patient navigation: Development

of a research program to address disparities. Clinical J of Oncology Nursing, 11(5), 633‐639.

Smedley, B.D., Stith, A.Y., & Nelson, A.R., eds. (2003). Unequal treatment: Confronting racial and

ethnic disparities in health care. Washington, DC: National Academies Press.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge

University Press: Cambridge.
Appendix A. Hope Health Clinic Patients’ Demographics: October 2011‐ March 2013

Response options Oct‐Dec

2011

Jan‐Mar

2012

Apr‐Jun2012

Jul‐Sep

2012

Oct‐Dec

2012

Jan‐Mar

2013

Total 

r

Male 52 86 89 80 46 53 469 

Gende Female Total 104 156 159 245 148 237 178 258 91137 89142 877 1346 Age

< 5 years

5 ‐ 18 years

19 ‐ 39 years

40 ‐ 64 years

65+ yearsTotal

0059961156

111041354245

001291071237

011381190258

0159761137

0272680142

16645683

111346

Race

White/CaucasianBlack/African‐American

N. Amer./HI or API

Asian

143121

206310

199510

215900

126300

107711

11323582

Missing/DeclinedTotal

9156

35245

32237

34258

8137

26142

1691346

Ethn.

HispanicNon‐Hispanic

Total

9147156

35210245

32205237

34224258

8129137

26116142

16911771346

Lang.

EnglishSpanishTotal

1515156

23114245

237237

24612258

1334137

12517142

1280661346

17 | Page
18 | Page

Response options Oct‐Dec Jan‐Mar Apr‐Jun Jul‐Sep Oct‐Dec Jan‐Mar Total 

2011 2012 2012 2012 2012 2013

100% or less 84 104 37 17 101 123 595 

101% ‐ 150% 29 81 20 14 13 192 

me

FPL)151% ‐ 200% 3 0 0 2 13 6 31 

Inco

(%More than 200% 0 0 0 9 0 9 

Missing/refused 40 60 180 239 0 0 519 

Total 156 245 237 258 137 142 1346

ntUninsured 156 245 237 258 137 142 1346 yme Medicaid 0 0 0 0 0 0 0 Pa

TypeMedicare 0 0 0 0 0 0 0 

e/ranc Private Insurance 0 0 0 0 0 0 0 Insu Total 156 245 237 258 137 142 1346

Oldham 555 72 80 791 

Trimble 113 15 17 167 

sidence Carroll 48 9 12 90 ReHenry 169 37 31 281 orJefferson 6 4 2 12 County Owen 1 0 0 1 

Shelby 4 0 0 4 

Total 896 137 142 1346
19 | Page

Hope Health Clinic Patients’ Demographics: April 2013‐September 2014

Response options Apr‐Jun

2013

Jul‐Sept2013

Oct‐Dec2013

Jan‐Mar

2014

Apr‐Jun2014

Jul‐Sept2014

Total 

r

Male 63 55 60 27 46 30 281 

Gende Female Total 108 171 109 164 67 127 5784 4995 70 100 460 741 Age

< 5 years

5 ‐ 18 years

19 ‐ 39 years

40 ‐ 64 years

65+ yearsTotal

0184824171

0079841164

0263611127

014538084

024345595

0363322100

09377342

13741

Race

White/CaucasianBlack/African‐American

N. Amer./HI or API

Asian

136730

157421

90312

64100

62200

47200

5561963

Missing/DeclinedTotal

25171

0164

31127

1984

3195

51100

157741

Ethn.

HispanicNon‐Hispanic

Total

25146171

30134164

3196127

196584

316495

5149100

187554741

Lang.

EnglishSpanishTotal

15714171

14519164

10027127

671784

732295

6436100

606

135741
20 | Page

Response options Apr‐Jun

2013

Jul‐Sept2013

Oct‐Dec2013

Jan‐Mar

2014

Apr‐Jun2014

Jul‐Sept2014

Total 

100% or less 129 132 97 62 69 82 571 

101% ‐ 150% 35 28 17 19 25 15 139 

me

FPL)151% ‐ 200% 7 4 13 3 1 3 31 

Inco

(%More than 200% 0 0 0 0 0 0 0 

Missing/refusedTotal

0171

0164

0127

084

095

0100

0741

ntUninsured 171 164 127 75 89 96 722 yme Medicaid 0 0 0 2 0 0 2 e/ Pa

TypeMedicare 0 0 0 4 0 0 4 

Insuranc Private Insurance Total 0171 0164 0127 384 695 4100 13741 

Oldham 84 94 74 49 49 58 408 

Trimble 22 13 13 7 9 8 72 

ceCarroll 21 20 12 10 11 13 87 or sidenRe Henry Jefferson 440 324 262 144 196 145 149 21 CountyOwen 0 0 0 0 0 0 0 

