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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. MacKenzie, MD, MSPH; and Raymond O. Estacio, MD
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. Estacio, MD; and Andrew W. Steele, MD, MPH, MSc
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. MacKenzie, MD, MSPH; and Raymond O. Estacio, MD
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.
The intervention group performed signifi cantly better than the usual-care group on our primary outcome, the percent of patients with an LDL less than 100 mg/dL in the preceding year (increased from 52.0% to 58.5% vs decreased from 55.6% to 46.7%, P <.01, Table 2). An “on-intervention” analysis (Table 3) compared lipid outcomes for the control group versus intervention patients with 3 or more contacts by the nurses during the study period. A higher percentage of the patients in this intervention subgroup were at goal at study end for the primary lipid outcome of LDL less than 100 mg/dL (69.1% vs 46.7%, P <.01). Among those patients with cardiovascular disease, only the “on-intervention” patients achieved the goal of LDL less than 70 mg/dL more than the control patients (50.0% vs 30.4%, P = .02). The intervention did not impact glycemic and blood pressure outcomes.

Healthcare Utilization and Costs

Table 4 provides a comparison between the intervention and control groups regarding utilization of healthcare services. At baseline, the control and treatment groups experienced similar rates of outpatient and emergency department (ED) visits. The control patients at baseline experienced more hospital admissions and higher associated cost per patient than the intervention patients. During the study period, the rate of inpatient admissions decreased for the intervention group whereas there was an increase for the control group that trended toward statistical significance (adjusted GEE, P = .06).

The case management intervention was not associated with a change in the number of outpatient visits (adjusted GEE, P = .60) or the number of emergency care visits (adjusted GEE, P = .19). For the nurse telephone intervention group, we observed a significant decrease in total costs of $60,802 when comparing the 18-month time period prior to randomization with the same time period post randomization. In contrast, there was an increase of $477,467 for the

same comparison in the control group (adjusted GEE, P = .01). Similar results were demonstrated with hospitalization costs; the intervention group was associated with a decrease of $109,077 versus an increase of $268,723 in the control group (adjusted GEE, P =.02). A similar trend was seen for ED costs (decreased by $13,702 vs increased by $39,890 in the control group, adjusted GEE, P = .05). There was not a significant intervention effect on outpatient costs (adjusted GEE, P = .47). This pattern of healthcare utilization was not altered in our “on-intervention” analysis (data not shown).

The direct programmatic costs for the case management program were $134,750 over the 20-month intervention. Incorporating programmatic costs, the average cost per patient to the healthcare system for patients enrolled in the nurse case management program was $6600 whereas the average cost per patient for those with diabetes not enrolled in the program was $9033. The difference in average per-patient cost between these 2 groups was $2433 (adjusted GEE, P = .03).

DISCUSSION

This nurse-run, telephone-based case management program served a vulnerable, underinsured population and was associated with improved lipid control and a decrease in overall healthcare utilization. The percentage of patients achieving the LDL goal of less than 100 mg/dL increased in\the intervention group and decreased in the usual-care group. The intervention was also associated with higher percentage of patients with pre-existing cardiovascular disease reaching a more rigorous target of LDL less than 70 mg/dL. Additionally, the intervention was associated with lower healthcare utilization and total costs, which was primarily attributable to less inpatient and emergency department utilization. There was no significant difference in other secondary outcomes, including glycemic control and blood pressure control.

The strength of the current study is that it was completed in a vulnerable underinsured population composed mainly of Latino patients while utilizing less than 1 full nurse full-time equivalent (FTE). This population is often difficult to reach and has historically had low rates of compliance and clinic attendance. This is certainly exemplified in our study, in which the nurse was unable to contact 65 of the 381 patients. Of the remaining 316 patients, 217 were contacted 3 or more times throughout the intervention. Despite these obstacles, the intervention demonstrated a positive effect on achieving lipid control when compared with the control group. Furthermore, there appeared to be a dose effect of the intervention. When including only patients who had at least 3 contacts with the nurse, the percentage reaching an LDL goal of less than 100 mg/dL increased to nearly 70%.

