Telehealth programs for congestive heart failure have been shown to be clinically effective. This study assesses clinical and economic consequences of providing telehealth programs for CHF patients. A Markov model was developed and presented in the context of a home-based telehealth program on CHF. Incremental life expectancy, hospital admissions, and total healthcare costs were examined at periods ranging up to five years. One-way and two-way sensitivity analyses were also conducted on clinical performance parameters. The base case analysis yielded cost savings ranging from
Congestive heart failure (CHF) is caused by any condition which reduces the efficiency of the heart muscle and results in insufficient blood supply to the human body. The high prevalence and costs associated with congestive heart failure (CHF) place an enormous economic and social burden on patients and society. Between 4 and 7 million people were estimated to suffer from CHF in the United States [
Telehealth is defined as the use of video, electronic, or other telecommunication information to monitor patients and transmit data related to patient health status at a distance [
This study designs and applies a Markov model to assess long-term clinical outcomes and economic consequences of providing CHF telehealth programs. Costs are modeled, from the perspective of an American payer. The analysis includes telehealth install/uninstall costs, monthly monitoring costs, costs for nursing resources for data review and home visits, and pharmacy costs, as well as the usual CHF outpatient and inpatient cost. Scenario analysis was performed to assess clinically and economically feasible product performance-cost combinations. Through the model, we will be able to address the research questions of when and with whom the optimal cost saving can be achieved by deploying telehealth programs.
The model was developed and is presented in the context of a home-based telehealth program on CHF. Telehealth programs can be conceptualized as having two primary components [
Schematic of a home-based telehealth program for monitoring CHF patients. Note that TEST includes one or more of the monitoring measures: activity monitoring, biomarker monitoring, questionnaires, and symptom monitoring; TREAT includes one or more of the following: case manager reviewing data, telephone triage, physicians’ initiation of medication package, and nurse home visit (if needed).
Markov models are state transition models commonly used to estimate the cost-effectiveness of a new treatment [
Markov model diagram.
The usual care cohort is defined as the cohort without receiving any telehealth intervention. The risks of hospitalization and mortality for the usual care group were derived from previous models [
Probability of mortality and hospitalization.
Usual care | Definitions | NYHA II or III | NYHA III or IV |
---|---|---|---|
|
|||
|
|||
Death rate | |||
|
0.007 [ |
0.01 (0.01–0.015 [ | |
|
0.100 (0.07–0.1 [ |
0.100 (0.07–0.1 [ | |
Hospitalization | |||
|
No prior hospitalization | 0.008 [ |
0.008 [ |
|
Index admission | 0.052 [ |
0.168 [ |
|
2 previous admissions | 0.106 [ |
0.213 [ |
|
3 previous admissions | 0.121 [ |
0.268 [ |
|
4+ previous admissions | 0.180 [ |
0.334 [ |
The mortality and hospitalization risks for patients in the telehealth group are affected by telehealth program efficacy. These risks are estimated in our previously published meta-analysis performed on 33 randomized control trials (RCT) between 2001 and 2012 from more than 9 countries with a total of 7530+ patients [
Reduction effectiveness of different types of telehealth programs [
Measure | Models | Effect | 95% CI |
|
Heterogeneity ( |
|
Public bias | Effectiveness | |
---|---|---|---|---|---|---|---|---|---|
Mortality | RR | FE | 0.76 | (0.66, 0.88) | <0.001 | 18.3% |
25.4 (0.49) |
No | 24% reduction |
CHF hosp | RR | RE | 0.72 | (0.61, 0.85) | <0.001 | 66.3% |
61.8 (<0.001) |
No | 28% reduction |
CHF LOS | MD | RE | −1.41 | (−2.43, −0.39) | 0.007 | 71.3% |
38.6 (<0.001) |
No | 1.41-day reduction |
RR: risk ratio; MD: mean difference.
FE: fixed effect model. RE: random effect model.
All costs were fixed at 2013 US dollars (
Our models synthesized inpatient and outpatient contributions to both CHF and non-CHF healthcare costs based on previously published studies. The cost estimates are summarized in Table
Cost estimates.
