Marcia J. Wade, MD, FCCP, MMM; Akshay S. Desai, MD, MPH; Claire M. Spettell, PhD; Aaron D. Snyder, BA; Virginia McGowan-Stackewicz, RN, CCM; Paula J. Kummer, RN, BA; Maureen C. Maccoy, RN, MBA; and Randall S. Krakauer, MD, FACP
Heart failure (HF) is a major public health problem in the United States. It represents the most common cause for hospitalization among elderly Americans1 and was the nation’s most expensive medical condition to treat in 2005, costing $76 billion. While recent advances in medical treatments for HF have improved patient outcomes, overall morbidity and mortality remain high,2
with approximately 30% to 40% of patients requiring readmission within 6 months of hospitalization.
We conducted a randomized controlled clinical trial to explore the impact of supplementing nurse case management (CM) with the Intel Health Guide System (HGS) in an elderly population with HF and a high risk for rehospitalization. The goal was to assess impact on outcomes and quality of life, and also to assess the feasibility of utilizing an Internet-based telemonitoring technology to facilitate CM in an elderly HF population with multiple comorbidities.
The study design was a prospective, randomized, active controlled study, in which members were allocated to the telehealth system with CM (THCM) or to CM alone.
Aetna Medicare Advantage members with medical and pharmacy benefits were identified through analysis of claims and eligibility information. Criteria for invitation to the study were residence in New Jersey, New York, or Pennsylvania; medical claims for chronic HF in medical claims in the past 3 years; inpatient admission or 2 or more emergency department (ED) visits for any cause within the 6 months before identification; and high risk for a subsequent admission or ED visit based on the health plan’s proprietary predictive model.
Exclusion criteria included claims or utilization records indicating a terminal condition, end-stage renal disease, dementia, use of a ventricular assist device, active listing for cardiac transplantation, completed heart transplant, or requirement for chronic or ongoing intravenous HF medication. Members managed in the health plan’s CM program within the 30 days prior to randomization were also excluded.
Recruitment, consent procedures, and study design were approved by Western Institutional Review Board (Olympia, WA, Protocol 20081787). Oral and written consent to telehealth and the study were sought after randomization for THCM. For those assigned to CM, consent was implied by their oral agreement to participate in CM. Participation began for both groups as telehealth systems were deployed between December 2008 and June 2009.
All participants in both arms of the study received Aetna Medicare’s current standard CM services, which are designed to meet the frequently complex needs of older adults. These services began upon enrollment. For CM, case closure occurred when member goals were deemed met. For THCM, CM continued throughout the monitoring period. Thus, in some cases THCM members received CM longer than members in CM alone. Nonparticipants among both randomized groups continued to be eligible for identification and outreach for CM. Case managers were not directly responsible for patient care, but worked closely with the members, physicians, subspecialty providers, and others to facilitate care to manage the complex cardiac, oncologic, psychiatric, social, other medical, and/or end-of-life needs of the participants. Identified needs were addressed on scheduled calls with the member (typically 2-3 times per week initially, with frequency decreasing as issues resolved). Common issues covered included health education, safety and emergency measures, medication regimen, and care coordination needs.
Telehealth Monitoring With Case Management Group
In addition to the CM intervention outlined above, the THCM subjects received home monitoring of weight and blood pressure. They also answered questions about adherence and their health regimen through their use of the HGS.
Telehealth System Description
The Intel HGS is a US Food and Drug Administration–approved system comprising a touch screen, a remote patient management unit placed in the member’s home, and a case manager user interface accessed via secure Internet link through a broadband connection. A wired blood pressure monitor and a wireless weight scale transmitted patient data to the CM team. The system was also programmed to ask members about their health status, activities, and medication adherence, and to offer and show educational videos. HGS telemonitoring sessions were administered weekdays at a time the participant selected, according to the member-customized rotocol the case manager had entered. Alerts to the case manager occurred when measurements or response violated threshold values set in alignment with the Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure blood pressure guidelines,3
HF management guidelines,4,5
and the physician’s care plan. Participants in THCM transmitted their weight and blood pressure as often as every weekday. Periodically, they were asked to input responses about medication adherence, exercise, and any recent ED visits or hospital stays. The HGS-mediated data flow between the participants and case managers catalyzed frequent CM interactions between the participant, his or her case manager, and the participant’s physician. Educational content was also available to participants on demand through the HGS. Case managers were instructed to check the Web-based clinical user interface for alerts on each member’s condition and to respond with care coordination assistance as needed. Case managers modified members’ care protocols and thresholds as needed to follow the protocol and the physician’s care plans.
Outcome Measures and Statistical Analyses
The primary outcome measure was admission to an acute care hospital, an ED visit, or death during the measurement period, as determined through claims and Medicare eligibility data.
