Abstract
INTRODUCTION
Persons living with dementia (PLWDs) have complex needs and high expenses, and primary care (PC) plays a key role in their care. However, few American PLWDs receive adequate care, and many have unmet but potentially modifiable dementia‐related care needs. Embedding effective evidence‐based care strategies into PC settings is an opportunity to reduce excess burden but remains rare. This study evaluated the feasibility and potential impact of adapting and implementing the MIND at Home Dementia Care Navigation Program (MIND at Home) into PC at two large unique health systems in the United States.
METHODS
A pilot pragmatic clinical trial evaluated the feasibility and potential impact of integrating MIND at Home into primary care. Process measures included identifying and addressing a comprehensive set of dementia‐related needs for PLWDs and care partners, conducting home visits, and capturing clinic‐based outcomes such as hospital transfers and medications. Data were collected on intervention patients, with additional data obtained for a validation group within the health systems based on eligibility criteria.
RESULTS
A total of 105 PLWD–care partners (dyads) were enrolled for a 3‐month intervention period. All dyads received a comprehensive needs assessment, a personalized care plan, and care navigation. Seventy‐four percent of identified needs were addressed. Health care utilization measures were feasible to collect from both sites, based on validation data. Medication data were collected but were difficult to interpret.
DISCUSSION
MIND at Home was successfully implemented into the PC setting and key outcomes were ascertained using electronic health record data. A comprehensive evidence‐based approach that combines the benefits of clinic‐based health care and home‐based supportive services for PLWDs, their families, and their care partners has the potential to reduce unmet care needs and reduce hospitalizations. Observed trends in hospital transfers suggest a potential association that warrants further investigation, especially among PLWDs with more advanced dementia.
Highlights
Persons with dementia and their care partners receive fragmented, suboptimal care and support.
The MIND at Home Dementia Care Navigation Program (MIND at Home) provides a solution by adding home‐based assessments and primary care (PC) integration.
MIND at Home was embedded successfully into PC with fidelity into two health systems.
Patient and care partner needs were identified and addressed via a care plan.
Key utilization outcomes were ascertained using electronic health record data.
Keywords: Alzheimer's disease, care coordination, continuity of patient care, dementia, home care services, home‐based care, patient navigation, primary health care
1. BACKGROUND
Community‐dwelling persons living with dementia (PLWDs) represent high‐need, 1 high‐cost individuals, 2 , 3 and primary care (PC) plays a pivotal role in the detection, diagnosis, and service delivery for this vulnerable population. 4 , 5 Although numerous evidence‐based approaches to dementia care exist, 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 few PLWDs receive adequate care. 18 , 19 Embedding effective evidence‐based interventions and care navigation services into PC settings presents an opportunity to reduce excessive patient and family burden but is not yet widely done. 19 , 20 , 21 , 22 , 23 , 24
Health policy, research, and payment model developments focusing on PC as the nexus of care, 25 , 26 coupled with the availability of affordable evidence‐based care management approaches for dementia, 27 , 28 , 29 present a timely opportunity to embed effective support into PC to drive value‐based care, transforming how care is delivered for PLWDs. The MIND at Home Dementia Care Navigation Program (MIND at Home) is a comprehensive, home‐based dementia care navigation program. MIND at Home takes an interdisciplinary, family‐centered care approach by systematically assessing and addressing the dementia‐related care needs of PLWDs and their care partners (CPs) that place both at increased risk for poor outcomes.
With strong evidence demonstrating its impact on reducing unmet needs, delaying transition to institutionalized settings, improving quality of life, and saving cost through reduced hospitalizations and skilled nursing use, 15 , 30 , 31 , 32 , 33 , 34 , 35 we embedded MIND at Home into PC for the first time. The goal was to elevate the role of health system staff to memory care coordinators (MCCs), increase PC provider access to interdisciplinary dementia care, and combine the benefits of clinic‐based services with home‐ and telephone‐based assessment and care to support family‐centered care planning and implementation for PLWD+CPs. Use of MIND at Home within PC brings a home‐based component to what is often exclusively clinic‐based care delivery. The MIND at Home program was developed to address key priorities across stakeholder groups, including: PLWDs’ wish to remain in their homes as long as possible (evidence suggests that PLWDs have higher quality of life at home vs long term care) 36 ; CPs’ desire to avoid burnout while providing quality care for PLWDs; PC providers’ incentives to keep patients healthy with a high quality of life; health systems’ need to avoid unnecessary hospitalizations and emergency department visits within value‐based care; payors’ motivation to contain costs; and health officials’ impetus to improve population health.
The primary objective of this study was to assess the feasibility and acceptability of embedding MIND at Home within PC. Secondarily, the feasibility of collecting and validating electronic health related (EHR) outcomes data from participating health systems was assessed.