ShelbyMissing/OtherTotal

00171

01164

00127

0084

0195

20100

22741
21 | Page

Appendix B. Hope Health Clinic: Referrals January 2012 – March 2013

1. Tabular Breakdown of Referrals by County for Each Quarter

Countya

Frequency Percent Valid Percent Cumulative Percent

Oldham 38 53.5 60.3 60.3

Trimble 8 11.3 12.7 73.0

Henry 11 15.5 17.5 90.5

Valid

Carroll 5 7.0 7.9 98.4

Jefferson 1 1.4 1.6 100.0

Total 63 88.7 100.0

Missing System 8 11.3

Total 71 100.0

a. quarter = Jan 2012 ‐Mar 2012

Countya

Frequency Percent Valid Percent Cumulative Percent

Oldham 39 54.9 58.2 58.2

Trimble 12 16.9 17.9 76.1

Valid Henry 11 15.5 16.4 92.5

Carroll 5 7.0 7.5 100.0

Total 67 94.4 100.0

Missing System 4 5.6

Total 71 100.0

a. quarter = Apr 2012 ‐ Jun 2012

Countya

Frequency Percent Valid Percent Cumulative Percent

Oldham 40 58.0 63.5 63.5

Trimble 6 8.7 9.5 73.0

Henry 10 14.5 15.9 88.9

Valid

Carroll 6 8.7 9.5 98.4

Owen 1 1.4 1.6 100.0

Total 63 91.3 100.0

Missing System 6 8.7

Total 69 100.0

a. quarter = Jul 2012 ‐ Sept 2012

   
22 | Page

Countya

Frequency Percent Valid Percent Cumulative Percent

Oldham 79 58.1 61.2 61.2

Trimble 21 15.4 16.3 77.5

Henry 20 14.7 15.5 93.0

Valid

Carroll 4 2.9 3.1 96.1

Jefferson 5 3.7 3.9 100.0

Total 129 94.9 100.0

Missing System 7 5.1

Total 136 100.0

a. quarter = Oct 2012 ‐ Dec 2012

Countya

Frequency Percent Valid Percent Cumulative Percent

Oldham 125 58.7 62.5 62.5

Trimble 19 8.9 9.5 72.0

Henry 37 17.4 18.5 90.5

Valid

Carroll 15 7.0 7.5 98.0

Jefferson 4 1.9 2.0 100.0

Total 200 93.9 100.0

Missing System 13 6.1

Total 213 100.0

a. quarter = Jan 2013 – Mar 2013

2. Graphs of Quarterly Referrals: by County, by Quarter, and by Provider
23 | Page

3. Percentage Follow‐through on Referrals by Quarter

Fol‐thru a

Frequency Percent Valid Percent Cumulative Percent

patient did not follow through 4 5.6 9.1 9.1

Valid patient followed through or

rescheduled

40 56.3 90.9 100.0

Total 44 62.0 100.0

Missing

Total

System 27

71

38.0

100.0

a. quarter = Jan 2012 ‐Mar 2012

Fol‐thru a

Frequency Percent Valid Percent Cumulative Percent

patient did not follow through 11 15.5 17.7 17.7

patient followed through or 51 71.8 82.3 100.0

Valid

rescheduled

Total 62 87.3 100.0

Missing System 9 12.7

Total 71 100.0

a. quarter = Apr 2012 ‐ Jun 2012
24 | Page

Fol‐thru a

Frequency Percent Valid Percent Cumulative Percent

patient did not follow through 3 4.3 4.4 4.4

patient followed through or 65 94.2 95.6 100.0

Valid

rescheduled

Total 68 98.6 100.0

Missing System 1 1.4

Total 69 100.0

a. quarter = Jul 2012 ‐ Sept 2012

Fol‐thru a

Frequency Percent Valid Percent Cumulative Percent

patient did not follow through 3 2.2 2.3 2.3

patient followed through or 130 95.6 97.7 100.0

Valid

rescheduled

Total 133 97.8 100.0

Missing System 3 2.2

Total 136 100.0

a. quarter = Oct 2012 ‐ Dec 2012

Fol‐thru a

Frequency Percent Valid Percent Cumulative Percent

patient did not follow through 3 1.4 1.4 1.4

Valid patient followed through or

rescheduled

205 96.2 98.6 100.0

Total 208 97.7 100.0

Missing

Total

System 5

213

2.3

100.0

a. quarter = Jan 2013 ‐ Mar 2013

   
Count   

Fol‐thru * county Crosstabulationa

county Total

Oldham Trimble Henry Carroll Jefferson

patient did not follow through 1 0 2 0 0 3

Fol‐thru patient followed through or 77 20 17 4 5 123

rescheduled

Total 78 20 19 4 5 126

a. quarter = Oct 2012 ‐ Dec 2012

25 | Page

4. Referral Follow‐throughs Broken Down by County and Quarter

Count   

Fol‐thru * county Crosstabulationa

county Total

Oldham Trimble Henry Carroll Jefferson

Fol‐thru

patient did not follow through

patient followed through or

rescheduled

1

18

3

5

0

6

0

4

0

1

4

34

Total 19 8 6 4 1 38

a. quarter = Jan 2012 ‐Mar 2012

Count   

Fol‐thru * county Crosstabulationa

county Total

Oldham Trimble Henry Carroll

Fol‐thru

patient did not follow through

patient followed through or

rescheduled

9

26

0

11

2

6

0

5

11

48

Total 35 11 8 5 59

a. quarter = Apr 2012 ‐ Jun 2012

Count   

Fol‐thru * county Crosstabulationa

county Total

Oldham Trimble Henry Carroll Owen

Fol‐thru

patient did not follow through

patient followed through or

rescheduled

3

37

0

6

0

10

0

5

0

1

3

59

Total 40 6 10 5 1 62

a. quarter = Jul 2012 ‐ Sept 2012
26 | Page

Count   

Fol‐thru * county Crosstabulationa

county Total

Oldham Trimble Henry Carroll Jefferson

Fol‐thru

patient did not follow through

patient followed through or

rescheduled

3

120

0

19

0

36

0

14

0

3

3

192

Total 123 19 36 14 3 195

a. quarter = Jan 2013 ‐ Mar 2013

5. Summary Tables  

Table 1. Referrals by County

Frequency Percent Valid Percent Cumulative Percent

Oldham 321 57.1 61.4 61.4

Trimble 66 11.7 12.6 74.0

Henry 90 16.0 17.2 91.2

Valid Carroll 35 6.2 6.7 97.9

Jefferson 10 1.8 1.9 99.8

Owen 1 .2 .2 100.0

Total 523 93.1 100.0

Missing* System 39 6.9

