Telehealth Infrastructure, Accountable Care Organization, and Medicare Payment for Patients with Alzheimer's Disease and Related Dementia Living in Socially Vulnerable Areas

Abstract

Background:

Structural social determinants of health have an accumulated negative impact on physical and mental health. Evidence is needed to understand whether emerging health information technology and innovative payment models can help address such structural social determinants for patients with complex health needs, such as Alzheimer's disease and related dementias (ADRD).

Objective:

This study aimed to test whether telehealth for care coordination and Accountable Care Organization (ACO) enrollment for residents in the most disadvantaged areas, particularly those with ADRD, was associated with reduced Medicare payment.

Methods:

The study used the merged data set of 2020 Centers for Medicare and Medicaid Services Medicare inpatient claims data, the Medicare Beneficiary Summary File, the Medicare Shared Savings Program ACO, the Center for Medicare and Medicaid Service's Social Vulnerability Index (SVI), and the American Hospital Annual Survey. Our study focused on community-dwelling Medicare fee-for-service beneficiaries aged 65 years and up. Cross-sectional analyses and generalized linear models (GLM) were implemented. Analyses were implemented from November 2023 to February 2024.

Results:

Medicare fee-for-service beneficiaries residing in SVI Q4 (i.e., the most vulnerable areas) reported significantly higher total Medicare costs and were least likely to be treated in hospitals that provided telehealth post-discharge services or have ACO affiliation. Meanwhile, the proportion of the population with ADRD was the highest in SVI Q4 compared with other SVI levels. The GLM regression results showed that hospital telehealth post-discharge infrastructure, patient ACO affiliation, SVI Q4, and ADRD were significantly associated with higher Medicare payments. However, coefficients of interaction terms among these factors were significantly negative. For example, the average interaction effect of telehealth post-discharge and ACO, SVI Q4, and ADRD on Medicare payment was −$1,766.2 (95% confidence interval: −$2,576.4 to −$976).

Conclusions:

Our results suggested that the combination of telehealth post-discharge and ACO financial incentives that promote care coordination is promising to reduce the Medicare cost burden among patients with ADRD living in socially vulnerable areas.

Introduction

Care coordination has been shown to be effective in enhancing the quality of care and quality of life for patients with Alzheimer's disease and related dementia (ADRD).^1–5 The 2020 National Health IT Priorities for Research and emerging research have acknowledged the role of health information technology (HIT) as a valuable resource for facilitating care coordination, expanding health care access through the use of remote providers, streamlining treatment processes, and alleviating burnout among frontline health care providers.^6–11 Studies also indicate that HIT interventions after hospital discharges are promising to improve quality of life, reduce emergency department visits and avoidable readmissions, and improve the quality of follow-up care.^12–14

A recent study, using the framework of structural racism and discrimination, has argued that HIT's impact on health outcomes may vary by race and ethnicity and individual-level socioeconomic status.^15,16 Previous studies have found that hospital-IT infrastructure that aimed to improve care coordination was cost-saving for Black and Hispanic patients with ADRD, although it increased total Medicare payment among patients with ADRD on average.¹⁷

In addition to race and ethnicity, structural-level social determinants of health (SDoH; e.g., allocation of resources) are also a critical component of structural racism and discrimination.¹⁸ It is well known that structural social determinants have accumulated negative impacts on physical and mental health and health-related behaviors, which “can ultimately increase the risk, disparities, and inequities associated with AD/ADRD.”¹⁸

The National Plan to Address Alzheimer's Disease 2023 noted that it is critical to address structural and SDoH and promote culturally appropriate population assessment, prevention, and treatment of ADRD and risk factors for ADRD.² Empirical evidence of ADRD care by structural SDoH variation is still limited. Studies have examined rural and urban disparities and showed that hospital-HIT post-discharge decreased the odds of preventable hospitalizations in rural areas and can improve the efficiency and equity of ADRD care.^19,20 Structural-level SDoH, measured by the Social Vulnerability Index (SVI), has been linked to increased risks of adverse health conditions for ADRD patients.^21,22 Empirical evidence linking HIT with structural SDoH is still lacking.