2. METHODS
2.1. Study design and setting
Using a pragmatic trial design, 100 community‐residing PLWDs receiving PC were recruited into the MIND at Home program for 3 months to test the feasibility of recruitment and implementation in PC settings. (The original MIND at Home program was implemented in community settings for 18 months. To test the feasibility of PC integration, a modified 3‐month program was designed to focus on feasibility, not clinical efficacy.) In addition, 100 non‐intervention patients served as a data validation arm to assess data collection feasibility from the two participating health care organizations (HCOs). One HCO was a large, physician‐owned, multi‐specialty medical group located in the rural Midwest whose 280 providers and 1000 staff members serve more than one million patients annually, with three participating PC practices. The second HCO was a large health system with seven hospitals, over 300 primary and specialty care locations, and >2700 physicians in the southern United States, with nine participating PC clinics.
RESEARCH IN CONTEXT
Systematic review: The authors reviewed the literature using traditional (e.g., PubMed) sources and meeting abstracts and presentations. Previous studies have demonstrated the efficacy of MIND at Home Dementia Care Navigation Program (MIND at Home) in community settings, with evidence of reducing unmet needs, delaying transition to institutionalized settings, improving quality of life, and saving cost through reduced health care resource utilization; however, there are no studies that examine the impact of integration of MIND at Home into primary care.
Interpretation: Our findings demonstrated that MIND at Home was successfully implemented in primary care (PC) with fidelity in two large health systems and key outcome data were ascertained from electronic health records.
Future directions: An embedded pragmatic clinic trial is needed to fully evaluate the impact of MIND at Home in the clinical setting on targeted outcomes.
HCO staff from the participating PC practices were selected and trained to serve as MCCs, who led all program activities. One HCO selected registered nurses (RNs) and one selected nurse practitioners (NPs) to serve as the MCCs. For recruitment, MCCs or health system staff (coordinators) screened potential study participants to assess eligibility, gauge interest, and identify the CP. Study participants were identified through EHR queries and by PC provider (PCP) referral. MCCs conducted comprehensive dementia care assessments, including office‐ and home‐based evaluations, with input from the PC team, comprising providers, nurses, medical assistants, and social workers.
All dyads (PLWD+CP) received a home‐based evaluation, incorporating relevant information from PC office visits. Based on these evaluations, the MCC developed and implemented a collaborative, family‐centered care plan with ad hoc support from an interdisciplinary PC team, when requested. The team was supported by regular virtual case‐based mentoring sessions with structured case presentation templates and targeted short didactics. The interdisciplinary team comprised external geriatric and geriatric psychiatry dementia specialists, that is, the Johns Hopkins University (JHU) MIND at Home faculty clinical support team, to provide clinical consultation and mentorship, and to maintain quality and alignment to the model of care, based on the MIND at Home program training, support, and assessment of program fidelity described below.
2.2. Study population and inclusion/exclusion criteria
Enrolled PLWDs were community‐residing adults, ≥18 years with a dementia diagnosis (using Centers for Medicare & Medicaid Services’ [CMS] chronic care warehouse definition 37 ) or a dementia medication including donepezil, galantamine, rivastigmine, aducanumab, or memantine on the patient medication list or pharmacy claim in the past 3 years. Note that eligibility criteria were expanded to include prescriptions for dementia‐related medications, in response to inconsistent diagnosis documentation, often due to provider reluctance based on patient preference.
Enrollees were required to (1) be actively receiving PC within a participating HCO, defined as having had an ambulatory visit with any specialty within the past year; and (2) have an English‐speaking CP (or language known by the MCC) willing to participate in study visits and activities for the 3‐month study period. PLWDs in crisis, for example, with signs of abuse, neglect, or a risk of danger to self/others were excluded and referred to appropriate services. PLWDs on hospice or with end stage disease were excluded. Criterion 2 could not be reliably applied to the validation arm.
2.3. Intervention
(See the MIND at Home program five key components, materials, and needs assessment items in Appendix A, Table S1A, B.) Appendix B contains the Dementia Care Needs Assessment Care Team Checklist. The care team used a variety of tools and standardized implementation protocols. They implemented core evidence‐based care strategies per standardized protocol that included disease education and coaching; referrals and care coordination for linking to community‐based services and resources; symptom screening and monitoring; and emotional support.
2.3.1. Team orientation and support
MCCs completed an eight‐module, interactive online MIND at Home certification course (15 Continuing Medical Education (CME)/Continuing Education Unit (CEU)), focused on skills for PLWD care and program processes, followed by two virtual, half‐day scenario‐based learning sessions, conducted by the MIND at Home faculty clinical support and study teams. In collaboration with the MCC and PC team, the study team adapted resources, tools, and team structures to local settings to reflect the needs and cultures of the populations served and differences between RNs and NPs in the MCC role.
Throughout the study, MCCs met biweekly with the research team and attended biweekly virtual collaborative case conferences and clinical sessions with the JHU MIND at Home faculty clinical support team. These sessions supported co‐learning, mentorship, and review of new and complex case.
2.4. Process and clinical outcomes data collection
Program fidelity was evaluated through review of documented identified and addressed needs, as well as the corresponding care plans within the 3‐month study period. MCCs received program materials, including a care plan builder with descriptions of each need and interventions to address. In the care plans, MCCs selected interventions to address each identified need. At each dyad visit, the MCCs assessed if the need had been completely or partially met, based on the care plan. MCCs completed satisfaction surveys on each dyad. PLWD+CPs also rated their experience, as did providers.
