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© 2001 by the American Diabetes Association, Inc.
Clinical Care/Education/Nutrition |
Impact of Automated Calls With Nurse Follow-Up on Diabetes Treatment Outcomes in a Department of Veterans Affairs Health Care System
A randomized controlled trial
From the Center for Health Care Evaluation/HSR&D Center of Excellence (J.D.P., F.B.K.), VA Palo Alto Health Care System, Palo Alto; the School of Medicine (F.B.K.) and Department of Health Research and Policy, Center for Primary Care and Outcomes Research (J.D.P.), Stanford University, Stanford; the Department of Medicine (S.J.M.), University of California, San Francisco, California; and the Roudenbush VA Medical Center (M.W.), Indiana University School of Medicine, Regenstrief Institute for Health Care, Indianapolis, Indiana.
Address correspondence and reprint requests to John D. Piette, PhD, Center for Health Care Evaluation, VA Palo Alto Health Care System, Menlo Park Division (152), 795 Willow Rd., Menlo Park, CA 94025. E-mail: jpiette{at}stanford.edu .
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESEARCH DESIGN AND METHODSRESULTSCONCLUSIONSReferences |
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OBJECTIVE— We evaluated automated telephone disease management (ATDM) with telephone nurse follow-up as a strategy for improving diabetes treatment processes and outcomes in Department of Veterans Affairs (VA) clinics. We also compared the results with those of a prior ATDM trial conducted in a county health care system.
RESEARCH DESIGN AND METHODS— A total of 272 VA patients with diabetes using hypoglycemic medications were randomized. During the 1-year study period, intervention patients received biweekly ATDM health assessment and self-care education calls, and a nurse educator followed up with patients based on their ATDM assessment reports. Telephone surveys were used to measure patients' self-care, symptoms, and satisfaction with care. Outpatient service use was evaluated using electronic databases and self-reports, and glycemic control was measured by HbAlc and serum glucose testing.
RESULTS— At 12 months, intervention patients reported more
frequent glucose self-monitoring and foot inspections than patients receiving
usual care and were more likely to be seen in podiatry and diabetes specialty
clinics. Intervention patients also were more likely than control patients to
have had a cholesterol test. Among patients with baseline HbAlc
levels 
8%, mean end-point values were lower among intervention patients
than control patients (8.7 vs. 9.2%, respectively; P = 0.04). Among
intervention and control patients with baseline values 9%, mean end-point
values were 9.1 and 10.2%, respectively (P = 0.04). At follow-up,
intervention patients reported fewer symptoms of poor glycemic control than
control patients and greater satisfaction with their health care.
CONCLUSIONS— This intervention improved the quality of VA diabetes care. Intervention effects for most end points replicated findings from the prior county clinic trial, although intervention—control differences in the current study were smaller because of the relatively good self-care and health status among the current study's enrollees.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESEARCH DESIGN AND METHODSRESULTSCONCLUSIONSReferences |
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Nearly all diabetes management takes place during in-person encounters with clinicians in ambulatory care settings. Regular outpatient follow-up is important for all patients, and some need frequent attention because their health is unstable, their treatment regimen is complex, or their social supports are inadequate. However, many patients live with financial and nonfinancial access barriers that limit their use of outpatient services (1). Consequently, they fail to attend outpatient appointments (2) and experience worse outcomes than trials of aggressive management suggest is possible (3,4).
Telephone care programs are a viable strategy for bringing diabetes management services into patients' homes and improving their glycemic control (5,6). Automated telephone disease management (ATDM) systems can augment telephone care by providing frequent monitoring and health education to large patient panels while allowing clinicians to focus attention on individuals who need it most. ATDM systems use specialized computer technology to deliver messages and collect information from patients using either their telephone's touch-tone keypad or voice-recognition software. Findings from multiple studies indicate that chronically ill patients will participate in ATDM and that the information they report during ATDM assessments is at least as reliable as information obtained via structured clinical interviews or medical record reviews (7,8,9). Indeed, some patients are more inclined to report health problems during an automated assessment than directly to a clinician (10).
