Automations
This pillar covers clinical workflows that unify imaging, lab values, longitudinal health records, and physician notes into a single diagnostic support layer. The content should emphasize multimodal ingestion, explainable triage, alerting logic, and health-system integration patterns that improve clinician throughput while preserving safety, interpretability, and clinical oversight.
This foundational page details a custom, multi-agent workflow that ingests and fuses imaging, labs, notes, and longitudinal records into a unified diagnostic support layer. It explains the architecture for multimodal data ingestion, explainable triage logic, and integration with EHRs like Epic or Cerner to improve clinician throughput and diagnostic accuracy. Implementation focuses on building a production-grade orchestration layer with human-in-the-loop controls and audit trails for regulated healthcare environments.
This page covers a custom workflow that automatically assembles fragmented patient data from disparate visits and systems into a coherent, timeline-based health narrative. It addresses the business problem of manual chart review, detailing an agentic architecture that extracts, deduplicates, and sequences clinical events to save clinician time and reduce diagnostic oversights. Implementation involves integrating with FHIR APIs, applying temporal reasoning models, and presenting synthesized records within clinical dashboards.
This page outlines a custom automation that cross-references free-text physician notes with corresponding radiology or pathology images to flag discrepancies or confirm findings. It solves the manual correlation bottleneck, using NLP and computer vision agents to improve diagnostic consistency and catch potential errors. The architecture connects to PACS and NLP services, with implementation focusing on confidence scoring, exception routing to radiologists, and integration into reading workstations.
This page details a custom multi-agent system that analyzes fused patient data to generate risk-stratified diagnostic hypotheses with clear reasoning. It automates the initial sorting of complex cases, directing clinician attention to high-risk patients first to improve outcomes and operational flow. The build covers the orchestration of specialist LLM agents, integration of clinical rules, and the design of an interpretable UI that shows the 'why' behind each triage decision for clinician trust.
This page explains a custom workflow that monitors incoming lab results and imaging reports in real-time, applying context-aware rules to trigger clinically significant alerts. It replaces inefficient batch review, reducing time-to-intervention for critical findings like rising creatinine or incidental pulmonary nodules. Implementation involves building event-driven agents that subscribe to HL7 feeds, apply logic to fused patient context, and route prioritized alerts to the correct clinician via EHR in-basket or mobile push.
This page covers a custom orchestration layer that ensures urgent findings (e.g., critical cancer diagnoses, acute strokes) are never missed in communication chains. It automates the identification, routing, acknowledgment tracking, and failover escalation of critical results, directly addressing patient safety and legal risk. The architecture details agent roles for classification, communication, and audit logging, with implementation tied directly to radiologist dictation systems and hospital communication protocols.
This page describes a custom decision-support workflow where AI agents synthesize symptoms, history, labs, and imaging to generate and rank a differential diagnosis list. It augments clinical reasoning, helping reduce diagnostic error and cognitive load, especially in complex presentations. The solution architecture combines retrieval-augmented generation (RAG) over medical knowledge bases with patient-specific data fusion, requiring careful design for explainability, citation, and integration into physician note-taking workflows.
This page targets the technical build of a custom data-fusion layer that seamlessly bridges EHR (e.g., Epic) and imaging archive (PACS) systems. It automates the bidirectional flow of context, enabling images to be viewed with full clinical history and notes to be enriched with imaging findings. Implementation focuses on HL7/FHIR and DICOM web service integration, caching strategies for performance, and creating a unified API layer for downstream diagnostic applications.
This page details a custom workflow for ingesting high-frequency vital sign streams from ICU or floor monitors, summarizing trends, and injecting actionable insights into the patient's EHR record. It solves the data silo problem, giving clinicians a fused view without manual transcription. The architecture involves real-time data pipelines, anomaly detection agents, and EHR write-back APIs, with implementation challenges around data volume, latency, and clinical validation of automated summaries.