Although this pilot study was not powered to detect statistically significant changes in outcomes, we collected outcomes data for feasibility testing. Hospital transfers (admissions, emergency visits, and observation stays) and the percent of antipsychotic medication, acetylcholinesterase inhibitor, and memantine prescriptions were collected over 7 months. Pre‐intervention data were collected retrospectively. Basic patient demographic data were collected at enrollment including sex, age, race, and ethnicity. Cognitive assessments were conducted at enrollment (or recorded if done in past month) using one of the following cognitive tests—Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), or the Saint Louis University Mental Status Examination for Detecting Mild Cognitive Impairment (SLUMS). All study data originating from site EHRs were collected as part of routine clinical care.
Patient‐level validation data were collected from both study sites, from the study clinics, using the same inclusion/exclusion criteria. Sites were provided a detailed data specification document to ensure comparable data extraction. The target for validation data was 100 patients from each site. These data were collected as part of a feasibility assessment and were not intended to represent a formal comparison group.
2.5. Analysis
Patients were enrolled continuously from May–December 2023. Measurement periods were established individually for each patient over 7 months based on enrollment date: 3 months prior to enrollment, 3 months during, and 1 month post discharge from the program. Descriptive statistics of the study population and outcomes (i.e., needs identified and met, patient touches, and health care utilization) were calculated. All data processing and analysis were conducted using R software (version 4.4.0; R Foundation for Statistical Computing). Exploratory analysis assessed data completeness and field reliability for measure construction and planning a full randomized controlled trial. Survey results were reported using descriptive statistics to summarize individual survey items.
This trial is registered at ClinicalTrials.gov (NCT05406921) and was approved by the Advarra Institutional Review Board.
3. RESULTS
Figure 1 displays the consort diagram for study enrollment. A total of 274 dyads (PLWD+CP) were screened; of whom 105 (38%) were enrolled. The primary reason for non‐enrollment was unable to reach (41%); followed by declined/not interested (29%); ineligible, that is, hospice, assisted living, or death (19%); and unknown reason (11%). Of those, 80 completed the 3‐month study. Of the 25 PLWDs who discontinued from the study, 15 (60%) had received a comprehensive needs assessment and care plan. Data from 200 validation patients were abstracted from the two participating systems and 188 records were deemed valid.
FIGURE 1.
Consolidated Standards of Reporting Trials (CONSORT) diagram for a pilot study examining the feasibility and potential impact of implementing MIND at Home, a home‐based care coordination program for persons living with dementia (PLWDs). The diagram illustrates patient screening, enrollment, and the final analysis population, including reasons for exclusions at each study stage.
3.1. Baseline characteristics
Table 1 displays the baseline characteristics of the intervention (n = 80) and the validation (n = 188) PLWDs. Enrolled PLWD were 51% male, 84% non‐Hispanic White, 85% urban‐dwelling, 58% on traditional Medicare, and 55% with a moderate‐to‐severe comorbidity score using the Charlson Comorbidity Index. 38 Most (91%) had a dementia diagnosis and 86% had a dementia‐related prescribed medication. More patients in the intervention group had a dementia diagnosis (91% vs 75%, p < 0.01). Providers chose their cognitive assessment tool. Almost half (45%) of PLWDs were assessed using the MMSE, with a median score of 23/30. Another 38% received the MoCA, with a median score of 16/30, and 9% were assessed using the SLUMS, with a median score of 12/30. (See Table S2 (Appendix C).
TABLE 1.
Baseline demographics of the study population.
| Intervention (n = 80) | Validation (n = 188) | p‐value | |
|---|---|---|---|
| Age, mean (range) | 81 (64–92) | 79 (60–95) | 0.10 |
| Male, n (%) | 41 (51%) | 92 (49%) | 0.83 |
| Non‐Hispanic White, n (%) | 67 (84%) | 156 (83%) | 0.81 |
| Non‐Hispanic Black, n (%) | 9 (11%) | 25(13%) | 0.81 |
| Urban residence, n (%) | 68 (85%) | 152 (81%) | 0.52 |
| Fee‐for‐service (FFS) Medicare insurance, n (%) | 46 (58%) | 94 (50%) | 0.43 |
| CCI * moderate/severe, n (%) | 44 (55%) | 101 (54%) | 0.50 |
| Dementia‐related Rx † , n (%) | 69 (86%) | 162 (86%) | 0.73 |
| Dementia diagnosis, n (%) | 73 (91%) | 140 (75%) | <0.01 |
| Had MMSE, ‡ % (median score) | 45% (23/30) | – | – |
| Had SLUMS, ¶ % (median score) | 19% (12/30) | – | – |
| Had MoCA, § % (median score) | 38% (16/30) | – | – |
Charlson Comorbidity Index (CCI).
Dementia‐related Rx includes memantine and acetylcholinesterase (AChE) inhibitors.
Mini‐Mental State Examination (MMSE).