Total 562 100.0

* Missed recording county of patient on referral

Table 2. Referrals by Quarter

Frequency Percent Valid Percent Cumulative Percent

Jan 2012 ‐Mar 2012 71 12.6 12.7 12.7

Apr 2012 ‐ Jun 2012 71 12.6 12.7 25.4

Jul 2012 ‐ Sept 2012 69 12.3 12.3 37.7

Valid

Oct 2012 ‐ Dec 2012 136 24.2 24.3 62.0

Jan 2013 ‐ Mar 2013 213 37.9 38.0 100.0

Total 560 99.6 100.0

Missing System 2 .4

Total 562 100.0
27 | Page

Table 3. Referrals by Provider Type

Frequency Percent Valid Percent Cumulative Percent

Oldham Co Health Dept 68 12.1 12.9 12.9

UK clinics 200 35.6 37.9 50.8

local network specialist 123 21.9 23.3 74.1

*hospital test 92 16.4 17.4 91.5

advocate 3 .5 .6 92.0

*BHNE clinic 20 3.6 3.8 95.8

UofL 3 .5 .6 96.4

Valid Baptist Medical Associates 1 .2 .2 96.6

*Baptist East 1 .2 .2 96.8

Mercy 12 2.1 2.3 99.1

Blue Surgery Clinic 1 .2 .2 99.2

social services 1 .2 .2 99.4

**PHHC – same as HHC 1 .2 .2 99.6

**HHC 2 .4 .4 100.0

Total 528 94.0 100.0

Missing System 34 6.0

Total 562 100.0

* These three providers are the same referral site: hospital test, BHNE clinic, and Baptist East.

** HHC (Hope Health Clinic) is a referral for mental health counselors who do not work at the clinic but use the clinic offices to see

patients on site.
28 | Page

Appendix C. Referral Specialties and Other Network Categories, 2011‐14

Medical Specialists 

Adolescent medicine specialist 

Allergist (immunologist) 

Anesthesiologist 

Cardiac electrophysiologist 

Cardiologist 

Cardiovascular surgeon 

Colon and rectal surgeon 

Critical care medicine specialist 

Dermatologist 

Developmental pediatrician 

Diagnostic radiologist 

Emergency medicine specialist 

Endocrinologist 

Forensic pathologist 

Gastroenterologist 

Geriatric medicine specialist 

Gynecologic oncologist 

Gynecologist 

Hand surgeon 

Hematologist 

Hepatologist 

Hospice and palliative medicine specialist 

Hospitalist 

Hyperbaric physician 

Infectious disease specialist 

Internist 

Interventional cardiologist 

Medical oncologist 

Neonatologist 

Nephrologist 

Neurological surgeon 

Neurologist 

Nuclear medicine specialist 

Obstetrician 

Occupational medicine specialist 

Ophthalmologist 

Oral surgeon (maxillofacial surgeon) 

Orthopedic surgeon 

Otolaryngologist 

Pain management specialist 

Pathologist 

Pediatrician 

Perinatologist 

Physiatrist 

Plastic surgeon 

Preventive medicine specialist 

Psychiatrist 

Pulmonologist 

Radiation oncologist 

Radiologist 

Reproductive endocrinologist 

Rheumatologist 

Sleep disorders specialist 

Spinal cord injury specialist 

Sports medicine specialist 

Surgeon 

Thoracic surgeon 

Urologist 

Vascular surgeon 

Behavioral Health Other Service Providers 

Addiction treatment Dentist 

Anger management Audiologist 

Counselor (BH) Pastoral counseling 

Social Worker 

Clinical psychologist 

Therapist 

   
Changed Date of Not Other reaction:   Service Just OK My Life for Service Helpful Please describe the Better

10. Medical service at the clinic          

11. Lab procedures          

12. Dental care       

13. Diabetes management          

14. Weight loss help          

15. High blood pressure management          

16. Smoking cessation          

17. Financial counseling or service          

18. Job assistance          

19. Job training       

29 | Page

Appendix D.  Patient Survey

Hope Clinic Patient Survey  

Instructions – THIS IS AN ANONYMOUS SURVEY – please do NOT place your name on it. If you need

assistance with filling out this survey, seek help from a Hope Clinic volunteer.

Demographics

1. County     ___ Carroll ___ Henry ___ Oldham ___ Trimble

2. Gender    ___ Female ___ Male   

3. Age   ___ 18‐25 ___ 26‐35 ___ 36‐45 ___ 46‐60 ___ over 60   

4. Education ___ Didn’t graduate from high school

    ___ High school graduate

    ___ Technical school graduate or certification: ______________(give type)

    ___ Some college

    ___ College graduate

5. Employed? ___ Yes    ___ No    If yes, where? __________________________________

6. Marital Status ___ Single ___ Married ___ Divorced

7. Number of children ___ 0 ___ 1‐2   ___ 3‐4   ___ 5‐6   ___ 7 or more

8. Number of family members living in home ________

9. Primary language spoken in home __________________

Hope Clinic Services:  Which of the following services have you used and when did you use it? Please

indicate your level of satisfaction for each service.
30 | Page

30. What service or assistance is missing from Hope Clinic’s whole‐person approach to healthcare?

____________________________________________________________________________________

_______

____________________________________________________________________________________

_______

____________________________________________________________________________________

_______

How has Hope Clinic services increased your awareness of and participation in healthy behaviors?

