DISEASE MANAGEMENTVolume 10, Number 1, 2007© Mary Ann Liebert, Inc.DOI: 10.1089/dis.2006.629

Leveraging the Trusted Clinician: Documenting DiseaseManagement Program Enrollment

SHARON GLAVE FRAZEE, Ph.D.,1 PATRICIA KIRKPATRICK, R.N., B.S., CPHQ,2RAYMOND 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 highercontact 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 contactand enrollment rates for persons assigned to either IDM or traditional TDM protocols, andcompares them on contact and enrollment efficiency. The IDM protocol showed a significantimprovement in contact and enrollment rates over traditional TDM. Integrating a worksite-based primary care and pharmacy delivery system led by trusted clinicians with traditionalTDM increases contact and enrollment rates, resulting in higher patient engagement. TheIDM protocol should be adopted by employers seeking higher returns on their investmentin 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 maybenefit from the program and create a target list;(2) contact patients on the list by telephone andother communication media; (3) enroll the con-tacted patient as a participant in the program;and (4) execute patient intervention programs toachieve 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 diseasemanagement (IDM) protocol (patent-pending)*designed to improve efficiencies in the contactand enrollment phases of the model. Defini-tions for various terms can be found at the endof the text.

Although there is some variation in the effi-ciency levels at each of these four phases on avendor 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 successfullycontacted by the completion of phase 2; at theend of phase 3, 50% of contacted patients agreeto become program participants by enrolling(also called “opt-in”); and at the end of phase4, 50% of enrolled participants exhibit measur-able behavior change, which ultimately drivesimprovement 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, thismodel would expect only 12.5% of the origi-nally targeted patients to actually exhibit be-havior change. It follows that this relativelysmall percentage will be the group that drivesthe measurable improvement in outcomes forthe target population. Improvement in mea-surable outcomes could be derived from eitherimproving the effectiveness of the interventionand/or by improving the efficiency of eachphase 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 creatinga higher quality list of target patients in phase1 by a “predictive modeling” algorithm and/orby incorporating the patient’s “trusted clini-cian” into the enrollment decision making pro-cess. A higher quality list of target patientscould lead to a greater success rate in phase 2(“contact efficiency”) as well as a greater suc-cess rate in phase 3 (“enrollment efficiency”) ifmore 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 alarger 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 careand pharmacy delivery to form an IDM deliv-ery protocol (Pat. Pend.). An aim of this IDMmethodology is to improve the identification ofappropriate 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 administrativeclaims and health insurance eligibility data toimprove the contact information data elementsand clinical data elements. Additionally, a pre-dictive modeling algorithm is used to stratifythe population by avoidable healthcare costs.Those patients with relatively high avoidablecosts are selected for the target populationdatabase with the theory that, by targeting thetypes of patients who have avoidable costs, en-gagement rates and successive financial andclinical outcomes will show improvement.

The second goal of the IDM methodology isto leverage the patient’s relationship with thetrusted primary care and other worksite-basedclinicians when offering patients in the targetdatabase the opportunity to enroll in a popu-lation-based disease management (DM) pro-gram. This approach of involving the patient’sphysician in the DM program will be a key fac-tor for program effectiveness. In the past, theDM industry has often been accused of oper-ating independently of the patient’s primaryhealthcare provider. The IDM protocol (Pat.Pend.) seeks to engage patients and theirtrusted clinicians to work together within theDM framework.

The primary study objective is to documentthe contact efficiency and the enrollment effi-ciency of this novel IDM methodology. Secon-darily, and within the limitations of experi-mental design methodology, our researchhypotheses are (1) an IDM protocol (Pat. Pend.)will significantly increase the efficiency of suc-cessfully contacting patients on a target listcompared 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 ourstand-alone TDM experience.

We assert that efficiency gained by the IDMmethodology, even with no significant im-provement in percent of enrollees that exhibitbehavior change, should increase the numberof patients with positive change. Therefore, atthe population level, the overall effectivenessof 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.) describedherein is designed to improve the first threephases of population-based DM, with measur-able improvement expected in the combinedprocesses of phases 1 and 2 (Contact Efficiency)and measurable improvement in the processesof phase 3 (Enrollment Efficiency) for a DMprogram targeting individuals with diabetes,coronary artery disease (CAD), and hyperten-sion (HTN). These three chronic conditions areamong 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 oftarget patients (phase 1). This database drivesthe 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 forpatients who are exposed to the IDM protocol(Pat. Pend.) will be significantly higher thanthe contact rate for patients exposed to the tra-ditional TDM protocol (Fig. 1). The patientcontact rate metric is operationally defined asfollows:

Patient contact rate� 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 interventionprogram.