In a prior randomized trial, we evaluated the efficacy of ATDM calls with telephone nurse follow-up among 248 English- and Spanish-speaking diabetic patients treated in the general medical clinics of a county health care system. After 12 months, intervention patients reported more intensive self-care than usual-care control subjects (11). Intervention patients also had better glycemic control and reported fewer symptoms of hyperglycemia and hypoglycemia. Moreover, patients receiving the intervention were more satisfied with their health care than control subjects, especially with the technical quality and continuity of their care, their communication with providers, and the quality of their health outcomes (12).
We extended this work by conducting a similar study among patients treated in Department of Veterans Affairs (VA) out-patient clinics. VA patients face substantial nonfinancial barriers to accessing clinic-based diabetes care. In particular, travel distance poses a major barrier for many VA patients' follow-up and has been linked to decreased use of VA outpatient services, increased hospitalization rates, and increased mortality rates (13,14). Here, we report the results of this VA randomized trial of ATDM-supported diabetes care and compare them with results of the prior county clinic trial.
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RESEARCH DESIGN AND METHODS |
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TOPABSTRACTINTRODUCTIONRESEARCH DESIGN AND METHODSRESULTSCONCLUSIONSReferences |
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Methods of patient enrollment, the characteristics of the intervention, and the measurement of outcomes in this study were similar to those in the prior county clinic ATDM trial. Each of these aspects of the design has been described previously (11,12,15) and is summarized below.
Patient enrollment
Participants were recruited from three general medicine clinics and one
diabetes specialty clinic within a university-affiliated VA health care
system. Research assistants reviewed online and paper-based medical records
daily to identify adults with a diagnosis of diabetes and an active
prescription for a hypoglycemic agent. Patients were excluded if they were
>75 years of age, were mentally ill, had a life expectancy of <12
months, were newly diagnosed, planned to discontinue receiving services from
the clinic within the 12-month follow-up period, or did not have a touch-tone
telephone. Informed consent procedures were conducted according to a protocol
approved by medical center and university human subjects committees. Patients
were randomized using sealed envelopes containing group assignments and a
sequence generated using a table of random numbers. Patients, their
clinicians, and research staff were not aware of patients' group assignment
until after they consented to participate and the envelope was opened.
Description of the intervention
Automated telephone calls. The automated calls consisted of
hierarchically structured messages composed of statements and queries recorded
in a human voice. All calls were outbound (i.e., patients received the calls),
and each assessment lasted 5-8 min. During each ATDM assessment, patients used
their touch-tone keypad to report information about their self-monitored blood
glucose (SMBG) readings, other self-care activities, perceived glycemic
control, symptoms, and use of guideline-recommended medical care. At the end
of each assessment, patients were given the option of listening to health
promotion messages (16).
Telephone nurse follow-up. Each week, the study nurse reviewed patients' ATDM assessment reports and followed up with them using an established protocol. During these follow-up calls, the nurse interacted with patients much like diabetes nurse educators in other medical settings. The nurse educated patients about appropriate self-care, discussed symptoms, monitored medication adherence, and promoted appropriate use of preventive medical care. The nurse also made periodic calls to follow up on issues discussed in a prior week or to check on patients who responded to the ATDM calls infrequently. Unlike the nurse in the prior county clinic trial, the VA nurse had the ability to schedule clinic appointments. Neither of the nurses had the ability to authorize medication changes, although both recommended dosage adjustments to patients' primary care physicians.
The nurse communicated with primary care providers using an established protocol created by the research team, with input by the VA facility's Chief of Endocrinology (F.B.K.) and clinic staff. The nurse called or e-mailed providers to discuss reported health problems and remind them regarding the need for preventive care (e.g., cholesterol testing).
