The world is moving towards personalization. We are served personalized feeds on social media apps, personalized ads on google even personalized shopping items online. So it is no surprise that the medical sector has figured out a paradigm-altering method to personalize medicine with precision medicine. It is the onset of a shift in the healthcare industry. 

But as always, a new invention brings about a fresh set of challenges. With precision medicine, it is the same story. A revolutionizing technology in healthcare precision medicine is about administering drugs based on individual genes, environments, and lifestyles. The old system of prescribing one drug for all is not as effective. 

Precision medicine aims to identify which treatments work in different groups of people rather than one drug-fits-all method. This will enable healthcare practitioners to provide more targeted treatment to patients. 

Precision medicine has always been a part of healthcare. For instance, before any blood transfusion, the blood type is matched; before organ transplants, several genetic matches are checked to ensure the organ works efficiently in the receiver’s body. 

Although precision medicine is much more detailed and complex, it involves identifying the interactions between genes and drugs based on a lot of data from patients. 

The primary challenge is to turn precision medicine into a widely accepted practice. There are specific barriers to it, one of them being the handling and accessibility of patient data. 

Gathering the correct information about a patient’s health, past medications, physical and psychological issues is critical. Without the correct data, ordering tests and delivering results is impossible. This brings us to the first challenge in handling data for precision medicine treatments:

Data capturing 

If it’s for any other industry, the collection of transactions is enough to know about the health of a business. The simple calculations tell their sales, marketing, and other departments about the increase in revenue, profitability, how to change their sales strategy, etc. In healthcare, it’s a little more complicated than that. Especially for precision medicine, healthcare institutes need the data for different processes. From the ER to surgery and labs, the data set required will be different and require different assessments. 

While oncology is the most promising area where precision medicine can make the most significant impact, researchers are planning to apply it to other areas. The problem is that different medical areas operate separately; information relevant for oncology will differ from ophthalmology and orthopedics. 

Clearly, there is a need for data capture at various levels. And while the healthcare industry has been focusing on different systems, precision medicine will require a more centralized data collection center with all the records about a patient in one place. 

The commonly used Electronic Medical Record (EMR) does provide some ease but can’t be integrated into the workflow to run multiple tests. Its leverage is limited. 

Data types 

All data sets seem simple at first. For instance, to know the net profit, basic information about money spent and earned will do the trick. But to get an extra edge over the competition, a more detailed view of customer’s buying patterns, most sold products, demographics of sales, and others are required. 

With precision medicine, the need for segregation of patient health data is mandatory. It’s not just about prescribing drugs that work but identifying which ones might not.  

Data identification and segregation in different categories – genetic, environmental, psychological, family history, past use of drugs, and demographics – are required. 

Ambiguous data 

Not all hospitals and healthcare institutions have a standard system to document treatment procedures. Different providers have their style of recording symptoms and diagnoses. There are also other medical records like x-rays, MRIs, brain scans, surgery videos, etc. 

While physicians might disagree that structured data is essential for patient care, it’s not the case with precision medicine. A standard data collection system is necessary to treat