AHRQ announces funding opportunity for AI, digital health and clinical decision support

The Agency for Healthcare Research and Quality (AHRQ) announced a funding opportunity for research projects that test digital healthcare interventions aimed at improving quality at the point of care. The digital healthcare solutions tested should use advanced analytics; patient-centered clinician and patient facing digital technologies; or clinical decision-making tools. Applications are due June 16, 2021. For the first two-year phase, the project budget may not exceed $275,000. Projects continued into the second three-year phase may have a total project budget not exceeding $750,000.

For the research area of interest involving advanced analytics, the AHRQ suggested examples such as projects integrating AI during the provision of healthcare services and assessing AI's impact on practice workflow and quality of care, or projects applying machine learning against large health data sets to improve quality at the point of care. Research involving patient-centered digital healthcare technologies could include projects testing innovative technologies that facilitate information sharing between patients and providers, or the use of novel digital solutions for patients with multiple chronic conditions. Examples of projects focused on clinical decision-making support would be projects evaluating a digital point of care solution combining natural language processing with a decision support tool, or projects testing decision-making tools that incorporate the use of patient-generated data and patient-reported outcomes at the point of care.

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Published: 27 April 2021 27 April 2021

Last Updated: 27 April 2021 27 April 2021

• digital health
• AI/ML
• Clinical decision support

Stanford research study: Apple Watch useful in assessing frailty in CV patients

A recent Stanford University study compared the effectiveness of the Apple Watch in assessing frailty in cardiovascular patients, in comparison with an in-clinic six-minute walk test (6MWT). The study enrolled 110 patients who were scheduled for vascular or cardiac procedures at a Veterans Administration facility, supplied them with an iPhone and Apple Watch running the VascTrack research application, and followed them for six months. Periodic in-clinic 6MWT results were compared with in-home 6MWT performed using the app, and passive step information collected by the Apple Watch. The tests assessed frailty, defined as walking fewer than 300 meters on an in-clinic 6MWT. The Apple Watch was nearly as effective in assessing frailty via the 6MWT in the home setting as in the clinic setting (83% sensitive and 60% specific in assessing frailty in the home setting, versus 90% sensitive and 85% sensitivity in the clinic setting). Passive data collected at home was nearly as accurate as the 6MWT in-home assessment.

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Published: 30 March 2021 30 March 2021

Last Updated: 30 March 2021 30 March 2021

• digital health
• remote patient monitoring

Cleveland Clinic study finds smartphone ECG can be useful for monitoring AF

While availability of direct-to-consumer devices that enable the user to record heart rhythms continues to grow, use of the devices in clinical practice remains low. One reason for limited clinical use is that most devices allow the user to share an electrocardiogram (ECG) recording with their healthcare provider as an email attachment. It is impractical for busy clinicians to incorporate this raw data into normal workflows.

A small Cleveland Clinic study evaluated the use of the Kardia Mobile smartphone monitor coupled with the Kardia Pro (KP) platform for follow-up of patients following successful atrial fibrillation (AF) ablation. The KP platform utilizes artificial intelligence to triage preliminary interpretation of ECG tracings and route suspected abnormal recordings to the electrophysiologist’s in-basket. The study randomized 100 patients who presented three to four months after successful AF ablation into a self-monitoring group using the Kardia Mobile monitor, and a control group following normal standard of care. Healthcare utilization and anxiety were similar for the two groups, but more patients in the control group required additional ECGs or cardiac monitors compared to the self-monitoring group.

The study authors concluded that the KP platform can be effectively incorporated into the care of patients to assist in detection of AF recurrences. They noted:

The synergistic relationship between instantaneous interpretation via the automated algorithm, digital platform, patient, and healthcare provider is key for successful adoption of digital technology into busy clinical practices and will turn the technology into an asset rather than a burden and transform the relationship between the AF patient and the physician into a partnership rather than a unidirectional process.

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Published: 10 April 2021 10 April 2021

Last Updated: 10 April 2021 10 April 2021

• digital health
• AI/ML
• research
• remote patient monitoring