Clinical data extraction solution

Extracting free Text, Structured Data Via Pre-Built APIs & Extraction Tools

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Clinical Data Extraction Solution

 

Extracting patient specifics from large datasets is notoriously difficult and modern healthcare businesses are turning to technology to speed up the process. Let’s take clinical trials as an example: how does a company identify suitable clinical trial candidates from thousands of electronic health records (EHRs)?

Without specific solutions, it’s a painstaking process. However, there is an easier way, when all pertinent clinical data (structured and unstructured) is easily searchable and extracted by specific criteria.

Then, they can identify those eligible for trials and shorten the entire product development process. Of course, clinical data extraction has more to offer than streamlining the clinical trial process.

Its primary aim is to improve care delivery, regardless of the data type involved.

While there is no nationwide access to all EHRs, healthcare enterprises can ensure their own data provides maximum value by making actionable insights easily extractable. In addition, public databases containing millions of medical events (where data has been anonymised to protect patient privacy) can provide valuable information that aids clinical diagnosis.

For example, by identifying specific risk factors by disease for your existing patients, you can then schedule timely check-ups for the early diagnosis of at-risk patients. This also maximises the profit potential of the business when treatment rooms are utilised more effectively.

Our Clients

Jean Hailes for Women's Health is a national not-for-profit organisation dedicated to improving women's health across Australia. We have over 100 team members across several clinics and in our head office. One of the biggest challenges that we faced was that our back-office support team were spending hours upon hours each week manually generating reports from our clinical and practice management applications in order for us to track the overall performance of our organisation. The reports were generated using standard excel spreadsheets which were simply too basic and often not detailed enough to provide a comprehensive overview of our organisation's performance. Generating such metrics was time-consuming, frustrating, and often caused system downtime due to the load on the different databases.

We engaged Byte IQ to develop and design a data lake solution whereby we can centralise our data sets and use the data to improve our workflows. Byte IQ integrated their BI platform Huddle with our data lake, enabling us to generate all reports and metrics with a few easy clicks, without impacting our production systems. The outcome has been outstanding as we have been able to save hours per week on report generation and excel extracts, therefore permitting more time to focus on important tasks.

Jean Hailes For Women's Health - Sarah Sorenson, Chief Operations Officer

By using Huddle, I have been able to get a deeper insight on how my clinic is performing and furthermore, I have been able to work with the directors to improve our clinic’s efficiency and overall performance. Huddle is quick, intuitive and accurate. It has helped me reduce the time it takes to generate reports and additionally, remove the guess work when it comes to the business decision making process.

Elm Road Clinics - Tami Rose, Practice Manager

We engaged Byte IQ as for assistance in managing an important cancer-related data collected over more than 40 years, which was be manipulated into a format where the datails usable for research related purposes. Having worked in the healthcare industry for many years, I had heard a great deal of positive things about Byte IQ in the market and knew that this healthcare data engineering firm was highly experienced in working with complex data sets and making them usable. The Byte IQ team was able to handle the sensitive data and make it usable across different requirements such as clinical analytics, machine learning, and Al tools.

Their data engineers, data scientists, and software engineers were able to work on our data sets and centralise them in their data lake solution. From there, I could log into the online portal and generate many reports, dashboards and graphs across the data sets. As a result, I could identify key trends and understand the data in a completely new way. The Data Scientists presented several machine learning scripts that will be utilised across the research in primary screening for colorectal cancer in the primary care sector. Overall, the Byte IQ team was diligent, communicative, and met meet my essential requirements.

Denis W King OAM FRACS
Clinical Professor University of Wollongong
Adjunct Professor University of New South Wales Board Chair, Justice Health and Forensic Mental Health