A Review of Emerging Technologies for the Management of Diabetes Mellitus
The following is a review of Zarkogianni et al. 2015 review regarding the emerging technologies used for the management of Diabetes Mellitus: 
Due to the rise in cost of health care delivery across the United States in patients suffering from chronic diseases, there has been an increasing trend towards the prevention of such diseases since each day the number of patients which can afford a treatment for their disease constantly increases. Zarkogianni et al. 2015, explore the utilization of these new technologies as means to prevent the pitfalls of treatments in the population by using the latest sensoring technologies and CDS in order to facilitate self-managing in patients and support decision making in physicians.
The review evaluated the following technologies:
- Sensors for Glucose and lifestyle monitoring
- Clinical Decision Support Systems (CDSS) for diabetes management
- Predictive modeling using molecular data to assess the onset or progression of DM (Diabetes Mellitus)
- Sensoring technology is evolving from a traditional invasive procedure towards a non-invasive procedure. Although, this shift is taking place the reliability of such test conducted under non-invasive methods aren't as accurate as those in traditional invasive procedures thus total support can't be given to them, however their evolution is a fact and we are not far from experiencing such technologies.
- CDSS turns out to be the giant whose setting the path for the rest of this technologies. Backed-up with evidence-based medicine and support from a portion of the medical community, this tool can indeed increase the rate of un-diagnosed patients at risk of developing DM by physicians thus it is a must to maintain and optimize this technology so that its reliability and acceptance isn't lost by the medical community since its adoption is currently undergoing and hasn't ended to establish it as a permanent tool around the clinical setting.
- In regards to the use of molecular data, it is an new technology with solid scientific information used for the unveiling of correlations and patterns observed in the development of DM.
Zerkogianni et al. 2015, recognize the increase in the rate of the evolution of technologies and that their integration with other clinical systems in the health setting such as an EHR can optimize even more the information gathered through them to provide a higher quality of prevention rates within the cluster of not only DM, but other chronic illnesses. However, they also established that although there is an ongoing current adoption this process hasn't been fully achieved across every single health care setting. There are several clinical settings in which this systems haven't been adopted despite the focus on achieving meaningful use around the US. Moreover, they also acknowledge that none of the explored technologies are close to be perfect and an ongoing update and maintenance is required to fulfill the needs of those ill.
I would criticize that there wasn't any evaluation in regards to m-Health an ongoing and fairly new system been implemented as a medium to also support the tracking and self-managing in patients suffering from chronic illnesses. Also, although we still face a portion of the clinical population who restrains from using technologies such as the ones discussed there is more than enough evidence of the benefits financially for the physicians and in quality for the service delivered to the patients. Due to these reasons the shift and full or partial adoption of this technologies will eventually take place around the next decade being that evidence illustrates a better quality of health care in the different scopes of its delivery.
High prevalence of Diabetes Mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with Clinical Decision Support Systems (CDSS) facilitating self-disease management and supporting healthcare professionals in decision making.
A critical literature review analysis is conducted focusing on advances in:
- sensors for physiological and lifestyle monitoring
- models and molecular biomarkers for predicting the onset and assessing the progress of DM
- modeling and control methods for regulating glucose levels.
Glucose and lifestyle sensing technologies are continuously evolving withcurrent research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering and control approaches have been deployed for the development of CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM.
Integration of data originating from sensor based systems and Electronic Health Records (EHR) combined with smart data analytical methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized and participatory diabetes care.
The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and the related challenges were identified. Helping patients with self-management is definitely needed with a widespread (and lifestyle dependent) disease such as diabetes.
- Zarkogianni, K., Litsa, E., Mitsis, K., Wu, P., Kaddi, C., Cheng, C., ... & Nikita, K. (2015). A Review of Emerging Technologies for the Management of Diabetes Mellitus. http://www.ncbi.nlm.nih.gov/pubmed/26292334