An Australian Medical Organization contacted Positiwise Software to build an efficient Healthcare Data Warehouse mechanism for streamlining the following data operations:
Our team thoroughly understood the requirements, discovered challenges, and provided the appropriate solution. The Healthcare Data Warehouse mechanisms cover edge-to-edge requirements, assuring high-performance and maximum uptime.
While assessing the client requirements, the primary need was to develop a system assuring 24/7 uptime and efficient capabilities. As our client deals with a large number of patients daily, the user requests on the system would also be high.
In addition, our ally requested to create a consistent backup module responsible for backing up each bit of data as it gets stored in the primary memory unit.
The Healthcare Data Warehouse architecture should connect all the departments and collect assimilated, semi and unstructured data from every department. Moreover, the system must connect with other ERP, Human resources, and Public Health organizations.
Our client also has a dedicated department for insurance claims that links with external insurance companies. Therefore, the requirement to connect Data Warehouse with insurance databases is one of the main objectives.
Our partner faces complexities in organizing the patient data according to different wards, departments, latest appointments, online consultations, and other criteria.
The business requirement is to set up an automated indexing and data organization module. It should evaluate the patient information and store it in the appropriate database. In addition, access to every detail should be provided according to roles and responsibilities.
Our client is consistently upgrading their services with evolving patient and market needs. As a result, they have seen a rapid rise in their medical service demand.
To ensure all their stakeholders an active system and seamless services, the client required a system that can automatically visualize the demand and scale resources up and down.
Machine Learning algorithms are doing wonders in the medical industry. Our partner required a custom ML module to assess the clinical data with information available in public health databases. And the final result must provide a diagnostic model to the doctors.
In addition, the ML components must process medical documents and genetic details and focus on identifying and predicting the symptoms and appropriate diagnosis.
Our partner needs to align business standards and systems with all major medical regulations to prevent legal actions against it. Our business analysts precisely understand this requirement and searched about all the standards to which the data warehouse system must comply.
The top-most priority of this project is to secure the medical and personal records of all the patients along with the organization’s confidential data. Besides this, our client wants to isolate the backup server from the network and provides it access only to higher management.
Furthermore, all the users log into the online portal by filling in their username and password, and the system must store their credentials securely. And all the dataflow channels must be secured from malicious actors.
Healthcare Data Warehouse processes large data chunks in high amounts, including metadata, raw data, historical, real-time, and summarized data. Our team identified the requisite to build robust and flexible storage, enabling it to store different files with and without compressing them.
According to our client’s requirements, our healthcare software development team created a three-tier modular Data Warehouse architecture, which executed every data bit in a secure and high-performing environment.
All the systems get configured with the latest operating systems, Business intelligence tools, cloud storage facilities, and scalable computational resources.
We connected all the departments through a centralized database scheme. All the pre-configured ERP systems, HR management modules, and public databases gest linked with the new data warehouse.
In addition, we channelized the data between medical institutions and insurance companies to make the claim process easier for patients.
By integrating the tailored data-oriented components, our team fulfilled the requirement of automated information organization and patient data indexing. In addition, we also performed modifications in their Enterprise software without additional cost to enhance the system compatibility.
We embedded the cloud infrastructure with the Data Warehouse system and configured an automated load balancing mechanism.
It assures maximum uptime without lag and security issues. Whenever there are extensive user requests, it will automatically assign more resources. Our client only has to pay for the utilized resources.
Our machine learning experts added modules to analyze data in communication channels and stored in databases. Whether the data is structured, semi-structured, or unstructured, ML modules can analyze it to provide an authentic diagnostic model.
Moreover, it evaluates the previous history and helps to predict future scope.
Our software developers, analysts, and security experts collaborated to cover every crucial HIPPA, HITECH, and FDA standard.
Now the system follows all the essential standards and processes electronic medical records accordingly. Furthermore, we have created a secure patient environment and assured security against losing PHI.
Focusing on data security, all the data paths are secured using the 256-bit encryption mechanisms. Moreover, the data stored in any file type gets hashed. Only the internal team knows security configuration with security modification controls only present with upper management.
For accessing any BI tools, employees need to request permissions from the administrator.
Our cloud architects embedded scalable cloud storage for this project. It allows uploading any type of file from any source into any database. And for maintaining data integrity, we configured access controls and defined data retrieval as per roles.
After we completed the project and deployed it across our client’s organization, they have analyzed the following final results:
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