Technology-Driven Intervention for Cognitive Impairment
(CI Wizard Trial)
Recruiting in Palo Alto (17 mi)
+1 other location
Age: 65+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: HealthPartners Institute
Disqualifiers: Chemotherapy, Stage 4 cancer, Hospice, others
No Placebo Group
Approved in 1 Jurisdiction
Trial Summary
What is the purpose of this trial?Most experts advocate for early detection of cognitive impairment (CI) so that patients and caregivers can be prepared for making difficult decisions and to improve quality of life, but studies show that screening alone isn't sufficient to change clinician actions related to early detection. Using predictive modelling developed with machine learning methods and sophisticated clinical decision support (CDS) tools, it is possible to identify patients at elevated risk for CI and make it much easier for primary care to engage and support patients and caregivers in meaningful care planning. The goal of this study is to implement and evaluate a low-cost, highly scalable CI-CDS system integrated within the electronic health record that has high potential to improve early CI detection and care and translate massive public and private sector investments in health informatics into tangible health benefits for large numbers of people.
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 data supports the effectiveness of the treatment CI-CDS System for cognitive impairment?
Research shows that clinical decision support systems (CDSS) can improve patient care by helping healthcare providers make better decisions. While specific data on CI-CDS for cognitive impairment is not available, similar systems have shown potential benefits in managing other conditions, like insulin use and patient-reported outcomes.
12345Is the Technology-Driven Intervention for Cognitive Impairment generally safe for humans?
Clinical decision support systems (CDSS) are generally safe and can help prevent medical errors, but their design is crucial. Poor design can lead to new errors, so it's important that the system is well-designed and tested for usability to ensure safety.
678910How is the CI-CDS System treatment different from other treatments for cognitive impairment?
The CI-CDS System is unique because it uses technology to provide personalized support and decision-making assistance for cognitive impairment, enhancing the connection between patients and clinicians, unlike traditional treatments that may lack this personalized interaction.
1112131415Eligibility Criteria
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
Patient has no CI diagnosis documented in the EHR prior to the visit
Patient has no cognitive testing in the last 18 months and a risk of a dementia diagnosis in the next 3 years >=15% as calculated by the MC-PLUS algorithm developed in the R61 phase
First visit during the accrual period at which all prior inclusion criteria are met
+4 more
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.
Trial Timeline
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
Participant Groups
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.
2Treatment groups
Experimental Treatment
Active Control
Group I: CI-CDSExperimental Treatment1 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 Control1 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).
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
HealthPartnersBloomington, MN
OCHIN, Inc.Portland, OR
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Who Is Running the Clinical Trial?
HealthPartners InstituteLead Sponsor
National Institute on Aging (NIA)Collaborator
OCHIN, Inc.Collaborator
References
The technical landscape for patient-centered CDS: progress, gaps, and challenges. [2022]Supporting healthcare decision-making that is patient-centered and evidence-based requires investments in the development of tools and techniques for dissemination of patient-centered outcomes research findings via methods such as clinical decision support (CDS). This article explores the technical landscape for patient-centered CDS (PC CDS) and the gaps in making PC CDS more shareable, standards-based, and publicly available, with the goal of improving patient care and clinical outcomes. This landscape assessment used: (1) a technical expert panel; (2) a literature review; and (3) interviews with 18 CDS stakeholders. We identified 7 salient technical considerations that span 5 phases of PC CDS development. While progress has been made in the technical landscape, the field must advance standards for translating clinical guidelines into PC CDS, the standardization of CDS insertion points into the clinical workflow, and processes to capture, standardize, and integrate patient-generated health data.
An information management system for patients with tuberculosis: usability assessment with end-users. [2012]Information systems with clinical decision support (CDS) offer great potential to assist the co-ordination of patients with chronic diseases and to improve patient care. Despite this, few have entered routine clinical use.
Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study. [2023]Artificial intelligence-based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools.
The effects of clinical decision support systems on insulin use: A systematic review. [2021]A clinical decision support system (CDSS) is a computerized system using case-based reasoning to assist clinicians in assessing disease status, in selecting appropriate therapy or in making other clinical decisions. Previous randomized controlled trials (RCTs or trials) have shown that CDSSs have the potential to improve the insulin use, but the evidence was conflicting and uncertain. The purpose of our study was to determine whether a CDSS improves the use of insulin.
Computer-Based Clinical Decision Support Systems and Patient-Reported Outcomes: A Systematic Review. [2018]Evidence-based treatment guidelines embedded in computer-based clinical decision support systems (CCDSS) may improve patient-reported outcomes (PRO). We systematically reviewed the literature for content and application of CCDSS, and their effects on PRO.
How usability of a web-based clinical decision support system has the potential to contribute to adverse medical events. [2022]Clinical decision support systems (CDSS) have the potential to reduce adverse medical events, but improper design can introduce new forms of error. CDSS pertaining to community acquired pneumonia and neutropenic fever were studied to determine whether usability of the graphical user interface might contribute to potential adverse medical events.
A usability study to improve a clinical decision support system for the prescription of antibiotic drugs. [2020]A clinical decision support system (CDSS) for empirical antibiotic treatment has the potential to increase appropriate antibiotic use. Before using such a system on a broad scale, it needs to be tailored to the users preferred way of working. We have developed a CDSS for empirical antibiotic treatment in hospitalized adult patients. Here we determined in a usability study if the developed CDSS needed changes.