Secondary variables reflecting healthcare utilization were also measured using claims data. Measures included total hospital inpatient days, ED visits, primary care visits, specialist visits, prescriptions filled for selected types of cardiac medication, and the subset of hospital admission and ED visits for cardiovascular cause. Acute hospital inpatient days per member per year for both groups during the intervention period were also compared with the groups’ rate of hospital days in the year leading up to their randomization date.
Participant experience was measured telephonically before and after the intervention period, using the Quality Control Inc SF-12 Health Survey (SF-12) (http://www.sf-36.org/tools/sf12.shtml). For THCM, a member experience and satisfaction survey (5-point Likert scale with responses ranging from strongly disagree to strongly agree) was administered directly via HGS at the end of the study. Descriptive measures included the proportion of telehealth participants completing the planned 6 months’ monitoring and program adherence. Adherence was measured as proportion of the scheduled weekday protocol-based telehealth sessions completed.
Based on a proprietary predictive model showing an anticipated event rate of 49% in 6 months for the control arm, we calculated that a sample size of 138 patients per arm would provide 80% power to detect a statistically significant decrease of 30% in the primary composite outcome with THCM. Calculations were conducted using SAS version 9.1.3 (SAS Institute Inc, Cary, NC) using an alpha level of .05 for a 1-tailed test of significance.
Baseline characteristics were expressed as means and percentages, and compared between the 2 groups using X
2 tests and independent t tests. The analysis of the primary outcome was initially conducted according to intention-to-treat for the randomized population. However, since only a fraction of the randomized population ultimately enrolled in the study, the data are presented for the as-treated cohort to better assess the effect of the intervention as utilized in our population. These as-treated analyses compared outcomes for THCM members who consented and had HGS installed with those of members who began the CM intervention during the same time period.
The odds ratio for the primary end point was assessed using logistic regression models adjusted for baseline differences between the 2 groups that persisted after randomization and subsequent dropouts. The multivariate regression analyses also included numerous covariates to control for any differences between the groups, including age, state of residence, presence of various comorbid conditions, and prior cardiac events including coronary artery bypass surgery. Date of randomization was included as a covariate to account for temporal differences. Sensitivity analyses were performed on secondary outcomes capping outliers’ results at the 99th percentile; since no difference was seen, only the raw data are shown. Poisson regression analyses using generalized estimating equations were used to compare rates of events (number of hospital admissions, number of ED visits, number of acute inpatient hospital days).
Baseline Demographic, Clinical, and Utilization Characteristics
We initially identified 2314 members who met the study eligibility criteria. A total of 114 were excluded before randomization due to loss of health plan coverage or missing contact information. The remaining 2200 were randomized to THCM or CM. In the interval between randomization and study enrollment, there was considerable attrition in the study cohort. The Figure
details the specific reasons for nonparticipation among those initially randomized, such as CM for anotherreason, lack of desire to participate, or lack of success reaching members. Ultimately, 164 members consented and received THCM, and 152 were placed into the CM arm of the study.
The randomized groups were similar in demographics, prior healthcare utilization, and clinical conditions. Average age was 78.1 years, and the average individual had numerous comorbid conditions. The treated groups included a higher proportion of black/African American members than the nonparticipating group (24.4% of THCM and 20.4% of CM participants vs 16% of nonparticipants; relative risk 1.5 and 1.3, respectively, P <.05), but the groups were statistically similar with respect to other demographic and clinical aspects. Detailed baseline characteristics for the treated groups are shown in Table 1
. Treated members in the THCM arm of the study were younger than those in CM (75.8 vs 77.7 years, P = .015), were more likely to have had coronary bypass surgery (11.1% vs 3.9%, P = .019), and were more likely to be taking angiotensin receptor antagonists (31.1% vs 20.4%, P = .03). The burden of comorbid medical illness was high and was similar between groups, with a median of at least 5 comorbid conditions per person.
Process Results: Uptake, Adherence, Alert Activity, and Interventions
Eighty-five percent of the THCM group (140 participants) who had an HGS installed continued to use it for the entire 6-month monitoring period. A total of 24 (15%) discontinued use sooner: 12
expressed frustration with or disinterest in using the device; 3 cited poor health; 4 died; and 5 left the health plan. More than 15,000 device sessions were logged among those assigned to THCM, reflecting a mean of 180 sessions per member over the 6-month study duration. All data were transmitted to the members’ case managers for evaluation and disposition. Average adherence to requested daily biomonitoring sessions was 74%, and only 7 members had measured adherence lower than 25%. Beyond the assigned daily sessions, there were many ad hoc measurements as participants elected to use the HGS to measure weight or blood pressure or review educational materials.
