Reflection 12/6

This week we learned about Big Data and real world evidence/data and how it can ultimately revolutionize the industry. Big Data is the cumulation of health data from a multitude of sources such as electronic health records (EHRs), medical imaging, genomic sequencing, payor records, pharmaceutical research, wearables, and medical devices. This data is sourced in a cloud and is compiled is mass volumes. The difference between this and other data compilations is that it has more data than any other, it's moving at high velocity, and it has such high variability. Big Data allows the health industry to have access to more data from many different systems in order to allow them to make more quality and information based decisions. The two major aspects that Big Data can change are that this can allow the industry to switch from fee for service to value based and it allows for more evidence based decisions that increases efficiencies. The fee for service model means that physicians or medical staff get paid based on the treatments they give. In reality the more treatments they give, the more money they will receive so this leads to unneeded treatments and tests being performed. Having more information and data available will allow medical teams to know more about what are the most effective treatments and regimens that patients should be taking so they will be providing the patient more of a quick and effective course of treatment. The second aspect is just in terms of cutting down medical errors and costs since they will have a better knowledge of what is the correct approach. The over arching goal of the industry is to better the population health as a whole and in order to do this we need to be able to open our minds to more data silos. Although there are many positives to Big Data, there are also some challenges of getting it into use. When it comes to this approach to analyzing issues, there are many key players that play a role in the health industry. It is not just one person, there are many counterparts that you have to get on board and come to an agreement on how to use the data. On top of this, using Big Data more aggressively would mean that business models would have to change which can be hard to convince everyone to want to do because there are many down stream affects like training, costs, and advertising. Finally, the more obvious challenge is that there are privacy issues with the data. There are strict policies and regulations put into place for the health industry to follow in terms of making sure that each patients health information is protected per their request. This would limit the number of data being used as well as elongate the process of receiving data. Big Data, despite some downfalls, can over all change the industry for the better. It is an endless pool of information just waiting to be used but it is just a matter of how and when it can be used. 
We also learned about real world evidence and real world data which is similar to Big Data but used in a different way. Both of them are growing tremendously due to the usage of new technologies that track patient data daily. "RWE is data from sources other than clinical trials on the use and the potential benefits or risks of a drug. Congress describes RWD as data on patient health status and/or delivery of care that’s routinely collected from EHRs, claims and billing, patient-generated data, and more". It seems as though RWE is more of other studies and research done that portrays information about usage, adherence, and side effects of certain drug regimens. RWD seems like it is most readily related to Big Data. RWD and RWE is used in order to aid in the drug development sector of the health industry. When it comes to clinical trials for drugs, they are extremely costly and take a long time. There has been a recent push for trials to occur that result in more low cost drugs and getting drugs to market faster. This new real world information is used to make these trials more effective. They can be used in many different areas but most importantly to figure out who is the best volunteers to have in the trial. This data can specifically point out which patients are the best fit for the drug in terms of current health and their conditions at the moment. This decrease the spending annually used to recruit volunteers because now they have a better idea of who to seek out. This also can decrease the number of faulty volunteers who chose to leave the trial early or do not have the best outcomes. It is important to do this because this gives trials the best case scenario in terms of having a volunteer subject group that will have the most effective outcome. RWD and RWE can also be use to predict certain outcomes in a trial in terms of side effects or adverse events. If the trial volunteers had a better idea of these potential risks, then it would weed out those who do not want to participate due to that factor. Josh stated that a way that Acorn AI is using RWE and RWD is in a way that matches patients with a randomized control study. By matching them it gives the study outliners a better idea of who is the best fit for the drug. RWE and RWD are currently used in trials but the push to get them further used is growing. Having all of this data is a gold mine but it all depends on how it is used. If you do not understand the data, then it is useless. The problem at hand is sorting and analyzing the data in an efficient and effective way so that all can understand it. The amount of data out there is incredible and it only keeps growing. It has been at our finger tips for a while but now we have a chance to grasp it and change the world.