The objective of the study is to examine whether implementing telehealth for care coordination in the most disadvantaged areas, especially for residents with ADRD, was associated with reduced Medicare payment. Specifically, this study focuses on hospital-based HIT services that facilitate post-discharge care coordination. We further explore the combined association between Accountable Care Organizations (ACOs) affiliation and the presence of telehealth infrastructure. ACOs encourage providers to promote care coordination across various settings through payment policies, such as using Current Procedural Terminology codes to reimburse for dementia care management activities.^23–26

ACOs and the newly proposed Guiding an Improved Dementia Experience (GUIDE) model aim to enhance care coordination by focusing on tailored health care approaches and addressing SDoH.^27–29 We speculate that integrating HIT with the ACO model could lead to earlier diagnoses and timely treatments, potentially reducing costs, especially for ADRD patients residing in disadvantaged areas with access to these resources.^7,8,12–14

Methods

DATA

We merged the data sets of 2020 Centers for Medicare and Medicaid Services (CMS) Medicare inpatient claims data, the Medicare Beneficiary Summary File, and the American Hospital Annual Survey. Our study focuses on community-dwelling Medicare fee-for-service (FFS) beneficiaries aged 65 years and up. We merged the Medicare claims data with the Center for Medicare and Medicaid Service's (CDC's) SVI.³⁰ Then we linked the data set with the Medicare Shared Savings Program ACO to measure beneficiary-level ACO enrollment.³¹ This integrated longitudinal data set allowed us to compare annual total costs by patients' race and ethnicity, SVI, and ACO affiliation.

MEASURES

The total cost of Medicare payments per person per year was the summation of Medicare payments on major services,³² including acute inpatient, skilled nursing facility, hospice, home health, hospital outpatient, Part B physician, and Part D Medicare payments. Hospital-based HIT measures were obtained in the Facilities and Services section of the AHA Annual Survey. The telehealth post-discharge services measure was a dichotomous indicator that equaled one if the hospital adopted telehealth remote patient monitoring post-discharge and telehealth remote patient monitoring ongoing chronic care management. Different measures and analyses, including factor analyses, have been tested in previous studies to test the robustness of the telehealth post-discharge measure used in this study.^17,19,20

The CDC SVI has 15 social factors, including unemployment, persons living below the Federal Poverty Line, racial/ethnic composition, limited English proficiency, housing (housing units with more than one person per room, multiunit housing, residence in group quarters, and mobile homes) and transportation (access to vehicle). The SVI measure was categorized into four quantiles, ranging from the least vulnerable (quantile 1) to the most vulnerable (quantile 4), representing areas with the least socioeconomic resilience.

Other independent variables at the beneficiary level included race, age, sex, and health indicators. Health indicators included common comorbidities of ADRD, such as heart disease, diabetes, hyperlipidemia, hypertension, and asthma. Covariates at the hospital level included teaching status, type of controls (for-profit, not-for-profit, and government), and bed size. Area-level variables included the rurality index using the Core-Based Statistical Areas, the U.S. Office of Management and Budget.

ANALYSIS

We first summarized the population characteristics and compared the telehealth and ACO by four SVI levels. We used the generalized linear model (GLM) with log link and gamma variance distribution to estimate the total Medicare payments. Then, we estimated the association between Medicare payment with telehealth infrastructure, ADRD, and SVI. Interaction terms were used to test the variation of association between telehealth post-discharge infrastructure, ACO, SVI, and ADRD. Interaction effects were tested and computed post-estimation to test the significance and robustness of the findings. Particularly, the effect of the interacted variables was calculated through the partial derivative and the first difference.

The ginteff command was used. We tested different model specifications (e.g., with or without community characteristics or state fixed effects; with or without county-level measures of health care resources obtained from the Area Health Resources File) and regressions (e.g., linear regressions of the log of payment) as sensitivity analyses. Results are similar and available upon request. STATA18 MP4 was used to implement the study. The study was approved by the University of Maryland Institutional Review Board.

Results

Our study included 3,331,132 total Medicare FFS beneficiaries living in communities aged 65 and above. Among them, 30% were treated in hospitals with telehealth post-discharge services, 37% had ACO, and 22% were diagnosed with ADRD. Table 1 compares beneficiaries' characteristics across SVI levels.

Table 1.

