Technology-Driven Intervention for Cognitive Impairment

(CI Wizard Trial)

No longer recruiting at 1 trial location

Overseen By Bethany Crouse, PhD

Age: 65+

Sex: Any

Trial Phase: Academic

Sponsor: HealthPartners Institute

Disqualifiers: Chemotherapy, Stage 4 cancer, Hospice, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to improve early detection and care for cognitive impairment (CI) using a new tool integrated with electronic health records. The tool, called CI-CDS (Clinical Decision Support System for Cognitive Impairment), helps doctors identify patients at risk for CI, facilitating care planning and support. Eligible participants include those with recent abnormal cognitive test results or a high risk for dementia, visiting a participating clinic. Participants will either experience this new tool during their visit or continue with their regular care. As an unphased trial, this study offers participants the chance to contribute to innovative research that could enhance future cognitive care.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It is best to discuss this with the trial coordinators or your doctor.

What prior data suggests that this CI-CDS system is safe for cognitive impairment detection?

Research has shown that computerized systems assisting doctors with decisions about cognitive impairment (CI) are generally safe. These systems use data and computers to help doctors make better choices and are already used in some areas to improve dementia diagnosis.

Studies have found that patients generally accept these systems well. No reports of harmful side effects exist because they support decision-making rather than involve medication or surgery. These tools are part of electronic health records, which many clinics already use safely.

In summary, CI-CDS technology is safe and has been used in various clinical settings without any known risks to patients. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the Technology-Driven Intervention for Cognitive Impairment because it introduces a novel tool called CI-CDS. This tool is designed to assist healthcare providers during patient encounters by offering real-time decision support, which could lead to more personalized and effective care. Unlike standard treatments for cognitive impairment that often rely on medications or traditional therapy, CI-CDS leverages technology to enhance clinical decision-making. This approach represents a shift towards integrating digital solutions in healthcare, potentially improving patient outcomes by tailoring interventions to individual needs in real-time.

What evidence suggests that the CI-CDS system is effective for cognitive impairment?

This trial will compare the use of a special computer system, the CI-CDS, with usual care. Studies have shown that such systems can greatly improve the early detection of memory and thinking problems. The CI-CDS uses advanced technology to help doctors identify issues sooner, leading to better care planning for patients and their families. Research indicates that these tools guide doctors through a clear process to diagnose conditions like dementia more effectively. By integrating these systems into electronic health records, doctors can make quicker and more accurate diagnoses. This approach shows promise for improving the quality of life for many people by catching cognitive problems early. ^{1 3 4 5 6}

Are You a Good Fit for This Trial?

This trial is for people aged 65 or older who visit a participating primary care clinic, have no prior diagnosis of cognitive impairment (CI), and show signs of CI based on specific tests. They must not have had chemotherapy for advanced cancer in the last year, be in hospice or palliative care.

Inclusion Criteria

Primary care office visit at a randomized clinic during the accrual period

Patient has no CI diagnosis documented in the EHR prior to the visit

Patient has any abnormal score on a comprehensive cognitive assessment (MoCA, MMSE or SLUMS) in the prior 18 months

Exclusion Criteria

My cancer is at stage 4.

I am enrolled in a hospice or palliative care program.

I have had chemotherapy through injection or IV in the past year.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

Accrual period

All primary care visits during accrual period

Intervention

Implementation of CI-CDS system in primary care clinics to improve early detection and management of cognitive impairment

24 months

Primary care visits with CI-CDS tool usage

Follow-up

Participants are monitored for CI diagnosis and clinician confidence in CI detection and management

24 months

Regular primary care visits

What Are the Treatments Tested in This Trial?

Interventions

• CI-CDS

Trial Overview

The study is testing a new system called CI-CDS that uses machine learning to help doctors spot early signs of dementia. It's integrated into electronic health records and aims to improve detection and management of cognitive issues.

How Is the Trial Designed?

Group I: CI-CDS Experimental Treatment 1 Intervention

In clinics randomized to the CI-CDS, the providers will be given the option to use the CI-CDS tool during eligible patient encounters.

Group II: Usual Care (UC) Active Control 1 Intervention

In clinics randomized to UC, patients will receive usual care at their primary care visits over the accrual period (no intervention will be given).