Saint Louis University Mental Status Examination for Detecting Mild Cognitive Impairment (SLUMS).
Montreal Cognitive Assessment (MoCA).
3.2. Assessment of needs and patient touches
Table 2 shows the needs of PLWDs and CPs identified during MIND at Home assessments and addressed throughout the study (met or partially met). A total of 998 needs were identified among PLWD+CPs combined. Of the needs identified, 74% were partially or fully met by study completion. The most common needs identified among PLWDs were in general health care, for example, PCP/specialist follow‐up, care coordination, medication management, incontinence, pain, and home and personal safety, with 82% and 84% of those needs met, respectively. The most frequently identified CP need was caregiver education, with 74% of identified needs partially or fully addressed.
TABLE 2.
Needs identified and met in the intervention group (80 dyads) by domain.
| Needs | Identified n (%) | Partially/fully met n (%) |
|---|---|---|
| Total needs | 998 (100%) | 740 (74%) |
| Patient needs * | 672 (67%) | 521 (78%) |
| Cognitive symptom management | 32 (3%) | 26 (81%) |
| Neuropsychiatric symptom management | 101 (10%) | 70 (69%) |
| General health care | 127 (13%) | 104 (82%) |
| Home and personal safety | 223 (22%) | 188 (84%) |
| Daily activities | 103 (10%) | 69 (67%) |
| Legal concerns and advanced care planning | 70 (7%) | 54 (77%) |
| Care financing | 16 (2%) | 10 (62%) |
| Care partner needs * | 326 (33%) | 219 (67%) |
| Caregiver education | 189 (19%) | 139 (74%) |
| Caregiver mental health | 8 (1%) | 3 (38%) |
| Caregiver general health | 9 (1%) | 6 (67%) |
| Caregiver informal support | 77 (8%) | 45 (58%) |
| Caregiver daily living | 22 (2%) | 12 (55%) |
| Legal concerns | 21 (2%) | 14 (67%) |
Total patient and care partner needs add up to 100%. The specific needs identified for patients sum to 67% and the specific care partner needs identified sum to 33%.
Patient touches, defined as interactions between the MCC and the dyad (PLWD, CP, or both) averaged 4 touches (range: 1–16) over 3 months. Phone calls (39%) were most common, followed by home visits (23%), and telehealth visits (20%).
3.3. Satisfaction surveys
Surveys completed by MCCs on each dyad (n = 95, 100%), PLWDs+CPs (n = 23, 29%), and providers (n = 11, 23%) indicated high levels of satisfaction with the MIND at Home program. (See Table 3.)
TABLE 3.
Care coordinator, provider, and staff survey results.
| Survey question summary | Agree/Strongly agree |
|---|---|
| Memory care coordinators for each dyad (n = 95) | |
| Satisfied with the support they provided to dyads. | 91% |
| Home assessment important part of assessment and care plan. | 92% |
| As related to dyads | |
| Dyads were comfortable expressing needs and concerns. | 94% |
| Dyads’ disease education enhanced by program. | 90% |
| Care partner acquired new dementia knowledge and care skills. | 88% |
| Impacted patients’ lives for the better. | 78% |
| Changes made to medical care due to program. | 57% |
| Would benefit from continuing program beyond 3 months. | 37% |
| Patients/care partners (n = 23) | |
| Satisfied with program. | 100% |
| Care coordinator was respectful, clear, listened, and knowledgeable. | 100% |
| Care coordinator helped them feel confident and in control. | 95% |
| Care coordinator improved their understanding of dementia. | 95% |
| Primary care providers (n = 11) | |
| Enhanced services they can provide in primary care. | 100% |
| Provided caregiver education and support not usually received. | 100% |
| Clear understanding of program and goals. | 82% |
| Clear understanding of eligibility criteria. | 54% |
Over 90% of MCCs reported satisfaction with their support to the dyads, believed the home assessment was an important part of the needs assessment and care plan, felt the dyads were comfortable expressing needs and concerns, and felt that the dyads’ disease education was enhanced by the program. Over 75% felt that the CPs acquired new dementia knowledge and care skills and that they impacted PLWDs’ lives positively. More than half (57%) reported making changes to the PLWDs’ medical care, and 37% reported patient benefit from continuing the program beyond 3 months.
From the PLWD/CP perspective, 100% reported satisfaction with the program and that the MCC was respectful, clear, listened, and was knowledgeable. Almost all (95%) reported that the MCC helped them feel confident and in control, and improved their understanding of dementia.
All PCPs reported that MIND at Home enhanced the services they provide in PC and provided CP education and support not usually received. The majority (82%) reported a clear understanding of the program and its goals and 54% reported a clear understanding of the eligibility criteria.
3.4. Health care utilization and medication data
The intervention arm experienced 2.5 hospital transfers per 1000 person‐days in the pre‐intervention period compared with 1.9 in the validation arm. In the 3‐month during‐intervention period, hospital transfers were reduced to 1.5 and 1.2 per 1000 person‐days in the intervention and validation arms, respectively. By the post‐intervention period, the validation arm had 1.1 hospital transfers compared with the intervention arm of 2.1 transfers per 1000 person‐days. (See Table 4 for rates by study group.)
