Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch

Perspectives on AI use in medicine: views of the Italian Society of Artificial Intelligence in Medicine

The first annual meeting of the Italian Society for Artificial Intelligence in Medicine (Società Italiana Intelligenza Artificiale in Medicina, SIIAM) on December 7, 2023, marked a significant milestone in integrating artificial intelligence (AI) into Italy's healthcare framework. This paper reports on the collaborative workshop conducted during this event, highlighting the collective efforts of 51 professionals from diverse fields including medicine, engineering, data science, and law. The interdisciplinary background of the participants played a crucial role in generating ideas for innovative AI solutions tailored to healthcare challenges. Central to the discussions were several AI applications aimed at improving patient care and streamlining healthcare processes. Notably, the use of Large Language Models (LLMs) in remote monitoring of chronic patients emerged as an area of focus. These models promise enhanced patient monitoring through detailed symptom checking and anomaly detection, thereby facilitating timely medical interventions. Another significant proposal involved employing LLMs to improve empathy in medical communication, addressing the challenges posed by cultural diversity and high-stress levels among healthcare professionals. Additionally, the development of Machine Learning algorithms for standardizing treatment in pediatric emergency departments was discussed, along with the need for educational initiatives to enhance AI adoption in rural healthcare settings. The workshop also explored using LLMs for efficient data extraction and analysis in scientific literature, interpreting healthcare norms, and streamlining hospital discharge records. This paper provides a comprehensive overview of the ideas and solutions proposed at the workshop, reflecting the participants' forward-thinking vision and the potential of AI to revolutionize healthcare.

Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review

The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.

Prospective Cohort Study of Remote Patient Monitoring with and without Care Coordination for Hypertension in Primary Care

BACKGROUND

Out-of-office blood pressure (BP) measurements contribute valuable information for guiding clinical management of hypertension. Measurements from home devices can be directly transmitted to patients' electronic health record for use in remote monitoring programs.

OBJECTIVE

This study aimed to compare in primary care practice care coordinator-assisted implementation of remote patient monitoring (RPM) for hypertension to RPM implementation alone and to usual care.

METHODS

This was a pragmatic observational cohort study. Patients aged 65 to 85 years with Medicare insurance from two populations were included: those with uncontrolled hypertension and a general hypertension group seeing primary care physicians (PCPs) within one health system. Exposures were clinic-level availability of RPM plus care coordination, RPM alone, or usual care. At two clinics (13 PCPs), nurse care coordinators with PCP approval offered RPM to patients with uncontrolled office BP and assisted with initiation. At two clinics (39 PCPs), RPM was at PCPs' discretion. Twenty clinics continued usual care. Main measures were controlling high BP (<140/90 mm Hg), last office systolic blood pressure (SBP), and proportion with antihypertensive medication intensification.

RESULTS

Among the Medicare cohorts with uncontrolled hypertension, 16.7% (39/234) of patients from the care coordination clinics were prescribed RPM versus <1% (4/600) at noncare coordination sites. RPM-enrolled care coordination group patients had higher baseline SBP than the noncare coordination group (148.8 vs. 140.0 mm Hg). After 6 months, in the uncontrolled hypertension cohorts the prevalences of controlling high BP were 32.5% (RPM with care coordination), 30.7 % (RPM alone), and 27.1% (usual care); multivariable adjusted odds ratios (95% confidence interval) were 1.63 (1.12-2.39; p = 0.011) and 1.29 (0.98-1.69; p = 0.068) compared with usual care, respectively.

CONCLUSION

Care coordination facilitated RPM enrollment among poorly controlled hypertension patients and may improve hypertension control in primary care among Medicare patients.

Continuous remote patient monitoring in heart failure patients (CASCADE study): mixed methods feasibility protocol (Preprint)

Background

Heart failure (HF) is a prevalent chronic disease and is associated with increases in mortality and morbidity. HF is a leading cause of hospitalizations and readmissions in the United States. A potentially promising area for preventing HF readmissions is continuous remote patient monitoring (CRPM).

Objective

The primary aim of this study is to determine the feasibility and preliminary efficacy of a CRPM solution in patients with HF at NorthShore University HealthSystem.

Methods

This study is a feasibility study and uses a wearable biosensor to continuously remotely monitor patients with HF for 30 days after discharge. Eligible patients admitted with an HF exacerbation at NorthShore University HealthSystem are being recruited, and the wearable biosensor is placed before discharge. The biosensor collects physiological ambulatory data, which are analyzed for signs of patient deterioration. Participants are also completing a daily survey through a dedicated study smartphone. If prespecified criteria from the physiological data and survey results are met, a notification is triggered, and a predetermined electronic health record–based pathway of telephonic management is completed. In phase 1, which has already been completed, 5 patients were enrolled and monitored for 30 days after discharge. The results of phase 1 were analyzed, and modifications to the program were made to optimize it. After analysis of the phase 1 results, 15 patients are being enrolled for phase 2, which is a calibration and testing period to enable further adjustments to be made. After phase 2, we will enroll 45 patients for phase 3. The combined results of phases 1, 2, and 3 will be analyzed to determine the feasibility of a CRPM program in patients with HF. Semistructured interviews are being conducted with key stakeholders, including patients, and these results will be analyzed using the affective adaptation of the technology acceptance model.

Results

During phase 1, of the 5 patients, 2 (40%) were readmitted during the study period. The study completion rate for phase 1 was 80% (4/5), and the study attrition rate was 20% (1/5). There were 57 protocol deviations out of 150 patient days in phase 1 of the study. The results of phase 1 were analyzed, and the study protocol was adjusted to optimize it for phases 2 and 3. Phase 2 and phase 3 results will be available by the end of 2022.

Conclusions

A CRPM program may offer a low-risk solution to improve care of patients with HF after hospital discharge and may help to decrease readmission of patients with HF to the hospital. This protocol may also lay the groundwork for the use of CRPM solutions in other groups of patients considered to be at high risk.

International Registered Report Identifier (IRRID)

DERR1-10.2196/36741