The importance of evaluating telemonitoring devices in healthcare is illustrated by the many weekly studies showing the increasing importance of RPM technology in healthcare. In addition, the constant improvement of the quality of clinical trials on remote monitoring helps to provide information to define the best patient and technology to deliver customized care.
Tenovi publishes a summary of new studies in remote patient monitoring each week. This week’s research round-up includes an overview of the following four studies:
- Telemonitoring for perioperative care of outpatient bariatric surgery: Preference-based randomized clinical trial
- Effectiveness of Remote Fetal Monitoring on Maternal-Fetal Outcomes: Systematic Review and Meta-Analysis
- Promises and challenges of machine learning for device therapy in heart failure
Evaluating Telemonitoring Devices in Healthcare
This week’s round-up highlights evaluating telemonitoring devices in healthcare and their use in bariatric surgery, fetal monitoring, and blood pressure monitoring in pregnancy and heart failure patients. RPM devices can help primary care and specialist physicians treat patients with acute or chronic conditions regardless of where they live. With these devices, qualified healthcare professionals can monitor patient vital signs in real-time. For instance, some conditions managed are diabetes, high blood pressure, obesity, cancer, asthma, and chronic kidney disease.
Devices include Bluetooth-enabled blood pressure monitors, glucose monitors, scales, pulse oximeters, and peak flow monitors that all transmit, process, and store patient data, allowing providers to retrieve patient data exactly when needed.
Evaluating Telemonitoring Devices in Bariatric Surgery Health
This study, published on February 22, aimed to evaluate telemonitoring devices in healthcare that are used in outpatient recovery after bariatric surgery, supported by remote monitoring, compared to traditional care in patients in the Netherlands. The authors concluded that outpatient bariatric surgery supported with telemonitoring is comparable to conventional overnight bariatrics regarding the textbook outcome. Both approaches show results above the Dutch average. Furthermore, offering same-day discharge reduces the total hospitalization days while maintaining patient satisfaction and safety.
The theme of lower hospitalization days aligns with the July 2022 study published in JAMA Network Open, which showed that implementing a remote patient monitoring program led to positive outcomes for COVID-19 patients, including lower hospitalizations, intensive care use, and lengths of stay.
Evaluating Telemonitoring Devices: Healthcare For Pregnancy Monitoring
Traditional fetal monitoring can be time-consuming for healthcare professionals and patients. In contrast, remote fetal monitoring expands the availability of health services in remote areas and cuts down on in-office appointments. In addition, pregnant patients transmit fetal monitoring data with fetal telemonitoring devices for physicians and qualified medical staff to review and interpret remotely in real-time.
In a systematic review published on February 22 of randomized controlled trials, researchers found that remote fetal monitoring reduces the incidence of neonatal asphyxia and health care costs compared with routine fetal monitoring. Two studies performed a cost analysis, stating that remote fetal monitoring can contribute to reductions in health care costs compared to conventional care. In addition, remote fetal monitoring may lower visits and duration of hospital stay, but research needs to draw definite conclusions due to the limited number of studies. This research highlights the importance of evaluating telemonitoring devices for pregnancy-related healthcare delivery.
Machine-Learning Technology and Evaluating Telemonitoring Devices in Healthcare
The following study associated with evaluating telemonitoring devices in healthcare focuses on heart failure and machine-learning technologies. Heart failure is a significant health problem that affects more than 64 million people worldwide. Moreover, it is associated with considerable healthcare management challenges and enormous medical costs. Therefore, efforts to decrease health outcomes and economic burden are a global public health priority. The good news is that the incidence of heart failure has stabilized and is declining in industrialized countries. However, with an increase in the aging of the population, prevalence is also increasing.
Results on Machine Learning (ML) performance in healthcare are promising with consistent algorithms. However, whether algorithms can be translated into real-world management use in heart failure patients still requires further study. What is machine learning? It is a brand of artificial intelligence (AI) that can help medical professionals care for patients and manage clinical data. AI involves programming digital technologies to mimic how people think and learn.
Developments in Machine-Learning Technology
Machine learning in healthcare can help to develop better diagnostic tools to analyze medical images. For example, machine learning technology has the potential to predict cardiac arrest. Research published in the European Heart Journal on February 18 asked: Can a machine learning-structured (ML) approach help mitigate healthcare inequities?
Increased adoption of telemonitoring devices can empower patients to become more proactive with their health. However, simplifying patient-level requirements with minimal out-of-pocket costs is needed. Moreover, models must cover a broad range of populations: demographic and socioeconomic factors, such as race, sex, and ethnicity, are required to avoid biased and inaccurate results. ML can reduce healthcare professional workload and better reach heart failure patients in rural communities.
Telehealth and smartphones have shown promising results in engaging patients in managing their healthcare and making actionable changes to improve, for example, body mass index and visceral fat percentage. Moreover, telemonitoring has been effective in patients with heart failure. The researchers conclude that ML-based healthcare can potentially decentralize the traditional paradigm of health delivery centered on providers and hospitals to that of patient-empowered, data-driven, and community-centered personalized treatment workflows.
Evaluating Telemonitoring Devices Healthcare
When evaluating telemonitoring devices in healthcare, providing stakeholders with new research can directly impact the outcome of remote patient monitoring implementations. That’s why effectively and continuously communicating with stakeholders to build and enhance collaborative relationships is so important. Presenting stakeholders with new research helps generate questions and feedback and keeps all parties on the same page.
Get Relevant Remote Patient Monitoring Studies and Research with Tenovi
As you know, sifting through citations and conducting scientific literature reviews related to evaluating telemonitoring devices in healthcare can be time-consuming and difficult. Tenovi cites studies and provides links to every source. The links make it easy to quickly review research on the PRM or telehealth topic. Have you subscribed to our weekly study blog round-up? It’s easy! Enter your email address below to sign up. Save time by following Tenovi on LinkedIn and Twitter, where we summarize new weekly remote patient monitoring and telehealth studies and provide daily updates on telemonitoring devices in healthcare.
