Evaluating the Effectiveness of an Aggressive Case Management and ...

Professional Case Management Vol. 17, No. 2, 51-58 Copyright 2012 © Wolters Kluwer Health | Lippincott Williams & Wilkins

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Evaluating the Effectiveness of an Aggressive Case Management and Home Telehealth Monitoring Program for Long-Term Control of A1C Thomas F. Klobucar, PhD, Robin Hibbs, RN, MBA, MSN, CDE, Peg Jans, RN, and Margaret R. Adams, ARNP, BC-ADM, CDE

ABSTRACT Purpose/Objectives: To describe and assess the effectiveness of a case management and home telemonitoring program for patients with diabetes mellitus (DM) Primary Practice Setting: Case managers work in a mid-sized medical center for the Department of Veterans Affairs. Patients are veterans who participate in a home telemonitoring and case management program designed to assist with long-term control of serum glucose levels. Findings/Conclusions: The home telemonitoring/case management program process is shown to be effective in helping patients with long-term control of glycosylated hemoglobin (A1C). When compared with a control group, program participants showed significantly differentiated long-term improvement in A1C levels. Implications for Case Management Practice: • Home telemonitoring and aggressive case management together are effective in helping patients with diabetes self-care. • Case management practices for patients with diabetes should include a strong educational component, continuing throughout the process, that addresses lifestyle and dietary changes. • Home telemonitoring may serve as a patient “demand” indicator and workload regulator for case managers. • Case management and home telemonitoring have long-term effects in diabetes self-care even after active case management and home telemonitoring come to an end. Key words: case management, diabetes, home telemonitoring, telehealth, telemedicine

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he Veterans Health Administration (VHA) currently serves more than 1.2 million veteran patients with diabetes (approximately 20% of all veteran patients), and almost 220,000 veterans nationwide who have been diagnosed as having prediabetes by VHA physicians (VHA, unpublished data, 2011). Nationally, the Centers for Disease Control and Prevention estimates that nearly 26 million Americans have diabetes (Centers for Disease Control and Prevention, 2011), and there are credible models that predict an increase to as much as 33% of the total U.S. adult population by 2050 (Boyle, Thompson, Gregg, Barker, & Williamson, 2010). In addition, an estimated 79 million U.S. adults have prediabetes, a condition that increases the risk of type 2 diabetes, heart disease, and stroke (Centers for Disease Control and Prevention, 2011). A significant body of evidence demonstrates that robust self-management of blood glucose levels

considerably enhances quality of life for patients with diabetes, reduces inpatient length of stay, and helps avoid the poor outcomes that can lead to increased financial and social burdens in terms of health care dollars as well as community resources (Barnett et al., 2006, 2007; Chumbler, Neugaard, Kobb, et al., 2005b; Chumbler, Neugaard, Ryan, et al., 2005; Chumbler, Vogel, et al., 2005c; Pare, Jaana, & Sicotte, 2007). To decrease these burdens, and to assist patients with diabetes in their self-management efforts, aggressive case management, including continuous patient education and medication titration is indicated and has Address correspondence to Thomas F. Klobucar, PhD, Iowa City VA Health Care System, 601 Highway 6 West, Mailstop 152, Iowa City, IA ([email protected]). The authors report no conflicts of interest. DOI: 10.1097/NCM.0b013e31823ba3cb

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The Veterans Health Administration currently serves more than 1.2 million veteran patients with diabetes …. Nationally, the Centers for Disease Control and Prevention estimates that nearly 26 million Americans have diabetes and there are credible models that predict an increase to as much as 33% of the total U.S. adult population by 2050. been shown to be effective. Furthermore, some studies strongly suggest that aggressive case management combined with home telemonitoring is more effective than either component separately (Barnett et al., 2007; Chumbler, Neugaard, Ryan, et al., 2005; Darkins et al., 2008). In 2006, the Iowa City VA Health Care System (VAHCS) initiated an aggressive case management Care Coordination/Home Telehealth (CCHT) program with the goal of assisting veteran patients with diabetes through continuous education and active assistance with long-term serum glucose control. The objective of this study is to evaluate the quality of an established case management/home telemonitoring program using health outcome as the determinate of program effectiveness.

