Our team can follow a data analytics development approach that fits with the particular organization’s goals, available resources, and budget.
We conduct marketing, technical and business research and develop a bespoke analytical tool tailored to your objectives and seamlessly integrated into your infrastructure.
To make analytics accessible throughout the organization, we create analytical modules and integrate them into your healthcare software.
We implement a third-party analytics platform, adapting it to your business needs. We work with Microsoft Power BI, Tableau, and Qlik.
faster test runs
We upgraded a suite of healthcare and pharmaceutical analytics solutions, performing development, QA and DevOps services to enhance user experience and boost app performance.
faster data processing
We migrated the customer’s infrastructure to the cloud and redeveloped their data analytics solutions used by pharma corporations globally.
There are five types of data analytics used in healthcare, each with their distinct computing power and data requirements and potential impact.
What might happen? (What could complicate the condition?)
Why did it happen? (What caused the condition?)
What happened? (What does the condition look like?)
What should happen? (How should we treat the condition?)
How do we know it happened? (Which of the patient’s vitals should be used to determine the cause/type/outcome of the condition?)
Here are ten most popular applications of data analytics in the healthcare industry.
Analytical solutions enable healthcare organizations to use available data to their and patients’ advantage.
Patients’ health history analysis can help identify a variety of health risks in advance and prevent upcoming diseases. This allows care providers to avoid hospitalizations, alleviate pressure on the ER and ICU, and speed up patient recovery.
A more holistic understanding of all patient health factors helps create treatment plans best suited to each patient’s individual case, needs, and health specifics. This improves patient satisfaction, boosts treatment success, and makes care more inclusive.
Performance analysis helps detect redundant workflows, areas of unproductive resource and personnel usage, and poor management approaches. It also helps establish cost-effective operational strategies, create successful marketing campaigns, and discover opportunities for patient base expansion.
Predictive analytics recognize signs of an upcoming crisis like an epidemic, personnel shortages, and facilities overcrowding and find effective management approaches, like resources and tasks redistribution, data backup, or patient flow redirection.
In the pharmaceutical industry, big data analytics and modeling can speed up the processing of research and trial data while also immensely lowering its cost, making new drugs cheaper and safer.
Although healthcare analytics solutions give care providers undisputable advantages, their implementation and use may pose some challenges, especially for non-technical professionals.
With software interoperability yet to be achieved in many healthcare organizations, data coming from different systems is often in disparate formats and therefore difficult to process and share.
Our experts review and clean up a healthcare organization’s datasets as well as audit data quality to make sure all data is ready to be processed. Then we integrate analytical tools into digital environments and ensure their future interoperability.
Healthcare organizations generate high volumes of data and sometimes have to opt for cheaper storage options that make it hard to access data.
We build DWH architectures and ETL processes to establish seamless data connectivity, system stability and scalability, taking into account your organization’s requirements, data volumes, and budget.
A data governance strategy is the best way to ensure data quality and protection for reliable analytics outputs. However, many providers are unfamiliar with such practices.
Our consultants work closely with the organization’s IT department to ensure the adoption and proper execution of all activities aimed at keeping data reliable, structured, accessible, and protected. We can conduct personnel training, monitor system performance, and give recommendations for further data governance enhancement.
We provide software for secure patient data management, storage and sharing, enabling providers to have a full, up-to-date view of patients’ health history.
Our CRM solutions are tailored to the healthcare industry, helping enhance patient experiences and expand service area coverage.
We deliver solutions that digitize and facilitate all hospital operations, from locating ward equipment to task distribution.
Our solutions help pharma companies handle complex procedures from supply chain, order and pharmaceutical data management to dangerous substance control.
We create sophisticated telehealth suites that enable remote consultations, diagnostics, and treatment to make healthcare more accessible and help providers increase revenue.
Our patient engagement solutions, including patient portals and mHealth applications, facilitate communication between providers and patients, increase patients’ loyalty and help them proactively manage their health.
Here are some questions worth asking before healthcare analytics adoption.
Depending on your IT ecosystem, analytical algorithms can use surveys, EHR/EMR, CRM, insurance claims, laboratory systems and medical device data.
This term is often used to describe automated data analysis that uses techniques and tools more complex than traditional business intelligence (AI, machine learning, blockchain, etc.).
The “big five” algorithms used in medical data analytics are linear regression, logistic regression, classification and regression trees, K-nearest neighbors, and K-means clustering. However, you can ask the vendor what algorithm is used in a particular case.
The cost varies significantly across solutions depending on the functionality and the amount of data they are processing. You’d better discuss the cost with your developer company when you have a solid idea of your future analytical software.
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