Patients with poor nutrition status have higher healthcare costs compared with their well-nourished counterparts because of higher rates of healthcare resource utilization (HCRU; ie, higher 30-day readmission rates) and delayed recovery (ie, longer hospital stays).1 The annual total cost of disease-associated malnutrition in the United States is estimated to be more than $156 billion, with $15.5 billion in direct costs alone.2,3 With compromised nutritional status and chronic diseases posing significant challenges for community-dwelling older adults,4 and the prevalence of the risk for malnutrition ranging between 26% and 39%,5-7 the need to introduce effective nutritional interventions to alleviate the burden of poor nutrition is very important for this patient population.
Nutritional interventions, especially oral nutritional supplements, can significantly reduce the health and economic burden of poor nutrition in community-dwelling adults and in patients receiving postacute care.8,9 However, limited research exists on the economic value of nutritional care delivered at home via comprehensive quality improvement programs that provide optimal nutrition care for patients at risk for poor nutrition who are transitioning through multiple healthcare settings.
The current literature on nutrition-focused quality improvement programs is focused mainly on hospitalized at-risk or malnourished patients. These studies have demonstrated that nutrition-focused quality improvement programs lead to significant reductions in 30-day readmissions, length of hospital stay, infectious and noninfectious (eg, pressure ulcers) complications, and overall healthcare expenditures.10-14
The most comprehensive nutrition-focused quality improvement program to date assessed the impact of a hospital-based quality improvement program at 4 Advocate Health Care hospitals (an Illinois-based healthcare system) on 30-day readmission rates and hospital length of stay in 1269 hospitalized at-risk or malnourished patients.10 The total cost-savings for a 6-month period resulting from readmission and length-of-stay reductions were estimated to be more than $4.8 million, with net savings of $3858 per patient treated.15
However, this quality improvement program did not account for the costs and potential benefits of sustaining nutritional care for in-hospital patients who are transitioning home after discharge, but who are continuing to receive healthcare services. Therefore, the same research team assessed the impact of a comprehensive nutrition-focused quality improvement program on the hospitalization rate (including admissions and readmissions) and on HCRU for adults at risk for malnutrition who were receiving home healthcare services predominantly after a hospital discharge.7 The study showed a significant reduction in hospitalizations and in overall HCRU.7
Our study examined the 30-day economic impact of the quality improvement program from a hospital system’s perspective. The use of 30-day outcomes for this analysis is consistent with the Hospital Value-Based Purchasing program, in which the Centers for Medicare & Medicaid Services penalizes low-performing hospital systems for care that occurs up to 30 days after discharge.16 The 30-day time frame also reflects a sustainable and scalable period for continued nutritional support and patient compliance in postacute care settings.
The quality improvement program used in this study focused on bolstering patient-centric nutritional care. The program included nutritional screening for patient admission to home healthcare services17 by the admitting clinician, patient and caregiver education, motivational interviewing regarding nutrition, and the provision of general or disease-specific oral nutritional supplement use for 30 days for at-risk patients. The oral nutritional supplement was delivered to the patient’s home within 48 to 72 hours of enrollment in the program and was supported by retail discount coupons provided by Abbott Nutrition to be used by the patient after the initial 30 days of supplementation use.
The quality improvement program group and the control group were comprised of 3 subgroups of patients at risk for malnutrition. The quality improvement program group 1, which was the largest cohort, consisted of 1049 (67.9%) hospitalized patients who were discharged to home healthcare, group 2 consisted of 294 (19%) patients discharged from a skilled-nursing facility to home healthcare, and group 3 consisted of 203 (13.1%) patients who were referred to home healthcare from an outpatient clinic (Figure 1).
For the purpose of this analysis, all the patients in the different groups were considered transition-of-care patients, because more than 75% of the patients had had a recent hospitalization before enrollment in home healthcare, and their sociodemographic and clinical characteristics were comparable.
A historical group (N = 7413) and a concurrent control group (N = 5235) were established. In addition, a propensity score matching method was used to match the historical and concurrent control groups to patients in the quality improvement program based on the probability of their participation in the quality improvement program. This created historical and concurrent propensity score matching cohorts.
In-depth information on the methodology and implementation characteristics of the quality improvement program has been outlined in a previous publication by Riley and colleagues7 and at ClinicalTrials.gov (NCT03011944).
After the implementation of the quality improvement program, a web-based, dynamic budget impact model similar to the one designed previously for the hospital-based quality improvement program15 was developed by the Center for Applied Value Analysis, LLC, Northampton, MA, to assess the quality improvement program’s respective effects on cost-savings.