MEASURES AND METHODS Although daily monitoring of blood glucose levels plays a critical role in helping patients with diabetes manage their health in the short term, stable and reliable measurement of glucose control over the longterm is possible through the use of hemoglobin A1C (A1C) levels (American Diabetes Association, 2010). A1C testing measures the level of glycosylated hemoglobin molecules in blood cells over a period of 4 to 12 weeks. Thus, the A1C test gives clinician and patients an overall view of average glucose control for that period. The American Diabetes Association maintains that an A1C goal of 7% or less (estimated average serum glucose level of 154 mg/dl; 8.6 mmol/L) for patients with diabetes offers the greatest protection from diabetes complications, including reducing microvascular and neuropathic complications (nephropathy and retinopathy) and reducing the incidence of cardiovascular disease (American Diabetes Association, 2010). The American Association of Clinical Endocrinologists recommends attainment of a more ambitious A1C goal of 6.5% or less (estimated average serum glucose level of 140 mg/dl; 7.75 mmol/L) to achieve the greatest protection from diabetes complications (Rodbard et al., 52

2007) Although these are endorsed goals they are not always appropriate for all patients—in some patients the 7% or less or 6.5% or less standard presents too great a risk of hypoglycemia with its concomitant complications. In these instances, a clinician may negotiate with the patient to set a different, higher goal to optimize patient safety and care. For this study, A1C was used as an indicator of the quality and effectiveness of the Iowa City VAHCS’s aggressive case management CCHT program, with a baseline measure taken immediately before each patient is offered entry into the program and at regular intervals thereafter. We thus proposed to evaluate the quality of the CCHT program using long-term serum glucose control as our primary outcome. See Table 1 for a description of measurement points. This program effectiveness study was focused on a case management/home telemonitoring program that has been underway for almost 5 years. The program actively recruits patients with A1C level of 9% or more (212 mg/dl; mmol/L). Program participants must also have standard plain old telephone service (POTS), adequate electrical service to their homes, and be willing and physically able to participate in the program. Patients are identified through a diabetes registry, direct referral from providers, and through monitoring of Endocrine Clinic appointments. Case managers expend extensive effort contacting providers to inform them that this program is available to patients. Once patients are identified, they are contacted by a case manager to confirm eligibility through the use of a standard questionnaire. See Table 2 for a summary of admission criteria. Because this program is an active service provided to all qualified patients, randomized selection of a control group was not possible. Instead, those patients who met the health criteria for admission to the program and were either unwilling or unable to participate, due to a lack of POTS or electrical service, serve as a control. No demographic data were recorded—thus, it is impossible to detail the comparability of the control group versus the intervention group. We use three groups in this analysis: a nonrandomized control group that did not receive the intervention, a nonrandomized intervention group of current program participants, and a nonrandomized intervention group of program graduates. For the purposes of this program evaluation, we consider all program participants as a single intervention group, and for some analyses, divide them into two subgroups: program graduates and current participants.

INTERVENTION PROGRAM DESCRIPTION The VHA model of case management and home telemonitoring (Darkins et al., 2008) has been implemented

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TABLE 1 Hemoglobin A1C Measurement Points

t (Admission A1C) Identifies patient eligi-

t ⫹ 3 Months (3-Month A1C) By this time, participants

t ⫹ 6 Months (6-Month A1C)

t ⫹ 12 Months (1-Year A1C)

Patient continues to

At this point, assessment is

t ⫹ 18, t ⫹ 24, t ⫹ 36 Months (18, 24, and 36-Month A1C) Case managers continue to

bility for the program.

have received face-to-

receive face-to-face

made to determine if

manage and support

Assessment of barriers

face diabetes educa-

diabetes education and

patient is prepared for

those patients who remain

performed at program

tion and assessment

assessment. Primary

graduation from the

in the program for any

entry: continually

from diabetes case

focus is to understand

program or if significant

reason. Assessing program

assessed throughout

manager. Diabetes

the relationship of food,

barriers exist that require

graduates to ensure

the program. This

mellitus medica-

medications, and activ-

patient to remain in a

continued adherence and

measure is used as

tions monitored and

ity to blood glucose lev-

comprehensive case man-

effective self-management.

the start date for all

adjusted to meet

els to facilitate diabetes

agement program for their

included in this study.

patient’s current needs.

self-management.

diabetes.