Comparison of Medicare Payment, Hospital Telehealth, Beneficiary Accountable Care Organization Affiliation, and Other Characteristics Across Social Vulnerability Index Quantiles

	SVI Q1		SVI Q2			SVI Q3			SVI Q4 (THE MOST VULNERABLE AREA)
	n = 729,341		n = 726,597			n = 708,078			n = 1,167,116
	MEAN	SD	MEAN	SD	p	MEAN	SD	p	MEAN	SD	p
Total Medicare payment per person per year ($)	46,025.88	49,304.28	46,349.59	48,371.39	0.011	51,160.83	54,427.54	<0.001	55,800.68	61,876.31	<0.001
Telehealth-post discharge	0.33	0.47	0.32	0.47	<0.001	0.29	0.45	<0.001	0.28	0.45	<0.001
ACO affiliation	0.37	0.48	0.41	0.49	<0.001	0.41	0.49	<0.001	0.32	0.47	<0.001
Telehealth-post discharge and ACO affiliation	0.13	0.34	0.13	0.33	<0.001	0.13	0.33	<0.001	0.08	0.28	<0.001
Race and ethnicity
White	0.87	0.33	0.88	0.33	<0.001	0.80	0.40	<0.001	0.74	0.44	<0.001
Black	0.05	0.23	0.06	0.23	0.004	0.11	0.31	<0.001	0.11	0.31	<0.001
Hispanic	0.03	0.16	0.03	0.16	0.0458	0.05	0.22	<0.001	0.09	0.29	<0.001
Asian	0.02	0.14	0.01	0.12	<0.001	0.02	0.13	<0.001	0.03	0.16	<0.001
Native	0.00	0.06	0.01	0.07	<0.001	0.01	0.08	<0.001	0.01	0.09	<0.001
Unknown race	0.02	0.13	0.02	0.12	<0.001	0.02	0.13	<0.001	0.01	0.11	<0.001
Other race	0.01	0.08	0.01	0.08	<0.001	0.01	0.08	0.0752	0.01	0.09	<0.001
Age
65–74	0.44	0.50	0.46	0.50	<0.001	0.46	0.50	<0.001	0.47	0.50	<0.001
75–84	0.35	0.48	0.35	0.48	<0.001	0.35	0.48	0.0165	0.35	0.48	<0.001
85 up	0.20	0.40	0.19	0.39	<0.001	0.19	0.39	<0.001	0.18	0.39	<0.001
Sex
Female	0.51	0.50	0.50	0.50	0.06	0.51	0.50	0.0165	0.51	0.50	0.002
Chronic conditions
ADRD	0.22	0.41	0.21	0.41	<0.001	0.22	0.42	<0.001	0.24	0.43	<0.001
Heart disease	0.50	0.50	0.51	0.50	<0.001	0.53	0.50	<0.001	0.53	0.50	<0.001
Diabetes	0.39	0.49	0.41	0.49	<0.001	0.43	0.50	<0.001	0.45	0.50	<0.001
Hyperlipidemia	0.75	0.43	0.75	0.43	0.016	0.76	0.42	<0.001	0.74	0.44	<0.001
Hypertension	0.87	0.33	0.88	0.32	<0.001	0.89	0.31	<0.001	0.89	0.31	<0.001
Asthma	0.10	0.30	0.10	0.29	<0.001	0.10	0.30	0.206	0.10	0.30	0.129
Location
Metro	0.91	0.29	0.91	0.29	<0.001	0.95	0.22	<0.001	0.90	0.31	<0.001
Adjacent to a metro area	0.07	0.25	0.07	0.25	0.006	0.04	0.19	<0.001	0.09	0.28	<0.001
Completely rural area	0.03	0.16	0.02	0.15	<0.001	0.01	0.12	<0.001	0.02	0.13	<0.001
Hospital
Teaching hospital	0.17	0.37	0.22	0.42	<0.001	0.32	0.47	<0.001	0.27	0.44	<0.001
Nonteaching hospital	0.83	0.37	0.78	0.42	<0.001	0.68	0.47	<0.001	0.73	0.44	<0.001
Bed size small	0.06	0.23	0.05	0.22	<0.001	0.04	0.20	<0.001	0.05	0.21	<0.001
Bed size medium	0.28	0.45	0.22	0.41	<0.001	0.16	0.37	<0.001	0.21	0.41	<0.001
Bed size large >200 beds	0.66	0.47	0.73	0.45	<0.001	0.80	0.40	<0.001	0.75	0.44	<0.001
Government owned hospital	0.08	0.28	0.11	0.32	<0.001	0.08	0.28	0.678	0.12	0.33	<0.001
Not-for-profit hospital	0.86	0.35	0.84	0.37	<0.001	0.84	0.37	<0.001	0.74	0.44	<0.001
For profit hospital	0.06	0.23	0.05	0.21	<0.001	0.08	0.26	<0.001	0.13	0.34	<0.001

Notes: We merged the data set of 2020 CMS Medicare inpatient claims data, the Medicare Beneficiary Summary File, and the American Hospital Annual Survey. Our study focuses on community-dwelling Medicare fee-for-service beneficiaries aged 65 years and up. The analysis used 100% of CMS Medicare data.

