Big Data Application in Healthcare For Effective Patient Treatment

Within the first decade of the 21st century, the use of big data became very popular in many big industries. The methods for capturing big data have since evolved from traditional data lake systems to more integrated technologies that combine big data with all other systems within a company.¹

New data sources such as anonymous longitudinal patient data (APLD) and new joint venture datasets are challenging the old-school database and data lake way of thinking. Multi-billion row datasets are the new norm, and businesses are posing more challenging questions for business intelligence (BI) teams to solve. Combine this with leadership pushing for more insight-driven decisions and talent scarcity in the space, and you have a huge opportunity for disruption and new data science applications in healthcare.

Big data application in healthcare allows data scientists and researchers to establish new standards for effective patient treatment. IoT (internet of things) data within healthcare allows data scientists to gather more data about patients and medication than ever before. Data science in healthcare has become very useful for making treatments more patient-centric and administering better patient care.

In Pharma, the value in healthcare data analysis comes from discovering potential safety problems, new indications from real-world use, and delivering optimal treatment regimens tailored for specific patient populations.

How the Internet of Things (IoT) Data is Changing Healthcare for the Better

The Internet of Things (IoT) is making medicine and Pharma more efficient at capturing and analyzing data. The IoT uses sensors and similar technologies to collect data from patients in real-time. This is proving to be advantageous in healthcare as physicians can now track wireless sensors located in patients’ homes, access the data provided from the sensors through a secure cloud, and use this data to manage chronic diseases. According to McKinsey Global Institute, for congestive heart failure alone, IoT has the potential to reduce treatment costs of patients by 10-20 percent.²

The IoT poses several advantages, including the obvious fact that we can now do away with the risk of losing important information once stored in obscure paper records. Instead, healthcare practitioners can now access information in one place via a shareable cloud-based system, most notably, the Electronic Health Records (EHR) system.3

Another path through which IoT is changing healthcare for the better is precision medicine. Genomics has transformed the way patients are treated, especially patients with rare diseases and cancers. Today, big data can be interpreted and analyzed using systems such as the omics platforms and the Collaborative Cancer Cloud created by Intel and the Oregon Health and Science University for furthering cancer research.3

Additionally, doctors can now monitor patients through wearable devices and apps. An example is the Myo armband, an orthopedic device that patients can use to track their exercise progress after a fracture, and doctors can simultaneously use to measure movement angles.3 Another example is the Zio Patch which provides measurements for a patient’s heart rate and electrocardiogram (ECG).³

In Pharma, IoT is also changing how companies can learn about patient problems and new indications from real-world use through digitized medical products offered during drug development, clinical trials, and treatment of patients.3 Pharma products are connected to the web and mobile apps, and Pharma companies can track patient data by logging on to these systems. Examples of wearable devices offered by Pharma firms are the sensors offered to patients living with Parkinson’s disease and multiple sclerosis (MS).³

Big Data Application Can Set Healthcare and Pharma Organizations for Success

1. Big Data Encourages Personalized Treatment

The ability of healthcare professionals and Pharma firms to track patient data can stimulate their offering of patient-centric improvements to their services and products. The use of APLD, patient-level data used by healthcare organizations and pharmaceutical companies to track patient treatments over time, comes into play here. Healthcare and Pharma firms can obtain APLD from various sources, including electronic medical records, pharmacy records, physician surveys, hospitalized patient billings, medical claims data, and patient diaries. The advantages for the application of this data could be as simple as having the correct number of flu vaccinations on-hand during flu season based on past data, or something more complex like using surgical data to determine the most successful methods based on the qualities of a patient.

Strategic planning is one of the critical ingredients for success. In healthcare, strategic planning can lead to effective patient treatments. When patient treatments are more effective, it leads to a domino effect of decreased costs, which further leads to increased patient access to these effective treatments. Hospitals and private practices will be able to see more patients and accelerate growth without incurring additional costs. Four hospitals in Paris are already experimenting with using big data to predict daily and hourly admission rates for the future to help them with strategic planning.⁴

Success only happens when you are at the right place at the right time. If a patient cannot be helped at the exact time that help is needed, it renders the medication or product useless. How can healthcare organizations and Pharma firms harness this right place/right time philosophy? By using big data! A history of patient results, including the dosage, initial symptoms, side effects, age, gender, and overall physical health of the patient allow physicians to prescribe the right medication based on a variety of factors.

Patient data also allows pharmaceutical companies to discover positive, unintended side effects of drugs in trials. These drugs can then be repackaged and prescribed to patients who exhibit a variety of symptoms.

Conclusion

Big data can only be effective in patient treatment if its application is insight-driven. Gathering and tracking patient data should ultimately translate to administering better patient care through patient-centric treatments, strategic planning, and the right prescription at the right time.

Are you interested in using your big data to improve patient treatments? Learn more about RxDataScience’s solutions.

References

1. Davenport TH, Dyché J. Big data in big companies. Int Inst Anal. 2013;3.
2. Bauer H, Patel M, Veira J. The Internet of Things: Sizing up the opportunity | McKinsey & Company. https://www.mckinsey.com/industries/semiconductors/our-insights/the-internet-of-things-sizing-up-the-opportunity. Accessed December 20, 2017.
3. Dimitrov DV. Medical Internet of Things and Big Data in Healthcare. Healthc Inform Res. 2016;22(3):156-163. doi:10.4258/hir.2016.22.3.156.
4. Marr B. Big Data In Healthcare: Paris Hospitals Predict Admission Rates Using Machine Learning. Forbes. https://www.forbes.com/sites/bernardmarr/2016/12/13/big-data-in-healthcare-paris-hospitals-predict-admission-rates-using-machine-learning/. Accessed December 20, 2017.