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March 21, 2018
이전 슬라이드다음 슬라이드

Current Challenges in Healthcare

작성자:
Guest Author

Guest blog written by Bendi Sowjanya, a microbiologist and technologist at B3DS

Health issues affect all populations globally but the treatment and prevention of these issues varies widely depending on geographic location.  The effectiveness of health care is limited due to different socio-economic and environmental conditions as well as the ability to provide education for prevention.

A key challenge in healthcare is the evolution of pathogens.  New pathogens and vector strains are emerging that are resistant to existing drugs and the risk is that these diseases can become pandemic.  Understanding the cause of disease is vital to the mitigation, containment and prevention of widespread epidemics.  Some of the causes of disease can be linked to:

  • Endemic and epidemic infectious diseases
  • Hereditary diseases such as cancer, Alzheimer’s and others
  • Life style and work-related stress
  • Environmental conditions such as pollution

Other factors present roadblocks to delivering prompt and effective healthcare including the ability to effectively monitor health issues, logistics and service provisioning and limited access to healthcare due to economics or social disparities.

The solution to providing effective and accessible healthcare to global populations is early intervention with prompt analysis and prediction of problems before they manifest.

Role of Big data and Biosensors in Healthcare

Early intervention can only be implemented with early recognition of health issues.  Biosensors (sensors that collect and transmit bio data in real-time) are fundamental to the monitoring and predicting of health issues before they occur.

Sensors detect biological or physical activities of living forms and capture atmospheric or geophysical variables. They help monitor various health vitals, predict clinical procedural outcomes in the real world and impact research outcomes. Additionally, they optimize the supply chain and logistics aiding in timely intervention for epidemics by providing real-time statistics and efficient healthcare service provisioning.

Once data is collected, biosensors transmit this data in real-time using a secure protocol to an array of servers.  Once stored, the data can be explored and analysed to create impactful information for use in reports as well as data analysis for predictions regarding a particular condition or disease.

With the cost of biosensors going down dramatically, health care officials and providers have been widely deploying biosensors.  This wide scale adoption of sensors has resulted in massive amounts of information being collected and transmitted leading to data flooding.

iFARM and FEISH offers an IoT and Big data Integration solution

iFARM is an IoT and Big data framework built on Hortonworks Data Platform which provides business views for data analytics and visualization. The platform is constructed to process real-world data and to explore and discover complex relationships and predict behavioural outcomes. iFARM’s cognitive computing libraries, interactive graphical and hierarchal views of informational assets can unleash discoveries to help solve current healthcare problems.  Together B3DS and Hortonworks offer solutions for healthcare such as analytical and mobility for sensor (IoT), diagnostics, clinical, payer, provider and non-clinical data  and deep image analytics for disease prognosis.

The data model of iFARM is compliant with global data exchange, integration and modelling standards such as HL7, ICD, SNOMED, CDSIC, GA4GH, and OMOB for interoperability and compatibility.

FEISH is a web application for users that provides personalized views to users to manage, track and monitor health information.  Entire patient records and diagnostics, sensors and other machine data are provisioned according to the users on the platform.

Sensory data is pushed to FEISH and iFARM for analysis and reporting. FEISH retains an aggregated view of data for certain periods but does long-term archiving in the iFARM data-lake. Sensor data is linked with provider or payer anonymous data such as EMR, PHR or EHR and other data domains such as genomics, pathways, and drugs in the data repository of iFARM.

Conclusion

iFARM and FEISH platform are designed to manage data of any scale thereby reducing efforts in the understanding and identification of many health problems. The benefits to healthcare and the life science industry result in substantial reductions in CAPEX and OPEX over a period of time. Its real world application in business, service delivery, disease control, epidemiological studies, personalized medication, patient safety, drug engineering and discovery would enables solutions to today’s healthcare challenges. Hortonworks and B3DS are committed to continue working on platform design and product innovation together.

To learn more, please visit: 

Solution Brief: B3 Digital Solutions and Hortonworks Data Platform
Website: www.b3-ds.com

Comments

Sumit sinha says:

Awesome

Tushar soni says:

Great Post!!
I seen your post and It’s really helps you to solve challenges in healthcare system. Main things of healthcare solutions provided in this post.

Thanks for sharing!!
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Scarlet says:

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