Empowering Healthcare Transformation with Infor Business Intelligence & Analytics
In the era of digital health, data is no longer a byproduct — it’s a strategic asset. For healthcare organizations striving to deliver high-quality care while managing costs, turning raw data into actionable insight is essential. That’s where Infor Business Intelligence and Analytics (especially through its Birst platform) comes into play. In this post, we will explore:
- What makes BI & analytics vital in healthcare
- The technical underpinnings of Infor’s approach
- Real-world benefits, use cases, and challenges
- A roadmap for adoption
1. Why BI & Analytics Matter in Healthcare
1.1 The Data-Driven Imperative
Healthcare generates massive volumes of data — from electronic health records (EHRs) to imaging, lab systems, billing, patient wearables, and more. Yet many organizations struggle to harness this for strategic insight. According to a systematic review covering 52 peer-reviewed studies between 2010 and 2023, BI & analytics significantly enhance clinical decision-making, improve patient outcomes, and optimize operational efficiency — though barriers like data integration, privacy, and adoption persist.
Another study of the “use of Big Data Analytics in healthcare” notes that innovations in BDA (Big Data Analytics) allow more precise diagnostics, predictive modeling, and health management. A survey of BI evolution also highlights how healthcare has become one of the more challenging but high-stakes domains for next-generation BI architectures.
When you consider that many U.S. hospitals (over 80%) have adopted some form of EHRs already Wikipedia, the next frontier is making sense of that data — converting it into actionable, real-time intelligence.
1.2 Outcomes, Efficiency & Cost Control
Healthcare organizations often balance three imperatives:
- Improve patient outcomes
- Maintain or reduce cost per patient
- Comply with regulations & quality standards
BI and analytics enable this by:
- Early disease detection, risk stratification, readmission prediction
- Capacity optimization (beds, staff shifts)
- Supply chain and inventory optimization (avoiding stock-outs or overstock)
- Financial analytics — e.g. payer mix, claims denial analysis
- Population health / preventive care initiatives
In fact, one source reported that 66% of U.S. providers have adopted predictive analytics, a core BI component.
And according to Virginia’s data science group, analytical use in healthcare has already helped reduce treatment costs, predict epidemics, and improve quality of life. But to truly deliver, the analytics platform must satisfy high standards for reliability, security, domain specificity, and usability — which is where Infor’s BI/analytics offering becomes compelling.
Ready to Empower Healthcare Transformation with Infor Business Intelligence?
Sama Consulting helps healthcare organizations harness Infor BI and analytics to unlock data-driven insights, enhance patient outcomes, and drive operational excellence across every department.
2. Introducing Infor’s BI & Analytics Stack (Birst & Analytics Platform)
To understand how Infor supports analytics in healthcare, we need to dig into its architecture and feature set.
2.1 The Core: Infor Birst & Analytics Platform
Infor’s BI platform centers on Birst, embedded within its broader analytics offering.
- Birst is Infor’s cloud-based analytics platform, offering pre-built industry and role-specific content, metrics, and centralized-decentralized data models.
- The Infor Analytics Platform incorporates Birst capabilities into healthcare-specific contexts: pre-built data models, dashboards, and reports tailored to clinical, operational, and financial functions.
Key architectural features:
- Prebuilt industry models & metrics — accelerates deployment by providing templates for common healthcare KPIs (e.g. patient throughput, readmission rates, cost per case).
- Drag-and-drop dashboards, drill-anywhere, filters — making the system usable by non-technical business users.
- Networked BI architecture — a hybrid model that unifies IT-managed enterprise data with user-managed data, enabling governance and flexibility.
- Adaptive UI / embedded analytics — insights can be embedded into native workflows or healthcare applications, reducing context switching.
- Cloud-native, multi-tenant design — Infor emphasizes its modern data architecture built for scalability and minimal overhead.
The combination provides “analytics embedded where the users need them” and speeds time to value with prepackaged models and role content.
2.2 How Infor’s Analytics Tech Stack Supports Healthcare
Infor’s architecture is designed for the complexity of healthcare environments:
- Data integration: It must pull from clinical systems, billing, scheduling, lab systems, EHRs, external datasets. Infor’s Data Fabric and Data & Insights suite support connecting disparate sources.
- Governance & security: Because healthcare data is highly sensitive, analytics must include robust security, audit trails, role-based access, and compliance (HIPAA, GDPR, etc.).
- Embedded analytics & contextual insights: Instead of asking users to leave their workflow for analysis, Infor enables widgets and dashboards in context. This reduces friction and increases adoption. (This is essentially embedded analytics)
- Scalability & performance: The cloud-native architecture supports scaling with data volume. Pre-packaged data models reduce ETL burden.
- Agility & self-service: Business users (e.g. managers, clinicians) can build ad hoc analyses or modify reports without heavy IT dependence.
3. Use Cases & Benefits in Healthcare
Let’s look at concrete ways Infor’s BI & analytics powers value in healthcare, through use case scenarios.