Service Date of

Service

Not

Helpful Just OK

Changed

My Life for

the Better

Other reaction:  

Please describe

20. Housing assistance   

21. Food assistance   

22. Child care assistance   

23. Spiritual counseling   

24. Marriage counseling   

25. Exercise class(es)   

26. Nutrition class(es)   

27. Emotional or mental assistance   

28. Other: _____________________   

29. Other: _____________________   

_

_

_

Healthy Behavior

Think

about it

less often

Think

about it

more often

Actively

participate

Tell family

or friends

about it  

Get family

or friends

involved

Does

not

apply

31. Get healthy check‐up    

32. Get needed healthcare    

33. Take care of my teeth    

34. Stop smoking    

35. Check blood pressure    
31 | Page

Healthy Behavior

Think

about it

less often

Think

about it

more often

Actively

participate

Tell family

or friends

about it  

Get family

or friends

involved

Does

not

apply

36. Healthy weight    

37. Daily physical activity

moderate or vigorous

–    

38. Eat healthy foods    

39. Sleep well for 6‐8 hours    

40. Anger management    

41. Stress management    

42. Enjoy life    

43a. In the last six months, have you not obtained medical treatment due to cost or other barriers?

______ Yes ______ No

43b. If yes, what was the reason you did not obtain medical treatment?  Check all that apply.

___ Cost for doctor’s visit     ___ Transportation problem

___Cost of medication       ___ Too far from home  

___ Too many health problems     ___ Fear of what I would find out

___ Dislike of doctors       ___ Can’t get off work

___ Other – please name it:

_________________________________________________________________

___ Other – please name it:

_________________________________________________________________

44. How has Hope Health Clinic changed the way you get the medical care you need?  Check all that

apply.

___ It hasn’t changed – I still use the emergency room for non –emergencies.

___ I no longer use the emergency room except for emergencies.

___ I still have to be admitted to the hospital to treat my condition, which is ______________________.

___ I no longer need to be admitted to the hospital.

___ I now have a primary care provider that I see regularly (at the clinic or at another medical office).

___ I go to the doctor when I need to.

___ I take medicines as the doctor prescribes.

___ I take better care of myself now by: ____________________________________________________.
32 | Page

___ Other – please name it:

______________________________________________________________.

___ Other – please name it:

______________________________________________________________.

Thanks so much for your time!

   
33 | Page

Appendix E. Self‐efficacy Form (also provided in Spanish)

Patient Name _____________________           Date _______________

Hope Health Clinic

Patient Ability to Manage Ongoing Illness

We would like to know how confident you are in doing certain activities. For each of the 

following questions, please choose the number that corresponds to your confidence that you 

can do the tasks regularly at the present time. 

1. How confident are you that you can 

 keep the fatigue caused by your 

 disease from interfering with the 

 things you want to do? 

 _______________________ 

 not at all | | | | | | | | | | totally 

confident 1 2 3 4 5 6 7 8 9 10 confident 

 2. How confident are you that you can 

 keep the physical discomfort or pain 

 of your disease from interfering with 

 the things you want to do? 

 _______________________ 

 not at all | | | | | | | | | | totally 

confident 1 2 3 4 5 6 7 8 9 10 confident 

3. How confident are you that you can 

 keep the emotional distress caused 

 by your disease from interfering with 

 the things you want to do? 

 _______________________ 

 not at all | | | | | | | | | | totally 

confident 1 2 3 4 5 6 7 8 9 10 confident 

 4. How confident are you that you can 

 keep any other symptoms or health 

 problems you have from interfering 

 with the things you want to do? 

 _______________________ 

 not at all | | | | | | | | | | totally 

confident 1 2 3 4 5 6 7 8 9 10 confident 

 5. How confident are you that you can do 

 the different tasks and activities needed 

 to manage your health condition so as 

 to reduce your need to see a doctor? 

 _______________________ 

 not at all | | | | | | | | | | totally 

confident 1 2 3 4 5 6 7 8 9 10 confident 

 6. How confident are you that you can do 

 things other than just taking medication 

 to reduce how much your illness affects 

 your everyday life? 

 _______________________ 

 not at all | | | | | | | | | | totally 

confident 1 2 3 4 5 6 7 8 9 10 confident 

This scale is free to use without permission.

Stanford Patient Education Research Center 

1000 Welch Road, Suite 204, Palo Alto CA 94304 

(650) 723-7935, self-management@stanford.edu 

http://patienteducation.stanford.edu 

Funded by the National Institute of Nursing Research (NINR) 
34 | Page

Appendix F.  Social Network Analysis

Hope Health Clinic Social Network Analysis 

Social network analysis was used to examine the Hope Health Clinic (HHC) healthcare provider social 

network. This analytical technique was used to explore the evolution of the HHC service delivery process, 

examining the network members and their exchanged resources within the social network. 

The exploration will aid in evaluating the community health clinic and its intended network of providers 

for increasing access to quality healthcare; improving the level of care among low-income and uninsured 

chronic clients; and reducing the cost of care, especially in the reduction of non-emergency ER services. 