TABLE 4.
Hospital transfers per 1000 person‐days in the pre‐, during, and post‐intervention periods, and by dementia severity.
| Intervention n = 80 | Validation n = 188 | ||
|---|---|---|---|
| Pre‐intervention, mean (SE) | 2.50 (0.60) | 1.89 (0.65) | |
| During intervention, mean (SE) | 1.53 (0.36) | 1.24 (0.45) | |
| Post‐intervention, mean (SE) | 2.08 (0.59) | 1.06 (0.76) | |
| By dementia severity a | Mild dementia n = 38 | Moderate/severe dementia n = 32 | |
|---|---|---|---|
| Pre‐intervention, mean (SE) | 0.9 (0.50) | 5.02 (2.20) | |
| During intervention, mean (SE) | 0.9 (0.50) | 1.79 (0.73) | |
| Post‐intervention, mean (SE) | 0.9 (0.89) | 3.23 (1.77) |
Dementia severity was determined by one of three cognitive screening tests. (See Table 1.)
Because there were differences in the severity of dementia among the intervention patients, we conducted a sub‐analysis of 70 intervention patients, stratified by dementia severity based on cognitive assessment scores (moderate to severe vs mild). The mild dementia patients (n = 38) had 0.9 hospital transfers per 1000 person‐days in the pre‐intervention period and remained unchanged in the during‐ and post‐intervention periods. In contrast, patients with moderate to severe dementia (n = 32) had a mean of 5.0 hospital transfers per 1000 person‐days in the pre‐intervention period that were reduced to 1.8 and 3.2 hospital transfers per 1000 person‐days in the during‐ and post‐intervention periods, respectively.
The average number of active medications remained stable during the intervention. Intervention patients saw a slight decrease (10.7 to 10.3), whereas validation patients demonstrated a modest increase (10.5 to 11.0). The proportion of dementia‐related medications remained unchanged among the intervention patients (91%) and increased from 81% to 84% in the validation arm. Antipsychotic medications increased in both groups—from 17% to 24% and 22% to 29% among the intervention and validation patients, respectively (Table S3).
4. DISCUSSION
This study demonstrated the feasibility of embedding and implementing MIND at Home into primary care (or PC) at two large HCOs. PLWDs and their CPs were successfully identified and enrolled in a 3‐month home‐based care coordination program within the PC setting. All dyads participated in a comprehensive, home‐based dementia care needs assessment, which identified health care, environmental, and social needs with fidelity. Care plans were developed and implemented in collaboration with the PC team for all dyads. As a result, 74% of needs were addressed.
Valid health care utilization data were collected from both sites. Medication data were collected but were difficult to interpret (see 5. Limitations). Patient medication lists were reported at one site and all prescriptions per patient were reported at the second site. However, it was impossible to unequivocally determine what medications a patient was actively taking. This was influenced by a short 7‐month study period and long prescription timeframes (typically 12 months).
The 3‐month intervention was designed to test feasibility, not to detect statistically significant outcomes. However, we observed trends in hospital transfers—particularly among PLWDs with moderate to severe dementia—suggesting a potential association warranting further investigation. These findings support the hypothesis that intervention intensity, that is, dose, may need to vary by disease severity. Some dyads benefited from a single home visit that addressed a few care plan items, whereas those with greater needs may have required a longer intervention period, as noted by MCCs and providers. In addition, differences in dementia diagnoses (91% intervention; 75% validation) may have influenced utilization outcomes (see 5. Limitations). A larger sample will help control for confounders in future research.
Previous studies of the implementation of MIND at Home in community settings have shown reduced unmet needs, delayed transition from home to institutional care, improved quality of life, and cost savings through reduced hospitalizations and skilled nursing use. 15 , 30 , 31 , 33 , 34 , 35 , 39 A 2024 trial 40 found that a remotely‐delivered personalized support program for dyads increased independence, reduced hospital and home care admissions, and generated health system cost savings. The implementation of MIND at Home into PC settings appears to align with and trend toward similar outcomes, with the PC‐centered model serving as the key differentiator.
A key strength of the MIND at Home program is its flexible delivery model, which allowed successful implementation in two distinct clinical environments. Given that home visits are not always feasible, telephone and video visits may need to be substituted periodically. The needs assessment may also require modification/consolidation to reduce time burden. Care team composition differed across sites: one site trained RNs who ran Medicare's chronic care management (CCM) program as MCCs, whereas the other trained NPs with expertise in dementia care. However, based on success in MIND at Home community‐based programs, 15 , 30 , 31 , 33 , 34 , 35 , 39 other roles are appropriate including supervising non‐clinical staff. These adaptations accommodated the distinct skills, workflow, and availability of MCCs across study sites. Flexibility was critical for successful implementation across PC settings and should be prioritized in future applications.
