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The American Journal of Managed Care February 2012
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Nurse-Run, Telephone-Based Outreach to Improve Lipids in People With Diabetes
Henry H. Fischer, MD; Sheri L. Eisert, PhD; Rachel M. Everhart, MS; Michael J. Durfee, MSPH; Susan L. Moore, MSPH; Stanley Soria, RN; Diana I. Stell, RN; Cecilia M. Rice-Peterson, RN, BSN; Thomas D. M
A Dementia Care Management Intervention: Which Components Improve Quality?
Joshua Chodosh, MD, MSHS; Marjorie L. Pearson, PhD, MSHS; Karen I. Connor, PhD, RN, MBA; Stefanie D. Vassar, MS; Marwa Kaisey, BS; Martin L. Lee, PhD; and Barbara G. Vickrey, MD, MPH
Hospital Readmission Rates in Medicare Advantage Plans
Jeff Lemieux, MA; Cary Sennett, MD; Ray Wang, MS; Teresa Mulligan, MHSA; and Jon Bumbaugh, MA
Early Evaluations of the Medical Home: Building on a Promising Start
Deborah Peikes, PhD; Aparajita Zutshi, PhD; Janice L. Genevro, PhD; Michael L. Parchman, MD; and David S. Meyers, MD
Identifying Patients With Osteoporosis or at Risk for Osteoporotic Fractures
Yong Chen, MD, PhD; Leslie R. Harrold, MD, MPH; Robert A. Yood, MD; Terry S. Field, DSc; and Becky A. Briesacher, PhD
Care by Cell Phone: Text Messaging for Chronic Disease Management
Henry H. Fischer, MD; Susan L. Moore, MSPH; David Ginosar, MD; Arthur J. Davidson, MD, MSPH; Cecilia M. Rice-Peterson, RN, BSN; Michael J. Durfee, MSPH; Thomas D. MacKenzie, MD, MSPH; Raymond O. Estac
Systematic Review of the Impact of Worksite Wellness Programs
Karen Chan Osilla, PhD; Kristin Van Busum, MPA; Christopher Schnyer, MPP; Jody Wozar Larkin, BSN, MLIS; Christine Eibner, PhD; and Soeren Mattke, MD, DSc
Adaptation and Psychometric Properties of the PACIC Short Form
Katja Goetz, PhD; Tobias Freund, MD; Jochen Gensichen, MD, MA, MPH; Antje Miksch, MD; Joachim Szecsenyi, MD, MSc; and Jost Steinhaeuser, MD
EHRs in Primary Care Practices: Benefits, Challenges, and Successful Strategies
Debora Goetz Goldberg, PhD, MHA, MBA; Anton J. Kuzel, MD, MHPE; Lisa Bo Feng, MPH; Jonathan P. DeShazo, PhD, MPH; and Linda E. Love, LCSW, MA

Nurse-Run, Telephone-Based Outreach to Improve Lipids in People With Diabetes

Henry H. Fischer, MD; Sheri L. Eisert, PhD; Rachel M. Everhart, MS; Michael J. Durfee, MSPH; Susan L. Moore, MSPH; Stanley Soria, RN; Diana I. Stell, RN; Cecilia M. Rice-Peterson, RN, BSN; Thomas D. M
Nurses can improve lipid control in people with diabetes in a primarily indigent population through telephone care using moderately complex algorithms. Telephone-based outreach may decrease resource utilization.
Background: There is a need for randomized, prospective trials of case management interventions with resource utilization analyses.


Objectives: To determine whether algorithm-driven telephone care by nurses improves lipid control in patients with diabetes.


Design: Prospective, randomized, controlled trial.


Participants: Adults with diabetes at a federally funded community health center were randomly assigned to intervention (n = 381) or usual-care (n = 381) groups.


Interventions: Nurses independently initiated and titrated lipid therapy and promoted behavioral change through motivational interviewing and self-management techniques. Other parameters of diabetes care were addressed based on time constraints.


Main Measures: The primary outcome was the proportion of patients with a low-density lipoprotein (LDL) less than 100 mg/dL. Secondary outcomes included the number of hospital admissions, total hospital charges per patient, and the proportion of patients meeting other lipid, glycemic, and blood pressure guidelines.


Key Results: The percent of patients with an LDL <100 mg/dL increased from 52.0% to 58.5% in the intervention group and decreased from 55.6% to 46.7% in the control group (P <.01). Average cost per patient to the healthcare system was less for the intervention group ($6600 vs $9033,= .03). Intervention patients trended toward fewer hospital admissions (P = .06). The intervention did not affect glycemic and blood pressure outcomes.