A disappointing finding, but not surprising given the nurse focus on lipids, was that the intervention did not impact blood pressure or glycemic control when compared with the control group. These results are compared with studies performed by Piette et al in 2 county health clinics21 and in the Veterans Administration Healthcare System12 in which automated calls combined with a nurse follow-up call resulted in improved glycemic control, fewer hypoglycemic reactions, and greater patient satisfaction with their healthcare. In both these studies, there appeared to be more patient interaction with the healthcare system when compared with our intervention. Improving blood pressure and glycemic control often requires more complex medication regimens than lipid control. For lipid control, using 1 medication, a statin, would often be sufficient to get the patient to goal. A major difference between our study and the studies of Piette et al is the method of recruitment of patients into the study. For our study, we purposely randomized patients from our diabetes registry prior to contacting them as opposed to recruiting at clinic visits. This design enabled us to evaluate the effi cacy and effectiveness of the intervention across a broad sample of our diabetes population. As a result, the study nurse was unable to contact 17% of the 381 patients randomized to the intervention.

At baseline, over 20% of the intervention population was hospitalized at least once 18 months prior to randomization and more than 25% made at least 1 ED visit. During the follow-up period of nearly 20 months, the intervention was associated with lower overall costs and a trend toward less hospitalizations. It is difficult to establish causality between the intervention and the lower healthcare utilization. One could hypothesize that the increased number of inpatient admissions in the control group related to hyper- and hypoglycemia, as well as secondary infection (pneumonia and pyelonephritis), might have been prevented with closer nurse-based monitoring. Frequent contact would allow nurses to identify worrisome clinical indicators and coordinate care to prevent worsening clinical status. Chart review revealed that of the 5 control patients with the highest charges, 3 failed to follow up as recommended by their primary care providers. One of these 3 patients was admitted with hyperglycemia and sepsis and required additional admits for secondary complications; another was admitted for hyperglycemia and osteomyelitis, which also led to subsequent admits; and the third patient developed recurrent cellulitis and urinary tract infections.

Were there baseline differences between the intervention and control groups that may have affected our results? The control and intervention groups did not differ significantly in baseline comorbidities, except for a higher baseline rate of cerebrovascular disease in the control group, a difference that was small but statistically signifi cant (14% vs 9%, P = .04). Also, a higher percentage of control patients were using insulin at baseline (38% vs 24%, P = .01). In our healthcare utilization analysis, GEE equations adjusted for the higher percentage of females in the intervention group. Most strikingly, the control group had higher baseline hospitalization rates and total costs. As discussed in the methods section, the CPDS risk adjustment methodology was well suited to the DHMC patient population, which demonstrated equivalent baseline risk between the 2 groups. Although we demonstrated impressive results regarding a decrease in both hospitalizations and total costs in the intervention group while both increased in the control group, our results should be interpreted with caution given the baseline differences noted above.

How can we improve the design of this case-management intervention? As noted, we purposely designed this randomized study to evaluate the effectiveness and the “reach” of this program. This aspect of the study is important since many safety net healthcare institutions, like ours, provide care to thousands of patients with diabetes and confront similar obstacles for their patients accessing care. The major obstacle as reported by the nurses for this study was the diffi culty contacting and obtaining buy-in from some patients. Unfortunately we did not include focus groups or individual interviews to clearly identify obstacles and potential solutions with the patients.

Nurses could expand their panel size in 2 ways. First, medical assistants could take the lead role in using the diabetes registry on a regular basis to identify and contact those patients due for labs or follow-up. Or, ideally, automated fl agging of and outreach to at-risk patients would occur through an electronic health record through various modalities, such as text message, automated phone calls, e-mail, Skype, audio video telemedicine, patient portals, or smart phone applications, based on patient preference. Second, nurses could change their focus to starting statin medications, as opposed to up-titrating them, which is a time-consuming, lower-yield process. The nurses would need to be allotted sufficient time to also implement basic blood pressure and glycemic control

algorithms, taking much of the chronic-disease management out of the PCP’s hands. In this primary care delivery redesign, the physician would increasingly supervise nurses and staff. Physician visits would focus on complex patients and allow more time to respond to patient-centered agendas.22 To implement this redesigned chronic-disease management, we will need to overcome important barriers, including getting buyin from our patients, providers, staff, and institution, as well as the pressures tied to the external reporting of performance measures, such as provider productivity.

 
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