ACO, Accountable Care Organization; ADRD, Alzheimer's disease and related dementia; CMS, Centers for Medicare and Medicaid Services; SD, standard deviation; SVI, social vulnerability index; SVI Q1 was the reference group.

Compared with individuals living in SVI Q1, beneficiaries residing in SVI Q4 (i.e., the most vulnerable areas) reported significantly higher total Medicare costs ($55,800 vs. $46,026, p < 0.001) and were least likely to be treated in hospitals that provided telehealth post-discharge services (28% vs. 33%, p < 0.001) or have ACO affiliation (32% vs. 37%, p < 0.001). Meanwhile, the proportion of the population with ADRD was the highest in SVI Q4 compared with other SVI levels (24% in SVI Q4 vs. 21–22% otherwise).

Table 2 presents results from the GLM regression controlling for all the covariates presented earlier. Model 1, the baseline model, showed that hospital telehealth post-discharge, ACO affiliation, and ADRD were associated with significantly higher Medicare payments. In addition, individuals living in SVI Q4 and Q3 encountered substantially higher costs than those living in SVI Q1 areas. Model 2 showed that ADRD patients living in SVI Q4 and telehealth adopted in SVI Q4 were also associated with higher costs, whereas ADRD patients receiving telehealth post-discharge services had lower costs.

Table 2.

Regression Results of the Association Between Health Information Technology, Accountable Care Organization, Social Vulnerability Index, Alzheimer's Disease and Related Dementia, and Total Medicare Payment

MODEL 1: HIT, SVI, ADRD	COEFFICIENT	95% CI		p
Telehealth-post discharge	0.069	0.066	0.072	<0.001
ACO	0.112	0.109	0.114	<0.001
ADRD	0.150	0.147	0.152	<0.001
SVI Q1: 0–28.7%	Ref.
SVI Q2: 28.7–52.5%	−0.005	−0.008	−0.001	0.006
SVI Q3: 52.5–69.2%	0.048	0.044	0.051	<0.001
SVI Q4: >69.2% (the most vulnerable area)	0.076	0.072	0.080	<0.001

MODEL 2: HIT × SVI × ADRD	COEFFICIENT	95% CI		p
Telehealth-post discharge	0.048	0.044	0.051	<0.001
SVI Q4 (the most vulnerable area) vs. the rest	0.024	0.020	0.028	<0.001
ADRD	0.143	0.140	0.146	<0.001
Telehealth-post discharge × SVI Q4	0.086	0.080	0.092	<0.001
Telehealth-post discharge × ADRD	−0.026	−0.032	−0.020	<0.001
SVI Q4 × ADRD	0.044	0.039	0.050	<0.001
Telehealth-post discharge × SVI Q4 × ADRD	−0.043	−0.054	−0.032	<0.001

MODEL 3: ACO × SVI × ADRD	COEFFICIENT	95% CI		p
ACO affiliation	0.132	0.129	0.135	<0.001
SVI Q4 (the most vulnerable area) vs. the rest	0.064	0.060	0.068	<0.001
ADRD	0.163	0.159	0.166	<0.001
ACO × SVI Q4	−0.020	−0.025	−0.014	<0.001
ACO × ADRD	−0.064	−0.070	−0.058	<0.001
SVI Q4 × ADRD	0.030	0.024	0.036	<0.001
ACO × SVI Q4 × ADRD	−0.012	−0.022	−0.002	0.022

MODEL 4: HIT AND ACO × SVI × ADRD	COEFFICIENT	95% CI		p
Telehealth-post discharge and ACO	0.131	0.126	0.135	<0.001
SVI Q4 (the most vulnerable area) vs. the rest	0.051	0.047	0.054	<0.001
Telehealth-post discharge and ACO × SVI Q4	0.144	0.141	0.147	<0.001
ADRD	0.061	0.051	0.072	<0.001
Telehealth-post discharge and ACO × ADRD	−0.063	−0.072	−0.054	<0.001
SVI Q4 × ADRD	0.032	0.027	0.037	<0.001
Telehealth-post discharge and ACO × SVI Q4 × ADRD	−0.043	−0.060	−0.025	<0.001