The second research hypothesis (EnrollmentEfficiency Hypothesis) is that the enrollmentrate 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 diseasemanagement (TDM). CAD, coronary artery disease.

Patient enrollment rate� 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 tothe Contact Efficiency (and in this study is ab-breviated as “C”); and the Patient EnrollmentRate is related to the Enrollment Efficiency (andin this study is abbreviated as “E”).

Figure 1 illustrates the overall study designand metrics. There are four study groups: thefirst is assigned to the IDM protocol (Pat. Pend.),and all others are assigned to the TDM protocol:

1. Health Center Users (IDM protocol [PatPend.])

2. Proximate Non-Health Center Users (TDMprotocol)

3. Non Proximate (TDM protocol)4. Historical stand-alone TDM (TDM protocol)

The Contact Efficiency metric for each studygroup is referred to as “C” and the group num-ber as C1–C4. The Enrollment Efficiency metricis referred to as “E” and the group number asE1–E4.

In short, the testable hypotheses are (a) C1will 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 alongwith their adult dependents was selected forthis study. This employer location has an on-site primary care health center and full-servicepharmacy available to active and retired em-ployees and their dependents. The full em-ployee, retiree, and dependent population atthis site consisted of 10,399 individuals, 7,818of whom were age 18 or older on July 1, 2005(claims and other data were available throughJune 30, 2005). The adult population eligible forthis study was 47% male with an average ageof 58. The composition of each group in termsof employment status (ie, actively employed,dependent, retiree, or early retiree) was ap-proximately equal although the TDM groupshad slightly higher percentages of dependentsthan the IDM group, and the proximate groups(Group 1/IDM and Group 2/ Proximate Non-Health Center Users) had a higher percentageof active employees than Group 3, Non-Proxi-mate. Demographic information on the studypopulation and the three groups defined by ac-cess and use of the health center are shown inTable 1.

This population showed a higher prevalencethan national estimates obtained from the Cen-ters for Disease Control for diabetes,2 CAD,

LEVERAGING THE TRUSTED CLINICIAN: DM ENGAGEMENT RATES 19

TABLE 1. STUDY POPULATION DESCRIPTIVES

Group 1Full study population: Group 2population: proximate population: Group 3

Estimated identified from health center proximate non- population: non-national medical claims users users proximate

prevalence (n � 7,818) (n � 1,821) (n � 4,694) (n � 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 55.9Active 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 foundthat about 62% of the population had at leastone medical claim consistent with a primary di-agnosis code for diabetes, CAD, or HTN. Thisis considerably higher than expected andhigher than the prevalence for this employer’spopulation overall. However, the high preva-lence rate might be at least partially explainedby the high average age of the population andby the fact that the geographic region of the par-ticular worksite chosen (southeastern UnitedStates) has a higher than national averageprevalence for these conditions.4

Selection of patient population

The selection protocol for study participantsinvolved several steps. First, the population ofemployees, retirees, and dependents age 18 andolder who were eligible for health benefits atthe start of the study were identified. Primarydiagnosis codes (International Classification ofDiseases, 9th Revision [ICD-9]) from medicalclaims were used to identify individuals withrecorded diagnosis codes for diabetes, CAD, orHTN. Health Plan Employer Data and Infor-mation Set (HEDIS) methodology was used todetermine the ICD-9 codes identifying theseconditions. HEDIS methodology also was usedto define encounter frequency and type. Datafor these individuals were included in a pro-prietary predictive model which determinedpredicted future and avoidable costs for eachmember. Predicted costs are those costs the in-dividual is expected to incur while avoidablecosts are that portion of the predicted costs thatmight be changed through some type of inter-vention.

