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Healthcare

Enhancing Data Analytics for Faster Diagnosis in Healthcare

Interactive dashboard displaying real-time patient health data and diagnostic insights powered by advanced analytics.

Focus Areas

Clinical Data Analytics

Diagnostic Decision Support

Data Integration & Visualization

Cloud-based analytics environment supporting scalable healthcare diagnostics.

Business Problem

A multi-specialty healthcare provider experienced delays in diagnosis and inconsistent clinical decision-making due to fragmented data sources and limited use of advanced analytics. Physicians struggled to access timely insights from EHRs, imaging systems, and lab results. The organization sought to harness real-time analytics to support faster, more accurate diagnoses and improve patient outcomes.

Key challenges:

  • Data Fragmentation: Clinical, imaging, and lab data were spread across siloed systems with limited interoperability.

  • Manual Data Review: Physicians spent excessive time reviewing records, delaying treatment decisions.

  • Limited Predictive Capability: Lack of AI or ML tools to identify early warning signs or prioritize high-risk patients.

  • Poor Visualization Tools: Raw data lacked clinical context, making it difficult for clinicians to interpret quickly.

  • Scalability Issues: Legacy infrastructure struggled with high-volume data ingestion and processing.

The Approach

Curate collaborated with the provider to design an integrated analytics ecosystem powered by real-time data pipelines, AI-driven diagnostic models, and intuitive dashboards tailored to clinical workflows. The goal was to reduce diagnostic latency and enable data-driven decision-making at the point of care.

Key components of the solution:

  • Discovery and Requirements Gathering:

    • Clinical Workflow Mapping: Identified key diagnostic workflows across cardiology, oncology, and emergency medicine.

    • Data Source Audit: Evaluated EHRs, RIS/PACS, lab systems, and IoT devices to map available datasets.

    • Stakeholder Interviews: Worked with physicians, radiologists, and lab technicians to understand user needs.

    • Compliance Review: Ensured HIPAA-compliant data handling and patient privacy protections.

  • Solution Design and Implementation:

    • Unified Data Platform: Built a cloud-native data lake integrating EHR, imaging, and lab data using FHIR APIs and HL7 connectors.

    • Predictive Analytics Models: Developed ML models for early detection of sepsis, stroke risk, and abnormal lab trends.

    • Clinical Dashboards: Deployed real-time dashboards in Power BI and Looker with patient-specific diagnostic alerts.

    • Data Pipeline Automation: Implemented ETL workflows using Airflow and dbt for daily data refreshes and anomaly detection.

    • Natural Language Processing (NLP): Extracted key insights from unstructured clinical notes using spaCy and Amazon Comprehend Medical.

  • Process Optimization and Change Management:

    • Clinical Engagement: Co-designed dashboards with clinicians to ensure relevance and usability.

    • Training and Adoption: Delivered targeted training sessions to boost adoption and integrate tools into daily routines.

    • Feedback Loops: Established continuous feedback mechanisms for improving model accuracy and UI/UX.

    • Governance: Created a data governance council to oversee model updates and ensure ethical use of analytics.

Business Outcomes

Faster Diagnosis and Treatment Initiation


Real-time insights allowed clinicians to identify high-risk cases more quickly and act on early warning signs.

Improved Clinical Decision Support


Predictive models and intuitive dashboards reduced diagnostic uncertainty and variability across departments.

Streamlined Data Access and Analysis


Unified views eliminated manual data hunting and empowered clinicians with actionable insights in minutes.

Higher Patient Satisfaction


Faster, data-informed diagnoses led to shorter wait times, fewer unnecessary tests, and more timely care.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Average time to diagnosis 6.5 hours 2.2 hours 66% reduction
Time spent retrieving clinical data 45 min/day 10 min/day 78% reduction
Sepsis early detection rate 56% 87% 31%
Diagnostic error rate 12% 5% 90% reduction
Clinician satisfaction with tools 42% 89% 112% Improvement
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

Customer Value

Accelerated Clinical Decision-Making


Enabled timely interventions, reducing complications and hospital stays.

Data-Driven Precision Care


Supported personalized treatment by highlighting key diagnostic patterns.

Sample Skills of Resources

  • Clinical Data Scientists: Developed and validated machine learning models for diagnosis support.

  • Data Engineers: Built robust, scalable pipelines for integrating and processing healthcare data.

  • UX/UI Designers: Created clinician-friendly dashboards aligned with workflows.

  • NLP Engineers: Extracted structured insights from clinical text and notes.

  • Healthcare Analysts: Mapped and measured clinical KPIs and model effectiveness.

Tools & Technologies

  • Data Integration: HL7, FHIR, Apache NiFi

  • Analytics & Visualization: Power BI, Looker, Tableau

  • Machine Learning: Python, scikit-learn, TensorFlow, Amazon SageMaker

  • NLP & Text Analytics: spaCy, Comprehend Medical, Spark NLP

  • ETL & Orchestration: dbt, Apache Airflow, Snowflake

  • Compliance & Security: Azure Key Vault, HIPAA Guardrails, OneTrust

How predictive analytics reduces time to diagnosis by identifying disease symptoms early.

Conclusion

By enhancing data analytics capabilities, Curate enabled the healthcare provider to deliver faster, more accurate diagnoses, improving patient outcomes and operational performance. The initiative successfully bridged data silos, brought predictive insights into clinical workflows, and laid the foundation for AI-enabled precision healthcare.

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