at the Iowa City VAHCS to provide for aggressive case management and daily monitoring for veteran patients whose health is at risk from poorly controlled diabetes. For each patient, the program is designed to continue for 12 consecutive months, although it can continue for a longer period, depending upon the judgment of the provider as informed by the case manager. Program participants agree to use home telehealth equipment (manufactured by Vitel Net) to send required data to a secure server and answer disease related interrogatories on a daily basis (the precise wording of these questions may be found in Appendix A). Failure to fulfill these obligations can result in discharge from the case management/home monitoring program. Upon enrollment, Registered Nurse (RN) case managers educate the patients face-to-face on the use of their assigned home telemonitoring equipment and ask the enrollees to demonstrate competency. During the same face-to-face interaction, participants also receive individualized diabetes education relevant to their specific needs. Every nonholiday weekday for the ensuing 12 months, patients transmit data including blood glucose levels (in mg/dl), weight, and blood pressure to a secure server, which is subsequently accessed by a nurse/case manager who reviews these data for variance from expectations. Expectations for transmitted health data are determined for each patient by the treating physician in combination with the RN case manager. When discrepancies are noted (e.g., blood glucose levels out of range, changes in weight or blood pressure, unexpected responses to daily interrogatories), the case manager judges whether to contact the responsible provider for orders or to call the patient directly to better evaluate the patient’s status. In either event, the patient is contacted by the case manager either

with the new orders or questions about their status. Case managers also use this opportunity to further supplement and support diabetes education previously provided to the patient. As part of the normal program process, A1C tests are ordered in conjunction with regular clinic visits. Patients are evaluated using A1C level at admission, 3 months postadmission, 6 months postadmission, and at the 1-year mark. The general guideline is for patients to achieve their targeted goal expressed in terms of A1C. After 1 year of the intervention, if the patient has met the program self-management goals negotiated during initial assessment, they are “graduated” from the program. Procedure at graduation includes presentation of a certificate from the case manager and information on how to reestablish contact if conditions warrant. Case managers continue to monitor A1C for program graduates for at least another 2 years. TABLE 2 Summary of Admission Criteria Diagnosis of diabetes mellitus, A1C ⱖ 9% Home environment allows daily care and management of medical problems Access to utilities (electricity, POTS) for appropriate installation of equipment Acceptance of home monitoring technology by patient or caregiver or both Patient, caregiver, or both able to use and maintain monitoring equipment Patient agreement to send clinical data and answer interrogatories Note. POTS ⫽ plain old telephone service.

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The average number of participants managed by a single case manager ranges from 70 to 80 patients. A large number of those who are not graduated from the program after 1 year may have achieved targeted diabetes goals, but remain in the case management/home telemonitoring program for unrelated reasons (e.g., functional status, cognitive status, multiple comorbidities). In addition, those who continue to demonstrate problems with diabetes self-management or do not meet program goals, may also remain in the program. The other program participants are a fluid panel of patients, revolving through the program by admission and successful discharge (see Appendix B for program discharge criteria), not requiring continued home telemonitoring and aggressive case management to maintain the targeted diabetes goals.

RESULTS Table 3 displays the timeline and mean A1C levels for the control group and the intervention groups (program graduates, current participants, and their aggregate). Analysis of variance shows that, with no significant difference in mean A1C between groups at the beginning of the study period, statistically significant differences (95% CI; p ⬍ .001) between the control and aggregate intervention groups are evident from t + 3 months through t + 36 months. Comparison of the first two data columns illustrates the differences in means (95% CI; p ⬍ .001) between the intervention subgroups. Although A1C control differs significantly between the two intervention subgroups, both remain significantly distinguishable from the control. The median A1C for the program graduate group reaches the ADA-recommended guideline of A1C goal of 7% or less from t ⫹ 6 months forward, whereas the median for other groups does not. Analyzing within group changes over time, the program graduate subgroup shows significant progress from t through t + 12 months, stabilizing at or near the ADA-recommended target A1C of 7% or less (154 mg/dl; 8.6 mmol/L). Similarly, the current participant group shows significant progress through the same period, although the mean A1C did not reach the 7% or less mark. The control group, in isolation, shows a significant mean reduction in A1C of 1.2% at t + 3 months, but no significant change thereafter.