Development, validation and evaluation of the Goal-directed Medication review Electronic Decision Support System (G-MEDSS)©. [2022]1) To understand and investigate the experiences of accredited clinical pharmacists (ACP) using computerised clinical decision support systems (CCDSS) during medication reviews for older people, including those living with dementia; 2) To design, develop, validate, and evaluate a CCDSS that incorporates pharmacological and other deprescribing tools to aid person-centred management of high-risk medications in older adults living with and without dementia.
Clinical decision support in critical care nursing. [2019]A clinical decision support system (CDSS) is a computerized application that helps clinicians detect and prevent untoward clinical events such as drug interactions, errors of omission, and trends in symptomatology. A CDSS in healthcare usually is built around an alerting system based on rules of logic. The alerting system of a CDSS can notify clinicians immediately on clinical data entry, or it can generate alerts over time after relating data from multiple sources. A CDSS for nurses and patients offers immediate benefits for nurses and patients by detecting potential drug-laboratory and drug-drug combinations and impending pharmacologic complications, monitoring microbiology results, and helping nurses relate symptoms to pharmacology and medication side effects. Other benefits include savings in time and money and reductions in morbidity and mortality. A CDSS presents an opportunity for nursing informatics and critical care nursing to collaborate for the benefit of the patient and the profession.
Design of decision support interventions for medication prescribing. [2015]Describe optimal design attributes of clinical decision support (CDS) interventions for medication prescribing, emphasizing perceptual, cognitive and functional characteristics that improve human-computer interaction (HCI) and patient safety.
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. [2023]Age-related cognitive impairment (ARCI) has a profound impact on individuals, families, health care systems, and societies at large. Evidence suggests that ARCI is the consequence of underlying brain pathology. Therefore, efforts to minimize the impact of ARCI and thus closing the gap between health span and life span, which has widened in recent years, requires early detection and timely deployment of targeted, personalized interventions. Access to clinical experts is limited and technology screening and assessment methods are thus appealing. However, as traditionally implemented patients were deprived of the benefit of personalized connection with a clinician, which is particularly critical for the prescription and to ensure the adherence to and ultimate success of therapeutic interventions. We present the concept of Intelligent Technology Therapy Assistant (ITA) as a scalable solution that increases the reach of clinical experts while sustaining the personal connection between each patient and their clinician. We illustrate ITA with the "Guttman Neuro Personal Trainer"®, a tele-rehabilitation platform that provides neuropsychological evaluation and care, and the Barcelona Brain Health Initiative (BBHI) multimodal intervention coaching app, a mobile-based platform that provides lifestyle coaching support in domains related to brain health. In addition, we discuss the translation of these models to a large-scale enterprise with Linus Health. To this end, we conclude with a discussion of challenges and opportunities to move the field forward.
Results of the Italian RESILIEN-T Pilot Study: A Mobile Health Tool to Support Older People with Mild Cognitive Impairment. [2023](1) Background: The RESILIEN-T system addresses the need for innovative solutions to support self-management in older people with Mild Cognitive Impairment (MCI). Despite the increasing prevalence of dementia and MCI, there is a lack of tailored solutions for these individuals. The RESILIEN-T system aims to empower and engage people with cognitive decline by providing a modular platform for self-management and coaching services. (2) Methods: Italian data collected for the RESILIEN-T project involved 62 older participants randomly assigned to the intervention or control group. Data were collected through questionnaires and user interactions with the system over a three-month period. (3) Results: Quantitative outcomes showed no significant differences between the intervention and control groups, except for an improvement in perceived memory capability in the intervention group. The usability assessment indicated a high level of acceptance of the RESILIEN-T system. (4) Discussions: Although no significant improvements were observed in most quantitative measures, the high user engagement and acceptance suggest the potential effectiveness of the RESILIEN-T system. Future improvements could involve integrating smart objects and interactive virtual agents. Overall, RESILIEN-T represents a promising step toward empowering individuals with cognitive impairment in their self-management and decision-making processes.
Effectiveness of computer-based interventions for community-dwelling people with cognitive decline: a systematic review with meta-analyses. [2023]Cognitive deficits arise with age and can increase the risk for subjective cognitive decline (SCD) and mild cognitive impairment (MCI), which may result in dementia, leading to health problems, care dependency and institutionalization. Computer-based cognitive interventions (CCIs) have the potential to act as important counteraction functions in preserving or improving cognition concomitant to available pharmacological treatment. The aim was to assess the effectiveness of CCIs performed individually with a personal or tablet computer, game console, virtual, augmented, or mixed reality application on cognition in community-dwelling people with SCD, MCI and dementia.
Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. [2020]In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics.
Usability and User Experience of Cognitive Intervention Technologies for Elderly People With MCI or Dementia: A Systematic Review. [2022]Incorporating technology in cognitive interventions represents an innovation, making them more accessible, flexible, and cost-effective. This will not be feasible without adequate user-technology fit. Bearing in mind the importance of developing cognitive interventions whose technology is appropriate for elderly people with cognitive impairment, the objective of this systematic review was to find evidence about usability and user experience (UX) measurements and features of stimulation, training, and cognitive rehabilitation technologies for older adults with mild cognitive impairment (MCI) or dementia.