In addition, each patient’s proximity to theprimary care medical center and pharmacy(PCRx) was calculated based on the patient’shome address zip code. Patients whose resi-dence was within 35 miles of the PCRx wereconsidered to be geographically proximate andhave access to the center for the medical careof their chronic condition. Patients with accessto the PCRx were classified either as HealthCenter 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 theHealth Center User list to identify patients whoutilized the health center for only acute caretreatment rather chronic condition treatment.Those patients utilizing the health center forepisodic acute care services only were reclassi-fied as Proximate Non-Users. Patients beyondthe 35-mile proximity radius were classified asNon-Proximate (Group 3). The resulting pa-tient population was then stratified on costsand those with relatively high avoidable costs(top two quintiles) were selected as having thepotential for the most significant improvementand included in the final study target popula-tion. The target patient population selectionprocess is illustrated in Figure 2.

The goal of this patient selection process wasto identify a relatively homogeneous popula-tion of patients with the target diseases to besubjected to the IDM protocol (Pat. Pend.) or theTDM protocol based on whether they utilizedthe worksite primary care health center or com-munity-based care. The final target populationconsisted of 1,890 patients. Analysis of the dis-ease prevalence for the study groups showed arelatively consistent burden of disease amongthe groups (Table 2). Thus, at this level of anal-ysis, it appears that the disease prevalence ofthese groups is comparable. The groups wereapproximately equal on other pertinent demo-graphic characteristics as well, although Group3, the Non-Proximate group, had fewer activeemployees than the other groups. This was notunexpected based on the definition of thisgroup given that most people live within 35miles of their workplace.

Contact rates C1, C2, C3, and C4 and enroll-ment rates E1, E2, E3, and E4 were calculatedfor each study group. Tracking of the patientcontact and enrollment process was performedusing 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. Thefourth group, with metrics C4 and E4, is basedon experience from our previous stand-aloneTDM programs using the same DM informa-tion system software. We considered usingother TDM industry performance rates, but de-

FRAZEE ET AL.20

finitive data were limited, and methods aroundTDM contact protocols vary widely in terms ofnumber of attempts, types of patients, types ofmessages left, and non-telephonic contactmethods. It was determined that a more validcomparison group would be our own TDM

contact experience, using historical data forsimilar patient groups and processes. Our pastTDM experience has shown that for a sampleof over 21,688 patients selected to be contactedfor telephonic DM services, 28% were con-tacted and 54% enrolled, leading to a 15% en-

LEVERAGING THE TRUSTED CLINICIAN: DM ENGAGEMENT RATES 21

FIG. 2. Protocol for the selection of the target patient population. CAD, coronary artery disease.

TABLE 2. TARGET POPULATION DISEASE PREVALENCE AND DESCRIPTIVES

Group 1population: Group 2proximate population: Group 3

health center proximate non- population: non-users users proximate

(n � 423) (n � 1,279) (n � 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 HTNMean no. of comorbidities 2.2 2.2 2.2

(out of 7a)Mean age, years 59.3 59.9 59.1Active 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 enrollmentrates for “ever enrolled” after a 12-month en-rollment period). These contact, enrollment,and engagement rates are fairly typical for theremote traditional DM industry.1 (Our pastTDM enrollment rate is at the lower end of theindustry 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 diseasestates and could have required up to nine as-sessments be completed.)

Some discussion of the issue of being able tocompare various DM programs either betweenor within DM vendors is warranted here. Theliterature 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 differentlyacross DM providers, the process to determinewhat equals “enrolled” also differs across DMproviders. For instance, opt-out programswhere the enrollee must request removal fromthe DM program would necessarily have amuch different enrollment rate than programsthat are opt-in, where enrollees must agree toparticipate at a minimum and often to completeother requirements. Because of this, we felt thebest option was to use our own historical en-rollment performance, but even that offerssome difficulties. The program discussed inthis study focused on three disease states (ie,diabetes, CAD, HTN) and continues to supportthe additional five diseases, but in a less struc-tured manner. This was true for all threegroups in the current study (Health CenterUser, Proximate Non-User, Non-Proximate).However, our past TDM experience focused oneight disease states (which included the threedisease states in the current study but with theaddition of asthma, neck and back pain, chronicobstructive pulmonary disease, congestiveheart failure, and a catch-all group called“Quality of Life”). It should be noted that theinterpretation of enrollment is consistent be-tween our past TDM experience and the cur-rent study; only the number of disease statesand potential number of assessments requiredto be completed to be considered “enrolled”differs. While our past TDM experience is nota perfect metric for comparison to the current

study TDM and IDM groups, it does provide acomparison of the core programmatic compo-nents. The enrollment and outreach processwere identical for both our historical and cur-rent TDM groups, similar to many other DMprograms.

