2012	ANNUAL	

EVALUATION	REPORT	

St. Joseph Health Systems/ Kentucky

Health One

December	2013	
2012 ANNUAL EVALUATION REPORT 

St. Joseph Health Systems/ Kentucky Health One 

December 2013 

Acknowledgements 

This report was developed under grant #10SIHKY001
Kentucky Healthy Futures Initiative 

Social Innovation Fund

2012 Annual Report for Saint Joseph Health System: 

Virtual Primary Care Delivery

I. Introduction

Kentucky is faced with significant challenges: high poverty, low health status, geographic 

barriers especially in eastern Appalachian counties, shortage of physicians and other allied 

health professionals, and a high number of uninsured citizens. 

 Beginning in 2008, to understand the scope of unmet needs in Appalachia, Saint 

Joseph Health Systems (SJHS) reviewed population and health service data available from 

delivery of care through the Eastern KY Mobile Health Service (EKYMHS), Saint Joseph Mt. 

Sterling and the Bath County Rural Health Clinic, as well as secondary data available through 

State Health Rank, United States Census and the Kentucky Institute of Medicine. This 

assessment indicated that within SJHS’ service area, the need was greatest in Wolfe and 

Powell Counties. The Kentucky Institute of Medicine Report (2007)1

, ranked Wolfe County last 

and Powell County as 112 out of the 120 Kentucky counties in overall health status; Kentucky 

ranked 44th in overall state health status in 2012.2 Additionally, in these locales the need for 

safety net services is significant. Both counties have a higher percentage of residents with 

income below the federal poverty level and without health insurance, lower primary care 

provider to population ratio than the state averages and limited access to primary and specialty 

care services. Within these two counties, chronic disease rates specifically cardiovascular 

deaths, cancer deaths, premature deaths and total mortality, exceeded state averages. 

To address these health issues SJHS applied for a 2010-2012 Foundation for a Healthy 

Kentucky grant Community Based Delivery Model: Virtual Care. The main goal was to increase 

access to primary and specialty care services by establishing two fixed-site primary care clinics, 

in Clay City/Powell County and Campton/Wolfe County. The clinics would be managed by 

Advanced Practice Registered Nurses (APRN) and virtually linked to physician specialists in 

Lexington and Mt. Sterling via telehealth technologies. With collaboration from Kentucky River 

Community Care (KRCC), the clinic in Campton was developed as an integrated 

physical/behavioral health model. 

 By developing fixed clinic sites in Wolfe and Powell counties, SJHS’s intent was to 

create a portal where individuals could have consistent availability for primary and specialty care 

without duplicating existing services. To ensure effective integration and coordination of 

services in Powell and Wolfe counties, SJHS worked with local legislative and governing 

representatives, the local health departments and mental health providers in the communities. 

 

1

 Kentucky Institute of Medicine. The Health of Kentucky: A County Assessment. Lexington, KY, 2007. 

<http://www.kyiom.org/healthky2007a.pdf>.. 

2

 United Health Foundation. America’s Health Rankings, 2012. http://www.americashealthrankings.org/KY. 
Program Logic Model

 The KHFI grant allowed SJHS to establish and test this new community-based model for 

health service delivery. In a third year continuation grant it plans to expand it to other Kentucky 

counties within its service area. Below is the logic model that was developed to describe the 

Community Based Health Delivery Model (CBHDM) and the associated outcomes. 

II. Methods

The purpose of this evaluation was to provide program staff and funders information 

about the implementation of these two rural clinics and provide preliminary evidence of 

program impact by documenting program outputs and outcomes. The research design 

called for longitudinal data collection on patient outcomes. 

 Our research study design is intended to provide preliminary levels of evidence as 

outlines in the guidance provided on behalf of the Corporation for National and Community 

Service’s Social Innovation Fund. Preliminary evaluation questions include: 

1. Who are the patients visiting the new clinics and obtaining specialty care services 

and what segments or portions of the county population are accessing services? 

2. To what extent has the new model improved primary and specialty care utilization 

and access? 
3. To what extent do patients perceive improvements in their overall health as a result 

of receiving services through the CBHDM? 

4. To what extent are patients with chronic disease better able to self-manage their 

conditions? 

5. To what extent are patients and providers satisfied with telehealth consults as a way 

to access specialty care? 

6. What are lessons learned from the implementation of this project that could be used 

to reduce barriers for future research, and produce more moderate or stronger levels 

of evidence in study design and evaluation? 

A. Data Collection 

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

reach and demographics of patients, utilization and access, health improvements, chronic 

disease management and satisfaction with telehealth consults. 

To collect information on patient demographics and utilization, data collection plans 

called for the use of both the clinic systems electronic medical record (EMR), AllScripts, and 

self-administered patient surveys completed during clinical contacts. The design allowed for the 

substitute collection of clinical service and patient demographic information from AllScripts 

collected during the patient registration process. The EMR contains information on gender, age 

and insurance coverage/payment type. Study forms included demographic information on age, 

race, ethnicity, and preferred language. The only demographic category covered by both 

AllScripts and the study forms was age. This manner of reporting using AllScripts, was initiated 

to provide quarterly data during a time when data from study forms could not be obtained, 

because study forms had not received human subjects’ approval from our contracted 

Institutional Review Board, Western IRB, in Olympia, Washington. After six quarters of reporting 

in this manner, the program continues to use AllScripts data along with data from study forms to 

maintain data consistency. 

To provide as much demographic data as possible, the program was forced to report 

some data on the entire population of patients (reach, service delivery, some demographics 

available in the AllScripts record, and other data on a much smaller patient population who 

consented to participate in the evaluation process. The initial IRB approval required the 

protection of study participants’ anonymity, and given the protocols in place, it was not possible 

in this first year to aggregate AllScripts and study form data for the patients who participated as 

planned, and we need to discuss these two groups separately. Study protocol and study 

participant identification changes have been sent to the IRB for review and approved so that in 

the future, we can link data from the two different data sources, the EMR and study protocols 

Data collection on other areas of interest included: 

 Patient survey at every visit 

 Telemedicine referring/consulting provider survey 

 Telemedicine patient survey 
 Review of program documents 

B. Patient recruitment 

To comply with IRB requirements, patients were recruited and consented to participate 

in the study. Study protocols received IRB approval on May 15, 2012 after a lengthy application 

and approval process. Patient Services began in Powell County in July 2011. Wolfe County 

clinic opened in August 2012 and began study recruitment immediately. 

During 2012, a total of 151 (of a patient base of 687) study participants were recruited. 

Powell County recruited 99 unique patients. Wolfe County recruited 52 study participants. The 

study protocol required participants to be recruited at the initial patient visit to the clinic. Data 

verification identified non-compliance with the study protocol regarding patient recruitment. 

Patients were being recruited at follow-up visits if they declined participation during their initial 

visit. Thus, recruitment percentages cannot be calculated for 2012. Additionally, due to a 

communication error no data were collected and no study participants were recruited for six 

weeks of fourth quarter. It is believed this reduced the number of study participants recruited for 

that quarter. Total study participants for 2012 are lower than expected due to these problems. 

