Healthcare data analytics interprets current and historical data from legacy systems, cloud, portable devices, and external sources into actionable insights for medical teams. KPi-Tech builds custom healthcare data analytics solutions using suitable data analysis techniques as well as Artificial Intelligence (AI) and Machine Learning (ML).

How the Healthcare Industry Utilizes Data Analytics

Improved performance

Around 56% of leading healthcare organizations worldwide have initiated the adoption of predictive analytics software, as per Statista.

Scalable apps

Data analytics is considered crucial for achieving business objectives by 85% of healthcare executives.

Cost efficient

51% of healthcare organizations view data integration and interoperability as primary obstacles to efficient data analytics implementation.


Post-hospital data analytics implementation, healthcare providers report that timely care interventions can prevent 70-80% of emergency readmissions

Our Services

Healthcare Analytics Services We Offer

Healthcare Data Analytics Consulting

Healthcare Data Analytics Consulting

  1. Assessing your requirements to determine how your organization can leverage data analytics effectively.
  2. Crafting a comprehensive strategy tailored specifically for healthcare analytics.
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Full Healthcare Analytics Outsourcinge

Full Healthcare Analytics Outsourcing

  1. Conducting a thorough analysis to create a personalized healthcare analytics solution.
  2. Selecting the appropriate healthcare data analytics platform and technology stack.
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Software Implementation

Software Implementation

  1. Our team handles all aspects of software implementation, whether it involves building from scratch, customizing, or integrating with third-party applications to meet your organization's needs. We also provide post-deployment assistance and long-term maintenance and support.
Integration and compliance

Integration and compliance

  1. Updating existing solutions to ensure security and compliance with relevant regulations such as HIPAA, IEC 62304, GMP, FDA 21 CFR Part 820, as well as adherence to standards like HL7 FHIR and OWASP, and compliance with the DICOM standard.
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  1. Handling database, storage, and application data migration in compliance with industry security standards, including transitioning legacy systems and data to thecloud environment if necessary.


  1. Fine-tuning, upgrading, or completely re-engineering legacy applications to meet current provider needs, healthcare ensuring compliance with modern security and regulatory standards, and optimizing system functionality.

Healthcare Data Analytics Options You get

Big Data Analytics

Big Data Analytics

We provide tailored treatment plans, condition management strategies, prevention measures, and fraud detection by processing various data sources.

Image Analysis

Image Analysis

Our ML models enhance diagnostic precision by identifying details in medical images that may be missed by humans, thus reducing patient exposure

IoMT Analytics

IoMT Analytics

We leverage the power of the Internet of Medical Things (IoMT) by leveraging machine learning to analyze real-time data from medical devices within IoMT networks. ...

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Business Analytics

Business Analytics

Our custom analytics software monitors facility operational efficiency and personnel performance in real-time, aiding in disruption prevention and resource management.

Data Warehousing

Data Warehousing

We ensure secure storage and proper structuring of protected health information from multiple healthcare systems for further processing.

Predictive Analytics

Predictive Analytics

ML-based solutions provide insights into potential outcomes such as condition developments or supply chain interruptions, facilitating informed decisions.

Data Visualization

Data Visualization

Our solutions transform medical data into intuitive dashboards, reflecting real-time personnel, facility performance, and patient health trends.

Population Health Analytics

Population Health Analytics

We develop solutions to extract and analyze data from diverse sources to detect and report on cohort health and behavior patterns.

Medical Data Analytics Development Scenarios

Our team is capable of tailoring a data analytics development strategy that aligns with your organization's objectives, resources, and budget. Through thorough research, we craft a bespoke analytical solution that harmonizes with your goals, seamlessly integrating it into your existing infrastructure.