Notes: We used the generalized linear model with log link and gamma variance distribution with state fixed effect. All other covariates included race, age, sex, and health indicators. Health indicators included common comorbidities of ADRD, such as heart disease, diabetes, hyperlipidemia, hypertension, and asthma. Covariates at the hospital level included teaching status, type of controls (for-profit, not-for-profit, and government), and bed size. Area-level variables included the rurality index using the Core-Based Statistical Areas, the U.S. Office of Management and Budget. Two-way interaction effects were tested and computed post estimation to test the significance and robustness of the findings. Particularly, the effect of the interacted variables was calculated through the partial derivative and the first difference. The ginteff command was used. ∂ Telehealth-Post Discharge × ∂ SVI Q4 × ∂ ADRD = −$1,431.5 (95% CI: −$1,905.4 to −$957.6); ∂ ACO × ∂ SVI Q4 × ∂ ADRD = −$685.6 (95% CI: −$1,107.3 to −$263.8); and ∂ Telehealth-Post Discharge and ACO × ∂ SVI Q4 × ∂ ADRD = −$1,766.2 (95% CI: −$2,576.4 to −$976).

CI, confidence interval; HIT, Health Information Technology.

The interaction between telehealth post-discharge, SVI Q4, and ADRD was significantly associated with reduced total cost (GLM coef = −0.043, p < 0.001). Model 3 showed that ACO affiliation was associated with higher costs, whereas ADRD patients with ACO affiliation received lower costs. The interaction term showed that ADRD beneficiaries living in SVI Q4 and enrolled in ACO had significantly lower total costs (GLM coef = −0.012, p < 0.05).

Finally, we examined the combined effect of telehealth post-discharge and ACO enrollment. Results of Model 4 showed that telehealth and ACO in SVI Q4 was associated with higher costs, whereas telehealth and ACO for ADRD was associated with lower costs. The interaction of telehealth post-discharge and ACO and SVI Q4 and ADRD was associated with lower total cost (GLM coef = −0.043, p < 0.001). The ginteff command was applied after the estimation and the findings from the GLM were confirmed. Specifically, ∂ Telehealth-Post Discharge × ∂ SVI Q4 × ∂ ADRD = −$1,431.5 (95% confidence interval [CI]: −$1,905.4 to −$957.6); ∂ ACO × ∂ SVI Q4 × ∂ ADRD = −$685.6 (95% CI: −$1107.3 to −$263.8); and ∂ Telehealth-Post Discharge and ACO × ∂ SVI Q4 × ∂ ADRD = −$1,766.2 (95% CI: −$2,576.4 to −$976).

Discussion

Our results showed that in 2020, during the telemedicine boom of the early COVID-19 pandemic, the combination of telehealth post-discharge infrastructure and the ACO model reduced Medicare costs among patients with ADRD living in socially vulnerable areas. Although telemedicine volume has since dropped, there is ample evidence that HIT can facilitate remote treatment and disease management and assist care teams in coordinating social support that is needed for improving well-being.^33–35 These findings are timely, as many Medicare COVID-19-era telemedicine flexibilities are set to expire in December 2024, and a recent study showed that, overall, high virtual care use is associated with a modest increase in patient health care spending.³⁶

Our study suggests that maximizing post-discharge remote monitoring services plus the ACO model, which financially incentivizes care coordination, can reduce total Medicare costs for ADRD patients living in SVI Q4. Our study illustrates the variability in telemedicine-related cost-savings by patient population, social vulnerability, and payment model. It suggests that post-discharge HIT and a shared risk payment model can effectively reduce medical expenditures for specific complex vulnerable patient populations.

Specifically for ADRD populations, HIT and telemedicine are promising for care coordination, which is vital given the complex health care needs of patients with ADRD. Integration allows health care professionals to link patients with various health care services, thus improving the quality and efficiency of health care delivery. For example, telehealth programs can enhance support in areas such as medication management and facilitate referrals to long-term care services.³⁷

Technologies such as mobile applications can also support home-based patient assessment and monitoring, train caregivers, and improve communication between health care providers and caregivers using patient portals.^38,39 Delayed ADRD diagnosis, treatment, or lack of care can result in disease progression, which is associated with escalating medical costs.⁴⁰ Thus, technology that connects patients with health care is especially critical for ADRD patients who reside in regions with limited health care resources.

Numerous health care systems have adopted programs to provide comprehensive care management and coordination for patients with ADRD. For example, the University of California Los Angeles Health System and the Veterans Health Administration (VHA) have collaborated with the Alzheimer's Association to help patients and caregivers access community-based services, including home-based care and support services such as transportation.^41,42 The VHA model reduced the number of hospitalizations, demonstrating that such strategies can improve the quality of care and control the high cost associated with ADRD care.⁴³

Innovative payment models such as ACOs are incentivized to focus on outcomes and cost efficiency.⁴⁴ Morenz and Liao call for evaluating telehealth costs through such models, which stand to gain financially from quality outcomes and cost efficiency. Our study findings support that telemedicine post-discharge infrastructure is cost-saving specifically for ADRD patients with complex health care needs living in areas with the least socioeconomic resilience.

