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
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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
Challenges
Public health data engineering faces several key challenges including ensuring data quality, managing diverse and complex datasets, addressing privacy and security concerns, and navigating ethical considerations. These challenges are crucial to address for effective public health surveillance, research, and interventions.
Addressing these challenges is crucial for realizing the full potential of data engineering in public health, enabling better surveillance, research, and ultimately, improved health outcomes.
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1. Data Quality and Integration
- Heterogeneous Data SourcesPublic health data comes from a wide variety of sources, including electronic health records, vital statistics, surveys, and social media, each with its own format, structure, and quality.
- Data StandardizationLack of clear standards for data collection, storage, and exchange hinders interoperability and efficient data sharing.
- Incomplete and Missing DataPublic health data is often incomplete, with missing values or inconsistent reporting, which can affect the accuracy of analyses.
- Data Cleaning and TransformationPreparing data for analysis requires significant effort in cleaning, transforming, and integrating data from various sources.
2. Privacy and Security
- Protecting Sensitive InformationPublic health data often contains sensitive information about individuals, requiring robust security measures to prevent unauthorized access and breaches.
- Anonymization and De-identificationBalancing the need for data analysis with privacy concerns often requires anonymization or de-identification techniques.
3. Computational and Analytical Complexity
- ScalabilityPublic health data can be massive, requiring scalable infrastructure and algorithms to handle the volume, velocity, and variety of data.
- Real-time DataMany public health applications require real-time or near real-time analysis of data streams, which poses significant computational challenges.
- Analytical ExpertiseAnalyzing complex public health data requires specialized skills in areas like epidemiology, biostatistics, and data science.
4. Ethical Considerations
- Bias in Data and AlgorithmsData used for public health analysis can reflect existing societal biases, leading to inaccurate or unfair outcomes if not addressed.
- Data Accessibility and EquityEnsuring equitable access to data and analytical tools is crucial for addressing health disparities and promoting health equity.
- Transparency and AccountabilityPublic health data analysis should be transparent and accountable, with clear explanations of how data is used and decisions are made.
Research
Research in public health leverages data engineering to extract, transform, and load (ETL) data from various sources, enabling the analysis needed to improve population health and address public health challenges. This field focuses on applying data science techniques to public health data, including disease surveillance, outbreak detection, and understanding health trends.
By combining data engineering with public health principles, researchers can gain valuable insights into health issues, develop effective interventions, and ultimately improve the health and well-being of populations.
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1. Data Engineering for Public Health Research
- Data Integration
Data engineering plays a crucial role in integrating diverse datasets from clinical settings, public health agencies, and other sources. This allows for a more holistic view of health determinants and outcomes.
- Data Quality and Management
Ensuring data accuracy and consistency is vital. Data engineering solutions help standardize and clean data, making it suitable for analysis.
- Real-time Monitoring
Data engineering facilitates real-time monitoring of health trends and potential outbreaks, enabling timely interventions.
- Enabling Data-Driven Decisions
By providing reliable and accessible data, engineering empowers public health officials and researchers to make informed decisions and allocate resources effectively.
2. Applications in Public Health
- Disease Outbreak Detection
Aggregating data from various sources (e.g., hospitals, labs, social media) to identify unusual patterns and potential outbreaks.
- Monitoring Health Trends
Tracking the prevalence of chronic diseases and other health indicators to inform prevention programs.
- Evaluating Interventions
Assessing the effectiveness of public health interventions and programs using data analysis.
- Precision Public Health
Utilizing big data to identify high-risk populations and tailor interventions for maximum impact.
3. Key Areas of Focus
Data engineering supports epidemiological studies by providing tools for analyzing disease patterns and risk factors.
Data science helps track and control the spread of infectious diseases through surveillance and analysis.
Data engineering supports the study of healthcare systems and the delivery of public health services.
Integrating data on environmental factors with health data to understand their impact on population health.
4. Importance of Reproducibility and Ethical Practices
- Public health data science emphasizes clear, reproducible research methods and ethical data handling to ensure transparency and build trust.
Projects
Data engineering in public health focuses on building efficient and robust systems for collecting, managing, and analyzing health data to inform public health initiatives and improve population health outcomes. Recent and future trends in this field involve leveraging advancements in technology and expanding the scope of data utilization.
In summary, the public health sector is undergoing a significant digital transformation, driven by data engineering and advancements in areas like AI, IoMT, and data standardization.
The focus is on leveraging these technologies to improve disease surveillance, personalize care, optimize resource allocation, enhance data-driven decision-making, and ultimately improve population health outcomes.
This transformation is an ongoing effort that requires continuous improvement, collaboration, and a focus on building a skilled workforce capable of harnessing the power of data for public health.
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Recent Trends & Projects
- Data Modernization Initiatives (DMI)
- The CDC’s DMI, launched in 2019 and funded with significant investment, aims to modernize public health data management across the U.S..
- This initiative focuses on improving data infrastructure and promoting interoperability between different public health data systems.
- One example is the rapid deployment of advanced disease surveillance systems and data analytics platforms during the COVID-19 pandemic.
- Increased Use of AI and Machine Learning
- AI and ML are being used to analyze large datasets to identify disease patterns, predict outbreaks, and inform targeted interventions.
- This includes predictive modeling for infectious disease spread and analysis of social media data for public health surveillance.
- AI-powered diagnostics are also becoming more prevalent, aiding in early disease detection and treatment planning.
- Focus on Interoperability & Standardization
- A significant challenge in public health data engineering is the lack of standardized data formats across different healthcare organizations.
- Efforts are being made to promote data standardization and interoperability, with the Fast Healthcare Interoperability Resources (FHIR) standard gaining wider adoption.
Future Directions & Projects
- Real-time Data Streaming & Internet of Medical Things (IoMT)
- The proliferation of IoMT devices like wearables and implants will generate vast amounts of real-time data.
- Data engineering needs to adapt to handle continuous streams of data efficiently to enable real-time patient monitoring and timely interventions.
- Blockchain for Data Security
- Blockchain technology is being explored for secure and transparent data sharing in public health informatics.
- This will enhance data security, integrity, and traceability, giving patients more control over their data.
- Integration with Genomics & Precision Medicine
- Public health informatics will increasingly integrate with genomics and precision medicine, enabling personalized public health interventions.
- Increased Emphasis on Data-Driven Decision Making & Data Literacy
- Public health professionals will rely more heavily on data and insights to inform policy development and decision-making.
- There’s a growing need to equip public health professionals with data science skills and data literacy to effectively utilize this information.
- Development of “Response-Ready” Systems
- The goal is to move from siloed data systems to connected, resilient, and adaptable systems that can effectively respond to emerging health threats.
- This includes strengthening early warning systems and improving the timeliness and completeness of data reporting to agencies like the CDC.