This budget impact model’s purpose was to assess the economic impact of the implementation of the quality improvement program from a hospital system’s perspective. The model estimates the differences in the number of admissions and readmissions, emergency department visits, and outpatient visits over 30 days for the patients in the program versus a pre–quality improvement program historical or concurrent group, as well as 2 other comparative groups, namely, the historical and the concurrent propensity score matching cohorts. This model enables predictive outcome simulations for a time horizon between 1 and 3 years, as well as flexible comparison of all simulated alternatives. Static representations of the cost results are reported throughout this article.
The model used in this study was designed to be highly customizable for a facility, with multiple inputs that can be defined by the user. To facilitate the use of the model, all such variables are prepopulated with patient-related data sourced from the underlying study sample. The main population inputs consist of the number of patients receiving care at home and the proportion of patients at risk for poor nutrition status.
The base-case calculations were informed by a baseline risk for malnutrition rate of approximately 27.2% (including 5688 patients screened and 1546 at-risk or malnourished patients), which was the rate of those at risk for malnutrition in the quality improvement program cohort in the previous study used in this analysis.7
The fixed and variable costs (not shown) of the quality improvement program were estimated based on the specific staff time recorded for the implementation of the program and the associated hourly wage rates reported by the 2017 Bureau of Labor Statistics.18
We performed all the analyses using the existing web-based budget impact model described earlier. The main cost calculations compare the quality improvement program cohort and the pre–quality improvement program control group.
In addition, we performed sensitivity analyses for the central cost inputs (ie, hospitalization, outpatient visits and emergency department visits, cost per oral nutritional supplement bottle, and screening cost per patient, which were all varied by ±20% change; observation time period, 30 or 60 days vs 90 days), the variation of oral nutritional supplement delivery costs, and for the concurrent and propensity score matching comparison cohorts.
The results were presented as costs per 1000 patients, comparing the costs for the quality improvement program group versus the historical control group and sorted by the parameters with the highest influence on the results.
The cost analysis was based on the costs associated with screenings for 5688 patients and assessments for all patients admitted to home healthcare during the quality improvement program period, amounting to $67,043. In addition, the costs for patient and caregiver education, follow-up reminders and calls, patient survey administration, and other quality improvement program procedures for patients who were at risk for malnutrition totaled $164,470, for the 1546 patients in the quality improvement program.
During the 30-day follow-up period, significant reductions in HCRU were found for the overall quality improvement program patient population (relative risk, 0.93; 95% confidence interval [CI], 0.91-0.95; P <.0001); Table 1). There were also significant reductions in inpatient and outpatient visits for the overall quality improvement program patient population compared with the historical controls.
The relative risks for the inpatient and outpatient visits were 0.81 (95% CI, 0.74-0.90; P <.0001) and 0.83 (95% CI, 0.81-0.85; P <.0001), respectively; the increase in emergency department visits was also significant (relative risk, 1.46; 95% CI, 1.27-1.69; P <.0001) for the overall quality improvement program patient population (Table 1).
The cost-savings associated with the significant reductions in overall HCRU 30 days after the quality improvement program are shown in Table 2. The total cost-savings resulting from the reduced overall HCRU—including those for admissions, readmissions, and outpatient visits—amounted to $2,408,668 per 1546 patients in the quality improvement program, with a net savings of $1558 per patient who received treatment.
The cost of oral nutritional supplement bottles and delivery was $171,281 for the 1546 patients in the program (Table 2). The hospitalization cost of $18,296 was extracted from the 2013 Healthcare Cost and Utilization Project report, which reported the costs for malnourished adults using 2013 data for US hospital admissions,1 and was then inflated to 2017 dollars using the Consumer Price Index (CPI).19
The average costs of the emergency department and outpatient visits were $1312 and $535, respectively, and were obtained from the 2013 Medical Expenditure Panel Survey,20 which were also inflated to 2017 dollars using the CPI.
The results of the sensitivity analyses were consistent with the original findings, as shown in Table 3 and Figure 2. The parameters with the highest influence on cost-savings were the observation period of 30 days, followed by the cost per hospital admission or readmission and the selected control cohort. Regardless of the sensitivity analysis performed, considerable cost-savings were associated with the quality improvement program, which ranged between $1229 and $2879 per patient for the 1546 patients included in this program.
An extensive body of evidence supports the effectiveness of comprehensive nutritional care in reducing the burden of poor nutrition on patient health and economic outcomes across the different care settings.9,15,21,22
The results of this budget impact analysis show how implementing sustained nutritional interventions for patients at risk for malnutrition who are transitioning to a postacute care environment results in significant cost-savings of more than $2.4 million for the overall quality improvement program population of 1546 (per-patient savings of >$1500) that results from a reduction in HCRU over a 30-day period. The cost-savings per 1000 patients extended longitudinally to 60 days ($2,878,670; $2878 per patient) and 90 days ($1,560,931; $1560 per patient), which warrants future research to study the long-term impact of a quality improvement program on HCRU and costs.