Who Is Running the Clinical Trial?

HealthPartners Institute

Lead Sponsor

Trials

196

Recruited

3,721,000+

National Institute on Aging (NIA)

Collaborator

Trials

1,841

Recruited

28,150,000+

OCHIN, Inc.

Collaborator

Trials

24

Recruited

9,964,000+

Published Research Related to This Trial

A study analyzing the usability of clinical decision support systems (CDSS) for community-acquired pneumonia and neutropenic fever found 422 recorded events, including 3 mistakes that could have been life-threatening, highlighting the critical need for careful design and evaluation of these systems.

Incorporating usability engineering principles early in the design of CDSS can help identify interface issues that may lead to adverse medical events, emphasizing the importance of user-friendly software in improving patient safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

How usability of a web-based clinical decision support system has the potential to contribute to adverse medical events. Graham, TA., Kushniruk, AW., Bullard, MJ., et al.[2022]

Computer-based cognitive interventions (CCIs) have shown significant benefits in improving specific cognitive domains such as memory and attention in individuals with mild cognitive impairment (MCI), based on a systematic review of 24 randomized controlled trials.

While CCIs did not demonstrate significant improvements in global cognition for individuals with dementia, one study indicated that CCIs could lead to memory improvements in those with subjective cognitive decline (SCD), suggesting that early intervention may be key for cognitive preservation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effectiveness of computer-based interventions for community-dwelling people with cognitive decline: a systematic review with meta-analyses. Zuschnegg, J., Schoberer, D., Häussl, A., et al.[2023]

A systematic review of 10 studies on cognitive interventions for older adults with mild cognitive impairment (MCI) or dementia found that most technologies were computer-based programs, often in the form of serious games, which showed positive user experiences despite some interface difficulties.

Elderly users required more time and assistance to complete tasks, highlighting the need for technology that is tailored to their capabilities, and emphasizing the importance of usability and user experience measurements in developing effective cognitive interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Usability and User Experience of Cognitive Intervention Technologies for Elderly People With MCI or Dementia: A Systematic Review. Contreras-Somoza, LM., Irazoki, E., Toribio-Guzmán, JM., et al.[2022]

In a study of 429 patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI), the PredictND tool did not significantly improve overall prognostic accuracy for predicting progression to dementia, but it did show promise in a subgroup with more certain classifications, increasing accuracy by 6.4%.

The use of the PredictND tool significantly boosted clinicians' confidence in their predictions, indicating its potential value in clinical settings for assessing patients with cognitive complaints.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. Bruun, M., Frederiksen, KS., Rhodius-Meester, HFM., et al.[2020]

The RESILIEN-T system, designed for older adults with Mild Cognitive Impairment (MCI), showed high user acceptance and engagement, indicating its potential as a supportive tool for self-management despite no significant differences in most quantitative outcomes.

Participants using the RESILIEN-T system reported an improvement in their perceived memory capability, suggesting that while overall efficacy was not statistically significant, the system may still positively impact users' self-perception of cognitive function.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Results of the Italian RESILIEN-T Pilot Study: A Mobile Health Tool to Support Older People with Mild Cognitive Impairment. Bevilacqua, R., Felici, E., Cucchieri, G., et al.[2023]

A new computerized clinical decision support system (G-MEDSS) was developed to assist accredited clinical pharmacists in managing high-risk medications for older adults, including those with dementia, based on feedback from healthcare professionals and caregivers.

The G-MEDSS demonstrated fair to good usability with a mean score of 69.0 on the System Usability Scale, indicating it effectively supports person-centered medication reviews tailored to individual patient goals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development, validation and evaluation of the Goal-directed Medication review Electronic Decision Support System (G-MEDSS)©. Kouladjian O'Donnell, L., Reeve, E., Hilmer, SN.[2022]

The article highlights the need for improved patient-centered clinical decision support (PC CDS) tools to enhance healthcare decision-making, emphasizing the importance of making research findings more shareable and publicly available.

