빅 데이터는 의료가 전달되는 방식을 재정의하고있다. 그것은 기존의 의료 시스템은 폐기되고 있지 않은지하지만 어떤 중요한 변화는 근본적인 수준에서 일어나고있다. 어떤 변화가 가장 눈에 띄는 있습니다: 의료 기관은 점점 맞춤형 구축 데이터에 의존,,en,개인화 된 치료 모델,,en,강조는 환자의 건강에 대한 데이터를 수집에 데이터를 기반으로,,en,예방 단계를 취할 수 있도록 질병의 발병을 예측,,en,데이터는 또한 의사가 얻을 도움이됩니다,,en,환자의 건강도보기,,en,빅 데이터는 기존의 의료 시스템을 보완하고있다,,en,빅 데이터 전에 국가는 의료에 도입,,en,빅 데이터는 의료 시스템에 도입하기 전에,,en,환자의 치료에있어서의 데이터의 역할은 제한되었다,,en,병원 이름 등의 환자 데이터를 수집하는 것,,en,질병 설명,,en,당뇨병 프로필,,en,의료 보고서 및 질병의 가족력,,en,적용 중,,en,이러한 데이터는 환자의 건강 문제의 제한된보기를 제공합니다,,en, personalized treatment models. The emphasis is on collecting data on the health of patients and based on the data; predict the onset of diseases so that preventive steps can be taken. The data is also helping doctors get a 360 degree view of the patient’s health. Big data has been complementing the existing healthcare system.
State before big data was introduced to healthcare
Before big data was introduced to the healthcare system, the role of data in the treatment of a patient was limited. Hospitals would collect such patient data as name, age, disease description, diabetic profile, medical reports and family history of illnesses, whichever applicable. Such data provides a constrained view of the patient’s health problems. For example, for a patient who has been diagnosed with heart diseases, the typical information gathered would be family history, diet, symptoms, age and other existing diseases. While such information provides a detailed view of the disease, the data is unable to provide other perspectives into the problem. There are other ways also to view the problem from which a better treatment plan can potentially emerge.
In a statistics published in the Nature journal, it has been found that among the 10 highest grossing drugs prescribed in the US helps only 1 ...에서 25 or 1 ...에서 4 patients. And for cholesterol drugs, the success rate is only 1 ...에서 50 patients. So the probability of success is very lower compared to the expenditure made on research, approval and other activities.
imprecision medicine
Image Source Link: http://www.nature.com/news/personalized-medicine-time-for-one-person-trials-1.17411
The above image shows the effect of imprecision medicine on patients. But now the paradigm is rapidly changing with the help of big data and IT.
How has big data changed healthcare and medicines?
Big data has added a dimension to the treatment of diseases. Doctors now are able to understand diseases better and deliver accurate, personalized treatment. They are also able to predict recurrences and suggest preventive steps.
Comprehensive view of diseases
Big data has helped healthcare institutions take a 360 degree view of a patient’s health problems. This has led to new findings, novel treatment plans and more accurate diagnosis. Availability of data has brought to attention hitherto unknown factors that are associated with health problems. For example, certain races are genetically more predisposed to heart diseases than other races. Now, when a patient representing one of such races suffers from heart diseases, it is time to examine the data of patients belonging to the same race who have complained of heart problems. It helps to find out more about such patients — dietary habits, lifestyle, genetic structure, family DNA, proteins, metabolites to cells, tissues, organs, organisms, and ecosystems.
Predicting diseases
This follows the first change actually. When a patient is treated, the healthcare institution is able to obtain huge volumes of meaningful data about the patient. The data can be used to predict recurrences of diseases with a certain degree of precision. For example, if a patient has suffered stroke, the hospital can have data on the time of stroke, gap between strokes in case of multiple strokes in the past, influencing events preceding the stroke such as a psychologically stressful event or heavy physical activities. The hospitals can provide clear steps to prevent strokes based on the data.