An important finding was the persistent underdiagnosis of dementia due to lack of diagnostic specialists, hesitancy among PCPs to diagnose due to stigma, and patients’ resistance to a formal diagnosis in their medical record. This challenge highlights the need to reduce stigma surrounding dementia and support PCPs in making timely, accurate diagnoses to enable earlier intervention including care planning and support of CPs.
Enrollment was challenging but easier when patients were part of an established program, for example, chronic care management, or when providers referred patients directly. Future efforts should leverage existing programs and relationships. PCPs’ understanding of the program and eligibility requirements, and direct referrals, is important to building trust for enrollment and engagement. Targeted outreach and concise provider‐facing materials, such as tip sheets, EHR prompts, or brief training sessions, may help increase awareness or the program and clarify eligibility criteria in the future. In addition, communicating the care plan and recommendations back to the PCPs may help raise program awareness and increase perceived value.
MCCs reported that home visits offered valuable insights into daily living not captured during clinic visits and were instrumental for comprehensive care planning, despite coordination challenges. Interdisciplinary team‐based case discussions were considered important, with consistently high attendance throughout the study.
5. LIMITATIONS
This study had several limitations. As a feasibility study, it was not powered to detect differences in clinical or utilization outcomes, thereby limiting the ability to assign causality to observed longitudinal trends. Differences in patient identification resulted from variation in how dementia diagnoses were prioritized. In the validation arm, patients were included based on the first occurrence of a diagnosis or prescription. In the intervention arm, inclusion began with diagnosis codes but was later expanded to include a prescription only, due to provider reluctance to formally diagnose dementia. EHR data had limited reliability in capturing PLWD residence status. Because long‐term care transfers are often undocumented unless care continues within the same system, future studies should improve residence data capture and randomize to ensure valid comparison populations.
Accurate medication data were challenging to obtain. EHR data often fail to reflect discontinued medications, which often persist in records after discontinuation, making it difficult to detect changes in anti‐psychotic medication use, particularly given the 7‐month study period. Future studies should consider a longer study period and manually collected, reconciled medication data to more accurately reflect active patient medication lists for medication‐related outcomes.
Needs assessments and care plans were not fully integrated into the EHRs at the study sites, creating additional burden for MCCs. Full integration of MIND at Home program materials, including needs assessments and care plans, into the EHR should be prioritized to support a more efficient workflow and scalability.
Integration of MIND at Home into PC has several important implications: (1) improved continuity of care across clinical and community settings, including referrals to specialists and support services; (2) combined home‐ and clinic‐based assessments surfacing social and medical issues that might otherwise go unaddressed; and (3) alignment with value‐based care goals through reduced unnecessary acute care and better financial incentives.
6. CONCLUSIONS
MIND at Home, an evidence‐based dementia care coordination program developed and tested in community‐based settings, was successfully implemented with fidelity in PC settings, and key outcomes were captured through EHRs. To meet the local needs and context of each HCO, pragmatic program modifications included optimizing eligibility criteria, streamlining program assessment/documentation within EHRs, and waiving the home visit requirement in special cases. An embedded pragmatic clinic trial is needed to fully evaluate the impact of MIND at Home in the clinical setting on targeted outcomes.
CONFLICT OF INTEREST STATEMENT
The authors declare no relevant conflicts of interest.
CONSENT STATEMENT
We received a waiver of informed consent as we met all five required criteria for this designation: (1) research involved no more than minimal risk to subjects; (2) research could not be practicably carried out without the waiver; (3) research involved using identifiable information and could not be practicably carried out without using data in an identifiable format; (4) the waiver would not adversely affect the participants’ rights and welfare; and (5) participants were provided additional pertinent information after participation.
Supporting information
Supporting Information
Supporting Information
ACKNOWLEDGMENTS
The authors wish to express their gratitude to the team members at the two participating HCOs for their commendable dedication to enhancing care for people living with dementia and their care partners, as well as for their tireless efforts to conduct this study with the utmost integrity. This work was supported by the National Institutes on Aging (NIA), through the NIA Alzheimer's Disease /Alzheimer's Disease and Related Dementias (AD/ADRD) Health Care Systems Research Collaboratory awarded to Brown University (Award No. 5U54AG063546‐02). The work was also supported by a BrightFocus Foundation, Special Award, titled “Dissemination of MIND at Home dementia care model to drive health care transformation and greater value.”