Data collection and measures
At baseline and 12 months, patients were surveyed over the telephone
regarding their self-care behavior, symptoms, and perceptions regarding
telephone care. Patients' satisfaction with care was measured using the
Employee Health Care Value Survey
(17). Intervention patients
also responded to a series of questions regarding their satisfaction with the
ATDM calls. Data on patients' use of specialty outpatient services were
obtained from electronic utilization databases and survey self-reports.
HbAlc and serum glucose levels were measured at baseline and 12
months in one laboratory by staff who were blinded to patients' experimental
condition.
Statistical analysis
All analyses of intervention effects were conducted on an intent-to-treat
basis, and all P values were two-tailed. Despite randomization,
intervention and control groups were not equivalent at baseline with regard to
patients' race and number of diabetes complications. To adjust end-point
measures for these differences, as well as for baseline values of each end
point, we fit multivariate least-squares and logistic regression models.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESEARCH DESIGN AND METHODSRESULTSCONCLUSIONSReferences |
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Patient characteristics and baseline values for end-point measures
A total of 489 eligible patients were identified, and 272 (93% of patients randomized) contributed outcome data at 12 months (Fig. 1). Intervention and control groups had similar characteristics at baseline (Table 1). However, intervention patients were more likely than control patients to be white and have somewhat more complications. Overall, only 44% of patients had baseline HbAlc
8%, the level at which additional clinical intervention
is recommended (18).
Intervention patients were significantly more likely than control patients to
be seen in ophthalmology clinics in the year before their enrollment; however,
there were no differences between intervention and control groups in baseline
measures of other end points (Table
2).
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The intervention process
On average, intervention patients completed 15 ATDM assessment and
self-care education calls and reported 12 SMBG readings
(Table 3). Overall, 50% of
intervention patients reported that they were very satisfied with the ATDM
calls, and 31% reported that they were moderately satisfied. A total of 97%
reported that the messages were mostly or always easy to understand, 76%
reported that the calls mostly or always made them more reassured that their
doctors knew how they were doing, and 67% reported that the calls reminded
them to engage in self-care activities. Of the intervention patients, 79%
reported that they would be more satisfied with their health care if ATDM
calls were available to patients, and 73% reported that they personally would
choose to receive such calls.
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The nurse communicated with patients by telephone an average of 1.1 times per month. During these calls, adherence problems and side effects of hypoglycemic medications were discussed 66% of the time, and glucose self-monitoring was discussed 60% of the time. Nondiabetes medications were discussed 32% of the time, nondiabetes symptoms were discussed 37% of the time, and psychological problems such as depression and anxiety were discussed in 24% of follow-up calls.
Of all episodes of nurse—patient contact, 23% resulted in follow-up contacts with the patient's primary care provider. Although providers were not systematically surveyed to evaluate their satisfaction with the ATDM service, there were no complaints about the intervention, and none of the providers disenrolled a patient from the trial. Anecdotally, some providers expressed enthusiasm for the intervention, noting that it gave them greater confidence that their patients were being monitored and that self-care problems were being addressed.
Intervention effects
Impacts on processes of care. Patients receiving the intervention
reported more frequent SMBG and foot inspections at 12 months than patients
receiving usual care (both P = 0.05,
Table 2). There were no
differences between groups in the frequency of weight monitoring or the
proportion of patients reporting one or more medication adherence
problems.
Intervention patients were more likely than control subjects to be seen in podiatry clinics (62 vs. 42%, respectively; P = 0.003) and diabetes specialty clinics (61 vs. 25%, respectively; P = 0.03). Intervention patients also were more likely to report having had their cholesterol tested in the 6 months before their end-point interview (86 vs. 78%, respectively; P = 0.05) and to have been encouraged by their physicians to check their feet for cuts and sores (92 vs. 72%, respectively; P = 0.0002). The total volume of outpatient service use was slightly higher than what is typical for patients with diabetes (19), suggesting that nonclinical visits may have been counted in patients' estimates (e.g., visits solely for medication refills, eligibility determination, or collection of specimens). At the time of their end-point interview, intervention patients reported more visits on average than control subjects (8.9 vs. 7.2 visits, respectively; P = 0.006).
