Data engineering in healthcare is taking a giant leap forward with rapid industrial development. Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords these days with developments of Chat-GPT, Bard, and Bing AI, among others. However, data collection and analysis have been commonplace in the healthcare sector for ages.
Data Engineering in day-to-day hospital administration can help with better decision-making and patient diagnosis/prognosis. Management of thousands of records can be tedious, especially in paper format. Electronic health records have been around for some time, but leveraging them for recurring treatments can prove effective for doctors and patients. In the long run, hospitals can save a lot more lives and operational expenses when applying a data engineering strategy.
In this blog, we will cover the advancements made in data engineering technology that aims to help navigate the challenges and opportunities of implementing a data engineering strategy in the healthcare industry.
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
Statistically, disease-causing pathogens follow similar life cycles even in varying patients. Capturing and maintaining data on a large population can help doctors chart the best course of action according to their previous diagnoses.
The use of deep learning and machine learning in healthcare is also increasing. For example, utilizing scientific research on how neural networks improve the predictability of certain diseases is becoming more common.
Researchers can use the TOP5 criterion to identify the five most likely health problems. The accuracy of this research is significant, and it depends on how well data is organized and stored. Once the most likely diseases are listed, doctors can chart a course for identification and treatment. This helps to prevent misdiagnosis and leads to a faster resolution and discharge of patients.
Additionally, access to the family medical history can significantly improve the chances of a quicker diagnosis. Thus, using data engineering is a must in 2023 for hospitals. A few hospitals in the United States are already using this technology. The well-revered John Hopkins Hospital is partnering with GE and Cleveland Clinic’s Microsoft alliance to help ensure a smooth implementation.
Patient Care and Record Management with Personalized Treatment
Personalized treatment plans are the future of medicine. Researchers suggest that by 2030 it will be the norm in healthcare worldwide. Patients can choose to provide healthcare businesses with access to their information. The adoption of Electronic Health Records, or EHRs, is widespread across the global healthcare system. Through the consent of patients, the information is easily processable using data engineering in healthcare. Some early adopters of personalized treatment using data engineering are NHS UK, Children’s Mercy Hospital in Kansas City, and Providence St. Joseph Hospital.
Operational Efficiency and Resource Optimization
Hospitals worldwide are shifting toward utilizing data to drive business decisions, reducing the risk of human error. If you have proper historical data about the operations of your hospital, you could use data engineering and analytics to predict and restock hospital inventory before shortages occur. Chinese hospitals are already using data engineering to manage their supply chains.
Data engineering can also be used to better understand trends and keep hospitals prepared for upcoming health-related scenarios. Assistance Publique-Hôpitaux de Paris (AP-HP) uses these data analytics models to predict how many patients will visit them each month as outpatients and for emergency reasons.
Vaccine Research and Clinical Trial Management
The pandemic taught healthcare workers and researchers the importance of big data analytics in vaccine research and clinical trials. The widespread sharing of information amongst multiple institutions allows us to achieve the desired results quicker. Data engineering in research helped to study vaccines better. Pfizer uses Snowflake Data Cloud to improve patient outcomes quicker and predictive analysis to ensure accurate diagnoses.
They used the power of Snowgrid to provide a single source of truth (SSOT) for their many teams, which were distributed all over the world to make the most efficient data-driven decisions. This ensured that everyone was up-to-date with the latest vaccine breakthrough and that all clinical trial data was easily accessible.
Public Health Management and Outbreak Prevention
Public health committees in various countries exist to provide guidelines to ensure the maximum health standards for its citizens. These committees are responsible for tracking the people diagnosed with different types of diseases. Data engineering models can help public health systems identify isolated cases before they explode into a health crisis.
As the saying goes, “An ounce of prevention is worth a pound of cure.” In Australia, the government’s healthcare branch uses Data Integration Partnership for Australia (DIPA) to identify adverse events. Norway is also making use of big data analytics to keep track of national health trends.
Challenges and Opportunities
So far, we’ve highlighted many healthcare institutions worldwide that are utilizing data engineering. However, the technology is still relatively new, making it open for further research and refinement but also susceptible to errors in implementation. Let’s identify the areas of opportunity and challenges for big data in healthcare.
Challenges in the Implementation of Data Engineering
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.
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.
What are the Data Engineering Opportunities for the Healthcare Sector?
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.
Looking at the below chart demonstrating the modern data architecture makes it easy to see that data lies at the heart of everything. Implementing Snowflake is a simple first step in helping organizations become future-ready.
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.
Data engineering in healthcare provides a plethora of opportunities. Transforming the healthcare industry through evidence-based decision-making is now a possibility. The days of reducing the chances of human error in the healthcare process are closer. The genome sequence and storage capabilities advancements allow us to save more lives each year. Identify the big data vision for your healthcare business and make it a reality today.
We at phData, have worked on multiple healthcare projects aimed at helping our clients’ businesses grow by implementing the best data solutions.
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The most useful data is clinical data for most healthcare research purposes. It can be collected during the patient's treatment or as a part of a clinical trial program. Clinical data falls into six major types:
Electronic health records
Patient / Disease registries
Clinical trials data
Data engineering can help develop evidence-based medicines, optimize supply chain processes, detect fraud, provide real-time data on demand, and more.