The Trusted Clinician in Population Health Management

DISEASE MANAGEMENT
Volume 10, Number 1, 2007
© Mary Ann Liebert, Inc.
DOI: 10.1089/dis.2006.629
Leveraging the Trusted Clinician: Documenting Disease
Management Program Enrollment
SHARON GLAVE FRAZEE, Ph.D.,1 PATRICIA KIRKPATRICK, R.N., B.S., CPHQ,2
RAYMOND FABIUS, M.D.,3 and JOSEPH CHIMERA, Ph.D.1
ABSTRACT
The objective of this study was to test the hypothesis that an integrated disease management
(IDM) protocol (patent-pending), which combines telephonic-delivered disease management
(TDM) with a worksite-based primary care center and pharmacy delivery, would yield higher
contact and enrollment rates than traditional remote disease management alone. IDM is char-
acterized by the combination of standard TDM with a worksite-based primary care and phar-
macy delivery protocol led by trusted clinicians. This prospective cohort study tracks contact
and enrollment rates for persons assigned to either IDM or traditional TDM protocols, and
compares them on contact and enrollment efficiency. The IDM protocol showed a significant
improvement in contact and enrollment rates over traditional TDM. Integrating a worksite-
based primary care and pharmacy delivery system led by trusted clinicians with traditional
TDM increases contact and enrollment rates, resulting in higher patient engagement. The
IDM protocol should be adopted by employers seeking higher returns on their investment
in disease management programming. (Disease Management 2007;10:16–29)
INTRODUCTION
THE MODEL FOR IMPLEMENTING population-
based, telephonic-delivered disease man-
agement (TDM) programs includes four succes-
sive phases: (1) identify patients who may
benefit from the program and create a target list;
(2) contact patients on the list by telephone and
other communication media; (3) enroll the con-
tacted patient as a participant in the program;
and (4) execute patient intervention programs to
achieve behavior change and subsequent im-
provement in outcomes. The efficiency of each
of these phases drives the overall program ef-
fectiveness and success at the population level.
This paper describes a new integrated disease
management (IDM) protocol (patent-pending)*
designed to improve efficiencies in the contact
and enrollment phases of the model. Defini-
tions for various terms can be found at the end
of the text.
Although there is some variation in the effi-
ciency levels at each of these four phases on a
vendor and program basis, an industry esti-
mate is a 50%1 success rate at each phase. Start-
ing with 100% at the beginning of phase 1, 50%
1CHD Meridian Healthcare, Nashville, Tennessee.
2CHD Meridian Healthcare, Omaha, Nebraska.
3CHD Meridian Healthcare, Chadds Ford, Pennsylvania.
*The IDM protocol developed by CHD Meridian Healthcare is patent pending, abbreviated in remainder of text
as “Pat. Pend.”
16

of the target patient population is successfully
contacted by the completion of phase 2; at the
end of phase 3, 50% of contacted patients agree
to become program participants by enrolling
(also called “opt-in”); and at the end of phase
4, 50% of enrolled participants exhibit measur-
able behavior change, which ultimately drives
improvement in outcomes. Thus, the cumula-
tive efficiency, or engagement rate, at the com-
pletion of the target patient identification, con-
tact (outreach), and enrollment phases is 25%,
or one out of four patients on the target list en-
roll in the program. At the final phase, this
model would expect only 12.5% of the origi-
nally targeted patients to actually exhibit be-
havior change. It follows that this relatively
small percentage will be the group that drives
the measurable improvement in outcomes for
the target population. Improvement in mea-
surable outcomes could be derived from either
improving the effectiveness of the intervention
and/or by improving the efficiency of each
phase of the general TDM model.
This paper focuses on the latter. For exam-
ple, improvement in cumulative enrollment ef-
ficiency (phases 1–3) could come from creating
a higher quality list of target patients in phase
1 by a “predictive modeling” algorithm and/or
by incorporating the patient’s “trusted clini-
cian” into the enrollment decision making pro-
cess. A higher quality list of target patients
could lead to a greater success rate in phase 2
(“contact efficiency”) as well as a greater suc-
cess rate in phase 3 (“enrollment efficiency”) if
more appropriate patients are identified for in-
clusion in the target population. Additionally,
incorporating the patient’s “trusted clinician”
into the enrollment decision may improve pa-
tient enrollment rates. Both of these improve-
ments should, therefore, ultimately lead to a
larger percent of the target population exhibit-
ing true behavioral change and associated im-
proved outcomes in phase 4.
We have designed a methodology that inte-
grates TDM with worksite-based primary care
and pharmacy delivery to form an IDM deliv-
ery protocol (Pat. Pend.). An aim of this IDM
methodology is to improve the identification of
appropriate patients to enroll. This can be ac-
complished by enhancing the quality of the tar-
get population database by combining health
center encounter data with administrative
claims and health insurance eligibility data to
improve the contact information data elements
and clinical data elements. Additionally, a pre-
dictive modeling algorithm is used to stratify
the population by avoidable healthcare costs.