Custom Electronic Data Warehouse

  1. Smooth Data Sharing for Effective Monitoring
  2. Data Sharing Aids in Monitoring and Error Identification
  3. Predict Time and Cost Consumption for Further Planning
  4. Target Critical Patients and Optimize Care Delivery
  5. Manage Population Health Using Data Warehousing

Custom Data Management and Predictive Analytics

  1. Utilize Data from Various Sources to Enhance Patient Engagement and Outcomes
  2. Improved Accessibility to Information like CRM and Pricing
  3. Implement Predictive Modeling to Analyze Health Data
  4. Provide Accurate Predictive Insights for Diagnostics
  5. Tailor to Different Healthcare Stakeholders' Needs with Predictive Analytics

Interactive Dashboards and Reporting

  1. Develop Personalized, User-Friendly Dashboards for Performance Metrics Visualization
  2. Enhance Reporting and Analysis by Eliminating Inconsistent Data
  3. Effectively Track Clinical and Administrative Outcomes of Hospitals and Health Companies
  4. Improve Physician Performance and Patient Satisfaction
  5. Streamline Workloads, Allow Providers to Focus on Quality Care, and Save Costs
  6. We customize third-party analytics platforms to meet your specific business requirements and integrate them into your operations.

Healthcare Analytics Tools

  • Python: Widely used for data analysis and manipulation, with libraries like Pandas, NumPy, and SciPy.
R tool
  • R: Popular for statistical analysis and visualization, with packages like ggplot2, dplyr, and tidyr.
  • SQL: Essential for querying and managing structured data in relational databases.
  • Tableau: Allows users to create interactive visualizations and dashboards from various data sources.
  • Power BI: Microsoft's BI platform for data visualization, reporting, and analytics.
  • QlikView/Qlik Sense: Enables self-service BI with interactive dashboards and data discovery capabilities.
  • SPSS: Statistical software used for data analysis, modeling, and reporting.
  • SAS: Widely used for advanced analytics, data management, and predictive modeling.
  • MATLAB: Offers built-in functions and toolboxes for statistical analysis and machine learning.
  • Weka: Open-source software for data mining and machine learning tasks.
  • RapidMiner: Provides a visual interface for building predictive models and data analysis workflows.
  • TensorFlow, scikit-learn: Python libraries for machine learning, deep learning, and predictive modeling.
Apache Hadoop
  • Apache Hadoop: Distributed storage and processing framework for handling large datasets.
Apache Spark
  • Apache Spark: In-memory computing engine for big data processing, with support for SQL, streaming, and machine learning.
Apache Kafka
  • Apache Kafka: Distributed event streaming platform for real-time data processing.
  • D3.js: JavaScript library for creating interactive and customizable data visualizations on the web
  • Plotly: Offers interactive plotting capabilities for Python, R, and JavaScript.
Microsoft Excel
  • Microsoft Excel: Widely used for basic data visualization and reporting tasks.
  • OpenRefineOpen: source tool for cleaning and transforming messy data
trifact wrangler
  • Trifacta Wrangler: Enables data wrangling and preparation with a user-friendly interface.
  • Pandas, tidyverse: Python and R libraries for data manipulation, cleaning, and transformation.
Informatica powercenter
  • Informatica PowerCenter: Enterprise data integration platform for building ETL workflows.
  • Talend: Open-source data integration and ETL tool with a graphical interface.
apache nifi
  • Apache NiFi: Data flow automation tool for orchestrating data movement and transformation
  • Reporting platforms like Premier Quality Advisor and Medisolv enable healthcare organizations to comply with quality reporting requirements, such as those outlined by CMS (Centers for Medicare & Medicaid Services), by analyzing and reporting on clinical quality measures and performance indicators.
  • Population health management solutions like, Innovaccer, and Philips Wellcentive aggregate and analyze data from multiple sources to identify high-risk patients, coordinate care, and improve health outcomes for entire patient populations.
  • NLP technology, such as Linguamatics and Clinithink, is used to extract insights from unstructured clinical notes, medical literature, and other textual data sources to support clinical decision-making, research, and quality improvement initiatives.
Why Choose Us?

Unlock the Power of Healthcare IT with KPi-Tech

With 22 years of experience in Healthcare IT and data analytics, we specialize in healthcare interoperability and custom software development services. Our seasoned team of experts is well-versed in regulatory compliance (including HIPAA, HITECH, GDPR), ensuring the highest standards of data security. Trust us as your partner to deliver innovative solutions tailored to your healthcare analytics needs.

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Ready to take the first step towards revolutionizing your healthcare analytics

Contact us now for a personalized cost estimate and ROI projections. Let's transform your data into actionable insights together!