Study protocols have been revisited and these problems corrected for 2013 data collection. 

 C. Data analysis

Data analysis was performed in SPSS. For categorical characteristics, descriptive 

statistics such as frequencies and percentages were calculated. For continuous characteristics, 

means, medians, standard deviations and ranges were used. Longitudinal plots were used to 

graph overall efficacy for each patient over time. 

D. IRB Review and Compliance 

We used the Western Institution Review Board (WIRB), Olympia, WA, as our IRB for this 

study. The Principal Investigator, Evaluator, Research Assistant, APRNs and Office 

Coordinators for Saint Joseph Primary Care Clinic – Clay City (Powell County) and Campton 

(Wolfe County) completed Human Subjects Protection training. 

All study instruments are stored in a secure and locked location. 

Our APRNs, LPNs, office staff, social worker and research assistant have completed 

statewide criminal background checks, FBI fingerprint check and National Sex Offender checks. 

The LPN at Campton had to redo the fingerprint check because of an ink smudge and we are 

awaiting the FBI clearance letter. 
III. Results 

 

A. Reach

In 2012, there were 687 unduplicated patients seen in all clinic locations. Powell County was 

operational for all 12 months of 2012 and received a total of 562 unique patient visits and 1182 

total visits. Wolfe County clinic opened in August, and saw patients for 5 months of the data 

collection period covered by this report. Wolfe County reported 141 unique patients in 395 visits. 

Sixteen patients were seen in both clinics, thus the total unduplicated patients for all clinics is 

687. Table 1 provides reach information from patients seen in both clinics combined. 

Table 1. Clinic Reach

Indicator Powell Wolfe All

Total number of patients reached 562 141 687

Total number of patient visits 1182 395 1577

Number of telemedicine consults completed 2 1 3

Number of study participants enrolled 99 52 151

Number of study participants with disease of interest (CHF, 

COPD, Pneumonia, CAD, Diabetes, AMI or Cancer)

46 23 69

Number of study participants completing the chronic disease 

self-efficacy scale. 

33 8 41

Implementation of telemedicine services was delayed through the end of 2012. Only a 

few telemedicine consults were held to test the system and equipment. The absence of 

telemedicine services significantly lowered the anticipated return patient visits that would have 

likely been for telemedicine consultations with specialty providers. We were not able to track our 

patients’ follow-up or specialty care visits at other facilities in this evaluation. 

As discussed above, the clinics enrolled 151 patients in the study. Of those, there were 

69 that had chronic diseases of interest, which made them eligible to complete the chronic 

disease self-efficacy scale. 41 people completed the self-efficacy scale—59% of those who 

were eligible to take it. 

All attained year-to-date reach numbers are below anticipated projected year values. At 

the Powell County Clinic, health issues from the APRN affected number of patients seen at the 

clinic for second quarter. The Wolfe County Clinic didn’t open until August, which gives only 5 

months of data for 2012. As stated above, all/returning patient visits were significantly lower 

than anticipated due to telemedicine delays, which was anticipated to lead to a number of 

follow-up appointments. Based on the steady growth experienced by the clinics, and the steady 

growth seen in the other clinic, it is reasonable to assume that the projected values would have 

either been attained or much closer had the telemedicine program gotten started and the Wolfe 

County Clinic had opened sooner. 

In regards to recruiting study participants, the IRB had not approved study forms until 

end of second quarter, thus study data was not able to be attained until start of third quarter. 

Additionally, in the Powell County Clinic, six weeks of data was not collected due to a 

communication error with clinic staff. 
Clinic staff has been retrained in study protocols to prevent data collection errors going 

forward. Additionally, recruitment methods were addressed to assist in increasing participation. 

Some modifications were made to the study forms by reducing the number of questions in the 

surveys. Additionally, the Chronic Disease Self-Efficacy scale was reduced from 32 to 6 

questions. These changes have been submitted to IRB for review. Until IRB approval is 

attained current study forms will continue being utilized for data collection. 

B. Patient Demographics

1. Age, Gender and Income - All patients (n=687)

Table 2. Age, Gender, and Income of Patients – Both Clinics

Reporting Period: January 1 - December 31, 2012

Variable Response options Patients Distribution All Visits Distribution -

All Visits

Visits/ 

person

Gender

Male 317 46.1% 708 44.9% 2.2

Female 370 53.9% 869 55.1% 2.3

Total 687 100.0% 1577 100.0%

Age

< 5 years 22 3.2% 42 2.7% 1.7

5 - 18 years 55 8.0% 128 8.1% 2.3

19 - 39 years 208 30.3% 391 24.8% 1.9

40 - 64 years 324 47.2% 808 51.2% 2.5

65+ years 78 11.4% 208 13.2% 2.7

Total 687 100.0% 1577 100.0%

Insurance/ 

Payment

Type

Private Insurance 183 26.6% 372 23.6% 2.0

Medicaid 127 18.5% 284 18.0% 2.2

Medicare 125 18.2% 377 23.9% 3.0

Self-pay (no insurance) 252 36.7% 544 34.5% 2.2

Total 687 100.0% 1577 100.0%

Table 3. Age, Gender and Income of Patients – Individual Clinics 

Reporting Period: January 1 - December 31, 2012

Patients

Powell

Clinic Visits

Powell

Visits/

period

Patients

Wolfe

Clinic Visits

Wolfe

Visits/

period

Gender

Male 259 522 2.0 66 186 2.8

Female 303 660 2.2 75 209 2.8

Total 562 1182 NA 141 395 NA

Age

< 5 years 15 27 1.8 7 15 2.1

5 - 18 years 47 118 2.5 8 10 1.2

19 - 39 years 166 288 1.7 42 103 2.4

40 - 64 years 264 560 2.1 75 248 3.3

65+ years 70 189 2.7 9 19 2.1

Total 562 1182 NA 141 395 NA

Paym

ent

Type

Private Insurance* 165 338 2.0 18 34 1.9

Medicaid 99 208 2.1 28 76 2.7
Medicare 106 295 2.8 22 82 3.7

Self-pay (none)* 192 341 1.8 73 203 2.8

Total 562 1182 NA 141 395 NA

 The patient base at the clinics consists of slightly more female than male patients which 

is not significantly different from the gender distribution in the two counties. Of the 687 

patients, 317 (46.1%) were male and 370 (53.9%) were female. However, women are more 

likely to have multiple clinic visits in the reporting period. Of the 1577 clinic visits, 708 were 

male and 869 were female (2.3 visits for women and 2.2 visits for men over the period). 

Females were significantly more likely to have returned for multiple visits over the year. In 

every quarter for all clinics there are more female patients than male patients, showing this 

is a stable pattern within the gender demographics. 