Those patients with relatively high avoidable
costs are selected for the target population
database with the theory that, by targeting the
types of patients who have avoidable costs, en-
gagement rates and successive financial and
clinical outcomes will show improvement.
The second goal of the IDM methodology is
to leverage the patient’s relationship with the
trusted primary care and other worksite-based
clinicians when offering patients in the target
database the opportunity to enroll in a popu-
lation-based disease management (DM) pro-
gram. This approach of involving the patient’s
physician in the DM program will be a key fac-
tor for program effectiveness. In the past, the
DM industry has often been accused of oper-
ating independently of the patient’s primary
healthcare provider. The IDM protocol (Pat.
Pend.) seeks to engage patients and their
trusted clinicians to work together within the
DM framework.
The primary study objective is to document
the contact efficiency and the enrollment effi-
ciency of this novel IDM methodology. Secon-
darily, and within the limitations of experi-
mental design methodology, our research
hypotheses are (1) an IDM protocol (Pat. Pend.)
will significantly increase the efficiency of suc-
cessfully contacting patients on a target list
compared to a TDM-only protocol (Contact Ef-
ficiency Hypothesis), and (2) an IDM protocol
(Pat. Pend.) will significantly increase the en-
rollment efficiency of contacted patients com-
pared to a TDM-only protocol (Enrollment Ef-
ficiency Hypothesis) and compared to our
stand-alone TDM experience.
We assert that efficiency gained by the IDM
methodology, even with no significant im-
provement in percent of enrollees that exhibit
behavior change, should increase the number
of patients with positive change. Therefore, at
the population level, the overall effectiveness
of the IDM methodology should improve ag-
gregate outcomes when compared to tradi-
tional TDM.
LEVERAGING THE TRUSTED CLINICIAN: DM ENGAGEMENT RATES 17

METHODS
Study objectives and design
The IDM program (Pat. Pend.) described
herein is designed to improve the first three
phases of population-based DM, with measur-
able improvement expected in the combined
processes of phases 1 and 2 (Contact Efficiency)
and measurable improvement in the processes
of phase 3 (Enrollment Efficiency) for a DM
program targeting individuals with diabetes,
coronary artery disease (CAD), and hyperten-
sion (HTN). These three chronic conditions are
among the most commonly offered DM pro-
grams for large, self-insured employers.1
IDM leverages administrative claims data
(medical and pharmacy), health center en-
counter data, and predictive modeling in an at-
tempt to produce a higher quality database of
target patients (phase 1). This database drives
the second phase of attempting to contact pa-
tients in the target database to discuss enroll-
ment in the DM program.
The first research hypothesis (Contact Effi-
ciency Hypothesis) is that the contact rate for
patients who are exposed to the IDM protocol
(Pat. Pend.) will be significantly higher than
the contact rate for patients exposed to the tra-
ditional TDM protocol (Fig. 1). The patient
contact rate metric is operationally defined as
follows:
Patient contact rate
ϭ no. of patients successfully contacted/
no. of patients in target population
Once a patient is contacted in the IDM proto-
col (Pat. Pend.), a clinician attempts to enroll (ie,
by a referral) the patient into an intervention
program.
The second research hypothesis (Enrollment
Efficiency Hypothesis) is that the enrollment
rate for patients exposed to the IDM protocol
(Pat. Pend.) will be significantly higher than pa-
tients exposed to the TDM protocol. The pa-
tient enrollment rate metric is operationally de-
fined as follows:
FRAZEE ET AL.18
FIG. 1. Evaluation of process metrics for integrated disease management (IDM) versus telephonic-delivered disease
management (TDM). CAD, coronary artery disease.

Patient enrollment rate
ϭ no. of patients enrolled/
no. of patients successfully contacted
Thus, there is a principal metric for each hy-
pothesis: The Patient Contact Rate is related to
the Contact Efficiency (and in this study is ab-
breviated as “C”); and the Patient Enrollment
Rate is related to the Enrollment Efficiency (and
in this study is abbreviated as “E”).
Figure 1 illustrates the overall study design
and metrics. There are four study groups: the
first is assigned to the IDM protocol (Pat. Pend.),
and all others are assigned to the TDM protocol:
1. Health Center Users (IDM protocol [Pat
Pend.])
2. Proximate Non-Health Center Users (TDM
protocol)
3. Non Proximate (TDM protocol)
4. Historical stand-alone TDM (TDM protocol)
The Contact Efficiency metric for each study
group is referred to as “C” and the group num-
ber as C1–C4. The Enrollment Efficiency metric
is referred to as “E” and the group number as
E1–E4.
In short, the testable hypotheses are (a) C1
will be significantly greater than C2, C3, or C4;
and (b) E1 will be significantly greater than E2,
E3, or E4.