In general, patients receiving the intervention were more favorably disposed to telephone care at follow-up than patients receiving usual care. Intervention patients were more likely than control patients to strongly agree that they could telephone a doctor or nurse for help with medical problems (69 vs. 55%, respectively; P = 0.02), that they could use the telephone to avoid office visits (33 vs. 18%, respectively; P = 0.005), and that their doctors could help them with a medical problem if they called them at home (36 vs. 26%, respectively; P = 0.05).
Impacts on outcomes. For the sample overall, mean end-point
HbAlc values were similar among intervention and control patients
(Table 2). However, among
patients whose baseline HbAlc was 8%, adjusted mean end-point
HbAlc values differed by 0.5% between intervention and control
groups (8.7 vs. 9.2%, respectively; P = 0.04). Among patients whose
baseline HbAlc was 9%, adjusted mean end-point values differed
by 1.1% (9.1 vs. 10.2%, respectively; P = 0.04).
In the sample overall, intervention patients at follow-up reported fewer symptoms of poor glycemic control than patients receiving usual care (P = 0.04). There was a small but statistically significant difference between intervention and control patients' satisfaction with care. Overall differences in satisfaction mainly reflected differences in patients' satisfaction with the interpersonal aspects of their care and the quality of their health outcomes.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONRESEARCH DESIGN AND METHODSRESULTSCONCLUSIONSReferences |
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In this randomized controlled trial, ATDM calls with telephone nurse follow-up increased the frequency with which patients self-monitored their blood glucose and checked their feet for problems. The intervention increased the proportion of patients seen in podiatry and diabetes specialty clinics and the proportion of patients who had a cholesterol test and physician counseling about foot self-care. Although there was no impact on HbAlc levels in the sample as a whole, statistically significant and clinically meaningful improvements in HbAlc were observed among patients with relatively poor glycemic control at baseline. The intervention also decreased patients' diabetes-related symptoms, increased their receptivity to telephone care, and increased their satisfaction with care.
Comparison of nurse activity and intervention effects in the current
VA trial and prior county clinic ATDM trial
Intervention effects were generally smaller in this VA trial than in the
prior county clinic trial, a seemingly surprising result given that the VA
nurse spent more time with patients and had access to a wider range of
treatment resources
(11,12).
On average, the VA nurse made 13 contacts with each patient for a total of 3.8
h of contact time. In comparison, the nurse in the county clinic trial made an
average of six contacts per patient for 1.2 h of total contact time. The nurse
in the VA trial had access to the facility's Intranet and used it to review
clinical records, schedule appointments, and e-mail providers. The nurse in
the prior trial had no Intranet access because she was located outside of the
county clinics. Thus, the clinical information available to her was limited to
data collected at enrollment and her own progress notes, and communication
with other providers was done exclusively via fax and telephone calls.
Both the VA and county clinic nurses discussed SMBG frequently (60 vs. 57% of all contacts, respectively), and, in both studies, non—diabetes-related medications were discussed during 32% of all contacts. Compared with the county clinic nurse, the VA nurse more frequently discussed hypoglycemic medications (66 vs. 45% of the time, respectively), diabetes-related symptoms (32 vs. 26%, respectively), and mental health problems (24 vs. 18%, respectively) but less frequently discussed non—diabetes-related symptoms (37 vs. 49%, respectively).