 The adult population aged 19-64 included 76.0% of all visits. Children, birth to 18 years, 

represent 10.8% and elderly, 65 years or older, represent 13.2% of all visits. These 

percentages are stable across each quarter and each clinic, representing a stable pattern of 

age demographics. Figure 1 shows the distribution of visits/patient by age for the Powell 

Clinic, Wolfe Clinic, and combined. We can conclude that across both clinics, adults aged 19-

64 are the predominant age demographic of our patient base and that as age increases 

patients are more likely to return to the clinic for multiple visits throughout the year with the 

exception of the 19-39 year age group, which is less likely to return for multiple visits.

 

0

0.5

1

1.5

2

2.5

3

3.5

< 5 years 5 - 18

years

19 - 39

years

40 - 64

years

65+ years

Visits/Patient-Powell

Visits/Patient-Wolfe

Visits/Patient-All

The insurance/payment type category was limited to the primary insurance for each 

patient. Cases with no insurance coverage were classified as self-pay. Overall, 36.7% of 

patients are Self-pay/uninsured, 36.7% have Medicaid or Medicare, and 26.6% have private 

Figure 1: Patient Visits by Age 
insurance. These percentages fluctuated slightly per quarter and per clinic. Looking at “all visits” 

shows that patients with Medicare are more likely to return for multiple visits in a year while 

those with other insurance types are slightly less likely to have multiple visits. 

Combining all clinic insurance and payment type can mask the differences in specific 

counties. As shown in Figure 2, there are differences in payment type between the two clinics. 

Wolfe County has significantly lower private insurance and significantly higher self-pay/no 

insurance compared to Powell County. While 52% of the patient base in Wolfe County is 

uninsured, in Powell County it is 34%. The differences between areas served are important to 

identify to determine areas of program service enhancement, growth and improvements. 

Different types of insurance relate to differences in patient visits at the clinic level, even 

with low-barriers and low-cost. The Medicare population is either disabled or over the age of 65, 

making it the oldest, and in this region also the most burdened with disease. Those patients with 

Medicare averaged three visits to a clinic in the period, the most of any group, with Medicare 

patients in Wolfe County having the most visits overall, on average 3.7 visits. In fact, with the 

exception of Wolfe County patients with insurance, the patients in the Wolfe Clinic had more 

visits than patients of the Powell County clinic regardless of insurance/payment type. 

 

Figure 2: Insurance type by Clinic 

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Private

Insurance

Medicaid Medicare Self-pay/No

Insurance

Powell

Wolfe

Figure 3: Visit Frequency by Insurance Type 

0

0.5

1

1.5

2

2.5

3

3.5

4

Private

Insurance

Medicaid Medicare Self-pay/No

insurance

Number of Visits 

Insurance Type 

Powell

Wolfe

All Clinics
1. Additional Demographics – Study participants (n=151) 

Table 4, below, provides demographic information for both clinics from patients who 

agreed to participate in the evaluation process (n=151) for patients and visits. As previously 

mentioned, these patients are a subset of the clinic population, but for them self-reported race, 

ethnicity, language, education level, and number living in the home was collected. Table 3 offers 

counts and distributions of patients by demographic categories not available through the EMR 

or available clinic records. These individuals cannot be matched to themselves in the 

population-level tables. 

Table 4. Additional Demographics for Study Participants 

Variable Response options Patients Distribution Patient Visits Distribution 

Patients Patient Visits

Age

< 5 years 3 2.0% 3 1.4%

5 - 18 years 10 6.6% 12 5.6%

19 - 39 years 42 27.8% 54 25.2%

40 - 64 years 82 54.3% 127 59.3%

65+ years 13 8.6% 16 7.5%

Missing 1 0.7% 2 0.9%

Total 151 100.0% 214 100.0%

Race

White/Caucasian 143 94.7% 205 95.8%

Black/African-American 0 0.0% 0 0.0%

Native American/Hawaiian/API 2 1.3% 2 0.9%

Mixed race (biracial, etc.) 1 0.7% 1 0.5%

Other 4 2.6% 4 1.9%

Missing 1 0.7% 2 0.9%

Total 151 100.0% 214 100.0%

Ethn.

Hispanic 3 2.0% 3 1.4%

Non-Hispanic 148 98.0% 211 98.6%

Missing 0 0.0% 0 0.0%

Total 151 100.0% 214 100.0%

Language

English 148 98.0% 210 98.1%

Spanish 0 0.0% 0 0.0%

Other 1 0.7% 1 0.5%

Missing 2 1.3% 3 1.4%

Total 151 100.0% 214 100.0%

Income per Year

Less than $10,000 75 49.7% 115 53.7%

$10,000 - $10,999 8 5.3% 13 6.1%

$11,000 - $20,999 25 16.6% 36 16.8%

$21,000 - $30,999 13 8.6% 15 7.0%

$31,000 - $40,999 11 7.3% 13 6.1%

Over $41,000 12 7.9% 13 6.1%

Missing 7 4.6% 9 4.2%

Total 151 100.0% 214 100.0%

Education

Finished Elementary School 38 25.2% 48 22.4%

Finished High School 88 58.3% 132 61.7%

Finished College 11 7.3% 11 5.1%

Finished Graduate School 5 3.3% 6 2.8%

Finished Vo-Tech School 7 4.6% 14 6.5%

Missing 2 1.3% 3 1.4%

Total 151 100.0% 214 100.0%

Employment

Work Full-Time Now 40 26.5% 52 24.3%

Not Working Full-Time Now 42 27.8% 52 24.3%

Disabled and Cannot Work 49 32.5% 82 38.3%

Retired 11 7.3% 13 6.1%
Work Part-Time Now 7 4.6% 11 5.1%

Missing 2 1.3% 4 1.9%

Total 151 100.0% 214 100.0%

# Living at Home

Only 1 Person 29 19.2% 49 22.9%

2 People 57 37.7% 86 40.2%

3 People 31 20.5% 37 17.3%

4 People 19 12.6% 23 10.7%

5 People 6 4.0% 6 2.8%

6 or More People 8 5.3% 10 4.7%

Missing 1 0.7% 2 0.9%

Total 151 100.0% 214 100.0%

The one variable that was collected for both groups was age. Among the study 

participants, the adult population aged 19-39 includes 27.8% of participants (N=42) compared to 

30.3% in the entire patient base aged 19-39 (N=208). Among the study participants, the adult 

population aged 40-64 includes 54.3% of unique participants (N=82) compared to 47.2% in the 

entire patient base aged 40-64 (N=324). Children, birth to 18 years, represent 8.6% of study 

participants (N=13) and 11.2% of the entire patient base (N=77). Elderly, 65 years or older, 

represent 8.6% of study participants (N=13) and 11.4% of the entire patient base (N=78). 

These data were subject to a comparison of proportions test to determine if the two 

groups were comparable based on age. In those cases where there was sufficient data for the 

test, the difference in the proportions of 19-39 and 40-64 year olds groups in both samples are 

not statistically different at a 95% level. The other age categories do not contain enough people 

to calculate the significance of their proportional differences. Though not identical, this is 

reflecting acceptable representation from these age ranges in the proportions they are found in 

the patient base. 

Race was self-identified by study participants. There is no statistically significant 

difference between study participant base and those returning for multiple study visits. 