Study population
One location of a large, self-insured em-
ployer’s active and retiree population along
with their adult dependents was selected for
this study. This employer location has an on-
site primary care health center and full-service
pharmacy available to active and retired em-
ployees and their dependents. The full em-
ployee, retiree, and dependent population at
this site consisted of 10,399 individuals, 7,818
of whom were age 18 or older on July 1, 2005
(claims and other data were available through
June 30, 2005). The adult population eligible for
this study was 47% male with an average age
of 58. The composition of each group in terms
of employment status (ie, actively employed,
dependent, retiree, or early retiree) was ap-
proximately equal although the TDM groups
had slightly higher percentages of dependents
than the IDM group, and the proximate groups
(Group 1/IDM and Group 2/ Proximate Non-
Health Center Users) had a higher percentage
of active employees than Group 3, Non-Proxi-
mate. Demographic information on the study
population and the three groups defined by ac-
cess and use of the health center are shown in
Table 1.
This population showed a higher prevalence
than national estimates obtained from the Cen-
ters for Disease Control for diabetes,2 CAD,
TABLE 1. STUDY POPULATION DESCRIPTIVES
Group 1
Full study population: Group 2
population: proximate population: Group 3
Estimated identified from health center proximate non- population: non-
national medical claims users users proximate
prevalence (n ϭ 7,818) (n ϭ 1,821) (n ϭ 4,694) (n ϭ 1,303)
Diabetes 7% 16% 18% 17% 13%
CAD 6% 12% 12% 13% 10%
HTN 33% 41% 48% 39% 37%
Male, % 47% 51% 46% 47%
Mean no. of 1.1 1.2 1.1 0.9
comorbidities
(out of 7a)
Mean age, years 58.5 59.6 58.7 55.9
Active employees, % 17% 20% 15% 19%
Active dependents, % 18% 15% 19% 21%
Retired employees, % 39% 42% 39% 37%
Retired dependents, % 26% 23% 27% 23%
aChronic diseases included are diabetes, coronary artery disease (CAD), hypertension (HTN), chronic obstruc-
tive pulmonary disease, asthma, congestive heart failure, and chronic back pain.

and HTN.3 These are also shown in Table 1.
Overall, the analysis of medical claims found
that about 62% of the population had at least
one medical claim consistent with a primary di-
agnosis code for diabetes, CAD, or HTN. This
is considerably higher than expected and
higher than the prevalence for this employer’s
population overall. However, the high preva-
lence rate might be at least partially explained
by the high average age of the population and
by the fact that the geographic region of the par-
ticular worksite chosen (southeastern United
States) has a higher than national average
prevalence for these conditions.4
Selection of patient population
The selection protocol for study participants
involved several steps. First, the population of
employees, retirees, and dependents age 18 and
older who were eligible for health benefits at
the start of the study were identified. Primary
diagnosis codes (International Classification of
Diseases, 9th Revision [ICD-9]) from medical
claims were used to identify individuals with
recorded diagnosis codes for diabetes, CAD, or
HTN. Health Plan Employer Data and Infor-
mation Set (HEDIS) methodology was used to
determine the ICD-9 codes identifying these
conditions. HEDIS methodology also was used
to define encounter frequency and type. Data
for these individuals were included in a pro-
prietary predictive model which determined
predicted future and avoidable costs for each
member. Predicted costs are those costs the in-
dividual is expected to incur while avoidable
costs are that portion of the predicted costs that
might be changed through some type of inter-
vention.
In addition, each patient’s proximity to the
primary care medical center and pharmacy
(PCRx) was calculated based on the patient’s
home address zip code. Patients whose resi-
dence was within 35 miles of the PCRx were
considered to be geographically proximate and
have access to the center for the medical care
of their chronic condition. Patients with access
to the PCRx were classified either as Health
Center User (Group 1) or as a Proximate Non-
User (Group 2) based on whether or not an en-
counter for an office visit at the health center
associated with medical care was recorded. Ad-
ditionally, health center clinicians reviewed the
Health Center User list to identify patients who
utilized the health center for only acute care
treatment rather chronic condition treatment.
Those patients utilizing the health center for
episodic acute care services only were reclassi-
fied as Proximate Non-Users. Patients beyond
the 35-mile proximity radius were classified as
Non-Proximate (Group 3). The resulting pa-
tient population was then stratified on costs
and those with relatively high avoidable costs
(top two quintiles) were selected as having the
potential for the most significant improvement
and included in the final study target popula-
tion. The target patient population selection
process is illustrated in Figure 2.
The goal of this patient selection process was
to identify a relatively homogeneous popula-
tion of patients with the target diseases to be
subjected to the IDM protocol (Pat. Pend.) or the
TDM protocol based on whether they utilized
the worksite primary care health center or com-
munity-based care. The final target population
consisted of 1,890 patients. Analysis of the dis-
ease prevalence for the study groups showed a
relatively consistent burden of disease among
the groups (Table 2). Thus, at this level of anal-
ysis, it appears that the disease prevalence of
these groups is comparable. The groups were
approximately equal on other pertinent demo-
graphic characteristics as well, although Group
3, the Non-Proximate group, had fewer active
employees than the other groups. This was not
unexpected based on the definition of this
group given that most people live within 35
miles of their workplace.
