Logibec MMS - Material Management System

Procurement and logistics management automation, an advantage for medical teams and their patients

Logibec MMS - Material Management System automates the procurement process and logistics for hospitals. From needs identification and electronic order transmission to receipt of goods and online invoice management, Logibec MMS supports all teams involved and ensures the sustainability of healthcare.

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Average ratio of transactions processed electronically, without human intervention*

92 % 

Average ratio of invoices processed electronically, without human intervention.*

80 % 

Average ratio of purchase order transmitted through the EDI.*

* Average rate of healthcare facilities using the MMS solution.

The right product at the right place, at the right time

Logibec MMS is an automated solution that significantly reduces the risk of errors associated with order indexing that can disrupt logistics, affect merchandise warehousing and compromise the efficiency of medical interventions and patient safety.

Letting teams focus on patient care

The automation of material management, which includes needs identification, replenishment and requisitions, gives back precious time to clinical teams who can then focus on what matters most: their patients.

Improve staff efficiency and effectiveness

Logibec MMS decreases by at least 90% the number of human operations associated with repetitive tasks such as invoice and transaction processing; letting the staff work on high-value tasks.

Access quality data in real time
Logibec MMS empowers authorized stakeholders to access relevant information about the status of the budget, expenses, pending orders, inventory issues, etc., optimizing logistics and the procurement process.
Leverage evolved management reports and  analytical tools

With its cross-referenced data warehouse available in real time and its analytical dashboard, Logibec MMS provides managers with the support they need to make strategic procurement and logistical decisions.

Procurement Management
Computerized management of replenishment, product and service requisitions, returns to suppliers, contracts, calls for tenders, usage proposals and requests for quotations.
Advance Purchasing, Replenishment and Inventory Management

Automated purchasing, replenishment and inventory management based on a processing calendar. Includes integrated interfaces with dedicated systems used in the healthcare facility’s operations, automatic transmission of orders sent by EDI or email, ordered product management (traceability) and consignment inventory.

Automated Transaction Generation and Exchange

Automation of interfaces that facilitate the creation and bidirectional exchange of transactions toward and from generic processing screens, without human intervention.

Automated Transaction Accounting and Financial Statistics

Advanced accounting for transactions resulting from procurement activities as well as product receipts and deliveries and accounts payable. Management of accounting entries and commitments, production of real-time purchasing and usage statistics.

Electronic Requisition Management

Complete computerization of the requisition process, which allows requesters from various care units to send product and service requests electronically (using bar codes or RFID) to the procurement and logistics division.

Calls for Tenders, Usage Proposals and Requests for Quotations

Automatic tender, contract and order generation, synchronization with GPOs and purchasing groups. Integration of calls for tenders, usage proposals and requests for supplier quotations in the Electronic Requisition Management module.

Electronic Document Interchange (EDI)

Computerization and automation of transaction processing including functional acknowledgements of receipts (997), catalogs and contracts (832), orders (850), order acknowledgements (855), delivery notices (856) and invoices (810).

Electronic Document Management – Financial and Material Management

Integrates into Logibec’s FMS (Financial Management System) and MMS. Digitizes payable invoices using a character recognition system and automates the processing of received invoices (PDR or paper) within the same electronic processing and approval flow as the EDI.

Automatic Invoice and Credit Note Matching

Automatic consolidation of invoices and credit notes through basic data entry. Information is then recorded in Accounts Payable, and automatically applied to the accounts concerned.

Inventory Products Distribution Centre and Purchase Order Cross-Docking

Management and centralization (partial or complete) of inventory products in an external warehouse. Products are identified at the distribution centre, then ordered as direct purchases from suppliers affiliated with this centre prior to being delivered to the institution.

Fixed Asset and Project Management
Computerization of the fixed asset ordering process which tracks items by serial and batch numbers and monitors projects and amortization related to the Fixed Asset Register.
Data Mining and Analytical Dashboards

Data is extracted from requisitions, orders, returns, etc., and made available in real time through interactive dashboards and graphs. This facilitates supplier performance assessments and simplifies the procurement process.

Integrated Resource Management (IRM-BI)

Cross-references data from Logibec FMS, MMS and Human Resources and Payroll Management in a single report to provide a comprehensive view of the financial data related to clinical activities.