This page covers a custom automation that ingests and interprets data from consumer (e.g., Apple Watch) and medical-grade wearables, transforming it into clinically relevant observations in the EHR. It addresses the growing need to leverage remote patient-generated data for chronic disease management and pre-operative assessment. Implementation involves building connectors to wearable APIs, applying medically validated algorithms for trend analysis, and designing clinician-facing summaries that highlight deviations from baselines.
This page explains the build of a custom operational data store and presentation layer that unifies all patient data—EHR, claims, SDOH, patient portal entries—into a single, queryable profile. It eliminates the need for clinicians to hunt across 10+ tabs, saving significant time per encounter. The architecture focuses on identity resolution, data normalization, and a responsive UI, with implementation detailing incremental rollout, data governance, and performance optimization for real-time clinical use.
This page details a custom workflow where AI agents pre-process incoming imaging studies, run abnormality detection models, and prioritize the radiologist's worklist based on urgency and complexity. It directly increases radiology department throughput and reduces time-to-diagnosis for critical cases. The build covers PACS integration, the orchestration of multiple AI inference engines (e.g., for bleeds, fractures, masses), and the design of a triage dashboard that integrates seamlessly with existing radiology information systems (RIS).
This page outlines a custom automation that combines structured data from stress tests (ECG, blood pressure) with echocardiogram images and measurements into a unified report. It eliminates manual cut-and-paste between systems, reducing report generation time and improving data consistency for cardiologists. Implementation involves parsing proprietary device outputs, extracting measurements from echo report text, and using LLM agents to draft an integrated narrative for physician review and sign-off.
This page covers a highly specialized workflow for oncology, where agents correlate histopathology slide images (WSI) with genomic variant reports to suggest targeted therapies or clinical trial matches. It automates a complex, manual research task for pathologists and molecular tumor boards. The architecture combines WSI AI models, NLP for genomic report parsing, and knowledge graph retrieval, with implementation focusing on a secure, HIPAA-compliant platform for multimodal diagnostic support.
This page details a custom automation that assembles all relevant data for a cancer patient—imaging, pathology, genomics, prior treatments, performance status—into a standardized, pre-meeting briefing packet for the tumor board. It saves hours of manual preparation per patient, allowing clinicians to focus on decision-making. The workflow involves agents that query multiple hospital systems, summarize key findings, and generate a structured document, with implementation requiring tight integration with oncology-specific EHR modules and molecular diagnostics platforms.
This page explains a custom, high-acuity workflow that fuses real-time streams from ventilators, infusion pumps, monitors, and labs to detect early signs of patient deterioration (e.g., sepsis, ARDS). It moves beyond simple threshold alerts to multivariate pattern recognition, enabling earlier intervention. The architecture is built for low-latency processing, using stateful agents to track patient state over time, with implementation challenges around alarm fatigue, clinical validation, and integration into nurse central monitoring stations.
This page targets the revenue cycle, detailing a custom workflow where AI agents read clinical notes and discharge summaries to suggest accurate medical codes. It reduces coder burden, accelerates billing, and improves compliance by ensuring documentation supports the codes. The architecture uses LLMs fine-tuned for clinical language, integrated with encoder rules, and includes a human-in-the-loop review queue for complex cases, with implementation focused on EHR integration and coder productivity metrics.
This page covers a custom workflow that automates the most labor-intensive part of prior auths: gathering and formatting the required clinical evidence from the EHR to support a payer's request. It drastically reduces administrative time for clinical staff. The solution uses agents to identify relevant notes, labs, and imaging reports based on payer-specific criteria, compile them into a submission package, and even draft the clinical rationale, with implementation detailing integration with utilization management platforms.
This page details a custom workflow that automatically generates a first draft of the hospital discharge summary by synthesizing the entire admission record. It addresses a major source of physician burnout and documentation delay, improving compliance with timely discharge communication. Agents extract key events, medications, lab trends, and follow-up plans from fused data, producing a structured summary for the attending physician to review, edit, and sign, with implementation focused on EHR templating and physician workflow integration.