Information stemming from this data can help make sense of what occurs within service delivery 

networks as these services are provided. Additionally, the resulting information can be used as a feedback 

mechanism that not only aids those shaping the network to make more informed decisions, but can also 

help the evaluation team create new ways of measuring change in the networks. This analysis can better 

reveal how the network is evolving in ways that are relevant to the delivery of medical and social 

services. 

Social Network Analysis Research 

Researchers define social networks as collections of individuals and organizations plus the shared 

relationships among them (Wasserman & Faust, 1994). Social network analysis employs qualitative and 

quantitative approaches to understand the collective network, its members, and their shared relationships. 

This analysis includes the creation of sociograms, or visual depictions of the networks with their members 

and relationships. Within a sociogram, a single point, or node, represents a network member, and a line 

drawn between two nodes indicates a relationship between two members. Sociograms display the 

complete collection of nodes and relationships within the network. Researchers also can create a series of 

sociograms for different time periods, and these diagrams provide insights into the network’s evolution, 

such as when members joined the network and how they interacted over time (de Nooy, Mryar, & 

Batageli, 2005). 

Sociograms and an evaluation of network relationships can help identify key providers. Members with 

numerous relationships have greater access to information and other resources, and often reside at or near 

the network’s center. Conversely, members with less influence have fewer relationships and often 

congregate along the network’s outskirts (de Nooy et al., 2005). Thus, healthcare providers located at the 

network’s center would likely have more shared relationships and serve as network hubs within the 

network, disseminating information and resources to other providers and clients. 

Researchers have used SNA in diverse areas such as educational reform (Kochan & Teddlie, 2006), sport 

communication (Hambrick, 2012; Hambrick & Sanderson, 2013), and governmental service delivery 

(Fredericks, 2006). The studies revealed the advantages of examining network members and their 

relationships as well as understanding how their interactions can combine to improve the network. For 

example, Fredericks (2006) studied a government agency’s programs and quality of service delivery. 

Sociograms were used to track the flow of information and services among various network members, 

which included other government agencies, non-profit organizations, and service recipients. The 

sociograms revealed a concentration of network members at the network’s center. More centrally located 

members generated and controlled program-related information and resources, while members on the 

network’s boundaries were left wanting. The researcher concluded the social network limited the spread 

of new ideas and information and hampered the program’s effectiveness in providing services to its 

clients (Fredericks, 2006). These same evaluation techniques can be used within a health care delivery 

context to understand key network members and relationship and how the network has evolved over time. 
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The social network data and sociograms can offer more insights into the service delivery network’s 

structure and reveal opportunities to increase its efficiency and resource flow. 

Research Purpose and Questions 

The purpose of this study was to evaluate whether the holistic-based HHC, using a medical home model 

and navigation system, improved health outcomes and reduced healthcare costs for low-income, 

uninsured residents in its four-county service area. The study addressed the following research questions: 

RQ1: How does a medical service network expand from an initial baseline state? 

RQ2: How do patients with different needs and characteristics flow through a medical service 

network? 

RQ3: How do different subgroups (e.g., cancer versus diabetic patients) flow through the 

network, and how can network density help service providers understand the network? 

The study used social network analysis to measure the development of and outcomes associated with 

HHC healthcare delivery. HHC, with its collection of healthcare delivery providers, clinic staff, 

volunteers, and patients, represents a social network, and social network analysis was used to examine the 

network in multiple ways: identifying (a) which members existed within the network, (b) what 

relationships connected members together, (c) what resources were exchanged through these 

relationships, and (d) how the resource exchanges evolved over time. 

Social network analysis was used to gain a better understanding of the healthcare provider network and its 

resource exchanges, as the analytical technique was used to explore the clinic and its network members in 

more detail. 

Method 

Data were collected from the HHC’s Health Navigator patient referral logs, which provide information 

about the health services providers used and services obtained. The patient referral logs spanned two time 

periods: (a) Period 1 occurred from Quarter 1 (January-March) 2012 to Quarter 1 (January-March) 2013, 

and (b) Period 2 occurred from Quarter 2 (April-June) 2013 to Quarter 3 (July-September) 2014. 

Social network analysis software Gephi was used to analyze the data. The software can help explore a 

network’s members and relationships as well as the diffusion process with the spread of services and 

other innovations within the network (Gephi, 2014). Researchers can also use the software to create visual 

depictions of and quantitative details about the network (Batagelj & Mrvar, 2010). 

This study included three steps: (a) classifying network members into two categories (healthcare 

providers and clients); (b) creating sociograms to display the social network, its members, and their 

relationships; and (c) using qualitative and quantitative analysis to describe the network. Sociograms were 

used to display network members and their individual relationships, and offered insight into the network’s 

structure with its members and their levels of connection to one another. The sociograms were 

constructed at various points in time--across 11 three-month periods from Quarter 1 2012 to Quarter 3 

2014--to explore the development and evolution of the networks over time (de Nooy, Mrvar, & Batagelj, 

2005). 

In addition to sociograms, social network analysis software can provide quantitative details about the 

social networks. This data gives information about the number of members joining the network, what 

relationships develop among them, and how they use relationships to share resources and provide 

services. This can be quantified via network density, or the ratio between the actual number of 

relationships within the network versus the maximum number of possible relationships within the 

network. Density values range from zero to one, where .000 indicates no relationships among network 
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members and 1.000 reveals all members share direct connections to one another. Higher density values 

indicate information and resources can flow more quickly and efficiently through the network, while 

lower values indicate a slower and less efficient flow of resources (de Nooy et al., 2005). In this study, 

density values along with the number of nodes and relationships were examined to track the evolution of 

the HHC social network over time. 

