Healthcare Data Management: Challenges and Benefits
With the digital revolution, the amount of data significantly increased in many industries, and healthcare is no exception. From EMR to EHR and wearables, the data pool became too vast and complex for us to analyze it using traditional tools. That’s where AI and similar technologies come into play. Healthcare experts agree that proper storing, management and data analysis will drive the future of healthcare.
What Is Data Management
Data management refers to storing, managing, and utilizing data. In healthcare, it consists of storing and managing health-related data and using it to improve healthcare delivery and clinical outcomes.
There are many types of data within the healthcare industry:
Demographic information (name, gender, ethnicity, age)
Administrative information (billing, scheduling, insurance)
Medical history and treatment (family history and visits)
It’s no surprise that we needed to come up with new methods of analyzing this bundle of big data in healthcare. Proper data management for healthcare is necessary, considering that, today, 30% of the world’s data volume comes from healthcare. By 2025, this number will reach 36%, making it an industry with an annual growth rate faster than manufacturing, financial services, and media and entertainment. With such yearly data quantity growth, it became apparent that we can utilize this information for the betterment of patients and providers alike.
Why Is Health Data Management Important
Proper data management gives care providers valuable insights into how they can improve their healthcare delivery. Patients are involved in the process through various telemedicine solutions, such as mobile apps and software that turns them into activated patients. They can generate data using technologies like wearable medical devices, and medical professionals can, in turn, rely on remote patient monitoring.
By giving providers a 360-degree view of patients, data management in healthcare serves to tackle the five “Vs” of big data. These are:
Volume: the size and the amount of data
Value: the insights and pattern recognition that can arise from data analytics
Variety: the diversity and different types of data
Velocity: the speed at which the data is accumulated
Veracity: the accuracy of data
As we mentioned, medical data management is related to utilizing this information using tools at our disposal. In healthcare, several tools exist, such as clinical decision support systems or machine learning algorithms. These tools are already helping doctors with many areas of healthcare, from improved diagnosis to reducing medical errors. Diagnosis errors, for example, still account for 17% of preventable medical errors.
With proper technologies and systems in place, medical software can alert health practitioners about potential misdiagnoses and errors in treatment. Patient data management plays a pivotal role here: by having complete information about the patient’s health history, family history, and previous medications, these technologies can save lives.
Healthcare Data Quality
It is crucial to remember the quantity of data and its quality. The reason for this is the fact that most health information falls under the category of protected health information (PHI). This data is extremely sensitive: not only does it need to be secure from internal mistakes, but we also must ensure that it is safe from outside threats.
Proper health data management solutions are essential, but they go along with standardization and mandated health data protection. From HIPAA to HITECH Act in 2009, these mandates gave us directions on storing and appropriately protecting data in healthcare. What is the purpose of HITECH? To incentivize the adoption of EHR systems and improve the quality of care, engage patients, increase coordination of care, improve the population's health status, and improve privacy and security.
To manage data in healthcare is to protect it. For twelve consecutive years, the healthcare industry has been the number one target of cybercriminals. The cost of a single stolen record in healthcare can go up to $430. The suggestion is clear: increasing the budget for medical organizations’ protection against online threats is necessary.
If a medical organization correctly implements all of the above steps, it will reap the benefits of proper data management.
Benefits of Healthcare Data Management
As we mentioned above, there are several benefits linked with proper data management. These are not limited only to care providers or patients. Instead, every party stands to gain something, such as:
Complete patient health overview: knowing all the patient information, including health history, family history, administrative and insurance data, medication history, etc. All of these allow for better treatment and prediction, improving clinical outcomes.
Improved patient engagement: proper data management in healthcare means that patients become more active in their health delivery. The increased engagement has been linked to better clinical outcomes.
Make educated, well-informed business decisions: care providers can utilize this data to make marketing or operational decisions, leveraging it to increase their patients based on patient needs.
Reduced cost: having these patient insights means that providers can operate knowing what they need to focus on, reducing times spent on unnecessary tasks and focusing on what truly matters.
Expands access to healthcare: data management for healthcare is vital as improved processes reduce cost, and lowered prices mean that more people would be able to access expensive healthcare services.
Challenges of Health Data Management
Implementation of data management technology and processes has its challenges. Here is a non-exhaustive list of some hurdles that providers would need to overcome to implement and utilize this technology fully:
Fragmented data: given the number of data sources, it is necessary to standardize and make your institution interoperable. It means making data from EHRs, administration, insurance companies, etc., easily stored and shareable.
Data volatility: The proper data management system considers the constantly changing data landscape. That means making regular updates and ensuring that there is no duplicate data.
Privacy and security: Given this data's sensitive nature, it is necessary to take proper precautions to secure it. That means completing regular updates, training staff, and implementing internal security measures.
Data storage: the overwhelming amount of health data needs to be stored somewhere. Instead of keeping data on the premises, a simple solution would be to utilize cloud services.
Our Expertise
Vicert has decades of experience within the health industry. From interoperability challenges to cloud solutions, we can answer your needs and develop a perfect healthcare data management solution for your needs. Book a call with us to learn more!