Although these differences in the content of nurse follow-up may have contributed to the different effect sizes in the two studies, a more likely explanation is that the relatively good self-care and health status among VA enrollees made it more difficult to demonstrate intervention impacts. Participants in the VA trial were better off at baseline than those in the county clinic trial with regard to their self-care, glycemic control, symptoms, and satisfaction with care. End-point values in the VA trial's intervention group were as good as or better than those in the intervention group of the county study. Nevertheless, intervention effects in the VA trial were smaller because of the better end-point values in the study's control group. Indeed, many end-point scores among control patients in the VA trial were better than end-point scores achieved among county clinic intervention patients.
Mean end-point HbAlc levels in the intervention groups of the
two studies were essentially the same. However, the VA intervention had a
greater impact on HbAlc relative to usual care among patients with
baseline HbAlc 8% because of the worse control among usual-care
patients in this VA patient subgroup. This exception to the general pattern of
effects supports the interpretation that ceiling effects in the VA trial,
rather than differences in the effectiveness of the two interventions, were
responsible for the different magnitude of intervention—control
differences in the two studies.
Issues to consider when evaluating a potential ATDM service
Cost issues. Various options are available for purchasing ATDM
services. The automated system used in the current study and the prior county
clinic study was a Teleminder Model IV automated telephone-messaging computer
produced by Decision Systems (Los Altos, CA). Vendors such as Decision Systems
sell computer equipment to health care providers who then develop and manage
the ATDM system with occasional technical support from the vendor. The cost
for such a system is 
$15-25 per patient annually, including the costs of
computer programming and database management but excluding the service's
impact on nursing care costs. Other vendors develop Web-based automated
telephone-messaging services according to a health system's specifications,
manage patient calls, and process patients' health data for providers. These
services often are priced in units of patient contact time. The cost of an
intervention similar to the one evaluated in the current study would be in the
range of $17-30 per patient annually (again, excluding the costs of nursing
care). Still other businesses market comprehensive disease management programs
that include automated telephone calls with initial follow-up by the company's
clinicians. Because they include clinician follow-up, such services tend to be
more costly than fully automated systems. Each of these methods of purchasing
ATDM capacity is viable, and the choice for a health care system will depend
on factors such as the organization's ability to manage the system in-house
and its preference for capital outlays versus ongoing predictable service
charges.
The intervention nurse in this study encouraged some patients to seek outpatient care for preventive screening or diagnostic evaluation, likely increasing usual-care costs in the short term. Because there are no estimates of the prognostic significance of ATDM-reported information, the nurse sometimes erred on the side of patient safety when the implications of a given report were uncertain. In our ongoing research, we are evaluating the empirical relationship between ATDM health assessment information and subsequent health problems so that ATDM reports can be used more effectively to differentiate between patients who do need additional care and those who do not.
Over the long term, improvements in adherence to treatment guidelines
associated with ATDM use may result in cost savings. As an illustration, we
estimated the intervention's potential impact on VA treatment costs if it were
implemented nationally, and the intervention had positive effects on glycemic
control comparable with those in the current trial. Estimates of the cost
savings associated with various levels of improvement in HbAlc were
taken from a prior study (20).
The distribution of HbAlc levels among VA diabetic patients as well
as the prevalence of hypertension and heart disease in this population were
taken from the current study's baseline data. These analyses suggest that
nationwide implementation of this intervention could save VA >$100 million
annually. Although crude, these analyses may be conservative because they
assume that the relatively low baseline prevalence of HbAlc levels
8% in the current study is typical in VA clinics nationally and that VA
medical care costs are no greater than those of the private managed care
organization from which cost estimates were obtained.
Patient targeting. Data from the current study suggest that ATDM
services may be most effective for patients with HbAlc levels
8%. Other eligibility criteria that reflect treatment problems more
prevalent among patients in the prior county clinic study (e.g., multiple
symptoms or medication nonadherence) also could be useful in identifying
patients for whom these services could be most beneficial. Given that the
sensitivity and specificity of ATDM health status reports remain undetermined,
it is especially important that ATDM services focus on populations in which
false-positive and false-negative errors will be minimized, specifically those
in which the prevalence of management problems is relatively high.