The sensitivity analyses demonstrated that regardless of the parameter value changes applied, the results were robust and show significant cost-savings with the implementation of a quality improvement program. In addition, the results showcase the conservative approach applied in the base-case analysis and suggest that there is still room for higher cost-savings than those presented in the base-case (eg, 60-day time horizon, propensity score matching cohort, no oral nutritional supplement delivery cost).
Our findings are consistent with those of previous hospital-based studies that identified nutritional interventions using cost-effective models of value-based care for patients at risk for malnutrition.7-14,21-27 The results of the previous analysis of cost-savings of the hospital-based nutrition-focused quality improvement program,15 combined with the present analysis, suggest that the adoption of improved nutritional practices in the inpatient and postacute or community settings can produce significant cost-savings while also improving patients’ health outcomes.
For example, the per-patient cost-savings resulting from reduced 30-day readmissions with the hospital nutrition quality improvement program was $674 (with the rest of the cost-savings resulting from a reduced length of stay), and more than $1100 with the home-based nutrition quality improvement program as a result of reduced admissions and readmissions. The benefits of this quality improvement program provide the rationale for the implementation of similar protocols for community-dwelling adults who are receiving nutritional care at home to optimize their care.
In addition, the combined savings of the hospital- and home-based quality improvement programs suggest that optimized nutritional care across the care continuum is a critical component of cost-effective healthcare delivery and financial sustainability of hospital systems.
This analysis has several limitations. The limitations outlined by Riley and colleagues, including the application of a real-world pragmatic study design rather than a randomized one, reliance on administrative and self-reported data for healthcare resource use, and oral nutritional supplementation consumption,7 apply to this analysis as well.
Because our study focused on a population-based management intervention, individual-level cost data were not collected. In addition, we relied on national cost data from 2013 rather than institution-level cost data, following a similar, previously published hospital budget impact model.15
The objective of this study was to estimate the cost-savings using a population-based management approach to nutrition in the postacute care setting, which can be generalized to other hospital systems across the United States. Therefore, the goal was not to assess the direct impact of the quality improvement program on episode cost data. However, future analysis could use institution-level or more recent periods of national-level cost information.
Furthermore, this base-case analysis does not account for variability in program fidelity over time that could attenuate the effects on outcomes and cost; however, the model expands over 3 years, so adjustments to outcomes and cost data can be applied. The model also examines the economic impact at only 1 hospital system; the results at other hospital systems may vary and may not be generalizable. Similarly, the analysis does not incorporate the societal perspective; future research and budget impact analyses would be required.
Finally, although preventing 30-day unplanned hospital readmissions is a goal for acute and postacute care providers and a quality of care measure that informs reimbursement,28-30 the prevention of hospital admissions is not necessarily associated with cost-savings, so future studies should focus on the impact of similar quality improvement programs on readmissions alone rather than on a combination of admissions and readmissions.
This analysis is the first to assess the impact of a novel and cost-saving nutrition-focused quality improvement program across the care continuum in association with the design of a budget impact model. Our budget model analysis enables providers and healthcare administrators to assess how an effectively implemented nutrition-focused quality improvement program can deliver significant health and economic improvements in the postacute care settings for patients discharged from a hospital as well as for people at outpatient settings who are at risk for malnutrition.
Furthermore, this study shows that significant economic benefits can be generated from implementing nutrition-focused interventions as a value-based driver of change for healthcare systems that offer postacute care to their hospitalized patients and/or those at the outpatient setting. This study provides additional support for nutrition-focused quality improvement programs as an innovative and low-cost model to help hospital systems advance value-based nutrition across the care continuum. The results also support the critical need of elevating the role of nutritional care and related interventions in advancing population-based management for these populations specifically.
Finally, these findings underscore how the optimization of nutritional care, although frequently overlooked by providers and healthcare administrators, is an important aspect of optimized, value-based healthcare delivery.
We would like to thank John Robinson of the Center for Applied Value Analysis for his help with the construction of the budget impact model. Also, special thanks to the Advocate Health Care study team.
Funding for the study was provided by Abbott, USA.
Author Disclosure Statement
Dr Sulo is an employee and stockholder of Abbott. Mr Lanctin and Dr Partridge were employees and stockholders of Abbott during the study. Mr Feldstein and Mr Schwander have received financial support from Abbott for the development of the budget impact model. Ms Landow and Mr Zöllner have no conflicts of interest to report.
Portions of this study were presented at the International Society for Pharmacoeconomics and Outcomes Research Conference, May 2019, in New Orleans, LA.
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