Information is the lifeblood of any modern business. Using data to inform decisions helps businesses adjust to a volatile environment. One of the most important aspects of business analytics is the capacity to generate reports based on the data collected.

The quantity of data grows at an exponential rate, and keeping up with this growth requires that data management systems be scalable at all times. Without prior experience, it will be difficult to construct a large-scale big-data pipeline and integrate it with preexisting analytics ecosystems.

A data pipeline is a path taken by data from its origin to its final storage location, which is typically a data warehouse or data lake. Since most firms will have their data spread across many platforms, the origin and structure of that data may vary widely. Therefore, the success of any data pipeline depends on its ability to reliably and efficiently collect data from its sources and transport it to its final destination.

As new technologies become more widely used, the healthcare sector is beginning to undergo a digital transformation. In order to better serve their patients, healthcare providers are shifting toward a more interdependent and cooperative healthcare environment.

Any data-driven firm realizes the significance of high-quality, scalable data pipelines in data science. Because of the sheer volume and variety of healthcare data, it is imperative that healthcare providers employ data-driven methods for streamlining clinical decision-making.

Healthcare data pipelines are the interconnects that facilitate the transmission and transformation of healthcare data between various data infrastructures. A high-quality data pipeline helps healthcare organizations access fresh and reliable medical data for accurate diagnosis and prognoses.

Let’s first go through the challenges and hardships of constructing safe healthcare data pipelines before diving into the different processes involved.

Existing Challenges in Building Healthcare Data Pipelines

The healthcare business processes massive amounts of data from numerous sources. Healthcare data can provide a number of challenges when designing data pipelines, including the following:

1. Clutter Data

A company’s data and information will come from a wide range of places and will alter and develop over time. This can make it difficult to process the data and transfer it to its final destination, which could be a data warehouse or a data lake. It’s important to think about how these will affect your data pipelines and whether or not they were designed to manage this kind of data. Finding and eliminating data noise through identification and removal of data clutter frees up attention for decision-makers.

2. Different Methods of Data Collection

To aggregate healthcare data, information must be gathered from numerous sources and transformed. Medical records, administrative data, physician files, and patient/physician customer surveys are all examples of healthcare records.

Raw data generally includes errors, inconsistencies, and other difficulties. Ideally, data-collecting procedures are meant to eliminate or reduce such difficulties. But that is rarely 100% accurate. As a result, obtained data normally has to be placed through data profiling to discover errors and data cleansing to remedy them.

Most big data environments have a mix of structured, unstructured, and semistructured data in massive quantities. That makes the first data collection and processing stages more challenging.

Data serialization is a necessary step before any structured data, such as text, numbers, or graphics, can be sent into the pipeline. In order to share or save structured data in a form that facilitates the recovery of the original structure, the data must be serialized.

In order to maintain uniformity across the many modules in the data processing pipeline, data serialization is necessary. The most often used data serialization types include XML, CSV, YAML, and JSON.

3. Data Size

There are so many levels of medical records that are hard and time-consuming to analyze. Before these files can be combined, they must undergo a series of modifications to standardize the format of the raw data. As a result of these insights, hospitals may considerably enhance their disease prevention and prediction systems.

Good data pipelines should be designed with scalability in mind. If you have scalable data pipelines, they should be able to handle massive amounts of data coming in from various sources if you have those pipelines in place. Pipelines for processing data will be designed to scale up or down as needed to accommodate fluctuations in data volume.

4. Define Important Data

This is a core problem that arises during the initial gathering of raw data and again when users compile data for analytics programs. Collecting data that isn’t required adds time, expense, and complexity to the operation. But taking out useful data might limit a data set’s economic value and influence analytics outcomes.

Proper data organization and collection are essential for making informed decisions. Organizations must connect their products to as many data repositories and formats as possible, particularly unstructured data. However, it’s difficult to decide what to use and aggregate, transform, and absorb that data. The use of data curation methods helps make it easier to identify and access data.

5. Remove Duplicate Data

Duplicate information can arise from a variety of causes, including data aggregation and typographical mistakes. When there are multiple records for the same patient, it can be difficult to draw accurate conclusions about that patient’s habits. Likewise, if there are multiple records with the same action taken against them, it will be difficult for the customer support team to determine what exactly is causing the patient’s problem. 

It will have a detrimental effect on all aspects of customer service and marketing communications. This is why it is important for firms to consider eliminating duplicates from their databases.

6. Verify Data Credibility

Keeping data trustworthy and intact throughout its lifecycle is a major concern when designing data pipelines. In order to make sound medical judgments, clinicians must have access to reliable information. Since even a seemingly insignificant change to medical data can have far-reaching effects on a patient’s health, ensuring the reliability of healthcare data is crucial.