DISCUSSION These data are evidence of the effectiveness of home telemonitoring paired with aggressive case management. The performance of the control group, which showed a significant A1C reduction in the first 3 months of the program, but a steady upward 54

(higher A1C) trend thereafter, stands in sharp contrast with the performance of program participants. For the intervention group, there were significant reductions through 12 months, with stable A1C levels thereafter. The case management process in this intervention is demand driven—after initial patient contact for education and so on, case managers contact participants when telemonitoring equipment indicates that daily clinical parameters exceed parameters established by their care team. There is some evidence in the literature that frequency and duration of case manager contact is associated with positive outcomes (Pimouguet, Le Goff, Thiebaut, Dartigues, & Helmer, 2011). In this program, to the extent that frequency and duration of contact are a function of patient health status, it may be reasonable to suggest that telemonitoring clinical data transmitted on a daily basis serves as a case manager’s workload “regulator” in that case managers can choose to make contact only when a patient’s vitals are out of acceptable parameters. By extension, enrolled participants’ case management dosage may be “just right” in that it meets the patients’ minimum requirements to achieve positive health outcomes. The statistically significant differences between the graduate subgroup and the current participant subgroup are difficult to interpret. It is possible that the reason participants are currently in the program is the very fact that they have not achieved their negotiated goals, which explains their higher mean A1C. Our data reveal no insight in this regard. It is also possible that this phenomenon—the content of the interchange between the case manager and the patient differs in some way—is case manager dependent. Thus, some of those patients currently in the program respond to their case managers in a manner that is less effective in controlling their blood glucose levels. Again, the data gathered for this study do not allow us to investigate this possibility.

LIMITATIONS OF THE STUDY There are a number of shortcomings in this observational retrospective design. None of these subjects were randomly assigned and the population consisted entirely of veteran patients who share many of the same demographic characteristics. Furthermore, the intervention group was limited to those who had POTS in their homes, effectively excluding those who use exclusively wireless phones. We also chose not to record patient demographic characteristics, which weakens our analysis and precludes review for comparability of data that might have allowed us to distinguish a bias in our method of selection. In addition to these shortcomings, this study lacks a cost–benefit

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TABLE 3 Hemoglobin A1C Over Time

Baseline (t)

t + 3 months

Intervention Subgroup: Program Graduates

Intervention Subgroup: Current Participants

Current vs. Graduate Significance (95% CI)a

Aggregate Intervention (Graduates + Current Participants)

Control Group

Control vs. Aggregate Significance (95% CI)a

Mean (SD)

10.2 (1.7)

10.1 (1.9)

⬍.50

10.1 (1.8)

10.4 (2.0)

⬍.15

Median (range)

10.1 (9.7)

9.9 (12.7)

10.0 (12.7)

10.1 (13.3)

8.2 (1.4)

9.2 (1.8)

7.9 (9.2)

8.7 (7.8)

Mean (SD)

7.6 (1.4)

8.3 (1.4)

Median (range)

7.3 (7.1)

8.2 (8.3)

Mean (SD)

7.3 (1.4)

8.1 (1.5)

Median (range)

7.0 (7.2)

7.8 (10.8)

t + 12 months

Mean (SD)

7.0 (1.1)

7.9 (1.4)

Median (range)

6.7 (5.2)

7.8 (7.9)

t + 18 months

Mean (SD)

7.2 (1.3)

8.1 (1.4)

Median (range)

6.8 (6.6)

7.8 (6.8)

Mean (SD)

7.2 (1.3)

8.2 (1.7)

Median (range)

7.0 (5.7)

7.7 (7.5)

Mean (SD)

7.4 (1.7)

7.9 (1.1)

Median (range)

6.9 (3.8)

7.8 (5.1)

t + 6 months

t + 24 months

t + 36 months

⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.003 ⬍.59a

7.9 (1.5)

9.0 (1.5)

7.6 (10.9)

8.6 (6.9)

7.7 (1.4)

8.8 (1.0)

7.6 (8.3)

8.8 (3.7)

7.7 (1.4)

9.4 (2.2)

7.5 (7.4)

9.1 (10.9)

7.9 (1.7)

9.3 (2.3)

7.6 (8.1)

9.1 (10.2)

7.8 (1.1)

9.7 (2.3)

7.8 (5.1)

9.3 (9.2)

⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.001

a

One-way ANOVA using PASW Statistics v18.0

analysis. Clearly, there are significant start-up costs in establishing a home telemonitoring program— the cost of the in-home equipment alone can range from $1,000 to $5,000 per patient and the average RN case manager’s salary in the U.S., $67,720 (Occupational Employment Statistics, 2010) as of May 2010, significantly increases these costs. This study did not include a return-on-investment analysis, and although others have looked at the value of such a regime using a variety of metrics to assess its value (Chumbler, Neugaard, Ryan, et al., 2005; Chumbler, Vogel, et al., 2005) more research and analysis in this area is required.