     Logibec MMS has met the challenge and automated our processes, from needs identification and electronic order transmission to the receipt of goods and electronic document and invoice management .


    - Ginette Proulx, Director, Procurement and Logistics at the CHUM

More about Logibec

Logibec is Stepping Into Predictive Analytics and Machine Learning to Help Prevent Hospital Readmissions

Dec 26, 2019, 22:05 PM by Yiou Huang

Logibec is in partnership with three healthcare organizations in Quebec, Canada, to develop and validate a readmission risk prediction model that will identify a patient’s likelihood of being readmitted within 45 days of discharge.

Nurse helping a long-term care patient get moving

Logibec is in partnership with three healthcare organizations in Quebec, Canada, to develop and validate a readmission risk prediction model that will identify a patient’s likelihood of being readmitted within 45 days of discharge.

Known for being costly, unplanned readmissions are one of the greatest challenges for healthcare organizations who are working towards offering the best care treatments to their patients. Several discharge and transitional care interventions aimed at lowering the number of unplanned readmissions have emerged in various jurisdictions; however, there is limited evidence in literature to support an understanding of which interventions are most effective.

In addition, it is important to underline that the return on investment of these interventions depends greatly on the hospital’s ability to accurately assess which patients are at high risk of readmission and will most likely benefit from these resource-intensive discharge and transitional care interventions.

Readmission risk prediction models designed for healthcare organizations have gained momentum in recent years. This trend is due to a combination of better access to individual-level electronic data and improvements in computing power, which has made available the use of predictive models and tools to hospitals for preventive management of high risk readmission patients. 

It is especially true in the United States since the introduction of the Medicare’s Hospital Readmissions Reduction Program (HRRP) under the Affordable Care Act that imposes a financial penalty on hospitals that have an excessive readmission rate.


Logibec’s Research Collaborative from Phase I to Phase II

With our partners – the Centre Hospitalier de l’Université de Montréal (CHUM), the North of Montreal Island Integrated University Health and Social Services Centre (CIUSSS du Nord de l’île de Montréal) and the Integrated Health and Social Services Centres of Laval (CISSS de Laval) – the two first objectives of the Research Collaborative consisted in reviewing the literature on existing readmission prediction models and in performing an external validation of the performance of the LACE index.  Our initial results were presented at the 2017 International Health Economics Association Congress in Boston.

The LACE index uses four variables to predict the risk of death or unplanned readmission within 30 days after hospital discharge among medical and surgical patients. It was developed in Ontario in 2010 by van Walraven et al. using data from 11 hospitals.

By applying the LACE index on our partners’ historical data (all adult inpatients discharged with the exception of psychiatric, obstetric or palliative care admissions), we validated the performance of the LACE index in adequately predicting 30-day death or urgent readmission after hospital discharge.

Our results suggested that the LACE index is a poor predictor of death or readmission within 30 days for patients who were at their first admission (i.e. having no historical acute care data) with a concordance C-statistic below 0.7 in all three studies.

The C-statistic, one of many measures used to assess the performance of a prediction model, tells how well the model distinguishes patients who are readmitted within 30 days from those who are not readmitted. A C-statistic of 0.5 indicates that the model is no better than chance for making a prediction. Prediction models are typically considered reasonable when the discriminative ability (C-statistic) is higher than 0.7 and strong when the C-statistic exceeds 0.8.

Considering patients with multiple admissions by randomly selecting one of their episodes, the C-statistic of the LACE index improved to 0.72 for two of the three organizations; however, the goodness-of-fit test results were not satisfactory.

The inconsistent results of the LACE index in predicting the risk of death or 30-day readmission using our partners’ local data speak to the importance of careful model construction and rigorous testing prior to adopting a predictive model in a hospital setting. Our Research Collaborative has evolved to Phase II, Logibec’s BI team composed of statistical, clinical, and IT experts are currently developing and validating a customizable Readmission Risk Prediction Tool. 

We have built the model using both traditional statistical methods and machine learning methods to predict readmission, and compared model discrimination and predictive range of the various techniques so as to build a model that delivers the highest level of accuracy while passing the various test criteria.

Being rigorous and cautious about the data chosen when building a prediction model is essential to its success when deployed in the real world!


Learn More About Logibec IA Readmission > 

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