Approximately 95% participants identified as White/Caucasian and 5% identified from another 

race group in rates similar to the racial distribution in of the counties themselves. The US 

Census lists the White/Caucasian percentage as 98.0% for Powell County and 99.0% for Wolfe 

County. Therefore, the percentage of patients’ races is representative of the counties served. 

Ethnicity was self-identified by study participants: 3 Hispanic (2.0%) and 148 NonHispanic (98.0%). Total study visits include: 3 Hispanic (1.4%) and 211 Non-Hispanic (98.6%). 

The U.S. Census reports Hispanic ethnicity as 1.1% in Powell County and 0.5% in Wolfe 

County. Therefore, the low percentages of 2.0% Hispanic patients in the study base and 1.4% 

returning study visits are representative of the counties served. 

Preferred language was self-identified by study participants. 98% of patients reported 

speaking English as their preferred language. Total study visits also were about 98% in 

English. The U.S. Census reports language other than English spoken at home as 1.1% in 

Powell County and 0.3% in Wolfe County. Therefore, the high percentage of 98% English 

speaking patients for the study and all study visits is representative of the counties served. 

There is no significant statistical difference between the study base and all study visits. 
Income was self-reported by study participants. About half of study participants (49.7%) 

and a slight majority (53.7%) of the study visits were among patients reporting an income of 

“less than $10,000/year.” In fact, patients and visits in this income group exceeded all other 

categories combined excluding the missing responses. Participants in the three lowest income 

categories (Income < $21,000/year) are slightly more likely to have multiple study visits than the 

three upper income categories (Income >= $21,000/year). This is an indication that some of the 

poorest residents in the county are seeking care at this clinic. However, it should be noted that 

the survey question did not specify whether “income” was defined as household income or 

individual income, so the results are somewhat ambiguous. 

 Education was self-reported by study participants. In the base study population more 

participants only finished elementary school (25.2%) than all participants who had attained any 

college, graduate or Vo-Tech education past high school (15.2%) excluding missing responses. 

The study base population identified 83.5% with high school education or less. Study 

participants who finished high school or Vo-Tech school were slightly more likely to return for 

multiple study visits than all other categories. 

 Employment was self-reported by study participants. Roughly a third of the study base 

reports they are disabled (32.5%). Slightly over a quarter of the study base is not working fulltime or is unemployed now (27.8%). Participants in all employment categories are slightly less 

likely to return for study visits except for the disabled and cannot work category. 

1. Location of Patients – All patients 

Figure 4 provides zip code information from unique patients seen in both clinics. The all 

visits category can give a rough estimate when compared to the patient base to identify which 

zip codes are generating returning patients and those utilizing the clinics most frequently. 

One of our goals in this evaluation was to determine where our patients lived to 

understand distance to access to primary and specialty care. Although these clinics are at 

present only able to provide primary care, we collected baseline zip code data on all our 

patients. Zip code data was collected during the registration process in AllScripts. 

Overall, patients utilizing the clinics came from 43 different Kentucky zip codes3.Only 6 

patients came from outside Kentucky. A majority (60.3%, n= 414) of both clinics’ patients reside 

in two Powell County zip codes (40312 and 403804). An additional 12% (n=84) reside in 41301 

(Campton) which is largely in Wolfe County.5 The only other zip code with more than 3% of 

patients was 41472, West Liberty, in Morgan County. For all visits the same three zip codes 

were the most common. 

 

3

In Kentucky, zip codes are not limited to a single county and a single county can have multiple zip codes

4

In addition to Powell county, 40380 also includes parts of Estill County 

5

Also includes parts of Morgan and Powell Counties. 
Figure 4: Patients & Clinic Visits by Zip Code

40312 40380 41301 41472 40391 41425 41332 40337 40353

All

Other

KY

Other

states

Clients 39.3% 21.0% 12.2% 4.7% 2.2% 1.9% 1.7% 1.7% 1.6% 12.8% 0.9%

Visits 37.9% 22.1% 16.0% 3.0% 1.5% 1.9% 1.6% 1.3% 2.0% 11.9% 0.8%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Percent

Kentucky counties were assessed for recurring clinic visits to see where patients were 

traveling from the most frequently. See Table 5. 

Table 5: Counties with Multiple Patient Visits 

Counties with > 2 visits /patient Counties with > 4 visits/patient

 Bath 

 Breathitt 

 Clark 

 Estill 

 Fayette 

 Franklin 

 Jackson 

 Lawrence 

 Morgan 

 Powell 

 Wolfe 

 Woodford

 Clark 

 Estill 

 Fayette 

 Franklin 

 Morgan

2. Disease status – study participants 

 

Baseline data relating to disease status was defined as having or ever having had a 

diagnosis of seven conditions of interest: Congestive Heart Failure (CHF), Chronic Obstructive 

Pulmonary Disease (COPD), pneumonia, Coronary Artery Disease (CAD), diabetes (DM), Acute 

Myocardial Infarction (AMI) or any cancer. Each disease was calculated to determine what 

percentage of the study population reported each condition. It is important to remember that 

individuals can have more than one disease. Calculations were then made to identify total 

number of chronic diseases reported overall and number and percent of patients who reported 

any chronic disease. 

Almost half (46%) of the study population reported having at least one disease (see 

Table 6; of those 69 individuals, they reported 113 total diseases. The most common diseases 
reported were pneumonia (21%), diabetes (18%), and COPD (11%). Multiple study visits were 

more likely among CHF, Pneumonia, COPD, CAD and AMI patients and less likely for cancer 

patients. Overall, among study participants, those with these illnesses did not have any more 

frequent visits to the clinics than those who did not report illness. However, it is likely that the 

severity of disease in the sample is lower than the severity of illness among the total population 

with any disease, which may account for the parity in frequency. 

Table 6: History or Presence of Disease

Disease Patients % Patients Visits % of visits

CHF 11 7.3% 19 8.9%

COPD 17 11.3% 28 13.1%

Pneumonia 31 20.5% 49 22.9%

CAD 3 2.0% 8 3.7%

Diabetes 27 17.9% 37 17.3%

AMI 10 6.6% 18 8.4%

Cancer 14 9.3% 17 7.9%

Total # Chronic Disease Reports 113 N/A 176 N/A

# Patients ANY Chronic Disease Present 69 45.7% 98 45.8%

 

 

A. Access to Care & Medications

 In the long-term, our goal is for all residents in Wolfe & Powell counties to have access 

to coordinated care, at an appropriate level, close to home. We have seen preliminary evidence 

of this in the stories we have provided in our quarterly reports. The examples illustrate how 

patients have access to primary care and the APRN directs that patient to the next level of care 

needed, whether it is an emergency room visit (and in some cases calling the ambulance to pick 

up the patient at the primary care clinic) or obtaining an in-person or telehealth specialty 

consultation for the patient. To quantitatively assess improvements in access to care, the 

evaluation looked at distance traveled to appointments and prior health care utilization. The 

evaluation also looked at access to needed medications. 