DIRECTION FOR FURTHER STUDY The control group for this study comprises patients who did not enter the program, either because they had no landline phone or they simply chose not to participate. For this particular group of patients further exploration of barriers to their care should be investigated. Of particular interest is the incorporation of wireless technology into the home monitoring regimen: as many as 10% of those in this study’s control group indicated they could not participate because they had no POTS, and the latest estimates indicate that 23% of adults have only cellular service in their homes, a portion of the population that is continually increasing. As of 2009, an estimated 15% of those in the 45–64 age group, the cohort

most likely to be diagnosed with diabetes, use only wireless phones (Blumberg et al., 2009). There are three technological approaches to solve this dilemma: 1. The use of interactive voice response using a cell phone for manual input of health data (blood glucose, weight, or blood pressure), 2. The adaptation of home telehealth units to use wireless signals to communicate with servers as landline phones do now, and 3. The use of a personal computer interface that acts as the home terminal and communicates data to the server. A comparative study of these three technologies as part of a case management/home telemonitoring program would assist health care decision makers in making appropriate choices to improve the quality (and quantity) of programs like this. Furthermore, although there is some evidence (Chumbler, et al., 2005) that case management with daily telemonitoring is more effective than less frequent telemonitoring. A comparative study of these different interactive methods, carefully logging and noting the content of communications between case manager and patient could provide even more insight into the process and give managers the information they need when making choices about program adoption and process improvement. There is an important opportunity for collaboration in further developing research into case management with home telemonitoring, Vol. 17/No. 2

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and such an effort should involve the U.S. military treatment facilities and private contractors who also provide analogous services to active duty military and civilian patients. Such a collaborative study, with a significantly larger number of patients, will substantially increase our knowledge in this understudied area. Finally, disentangling the influence of case management from the effect of home telemonitoring should be a quality improvement research priority. Telemonitoring, although becoming less costly over time, is an expensive proposition with expensive equipment, and there is not an abundance of evidence indicating that case management is improved when it is used in conjunction with home telemonitoring. Although the case management literature is somewhat more developed than that for telemonitoring, there are sufficient lacunae in both areas that further investigation is required regarding the content, duration, and frequency of case managers’ communications with patients and the effectiveness of telemonitoring in isolation. Nonetheless, these findings that patients have greater long-term A1C control when they participate in this particular case management/home telemonitoring program provide support for continuance of the program. Such a regimen gives case managers the opportunity to make frequent, on-demand contact with patients to provide support, comprehensive diabetes education, lifestyle advice, and also may expedite interaction with providers to facilitate aggressive medication management, all of which contribute to optimal diabetes outcomes.

ACKNOWLEDGMENTS The research reported here was supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Rural Health, Veterans Rural Health Resource Center-Central Region (VRHRC-CR) and the Iowa City VA Health Care System.

REFERENCES American Diabetes Association (2010). Standards of medical care in diabetes–2010. Diabetes Care, 33(Suppl 1), S11–S61. Barnett, T. E., Chumbler, N. R., Vogel, W. B., Beyth, R. J., Qin, H., & Kobb, R. (2006). The effectiveness of a care coordination home telehealth program for veterans with diabetes mellitus: A 2-year follow-up. The American Journal of Managed Care, 12(8), 467–474. Barnett, T. E., Chumbler, N. R., Vogel, W. B., Beyth, R. J., Ryan, P., & Figueroa, S. (2007). The cost-utility of a care coordination/home telehealth programme for veterans with diabetes. Journal of Telemedicine and Telecare, 13(6), 318–321.

