Medical Operations Control Center (MOCC)

Medical Operations Control Center (MOCC)

Advent of Medical IOT

Medical services, especially Telemedicine, is one of the areas were IOT will have its strongest impact.  The application of focused medical treatments nowadays depends more and more on digital files, an ever expanding array of connected sensors, and different types of analytics (whether preventive or prescriptive).

Due to the multitude of data sources, this is becoming a big data exercise, where inter sensor-correlation, machine learning, outlier analysis and other comparable methods are the means to distil insights out of huge and disparate datasets.

Additionally, tactile internet reliant on ultra-reliable low latency networks, has opened the possibly of providing remote care and implementing remote procedures to an extent never heard of before.

Challenging trends, but help is on the way

Medical IOT can also help cope with recent problematic trends that are posing serious challenges to the population in general and the medical world in particular:

  • A growing aging population
  • Decreasing doctor to patient ratio
  • Resistant strains of bacteria
  • Worsening climatic conditions
  • An increase in vector-borne diseases

What is common to all tele-medicine services?

Communication – While for some this is an afterthought; the truth is that Telemedicine is a chain of processes that is weakest as its weakest link. In order for any Telemedicine service to work, the ‘Tele’ part must be ensured, e.g. ensuring the availability of the communication link between the patient/sensor and the provider of medical services.

Operations and the application of focused policies – While the communication links are responsible for ensuring the availability of the data transported between patients and sensors and the medical service providers (and other parties as required), the operations of the medical services and the policies applied to optimize their availability ensure that the right actions are taken according to the information type, its content, and the situational context.

Sensors and actuation Sensors are slowly but surely inundating our personal space, from  smartwatches measuring heart rate, location and sleep patterns, through fall detection wristbands and up to more sophisticated sensors such as wearable Oximeters, eTextile based wearable Spirometers and minimally/non invasive CGM. Any piece of such information can be very valuable, and sometimes critical by itself in its ability to save lives, but overall, the effectiveness of Remote Patient Monitoring (RPM) can dramatically increase with the application of analytical tools such as correlation and RCA (Root Cause Analysis), and the evaluation of trends and outliers.

MOCC to Assist

With more than 25 years of experience in the area of operational monitoring, initially of networks of any kind but recently expanded to multiple IOT verticals, we are in a privileged position to assure the efficiency, availability and reliability of Telemedicine services by providing operational tools to enable the following:

  • Service Assurance Solutions that enable the 24/7 monitoring of the availability and quality of the required communication links relevant to all the different Telemedicine services provided, including sophisticated SLA analysis, RCA, Analytics and the management and operation of resolution processes via mechanisms such as notifications, escalations, automated commands and ticketing.
  • Sophisticated algorithms and programs that permit the application of customizable analytics to the sensor data (e.g. correlation, trend analysis and forecasting, outlier detection and alarming, machine learning, generation of reports and online dashboards). This enables the proactive identification of problems and the implementation of resolution processes according to customizable policies and automation rules.
  • A platform that provides with the capability to integrate multiple data layers to enable the analysis of the contextual situation to optimize the speed of problem detection and the efficiency of the resolution processes.
  • Customizable automated rules and processes to enable the interaction and data sharing with other bodies (e.g. first responders, smart city operators, paramedics, specialists, etc.) as needed .
  • Open APIs for the integration with other information sources that can help better understand the problem, the situational context and the best way to approach the problem (e.g. weather data, pollution data, data from emergency systems, traffic data, road closure data, etc.).

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