LITERATURE REVIEW: THE USE OF CLINICAL SYSTEMS TO IMPROVE OUTCOMES AND EFFICIENCIES

Remote Patient Monitoring (RPM) for Diabetes Management Student’s Name Institution Course Number and Name Instructor’s Name Date Remote Patient Monitoring (RPM) for Diabetes Management The rapid advancement of technology has led to the rise of numerous clinical systems seeking to improve health outcomes and boost the efficiency of healthcare organizations. One such system is remote patient monitoring (RPM), which allows providers to monitor and manage different health conditions. Diabetes is a chronic condition that poses a significant burden to the American healthcare system. Solutions tailored towards reducing the burden of diabetes can be integral in addressing the rising healthcare costs and improving the health outcomes of different communities in the US. Several research studies have explored the utilization of RPM in the management of diabetes. This paper examines four such research articles and provides important lessons about the role of RPM in diabetes management. Amante, D. J., Harlan, D. M., Lemon, S. C., McManus, D. D., Olaitan, O. O., Pagoto, S. L., . . . Thompson, M. J. (2021). Evaluation of a diabetes remote monitoring program facilitated by connected glucose meters for patients with poorly controlled type 2 diabetes: randomized crossover trial. JMIR diabetes, 6(1). https://doi.org/10.2196/25574 Amante et al. conducted a randomized crossover trial to evaluate the effect of a diabetes RPM program on blood sugar levels and patient satisfaction. The research utilized the Livongo for Diabetes Program, which incorporates diabetes educators and education specialists to offer real-time feedback based on the glucose monitoring data. The study recruited patients (n=119) with type 2 diabetes from the University of Massachusetts Medical Center. The intervention comprised a cellular-connected glucose meter as well as diabetes coaching delivered through the phone. The glucose meter automatically uploaded blood glucose recordings to a secure portal after the patients regularly tested their blood glucose levels. In case of abnormal blood glucose levels, certified diabetes educators would contact the patient immediately and assess whether they needed urgent medical attention. The findings of the study indicated that participants experienced improvements in blood glucose levels similar to if they received care at specialized diabetes centers. The study was also associated with patient satisfaction with the program. The study is significant in the sense that it reports on the effectiveness of a RPM program. An important lesson from the study is the quick response by diabetes educators in case of an abnormal recording. In this regard, quick responses to diabetic patients can be significant in improving outcomes. Ramesh, J., Aburukba, R., & Sagahyroon, A. (2021). A remote healthcare monitoring framework for diabetes prediction using machine learning. Healthcare Technology Letters, 8(3), 45-57. https://doi.org/10.1049/htl2.12010 In their study, Ramesh et al. proposed an automated RPM framework for the management and prediction of diabetes. The traditional methods of disease management utilize score estimations and rule-engines for identifying diabetes risk, which are less effective than machine learning tools in detecting health conditions. The proposed framework utilizes data from consumer wearables and personal health devices to detect diabetes risks early to keep health professionals updated. The vitals that are continually monitored include blood oxyg...