Impacts on the organization of health services. Consideration should be given to the way in which ATDM information is reported to clinicians. In the current study, as well as the prior county clinic study, ATDM health status reports were reviewed by a single nurse who served as the interface between the intervention and patients' primary care teams. Other models of integrating ATDM into diabetes management are possible, such as delivering patients' reports directly to their primary care providers (21). If ATDM monitoring is subcontracted to an outside organization, consideration should be given to how patient data will be shared and who will be the patients' point of contact for administrative and clinical questions (22).
The extent to which ATDM implementation is intended to supplement or supplant in-person nursing care also is important. Investigators have examined a range of diabetes telephone treatment models, some being based almost exclusively on direct nurse—patient communication (5,6) and others relying on computer automation for the bulk of telephone contact (23,24,25). Because of differences in study design, eligibility criteria, and treatment settings, direct comparisons across these studies would be tenuous. More research will be needed before conclusions can be drawn about the best balance between automated follow-up and nursing care and the relative emphasis that should be placed on ATDM as a health-monitoring tool versus a means of increasing patients' access to self-care education.
In conclusion, this VA study and our prior county clinic ATDM trial included follow-up by various nurses working under somewhat different constraints. As a result, we cannot estimate the extent to which variation in outcomes of the two trials reflects differences in the nurses' skills versus differences in the potential impact of ATDM generally. Moreover, wide-scale implementation may result in less-consistent adherence to the intervention protocols and more variable outcomes. With these caveats, we conclude that ATDM with nurse follow-up improved the process and outcomes of VA diabetes care. Intervention effects for most end points replicated findings from the prior county clinic trial, although intervention-control differences in the current study were smaller because of the relatively good self-care and glycemic control, low frequency of symptoms, and high satisfaction with care among the VA enrollees.
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ACKNOWLEDGMENTS |
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This research was supported by the Health Services Research and Development Service, Mental Health Strategic Health Care Group, and Quality Enhancement Research Initiative, Department of Veterans Affairs, and by the American Diabetes Association.
We thank the clinicians and patients of the VA Palo Alto Health Care System and Dorleen Turner Von-Raesfeld for her contribution as intervention nurse; Connie A. Mah, for managing the study; and Edgar O. Alvarez, Dara J. Amboy, and Carol Sundermeyer for assistance with data collection and data processing. Stephanie Tobin and LaTonya Trotter gave helpful feedback on earlier drafts.
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FOOTNOTES |
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Abbreviations: ATDM, automated telephone disease management; VA, Veterans Affairs; SMBG, self-monitored blood glucose.
The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.
A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.
Received for publication April 27, 2000. Accepted for publication October 20, 2000.
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 H. M. Krumholz, P. M. Currie, B. Riegel, C. O. Phillips, E. D. Peterson, R. Smith, C. W. Yancy, and D. P. Faxon A Taxonomy for Disease Management: A Scientific Statement From the American Heart Association Disease Management Taxonomy Writing Group Circulation, September 26, 2006; 114(13): 1432 - 1445. [Abstract] [Full Text] [PDF]
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 K. G. Shojania, S. R. Ranji, K. M. McDonald, J. M. Grimshaw, V. Sundaram, R. J. Rushakoff, and D. K. Owens Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA, July 26, 2006; 296(4): 427 - 440. [Abstract] [Full Text] [PDF]
