Easy Data Extraction: Extract key information from requisition forms automatically with AI, minimizing manual work and human error.
Enhanced Operational Efficiency: Free lab staff from data entry tasks, streamline workflow, and optimize resource allocation.
Improved Accuracy & Scalability: Ensure data accuracy for patient testing and empower scalable operations through cloud-based infrastructure.


Faster Results. Enhanced Accuracy. Improved Patient Care.

Requisition AI leverages the power of OCR, Vision ML, and Generative AI to automate data extraction from requisition forms. Reduce manual review and data entry by up to 90%, freeing up lab staff time and minimizing errors. Boost operational efficiency, enhance data accuracy in patient testing, and empower your lab to scale with ease.

The Main Problem

Inefficient workflow in patient requisitions

Manual processing of patient requisition forms creates a bottleneck in lab operations. Lab technicians spend a significant amount of time reviewing and entering data from paper forms, hindering efficiency and compromising accuracy. This time-consuming process can delay test results and, hence, impact patient care.

Labor-intensive manual data entry
Error-prone manual processes
Workflow bottlenecks affect lab throughput

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Pain Point #1

Lab Technicians Drowning in Paperwork?

Lab technicians spend a substantial amount of time, often hours per day, reviewing and entering data from paper forms. This repetitive and time-consuming task diverts their attention away from higher-value activities crucial for patient care.  Imagine a skilled lab technician spending hours on data entry instead of analyzing test results, interpreting data to identify potential issues, or consulting with physicians about critical tests. This not only hinders lab efficiency but also limits the opportunity for lab staff to contribute their expertise to patient care.

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Pain Point #2

Errors in Manual Data Entry Delaying Test Results

Handwritten data entry is prone to errors, which can have serious consequences for patient care.  Typos, misinterpretations, or incorrect information on requisition forms can lead to delays in test results. This can create a backlog, forcing patients to wait longer for critical diagnoses and treatment decisions.  In some cases, errors can even lead to misdiagnosis and improper treatment, jeopardizing patient health and incurring additional healthcare costs.

Pain Point #3

Struggling to Scale Lab Operations Efficiently

Manual processes create a rigid and inflexible workflow, limiting a lab's ability to scale its operations efficiently.  As testing volumes increase, labs using manual processes can experience backlogs and longer wait times for patients. Additionally, the repetitive nature of manual data entry tasks can make it difficult to attract and retain qualified lab staff.  This limited scalability can hinder a lab's ability to handle new types of tests or procedures, ultimately impacting the quality of service they can deliver to patients.

By eliminating these pain points, Requisition AI empowers labs to streamline workflows, improve data accuracy, and achieve greater operational efficiency. This translates to faster turnaround times for test results, improved patient care, and the ability to scale effectively to meet growing patient demands.

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JP Grace
Chief Technology Officer

As our customer base grew, we ran into PostgreSQL vertical scalability limits and problems like CPU, memory and connection exhaustion. We were thrilled the solution gave us a drop-in PostgreSQL replacement with much more efficient reads and writes. The solution requires less CPUs to hit our throughput and latency goals, lowering our cost by 40-50% and preparing us for the next phase of customer growth.

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