The initial enrollment campaign began onFebruary 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 thecontacting 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, aseach contacted patient was subjected to eitherthe IDM (Pat. Pend.) or TDM enrollment pro-tocol, a standardized comment was added tothe DM application recording the dispositionof the enrollment event. Enrollment was de-fined as agreement by the contacted individualto participate and the completion of an initial15–20 minute intake assessment.

Outreach to the adjusted target populationconsisted of two different processes. First, forthose individuals currently utilizing the healthcenter (Group 1), clinicians at the PCRx eithersolicited enrollment during a scheduled officeappointment or made a telephone call to ex-plain program benefits and request agreementto participate. This agreement was followed bya call center nurse contact to complete the en-rollment process and complete an initial clini-cal assessment and goal setting session. Thiswas the outreach protocol defined for IDM protocol (Pat. Pend.). Second, two additionalgroups of non-health center users were alsostudied. The outreach for these two groupsconsisted of either a pharmacy clinician expla-nation of the program when the patient filleda prescription, or a series of two outbound callsfrom call center staff with a postcard reminderif the person was unreachable by phone. Thisoutreach method is defined as TDM. Therewere two TDM groups in the study: ProximateNon-Health Center Users (Group 2) and Non-Proximates (Group 3). Proximate Non-HealthCenter 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 centerfor chronic DM. The other group, the Non-Proximates (Group 3), consisted of individualsresiding further than 35 miles from the healthcenter.

Contact and enrollment statistics were thenreported by DM application, 90 days after theinitiation of the program.

RESULTS

The overall population included 7,818 activeemployees, retirees, and early retirees eligiblefor health benefits at the self-insured employer.Of these, 1,890 (24%) were identified as havingone or more of the eligible diseases (diabetes,CAD, HTN) and having avoidable costs in thetop two quintiles, and they comprised the tar-get population. Seventy-five individuals (4%)were excluded during the enrollment processfor such reasons as death, terminated employ-ment, or not having one of the eligible diseases.The adjusted target population totaled 1,815, or23% of the overall population. Successful con-tacts were achieved with 1,123 individuals, or59% of the target population and 62% of the ad-justed target population. Of those successfullycontacted, 693 (62%) agreed to participate. Asdescribed in the methods section, enrollmentwas defined as agreement to participate andcompletion of the initial 15–20-min intake as-sessment call. The overall engagement rate was38%, a 153% increase over past CHD MeridianTDM experience of 15% (significant at p � 0.01level).

As described more fully in the methods sec-tion, outreach to the adjusted target populationconsisted of two different processes: the IDMprotocol (Pat. Pend.) used for Group 1 involvedthe trusted clinicians in the contact and enroll-ment process, while the TDM protocol used forGroups 2 and 3, as well as the comparisongroup of our prior TDM experience (Group 4),focused on telephonic nurse-based outreach.

Group 1, those currently utilizing the healthcenter for the care of their chronic disease, con-sisted of 423 individuals. The contact rate forGroup 1 was 96% (n � 407). This high contactrate was primarily related to the health centerpossession 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 muchhigher enrollment rates than the other groups,with enrollment at 79% (n � 320) of those suc-cessfully contacted. The overall engagementrate for Group 1 therefore was 76%. This is il-lustrated in Figure 3. Contact rates, enrollmentrates, and engagement rates for Group 1 weresignificantly higher (p � 0.01 level) than forGroups 2–4.

For the two TDM groups in the study(Groups 2 and 3), contact and enrollment rateswere not as high as in Group 1. The first, Prox-imate Non-Health Center Users (Group 2),comprised individuals residing within 35 milesof the health center, but not utilizing the healthcenter primarily for chronic DM. Contact forthis group was initiated either by a pharmacyclinician explanation of the program at the timethey filled a prescription for the covered pa-tient, or by a series of two outbound calls madeby call center nursing staff and a postcard re-minder for those the call center was unable toreach. Group 2 consisted of 1,279 individuals.The contact rate was 50% (n � 641). This is astatistically significant improvement (p � 0.01)over our historical TDM contact rate of 28%. Itshould be noted that the promotion by thepharmacy clinician has not been part of our his-torical TDM protocol. Of those successfullycontacted from Group 2, 327 or 51% agreed toparticipate. Thus, the engagement rate was26%, a statistically significant improvementover our historical TDM experience (p � 0.01).The other TDM group, Non-Proximate (Group3), consisting of 188 individuals residing far-ther than 35 miles from the health center. Allcontact for this group was by telephone fromthe call center nursing staff with a postcard sentto those who were unreachable. The contactrate was 40% (n � 75), while 61% of those con-tacted enrolled (n � 46), for an overall engage-ment rate of 24%. The contact rate and en-gagement rate were significantly higher (p �0.01 level) than our historical TDM experience(Group 4), but the enrollment rate was not sig-nificantly different.