1. Distance traveled 

Distance travelled to care has been identified as a barrier to access. Distance travelled 

for each clinic was calculated by taking the actual street address (no post office boxes used for 

mailing addresses) of all patients and mapping the distance to the clinic they attended. All 

patients for two quarters were calculated and average distance travelled to each clinic by zip 

code was determined. These averages are considered “unadjusted” in Table 7. Average 

distance travelled per unique patient were 3.0 miles for Powell County (Clay City), 7.1 miles for 

Wolfe County (Campton) and all clinics combined averaged 5.1 miles. Average distance 

travelled per visit were 11.4 miles for Powell County (Clay City), 20.6 miles for Wolfe County 

(Campton) and all clinics combined averaged 16.0 miles. 

Of note, several patients from Lexington and the surrounding cities “followed” the 

APRNs from previous employment to Powell and Wolfe Counties. These patients raised the 

average for distance travelled significantly. Removing these patients from the average reduces 

the average distance travelled as follows: Powell County unique patient from 3.0 to 1.8 miles, 

Powell County per visit from 11.4 to 9.4, Wolfe County unique patient from 7.1 to 3.8 miles, 
Wolfe County per visit from 20.6 to 11.6, All clinics combined averages change per unique 

patient from 5.1 to 2.8 and per visit from 16.0 to 10.5. These patients report addresses for other 

states, Tennessee, or distant counties in Kentucky and these patients would not normally be 

utilizing these clinics. At this time, no adjustments can be made for individual patients attaining 

care at the clinics who are temporarily in the area due to “following” their provider or while 

visiting the area where clinics are located. 

Reporting 

Table 7: Distance 

Period: January 1 -

Traveled

December 31, 2012

Program 

area

Unadjusted 

average 

distance 

travelled 

per unique 

patient

Adjusted 

average 

distance 

travelled 

per unique 

patient

Unadjuste

d average 

distance 

travelled 

per visit

Adjusted

average 

distance 

travelled 

per visit

Year to 

date miles 

travelled 

for all 

visits

Year to date 

estimated 

travel cost 

($0.55 per 

mile) for all 

visits

Access - Travel and Cost

Powell 

County 

(Clay City)

3.0 1.8 11.4 9.4 13483.2 $7,415.76

Wolfe 

County 

(Campton)

7.1 3.8 20.6 11.6 8117.7 $4,464.74

All

5.1 2.8 16.0 10.5 21600.9 $11,880.50

 Average cost of travel was calculated for each clinic individually and an all clinics total. 

Travel was estimated at $0.55 a mile. Miles travelled for each clinic was calculated by taking the 

average miles travelled per zip code and multiplying by number of patients reporting an actual 

street address (no post office boxes) for that zip code for all visits. Powell County (Clay City) 

year to date total for 2012 was 13,483.2 miles equating to $7,415.76 in travel costs for patients. 

Wolfe County (Campton) year to date total for 2012 was 8,117.7 miles equating to $4,464.74 in 

travel costs for patients. All clinics year to date total for 2012 was 21,600.9 miles equating to 

$11,880.50 in travel costs for patients. 

 The intention of this outcome measure was to compare the distance to the primary care 

clinic for a telehealth consultation, to the distance that would have otherwise been traveled to a 

face-to-face visit to a specialist. This comparison would provide cost savings in 

mileage/transportation costs and time, as well as increased access to specialty care. Because 

of the delay in telemedicine implementation, this outcome measure only shows distance 

traveled/access to care to the primary care clinic. 
2. Access to Specialty Care

Telemedicine implementation was delayed in all clinics through the end of 2012 due to 

equipment and connectivity issues. Increased bandwidth was needed to support telemedicine 

implementation, a process requiring extensive negotiations and new lines to be installed. The 

increase in bandwidth was necessary because of the unforeseen implementation of the 

AllScripts Medical Record, which utilized all of the bandwidth that had been planned for 

telemedicine consultations. Despite these delays each clinic had 1-2 telemedicine consultations 

to test the equipment and identify other potential issues. Changes were made in the equipment 

to facilitate better patient care. At the end of 2012, the telemedicine implementation was moving 

forward toward implementation. Distance traveled will become an important access marker 

once the telemedicine component is implemented. Travel to specialty providers from Powell and 

Wolfe counties currently involves a significant amount of travel that will be saved through 

utilizing the telemedicine capability of the local clinic. 

Many of the other short-term outcomes which were identified in original SEP have not been 

evaluated because of delay in telemedicine implementation. These outcomes will be addressed 

in the 2013 Annual Report. They include:

o Increased utilization of appropriate specialty care;

o Greater efficiency of service through:

 reduced wait time (compared with wait to see specialists face-to-face); 

 reduced travel and costs to specialists for patients; 

 greater patient compliance with appointments 

 appropriate patient transfer to different levels of care; 

3. Prior Health Care Utilization

From this evaluation, we have realized that it is more difficult to obtain reduced 

emergency room visits and readmission rates, since patients can go to many different ERs and 

Hospitals, and unless they return to the primary care clinic, we are unable to obtain this 

information. We can only report a patient’s self-report on number of times of ER visits and

hospital readmissions at their first clinic visit, and any additional self-reported information at their 

returning primary care visits. Table 8 provides data on self-reported health care utilization over 

the past 12 months. 

The last doctor visit was utilized to determine length of time since the patient last visited 

a medical provider for primary care. Almost 70% of study participants reported seeing a doctor 

within the past 12 months. An additional 17% had seen a doctor within the last 24 months. 

There was a slightly higher likelihood a participant would have multiple visits if they had last 

seen a doctor in 0-1 months. One reason for this may be that patients with a chronic disease 

require more care than those patients who do not have a chronic disease.

Specialty care utilization was identified by asking how many times a patient had seen a 

specialist within the last 12 months. Roughly 40% of the study participants utilized specialty 
providers within the last 12 months, with 10% having had 5 or more visits. Multiple study visits 

were less likely for participants who had not seen a specialist in the past 12 months. 

Emergency care visits were identified by asking low many ER visits the patient had within the 

last 12 months. Roughly 41% of the study participants had utilized the emergency room within 

the last 12 months, with half of those people only having one visit. 

