Summary
Data engineering in the context of Public Health Management (PHM) is the process of building and managing systems and infrastructure to collect, store, process, and analyze diverse public health data. This data comes from various sources, including:
- Electronic Health Records (EHRs): Patient medical information, diagnosis, treatments, etc.
- Public health surveillance data: Information on disease outbreaks, immunizations, vital records, etc.
- Administrative claims data: Information from insurance companies related to patient care and costs.
- Wearable devices and sensors: Real-time data on patient health metrics, especially for chronic disease management.
- Survey data: Information collected through health surveys and research studies.
In summary, data engineering in public health management is crucial for ensuring that public health initiatives are effective and efficient, leading to improved population health outcomes.
OnAir Post: Public Health
About
DE Use Cases in Public Health
Use Cases of Data Engineering in Healthcare
The use cases of data engineering in healthcare are endless as advancements in technology continue to be made. With the right patient records, healthcare has the ability to be personalized for patients, and the entire operational chain can be optimized.
Data engineering can serve as the foundation for every data need within an organization. To ensure long-term viability and sustainability, an efficient data engineering strategy is needed.
Predictive Analysis for Disease Prevention and Precautionary Steps
Patient Care and Record Management with Personalized Treatment
Operational Efficiency and Resource Optimization
Vaccine Research and Clinical Trial Management
Public Health Management and Outbreak Prevention
Source: phData
Challenges
The interest is ever-increasing in data analytics projects for hospitals and doctors. Here are some challenges you might encounter during the implementation process:
- Data Privacy and Security Concerns
Patient privacy standards are unique in every country. The restrictive nature of these regulations ensures that sensitive information doesn’t fall into the wrong hands. In addition to patient consent, there also needs to be clarity on the ethics of how to store the data in third-party servers. Snowflake is a market leader and sets the standard for data security. - Quality of Data in Research Activity
Sources are crucial for analyzing data and developing appropriate conclusions. Research can be halted due to inadequate controls during data sourcing. As it becomes increasingly challenging to source information together, cloud warehouses like Snowflake work to maintain consistency and concurrency throughout the data engineering and research processes. - Scalability Requirements and Infrastructure
Server infrastructure can become expensive when hosting data for thousands of patients. Many popular data warehousing tools require users to have a high budget just to get started. This can produce a significant barrier to entry into the market.As a result, a cloud ecosystem has risen that allows users to pay as they use it. This option helps to level the playing field and gives organizations the opportunity to scale up as they grow and increase revenue. - Talent and Skill Gaps
The technology supporting data engineering is relatively new and constantly evolving. The human resource capable of implementing such projects can become quite costly. Additionally, finding and maintaining a team that remains up-to-date as technology advances can be challenging. Many technology providers offer certification programs to assist in sharpening the skills of an organization’s internal data engineering team. One tip when exploring a tool is to always check its partner page and certifications page. The top 5 should be your first choice to go for. - Data Exchange
Sharing sensitive data is challenging for healthcare institutions. The maximum security standards must be met while ensuring the transfer of even a single patient file among hospitals. Thankfully, Snowflake helps tackle these challenges head-on and has the best Data exchange with security protocols in place.
Source: phData
Opportunities
When it comes to data engineering, the possibilities of impact for the healthcare sector are endless. We will cover the most revolutionizing concepts below.
- Leveraging Advanced Analytics Techniques in Disease Diagnosis
Disease diagnosis is changing for the better in 2023. Data engineering helps identify trends across multiple patients. There is scope for the growth of these disease detection systems to one day become significantly better at accurately diagnosing patients quicker. These predictions, of course, rely on how much accurate historical data is available to analyze. - Utilizing Wearable Devices for Self-Tracking by Patients
The history of wearable devices in healthcare dates back to the invention of eyeglasses. Now, with the development of smartwatches, users can opt for real-time collection of health markers like heart rate, BMI, and more.Taking advantage of the available opportunities for self-reporting will enable patients to provide additional relevant health data that can be further utilized to advance healthcare and the analysis of patient information. - Collaboration with Other Institutions for Quicker Research Outcomes by Data Sharing
The practice of data sharing is commonplace among researchers. However, nobody can share patient data without consent. Thus, creating an automated process for granting consent in data sharing is crucial. Rapid advancements in the research outcomes for various medications and vaccines exist but can be amplified through strong data-sharing practices. - Future of Data Engineering in Healthcare
Data engineering in healthcare is making considerable strides to transform healthcare. There is potential to revolutionize the industry by 2030. Now is the time for healthcare organizations to lay the foundation necessary for data engineering. - Real-Time Data Processing and Predictive Insights for Patients
Healthcare professionals need to make quick and informed decisions to help save lives. Through big data models, hospitals can identify trends that guide smart decision-making. Regular monitoring of vitals and necessary health metrics will help them chart the best course for patients.Predictive insights ensure a quick diagnosis and timely intervention. Real-time data analysis could also detect irregular heartbeats that could save lives. - How AI and ML Can Leverage the Data Warehouse
Early detection using artificial intelligence and machine learning can assist in curing diseases quicker. The data gathered across multiple areas, such as lab results, scans, X-rays, family records, etc., can be interpreted much quicker using AI and ML. This quick analysis makes it simple for doctors to provide a personalized treatment plan for each patient. - Data Ecosystems for Easy Patient Information Transfer
The existence of data banks and data ecosystems is new in 2023. Utilizing granular data sets available in most modern hospitals’ pre-existing records management tools can promote advancements for learning models in data engineering systems.For instance, Pfizer and Johnson & Johnson shared patient information during the pandemic as they worked towards a common goal of developing a COVID-19 vaccine. Snowflake also shares in this common goal to unite all data and eliminate technical and institutional data silos.
Source: phData