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Blumberg, S. J., Luke, J. V., Davidson, G., Davern, M. E., Yu, T. C., & Soderberg, K. (2009). Wireless substitution: State-level estimates from the National Health Interview Survey, January-December 2007. National Health Statistics Report (14), 1–13, 16. Boyle, J. P., Thompson, T. J., Gregg, E. W., Barker, L. E., & Williamson, D. F. (2010) Projection of the year 2050 burden of diabetes in the US adult population: Dynamic modeling of incidence, mortality, and prediabetes prevalence. Population Health Metrics, 8, 29. Carson, A. P., Reynolds, K., Fonseca, V. A., & Muntner, P. (2010) Comparison of A1C and fasting glucose criteria to diagnose diabetes among U.S. adults. Diabetes Care, 33(1), 95–97. Centers for Disease Control and Prevention (2011). Number of Americans with diabetes rises to nearly 26 million: More than a third of adults estimated to have prediabetes (Press Release). Retrieved from: http://www. cdc.gov/media/releases/2011/p0126_diabetes.html. Chumbler, N. R., Neugaard, B., Kobb, R., Ryan, P., Qin, H., & Joo, Y. (2005). Evaluation of a care coordination/ home telehealth program for veterans with diabetes: Health services utilization and health-related quality of life. Evaluation and the Health Professions, 28(4), 464–478. Chumbler, N. R., Neugaard, B., Ryan, P., Qin, H., & Joo, Y. (2005). An observational study of veterans with diabetes receiving weekly or daily home telehealth monitoring. Journal of Telemedicine and Telecare, 11(3), 150–156. Chumbler, N. R., Vogel, W. B., Garel, M., Qin, H., Kobb, R., & Ryan, P. (2005). Health services utilization of a care coordination/home telehealth program for veterans with diabetes: A matched-cohort study. Journal of Ambulatory Care Management, 28(3), 230–240. Darkins, A., Ryan, P., Kobb, R., Foster, L., Edmonson, E., Wakefield, B., Lancaster AE. (2008). Care Coordination/Home Telehealth: The systematic implementation of health informatics, home telehealth, and disease management to support the care of veteran patients with chronic conditions. Telemedicine Journal and e-Health, 14(10), 1118–1126. Pare, G., Jaana, M., & Sicotte, C. (2007). Systematic review of home telemonitoring for chronic diseases: The evidence-base. Journal of the American Medical Informatics Association, 14(3), 269–277. Pimouguet, C., Le Goff, M., Thiebaut, R., Dartigues, J. F., & Helmer, C. (2011) Effectiveness of disease-management programs for improving diabetes care: A metaanalysis. The Canadian Medical Association Journal, 183(2), E115–E127. Rodbard, H. W., Blonde, L., Braithwaite, S. S., Brett, E. M., Cobin, R. H., Handelsman, Y., … Zangeneh F. (2007). American Association of Clinical Endocrinologists medical guidelines for clinical practice for the management of diabetes mellitus. Endocrine Practice, 13(Suppl. 1), 1–68. Occupational Employment Statistics. (2010). Occupational Employment and Wages, May 2010. 29-111 Registered Nurses. Retrieved August 9, 2011, from http:// www.bls.gov/oes/current/oes291111.htm.

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SUGGESTED READING Baily, M. A., Bottrell, M., Lynn, J., Jennings, B., & Hastings, C. (2006). The ethics of using QI methods to improve health care quality and safety. The Hastings Center Report, 36(4), S1–S40. Dellifraine, J. L., & Dansky, K. H. (2008). Home-based telehealth: A review and meta-analysis. Journal of Telemedicine and Telecare, 14(2), 62–66. Gaikwad, R., & Warren, J. (2009). The role of home-based information and communications technology interventions in chronic disease management: A systematic literature review. Health Informatics Journal, 15(2), 122–146. Genuth, S. (2006). Insights from the diabetes control and complications trial/epidemiology of diabetes interventions and complications study on the use of intensive glycemic treatment to reduce the risk of complications of type 1 diabetes. Endocrine Practice, 12(Suppl. 1), 34–41. Hale, N. L., Bennett, K. J., & Probst, J. C. (2010) Diabetes care and outcomes: Disparities across rural America. Journal of Community Health, 35(4), 365–374. Hicks, L. L., Fleming, D. A., & Desaulnier, A. (2009). The application of remote monitoring to improve health outcomes to a rural area. Telemedicine Journal of e-Health, 15(7), 664–671. Jaana, M., & Pare, G. (2007). Home telemonitoring of patients with diabetes: A systematic assessment of observed effects. J Evaluation in Clinical Practice, 13(2), 242–253. Lu, Z. X., Walker, K. Z., O’Dea, K., Sikaris, K. A., & Shaw, J. E. (2010). A1C for screening and diagnosis of type 2 diabetes in routine clinical practice. Diabetes Care, 33(4), 817–819. Mitty, E. (2007). Hastings Center special report: The ethics of using QI methods to improve health care quality and safety. Journal of Nursing Care Quality, 22(2), 97–101. Noel, H. C., Vogel, D. C., Erdos, J. J., Cornwall, D., & Levin, F. (2004). Home telehealth reduces healthcare costs. Telemedicine Journal of e-Health, 10(2), 170–183. Polisena, J., Tran, K., Cimon, K., Hutton, B., McGill, S., & Palmer, K. (2009). Home telehealth for diabetes management: A systematic review and metaanalysis. Diabetes, Obesity and Metabolism, 11(10), 913–930. Ronnemaa, T. (2008). Intensive Glycemic Control and Macrovascular Disease in Type 2 Diabetes—A Report on the 44th Annual EASD Meeting, Rome, Italy, September 2008. The Review of Diabetic Studies, 5(3), 180–183.