However, our study also showed that HIT was less likely to be available in the most socially vulnerable areas. The finding aligns with the literature on the maldistribution of care coordination infrastructure and urban/rural disparities. For example, remote patient monitoring through HIT could be particularly beneficial for older rural residents in detecting biometric errors and enhancing the quality of in-home care. The lack of access to high-speed internet impedes the full utilization of these advanced technologies.⁴⁵ Another study suggests telehealth services may increase costs at the population level but be cost-saving for Black and Hispanic patients with ADRD.¹⁷

Yet, socially vulnerable regions, which commonly have a high concentration of racial and ethnic minority populations, were found to have less access to care coordination infrastructure.⁴⁶ It is worth noting that these areas also have a higher proportion of residents diagnosed with ADRD and face shortages of health professionals, specialists, and social services. This phenomenon highlights the disparity in HIT distribution, where the communities that could most benefit from HIT services are the ones with the least access.⁴⁷

The geographic maldistribution of ACO-affiliated institutions is also evident. ACOs provide financial incentives to providers to promote care coordination across care settings and transitions, such as hospitals, nursing facilities, and homes.^23,26 However, implementing ACO infrastructure can lead to increased administrative complexities and higher costs, particularly impacting smaller hospitals.⁴⁸ Hospitals that have adopted the ACO models are often large, located in resourceful areas, and have funding for care coordination programs.⁴⁹

Furthermore, ACOs could potentially exacerbate disparities, either due to lack of consistent measures of quality or the absence of comprehensive data on SDoH.⁵⁰ The results of the study offer a positive perspective, indicating that ACOs can be structured to encourage HIT investment in underserved areas, thereby improving access to HIT and potentially reducing Medicare costs for vulnerable populations.⁵¹ ACOs are critical in generating cost-savings, likely due to improved care coordination for comprehensive care.

This is achieved by delivering tailored health care and integrating structural and individual SDoH. Innovative models such as ACO, ACO Realizing Equity, Access, and Community Health (ACO REACH),²⁹ Rewarding Excellence for Underserved Populations,⁵² and the GUIDE Model are specifically developed to promote care coordination. For instance, the GUIDE model aims to enhance care quality while reducing expenses, building upon the ACO and ACO REACH frameworks.²⁷ The study's outcomes suggest that these CMS innovation models have the potential to close care gaps for ADRD by addressing structural SDoH, such as HIT investment among SVI Q4 hospitals.

Our study has several limitations. First, the study used a cross-sectional analysis, and the results cannot infer a causal relationship. Second, the measures of hospital-based telehealth services were based on an intent-to-treat approach, meaning we examined the availability rather than the actual utilization of telehealth services. Third, although the claims data set provides comprehensive information, it may lack details on disease progression/severity. Fourth, our data were limited to the Medicare FFS population and individuals living in the community.

We focused on Medicare Shared Savings Program ACO, which is the largest ACO and includes 438 ACOs and participation of >500,000 providers serving 11 million Medicare beneficiaries in 2022.⁵³ Hence, the results of our study might not be generalizable to those enrolled in Medicare Advantage, Medicare/Medicaid dual-eligible programs, or other health plans. Future studies should further explore specific types of ACO models and investigate the details of ACO payment structures.

Future studies should also examine the use of HITs in post-acute care settings, including long-term care hospitals, inpatient rehabilitation facilities, skilled nursing facilities, and home health agencies, for ADRD patients. Finally, future studies should evaluate telehealth post-discharge infrastructure and ACO and SVI Q4 and ADRD during nonpandemic volume telemedicine utilization as data become available.

Conclusion

The results of our study suggest that ACO models can be structured to facilitate the adoption of HIT to reduce health care costs by enhancing treatment efficacy and care coordination, especially for ADRD patients residing in underserved areas. These findings suggest that ACO or GUIDE are promising ways to reduce Medicare costs by promoting HIT infrastructure, especially for people with complex health needs such as ADRD.

Disclosure Statement

The authors report no conflicts with any product mentioned or concept discussed in this article.

Funding Information

This study is supported by the National Institute on Aging (Grant Nos. R01AG062315 and RF1AG083175).