Big Data’s Influence in Medicine and Pharmaceuticals

In today’s world, huge volumes of information are available on medication. This medication data can be utilized to construct improved and efficient well-being profiles of individual patients. This organized patient profile can be used to provide them better treatment and appropriate medicines. Here I will discuss how the medication data (which is big data) is used in medicine and the pharmaceutical industry to provide better treatment. (Read more in Can Big Data Save Health Care?)

Current Data Generation

The amount of big data generated every day is growing at an exponential rate. This means that new kinds of data are being generated for every field of human knowledge, including that of the pharmaceutical and medical fields. However, many organizations are still struggling to handle all the data and convert it into usable information. So, it is important that more and more companies know about the benefits of big data in the healthcare, medical and pharmaceutical fields in order to carry out better big data partnerships and provide more benefits to the masses.

Medication Data as Big Data

The advent of new tools for big data generation has resulted in an explosion of new data being generated every second. This is especially due to large organizations creating huge amounts of research data. This data comes in different types and formats.

In the field of healthcare and pharmaceuticals, this data is created by different kinds of sources. If the data is correctly used, then a large amount of revenue can be earned and newer medications can be developed too. The McKinsey Global Institute has conducted a study which estimates that if big data strategies are properly used, they can account for revenue up to $100 billion every year. The data provided by the pharmaceutical industries, i.e. the medical data, is becoming larger day by day, and it must be properly utilized for better decision making and healthcare.

Sources of Medication Data

In the field of medicine and pharmaceuticals, big data comes from many different types of sources. The primary sources of this kind of data include the process of R&D, caretakers of the patients, the patients themselves and from the retailers of the drug. Some other sources include data from disease outbreaks occurring, data from previous clinical trials, census records, treatment and therapy patterns, disease patterns, hospital and clinical records, etc.

With such diverse amounts of data coming in from different sources, it is necessary for the data collection and processing system to be accurate and quick enough to make sense of such a humongous amount of data. This data must be processed in as close to real time as possible, as this will ensure quick responses and faster decisions for the pharmaceutical firms.

However, a major hurdle is that about 80% of the total big data collected from these sources arrives in the form of unstructured data. These data sources include pathology reports, clinical notes, consultant notes, physician notes and hospital data. So, these also have to be processed as quickly as possible.

Insights From Medication Data

As big data contains the information collected from the masses, it can be properly processed for obtaining many medicinal insights. These insights include information about an outbreak of a disease, information about how it is currently being handled and information on certain medical problems faced by the masses, for example, obesity.

Forming a Better Patient Profile

The large amounts of data generated by the different medical sources, if used properly, can really redefine the creation of patient profiles. A specific branch of big data known as predictive analysis can help in this. Predictive analytics combines machine learning technology with the medical data about a patient, to make an accurate patient profile which can be used to predict the causes of the symptoms of the patient and cure them easily based on the historic data.

For this, the big data stores are thoroughly scanned for medical information about either a specific patient or the whole populace. Some extra data is collected from the external databases for enhancing the quality of the medical patient profile. With the use of predictive analysis, all this data collected can be combined to form a single database which will contain all the data that is needed for creating a highly accurate patient profile. Now, this profile can be used to help patients to deal with their ailments more easily. Thus, the patient profile creation system is becoming more and more accurate with the help of big data’s predictive analysis and machine learning techniques. (To learn more, read How Predictive Analytics Can Improve Medical Care.)

Big Data’s Influences in Medicine

Big data is having a huge impact in the field of medicine and the pharmaceutical industry. It is helping the medicinal industry in tackling plenty of real-world problems. These problems include analysis of the patterns of diseases in different countries, which require certain emergency medications to be prepared in advance for the disease’s prevention.

Big data is also influencing the field of drug discovery. The proper analysis of big data from resources like medical journals and clinical records helps a pharmaceutical company to target the specific ailments or find areas for use of their newly created drugs. This work is done in a very cost-effective manner too. It also helps in the proper management of the clinical trials performed, so that the side effects of new drugs are reported.

Some Practical Use Cases

Many medical and pharmaceutical firms are currently using big data for various uses. One such company is Explorys, which has a large healthcare database based on its collection and processing of big data. This database helps other bio-scientists to learn more about an ailment and determine the best medicine for countering its effects.

Another firm known as Propeller Health is using big data for proper asthma management. This firm uses data from various sources like sensors in asthma inhalers and smartphone apps to detect the patient’s condition and prevent an asthma attack based on the data pattern.

Conclusion

It is expected that in the near future, big data will be combined with the pharmaceutical industry to an even greater extent than it is today. With better big data mining and processing techniques, it will be used even more extensively and the pharmaceutical industry will be able to offer better solutions to the masses.