Contact rates C1, C2, C3, and C4 and enroll-
ment rates E1, E2, E3, and E4 were calculated
for each study group. Tracking of the patient
contact and enrollment process was performed
using a proprietary DM information system ap-
plication. This system was populated with de-
mographic and other contact data for each tar-
get patient classified as a Health Center User,
Proximate Non-User, and Non-Proximate. The
fourth group, with metrics C4 and E4, is based
on experience from our previous stand-alone
TDM programs using the same DM informa-
tion system software. We considered using
other TDM industry performance rates, but de-
FRAZEE ET AL.20

finitive data were limited, and methods around
TDM contact protocols vary widely in terms of
number of attempts, types of patients, types of
messages left, and non-telephonic contact
methods. It was determined that a more valid
comparison group would be our own TDM
contact experience, using historical data for
similar patient groups and processes. Our past
TDM experience has shown that for a sample
of over 21,688 patients selected to be contacted
for telephonic DM services, 28% were con-
tacted and 54% enrolled, leading to a 15% en-
FIG. 2. Protocol for the selection of the target patient population. CAD, coronary artery disease.
TABLE 2. TARGET POPULATION DISEASE PREVALENCE AND DESCRIPTIVES
Group 1
population: Group 2
proximate population: Group 3
health center proximate non- population: non-
users users proximate
(n ϭ 423) (n ϭ 1,279) (n ϭ 188)
Diabetes 41% 43% 41%
CAD 29% 30% 37%
HTN 80% 78% 75%
Male, % 69% 61% 64%
Mean no. of comorbidities (out of three): 1.5 1.5 1.5
diabetes, CAD, and HTN
Mean no. of comorbidities 2.2 2.2 2.2
(out of 7a)
Mean age, years 59.3 59.9 59.1
Active employees, % 24% 20% 14%
Active dependents, % 9% 12% 13%
Retired employees, % 50% 47% 53%
Retired dependents, % 17% 21% 20%
aChronic diseases included are diabetes, coronary artery disease (CAD), hypertension (HTN), chronic obstruc-
tive pulmonary disease, asthma, congestive heart failure, and chronic back pain.

gagement rate (using cumulative enrollment
rates for “ever enrolled” after a 12-month en-
rollment period). These contact, enrollment,
and engagement rates are fairly typical for the
remote traditional DM industry.1 (Our past
TDM enrollment rate is at the lower end of the
industry range, probably due to a rigid past in-
terpretation of “enrolled” as agreement to par-
ticipate and completion of all necessary as-
sessments; the program covered eight disease
states and could have required up to nine as-
sessments be completed.)
Some discussion of the issue of being able to
compare various DM programs either between
or within DM vendors is warranted here. The
literature on DM programs abounds with ref-
erences to the difficulty in comparing pro-
grams.5–10 Not only is the term “disease man-
agement” defined and practiced differently
across DM providers, the process to determine
what equals “enrolled” also differs across DM
providers. For instance, opt-out programs
where the enrollee must request removal from
the DM program would necessarily have a
much different enrollment rate than programs
that are opt-in, where enrollees must agree to
participate at a minimum and often to complete
other requirements. Because of this, we felt the
best option was to use our own historical en-
rollment performance, but even that offers
some difficulties. The program discussed in
this study focused on three disease states (ie,
diabetes, CAD, HTN) and continues to support
the additional five diseases, but in a less struc-
tured manner. This was true for all three
groups in the current study (Health Center
User, Proximate Non-User, Non-Proximate).
However, our past TDM experience focused on
eight disease states (which included the three
disease states in the current study but with the
addition of asthma, neck and back pain, chronic
obstructive pulmonary disease, congestive
heart failure, and a catch-all group called
“Quality of Life”). It should be noted that the
interpretation of enrollment is consistent be-
tween our past TDM experience and the cur-
rent study; only the number of disease states
and potential number of assessments required
to be completed to be considered “enrolled”
differs. While our past TDM experience is not
a perfect metric for comparison to the current
study TDM and IDM groups, it does provide a
comparison of the core programmatic compo-
nents. The enrollment and outreach process
were identical for both our historical and cur-
rent TDM groups, similar to many other DM
programs.
The initial enrollment campaign began on
February 13, 2006 and continued for 90 days.
As each patient in the target groups was sub-
jected to the contacting protocols, IDM pro-
tocol (Pat. Pend.) vs. TDM protocol, a stan-
dardized comment was added to the DM
application that records the disposition of the
contacting event. Individuals we were unsuc-
cessful in contacting were classified with a fi-
nal disposition comment, and classified as ei-
ther Unable to Contact or Termed. Likewise, as
each contacted patient was subjected to either
the IDM (Pat. Pend.) or TDM enrollment pro-
tocol, a standardized comment was added to
the DM application recording the disposition
of the enrollment event. Enrollment was de-
fined as agreement by the contacted individual
to participate and the completion of an initial
15–20 minute intake assessment.