Additionally, two subgroups within Group2 were studied further. These subgroups ac-

LEVERAGING THE TRUSTED CLINICIAN: DM ENGAGEMENT RATES 23

counted for 466 individuals, or 36% of Group2. The first subgroup, Pharmacy Clinician Re-ferral, was defined as the 238 study participantsfrom Group 2 who utilized the pharmacy co-located at the health center for prescription ser-vices. The contact rate for this subgroup was55% (131) with enrollment rates for those con-tacted at 63% (82) and an engagement rate of34%. The enrollment and engagement rates forthe Pharmacy Clinician subgroup were statis-tically higher than those in Group 2 overall (p �0.05 level). When compared with Group 4 (ie,our historical TDM experience), however, onlythe contact and engagement rates are signifi-cantly higher (p � 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, andincluded 228 individuals. For this subgroup,the contact rate was 46% (104), with an enroll-ment rate of 58% (61) and an engagement rateof 26%. None of these rates were significantlyhigher than the overall Group 2 contact, en-rollment, or engagement rates at the p � 0.05level but, like the Pharmacy Clinician Referralsubgroup, because the contact rate was signif-icantly higher than that of our historical TDM

experience (p � 0.01), engagement rates werealso significantly greater.

The results of this study have affirmed bothresearch hypotheses. The first hypothesis, thatthe contact rate for patients exposed to the IDMprotocol (Pat. Pend.) will be significantly higherthan patients exposed to the traditional TDMprotocol (Contact Efficiency hypothesis), wasproven as there is a statistically significant dif-ference (p � 0.01) between Group 1 and Groups2 through 4. The second hypothesis, that theenrollment rate for patients exposed to the IDMprotocol (Pat. Pend.) will be significantly higherthan patients exposed to the TDM protocol (Enrollment Efficiency Hypothesis), was alsoproven as the engagement rate for Group 1 wassignificantly higher than Groups 2–4 (p � 0.01).

DISCUSSION

With over 20 million Americans receivingtelephonic DM programs,11 improving the ef-ficiency of contacting and enrolling individualsin these programs has the potential to reduceoverall costs while increasing participationrates. The latter is expected to increase the over-

FRAZEE ET AL.24

FIG. 3. Evaluation of process metrics for cohorts. *Patent pending. #Engagement rate � contact rate � enrollment rate.

all impact of DM programs, even if the actualpercentage of patients showing improvements“downstream” does not change. This study hasreviewed a new protocol (Pat. Pend.) that showspromise for increasing both contact and en-rollment efficiency. It also helps quantify thevalue of the “trusted clinician” in the contactand enrollment process.

The value of the trusted clinician is most ap-parent in Group 1, where the primary carehealth center and full-service pharmacy oper-ated at the patient’s worksite was able to notonly provide improved selection and contact in-formation over what is traditionally available toDM programs, but also to become part of therecruitment 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. Figure4 shows the impact of the trusted clinician inthis study as compared to Groups 2 through 4.Note that 76% of the IDM group engaged,where the trusted clinicians played an integralrole in encouraging enrollment into the DMprogram. An engagement rate of 76% is fivetimes greater than our traditional TDM experi-ence and three times greater than engagementsrates for Groups 2 and 3. The importance of hav-ing a “trusted clinician at the workplace™” whoencourages participation in DM programs hasbeen shown to be so strong in this study that itwould 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 evenwhen the trusted clinician was a pharmacist.The value of pharmacy clinicians in DM pro-grams has been reported elsewhere12 as well. InGroup 2, the Pharmacy Clinician Referral sub-population had a contact rate of 55% (comparedto 50% for Group 2 overall) and an enrollmentrate of 34%, which is 31% higher than the en-rollment rate for Group 2 overall (p � 0.05). Ad-ditionally, the Acute Care Users subpopulationin Group 2 had a contact rate of 46% (comparedto 40% for Group 3). As interaction with healthcenter staff increases, the contact and enroll-ment success also improves. While it was nottested in this study, we would expect to findthat other types of trusted clinicians, such asmidlevel 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 thestudy population and then obtain contact in-formation involved several more steps than istypically found in telephonic DM programs.While this made the program implementationmore labor-intensive, these steps also contrib-uted to the success of the enrollment process.Like many DM vendors, we used an outsidevendor to provide a clean list of phone num-bers for those without a valid phone numberin the patient demographics provided by theemployer’s third-party administrator. How-ever, we actually found that we were able toobtain a higher contact rate from the phonenumbers provided by the primary care healthcenter than from those purchased from theclean list vendor.

