applications and examples of big data in health industry

Big data is transforming the world. Well, it is true. The role of big data in the healthcare industry is the perfect example of this evolution. The healthcare industry is experiencing complete disruption. Changes in legislative regulations and healthcare delivery models are changing the industry’s dynamics. Equally important is the deeply growing thirst for information. The heavy influx of information is dramatically changing the healthcare industry in many ways.

Healthcare management software solutions are not just about improving bottom-line results or increasing profits, but about improving quality of life, predicting epidemics, and improving quality of life. Data is driving the quest to understand patients in a better manner, picking up symptoms and signals as early as possible to treat serious illnesses. In the past decade, the healthcare industry has witnessed huge advances in the amount of data collected, as well as strategically using big data technology to personalize patient care.

Decision-makers across the health care organizations are looking for relevant, actionable information from a wide variety of sources to make timely strategic operational choices and provide life-saving healthcare treatment.

However, limited IT resources, trained staff, legacy systems, regulations, lengthy approval processes, and conflicting priorities can be barriers to meeting these needs.

From drug testing and patient care to genetic testing, there are several opportunities to leverage big data analytics solutions in improving outcomes. It is now possible for healthcare and life science organizations to apply fast and actionable analytics.

Here are the Benefits of Big Data Analytics in the Healthcare industry

Drug Discovery and Development

Introducing new drugs to the market is a daunting task that is often expensive requiring upfront investments running into millions. Several different techniques such as machine learning and visual analytics tools help streamline simulations across data sets.

Enhancing Patient Engagement

Many consumers use smart devices that record each step and vitals such as heart rates, as well as sleeping quality permanently. This vital information when combined with other trackable data can help identify potential health risks. Patients can directly monitor their health and get incentives from health insurance that can lead them to lead a healthy lifestyle.

Electronic Health Records (EHRs)

Every patient has a personal digital record including details such as demographics, medical history, allergies, and laboratory test results. Records are shared through secure information systems and are available for providers from both the public and private sectors. Every record consists of a file that allows doctors to implement changes over time with no paperwork and no danger of data replication.

EHRs can also generate warning signals and reminders for a patient when it is time to get a new lab test or check routine prescriptions to find if a patient has been following doctors’ orders.

Anticipate and Treat Illnesses

Access and analyzing voluminous data sets can improve our ability to anticipate and treat illnesses. This data can help track individuals who are at considerable risk for critical health problems. The ability to effectively use big data to identify waste in the healthcare system can significantly reduce the cost associated with healthcare across the board.

Fitness Tracking Devices

Keeping patients healthy and avoiding illness and disease comes across as the top priority list. Consumer health and fitness products like the Fitbit activity tracker and the Apple Watch keep tabs on the physical activity levels of individuals and can report on specific health-related patterns. The data collected is delivered to cloud servers, providing information to physicians who use this vital information for overall health and wellness programs.

Apple’s HealthKit, CareKit, and ResearchKit leverage the technology embedded in Apple’s mobile devices to help patients manage their conditions and enable researchers to collect data from hundreds of millions of users worldwide.

Eliminating Prescription Errors

Adverse drug events (ADEs) account for more than 3.5 million physician office visits and 1 million emergency department visits each year. It is believed that preventable medication errors impact more than 7 million patients and cost almost $21 billion annually across all care settings.

Healthcare organizations are using decision support tools empowered by big data analytics that help identify fatal prescription errors even before they occur. Big data analytics solutions allow, preventing a wide range of prescription errors relating to wrong drug, wrong patient, drug interactions, dosages, and allergies with higher precision than the existing systems.

Innovation in Healthcare Industry

Wearables and IoT

Wearables are now a rage everywhere, allowing the collection and measurement of vital information from sensors placed on human bodies. This information is relevant to maintaining the health of a person. A wearable device or sensor will provide a real-time feed of health records, which allows medical staff to monitor and later consult with the patient, either face-to-face or remotely.

Precision medicine

Precision medicine aims to understand a person’s genetics and lifestyle choices/habits to determine the best approach to either avoid or treat a disease. The long-term goals of the Precision Medicine Initiative focus on revamping healthcare on a large canvas.

Machine learning

Component of artificial intelligence and one that depends on big data is already helping physicians improve patient care. Machine learning together with healthcare big data analytics, multiply caregivers’ ability to enhance patient care.

Challenges facing the healthcare industry

There are several roadblocks to the rapid adoption and expansion of health analytics use. Hospitals have voluminous electronic health records (EHRs). However, several hospitals may not contain all the necessary clinical data. Organizations need to make data accessible to decision-makers and link data to actionable change.

Data Aggregation: Patient and finance (payment) data are often spread across different stakeholders such as payors, hospitals, and government agencies. Getting the information from all data producers to collaborate in the future as new data is produced requires planning. Organizations need to agree on the types and formats of big data and the accuracy of data. This requires data cleansing and data governance.

Policy and Process: Once data is validated and collected, various process- and policy-related issues such as HIPAA regulations, access control, authentication, security, and other rules considerably complicate the task. This multifaceted issue has been solved by cloud service providers, offering cloud services that comply with HIPAA and Protected Health Information (PHI).

Security: Data security is one of the most critical issues in front of healthcare organizations, given data breaches, hacking attempts, malware attacks, and more. It is important to maintain the integrity of data collected and stored at all times.

Conclusion

To succeed in a competitive environment, healthcare organizations must change the way they operate to build a world-class user experience. This effectively means building a big data infrastructure that allows sharing of information to maximize its value. Several healthcare organizations are yet in the phase of developing technology, processes as well as expertise to leverage the benefit of analytics. Applications and examples of big data in the health industry will empower users with actionable insights, as well as timely access to information to make decision-makers faster.


Looking to implement big data solutions for better patient care or streamlined operations? Get in touch with our team for details.