 Hospital admissions were identified by asking if the patient had an overnight hospital 

stay within the last 12 months. Roughly 20% of the study participants reported a hospital 

admission at least overnight within the last 12 months. Participants were more likely to have 

multiple clinic visits if they had one or more than 5 overnight hospital admissions within the last 

12 months. Overnight hospital stay ER Visits within 12 Specialist Visit within 12 Last Doctor Visit within 12 months months months

Table 8: Health Care Utilization – Study Participants 

Distribution Distribution Response options Patients All Visits patients Visits

0 - 1 Month 26 17.2% 41 19.2%

1 - 3 Months 37 24.5% 50 23.4%

4 - 6 Months 21 13.9% 29 13.6%

7 - 11 Months 19 12.6% 27 12.6%

12 - 24 Months 26 17.2% 37 17.3%

More than 36 Months 17 11.3% 22 10.3%

Missing 5 3.3% 8 3.7%

Total 151 100.0% 214 100.0%

No Visits (0 Times) 88 58.3% 114 53.3%

1 Visit 16 10.6% 25 11.7%

2 Visits 14 9.3% 18 8.4%

3 Visits 9 6.0% 14 6.5%

4 Visits 5 3.3% 9 4.2%

5 or More Visits 15 9.9% 23 10.7%

Missing 4 2.6% 11 5.1%

Total 151 100.0% 214 100.0%

No Visits (0 Times) 79 52.3% 108 50.5%

1 Visit 35 23.2% 42 19.6%

2 Visits 19 12.6% 29 13.6%

3 Visits 7 4.6% 12 5.6%

4 Visits 6 4.0% 7 3.3%

5 or More Visits 2 1.3% 5 2.3%

Missing 3 2.0% 11 5.1%

Total 151 100.0% 214 100.0%

No Visits (0 Times) 120 79.5% 162 75.7%

1 Visit 20 13.2% 30 14.0%

2 Visits 6 4.0% 8 3.7%

3 Visits 0 0.0% 0 0.0%

4 Visits 0 0.0% 0 0.0%

5 or More Visits 1 0.7% 3 1.4%

Missing 4 2.6% 10 4.7%

Total 151 100.0% 214 100.0%

 

It will be useful, in 2013, to compare study participants on all health care utilization 

questions to see if the number of overnight hospital admissions, emergency room visits, and 

length of time since having seen a physician for primary care decreases as patients have 

increased access to primary care. The data collected in 2012 from these study participants have 

established a baseline for comparison in future year end reporting. 
4. Access to Medication 

 The Social Worker rotated between the two primary care clinics beginning 3rd Quarter 

2012, and helped patients at Powell and Wolfe county clinics complete paperwork and obtain 

medications they need. Table 9 shows the reach of the Social Worker and the assistance 

provided to obtain medications. 

The Social Worker assisted 9 (38%) more patients at SJPCC-Campton in the 4th quarter 

than the previous, and was able to double the drug assistance orders to help patients receive 

the medications that they need, which was estimated to be a 79% increase in value for cost of 

drugs provided. 

The Social Worker assisted 19 (20%) more patients at SJPCC-Clay City in the 4th

quarter than the previous, and processed 5% more drug assistance orders to help patients 

receive the medications that they need. 

 The Social Worker also helped 50 patients in Campton and 55 patients in Clay City obtain 

Financial Assistance during the 2012 4th quarter. 

Table 9. Social Worker Assistance for Obtaining Medications 

Clinic 3rd 4th 3rd 4th Quarter 3rd 4th

Quarter Quarter Quarter Number of Quarter Quarter 

number number Number of Drug AWP AWP 

of of Drug Assistance Value Value

patients patients Assistance Orders 

Orders 

Campton (opened 

rd in 3 Quarter/2012)

246 33 82 164 $ 52,022 $ 93,078

Clay City (7/1-

12/31/2012) 

95 114 321 337 $197,386 $197,582

TOTAL 119 147 403 501 $249,408 $290,660

B. Health Improvement

Health outcomes were calculated by asking study participants to self-report their overall 

health status on a scale of 1=Excellent to 5=Poor. Powell County (Clay City) reported average 

overall health was 3.23. Wolfe County (Campton) reported average overall health was 3.85. All 

clinics combined reported average overall health was 3.54. Some variation was seen between 

quarters by clinic. As new patients enter the clinic who have not had access to care it is 

expected this average will be lower. Due to Wolfe County not opening until August it is not 

unexpected to see the average self-reported health to decrease rather than improve as new 

patients gain access. Though overall a slight improvement in overall self-reported health was 

 

6

 Campton Clinic opened on August 7, 2012. 3rd Quarter numbers reflect the period when a social worker was 

available to Wolfe County patients. 
seen it is inaccurate to combine all clinics into a single average due to their being in different 

stages of clinic growth and establishing a patient base. It is expected to see gradual 

improvement in these health outcomes through 2013 for both clinics. (See Table 10.) 

Study participants are more likely to have multiple study visits if their perceived health 

status is fair or poor. Due to the significant percentage of missing responses these percentages 

are of questionable validity and reliability. To address this issue the study form has been 

changed (pending IRB approval) to place this question at the top, rather than the bottom of the 

survey instrument to facilitate response completion. 

Table 10: Self-reported Health Status 

Health Status Patients Distribution

patients All Visits DistributionVisits

Visits/person

Excellent 8 5.3% 9 4.2% 1.12

Very Good 16 10.6% 17 7.9% 1.06

Good 30 19.9% 40 18.7% 1.33

Fair 49 32.5% 76 35.5% 1.55

Poor 16 10.6% 24 11.2% 1.50

Missing 32 21.2% 48 22.4% 1.50

Total 151 100.0% 214 100.0% 1.42

C. Ability to manage chronic disease 

Study participants self-identified presence of chronic disease by reporting that they had 

ever had, or currently have any of the following: CHF (congestive heart failure), COPD (chronic 

obstructive pulmonary disease), pneumonia, CAD (coronary artery disease), diabetes, AMI 

(acute myocardial infarction) or cancer. These participants were then asked to complete the 

Chronic Disease Self-Efficacy form at their initial and each subsequent visit. 

There were 35 patients who made at least 1 visit to either the Clay or Campton clinic and 

filled out the self-efficacy survey during the 3rd and 4th quarters of 2012. Of these 35 patients, 6 

had a second visit during that same period, and 3 of those 6 patients had a third visit during that 

period. 

Figures 5a and 5b plot the sum of the reduced 6-Item Scale for each patient visit (see 

http://patienteducation.stanford.edu/research/secd6.html). These 6 items are: 

1) How confident are you that you can keep the fatigue caused by your disease from 

interfering with the things you want to do? 

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? 

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? 

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? 
5) How confident are you that you can do the different tasks and activities needed to 

manage your health condition so as to reduce you need to see a doctor? 

6) How confident are you that you can do things other than just taking medication to reduce 

how much you illness affects your everyday life? 

Each of these items is on a scale from one to 10, with the lower end of the scale 

representing “Not at all confident” and the upper end of the scale representing “Totally 

Confident”.

Figure 5a and 5b plot the sum of the 6-item scale by patient and date of patient visit. Figure 

5a plots each patient in a separate panel, and Figure 5b shows all the patients and visits in one 

panel. Patients with only one visit are shown as dots. Lines are used connect multiple visits for 

patients who had more than one visit. 

Figures 5c and 5d are similar to Figures 1a and 1b respectively, except that the first visit for 

each patient is set at time 0, and each successive visit for a patient is shown as the difference in 

days from the first visit. 

Tables 11a shows descriptive statistics (mean, standard deviation, median, minimum, and 

maximum) for the sum of the 6 self-efficacy measures for each visit, and Table 11b shows the 

same descriptive statistics for the time from the first visit (in days) for the second and third visits. 

With only 6 patients that reported self-efficacy scores making more than one visit, it is not 

meaningful to conduct statistical analysis to assess changes in overall scores for this period. 