In addition, we stratified on avoidable coststo help identify the best potential candidatesfor intervention. Many DM programs stratifyon total costs but we feel that using avoidable

LEVERAGING THE TRUSTED CLINICIAN: DM ENGAGEMENT RATES 25

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, fewerthan approximately 1,500 employees) typically do nothave the level of utilization a full-service, on-site primarycare 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 reducibleby some type of intervention) is a bettermethod to identify individuals who are appro-priate candidates for DM services. Given thelimited resources most customers of DM ser-vices have for these programs, along with theneed for a return on their investment (or at leastcost neutrality), selecting the right target pop-ulation is integral to the program’s success.

We looked at the avoidable cost distributionand 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 contactstage (overall mean of $337). However, therewere some differences in the enrolled groups,with Proximate Health Center Users and Non-Proximates having lower mean avoidable coststhan the Proximate Non-User group ($299 and$255 versus $468). While it appears that theProximate Non-User group is different in someway from the other groups, the variation canbe explained by the existence of two very largeoutliers 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’shigh-cost individuals are not necessarily to-morrow’s high-cost individuals.13 However,the proprietary predictive model algorithm weemployed also generates statistically derivedweights from demographic and diagnostic datathat estimate an individual’s risk of becominghigh cost in the future, regardless of the cur-rent cost burden shown in the individual’sclaims history, which reduces this bias.14,15

Careful consideration was given to ensurethat the IDM and TDM groups were equiva-lent. In addition to looking at disease preva-lence and comorbidities (Tables 1 and 2), wealso tested the mean avoidable costs for eachgroup 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 ourown stand-alone TDM book of business expe-rience as shown in Figure 3. We believe ourstand-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 thanpast experience. This is due to the health cen-ter providing better contact information than istypically found using data provided by em-ployers or their third-party administrator. Inboth processes (ie, historical TDM experienceand the new IDM experience), a third-partyvendor was used to find phone numbers forthose individuals with missing contact infor-mation, but the most accurate contact informa-tion came from the health center where thepatient enjoys a relationship with a trustedclinician; the health center is more likely tomaintain current and correct phone and ad-dress information. Exclusions from the targetpopulation 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 tocheck the eligibility of potential participantsprior to including them in the target popula-tion group, which reduced the percentage ex-cluded due to termination of employment orhealth benefits. For the DM industry, havingdata this accurate reduces costs while greatlyincreasing engagement rates.

It should be noted in the enrollment phasedata shown in Figure 3 that individualsopted-in at a much higher rate than with ourstand-alone TDM experience. Because wehave operated on-site health centers andpharmacies at several locations for this em-ployer for several years, the target popula-tion may have been familiar with our brandand therefore more likely to be open to par-ticipation. Additionally, we believe that thisprocess was further enhanced by union andemployer endorsement. The overall enroll-ment rate as noted in the results section was153% higher (p � 0.01) than in our historicalTDM experience.

In order to better understand which contactand engagement processes were most influen-tial we analyzed the process flow. The IDMgroup had the highest enrollment rate at thefirst contact (83.9%), either via an outbound callfrom a health center nurse or via having theDM program discussed and enrollment offeredin person by the health center staff. The latteronly 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 rateoccurred on the first or second telephone con-tact attempt (37.6%), with ensuing effortsshowing diminishing returns.