Results 

The purpose of this study was to evaluate whether the holistic-based HHC, using a medical home model 

and navigation system, improved health outcomes and reduced healthcare costs for low-income, 

uninsured residents in its four-county service area. Social network analysis quantitative data and 

sociograms were used to address the three research questions. 

RQ1: How does a medical service network expand from an initial baseline state? 

The following quantitative data and sociograms provide details about the social network as it evolved 

over time. 

Period 1 - Quarter 1 2012 - Quarter 1 2013 

As a baseline state, the first quarter of 2012 reveals the social network contained 80 members with six 

provider categories: (a) BHNE Clinic, (b) HD, (c) Local Network Specialist, (d) UK Clinic, (e) UofL, and 

(f) Other (Table 1). Individual providers were categorized into one of six groups and depicted a 

parsimonious view of the data with interaction among clients and providers across the social network. 

These members shared 71 relationships, and each relationship was defined as a single interaction between 

a client and a network provider. Some clients met with the same provider more than one time during the 

quarter, and each interaction was recorded as a single relationship. 

The number of network members decreased to 75 in the second quarter of 2012, and the network 

members shared 71 relationships. The number of network members increased to 77 in the third quarter of 

2012, and the network members shared 69 relationships. The number of network members increased 

further in the fourth quarter of 2012 with 111 clients, and the network members shared 136 relationships. 

Finally, the number of network members increased again in the first quarter of 2013 with 140 clients, and 

the network members shared 213 relationships. 

The data provide evidence of the HHC provider network expansion over time as the number of new and 

existing clients used services across the network. Looking at the data by provider type reveals that the 

Local Network Specialist and UK Clinic offered the most services over the five quarters. The Local 

Network Specialist providers grew from 20 relationships in the first quarter of 2012 to 35 relationships in 

the first quarter of 2013. The UK Clinic increased its relationships from 26 in the first quarter of 2012 to 

63 relationships in the first quarter of 2013. The BHNE Clinic also witnessed a sizeable increase in 

services, growing from one relationship in the first quarter of 2012 to 69 in the first quarter of 2013. 

Next, sociograms were used to display the network relationships visually among the clients and their 

individual relationships with network providers. To protect the privacy of clients, each individual was 

given a random identification number, as this would allow clients to be depicted in the social network 

without revealing their identities. Each client and network provider was displayed as a node, or circle, and 

the lines connecting two nodes represented the shared relationship between the client and the provider. 

The series of sociograms shows the evolution of the network over time. 
Table 1. HHC social network attributes 

Quarter Q1 2012 Q2 2012 Q3 2012 Q4 2012 Q1 2013 Q2 2013 Q3 2013 Q4 2013 Q1 2014 

Network Attributes 

Members 80 75 77 111 140 156 87 69 72 

Relationships 71 71 69 136 213 262 130 102 93 

Density 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 

Providers 

BHNE Clinic 1 11 2 31 69 96 36 44 25 

HD 18 3 0 25 22 10 10 6 18 

Local Specialist 20 28 19 21 35 64 36 21 31 

UK Clinic 26 24 44 42 63 18 6 3 7 UofL 1 1 0 0 1 74 41 28 12 Other 5 4 4 17 23 0 1 0 0 

Total 71 71 69 136 213 262 130 102 93 
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Figure 1. HHC network relationships by quarter and cumulatively 

0

200400600800

1,0001,2001,400

12Q1 12Q2 12Q3 12Q4 13Q1 13Q2 13Q3 13Q4 14Q1 14Q2 14Q3
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Figure 2. HHC social network in Quarter 1 2012 

Similar patterns were found across the five sociograms during the first period. The pattern most evident was the star 

pattern, where the network provider was located at the center of a circular figure with clients attached to the provider. The 

UK Clinic provided services to 26 clients, and the clinic was shown at the nexus with the clients radiating outward in the 

star pattern. A similar pattern was revealed with HD and the Local Network Specialists. The other providers with fewer 

clients had less distinct star patterns. Some provided no services during this time period, and were bunched together in the 

figure above. Additionally, some clients used more than one service during the quarter, and linked two or more providers 

together. These linkages can help increase the density of the network as more network members share multiple providers 

and services. 

The density value for this sociogram was .011, which indicated that approximately 1% of the possible relationships among 

network members were captured within the network. While the number is small, density values may be smaller in a newly 
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formed network where individuals and organizations first join together. As existing clients learn about and use additional 

services, the network density could increase. Having said that, the addition of new clients and/or service provider groups 

without a corresponding increase in additional relationships could cause the network density to drop, as evidenced in 

some of the following sociograms. 

 
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Figure 3. HHC social network in Quarter 2 2012 

Moving from quarter one to quarter two revealed similar patterns, with distinct star patterns for the Local Network 

Specialists and UK Clinic. HD provided fewer services but remained part of the network. In this quarter, BHNE Clinic 

increased the number of relationships and also linked to UK Clinic via its clients. The number of network members 

decreased, while the number of relationships stayed the same, thereby maintaining the overall integrity of the network. 

The density value for this sociogram was .012, which indicates that approximately 1% of the possible relationships among 

network members were captured within the network. 