A comprehensive assessment involving a technical expert panel, literature review, and interviews with 18 stakeholders identified key areas for improvement, including the need for standardized processes to integrate clinical guidelines and patient-generated health data into PC CDS systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The technical landscape for patient-centered CDS: progress, gaps, and challenges. Dullabh, P., Heaney-Huls, K., Lobach, DF., et al.[2022]

A systematic review of 24 randomized controlled trials involving 7653 participants found that clinical decision support systems (CDSS) can improve insulin use and blood glucose control, with 70% of studies showing lower mean blood glucose levels after using computerized insulin dose adjustments.

While CDSSs showed promise in reducing hyperglycemia events and improving insulin therapy, the overall quality of evidence was moderate, indicating a need for better-designed and longer-term studies to confirm these findings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The effects of clinical decision support systems on insulin use: A systematic review. Jia, P., Jia, P., Chen, J., et al.[2021]

Age-related cognitive impairment (ARCI) is linked to underlying brain pathology, highlighting the need for early detection and personalized interventions to improve health span.

The Intelligent Technology Therapy Assistant (ITA) concept, exemplified by platforms like the Guttman Neuro Personal Trainer® and the Barcelona Brain Health Initiative app, aims to enhance access to clinical expertise while maintaining a personal connection between patients and clinicians, potentially improving the effectiveness of cognitive interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Aging in the Digital Age: Using Technology to Increase the Reach of the Clinician Expert and Close the Gap Between Health Span and Life Span. Gomes-Osman, J., Solana-Sánchéz, J., Rogers, E., et al.[2023]

A clinical decision support system (CDSS) helps healthcare providers by detecting potential drug interactions and errors, which can lead to improved patient safety and reduced clinical errors.

The implementation of a CDSS can save time and money while also decreasing morbidity and mortality rates, highlighting its effectiveness in enhancing patient care and supporting nursing informatics.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support in critical care nursing. Lyons, A., Richardson, S.[2019]

A clinical decision support system (CDS) for managing tuberculosis (TB) patients was positively received by 12 healthcare professionals, indicating its potential to improve patient care despite some initial usability challenges.

The assessment highlighted the value of decision support features and automated reporting, suggesting that while there were workflow issues, the system could enhance coordination and efficiency in TB management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An information management system for patients with tuberculosis: usability assessment with end-users. Darby, J., Black, J., Morrison, D., et al.[2012]

A survey of 64 ICU physicians revealed that 92% believe a discharge AI-CDS tool could be valuable for predicting patient readmission and mortality risk, indicating strong support for AI integration in clinical practice.

Despite differing opinions on the complexity of discharge decisions, most physicians (86%) felt that AI could enhance their decision-making, highlighting a positive outlook on the potential benefits of AI in improving patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study. van der Meijden, SL., de Hond, AAH., Thoral, PJ., et al.[2023]

A systematic review of 15 studies involving 13,480 patients found that computer-based clinical decision support systems (CCDSS) can have marginal positive effects on patient-reported outcomes (PRO), particularly in symptom management.

No negative effects were reported from the use of CCDSS, suggesting they are safe to implement, but further development of user-friendly systems with clear evidence-based guidance is needed to enhance their effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-Based Clinical Decision Support Systems and Patient-Reported Outcomes: A Systematic Review. Blum, D., Raj, SX., Oberholzer, R., et al.[2018]

Effective clinical decision support (CDS) systems for medication prescribing should focus on high-severity alerts that minimize workflow interruptions, while integrating relevant patient data to enhance decision-making accuracy and efficiency.

Design recommendations include using clear and consistent visual elements, minimizing information clutter, and allowing quick responses to alerts, which can significantly improve human-computer interaction and ultimately enhance patient safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Design of decision support interventions for medication prescribing. Horsky, J., Phansalkar, S., Desai, A., et al.[2015]

A usability study involving 8 medical residents identified 51 usability problems in a clinical decision support system (CDSS) for antibiotic treatment, highlighting areas for improvement in its design and functionality.

Most identified issues were minor or cosmetic, but some could lead to significant ordering errors, emphasizing the need to prioritize redesign efforts based on their potential impact on clinical outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A usability study to improve a clinical decision support system for the prescription of antibiotic drugs. Akhloufi, H., Verhaegh, SJC., Jaspers, MWM., et al.[2020]

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