This page explains a custom automation that continuously calculates a validated risk score (e.g., NEWS, MEWS) by fusing real-time vitals, labs, and nursing assessments. It provides an objective, automated early warning system for clinical teams. The workflow involves data ingestion agents, a scoring engine, and dashboard integration, with implementation focusing on model retraining, alert threshold configuration, and change management to ensure clinical adoption of the automated scores.
This page goes beyond simple scoring to detail a custom workflow that actively surveils for sepsis and other deterioration patterns, triggering specific, protocol-based alert bundles. It aims to reduce mortality and length of stay by automating the earliest steps of the sepsis response. The architecture uses sophisticated pattern-matching agents on fused data streams to identify suspected sepsis, then automatically pages the rapid response team and suggests initial orders (lactate, blood cultures, antibiotics) for nurse or physician approval.
This page covers a population health automation that scans fused patient records against guidelines (e.g., for mammograms, colonoscopies, vaccinations) to identify care gaps. It then triggers personalized patient outreach via portal, text, or call. This workflow shifts preventive care from reactive to proactive, improving quality metrics and revenue. Implementation involves batch processing of EHR data, patient matching, and integration with patient engagement platforms, requiring careful design for scalability and opt-out management.
This page details a custom workflow that automates the error-prone process of medication reconciliation at care transitions. Agents compare home medication lists with hospital orders and new prescriptions, flagging discrepancies, duplications, and dangerous interactions for pharmacist or physician review. It directly improves patient safety and saves clinician time. The build involves NLP for parsing unstructured medication lists, integration with pharmacy databases, and designing a clear reconciliation interface within clinician workflows.
This page targets the data governance and research use case, detailing a custom workflow that automatically strips protected health information (PHI) from clinical notes and reports at scale. It enables safe data sharing for research and AI model training while complying with HIPAA. The architecture uses a pipeline of NLP models and pattern-matching rules, with human review for edge cases, and implementation focusing on throughput, accuracy metrics, and integration with data lake ingestion pipelines.
This page explains a custom workflow that transforms free-text physician notes into structured, queryable data by extracting specific clinical concepts (e.g., symptoms, medications, procedures). It unlocks the value trapped in narrative text for analytics, quality reporting, and decision support. The solution uses LLM agents configured for specific extraction tasks, validates outputs against ontologies like SNOMED CT, and writes the structured data back to the EHR's discrete fields, detailing the architecture for high-volume, low-latency processing.
This page covers a custom workflow that acts as an automated chart auditor, scanning patient records for missing signatures, incomplete forms, unsupported diagnoses, or conflicting data. It helps hospitals avoid denials and maintain accreditation by proactively identifying documentation defects. Agents are programmed with institutional and regulatory rules, generating task lists for HIM staff or clinicians to resolve. Implementation involves deep EHR integration and designing actionable exception reports.
This page details a custom workflow for 'store-and-forward' telehealth, where agents prepare a patient case for remote specialist review. It ingests patient-submitted photos, history, and prior records, synthesizes them into a structured consult request, and prioritizes it in the specialist's queue. This automates administrative triage, reducing specialist time spent on case assembly. Implementation focuses on patient-facing app integration, data security, and creating an efficient specialist review portal that fuses all submitted data.
This page explains the build of a custom workflow where a conversational AI bot conducts initial patient triage, then seamlessly creates a structured intake note in the EHR and routes the patient to the appropriate service (e.g., urgent care video visit, primary care appointment, nurse line). It reduces call center volume and improves routing accuracy. The architecture connects the chatbot to the EHR's scheduling and registration APIs, with implementation detailing the handoff protocol and ensuring the bot's clinical logic is safe and auditable.