Baseline | Quoted references | |
---|---|---|
Usual care | ||
Per CHF hospitalization cost | $12,000 | $12.7K [ |
Annual CHF outpatient cost | $1,700 | $680–2700 [ |
Annual non-CHF healthcare cost | $10,000 | $7300–13000 [ |
Telehealth | ||
Install/uninstall cost amortized to each month | $15 | Based on field experts estimate |
Monthly monitoring cost | $80 | Based on field experts estimate |
Case manager cost per patient per month | $125 | Based on average nurse salary, assuming 75 patients are covered by one nurse |
Total monthly TEST cost | $220 | |
Physician contact/medication initialization cost per detected episode | $52 | Based on physician verbal order time and new medication cost |
Nurse home visit cost per detected episode | $135 | Based on field experts estimate |
Total TREAT cost per episode | $187 |
The costs of telehealth programs are affected by two additional technical parameters: (1) the sensitivity of the home-based exacerbation detection method (SEN) and (2) the specificity of the home-based exacerbation detection (SPE) [
Cost consequences of deploying telehealth programs with certain exacerbation detection sensitivity and specificity. Note that there are two cost saving channels: when true exacerbation (+) is converted to nonexacerbation status (−), cost is saved through reverted admission. Even when true exacerbation is not reverted, through telehealth monitoring and early intervention, the severity of exacerbation can be reduced such that even if the patients are admitted to hospital, the length of stay would be reduced.
The three main outcomes of the model are the number of incremental hospitalization incidences, incremental health outcome, and total cost difference. Health outcome is expressed as life years (LY). Utility values per disease state are not considered in this study. All costs were discounted at a rate of 3.0%, an accepted value for the United States [
Three pairs of cohorts, each consisting of a telehealth cohort and a usual care cohort, were constructed. Within all cohorts, patients were distributed in the NYHA II or III population. Cohort 1 (C1) begins at the time when patients have no hospitalization at all. This cohort indicates the lowest risk population of heart failure. Cohort 2 (C2) initially contains a 30%, 30%, and 40% distribution of patients with one, two, and three prior hospitalization incidences, respectively. This cohort resembles the clinical cohort of patients with middle-to-high risk who are also considered as the target population for current-day telehealth programs. Cohort 3 (C3) is composed entirely of patients who have already had at least four prior CHF hospital admissions. This cohort represents severe, very advanced patient population whose condition deteriorates fast and is subject to frequent hospital admissions. All cohorts were tracked through Markov cohort analysis over the five-year simulation horizon. First-year, third-year, and fifth-year results were recorded, and overall outcomes were estimated at these time points.
We additionally performed both one-way and two-way sensitivity analyses to investigate the effect of adjusting base case assumptions such as costs and transitional probabilities. Three scenarios for the performance of telehealth program were constructed to evaluate the impact of changing the telehealth efficacy parameters. Each scenario was defined by a different combination of five parameters as described in in Table
Telehealth clinical efficacy parameters.
Best scenario | Base case scenario [ |
Worst scenario | |
---|---|---|---|
Sensitivity | 90% | 80% | 70% |
Specificity | 90% | 80% | 70% |
Mortality reduction | 29% | 24% | 19% |
Hospitalization reduction | 38% | 28% | 18% |
LOS reduction | 30% | 25% | 20% |
We validated our model using the lifetime cost of the control arm, that is, the usual care cost of heart failure. Dunlay et al. estimated that total lifetime costs after heart failure diagnosis were
Furthermore, we estimated the current economic burden of CHF in the United States by constructing a cohort where the weights of admission status were derived from the real statistics of American patient population (i.e., 70.7%, 10.3%, 4.4%, 3.3%, or 11.3% with 0, 1, 2, 3, or 4 or more admissions, resp., [
Results for the three hypothetical cohorts are given in Table
Base case results.
Year 1 | Year 3 | Year 5 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cost | LF | AD | Cost | LF | AD | Cost | LF | AD | |||
C1 | Low risk | Usual | 12402 | 0.94 | 0.11 | 34982 | 2.47 | 0.48 | 54780 | 3.63 | 1.00 |
Tele |
+2502 | +0.01 | −0.02 | +6590 | +0.08 | −0.14 | +9826 | +0.21 | −0.28 | ||
|
|||||||||||
C2 | Intermediate risk | Usual | 25304 | 0.88 | 1.23 | 66812 | 2.07 | 3.51 | 93075 | 2.74 | 5.03 |
Tele |
−2832 | +0.03 | −0.27 | −5620 | +0.22 | −0.60 | −3422 | +0.46 | −0.55 | ||
|
|||||||||||
C3 | High risk | Usual | 32916 | 0.84 | 1.90 | 75515 | 1.91 | 4.39 | 99024 | 2.47 | 5.79 |
Tele |
−5499 | +0.04 | −0.36 | −7683 | +0.25 | −0.55 | −4456 | +0.50 | −0.4 |
AD: admission; LY: life years.
Base case analyses for three cohorts. (a) Cost saving curves as a function of number of years on telehealth programs; (b) hospitalization reduction curves as a function of number of years on telehealth programs.
The focus of the sensitivity analysis was to examine how different assumptions on telehealth efficacy would impact the estimated costs and clinical outcomes. We use cohort 2 in this analysis as this cohort represents the most clinically realistic population who might benefit most from telehealth programs.