While the results of this study are quitepromising for the DM industry, there are sev-eral limitations. First, only one self-insured em-ployer was studied. However, this employerhas a large employee population and the re-sults obtained statistical significance. While notdirectly related to the results of this study, itshould be noted that worksite primary care andpharmacy centers are most common with self-insured employers who have a geographicallyconcentrated workforce. The need for a criticalmass of employees at a given location (typicallyaround 1,500 employees) is primarily economicin nature as a certain level of utilization is nec-essary to provide an adequate return on in-vestment after the costs of implementing andoperating a worksite health center and phar-macy are taken into account.

Second, the nature of administrative claimsdata lends itself to some limitations. Claimsdata are collected primarily for billing pur-poses; thus, using coding algorithms to deter-mine the existence of disease may be incorrectinsofar as the data does not include all clini-cally relevant information. Another limitationis the inability to know with confidencewhether the claims available were exhaustive.Incomplete data would mean missing potentialparticipants who had at least one of the dis-eases targeted using the IDM and TDM proto-cols. The integration of clinical informationfrom the primary care clinicians can reduce thislimitation.

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 aswell.

Fourth, this research is based on an opt-inmodel and therefore may not be applicable toopt-out program models. That being said,however, we do feel that encouragement froma “trusted clinician” increases actual engage-ment.

Using a primary care setting to deliver chroniccare management has shown promise in recent

studies.16,17 Future research should extend thisprotocol to multiple clients in order to improvethe generalizability of the results of this IDMprotocol study. In addition, while this study fo-cused primarily on process metrics, future stud-ies are planned to evaluate enrollment erosionrates are well as the clinical, financial, and uti-lization outcomes of the patients enrolled inIDM versus those enrolled in the TDM protocol.

This study suggests that coordinating the“trusted clinicians at the workplace™” with remote telephonic nurse coaches—aligningcaregivers into a single, integrated deliverymodel—will bring us closer to realizing the po-tential value of population health managementthat encompasses healthier employees, re-duced healthcare costs, reduced absenteeism,and increased productivity.

DEFINITIONS

Active Enrollment: Those covered individualswho agreed to enroll in the disease manage-ment program and have not terminated due torequested disenrollment, death, or loss of eli-gibility of health benefits.

Adjusted Target Population: The resulting in-dividuals not excluded due to death, primaryresidence in a long-term care or hospice facil-ity, ineligibility for health benefits from theclient, or not having one of the eligible diseasesand 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 thatcan 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. Successfulcontact is talking with an individual.

Coronary Artery Disease (CAD): CAD canclinically present in various ways with cardiacpain, cardiac tissue injury, and rhythm distur-bances being most common according to theDictionary of Disease Management Terminol-

LEVERAGING THE TRUSTED CLINICIAN: DM ENGAGEMENT RATES 27

ogy (DMAA 2004). The primary diagnosiscodes for CAD include 410.xx, 411.00–411.89,412, 413.xx, 414.xx, and specific codes in the420, 423, 429 series.

Diabetes: Diabetes is Diabetes Mellitus with andwithout complications as indicated by a primarydiagnosis code of 250.xx. This definition in-cludes Type I and Type II diabetes and con-trolled and uncontrolled diabetes but does notinclude gestational diabetes, which is consistentwith the Health Plan Employer Data and Infor-mation Set (HEDIS) definition for this disease.

Engagement Rate: The weighted enrollmentrate which is the product of the contact ratemultiplied by the enrollment rate.

Enrollment Rate: Number of individuals en-rolled into the program divided by the numberof individuals successfully contacted. Enroll-ment is defined as securing an individual’sagreement to participate in the program and theindividual completing an initial 15–20 minuteassessment.

Ever Enrolled: The cumulative number of cov-ered individuals who ever agreed to enroll inthe disease management program, not onlythose actively enrolled.

Hypertension (HTN): More commonly knownas high blood pressure, HTN is one of the mosttreatable cardiovascular diseases but is also themost common with an estimated 65 millionAmericans suffering from it according to theCenters for Disease Control and Prevention(Healthy People 2000). Defined as a primary di-agnosis code of essential hypertension from the401 and 402 series of ICD9 codes.

Non-Proximate: Covered individuals who livefurther than 35 miles from the worksite-basedprimary care center.

Non-Users: Covered individuals who do notuse the worksite-based primary care center forhealthcare services.

Predicted Costs: The total expected futuremedical costs for an individual over the next12-month period.

Proximate: Covered individuals who livewithin 35 miles of the worksite-based primarycare 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 thepredictive modeling process as being treatedfor one or more eligible diseases (ie, diabetes,HTN, CAD) and having avoidable costs in thetop two quintiles.