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J. Leeman Interventions to Improve Diabetes Self-management: Utility and Relevance for Practice. The Diabetes Educator, July 1, 2006; 32(4): 571 - 583. [Abstract] [Full Text] [PDF]
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 A. Linden Evaluating the Effectiveness of Home Health as a Disease Management Strategy Home Health Care Management Practice, April 1, 2006; 18(3): 216 - 222. [Abstract] [PDF]
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 N. R. Chumbler, B. Neugaard, R. Kobb, P. Ryan, H. Qin, and Y. Joo Evaluation of a Care Coordination/Home-Telehealth Program for Veterans with Diabetes: Health Services Utilization and Health-Related Quality of Life Eval Health Prof, December 1, 2005; 28(4): 464 - 478. [Abstract] [PDF]
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N. C. Martinez and T. Tripp-Reimer Diabetes Nurse Educators' Prioritized Elder Foot Care Behaviors The Diabetes Educator, November 1, 2005; 31(6): 858 - 868. [Abstract] [Full Text] [PDF]
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M. Heisler and J. D. Piette "I Help You, and You Help Me": Facilitated Telephone Peer Support Among Patients With Diabetes The Diabetes Educator, November 1, 2005; 31(6): 869 - 879. [Abstract] [Full Text] [PDF]
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 E. B. Fisher, C. A. Brownson, M. L. O'Toole, G. Shetty, V. V. Anwuri, and R. E. Glasgow Ecological Approaches to Self-Management: The Case of Diabetes Am J Public Health, September 1, 2005; 95(9): 1523 - 1535. [Abstract] [Full Text] [PDF]
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G. T. McMahon, H. E. Gomes, S. Hickson Hohne, T. M.-J. Hu, B. A. Levine, and P. R. Conlin Web-Based Care Management in Patients With Poorly Controlled Diabetes Diabetes Care, July 1, 2005; 28(7): 1624 - 1629. [Abstract] [Full Text] [PDF]
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R. M. Anderson Is It Ethical to Assign Medically Underserved African Americans to a Usual-Care Control Group in Community-Based Intervention Research? Diabetes Care, July 1, 2005; 28(7): 1817 - 1820. [Full Text] [PDF]
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 S. S. Bull, B. Gaglio, H. G. Mckay, and R. E. Glasgow Harnessing the potential of the Internet to promote chronic illness self-management: diabetes as an example of how well we are doing Chronic Illness, June 1, 2005; 1(2): 143 - 155. [Abstract] [PDF]
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 J. Porthouse and D. J. Torgerson The Need for Randomized Controlled Trials in Podiatric Medical Research J Am Podiatr Med Assoc, May 1, 2004; 94(3): 221 - 228. [Abstract] [Full Text] [PDF]
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M. B. Davidson Effect of Nurse-Directed Diabetes Care in a Minority Population Diabetes Care, August 1, 2003; 26(8): 2281 - 2287. [Abstract] [Full Text] [PDF]
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M. B. Davidson The Case for "Outsourcing" Diabetes Care Diabetes Care, May 1, 2003; 26(5): 1608 - 1612. [Full Text] [PDF]
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 M. Safford, L. Eaton, G. Hawley, M. Brimacombe, M. Rajan, H. Li, and L. Pogach Disparities in Use of Lipid-Lowering Medications Among People With Type 2 Diabetes Mellitus Arch Intern Med, April 28, 2003; 163(8): 922 - 928. [Abstract] [Full Text] [PDF]
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C. B. Taylor, N. H. Miller, K. R. Reilly, G. Greenwald, D. Cunning, A. Deeter, and L. Abascal Evaluation of a Nurse-Care Management System to Improve Outcomes in Patients With Complicated Diabetes Diabetes Care, April 1, 2003; 26(4): 1058 - 1063. [Abstract] [Full Text] [PDF]
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 A. A. Rothman and E. H. Wagner Chronic Illness Management: What Is the Role of Primary Care? Ann Intern Med, February 4, 2003; 138(3): 256 - 261. [Abstract] [Full Text] [PDF]
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 S. R Weingarten, J. M Henning, E. Badamgarav, K. Knight, V. Hasselblad, A. Gano Jr, and J. J Ofman Interventions used in disease management programmes for patients with chronic illness---which ones work? Meta-analysis of published reports BMJ, October 26, 2002; 325(7370): 925 - 925. [Abstract] [Full Text] [PDF]
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