In one-way sensitivity analysis, we reduced the default efficacy from full capacity (base case from meta-analysis) to 50% effectiveness. As indicated in Figure
Sensitivity analysis of cost saving curve for cohort 2.
Two-way sensitivity analysis was performed to investigate the effect of adjusting admission costs (from
Two-way sensitivity analysis: telehealth 3-year incremental cost and cost-effectiveness with varying monthly telehealth service costs and hospital admission costs.
Telehealth monthly cost ($) | Admission cost ($) | |||||
---|---|---|---|---|---|---|
6K | 8K | 10K | 12K | 14K | 16K | |
Δ(cost) |
Δ(cost) | Δ(cost) | Δ(cost) | Δ(cost) | Δ(cost) | |
50 | −2609 | −5264 | −7920 | −10575 | −13230 | −15886 |
150 | 295 | −2359 | −5014 | −7670 | −10325 | −12980 |
250 | 3201 | 546 | −2109 | −4764 | −7419 | −10075 |
350 | 6106 | 3451 | 796 | −1858 | −4514 | −7169 |
450 | 9012 | 6357 | 3701 | 1046 | −1608 | −4264 |
Moreover, the base case analysis assumes that patients at different risk levels consume the same amount of telehealth services (TEST cost is
In the context of a cost-avoidance model, the break-even point was defined as the cost for which the total cumulative telehealth costs for the CHF patients equalled the total cost saving through hospitalization and LOS reduction (cost savings or ΔCost = 0). Results for this break-even analysis are given in Table
Break-even costs for different patient risk groups to reach cost saving in 1, 3, and 5 years.
Patient group | Maximum monthly service fee ($) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Best | Base case | Worst | ||||||||
Year 1 | Year 3 | Year 5 | Year 1 | Year 3 | Year 5 | Year 1 | Year 3 | Year 5 | ||
C1 | Low risk | $35 | $48 | $56 | Never | $16 | $23 | Never | Never | Never |
C2: | Intermediate risk | $634 | $552 | $404 | $472 | $414 | $303 | $313 | $277 | $204 |
C3 | High risk | $946 | $652 | $430 | $715 | $498 | $333 | $497 | $349 | $236 |
In this paper, we apply Markov methods for examining the potential cost consequences of home-based telehealth programs that attempt to reduce the frequency and severity of exacerbations in CHF. We investigated multiple scenarios for cost and clinical performance for the program and assessed the potential cost-saving capabilities of these programs from the perspective of an American payer. Through these analyses, we demonstrated the likely cost-saving capabilities of the CHF telehealth program and report on the technical and cost boundaries within which the program should operate.
Using meta-analysis results compiled over a broad range of clinical trials on CHF telehealth programs, we were able to define base case assumptions and scenarios. Our analysis suggests that, under the base case system performance and cost assumptions, telehealth programs are likely to be cost saving for higher risk patients (patients with one or more prior admissions) within the simulation duration (up to five years).
To our knowledge, this study is the first of its kind to assess telehealth economic and clinical consequences in chronic heart failure. The base case analysis yields cost savings ranging from
We chose a 5-year period as the longest observation period for analysis because most current-day telehealth programs were used for 6 months (33%) and 12 months (51.5%). Only 6% studies extended over 24-month time frame [
This study had a few limitations: first we did not include utility data into the analysis; second we obtained model data from existing literatures and assume that the effectiveness of telehealth programs is constant over time. Future work could include patient level data when available and create time-dependent transition probabilities.
We envision that the results of this study and the broad approach can aid payers in technology acquisition decisions. We also suggest that the results of this study can be used to set performance and price targets for those healthcare innovators engaged in the development of CHF telehealth programs. Payers could use the model developed in this study to simulate different scenarios that would help them assess how to best allocate telehealth resources among different patient risk. For example, payers can evaluate if the intensities and cost of the teleheath intervention are reasonable given the patient risk profile and if the cost impact of the intervention is satisfactory according to their perspectives.
(i) Telehealth programs have been both theoretically and empirically proved clinically beneficial, but current understanding of the cost consequences of these telehealth programs is still limited. (ii) This study develops a Markov model and assesses clinical and economic consequences of providing telehealth programs for CHF patients. This is the first attempt in this field. (iii) Telehealth programs can be cost saving for intermediate and high risk patients with one or more prior admissions over a 1- to 5-year window. The cost savings were most sensitive to patient risk, baseline cost of hospital admission, and the length-of-stay reduction ratio affected by the telehealth programs. Regions with high inpatient care costs and high readmission rate would receive the greatest financial benefit from telehealth programs.
The authors declare that they have no competing interests.
This study receives no funding from any outside sponsor, and there are no relationships to be declared for any of the authors. The corresponding authors would like to thank Michael Lee, John Ryan, Juliet Chon, and Linda Schertzer for the useful discussions and generous support.