2018-11-19
Before we can actually start utilizing big data in healthcare, we need to first integrate all the data we have in storage. In this article, we’ll take a close look into why integrating the data is so important and what benefits can be gained from the integration.
Medical big data integration means the collection of all medical data scattered across different departments. Here, scattered data includes various types of data such as clinical information, medical images, ECG, body temperatures, blood pressures, genomic information, use cases, whitepapers, clinical trial data, and etc.
Integrating medical big data provides a comprehensive view of a patient’s health status.
The computing power of artificial intelligence (machine-learning) algorithms enables high-speed analysis of big data which, when applied to healthcare, enables medical professionals to treat each patient with a more holistic perspective*. The concept of population health also has similar roots. It is an approach to health that analyzes various types of data from a specific group of people in efforts to anticipate and manage their health conditions.
Furthermore, integrating medical big data enables treatment tailored to individual patients.
Until now, if you have a specific symptom, you would go to the hospital for examination and most likely be prescribed the standard (one-size-fits-all)** treatment and medication. But it’s possible that patients A and B with exactly the same symptoms could react differently to the same treatment/medication. One of them could even experience side effects.
Precision medicine***, undoubtedly one of the hottest keywords in medical industry at the moment, addresses this issue by optimizing diagnosis and treatments based on an individual’s comprehensive health data including genetic, environmental and lifestyle information.
In order to implement precision medicine, however, it is necessary to first ‘integrate the medical big data’ at hand, which in itself is not an easy task. In fact, integrating all the data generated from a single medical facility is difficult enough.
*Jung Young-hoon, , Korea Consumer Agency, 2017. 31
**NIH, https://ghr.nlm.nih.gov/primer/precisionmedicine/definition
***Moon Se-young et al, , Korea Institute of S&T Evaluation and Planning, 2016. 9
Basically, medical data generated within a hospital is distributed across different departments. What’s more, medical modality and information system vendors all use different formats for storing and communicating data, making it extremely difficult to store and manage different types of data as a whole.
You need a ‘medical big data platform’ to store, manage and utilize the dispersed data in an integrated manner.
For example, you can compare a big data platform to a train station or an airport—places where a lot of people gather to meet up and interact, and where vending machines, restaurants, and shops emerge as a result. The idea of a platform is basically to create an ecosystem in which different players interact with each other to produce new values and benefits. The same applies to medical big data as well. A platform is needed to collect and integrate the data scattered across different departments and different medical devices and information systems, and to produce new values as a result.
In this sense, INFINITT Healthcare Platform (IHP) is just the right medical big data platform for you.
INFINITT Healthcare Platform (IHP) is a vendor neutral archive for integrating all data generated within a medical facility. Here, a vendor neutral archive refers to a medical information solution that receives, stores, manages and opens up (for viewing) all medical data in standardized format, regardless of what vendor modalities and systems are used.
IHP brings together medical data from different departments under one platform where all data is comprehensively managed from creation to storage and usage. So what are the benefits of using IHP?
First, you can provide patient-centric care and treatment based on the data integrated under IHP. This is because integrated data gives you a more holistic perspective on a patient’s health status. You get to see both the small and the big picture.
Second, the data integrated under IHP can be shared and utilized not only within the medical facility but also with other medical facilities for collaboration and research purposes.
Third, the data integrated under IHP can also be used for artificial intelligence (AI), precision medicine and population health applications.
Medical big data and its integration is clearly gaining momentum globally. In the UK, the Healthcare Big Data Consolidation Center** is established to promote the sharing and utilization of medical data, and the United States is also pursuing a data construction project*** that links approximately 1 million people with genetic information and health information.
__
We will continue the discussion on potential applications of medical big data in the next article of the series.
-
Product Essay
영상의학과의 데이터 활용, INFINITT Enterprise Search로 쉽고 빨라집니다
#INFINITT_Enterprise_Search #영상의학과 #데이터활용
-
Product Essay
INFINITT DoseM으로 시작하는 체계적인 방사선량 관리
#INFINITT_DoseM #방사선량_관리 #dose_management
-
Product Essay
의료 데이터를 바라보는 새로운 관점, INFINITT와 시작하는 데이터 자산화
#데이터_자산화 #enterprise_imaging #INFINITT_Healthcare_Platform
-
Product Essay
INFINITT PACS 7.0이 영상의학의의 새로운 역할을 지원하는 세 가지 방법
#INFINITT_PACS #인공지능 #협업 #판독_효율성_향상
-
Product Essay
INFINITT MDT와 함께하는 효율적인 다학제 진료
#다학제진료 #INFINITT_MDT
-
Product Essay
Enterprise Imaging, 의료 영상 및 데이터 통합 전략
#enterprise_imaging #의료영상데이터통합 #데이터공유 #다학제진료 #빅데이터 #AI
-
Product Essay
INFINITT Digital Pathology Solution으로 구현하는 디지털 병리 워크플로우
#디지털병리 #INFINITT_Digital_Pathology_Solution