Conclusions: Nurses can improve lipid control in patients with diabetes in a primarily indigent population through telephone care using moderately complex algorithms, but a more targeted approach is warranted. Telephone-based outreach may decrease resource utilization, but more study is needed.


(Am J Manag Care. 2012;18(2):77-84)
Nurses can improve lipid control in patients with diabetes in a primarily indigent population through telephone care using moderately complex algorithms, but a more targeted approach is warranted. Telephone-based outreach may decrease resource utilization, but more study is needed.

  • The percent of patients with a low-density lipoprotein <100 mg/dL increased from 52.0% to 58.5% in the intervention group as compared with decreasing from 55.6% to 46.7% in the control group.

  •  Average cost per patient to the healthcare system was less for the intervention group ($6600) than for the control group ($9033).

  • Intervention patients trended toward fewer hospital admissions.
Diabetes is a disease growing in epidemic portions worldwide with associated costs that signifi cantly impact healthcare systems. In the United States more than 20 million persons have been diagnosed with diabetes, a number which at the current rate will increase to more than 30 million by the year 2030.1,2 Estimates of diabetes costs rose from $130 billion in 2002 to $174 billion in 2007, and are projected to reach $192 billion by 2020.1,3,4

In our safety-net institution, Denver Health Medical Center (DHMC), the majority of patients with diabetes are of Latino ethnicity (59%) with a substantial minority being of African American race (21%). Most of the patients enrolled in our system have either no or inadequate insurance coverage. Consistent with national trends, the majority of our patients with diabetes do not meet the targets of care recommended by the American Diabetes Association (ADA) and National Cholesterol Education Program (NCEP) guidelines.5 These guidelines are based on numerous prospective interventional studies which demonstrated the delay or prevention of the microvascular and macrovascular complications associated with this disease.6,7

The reasons for suboptimal performance on diabetes outcomes are complex and involve the patient, the provider, the systems of healthcare delivery, the ability to track and assist patient populations, and societallevel factors.4 Potentially modifi able patient-level factors include lack of diabetes education, resources, and motivation. Competing demands in the 20-minute visit limit the provider’s ability to identify barriers and educate and motivate patients.8,9

Nurse-run case management programs may overcome some of these chronic disease management barriers through frequent patient contact, tracking of patients, and the use of motivational interviewing and selfmanagement techniques. Recent reviews of diabetes disease-management programs point to mounting evidence for the benefi ts of nurse-run case management and targeted interventions in the clinic.10,11 There is a growing but insuffi cient study of telephone-based case management in diabetes. Telephone-based diabetes interventions have often focused on glycemic control, targeting the veteran population as well as elderly, ethnically diverse patients,12-15 and often focus on glycemic control. To date, however, we are not aware of any study of a nurse-run, telephone-based intervention to improve lipid control in a population with diabetes composed mainly of minorities with no or inadequate insurance coverage. Also, there is a call for large, prospective diabetes case-management interventions with resource utilization analyses.11

METHODS

Design, Setting and Participants, and Randomization

The design, setting and participants, and randomization flow are described in a previous publication.16 Briefl y, this randomized, controlled trial was conducted at Denver Health’s Westside Family Health Center (Westside Clinic), a federally funded community health center which serves a primarily indigent, Latino population. We included only adult patients (aged >17 years) in our diabetes registry who were actively utilizing Westside Clinic for their primary care (at least 2 visits in the past year) and who spoke either English or Spanish. We sought to maximize the generalizability of the study and therefore had only minimal exclusion criteria: pregnant or lactating women, patients with end-stage renal disease (creatinine >3.0 mg/dL), and/or a comorbid illness with life expectancy less than 12 months (eg, terminal cancer or Child’s-Pugh Class C hepatic cirrhosis).