Ciemins E, Shields S, Amjad H, et al. Piloting the MIND at Home Dementia Care Navigation Program in primary care. Alzheimer's Dement. 2025;11:e70170. 10.1002/trc2.70170
REFERENCES
- 1. Black BS, Johnston D, Leoutsakos J, et al. Unmet needs in community‐living persons with dementia are common, often non‐medical and related to patient and caregiver characteristics. Int Psychogeriat. 2019;31(11):1643–1654. doi: 10.1017/S1041610218002296 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Jennings LA, Reuben DB, Evertson LC, et al. Unmet needs of caregivers of individuals referred to a dementia care program. J Am Geriatr Soc. 2015;63(2):282–289. doi: 10.1111/jgs.13251 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Zissimopoulos J, Goldman D. The Cost of Dementia in 2025. USC Schaeffer Center for Health Policy & Economics; 2025. doi:https://schaeffer.usc.edu/wp‐content/uploads/2025/04/The‐Cost‐of‐Dementia‐in‐2025.pdf [Google Scholar]
- 4. Boustani M, Schubert C, Sennour Y. The challenge of supporting care for dementia in primary care. Clin Interv Aging. 2007;2(4):631–636. doi: 10.2147/cia.s1802 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Yang M, Chang C‐H, Carmichael D, Oh ES, Bynum JPW. Who is providing the predominant care for older adults with dementia? J Am Med Dir Assoc. 2016;17(9):802–806. doi: 10.1016/j.jamda.2016.04.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Callahan CM, Boustani MA, Unverzagt FW, et al. Effectiveness of collaborative care for older adults with Alzheimer disease in primary care: a randomized controlled trial. JAMA. 2006;295(18):2148–2157. doi: 10.1001/jama.295.18.2148 [DOI] [PubMed] [Google Scholar]
- 7. Clevenger CK, Cellar J, Kovaleva M, Medders L, Hepburn K. Integrated memory care clinic: design, implementation, and initial results. J Am Geriatr Soc. 2018;66(12):2401–2407. doi: 10.1111/jgs.15528 [DOI] [PubMed] [Google Scholar]
- 8. Haggerty K, Epstein‐Lubow G, Spragens L, et al. Recommendations to improve payment policies for comprehensive dementia care. J Am Geriatr Soc. 2020;68(11):2478–2485. doi: 10.1111/jgs.16807 [DOI] [PubMed] [Google Scholar]
- 9. Hepburn K, Nocera J, Higgins M, et al. Results of a randomized trial testing the efficacy of Tele‐Savvy, an online synchronous/asynchronous psychoeducation program for family caregivers of persons living with dementia. Gerontologist. 2022;62(4):616–628. doi: 10.1093/geront/gnab029 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Jennings LA, Laffan AM, Schlissel AC, et al. Health care utilization and cost outcomes of a comprehensive dementia care program for Medicare beneficiaries. JAMA Intern Med. 2019;179(2):161–166. doi: 10.1001/jamainternmed.2018.5579 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Kim SK, Park M. Effectiveness of person‐centered care on people with dementia: a systematic review and meta‐analysis. Clin Interv Aging. 2017;12:381–397. doi: 10.2147/CIA.S117637 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Lee KH, Lee JY, Kim B. Person‐centered care in persons living with dementia: a systematic review and meta‐analysis. Gerontologist. 2022;62(4):e253–e264. doi: 10.1093/geront/gnaa207 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Possin KL, Merrilees JJ, Dulaney S, et al. Effect of collaborative dementia care via telephone and internet on quality of life, caregiver well‐being, and health care use: the care ecosystem randomized clinical trial. JAMA Intern Med. 2019;179(12):1658–1667. doi: 10.1001/jamainternmed.2019.4101 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Reuben DB, Evertson LC, Wenger NS, et al. The University of California at Los Angeles Alzheimer's and dementia care program for comprehensive, coordinated, patient‐centered care: preliminary data. J Am Geriatr Soc. 2013;61(12):2214–2218. doi: 10.1111/jgs.12562 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Samus QM, Johnston D, Black BS, et al. A multidimensional home‐based care coordination intervention for elders with memory disorders: the maximizing independence at home (MIND) pilot randomized trial. Am J Geriatr Psychiatry. 2014;22(4):398–414. doi: 10.1016/j.jagp.2013.12.175 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Tisher A, Salardini A. A comprehensive update on treatment of dementia. Semin Neurol. 2019;39(2):167–178. doi: 10.1055/s-0039-1683408 [DOI] [PubMed] [Google Scholar]
- 17. Vickrey BG. Mittman BS. Conner KI. The effect of a disease management intervention on quality and outcomes of dementia care. Ann Intern Med. 2006;145(10):713–726. doi: 10.7326/0003-4819-145-10-200611210-00004 [DOI] [PubMed] [Google Scholar]
- 18. Bynum JPW. Challenges and innovations in dementia care—evaluating comprehensive models. JAMA. 2025;333(11):943–945. doi: 10.1001/jama.2024.28066 [DOI] [PubMed] [Google Scholar]
- 19. National Academies of Sciences Engineering and Medicine . Meeting the Challenge of Caring for Persons Living with Dementia and their Care Partners and Caregivers: A Way Forward. The National Academies Press; 2021. [PubMed] [Google Scholar]
- 20. 2024 Alzheimer's disease facts and figures. Alzheimers Dement. 2024;20(5):3708–3821. doi: 10.1002/alz.13809 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Kallmyer BA, Bass D, Baumgart M, et al. Dementia care navigation: building toward a common definition, key principles, and outcomes. Alzheimers Dement (N Y). 2023;9(3):e12408. doi: 10.1002/trc2.12408 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Reuben DB, Epstein‐Lubow G, Evertson LC, Jennings LA. Chronic disease management: why dementia care is different. Am J Manag Care. 2022;28(12):e452–e454. doi: 10.37765/ajmc.2022.89258 [DOI] [PubMed] [Google Scholar]