Outreach to the adjusted target population
consisted of two different processes. First, for
those individuals currently utilizing the health
center (Group 1), clinicians at the PCRx either
solicited enrollment during a scheduled office
appointment or made a telephone call to ex-
plain program benefits and request agreement
to participate. This agreement was followed by
a call center nurse contact to complete the en-
rollment process and complete an initial clini-
cal assessment and goal setting session. This
was the outreach protocol defined for IDM
protocol (Pat. Pend.). Second, two additional
groups of non-health center users were also
studied. The outreach for these two groups
consisted of either a pharmacy clinician expla-
nation of the program when the patient filled
a prescription, or a series of two outbound calls
from call center staff with a postcard reminder
if the person was unreachable by phone. This
outreach method is defined as TDM. There
were two TDM groups in the study: Proximate
Non-Health Center Users (Group 2) and Non-
Proximates (Group 3). Proximate Non-Health
Center Users (Group 2) were individuals de-
fined as residing within 35 miles of the health
FRAZEE ET AL.22

center but who had not used the health center
for chronic DM. The other group, the Non-
Proximates (Group 3), consisted of individuals
residing further than 35 miles from the health
center.
Contact and enrollment statistics were then
reported by DM application, 90 days after the
initiation of the program.
RESULTS
The overall population included 7,818 active
employees, retirees, and early retirees eligible
for health benefits at the self-insured employer.
Of these, 1,890 (24%) were identified as having
one or more of the eligible diseases (diabetes,
CAD, HTN) and having avoidable costs in the
top two quintiles, and they comprised the tar-
get population. Seventy-five individuals (4%)
were excluded during the enrollment process
for such reasons as death, terminated employ-
ment, or not having one of the eligible diseases.
The adjusted target population totaled 1,815, or
23% of the overall population. Successful con-
tacts were achieved with 1,123 individuals, or
59% of the target population and 62% of the ad-
justed target population. Of those successfully
contacted, 693 (62%) agreed to participate. As
described in the methods section, enrollment
was defined as agreement to participate and
completion of the initial 15–20-min intake as-
sessment call. The overall engagement rate was
38%, a 153% increase over past CHD Meridian
TDM experience of 15% (significant at p Ͻ 0.01
level).
As described more fully in the methods sec-
tion, outreach to the adjusted target population
consisted of two different processes: the IDM
protocol (Pat. Pend.) used for Group 1 involved
the trusted clinicians in the contact and enroll-
ment process, while the TDM protocol used for
Groups 2 and 3, as well as the comparison
group of our prior TDM experience (Group 4),
focused on telephonic nurse-based outreach.
Group 1, those currently utilizing the health
center for the care of their chronic disease, con-
sisted of 423 individuals. The contact rate for
Group 1 was 96% (n ϭ 407). This high contact
rate was primarily related to the health center
possession of accurate demographic informa-
tion and the ability to leverage scheduled ap-
pointments with the trusted clinician. More-
over, the use of the IDM methodology (Pat.
Pend.) for this group also generated much
higher enrollment rates than the other groups,
with enrollment at 79% (n ϭ 320) of those suc-
cessfully contacted. The overall engagement
rate for Group 1 therefore was 76%. This is il-
lustrated in Figure 3. Contact rates, enrollment
rates, and engagement rates for Group 1 were
significantly higher (p Ͻ 0.01 level) than for
Groups 2–4.
For the two TDM groups in the study
(Groups 2 and 3), contact and enrollment rates
were not as high as in Group 1. The first, Prox-
imate Non-Health Center Users (Group 2),
comprised individuals residing within 35 miles
of the health center, but not utilizing the health
center primarily for chronic DM. Contact for
this group was initiated either by a pharmacy
clinician explanation of the program at the time
they filled a prescription for the covered pa-
tient, or by a series of two outbound calls made
by call center nursing staff and a postcard re-
minder for those the call center was unable to
reach. Group 2 consisted of 1,279 individuals.
The contact rate was 50% (n ϭ 641). This is a
statistically significant improvement (p Ͻ 0.01)
over our historical TDM contact rate of 28%. It
should be noted that the promotion by the
pharmacy clinician has not been part of our his-
torical TDM protocol. Of those successfully
contacted from Group 2, 327 or 51% agreed to
participate. Thus, the engagement rate was
26%, a statistically significant improvement
over our historical TDM experience (p Ͻ 0.01).
The other TDM group, Non-Proximate (Group
3), consisting of 188 individuals residing far-
ther than 35 miles from the health center. All
contact for this group was by telephone from
the call center nursing staff with a postcard sent
to those who were unreachable. The contact
rate was 40% (n ϭ 75), while 61% of those con-
tacted enrolled (n ϭ 46), for an overall engage-
ment rate of 24%. The contact rate and en-
gagement rate were significantly higher (p Ͻ
0.01 level) than our historical TDM experience
(Group 4), but the enrollment rate was not sig-
nificantly different.