 
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Figure 4. HHC social network in Quarter 3 2012 

The third quarter sociogram reveals a different pattern from the first two quarters. The UK Clinic assumed a larger role 

within the network and offered more services, as depicted via its distinct star pattern. The UK Clinic’s provision of 

services surpassed the Local Network Specialists, which also displayed a star pattern. Two clients linked the two 

providers together, while fewer clients utilized the other provider services during this time period. The density value for 

this sociogram was .011, which indicates that approximately 1% of the possible relationships among network members 

were captured within the network. 

 
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Figure 5. HHC social network in Quarter 4 2012 

Moving into the fourth quarter of 2012, the sociogram revealed a cohesive network as more clients used multiple services 

and more providers connected to one another via these shared relationships. UK Clinic and Local Network Specialists 

continued to play an integral role, but other providers offered more services to multiple clients across the network. This 

sociogram suggests clients took advantage of a variety of services and providers in turn leveraged one another to provide a 

cohesive package of services to interested parties. 

The density value for this sociogram was .010, which indicates that approximately 1% of the possible relationships among 

network members were captured within the network. As mentioned above, while the sociogram appeared more connected, 

an increase in the number of network members--and the potential for additional relationships--caused the density value to 

decrease. 

 
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Figure 6. HHC social network in Quarter 1 2013 

Finally, the first quarter of 2013 revealed an even greater level of cohesiveness across the network with the largest number 

of network members and relationships to date. This network evolution again provides evidence of the network provider 

activity and the willingness of clients to take advantage of these services. Assuming this pattern of growth continues, one 

would expect to see a further increase in the number of network members and relationships as well as a greater level of 

cohesiveness across the network. 

The density value for this sociogram was .009, which indicates that approximately 1% of the possible relationships among 

network members were captured within the network. An inverse relationship exists with the latest sociograms, where 

increases in network members and relationships lead to decreases in density value. While the sociograms appear more 

connected with more providers and relationships, an increase in the number of network members--and the potential for 

additional relationships--caused the density values to decrease. 

 
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Figure 7. HHC social network for Period 1 

Overall, Period 2 from Quarter 1 2012 to Quarter 1 2013 demonstrated cohesiveness across the network with a sizeable 

number of network members and relationships. The network evolved over these first five quarters, resulting in multiple 

interactions among the providers and clients. The continued network growth highlights the potential benefits of bringing 

together network providers to offer a myriad services to potential clients. Collaborations occurred among Local Network 

Specialists with the UK Clinic and BHNE Clinic, as clients sought an array of services from these providers. 

The density value for this sociogram was .003, which indicates that less than 1% of the possible relationships among 

network members were captured in the network. Similar to the findings in the Quarter 1 2013, the sociogram reflects a 

connected network of providers, clients, and relationships among them. However, the network as a whole grew, and 

reflects the desired strategic outcomes of the HHC stakeholders. 

Period 2 - Quarter 2 2013 - Quarter 3 2014 

Moving into the second period, data were examined from the second quarter of 2013 to the third quarter of 2014. As a 

baseline state, the second quarter of 2013 revealed the social network contained 156 members with six provider 

categories: (a) BHNE Clinic, (b) HD, (c) Local Network Specialist, (d) UK Clinic, (e) UofL, and (f) Other. These 

members shared 262 relationships, again defined as a single interaction between a client and a network provider. 

The number of network members decreased to 87 in the third quarter of 2013, and the network members shared 130 

relationships. The number of network members decreased to 69 in the fourth quarter of 2013, and the network members 
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shared 102 relationships. Moving into 2014, the number of network members increased to 72 with 93 relationships during 

the first quarter. The number of network members decreased to 50 in the second quarter, and the network members shared 

56 relationships. Finally, in the third quarter, the number of network members decreased, resulting in 35 network 

members and 35 shared relationships. 

In comparison to the previous period, the data from the current period outlines the HHC provider network’s contraction 

across the quarter as a more limited number of individuals took part in the network’s services in comparison to the 

previous quarters. The network’s growth peaked in the second quarter of 2013, attaining the largest number of individuals 

and relationships over Periods 1 and 2. However, the number of members and relationships within each quarter declined 

as the year progressed, and this tapering off continued into 2014. 

The largest declines occurred with the BHNE Clinic, Local Network Specialists, and UofL. The BHNE Clinic 

relationships dropped from 96 in the second quarter of 2013 to 10 in the third quarter of 2014. The Local Network 

Specialist provider relationships fell from 64 to 13 during the same time period. Likewise, the UofL relationships were 

reduced from 74 to 4 across the period. However, the overall structure of the network remained as those within the 

network continued to provide and use a variety of services. 

Similar to the previous period, sociograms were used in Period 2 to display the network relationships occurring within 

each quarter, and this visual depiction included the clients and their individual relationships with network providers. 

Clients were given a random identification number, which would allow individuals to be depicted in the social network 

without revealing their respective identities. Each client and network provider was displayed as a node, or circle, and the 

lines connecting two nodes represented the shared relationship between the client and the provider. The series of 

sociograms shows the evolution of the network over Period 2. 

Figure 8. HHC social network in Q2 2013 

Similar to the prior quarter, the second quarter of 2013 showed cohesiveness with the addition of the largest number of 

network members and relationships across the eleven quarters examined. This result points to the growing number of 
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healthcare organizations and individuals taking part in the service delivery process. A growing number of clients 

connected the different providers together, particularly the triad of the BHNE Clinic, UK Clinic, and Local Network 

Specialists. 

The density value for this sociogram was .008, which indicates that approximately 1% of the possible relationships among 

network members were captured within the network. Similar to the previous period examined, the sociogram appeared 

more cohesive with more relationships; however, an increase in the number of network members--and the potential for 

additional relationships--resulted in a relatively small density value. 