- 23. Samus QM, Black BS, Bovenkamp D, et al. Home is where the future is: the BrightFocus Foundation consensus panel on dementia care. Alzheimers Dement. 2018;14(1):104–114. doi: 10.1016/j.jalz.2017.10.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Reuben DB, Evertson LC, Jackson‐Stoeckle R, et al. Dissemination of a successful dementia care program: lessons to facilitate spread of innovations. J Am Geriatr Soc. 2022;70(9):2686–2694. doi: 10.1111/jgs.17900 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Centers for Medicare & Medicaid Services . States Advancing All‐Payer Health Equity Approaches and Development (AHEAD) Model. Centers for Medicare & Medicaid Services; 2023. https://www.cms.gov/priorities/innovation/innovation‐models/ahead [Google Scholar]
- 26. Centers for Medicare & Medicaid Services . ACO Primary Care Flex Model. Centers for Medicare & Medicaid Services; 2025. https://www.cms.gov/priorities/innovation/innovation‐models/aco‐primary‐care‐flex‐model [Google Scholar]
- 27. Guterman EL, Kiekhofer RE, Wood AJ, et al. Care ecosystem collaborative model and health care costs in Medicare beneficiaries with dementia: a secondary analysis of a randomized clinical trial. JAMA Intern Med. 2023;183(11):1222–1228. doi: 10.1001/jamainternmed.2023.4764 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Jutkowitz E, Pizzi LT, Shewmaker P, et al. Cost effectiveness of non‐drug interventions that reduce nursing home admissions for people living with dementia. Alzheimers Dement. 2023;19(9):3867–3893. doi: 10.1002/alz.12964 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Willink A, Davis K, Johnston DM, et al. Cost‐effective care coordination for people with dementia at home. Innov Aging. 2020;4(2):igz051. doi: 10.1093/geroni/igz051 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Tanner JA, Black BS, Johnston D, et al. A randomized controlled trial of a community‐based dementia care coordination intervention: effects of MIND at Home on caregiver outcomes. Am J Geriatr Psychiatry. 2015;23(4):391–402. doi: 10.1016/j.jagp.2014.08.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Samus QM, Black BS, Reuland M, et al. MIND at home‐streamlined: study protocol for a randomized trial of home‐based care coordination for persons with dementia and their caregivers. Contemp Clin Trials. 2018;71:103–112. doi: 10.1016/j.cct.2018.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Samus QM, Davis K, Willink A, et al. Comprehensive home‐based care coordination for vulnerable elders with dementia: maximizing independence at Home‐Plus‐Study protocol. Int J Care Coord. 2017;20(4):123–134. doi: 10.1177/2053434517744071 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Black BS, Johnston D, Leoutsakos J, et al. Unmet needs in community‐living persons with dementia are common, often non‐medical and related to patient and caregiver characteristics. Int Psychogeriatr. 2019;31(11):1643–1654. doi: 10.1017/S1041610218002296 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Black BS, Johnston D, Rabins PV, Morrison A, Lyketsos C, Samus QM. Unmet needs of community‐residing persons with dementia and their informal caregivers: findings from the maximizing independence at home study. J Am Geriatr Soc. 2013;61(12):2087–2095. doi: 10.1111/jgs.12549 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Hughes TB, Black BS, Albert M, et al. Correlates of objective and subjective measures of caregiver burden among dementia caregivers: influence of unmet patient and caregiver dementia‐related care needs. Int Psychogeriatr. 2014;26(11):1875–1883. doi: 10.1017/S1041610214001240 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Olsen C, Pedersen I, Bergland A, et al. Differences in quality of life in home‐dwelling persons and nursing home residents with dementia—a cross‐sectional study. BMC Geriatr. 2016;16:137. doi: 10.1186/s12877-016-0312-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Taylor DH, Jr. , Fillenbaum GG, Ezell ME. The accuracy of Medicare claims data in identifying Alzheimer's disease. J Clin Epidemiol. 2002;55(9):929–937. doi: 10.1016/s0895-4356(02)00452-3 [DOI] [PubMed] [Google Scholar]
- 38. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Med Care. 2005;43(11):1130–1139. doi: 10.1097/01.mlr.0000182534.19832.83 [DOI] [PubMed] [Google Scholar]
- 39. Samus QM, Amjad H, Johnston D, Black BS, Bartels SJ, Lyketsos CG. A multipronged, adaptive approach for the recruitment of diverse community‐residing elders with memory impairment: the MIND at home experience. Am J Geriatr Psychiatry. 2015;23(7):698–708. doi: 10.1016/j.jagp.2015.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Cooper C, Vickerstaff V, Barber J, et al. A psychosocial goal‐setting and manualized support intervention for independence in dementia (NIDUS‐Family) versus goal setting and routine care: a single‐masked, phase 3, superiority, randomized controlled trial. Lancet Healthy Longev. 2024;5(2):e141–e151. doi: 10.1016/S2666-7568(23)00262-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
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