Additionally, two subgroups within Group
2 were studied further. These subgroups ac-

counted for 466 individuals, or 36% of Group
2. The first subgroup, Pharmacy Clinician Re-
ferral, was defined as the 238 study participants
from Group 2 who utilized the pharmacy co-
located at the health center for prescription ser-
vices. The contact rate for this subgroup was
55% (131) with enrollment rates for those con-
tacted at 63% (82) and an engagement rate of
34%. The enrollment and engagement rates for
the Pharmacy Clinician subgroup were statis-
tically higher than those in Group 2 overall (p Ͻ
0.05 level). When compared with Group 4 (ie,
our historical TDM experience), however, only
the contact and engagement rates are signifi-
cantly higher (p Ͻ 0.01). The second subgroup,
Acute Care Users, was defined as patients uti-
lizing the health center for acute conditions,
such as colds or minor medical conditions, and
included 228 individuals. For this subgroup,
the contact rate was 46% (104), with an enroll-
ment rate of 58% (61) and an engagement rate
of 26%. None of these rates were significantly
higher than the overall Group 2 contact, en-
rollment, or engagement rates at the p Ͻ 0.05
level but, like the Pharmacy Clinician Referral
subgroup, because the contact rate was signif-
icantly higher than that of our historical TDM
experience (p Ͻ 0.01), engagement rates were
also significantly greater.
The results of this study have affirmed both
research hypotheses. The first hypothesis, that
the contact rate for patients exposed to the IDM
protocol (Pat. Pend.) will be significantly higher
than patients exposed to the traditional TDM
protocol (Contact Efficiency hypothesis), was
proven as there is a statistically significant dif-
ference (p Ͻ 0.01) between Group 1 and Groups
2 through 4. The second hypothesis, that the
enrollment rate for patients exposed to the IDM
protocol (Pat. Pend.) will be significantly higher
than patients exposed to the TDM protocol
(Enrollment Efficiency Hypothesis), was also
proven as the engagement rate for Group 1 was
significantly higher than Groups 2–4 (p Ͻ 0.01).
DISCUSSION
With over 20 million Americans receiving
telephonic DM programs,11 improving the ef-
ficiency of contacting and enrolling individuals
in these programs has the potential to reduce
overall costs while increasing participation
rates. The latter is expected to increase the over-
FRAZEE ET AL.24
FIG. 3. Evaluation of process metrics for cohorts. *Patent pending. #Engagement rate ϭ contact rate ϫ enrollment rate.

all impact of DM programs, even if the actual
percentage of patients showing improvements
“downstream” does not change. This study has
reviewed a new protocol (Pat. Pend.) that shows
promise for increasing both contact and en-
rollment efficiency. It also helps quantify the
value of the “trusted clinician” in the contact
and enrollment process.
The value of the trusted clinician is most ap-
parent in Group 1, where the primary care
health center and full-service pharmacy oper-
ated at the patient’s worksite was able to not
only provide improved selection and contact in-
formation over what is traditionally available to
DM programs, but also to become part of the
recruitment team encouraging patients to en-
roll. The commitment to participate is a signif-
icant decision for patients; when encourage-
ment came from their trusted clinicians,
enrollment rates dramatically increased. Figure
4 shows the impact of the trusted clinician in
this study as compared to Groups 2 through 4.
Note that 76% of the IDM group engaged,
where the trusted clinicians played an integral
role in encouraging enrollment into the DM
program. An engagement rate of 76% is five
times greater than our traditional TDM experi-
ence and three times greater than engagements
rates for Groups 2 and 3. The importance of hav-
ing a “trusted clinician at the workplace™” who
encourages participation in DM programs has
been shown to be so strong in this study that it
would behoove employers who are truly inter-
ested in the health of their employees to imple-
ment and cultivate a workplace health center.†
The value of the trusted clinician was seen even
when the trusted clinician was a pharmacist.
The value of pharmacy clinicians in DM pro-
grams has been reported elsewhere12 as well. In
Group 2, the Pharmacy Clinician Referral sub-
population had a contact rate of 55% (compared
to 50% for Group 2 overall) and an enrollment
rate of 34%, which is 31% higher than the en-
rollment rate for Group 2 overall (p Ͻ 0.05). Ad-
ditionally, the Acute Care Users subpopulation
in Group 2 had a contact rate of 46% (compared
to 40% for Group 3). As interaction with health
center staff increases, the contact and enroll-
ment success also improves. While it was not
tested in this study, we would expect to find
that other types of trusted clinicians, such as
midlevel clinicians (eg, nurse practitioners,
physician assistants), would have a similar ef-
fect in increasing contact, enrollment, and en-
gagement rates using the IDM protocol.
The criteria used to select individuals into the
study population and then obtain contact in-
formation involved several more steps than is
typically found in telephonic DM programs.
While this made the program implementation
more labor-intensive, these steps also contrib-
uted to the success of the enrollment process.
Like many DM vendors, we used an outside
vendor to provide a clean list of phone num-
bers for those without a valid phone number
in the patient demographics provided by the
employer’s third-party administrator. How-
ever, we actually found that we were able to
obtain a higher contact rate from the phone
numbers provided by the primary care health
center than from those purchased from the
clean list vendor.
In addition, we stratified on avoidable costs
to help identify the best potential candidates
for intervention. Many DM programs stratify
on total costs but we feel that using avoidable
FIG. 4. Cohort evaluation metrics graph. *Patent pending.
†From an economic perspective, workplace health cen-
ters are typically best suited to employers with a con-
centrated employee population at a given geographic lo-
cation. Smaller concentrations of employees (eg, fewer
than approximately 1,500 employees) typically do not
have the level of utilization a full-service, on-site primary
care and pharmacy would require to show a return on in-
vestment separately from any DM or other program out-
comes.