This page targets clinical research, detailing a custom workflow that automates the extraction of patient data from the EHR and its transformation/transfer into an Electronic Data Capture (EDC) system for a clinical trial. It eliminates manual double-data entry, reducing site burden and error. The architecture involves mapping EHR fields to EDC case report forms, handling data transformations, and submitting data via EDC APIs, with implementation requiring robust validation, audit trails, and reconciliation processes for regulatory compliance.
This page covers a custom workflow that continuously screens the health system's patient population against clinical trial inclusion/exclusion criteria. It automates patient recruitment, identifying eligible candidates in near real-time and alerting research coordinators. This dramatically accelerates enrollment timelines. The solution uses NLP on fused data to interpret complex criteria, with implementation focusing on privacy-preserving search, patient opt-in workflows, and integration with clinical trial management systems (CTMS).
This page details a custom workflow that predicts which patients are at risk of not taking their medications as prescribed, based on fused data like refill history, social determinants, and engagement patterns. It then triggers personalized interventions (e.g., automated reminders, pharmacist calls, simplified regimens). This improves outcomes and reduces readmissions. The architecture combines predictive modeling with an orchestration layer for multi-channel outreach, implemented within pharmacy or chronic care management platforms.
This page explains a custom workflow that automatically flags patients on complex medication regimens or high-risk drugs (e.g., anticoagulants, opioids) for pharmacist or geriatrician review. It systematizes a critical safety check that is often ad-hoc. Agents analyze medication lists, lab values (e.g., renal function), and diagnosis codes to identify risk, then create a review task with relevant context. Implementation involves integration with pharmacy systems and designing an efficient review interface that presents fused patient data.
This page covers a custom workflow that automatically generates tailored education handouts or videos for patients based on their specific conditions, treatments, literacy level, and preferred language. It moves beyond generic pamphlets, improving comprehension and adherence. Agents pull data from the visit summary and diagnosis, retrieve appropriate content blocks from a knowledge base, and assemble a personalized document for clinician approval and delivery. Implementation focuses on content management system integration and delivery via patient portals.
This page details a custom workflow that ingests and analyzes digital patient intake forms (symptoms, history, concerns) before an appointment, generating a concise summary for the clinician. It saves the first 5-10 minutes of the visit spent on data gathering. NLP agents extract key complaints, flag urgent symptoms, and organize information into a structured format pre-loaded into the EHR note. Implementation involves patient-facing form builders, secure data ingestion, and seamless integration into the clinician's pre-visit planning workflow.
This page explains a custom workflow that goes beyond displaying single lab values, by automatically analyzing longitudinal trends (e.g., HbA1c over 2 years) and correlating them with relevant clinical events documented in notes (e.g., medication changes, hospitalizations). It provides deeper diagnostic insight, saving clinician cognitive effort. The architecture uses time-series analysis and NLP agents to find correlations, presenting an annotated trend graph and narrative summary within the EHR's lab viewer.
This page covers a specialized workflow for precision medicine, where AI agents assist in interpreting complex genomic variant reports. They retrieve relevant literature, clinical trial information, and pathogenicity predictions for identified variants, summarizing the clinical significance for the oncologist or genetic counselor. This automates hours of manual research per case. Implementation involves building a RAG system over curated genomic databases and designing a report overlay that integrates seamlessly with existing laboratory information systems.
This page details a custom quality assurance workflow that compares preliminary radiology reports (e.g., from a resident or AI) with final attending reports, or compares current reports with prior studies, to automatically flag significant discrepancies. It helps catch potential errors and supports peer learning. NLP agents are used to align findings and assess semantic differences, with flagged cases routed to a QA committee. Implementation focuses on integration with voice dictation and reporting systems and managing a secure review workflow.
This page explains a custom workflow that captures images and interpretations from handheld ultrasound devices used at the bedside, automatically structuring the findings and inserting them into the patient's EHR record. It solves the problem of POCUS data being lost or poorly documented. The architecture connects to device APIs, uses computer vision to standardize image views, and employs NLP to parse clinician voice memos, creating a structured note for cosignature. Implementation addresses workflow integration for emergency medicine and critical care.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
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