In the field of medicine, patient data is unparalleled in significance. It includes a variety of aspects of a patient’s life, including their medical records, test results, and personal details. Incorrect or altered data might lead to the improper treatment of patients, whereas accurate data can assist produce trustworthy conclusions.

Verifying and correcting patient information in real-time can have the following effects:

• Enhanced medical care
• Predictive and diagnostic accuracy
• Patient care that is tailored to each individual
• Accurate and clear two-way exchanges between medical staff and their patients
• Expedite the results

Ensuring Data Security And Compliance In Healthcare

Technologies like Cloud and web services are being adopted by modern healthcare systems in order to improve patient care and data management. However, while improvements to these systems are improving accessibility, there is a growing risk that sensitive healthcare information will be breached.

The role of compliance in constructing safe healthcare data pipelines is similar to that of data security. Protecting patient confidentiality and personal information is a top priority for many healthcare standards. Some of the most well-known are:

• HIPAA, or the Health Insurance Portability and Accountability Act, is a law that sets guidelines for how medical institutions can use patients’ personal information.

• GDPR, or the General Data Protection Regulation, is the European Union’s data protection regulation.

• FHIR, or the Fast Healthcare Interoperability Resources, is a set of rules for the safe transmission of medical records over the internet.

Adherence to these guidelines will help the healthcare sector reinforce and maintain a level of data security. When it comes to sensitive medical information, data compliance is essential for both storage and transmission.

Picking Up The Right Technology

Because we are becoming increasingly conscious of the importance of data, we have begun producing and collecting more data on virtually everything by making technological advancements in this area.

Thanks to technological progress, we now generate so much data that it is impossible to manage effectively using the tools at our disposal. Because of this, Big Data has been coined to characterize massive amounts of data that are difficult to process. Therefore, we need innovative methods of data organization and information extraction if we want to address our current and future healthcare needs.

Developing a real-time biomedical and health monitoring system has spurred the creation and adoption of wellness monitoring devices and accompanying software that may produce alerts and exchange the patient’s health-related data with the corresponding healthcare professionals.

Cloud computing is a system like this, providing dependable services and using virtualized storage technologies. It provides robustness, scalability, independence, accessibility everywhere, dynamic resource discovery, and the flexibility to compose services from different components. Further, new methods and technologies need to be created to comprehend the data’s nature (structured, semi-structured, unstructured), complexity (dimensions, attributes), and volume in order to extract useful insights.

Build a Secure and Scalable Solution

Due to the prevalence of cyberattacks, hacks, phishing attempts, and ransomware, healthcare providers must take extra precautions to protect patient information. After detecting an array of weaknesses, a list of technical protections was devised for protected health information (PHI). These regulations, defined as HIPAA Security Rules, help guide enterprises with storing, transmission, authentication processes, and control over access, integrity, and auditing. 

A lot of headaches can be avoided by taking the most basic security precautions, such as keeping your anti-virus software up to date, setting up a firewall, encrypting critical data, and utilizing multi-factor authentication.

When designing scalable data pipelines, it’s crucial to have a firm grasp of the unique difficulties faced by the healthcare industry and its broader environment.

An estimated 10% increase in data visibility can result in over $65 million in net profitability for Fortune 1000 enterprises.

Deliver Insight Faster

With the advent of new digital technologies, the healthcare sector now stores and transmits vast quantities of patient information. Hidden within this information are numerous nuggets of wisdom that can significantly advance the quality of treatment provided to patients. Therefore, access to timely, appropriate healthcare information is essential for all parties involved in a healthcare setting and is crucial to manifesting superior medical data outcomes.

The algorithms used in machine learning allow for automatic scaling, making it possible to gain insights from the ever-increasing amounts of healthcare data. The use of data visualization tools that offer interactive dashboards for analyzing medical data is also on the rise.

The time it takes to deliver medical insights can be cut drastically by using tools that take advantage of these solutions to provide large-scale medical data analysis.

Conclusion

In today’s world of ever-increasing medical data, organizations can’t afford to be without a well-connected healthcare ecosystem.

To develop a truly collaborative healthcare environment, it is not sufficient to merely construct pipelines. There have to be strong safeguards in place to protect the flow of healthcare data.

Data Nectar has collaborated with industry leaders on a wide range of data pipeline development projects, delivering solutions that are both secure and scalable. Get in contact with us if you need assistance building a data pipeline to integrate diverse data sets, generate data feeds, and expose APIs to other parties so that you can generate actionable insights.