Termed: Individuals who were on the list ofthose to be invited to enroll into the diseasemanagement program, but who are no longereligible for services due to termination of em-ployer-sponsored health benefits.

Unable to Contact: Individuals who were onthe list of those to be invited to enroll into thedisease management program but who werenot able to be contacted after the agreed-uponnumber of attempts. Contact methods includedscheduled primary care center appointments,telephone calls, or other means such as mail.

Users: Individuals who use the worksite-basedprimary care center for healthcare services.

ACKNOWLEDGMENTS

All authors were employed by CHD Merid-ian Healthcare during this research project,which offers IDM services. CHD MeridianHealthcare provided all financial support.

REFERENCES

1. Lynch WD, Chen CY, Bender J, Edington DW. Docu-menting participation in an employer-sponsored dis-ease management program: selection, exclusion, at-trition, and active engagement as possible metrics. JOccup Environ Med 2006;48:447–454.

2. Centers for Disease Control and Prevention. Totalprevalence of diabetes among people aged 20 yearsor older, United States, 2002. Available at: www.cdc.gov/diabetes/pubs/estimates.htm#prev2. AccessedJuly 1, 2005.

3. American Heart Association. Heart disease andstroke statistics—2004 update. Available at: http://www.americanheart.org. Accessed July 1, 2005.

FRAZEE ET AL.28

4. Groseclose SL, Knowles CM, Hall PA, et al. Summaryof notifiable diseases—United States, 1998. MMWRMorb Mortal Wkly Rep 1999;47:1–93.

5. Congressional Budget Office. An analysis of the liter-ature on disease management programs. 2004. Avail-able at: www.cbo.gov. Accessed October 1, 2005.

6. Epstein RS, Sherwood LM. Outcomes research to dis-ease management: a guide to the perplexed. Ann In-tern Med 1996;124:832–837.

7. Goetzel RZ, Ozminkowski RJ, Villagra VG, Duffy J.Return on investment in disease management: a re-view. Health Care Financ Rev 2005;26:1–19.

8. Krause D, Charlton W, Courtemanche T. Longer termimpact of a chronic complex disease managementprogram versus matched reference groups. Presentedat DMAA 5th Annual Disease Management Leader-ship Forum, Chicago, 2003.

9. Linden A, Adams J, Roberts N. Generalizing diseasemanagement program results: how to get from hereto there. Manag Care Interface 2004;17:38–45.

10. Weingarten SR, Henning JM, Badamgarav E, et al. In-terventions used in disease management programmesfor patients with chronic illness—which ones work?Meta-analysis of published reports. BMJ 2002;325:925–940.

11. Health Industries Research Companies, Health andDisease Management Service. Appendix 1: leadingdisease management organizations. Comprehensivereport on the disease management industry. 2005.Available at: www.dismgmt.com/dmreport.asp. Ac-cessed October 1, 2005.

12. Cranor CW, Bunting BA, Christensen DB. TheAsheville Project: long-term clinical and economic

outcomes of a community pharmacy diabetes careprogram. J Am Pharm Assoc 2003;43:173–184.

13. Ash AS, Zhao Y, Ellis RP, Kramer MS. Finding futurehigh-cost cases: comparing prior cost versus diagno-sis-based methods. Health Services Res 2001;36:194–206.

14. Brody KK, Johnson RE, Ried DL, Carder PC, PerrinN. A comparison of two methods for identifying frailMedicare-aged persons. J Am Geriatr Soc 2002;50:562–569.

15. Cumming RB, Knutson D, Cameron BA, Derrick B.A comparative analysis of claims-based methods ofhealth risk assessments for commercial populations.Society of Actuaries, 2002. Available at: www.soa.org.Accessed October 1, 2005.

16. Platt GA, Orchard TJ, Emerson SE, et al. Translatingthe chronic care model into the community. DiabetesCare 2006;29:811–817.

17. Celeste-Harris S, Maryniuk M. Educating medical of-fice staff: enhancing diabetes care in primary care of-fices. Diabetes Spectrum 2006;19:76–78.

Address reprint requests to:Raymond Fabius, M.D.

CHD Meridian Healthcare4 Hillman Drive, Suite 130

Chadds Ford, PA 19317

E-mail: rjfabius@chdmeridian.com

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