During the 6-month monitoring period, the 164 THCM members generated a total of 29,612 threshold violations. On average, there were more than 50 alerts per member during the first month of monitoring and 27 to 30 alerts permember per month thereafter. Blood pressure alerts were the most common threshold violation (35% of the total). Weight thresholds and health assessment responses triggered 24% and 28% of the alerts, respectively. Thirteen percent were false-positives generated by failed peripheral device readings or transmissions. The alerts gave immediate feedback to the member through the device and also were viewed and managed by the case managers through the Web-based portal. Case managers facilitated 324 medication adjustments for 90 members in the THCM group. Telehealth events increased the CM resources required to support THCM. The average number of case manager contacts with each member was 9.4 per month over a 5-month period for THCM, compared with 3.5 per month over a 2-month period for the CM group.
The primary study outcomes are summarized in Table 2
. For the primary outcome of hospitalization, ED visit, or death, no significant difference was found between the THCM and CM groups, whether analyzed as intention-to-treat or as-treated. Of the 12 deaths, 8 were from a non-HF cause and/or occurred in the context of hospice care. The death rate during the 6-month intervention was under 4% for each group.
summarizes the secondary outcome results. The THCM and CM groups did not differ significantly in measures of hospital days, cardiovascular admissions, primary care visits, and annualized months of prescriptions filled for diuretics or angiotensin-converting enzyme inhibitors. Carndiology visits and angiotensin receptor antagonist prescription purchases were more frequent for THCM than for CM members (P <.05). Although the SF-12 physical and mental component scores were numerically lower for THCM than for CM participants at the start of the study (NS), the change in these scores from the beginning to the end of the study did not differ significantly between the groups.
Overall, compared with the year prior to randomization, the study population experienced clinical improvement in the form of fewer hospital days during the monitoring period (Table 4
). Mean acute days per member per year dropped 42% from approximately 10 days in the baseline year to 5.41 days during THCM and 6.44 for CM (NS) (Table 3).
shows the THCM participants’ self-reported experience. A total of 131 (81%) responded to the 10-question survey with a mean positive score of 4.4 on a Likert scale ranging from 1 to 5. More than 93% of THCM members expressed satisfaction with the equipment, the support that was provided, and the protection of their privacy while using the system.
We found no difference in clinical outcomes or quality of life among HF patients treated with HGS-supplemented CM support versus CM alone. Although frequent alerts from the HGS prompted more telephone contact with case managers and visits to healthcare providers, there was no reduction in frequency of hospitalizations, ED visits, or death. The Internet-based home monitoring system was implemented successfully in an elderly population in the context of CM support, with high participant satisfaction and adherence. Case mann agers contacted THCM participants more frequently and remained engaged with them longer than they did with CM participants. The participant population as a whole experienced 42% fewer inpatient days during the intervention period compared with the year before randomization, possibly underscoring the overall benefit of CM in this population.
Our results contrast with those of several prior studies that suggested reduction in morbidity and mortality associated with telehealth and care management for HF patients.
In Cleland et al’s randomized study of telehealth for 426 HF patients, hospital stays averaged 6 days shorter for THCM than for usual care.6
Mortality at 240 days was similar for THCM (29%) and CM (27%), with significantly higher mortality for usual care (45%). The Weight Monitoring in Heart Failure study of AlereNet’s electronic monitoring technology found 56% lower mortality with THCM, but no difference in the primary outcome of HF hospitalizations.7
A recent meta-analysis of randomized controlled trials found lower mortality (relative risk 0.83) and hospitalization (relative risk 0.93) with THCM compared with usual care.8
However, our results are consistent with those of 2 recently reported large, prospective, randomized controlled trials that found no incremental benefit of telemonitoring over standard HF care.9,10
Several factors may explain the lack of benefit for telemonitoring seen in our study. Our treated sample size was small and the intervention was limited to 6 months, limiting statistical power to detect differences in clinical outcomes. We cannot exclude the possibility that a benefit might have been observed in a larger population followed for a longer time. Only 11% of the population randomized to THCM actually enrolled; the high proportion of postrandomization exclusions may add further confounders that were not captured in our multivariate modeling. Additionally, the successful deployment of an effective CM intervention for the entire population may have limited the potential to identify incremental benefit with telemonitoring. In addition, because of the high comorbidity rate of the study population, an HF-focused intervention might not have had sufficient impact to reduce overall healthcare utilization. Finally, even if the telehealth intervention were successful in facilitating early detection of clinical problems, we did not control the speed or nature of clinical response to the detected threshold violations.
As others have noted, successful improvement in patient outcomes through remote monitoring may depend critically on delivery of an efficient and effective corrective response.11
Notably, our telehealth algorithms immediately advised participants with out-of-range weight or blood pressure to contact their physicians, and our results showed a significant increase in clinical visits for THCM.