Figure 9. HHC social network in Q3 2013 

The third quarter of 2013 continued to demonstrate the network’s cohesiveness, as the network structure displayed 

stability in relation to the previous period. Again, the triad of the BHNE Clinic, UK Clinic, and Local Network Specialists 

continued to play a pivotal role in the delivery of services. While the number of clients using the services declined in this 

period, the network’s structure still revealed active usage within this group. 

The density value for the sociogram was .014, which indicates that approximately 1% of the possible relationships among 

network members were captured within the network. The density value increased, resulting from the decrease in network 

relationships occurring in this quarter. 
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Figure 10. HHC social network in Q4 2013 

Moving into the fourth quarter, the number of network members and relationships declined in relation to the previous 

quarters. However, the network’s core structure remained as the BHNE Clinic, UK Clinic, and Local Network Specialists 

offered most of the services. This existing structure revealed a continuous level of network stability, where users 

gravitated to these three groups for the bulk of their healthcare needs. 

The density value for this sociogram was .018, which indicates that approximately 2% of the possible relationships among 

network members were captured within the network. The density value increased, resulting from a decrease in network 

relationships occurring in the fourth quarter. 

 
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Figure 11. HHC social network in Q1 2014 

The first quarter of 2014 presented a network structure that differed from the previous quarters, and shared similarities 

with the structures found in the first, second, and third quarters of 2012. Here, the network presented the previous star 

formations, where clients circled around HD, UK Clinic, BHNE Clinic, and the Local Network Specialists. Additionally, 

some clients used the services of Other providers within the network. A smaller number of members were in this quarter's 

network; however, the most popular providers found in previous periods remained. 

The density value for this sociogram was .016, which indicates that approximately 2% of the possible relationships among 

network members ere captured within the network. Continuing the trend of the previous quarters, the density value 

remained similar, reflecting an additional decrease in network relationships occurring in the first quarter. 

 
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Figure 12. HHC social network in Q2 2014 

Again, the second quarter of 2014 reflected the same formation found in the previous quarter, whereby star formations 

existed around HD, Local Network Specialists, BHNE Clinic, and UK Clinic. The number of network members and 

relationships decreased, but the network’s overall structure remained with the most popular providers continuing to offer 

their respective services. 

The density value for this sociogram is .021, which indicated that approximately 2% of the possible relationships among 

network members were captured within the network. The density value increased once again, reflecting the additional 

decrease in network relationships occurring in the second quarter. 

 
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Figure 13. HHC social network in Q3 2014 

The last examined quarter revealed a similar structure to the one found in the previous two quarters. Again, the number of 

network members and relationships decreased. However, the UK Clinic, BHNE Clinic, Local Network Specialists, and 

HD provided services to these members. 

The density value for this sociogram was .029, which indicates that approximately 3% of the possible relationships among 

network members were captured within the network. The density value increased an additional time, again reflecting the 

decrease in network relationships occurring in the third quarter. 

 
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Figure 14. HHC social network for Period 2 

This final sociogram depicts the entire network at the end of Period 2. Similar to the final sociogram for Period 1, the final 

network revealed a relatively high level of cohesiveness among the network members and relationships. The number of 

new patients and relationships tapered off across the individual quarters during this period, yet the network as a whole 

remained stable and comprehensive. Clients utilized multiple services, particularly among the Local Network Specialists, 

BHNE Clinic, and UK Clinic. Additionally, HD and Other providers played roles in the service delivery process and 

remained connected to the other groups via their clients. The sociogram highlights that clients continued to take advantage 

of the network providers and services. Assuming the growth trends remain the same, one would expect the network 

structure to exhibit a similar structure going forward. 

The density value for this sociogram was .004, which indicates that less than 1% of the possible relationships among 

network members were captured within the network. Similar to the findings in the previous period, the smaller density 

value reflected the many members and relationships within the network. 

RQ2: How do patients with different needs and characteristics flow through a medical service network? 
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RQ3: How do different subgroups (e.g., cancer versus diabetic patients) flow through the network, and how can network 

density help providers understand the network? 

At this time, the sociograms and quantitative data provide somewhat limited insights into research questions 2 and 3 as 

they relate to specific client needs and characteristics as well as how they might be served or flow through this social 

network. Further data would likely be required here, potentially in the form of interviews or other information to gain 

better insights into these network characteristics and behaviors. This information would help support the existing 

sociograms and provide greater depth of analysis, certainly leading to a clearer picture of the network’s evolution and 

resource exchanges over time. The interviews and other data may also provide insights into the network’s expansion and 

contraction from the latter quarters of 2013 into to the early quarters of 2014. 

Discussion 

Overall, the data provides insights into the HHC social network from a baseline perspective beginning in the first quarter 

of 2012 and how it continued to evolve over a time period of 11 quarters. The sociograms and quantitative data offer clear 

evidence of the increase in network members and relationships, which allowed the network to grow over time. Although 

the number of new members and relationships tapered over time, the network maintained cohesion and stability. The data 

also provided insights as to which providers--in this case, BHNE Clinic, Local Network Specialists, and UK Clinic--

offered the most services to clients within the network. The network’s evolution reveals an opportunity for continued 

growth as more clients enter the network and take advantage of the myriad of services. Additionally, an opportunity exists 

for providers to collaborate more with one another as they provide a comprehensive menu of services to their clients over 

time. 

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