Understanding the Intersection of Healthcare and Data Analytics
As technology continues to advance, many organizations are gathering and storing valuable raw data but not doing anything with it. With data analytics, organizations can take this raw data and turn it into valuable business insights. Let’s take a look at how the healthcare industry can improve business operations and patient care with data analytics.
Defining Healthcare Data Analytics
Data analytics is the process of transforming raw data and analyzing it to identify and predict trends to support data-driven decision-making. Healthcare organizations can use data analytics to identify health outcomes, analyze patient data, support decisions with clinical data, and improve health business management.
All participants in the health system can benefit from data analytics. Organizations can use advanced analytics to determine trends in healthcare facilities and analyze financial data to identify cost-cutting opportunities. Healthcare providers can analyze patient data to predict health outcomes based on demographic data and medical history. Lastly, patients can benefit from data analytics and improve patient outcomes based on the information the healthcare provider gathered.
The Role of Data Analytics in the Healthcare Sector
In the healthcare industry, data analytics is essential to all aspects of an organization, from health business management to improving the care patients receive. Data analytics in healthcare can:
- Transform how organizations evaluate potential health professionals
- Use predictive analytics to predict medical conditions
- Use machine learning technology in medical imaging to detect anomalies
This technology can transform how medical organizations manage their daily operations, and the care patients receive. By reducing operational costs through process automation and providing high-quality care, organizations can reap the benefits of the advances in healthcare analytics.
Key Advances in Healthcare Data Analytics
Healthcare data analytics are constantly evolving as technology continues to advance. Let’s take a look at some of the key advances in data analytics techniques, from using predictive analytics to improve patient care to applying blockchain technology in healthcare data.
Predictive Analytics and its Role in Healthcare
Predictive analytics is the process of analyzing historical and current data to predict trends using data mining practices and machine learning. In the healthcare industry, this includes analyzing clinical data to offer clinical decision support, predict health outcomes in patients, and effectively manage the spread of infection and disease.
The data used for predictive analytics in healthcare will include data related to the relevant health conditions, which can be gathered from electronic health records, medical surveys, and public health registries. This data can be used by healthcare companies to:
- Support effective disease control
- Improve clinical decision-making
- Automate healthcare organization’s administrative tasks
- Offer more accurate diagnoses at a faster rate
- Accurately calculate healthcare costs and insurance rates
Advancements in Genomics and Personalized Medicine
Genomics, often referred to as personalized medicine, develops a personal health care plan based on the information gathered from a patient’s genome, a complete set of DNA. By approaching health plans based on the patient’s DNA, health professionals can predict the risk of developing a disease before it happens, reveal the potential cause for disease and health conditions, and determine whether prescribed medications will work.
By using advanced data analytics techniques, healthcare professionals can offer patients a personalized care plan, improving patient outcomes and reducing the amount of time healthcare providers spend trying to identify the diagnosis. This allows healthcare professionals to speed up healthcare delivery and provide medical services to more patients.
Application of Blockchain Technology in Healthcare Data
Blockchain technology is an advanced database that allows information sharing with a network. This is commonly applied in the healthcare industry to facilitate the secure transfer of electronic health record data and strengthen the security of medical data.
As more healthcare organizations store health data and medical records on cloud databases, data sharing has become increasingly important to patient care. With blockchain technology, entities in the healthcare system can share and access vital patient information while ensuring strong data security.
Challenges and Opportunities in Healthcare Data Analytics
While using data analytics in healthcare can improve healthcare processes for organizations, providers, and patients, it’s still an evolving field. Knowing this, let’s take a look at some of the challenges and growth opportunities with advancing data analytics technologies.
Understanding the Limitations and Challenges
While there are numerous limitations and challenges to data analytics techniques, we’ll focus on three of the common challenges healthcare systems experience.
- Data structure: Due to natural language processing and artificial intelligence, healthcare data is typically unstructured. Without a structured, standardized format, data sharing can become a challenge due to the lack of interoperability. To effectively use data analytics to improve patient care, healthcare professionals first have to be able to get the data in a clear format.
- Data concerns: In the healthcare industry, data security is a top priority due to laws and regulations like HIPAA. By making historical data available to improve data sets and predictive models, the data is at risk for potential data breaches and cyber-attacks. Verifying the security and privacy of all the data will be an ongoing challenge for healthcare organizations utilizing data analytics.
- Data Biases: A common critique and complication with artificial intelligence and machine learning are the training data sets used. Depending on the data sets the machine learning models learned from, there could be biases in the data based on patient demographics, such as sex, race, age, and geographical location. Health organizations must keep this in mind when utilizing data analytics and performing independent medical research to determine the validity of the results.
Exploring the Opportunities and Future Scope
Big data includes large data sets produced by internet users and can only be used by performing advanced analytics with it. In the healthcare industry, big data analytics can improve the current data analytics processes by using large, diverse data sets. With a larger amount of high-quality data, health systems can improve diagnoses, operational processes, and patient care.
Effective Approaches for Implementing Healthcare Data Analytics
Most organizations in the medical industry are already gathering and storing the necessary data to perform advanced analytics. Let’s review the process for implementing data analytics and the best practices to set your organization up for success.
Best Practices for Implementing Data Analytics
When working with a software development company to build a system to perform data analytics, there are plenty of best practices to keep in mind. We’ll take a look at a few of the key practices when implementing data analytics.
- Understand Your Data: Before diving too deep into data analysis, an organization should clearly understand and define the data that needs to be collected and analyzed. This will give the software development team a better understanding of the data required by healthcare professionals, ensuring the analytics perform as intended.
- Standardize Data Structure: As mentioned earlier, one common challenge to data analytics is a lack of data structure. By using a standardized format that is widely used in the healthcare sector, users will have fewer challenges when it comes to systems integration. A common format for healthcare data exchange is Fast Healthcare Interoperability Resource (FHIR).
- Utilize User-Role Access Levels: One of the intentions behind using data analytics in healthcare is to improve diagnosis accuracy and patient care. However, this can only be achieved if the proper users have access to the necessary information in real-time. By utilizing user-role access levels, organizations can give users access to specific data, ensuring the analytics are being effectively used.
Ethical Considerations and Data Privacy
When it comes to medical data, ensuring data privacy and compliance with data regulations is one of the top priorities for healthcare organizations. This includes anonymizing patient data, restricting access to data sets, and using data encryption to store and transmit data securely.
To ensure the data is being used ethically, organizations should create guidelines on how the data should be collected, analyzed, and reported. This includes objectively analyzing clinical data by removing the potential risk of discrimination and bias. By including objective and clear explanations for insights gained from the analysis, users can transparently make data-driven decisions.
How Geneca Can Help
If your healthcare organization is ready to transform your patient and organizational data into valuable and actionable insights, contact Geneca today! Our team of business analysts and software developers are experienced in the healthcare industry and ready to support your business today.