3.1 Clinical / Care Quality Use Cases
- Patient outcomes & risk stratification
Using predictive models and historical data, healthcare organizations can identify high-risk patients (e.g. for readmission, complications). For example, models embedded in Infor analytics might flag patients with high readmission risk so care teams can design targeted interventions. - Clinical decision support & variation monitoring
BI dashboards can reveal variations in clinical practices — for instance, different surgeons using different post-op protocols — enabling standardization and quality improvements. - Population health & preventive care
Analytics can segment populations (e.g. diabetic patients not meeting glycemic control) and support outreach, monitoring, or intervention programs. - Operational & Capacity Management
a. Bed utilization & throughput — ensure that patient flow is optimized; forecast demand spikes vs. capacity
b. Staff scheduling & load balancing — assign nurses, physicians, and support staff aligned with predicted demand
c. Operating room efficiency — optimize OR schedules to reduce idle time or overtime - Supply Chain, Inventory & Asset Management
Healthcare is supply-intensive. Analytics can predict usage, flag stockouts, reduce waste (especially for perishable items), and optimize purchasing cycles. - Financial analytics & reimbursement
a. Payer mix, claims denials analysis — flag patterns of denials and root-cause them
b. Costing models — compute cost per diagnosis, procedure, or department to inform pricing and efficiency initiatives
c. Revenue leakage detection — identify gaps where services were delivered but not billed
3.2 Quantified Benefits (Where Available)
- In various BI-aided healthcare deployments, organizations have reported improvements in operational efficiency (e.g. 10–20% improvement in throughput or resource utilization).
- Infor claims that more than 750 healthcare organizations in North America run its solutions, processing 1 billion daily clinical data transactions across those systems.
- Infor also suggests that with analytics, they help reduce operational costs, manage supply chain continuity, and improve clinical/imaging decisions.
- Independent reviews of BI in healthcare consistently note reductions in readmissions, identifying at-risk patients, optimizing resource use, and supporting preventive care as highlights in successful cases.
While exact ROI figures vary by organization, the alignment of BI and healthcare outcomes is broadly validated across research and industry.
Ready to Empower Healthcare Transformation with Infor Business Intelligence?
Sama Consulting helps healthcare organizations harness Infor BI and analytics to unlock data-driven insights, enhance patient outcomes, and drive operational excellence across every department.
4. Challenges & Best Practices in Healthcare BI Implementation
Even with a strong platform like Infor’s, ambitious analytics programs must anticipate and address certain challenges.
4.1 Key Challenges
4.1.1 Data Integration & Quality
- Disparate systems (EHRs, labs, billing, imaging) often use different standards and formats
- Missing or inconsistent documentation or coding
- Upgrading or migrating legacy systems may complicate integration
- Real-time or near-real-time synchronization is non-trivial
Many studies cite data integration issues as a major barrier to BI&A adoption in healthcare.
4.1.2 Privacy, Security & Compliance
- Healthcare data is subject to stringent privacy laws (e.g. HIPAA, GDPR)
- Role-based access control, encryption in transit and at rest, auditing, de-identification for research contexts — all must be baked in.
- Patients’ trust and institutional risk make any breach extremely costly.
4.1.3 Change Management & Adoption
- Clinicians and staff may resist or distrust dashboards, especially if prior analytics efforts failed or were poorly designed
- Ensuring that insights translate into workflow improvements requires buy-in and training
- Poor data literacy or lack of an analytics culture may slow adoption
4.1.4 Governance & Oversight
- Who “owns” analytics? IT, analytics team, business units?
- Versioning, metadata, data lineage, governance policies are essential
- Ensuring consistency and correctness of metrics across departments
4.1.5 Scalability & Performance
- As data volume grows (images, genomics, sensor data), systems must scale
- Latency must stay low, especially for near-real-time or critical decision dashboards
4.2 Best Practices & Recommendations
- Start small, scale iteratively: Pilot with a use case (say readmission prediction) and then expand
- Use prebuilt models & domain templates: Platforms like Infor offer healthcare templates to shorten deployment cycles
- Strong data governance framework: Define ownership, access policies, standard metrics
- Embed analytics into workflows: Use embedded dashboards rather than forcing users to switch to separate BI tools
- Training & user engagement: Involve clinical and administrative users in design so dashboards are intuitive and trusted
- Continuous validation & feedback loops: Monitor model performance and adjust metrics as needed
- Focus on ROI and outcomes: Tie analytics to measurable outcomes (e.g. % drop in readmissions, cost per case)
- Ensure compliance from day one: Encrypt data, audit logs, role-based access, anonymization as needed
A maturity-model approach is often recommended: start with descriptive reporting, then diagnostics, predictive, and finally prescriptive analytics.
5. Architecting a Healthcare Analytics Roadmap with Infor
Below is a suggested phased roadmap that a healthcare organization might follow to adopt Infor BI/analytics in a robust, scalable way.