Also, since visits for each patient do not follow a regular pattern for time between visits, (i.e. a 

visit every 3 months), it is not advisable to try to assess or partition the data into 3 month 

periods. For example, the maximum time from the first to the second visit was 181 days, and the 

minimum was 8 days (Table 11b). 
Figure 5a: Chronic Disease Management 

Figure 5b: Chronic Disease Management
Figures 5a and 5b plot the sum of the reduced 6-Item Scale by Date of Visit for Each Patient; 3rd

and 4th Quarter 2012. Figure 5a has separate panels for each patient. Figure 5b shows all 

patient scores in the same plot. A, B, and C represents visits A, B and C for a patient 

respectively. ID’s to the left of each dot are patient ID. Patients with only one visit are shown as 

dots. Lines are used connect multiple visits for patients who had more than one visit. 

Figure 5c: Chronic Disease Management 
Figure 5d: Chronic Disease Management 

Figures 5c and 5d plot of the sum of the reduced 6-Item Scale by Date Difference (“A”

visit set at time 0) for Each Patient (3rd and 4th Quarter 2012). . Figure 5c has separate panels 

for each patient. Figure 5d shows all patient scores in the same plot. A, B, and C represents 

visits A, B and C for a patient respectively. ID’s to the left of each dot are patient ID. Patients 

with only one visit are shown as dots. Lines are used connect multiple visits for patients who 

had more than one visit. 

Table 11a: Descriptive Statistics for the sum of the reduced 6-Item Scale by Visit for Each 

Patient ; 3rd and 4th Quarter, 2012.

Visits Count Mean Stand. 

Dev.

Median Min Max

A 36 37.9 16.1 42 6 60

B 6 38.2 15.4 41.5 14 59

C 3 38.7 14.5 39 24 53

Overall 43 38.0 15.6 41.5 6 60

Only 6 patients had more than one visit (the A visit). Of those 6 patients, only 3 patients 

had a third visit over the 2012 3rd and 4th quarter period. 
Table 11b: Descriptive Statistics for Length of Time in Days of B and C visits from first 

visit; 3rd and 4th Quarter, 2012 

Visits Count Mean Stand. 

Dev.

Median Min Max

B 6 78.3 61.2 80.5 8 181

C 3 74 33 74 41 107

Health behavior addresses a patient’s ability to perform individual behaviors related to 

exercising regularly, understanding their disease condition, obtain help from community, family 

or friends and communicate with their physician. Baseline score was 7.22 and 3 month followup was 8.22. Baseline revealed a moderate to high degree of self-efficacy in patients. 

Symptom control addresses a patient’s ability to manage their disease, manage their 

symptoms and manage any shortness of breath. Baseline score was 6.10 and 3 month followup was 7.42. Baseline scores reveal a need for symptom control in patients and a moderate 

degree of self-efficacy. 

Role function addresses a patient’s ability to do household chores, take care of 

themselves and dependents as necessary and to engage in social and recreational activities. 

Baseline score was 5.73 and 3 month follow-up was 6.45. Baseline scores reveal a need for 

role function improvement in patients and a moderate degree of self-efficacy. 

 Emotional functioning addresses a patient’s ability to control or manage depression 

regarding their chronic disease. Baseline score was 5.64 and 3 month follow-up was 7.00. 

Baseline scores reveal a need for emotional functioning improvement in patients and a 

moderate degree of self-efficacy. 

 Improvement in self-efficacy scores were seen in all four categories from baseline to 

three month follow-up. However this result is based on small numbers and a short time period 

so it is premature to call this an established trend. It will be important in 2013 to continue 

analysis of these values to identify stable trends. It is expected to see further improvement as 

time passes and more participants are included in the data for analysis. It will also be useful in 

2013 to track self-efficacy scores overtime to see if access to primary care leads to greater selfefficacy. The data collected in 2012 from these study participants have established a baseline 

for comparison in future year end reporting. 

D. Satisfaction with telehealth consultation

 As mentioned above, telemedicine implementation was delayed. Three telehealth 

consults were held to test the system. Instruments have been developed to assess satisfaction 

with telehealth consultation and to track referrals to the telehealth consult, and will be used as 

the program is fully implemented. 

 
IV. Discussion 

 Despite many challenges with the program and evaluation, we have established two new 

access points for primary care in two underserved communities and overcome significant 

obstacles to establish the infrastructure for telehealth specialty consultation. 

 The two clinics served 687 patients during 2012, and we’ve collected baseline data on 

many indicators that will be used to measure changes in access, health status, and ability to 

manage chronic disease. 

 We have tested a model for delivering care through a Community Based Health Delivery 

Model which will be spread to other parts of the state and help us prepare for the 

implementation of the Affordable Care Act, when we will have 280,000 covered lives with new 

medial cards who are seeking access to health services. Because of the successful 

establishment of these two clinics, we have received a continuation grant to build out the model 

to two additional sites. 

This project proposed to establish a new integrated model of health services delivery 

between primary and specialty care services at the local patient level in two primary care clinics 

and establish a new integrated physical health and behavioral health model in one of the 

primary care clinics. The primary care clinics established a new APRN managed care model. 

All of these models are pushing the boundaries of established standards. We are working with 

legislators and state agencies to change regulations and reimbursement barriers that are 

impeding full implementation of our model. 

Many of the reasons why we chose to establish primary care clinics in Powell and Wolfe 

counties (geographic barriers to access to healthcare, poor economic development, etc.) 

became barriers to our own project implementation. The biggest challenge that we have faced, 

especially in implementation of our telehealth component in the two primary care clinics, has 

been lack of adequate telecommunications infrastructure. Multiple layers of service providers 

(Windstream, AT&T, local telco, KentuckyOne telecommunications connectivity, and Catholic 

Health Initiatives (CHI) national telecommunications connectivity) expanded complications to 

our progress. The telecommunications situation was compounded even further by the 

unprecedented system-wide phased roll out of the electronic medical record (Allscripts) in the 

clinics. At the time of our telehealth implementation there was no problem with our connectivity 

between the primary care site and a specialist. However, we began to experience severe 

degradation in audio/visual transmission. It took almost six months of tests and trials, vendor 

investigation and local, regional and national network monitoring to understand that the 

competition for bandwidth at the primary care clinics was between the electronic medical record 

and telehealth equipment. Convincing telecommunications companies to install new high 

capacity telecommunications lines to two primary care clinics in Powell and Wolfe counties in 

Appalachian Kentucky was costly and complicated. 

We have learned a valuable lesson which has better prepared us in planning additional 

primary care clinics. Our painful lesson has also been understood at a KentuckyOne level as the 
roll out of the electronic medical record within KOH hospitals has been halted because of lack of 

telecommunications infrastructure. 