costs (ie, those costs predicted to be reducible
by some type of intervention) is a better
method to identify individuals who are appro-
priate candidates for DM services. Given the
limited resources most customers of DM ser-
vices have for these programs, along with the
need for a return on their investment (or at least
cost neutrality), selecting the right target pop-
ulation is integral to the program’s success.
We looked at the avoidable cost distribution
and other comorbidities and found no statisti-
cally significant differences between the Prox-
imate Health Center Users, Proximate Non-
Users and Non-Proximate groups at the contact
stage (overall mean of $337). However, there
were some differences in the enrolled groups,
with Proximate Health Center Users and Non-
Proximates having lower mean avoidable costs
than the Proximate Non-User group ($299 and
$255 versus $468). While it appears that the
Proximate Non-User group is different in some
way from the other groups, the variation can
be explained by the existence of two very large
outliers in the Proximate Non-User group,
which when removed reduced the mean avoid-
able costs to $312.
Stratifying on costs is not without problems,
as it has been noted in some research, today’s
high-cost individuals are not necessarily to-
morrow’s high-cost individuals.13 However,
the proprietary predictive model algorithm we
employed also generates statistically derived
weights from demographic and diagnostic data
that estimate an individual’s risk of becoming
high cost in the future, regardless of the cur-
rent cost burden shown in the individual’s
claims history, which reduces this bias.14,15
Careful consideration was given to ensure
that the IDM and TDM groups were equiva-
lent. In addition to looking at disease preva-
lence and comorbidities (Tables 1 and 2), we
also tested the mean avoidable costs for each
group at each stage in the enrollment process.
As mentioned previously, because TDM pro-
grams differ so greatly in terms of contact pro-
tocol, we compared our metrics numbers to our
own stand-alone TDM book of business expe-
rience as shown in Figure 3. We believe our
stand-alone TDM experience is generally rep-
resentative of that seen with other similar ven-
dors. It should be noted that in the current
study, contact rates were much higher than
past experience. This is due to the health cen-
ter providing better contact information than is
typically found using data provided by em-
ployers or their third-party administrator. In
both processes (ie, historical TDM experience
and the new IDM experience), a third-party
vendor was used to find phone numbers for
those individuals with missing contact infor-
mation, but the most accurate contact informa-
tion came from the health center where the
patient enjoys a relationship with a trusted
clinician; the health center is more likely to
maintain current and correct phone and ad-
dress information. Exclusions from the target
population were also much fewer in the cur-
rent study than in our stand-alone TDM expe-
rience. The importance of having timely, accu-
rate data is reflected here as we were able to
check the eligibility of potential participants
prior to including them in the target popula-
tion group, which reduced the percentage ex-
cluded due to termination of employment or
health benefits. For the DM industry, having
data this accurate reduces costs while greatly
increasing engagement rates.
It should be noted in the enrollment phase
data shown in Figure 3 that individuals
opted-in at a much higher rate than with our
stand-alone TDM experience. Because we
have operated on-site health centers and
pharmacies at several locations for this em-
ployer for several years, the target popula-
tion may have been familiar with our brand
and therefore more likely to be open to par-
ticipation. Additionally, we believe that this
process was further enhanced by union and
employer endorsement. The overall enroll-
ment rate as noted in the results section was
153% higher (p Ͻ 0.01) than in our historical
TDM experience.
In order to better understand which contact
and engagement processes were most influen-
tial we analyzed the process flow. The IDM
group had the highest enrollment rate at the
first contact (83.9%), either via an outbound call
from a health center nurse or via having the
DM program discussed and enrollment offered
in person by the health center staff. The latter
only applied to patients in the target popula-
tion who had a visit scheduled within the en-
FRAZEE ET AL.26

gagement time frame. For the TDM groups
(Groups 2 and 3), the highest enrollment rate
occurred on the first or second telephone con-
tact attempt (37.6%), with ensuing efforts
showing diminishing returns.
While the results of this study are quite
promising for the DM industry, there are sev-
eral limitations. First, only one self-insured em-
ployer was studied. However, this employer
has a large employee population and the re-
sults obtained statistical significance. While not
directly related to the results of this study, it
should be noted that worksite primary care and
pharmacy centers are most common with self-
insured employers who have a geographically
concentrated workforce. The need for a critical
mass of employees at a given location (typically
around 1,500 employees) is primarily economic
in nature as a certain level of utilization is nec-
essary to provide an adequate return on in-
vestment after the costs of implementing and
operating a worksite health center and phar-
macy are taken into account.
Second, the nature of administrative claims
data lends itself to some limitations. Claims
data are collected primarily for billing pur-
poses; thus, using coding algorithms to deter-
mine the existence of disease may be incorrect
insofar as the data does not include all clini-
cally relevant information. Another limitation
is the inability to know with confidence
whether the claims available were exhaustive.
