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Record W4223570820 · doi:10.1111/jgs.17772

Transitioning an in‐person geriatric memory clinic to a virtual care model for rural primary care clinics

2022· letter· en· W4223570820 on OpenAlexaboutno aff
Tsai‐Ling Liu, Jennifer Woodward, Latonia Frazier, Whitney Rossman, Yhenneko J. Taylor, Deanna A. Mangieri

Bibliographic record

VenueJournal of the American Geriatrics Society · 2022
Typeletter
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersDuke Endowment
KeywordsMedicinePrimary careGeriatric careGeriatricsGerontologyFamily medicineNursingPsychiatry

Abstract

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Dementia is a leading cause of disability and dependency among older adults, with an estimated 6.2 million Americans affected in 2021 and $355 billion in direct care costs.1 Experts recommend management of less-complex patients in primary care and more complex patients by geriatricians.2-4 However, shortages in the geriatric workforce make access challenging, particularly in rural communities.5 Virtual care via video conferencing is a promising strategy for addressing specialist shortages while improving access.6-8 These models have been examined in select populations including veterans,9 but have not been studied using a team-based approach. Our memory clinic located in the Charlotte metropolitan region is a geriatrician-led, team-based clinic dedicated to diagnosis and management of dementias.8 Our prior research identified several program strengths, including the team-based approach to enhance care coordination and education. Here, we describe how we expanded our in-person memory care model through telehealth to improve access for patients living in rural settings. We partnered with two rural primary care clinics in Stanly and Cleveland counties (North Carolina), each with high numbers of patients aged 65 years and older (based on prior year visits). At each facility, we embedded the virtual memory clinic (VMC) into the primary care workflow. We adapted each component of our in-person clinic to accommodate virtual delivery of this tailored dementia screening and referral program (Figure 1). Our model leveraged existing patient–primary care provider (PCP) relationships along with centralized electronic medical records (EMR) and billing and scheduling systems shared across primary care sites and memory clinic locations. Patients were eligible for referral if their PCP suspected potential or existing memory issues. No prior formal memory screening was required. Virtual visit costs were covered by extramural funds. This program was deemed Quality Improvement by the Atrium Health Institutional Review Board. Our VMC model followed a protocolized approach. At initial visit, the local primary care practice's Medical Assistant (MA) roomed the patient and caregiver(s) off camera to collect basic information, vitals, and reconcile medications. The caregiver met independently with the geriatrician and social worker, via telehealth technology, to discuss the patient's condition and cognitive history while the MA completed the Montreal Cognitive Assessment and depression screening with the patient and shared results via the EMR for geriatrician review. Screening results were discussed with the caregiver along with educational resources and caregiver/respite care options. Next, the patient joined the telehealth room while the geriatrician reviewed disease history, screening results, medications, and nonpharmacologic interventions for memory. At visit completion, the MA provided a summary of tips and resources, medication changes, and follow up instructions (Figure 2). Within 1 week of the virtual visit, the navigator followed-up via phone with the caregiver to address remaining questions. Caregivers also received a satisfaction survey through text message. The navigator continued monthly phone follow-up with caregivers, focusing on advanced care planning, while the geriatrician followed-up virtually at least every 6 months. The VMC saw 115 patients, of whom 70.4% (n = 81) had never seen a specialist for memory loss issues and 87.0% (n = 100) were diagnosed with some level of cognitive impairment (mild cognitive impairment [MoCA = 18–25] 22.6%, mild/moderate dementia [MoCA = 11–17] 47.9%, moderate severe/severe dementia [MoCA ≤ 10] 16.5%). Among the 105 caregivers who answered the satisfaction survey, 93.3% would choose to use the VMC services again and 95.2% would recommend these services to others. Satisfaction surveys were administered to the 18 providers with patients eligible for VMC, with a response rate of 33.3% (n = 6). One respondent did not refer any patients because the referral process was too complicated. The other 5 respondents all strongly agree that they would refer their patients to the program again and would recommend the services to their colleagues. They also expressed that VMC is a great service, particularly with the length of time spent with the patient/caregiver. Despite implementation challenges (Table 1), we successfully translated all elements of the in-person memory clinic into virtual delivery via primary care. In addition to convenient, virtual access to a geriatrician, patients benefited from the continuity of having their cognitive screenings performed by familiar primary care staff. Caregivers received monthly phone calls from the navigator to build rapport, solicit questions, and provide non-pharmacologic interventions and resources. Like our in-person clinic, patients and caregivers shared that education and support facilitated by the navigator was a meaningful and valuable component of the VMC experience.10 PCPs also benefitted from VMC support in managing multiple aspects of routine care (e.g., caregiver education, advanced care planning, driving safety evaluation, dementia-related behavioral management, and reduced triage communication for behavioral issues). Designated site-based medical assistants (MAs) trained to administer: Strategies to improve engagement with site providers: Provider engagement and patient recruitment Lessons learned: COVID-19 pandemic Site-based structure and scheduling Barriers: Lessons learned: Our results support the feasibility of virtual team-based dementia care for improving access to care addressing the clinical and psychosocial needs of patients and caregivers. Scaling this model requires addressing implementation challenges in collaboration with stakeholders, tailoring content, and securing funding. Reimbursement for virtual visits precipitated by the COVID-19 pandemic hold promise for virtual care as a viable option for dementia care into the future. The authors of this article would like to acknowledge the participating clinics, including clinic staff, medical assistants, and primary care providers who helped with the implementation of the virtual memory clinic. It was a privilege to collaborate with the participating clinics. We also thank Dr. Marc Kowalkowski for providing critical feedback on the manuscript. All authors received no support from any organization for the submitted work, have no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years, and have no other relationships or activities that could appear to have influenced the submitted work. All authors contributed to the design and execution of the virtual memory clinic implementation. Tsai-Ling Liu drafted the manuscript; all coauthors provided intellectual content, critical revisions, and approval of the final draft. Sponsors had no role in the design, methods, subject recruitment, data collections, analysis, or preparation of the paper. Table S1 List of patient resources provided at the memory clinic and VMC. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.330
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2022
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Same venueJournal of the American Geriatrics SocietySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207