The fact that we have been able to accomplish this two year grant project within a period 

of local, regional and national upheaval is remarkable. As our grant was awarded, our SJHS 

organization went through an extensive one-year merger period. As we became KentuckyOne, 

our organization took on new partnerships with University of Louisville Hospital, James Brown 

Cancer Center. These new partnerships will be a catalyst for new telehealth applications 

(emergency telepsychiatry, telestroke, oncology) and new patient and professional educational 

opportunities. We believe these additional expert resources will benefit our patients along the 

continuum of care which includes our primary care clinics. It also provides us with additional 

research opportunities to enhance health care services for all of Kentucky citizens.

Evaluation challenges: The evaluation was a challenge from the beginning because the 

federal regulations and requirements that were not initially clear or totally absent in the original 

FHKY/KHFI RFP. These requirements became clearer as they were imposed weeks and 

months throughout the grant years. Further complications were introduced, beyond 

administrative and financial components, with the multiple layers of evaluation approvals, which 

resulted in multiple redesigns of our research instruments. As a result of these layers of 

approval, we did not receive final WIRB approval of all research instruments until August 2012, 

the last four months of the two-year grant project. Thus, we only had a very short window of 

opportunity to enroll participants in the study and gather data as planned in the SEP, yet, in the 

meantime, we had been required to submit quarterly reports on our progress. Once data 

collection began there were a number of other challenges, which have been mentioned 

throughout this report. We are working on addressing many of the challenges in 2013. 

V. Next Steps for the Program 

 The Community Based Health Delivery Model with the primary care clinic as the medical 

home is the core of delivering coordinated care in rural Kentucky. KentuckyOne Health, 

formerly Saint Joseph Health System is investigating additional ways that we can build upon this 

Community Based Health Delivery Model by adopting new technology, forging new 

partnerships, or creating new services to strengthen care coordination. 

 The expected impact of this coordinated-care strategy is community-wide improvements 

in health status, health outcomes, and quality of life for all residents throughout the SJHS 

service area. KentuckyOne leadership and CHI national leadership are supporting this initiative 

financially and with staff expertise on multiple levels. They are closely monitoring this 

Community Based Health Delivery Model, which may also expand beyond KyOne/SJHS service 

area, through regional replication and throughout the national Catholic Health I health system. 

 Our vision is to expand the Community Based Health Delivery Model to up to 10 total 

primary care clinics in underserved communities, which will serve as medical homes to those 

who do not have a primary care provider or easy access to specialty services. From that core 
primary care clinic network, patients may receive the appropriate level of care in an integrated 

manner. This model includes access to behavioral health services as developed in the 

integrated physical/behavioral health delivery system in Wolfe County/SJPCC through 

collaboration with Kentucky River Community Care (KRCC). The model also includes a Social 

Worker, a Nurse Transition Coach. Depending on community need, the model may also include 

radiological services, dental services, community health workers. 

 Primary Care. Within the next two years, we plan to open two new virtual primary care 

clinics in additional counties in rural Kentucky. To eliminate barriers for patients completing 

necessary paperwork, at these new and existing clinics at the point of registration, we are 

working toward providing patients with iPads or tablets with audible files. The purpose of this 

pilot is to assist patients with low literacy levels with the registration process, the process of 

consenting to participate in research studies such as this, and completing the necessary forms 

and survey instruments. We have received a one-year (2012) KHFI continuation grant to open 

the two primary care clinics and pilot the iPads. 

 Diagnostics. Enhancement of the Community Based Health Delivery Model with 

diagnostic services is being considered outside the scope of this grant funding, to determine 

costs/benefits of renovating the existing mobile coach to provide digital Mammography, Doppler, 

Ultra Sound, and schedule this Mobile Diagnostics Unit to travel to each of the primary care 

clinics to deliver these services. KentuckyOne Leadership is reviewing the mobile coach and 

feasibility of renovation. 

 School Telehealth. We are planning to enhance the Community Based Health Delivery 

Model with School Telehealth services outside the scope of this grant funding in a test pilot in 

Powell and/or Wolfe County. We have initiated discussion with the School Superintendents and 

other staff in both counties. However, we are also following the current legislative action defining 

health delivery in schools. There are multiple issues (sharing health information, level of skill for 

onsite provider, reimbursement etc.) that need definition and resolution before we can begin a 

School Telehealth pilot. We do have a vendor who may be willing to collaborate with us in this 

pilot by providing the telehealth equipment. 

 Health Department. We are working to enhance the Community Based Health Delivery 

Model in collaboration with Powell County Health Department. We were approached by the 

Director of the Health Department because of state funding cuts to their budget. We are working 

with KentuckyOne leadership to develop an external partner agreement that will enable us to 

provide identified services. 

 In addition, we are working with the CHI Institute for Research and Innovation (CIRI in 

participating in a Powell County Community Coalition to address community needs and seek 

further funding opportunities to accomplish coalition goals. 
BUDGET

FHK – Year End Report 2.28.13 

Budget Narrative Analysis 

There were no additional expenses incurred during our extension period of January 16 –

February 28, 2013. 

 As of December 31, there was a balance of $29,733 of Foundation State Funds and an 

outstanding Federal Fund distribution of $62,500remaining. The delay in receiving the last 

distribution of funds was due to the fact that our 4th quarter expense verification did not occur 

until after January 1, 2013. Therefore, a no cost-extension was requested on January 16, 2013 

to allow for a review of 4th quarter report and supporting documentation that would in turn 

support the final distribution of federal funds to us in January 2013. 

 As of February 28, 2013, $125,000 of Federal and Foundation funds, for a total of 

$250,000, have been expended. A match of $931.81 is reported against the projected amount 

of $1,319 in our letter dated January 15, 2013. The difference of $387.19 is non-productive time 

that was incorrectly included in the $34,614 estimate for 1st quarter grant funded personnel 

costs. This brings our total match for Year 2 grant to $502,186. 

Federal Financial Report (FFR) Grant Agreement: 2011KHF1013

Saint Joseph Health System

2012

2.28.13

Quarter

Funds 

Received

Funds 

Received 

Federal

Funds 

Received 

Foundation

Cash Match 

Raised

Funds 

Expended 

Federal

Funds 

Expended 

Foundation

 Funds 

Expended 

Cash Match Balance

1/1/12 - 3/31/12 -$ -$ -$ 62,500$ -$ -$ 112,843 $ 

4/1/12 - 6/30/12 62,500$ -$ 62,500$ 62,500$ -$ 37,667$ $ 24,833$ (FOU) 105,532

7/1/12 - 9/30/12 125,000$ 62,500$ 62,500$ 62,500$ 41,265$ 24,833$ $ 83,735$ ($21,235 (FED) $62,500 (FOU) 135,354

10/1/12 - 12/31/12 -$ -$ -$ 62,500$ 21,235$ 32,767$ $ 29,733$ (FOU) 147,526

Total 187,500$ 62,500$ 125,000$ 250,000$ 62,500$ 95,267$ $ 501,254

EXTENSION

1/1/13 - 2/28/2013 62,500$ 62,500$ -$ 62,500$ 29,733$ $ $0 Balance 931.81

GRAND TOTAL 250,000$ 125,000$ 125,000$ 250,000$ 125,000$ 125,000$ $ 502,186