In summary, in our randomized evaluation of a telemonitoring strategy compared with Medicare specialized CM for elderly patients at high risk for HF rehospitalization, there was no apparent incremental benefit with regard to morbidity, mortality, or quality of life. The reduction in hospital days for the trial participants compared with their prestudy year underscores the benefits of CM for such HF patients. Routine use of telemonitoring to supplement CM may not be an effective way to improve outcomes in HF patients. These results underscore the need for systematic evaluation of the benefits of specific remote monitoring interventions prior to widespread application in clinical practice.
1. Jessup M, Brozena S. Heart failure. N Engl J Med. 2003;348(20): 2007-2018.
2. National Center for Health Statistics. Health, United States 2005 With Chartbook on Trends in the Health of Americans. Hyattsville, MD: National Center for Health Statistics; 2005. http://www.cdc.gov/nchs/data/hus/hus05.pdf. Accessed February 14, 2011.
3. Chobanian AV, Bakris GL, Black HR, et al; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National Heart, Lung, and Blood Institute; National High Blood Pressure Education Program Coordinating Committee. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7). Hypertension. 2003;42(6):1206-1252.
4. The Heart Failure Society of America. Executive summary: HFSA 2006 comprehensive heart failure practice guidelines. J Cardiac Fail. 2006;12(1):10-38.
5. Hunt SA, Abraham WT, Chin MH, et al; American College of Cardiology; American Heart Association Task Force on Practice Guidelines; American College of Chest Physicians; International Society for Heart and Lung Transplantation; Heart Rhythm Society. ACC/AHA 2005 Guideline Update for the Diagnosis and Management of Chronic Heart Failure in the Adult: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Update the 2001 Guidelines for the Evaluation and Management of Heart Failure): developed in collaboration with the American College of Chest Physicians and the International Society for Heart and Lung Transplantation: endorsed by the Heart Rhythm Society. Circulation. 2005;112(12):e154-e235.
6. Cleland JG, Louis AA, Rigby AS, Janssens U, Balk AH; TEN-HMS Investigators. Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death: the Trans-European Network–Home-Care Management System (TEN- HMS) study. J Am Coll Cardiol. 2005;45(10):1654-1664.
7. Goldberg LR, Piette JD, Walsh MN, et al; WHARF Investigators. Randomized trial of daily electronic home monitoring system in patients with advanced heart failure: the Weight Monitoring in Heart Failure (WHARF) trial. Am Heart J. 2003;146(4):705-712.
8. Klersy C, De Silvestri A, Gabutti G, Regoli F, Auricchio A. A metaanalysis of remote monitoring of heart failure patients [published correction appears in J Am Coll Cardiol. 2010;55(19):2185]. J Am Coll Cardiol. 2009;54(18):1683-1694.
9. Chaudhry SI, Mattera JA, Curtis JP, et al. Telemonitoring in patients with heart failure [published correction appears in N Engl J Med. 2011;364(5):490]. N Engl J Med. 2010;363(24):2301-2309.
10. Anker SD. Telemedical interventional monitoring in heart failure (TIM-HF), a randomized, controlled, intervention trial investigating the impact of telemedicine on mortality in ambulatory patients with chronic heart failure. Paper presented at: American Heart Association 2010 Scientific Sessions; November 16, 2010; Chicago, IL.
11. Desai AS, Stevenson LW. Connecting the circle from home to heartfailure disease management. N Engl J Med. 2010;363(24):2363-2367.
Author Affiliations: From Aetna, Inc (MJW, CMS, ADS, VM-S, PJK, MCM, RSK), Princeton, NJ; Cardiovascular Division (ASD), Brigham and Women’s Hospital, Boston, MA; and Harvard Medical School (ASD), Boston, MA.
Funding Source: This study was funded by Aetna, Inc, and Intel, Inc.
Author Disclosures: Drs Wade and Spettell, Mr Snyder and Ms Kummer report being employees of Aetna, Inc, the funder of this study. Drs Wade and Spettell and Ms Kummer also report holding stock in the company. Dr Desai reports serving as a paid consultant to Intel Corp and Novartis. The other authors (VM-S, MCM, RSK) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (MJW, ASD, CMS, VM-S, PJK, MCM, RSK); acquisition of data (CMS, VM-S, MCM); analysis and interpretation of data (MJW, ASD, CMS, ADS, RSK); drafting of the manuscript (MJW, ASD, CMS, ADS, PJK, RSK); critical revision of the manuscript for important intellectual content (MJW, ASD, CMS, ADS, PJK, RSK); statistical analysis (ADS, VM-S); provision of study materials or patients (VM-S, PJK); obtaining funding (RSK); administrative, technical, or logistic support (VM-S, PJK); and supervision (MJW, ASD, VM-S, PJK, MCM, RSK).
Address correspondence to: Marcia J. Wade, MD, FCCP, MMM, Aetna, Inc, 3 Independence Way, MS F075, Princeton, NJ 08540. E-mail: firstname.lastname@example.org.