Intervention

The details of this intervention have been previously published.16 Briefl y, this telephone outreach program lasted 20 months, ending in May 2007, and was considered an adjunct to usual care. At the time of this intervention, usual care consisted of largely primary care provider–driven diabetes care with most providers recommending clinic visits every 1 to 3 months for patients not at diabetes goal, and every 6 months for those at goal. The study nurses focused on lipid management, using algorithms based on published guidelines from the most current NCEP and ADA recommendations.17,18 The nurses independently checked labs and initiated and titrated lipid-lowering medications over the telephone with a 2-week follow-up call to assess for medication side effects and a 6-week followup call to recheck lipids after medication changes.16 The nurses were also trained in motivational interviewing techniques and facilitation of patient self-management. Additionally, the nurses used algorithms addressing other aspects of diabetes care which included glycemic and blood pressure control, update on vaccinations, and facilitation of primary care provider (PCP) and subspecialty appointments based on time constraints. The nurses used pre-printed prescriptions signed by a physician champion who offered educational and management support.

Baseline Data

Relevant demographic and laboratory data were extracted from an integrated electronic health record for all patients. Comorbidities were determined based on all historical diagnosis codes that existed for the patients in our integrated system. Medication use was determined based on outpatient pharmacy records for prescriptions that were filled within our system in the 3 months leading up to study initiation.

Outcomes and Measures

The primary outcome is the proportion of patients (both with and without cardiovascular disease [CVD]) with a lowdensity lipoprotein (LDL) less than 100 mg/dL, secondary outcomes include: 1) average hospital charges per patient including inpatient, outpatient, emergency department, and intervention costs; 2) total number of inpatient admissions; 3) proportion of patients with CVD with an LDL less than 70 mg/dL per NCEP guidelines17; 4) percentage of patients with last blood pressure less than 130/80 mm Hg as recommended by The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure19 percentage of patients with glycated hemoglobin (A1C) less than 7 mg/dL per ADA guidelines.18

Statistical Analysis

Sample Size Calculations

We performed a conservative sample size estimate by assuming that 50% of control patients at study end would achieve the target LDL of less than 100 mg/dL (no improvement) as compared with 70% in the intervention group. Utilizing an alpha = 0.05, power of 90%, we estimated that we needed 129 patients in each randomization group. However, we expanded the sample size to 381 patients in each group since previous chronic disease management at DHMC suggested nurses could handle a panel of this size and allowed for cost analyses.

Clinical Outcomes Analyses

Differences in lipid, glycemic, and blood pressure outcomes were compared pre- and post-intervention for both the control and treatment groups using an intention-to-treat analysis. Patients without a lab value during the designated time period were counted as not being at goal for that time period. Multivariate logistic regression controlled for baseline lipid performance, age, gender, insurance, and ethnicity for the primary lipid outcomes. Statistical analyses were performed using SAS (version 9.1, SAS Institute Inc, Cary, North Carolina) software.

Measuring Healthcare Utilization and Costs


Nine separate multiple linear regression models were used to compare treatment and control groups over time according to the following utilization and cost measures: number of inpatient admissions; number of outpatient visits; number of emergency visits; total inpatient costs; total outpatient costs; total emergency costs; sum of all costs; and total cost per patient enrolled.

The linear models were adjusted to account for differences in age, race/ethnicity, gender, baseline levels for each outcome variable, and degree of illness, and included generalized estimated equations (GEEs) to account for the within-subject correlation of repeated measures by individual patients. Patients lost to follow-up, defi ned as no primary care visit in the year preceding the end of the study, were not assessed when comparing baseline with the intervention period, for consistency of results.

Costs were evaluated from the perspective of the healthcare system, which included case-management programmatic costs and the incremental costs associated with the change in the utilization of healthcare services. Cost-to-charge ratios were used to convert charges to the patients into costs to the healthcare system. These ratios are developed using the total costs divided by the total charges for inpatient admits, outpatient visits, and emergency visits separately. These total costs include indirect and direct costs. The cost-to-charge ratios applied were 0.43 for inpatient admits, 0.51 for outpatient visits, and 0.35 for emergency visits.

Risk Adjustment

The Chronic Illness and Disability Payment System (CDPS) diagnostic classifi cation model was used to risk adjust the study population. CDPS uses a diversity of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes to group and weight diagnoses for chronic and disabling diseases.20 A comparison among Medicaid beneficiaries found that CDPS compared favorably with other leading diagnostic classification systems, with moderate advantage over the Hierarchical Condition Category model and signifi cant advantage over the Adjusted Clinical Groups model.20 Given that more than 90% of the study patient population is composed of Medicaid patients and the uninsured, the CDPS model was chosen to best reflect the degree of illness among the study participants.

Role of the Funding Source

This study was in part funded by the ADA, which played no role in patient selection, implementation of the intervention, interpretation of the data, or writing of the manuscript.

RESULTS

Baseline Characteristics

 
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