Wearable devices
Wearable devices can do a wonderful job in detecting potential health problems even if there are no apparent symptoms. To evaluate the health of an apparently healthy person, a doctor needs to prescribe a series of medical examinations which is lengthy and costly. Wearable devices can reveal a number of health indicators based on which a doctor can make certain conclusions and decide on the future course of action. Already, a number of wearable devices and apps are able to measure such parameters as your heart rate, pulse, glucose levels and calorie levels. 장치 오늘날의 대부분은 오락 목적으로 사용하고있다지만,,en,그들은 심각한 가제트에 metamorphosing된다,,en,미국 식품의 약국 (FDA),,en,FDA,,en,포도당 모니터의 번호를 승인했다,,en,맞춤 의학에 빅 데이터의 영향,,en,전문가들은 빅 데이터는 크게 개인 의약품의 효능을 증가 할 것입니다 믿습니다,,en,이니셔티브의 숫자는 개인 의약품의 효과를 향상시킬 수있는 방법을 찾을 수있는 방법을 받고있다,,en,하나 개는 이러한 이니셔티브는 치료 선택에 대한 NCI-분자 분석으로 알려진 암 연구 프로그램이었다,,en,NCI-MATCH,,en,시도,,en,이 시험은 건강의 정밀 의학 이니셔티브의 국립 연구소의 중요한 부분입니다,,en,주도권에 대해 등록 할 것입니다,,en,사람과 특정 의약품 종양의 일치 특정 유형,,en, they are metamorphosing into serious gadgets. Already, the US Food and Drug Administration (FDA) has approved a number of glucose monitors.
Impact of big data on personalized medicine
Experts believe that big data is going to increase the efficacy of personal medicines significantly. A number of initiatives are under way to find out ways to improve the effectiveness of personal medicines.
One such initiative has been the cancer research program known as the NCI-Molecular Analysis for Therapy Choice (NCI-MATCH) Trial. This trial is an important part of the National Institute of Health’s Precision Medicine Initiative. The initiative is going to enrol about 1000 people and match specific types of tumors with specific medicines. 등록 사람들은 표준 암 치료에 반응하지 않은 종양이 있었다,,en,종양은 특정 유전자 마커에 기초하여 더 나은 결과를 생성하는 것으로 알려진 약물과 유사한 것,,en,일치의 결과를 바탕으로,,en,알려진 약물의 목록이 효과적 일 수 있도록 약물의 데이터베이스를 종양 해당 생성됩니다 것은 볼 수 있습니다,,en,이니셔티브는 종양의 진행 및 하나의 새로운 유형을 공부하고 해당 약물이 확인 될 것입니다,,en,재판은 올바른 약물 개인의 게놈을 일치시켜 경화 희귀하고 치명적인 암 종류의 비밀을 잠금을 해제 할 수있는 잠재력을 가지고있다,,en,암의 종류와 환자는 프로그램이 적어도이하는 것을 목표로하지만 재판에 등록 할 자격이,,en. The tumors will be matched with drugs known to produce better outcomes on the basis of certain genetic markers. Based on the outcome of the matching, a database of drugs will be created so that a list of drugs known to be effective for corresponding tumors is available. The initiative is an ongoing one and new types of tumors will be studied and corresponding drugs will be identified. The trial has the potential to unlock the secret of curing rare and fatal cancer types by matching the genome of an individual with the right drugs. A patient with any type of cancer is eligible to enrol for the trial though the program aims to have at least 25% of the total patients to have rare cancer. There are a number of parameters to evaluate whether the medicines are working. One parameter is to observe if the tumor size is shrinking, the second parameter is to find out whether the patient’s condition has worsened in the past 6 개월. The researchers will also take into account the side effects of the treatment.
As a result, precision medicine is gaining huge popularity and driving from all the sectors. US have also announced a US$215-million national Precision Medicine Initiative (Nature). It will include establishment of a national database of the genetic and other data of one million people in the United States.
personalized healthcare
The above image shows how the personalized healthcare is enabled.
Summary
There is no doubt that big data can revolutionize healthcare and personalized medicines. However, the pace of adoption across the globe is still slow and not uniform. Big data has the potential to significantly reduce unnecessary expenses on healthcare across the globe. Since adoption of big data represents a paradigm change, there has been resistance in certain quarters. But as the benefits become more obvious, adoption is going to become smoother. The biggest potential of big data lies in finding drugs for life threatening diseases.