Selvin, E., Steffes, M. W., Gregg, E., Brancati, F. L., & Coresh, J. (2011). Performance of glycated hemoglobin for the classification and prediction of diabetes. Diabetes Care. 34(1):84–89. Skyler, J. S., Bergenstal, R., Bonow, R. O., Buse, J., Deedwania, P., Gale, E. A., … Sherwin R. S. (2009). Intensive glycemic control and the prevention of cardiovascular events: Implications of the ACCORD, ADVANCE, and VA diabetes trials: A position statement of the American Diabetes Association and a scientific statement of the American College of Cardiology Foundation and the American Heart Association. Diabetes Care, 32(1), 187–192. Stone, R. A., Rao, R. H., Sevick, M. A., Cheng, C., Hough, L. J., Macpherson, D. S., … Derubertis FRl. (2010) Active case management supported by home telemonitoring in veterans with type 2 diabetes: The DiaTel randomized controlled trial. Diabetes Care, 33(3), 478–484. The Diabetes Control and Complications Trial Research Group. (1993). The effect of intensive treatment of diabetes on the development and progression of longterm complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine, 329(14), 977–986. Tkac, I. (2009). Effect of intensive glycemic control on cardiovascular outcomes and all-cause mortality in type 2 diabetes: Overview and metaanalysis of five trials. Diabetes Research and Clinical Practice, 86(Suppl. 1), S57–S62. UK Prospective Diabetes Study (UKPDS) Group. (1998). Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet, 352(9131), 837–853. Thomas F. Klobucar, PhD, is the Telehealth Evaluation Research Associate for the Veterans Health Administration, Office of Rural Health, Veterans Rural Health Resource Center—Central Region. His current research focuses on how the interaction between telehealth technology, patients, and providers affects health care outcomes. Robin Hibbs, RN, MBA, MSN, CDE, is the Inpatient Diabetes Nurse Specialist at the Iowa City Veterans Administration Healthcare System. She has 14 years of specialized experience focused in diabetes care and education in the inpatient and outpatient clinical care settings. Peg Jans, RN, is the Chronic Disease Case Manager, for diabetes at the Iowa City Veterans Administration Healthcare System. Ms. Jans has 40 years of nursing experience and has for the last 5 years focused her practice in the area of Chronic Disease Case Management for diabetes. Margaret R. Adams, ARNP, BC-ADM, CDE, is the Diabetes Clinical Nurse Specialist for the Iowa City VA Health Care System. Ms. Adams has 27 years of nursing experience, specializing in the education and care of patients, family, and staff in all aspects of diabetes care in the VA setting.

For more than 28 additional continuing education articles related to Case Management topics, go to NursingCenter.com/CE.

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Appendix A Wording of Daily Interrogatories Presented to Patients/Program Participants’ Home Monitoring Equipment Are you feeling lightheaded or dizzy today?

Appendix B Program Discharge Criteria (Patients must meet one or more of the following criteria.) Treatment goals have been met or reasonable treatment goals are determined to be unobtainable.

I am having problems following my diet.

The patient/caregiver has requested withdrawal from the project.

Yesterday, I did not exercise.

The patient has expired.

Are you having vision problems?

The patient has moved out of the catchment area.

Yesterday my blood sugar was above 250 mg/dl.

The patient is transferred to a nursing home.

Yesterday my blood sugar was below 70 mg/dl.

By order from a provider to discharge patient from chronic

I am having frequent headaches.

disease management.

Do you have sores, cuts, or open areas on your feet?

I am having frequent headaches.

Have you skipped any of your medications as prescribed?

Do you have sores, cuts, or open areas on your feet?

I feel discouraged about the future.

Have you skipped any of your medications as prescribed? I feel discouraged about the future.

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