Healthcare data analytics services: 
key types, features, and applications

Healthcare data analytics services: key types, features, and applications

Healthcare data analytics interprets current and historical data from legacy systems, cloud, portable devices, and external sources into actionable insights for medical teams. Itransition builds custom healthcare data analytics solutions using suitable data analysis techniques as well as AI and ML.

of world data volume is generated by the healthcare industry

RBC Capital Markets

of healthcare leaders in the US have adopted predictive analytics


expected CAGR of the global healthcare analytics market between 2021 and 2030

Precedence Research

Healthcare data analytics options

Big data analytics
We create big data analytics solutions that process numerous data sources to deliver tailored treatment plans, condition management strategies, timely prevention measures, and eliminate fraud.

Image analysis

Our experts train and implement ML models to distinguish details in medical images that may evade the human eye, enhancing the diagnostics’ precision and reducing patients' exposure.
IoMT analytics
We deliver ML-powered solutions that analyze real-time medical device data within IoMT networks and identify meaningful medical condition patterns and changes.
Business analytics
Itransiton’s team implements custom analytics software that monitors the facility’s operational efficiency as well as personnel performance in real time. It also helps prevent disruptions and manage resources.
Data warehousing
We create safe storage for protected health information from multiple healthcare systems and ensure proper data structuring and standardization for its further processing.

Predictive analytics

We deliver ML-based solutions that offer insights into probable outcomes, like condition developments or supply chain interruptions, and help make informed decisions.
Data visualization
We implement solutions that can turn medical data and metrics into intuitive dashboards, reflecting real-time personnel and facilities performance as well as patient health trends.
Population health analytics
We develop solutions that extract data from disparate sources, including research databases and demographic data, to detect and report on cohort health and behavior patterns.

Healthcare data analytics services we offer

Healthcare data analytics services
Our experts help healthcare organizations understand their business goals and compile a list of requirements for the future analytics solution. We also help healthcare providers outline their analytics implementation roadmaps and ensure its smooth and safe adoption.
Itransition’s engineers carry out full cycle software implementation, whether you need to develop the solution from scratch, or customize or integrate it with third-party apps according to the organization’s business requirements. We also assist our clients during the post-deployment period and offer long-term maintenance and multi-level support.
We carry out database, storage, and app data migration, moving health data in accordance with industry's security standards. Our team can also perform entire infrastructure overhauls when legacy systems and data have to be transferred to the cloud environment.
Itransition’s experts can fine-tune, upgrade, rework, or fully re-engineer legacy apps and solutions to fit the healthcare provider’s current business requirements, make them comply with modern security and regulatory standards, and optimize the system’s functionality.
We update legacy software or develop new solutions from scratch to comply with HIPAA, IEC 62304, GMP, FDA 21 CFR Part 820 policies, as well as follow FHIR and OWASP requirements, and adhere to the DICOM standard.

Looking to get actionable insights from healthcare data?

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Medical data analytics development scenarios

Our team can follow a data analytics development approach that fits with the particular organization’s goals, available resources, and budget.

Custom solutions

We conduct marketing, technical and business research and develop a bespoke analytical tool tailored to your objectives and seamlessly integrated into your infrastructure.

Embedded BI

To make analytics accessible throughout the organization, we create analytical modules and integrate them into your healthcare software.

Platform customization

We implement a third-party analytics platform, adapting it to your business needs. We work with Microsoft Power BI, Tableau, and Qlik.

Itransition’s medical data analytics portfolio

Healthcare analytics applications revamp


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.

Pharmaceutical data analytics suite


faster data processing

We migrated the customer’s infrastructure to the cloud and redeveloped their data analytics solutions used by pharma corporations globally.

Types of healthcare data analytics

There are five types of data analytics used in healthcare, each with their distinct computing power and data requirements and potential impact.

Healthcare data analytics types


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?)

Top 10 healthcare data analytics use cases

Here are ten most popular applications of data analytics in the healthcare industry.

Disease course prediction

Analyzing data from EHRs, patient surveys, RPM devices, and nation-wide research, healthcare institutions can identify patients at risk of various diseases or predict the worsening of existing conditions to intervene early and improve patient outcomes.

Treatment planning

By creating a treatment model based on a patient profile with data from a variety of sources, a care provider can better understand potential effects of different treatments and choose the safest yet the most effective one for each patient.

Chronic condition management

Chronic condition profiling coupled with continuous patient monitoring and analysis of the readings offers insights about the condition’s course, alerts patients and personnel about potential complications, and provides condition management recommendations.

Self-harm prevention

Mental state monitoring and analysis helps identify patients who are at risk of anxiety attacks, depression, suicidal or self-harm tendencies, or other mental disorders, and intervene before any dangerous event takes place.

Resource allocation

Identifying resource usage patterns and correlating them with other factors like time of the day, epidemiologic situation, or scheduled appointments helps predict future needs and timely acquire or redistribute the resources throughout the facilities.

Patient load management

Modeling patient flow patterns can alert care providers about the influx of patients to particular facilities or specialists, giving them time to prepare and meet the demand without disruptions.

Supply chain management

Descriptive and predictive analytics can reveal opportunities for more effective order placement, price negotiation, product variability reduction, or new equipment and supply vendor agreements for healthcare organizations.

Fraud prevention

AI-powered tools can detect insurance claims that don’t match the patient’s history, identify the provision of services unnecessary regarding the diagnosis, and detect duplicate or phantom billing.

Patient engagement

Learning patients’ behavior patterns can give providers insight into their wants and needs and help better tailor care service offers as well as ensure more transparent and satisfying communication with patients.

Data security

Identifying suspicious pattern changes in data access, sharing, and utilization helps healthcare organizations uncover security risks, while ML-based modeling can recommend ways to strengthen security.

Benefits of healthcare data analytics

Analytical solutions enable healthcare organizations to use available data to their and patients’ advantage.

Early diagnostics and disease prevention

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.

Care optimization and personalization

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.

More efficient operations

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.

Better crisis management

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.

Faster drug discovery and rollout

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.

Challenges of implementing healthcare data analytics

Although healthcare analytics solutions give care providers undisputable advantages, their implementation and use may pose some challenges, especially for non-technical professionals.

Common challenges

How we can help

Data formats inconsistency

Common challenges

How we can help

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.

Storage with slow access to data

Common challenges

How we can help

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.

Data governance

Common challenges

How we can help

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.

Our experts will solve all emerging issues during healthcare analytics development and integration

Let’s talk

Related services



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.

Healthcare CRM

Our CRM solutions are tailored to the healthcare industry, helping enhance patient experiences and expand service area coverage.

Hospital management software

We deliver solutions that digitize and facilitate all hospital operations, from locating ward equipment to task distribution.

Pharmacy management solutions

Our solutions help pharma companies handle complex procedures from supply chain, order and pharmaceutical data management to dangerous substance control.

Telehealth solutions

We create sophisticated telehealth suites that enable remote consultations, diagnostics, and treatment to make healthcare more accessible and help providers increase revenue.

Patient engagement tools

Patient engagement tools

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.

FAQ about healthcare data analytics

Here are some questions worth asking before healthcare analytics adoption.

Where does medical data come from?

Depending on your IT ecosystem, analytical algorithms can use surveys, EHR/EMR, CRM, insurance claims, laboratory systems and medical device data.

What is advanced analytics in healthcare?

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.).

What algorithms do data analytics platforms employ?

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.

What is the cost of healthcare analytics solutions?

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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