Incomplete data would mean missing potential
participants who had at least one of the dis-
eases targeted using the IDM and TDM proto-
cols. The integration of clinical information
from the primary care clinicians can reduce this
limitation.
Third, although we used our own propri-
etary predictive modeling algorithm, we be-
lieve the general findings would be repro-
ducible with other predictive modeling tools as
well.
Fourth, this research is based on an opt-in
model and therefore may not be applicable to
opt-out program models. That being said,
however, we do feel that encouragement from
a “trusted clinician” increases actual engage-
ment.
Using a primary care setting to deliver chronic
care management has shown promise in recent
studies.16,17 Future research should extend this
protocol to multiple clients in order to improve
the generalizability of the results of this IDM
protocol study. In addition, while this study fo-
cused primarily on process metrics, future stud-
ies are planned to evaluate enrollment erosion
rates are well as the clinical, financial, and uti-
lization outcomes of the patients enrolled in
IDM versus those enrolled in the TDM protocol.
This study suggests that coordinating the
“trusted clinicians at the workplace™” with
remote telephonic nurse coaches—aligning
caregivers into a single, integrated delivery
model—will bring us closer to realizing the po-
tential value of population health management
that encompasses healthier employees, re-
duced healthcare costs, reduced absenteeism,
and increased productivity.
DEFINITIONS
Active Enrollment: Those covered individuals
who agreed to enroll in the disease manage-
ment program and have not terminated due to
requested disenrollment, death, or loss of eli-
gibility of health benefits.
Adjusted Target Population: The resulting in-
dividuals not excluded due to death, primary
residence in a long-term care or hospice facil-
ity, ineligibility for health benefits from the
client, or not having one of the eligible diseases
and who meet avoidable costs criteria for se-
lection into the study group.
Avoidable Costs: The portion of the total pre-
dicted medical costs for a 12-month period that
can be avoided through some form of inter-
vention.
Contact Rate: Number of individuals success-
fully contacted divided by the number of indi-
viduals in the target population. Successful
contact is talking with an individual.
Coronary Artery Disease (CAD): CAD can
clinically present in various ways with cardiac
pain, cardiac tissue injury, and rhythm distur-
bances being most common according to the
Dictionary of Disease Management Terminol-

ogy (DMAA 2004). The primary diagnosis
codes for CAD include 410.xx, 411.00–411.89,
412, 413.xx, 414.xx, and specific codes in the
420, 423, 429 series.
Diabetes: Diabetes is Diabetes Mellitus with and
without complications as indicated by a primary
diagnosis code of 250.xx. This definition in-
cludes Type I and Type II diabetes and con-
trolled and uncontrolled diabetes but does not
include gestational diabetes, which is consistent
with the Health Plan Employer Data and Infor-
mation Set (HEDIS) definition for this disease.
Engagement Rate: The weighted enrollment
rate which is the product of the contact rate
multiplied by the enrollment rate.
Enrollment Rate: Number of individuals en-
rolled into the program divided by the number
of individuals successfully contacted. Enroll-
ment is defined as securing an individual’s
agreement to participate in the program and the
individual completing an initial 15–20 minute
assessment.
Ever Enrolled: The cumulative number of cov-
ered individuals who ever agreed to enroll in
the disease management program, not only
those actively enrolled.
Hypertension (HTN): More commonly known
as high blood pressure, HTN is one of the most
treatable cardiovascular diseases but is also the
most common with an estimated 65 million
Americans suffering from it according to the
Centers for Disease Control and Prevention
(Healthy People 2000). Defined as a primary di-
agnosis code of essential hypertension from the
401 and 402 series of ICD9 codes.
Non-Proximate: Covered individuals who live
further than 35 miles from the worksite-based
primary care center.
Non-Users: Covered individuals who do not
use the worksite-based primary care center for
healthcare services.
Predicted Costs: The total expected future
medical costs for an individual over the next
12-month period.
Proximate: Covered individuals who live
within 35 miles of the worksite-based primary
care center.
Target Population: Individuals identified as el-
igible for health benefits (ie, an employee or re-
tiree or the adult dependent/spouse of an em-
ployee or retiree) and identified through the
predictive modeling process as being treated
for one or more eligible diseases (ie, diabetes,
HTN, CAD) and having avoidable costs in the
top two quintiles.
Termed: Individuals who were on the list of
those to be invited to enroll into the disease
management program, but who are no longer
eligible for services due to termination of em-
ployer-sponsored health benefits.
Unable to Contact: Individuals who were on
the list of those to be invited to enroll into the
disease management program but who were
not able to be contacted after the agreed-upon
number of attempts. Contact methods included
scheduled primary care center appointments,
telephone calls, or other means such as mail.
Users: Individuals who use the worksite-based
primary care center for healthcare services.
ACKNOWLEDGMENTS
All authors were employed by CHD Merid-
ian Healthcare during this research project,
which offers IDM services. CHD Meridian
Healthcare provided all financial support.
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Address reprint requests to:
Raymond Fabius, M.D.
CHD Meridian Healthcare
4 Hillman Drive, Suite 130
Chadds Ford, PA 19317
E-mail: rjfabius@chdmeridian.com

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