Phase 1: Assessment & Strategy
- Inventory existing data sources (EHR, billing, labs, imaging systems)
- Evaluate existing analytics capabilities, pain points, and user needs
- Define strategic goals (e.g. reduce readmissions by X%, optimize staffing)
- Establish governance, data policies, and stakeholder roles
Phase 2: Pilot Implementation
- Choose a focused use case (e.g. readmission prediction, OR efficiency)
- Deploy a minimal data mart, dashboards, reports using Infor’s pre-built models
- Engage clinical and admin users for feedback
- Validate data integration, performance, and usability
Phase 3: Expansion & Integration
- Extend to additional functional areas: supply chain, finance, population health
- Embed analytics into core clinical and operational systems
- Implement role-based access and governance
- Enhance models (predictive, prescriptive)
Phase 4: Optimization & AI/ML Augmentation
- Monitor dashboards continuously, refine KPIs
- Introduce machine learning models, anomaly detection, prescriptive analytics
- Scale infrastructure to handle growing data volume
Phase 5: Innovation & Continual Improvement
- Enable self-service analytics for advanced users
- Explore advanced technologies (e.g. ontologies, knowledge graphs, semantic interoperability)
- Keep iterating — analytics is never “done”
Because Infor’s platform already includes many healthcare-specific dashboards and templates, the ramp-up is shorter. But success depends heavily on execution and organizational culture.
Ready to Empower Healthcare Transformation with Infor Business Intelligence?
Sama Consulting helps healthcare organizations harness Infor BI and analytics to unlock data-driven insights, enhance patient outcomes, and drive operational excellence across every department.
6. Why Choose Infor BI & Analytics (Birst) for Healthcare?
There are many BI tools out there (Power BI, Tableau, Qlik, etc.), but Infor offers differentiators especially suited to healthcare:
- Domain specificity: Pre-built healthcare data models, metrics, dashboards reduce the burden of customization
- Embedded analytics: Contextual insights within workflows improve adoption
- Networked architecture: IT-grade governance plus user agility
- Scalability & cloud-first: Built for performance and growth
- Comprehensive platform: Integrated with Infor’s broader healthcare stack (ERP, HCM, interoperability)
- Strong healthcare footprint: Infor claims 750+ healthcare customers in North America, 1B daily clinical transactions processed.
For more details on Infor Birst, you can explore the SAMA Consulting’s Infor Birst page: Infor Birst
And for broader consulting around Infor analytics, our main site is a good resource: https://samaconsultinginc.com/
7. Putting It All Together — Sample Silo Outline for Your Organization
Here’s an example of how an organization might structure its analytics silo using Infor:
| Silo / Domain | Key Data Sources | Typical Dashboards & Use Cases |
| Clinical Outcomes | EHR, labs, patient vitals | Readmission risk, mortality analysis, care variation |
| Operations & Capacity | Patient flow, scheduling, staffing | Bed utilization, OR efficiency, staff allocation |
| Finance & Revenue | Billing, claims, payer data | Revenue leakage, cost per case, payor mix |
| Supply Chain & Inventory | Procurement, usage logs | Stock-out alerts, optimization, spend analysis |
| Population Health / Preventive | Claims, community data | Chronic disease cohorts, intervention tracking |
Each silo still connects through a governed analytics backbone (Infor’s networked architecture), enabling cross-domain insights (e.g. linking clinical outcomes with cost data).
Ready to Empower Healthcare Transformation with Infor Business Intelligence?
Sama Consulting helps healthcare organizations harness Infor BI and analytics to unlock data-driven insights, enhance patient outcomes, and drive operational excellence across every department.
8. Future Trends & Innovations
As healthcare BI matures, several emerging trends are reshaping the landscape:
8.1 Ontology-driven semantic analytics
A recent review highlights how linking metadata to healthcare knowledge graphs (ontologies) enhances semantic interoperability, metadata discoverability, and decision support — an area ripe for integration with BI systems.
8.2 Real-time & streaming analytics
As IoT, wearables, and sensor data grow, analytics will shift toward real-time streaming, event-driven dashboards, and anomaly detection in near real-time.
8.3 Prescriptive analytics & autonomous systems
Beyond prediction, systems will increasingly suggest actions, simulate scenarios, or even automate adjustments (e.g. routing patients or rescheduling staff).
8.4 Federated data architectures & privacy-preserving analytics
Techniques like federated learning, secure multiparty computation, and differential privacy will gain traction in healthcare analytics due to privacy constraints.
8.5 Quantum & advanced computing
While still nascent, research into quantum neural networks in healthcare suggests future possibilities for compute-intensive analytics and optimization.
8.6 Greater consumer & patient analytics
With more patient-generated data (wearables, home devices), health systems will integrate that into analytics to support more proactive, personalized care.
Infor’s cloud, scalable architecture and its integration with data & insights, AI and data fabric solutions position it well to evolve alongside these trends.
9. Conclusion
Adopting business intelligence and analytics in healthcare is no longer optional — it’s essential for quality, efficiency, and financial viability. Infor’s BI & analytics stack (built around Birst and embedded analytics) offers a robust foundation tailored for healthcare realities: connecting disparate systems, ensuring compliance, supporting clinical and operational contexts, and scaling for tomorrow’s demands.
Ready to Empower Healthcare Transformation with Infor Business Intelligence?
Sama Consulting helps healthcare organizations harness Infor BI and analytics to unlock data-driven insights, enhance patient outcomes, and drive operational excellence across every department.
