Lead Horse Technologies is the Personalized Medicine Company that lowers healthcare costs for everyone by giving you better drug safety information than you can get anywhere else.        

Our product is the Medloom Decision Support system. In a pure SaaS model, Medloom is offered as a cloud-based app that bolts onto any electronic medical information system to make it better at lowering costs related to patient safety. It does this by using artificial intelligence that is a generation beyond the common ‘rules-based inference engines’ because it uses an AI technology known as Association Rule Discovery.  Like a cyborg, Medloom actually discovers significant associations on its own, surveying millions of real-world reports in real-time to identify certain kinds of patients that die or get hospitalized based on the meds they take and the kind of patient they are.  With an interface to any EMR system, Medloom then cross-references any patterns it has discovered in the ‘real world’ to identify patients under your care who may be at risk - and quantifies that risk - even before those patients call to complain of symptoms, automatically triggering clinicians to do something (order a lab, schedule an appointment, make a referral, etc.) to keep those events from ever happening. 

Neither the patient nor the clinician is identified with Medloom.  Cost-savings are metricked for demonstrable ROI, and a graphical display of analytics is provided.  Medloom does not replace existing drug-drug interaction (DDI) or adverse event (AE) information products – it supplements that information to prevent the loss of life and high rate of hospitalizations that continue to occur in spite of existing DDI and AE product offerings.  Medloom provides a solution to the unmet medical need for rapid access to real-world safety information at the point-of-care.

Medloom is affordable. Much like a cell phone plan, monthly hosting fees (that can come out of small, maintenance and operations budgets) require no high dollar authorizations and cover access, storage, and unlimited use for a defined number of users and an ability to add blocks of users to the contract, just as you would add blocks of minutes to your cell phone plan. Call today at 785-238-5666 to talk about making Medloom available to your company or institution.

Medloom bolts onto any electronic health information system. Integration within your electronic health information system, pharmacy database, or claims database enables hassle-free implementation at each customer site to provide cost savings associated with fewer Adverse Events, fewer deaths, fewer hospital 30-day re-admissions, and lower healthcare costs for the patient, the payer, and the provider.  Access to Medloom is provided in a cloud-based SaaS model through web services, HL-7, InterSystems Ensemble (a sophisticated HL-7 implementation), CCD, or any protocol required by its customers.

Medloom finds critical safety information using content from multiple data sources, including drug labels, but also pharmacovigilance registries. Besides integrating legacy safety data from drug labels, OTCs, and herbals, Medloom integrates post-marketing safety data from the US FDA’s MedWatch-populated Adverse Event Reporting System (AERS) database, as well as its own real-time AERS data, which are immediately available to Medloom users anywhere in response to ADR queries.

Medloom’s Ontology-Based Search Term Expansion technology allows an ADR query to be made using the term, e.g.,  liver toxicity, but it actually searches through our myriad data sources with hundreds of related clinical terms using concept maps to clinical ontologies like SNOMED-CT and others.  This approach avoids the frustrating results returned when a doctor knows there’s something in a drug label related to a specific problem but can’t find it in a medical reference system if they’re not stating it exactly as written.  We also have developed an artificial intelligence system that discovers significant Drug-ADR relationships in pharmacovigilance databases.

Medloom’s A.I. system lets users get a handle on important Adverse Drug Event associations in near real-time, whether it’s during a clinical trial or after a drug is launched.  Systems used elsewhere use advanced pharmacovigilance methods that include Empirical Bayes Geometric Mean (EBGM) and Proportional Reporting Ratio (PRR) methods.  Medloom’s A.I. technology uses an association rule discovery (ARD) approach to: 1) discover groups of drugs and events where an event depends on drugs; and 2) evaluate the importance of selected relations in a way that is something similar to PRR. While EBGM and PRR try to estimate how a specific drug or group of drugs is important for a specific adverse event, they don't explain how to select this drug or group of drugs and this event. Instead of waiting while somebody tries to evaluate how important a drug is for an event, Medloom’s A.I. technology discovers all potentially important relations and stores them in database. The association rule discovery approach that we use is not new, but what we did was develop a new algorithm that implements this approach. The innovation of Medloom’s embedded signal detection system is that, while it uses approaches similar to EBGM and PRR, it eliminates the need to specify the drug or the event, stores all potentially important relationships in a database, and then checks the discovered associations by any number of metrics, which can include PRR. The benefit is in application of ARD technology into an Adverse Event query algorithm that automatically implements this approach because an association of any specific drug with any specific adverse event may be insignificant without taking into consideration the role of complex polypharmacy profiles and other patient sub-type parameters; moreover, in general, it is very time-consuming to find all potentially important relations between all groups of drugs and all events. This is not so with Medloom’s A.I. technology.  Drug-Adverse Event discoveries can be made much more quickly with Medloom’s AI methodology compared to traditional pharmacovigilance methods. Results from Medloom’s A.I.-based searches are presented with levels of confidence and support, where “confidence” is the percentage of reports in the time period analyzed that have the specified AE among all reports with the specified set of drugs, and “support” is the percentage of reports in the time period analyzed that have the specified AE and the specified set of drugs.





Company Overview


Founder, President, & Chairman of the Board


VP of Product Development


Board Director &

Science & Medicine Advisory Board


Science & Medicine Advisory Board


Science & Medicine Advisory Board


Science & Medicine Advisory Board


Science & Medicine Advisory Board

Pottberg, Gassman, & Hoffman

Accounting Services

Medloom Videos

Medloom for iPhonehttp://www.youtube.com/watch_popup?v=oxyv1HUL-w0&vq=large
HIMSS 2011 Presentationhttp://www.youtube.com/watch_popup?v=24a8bWFdWQI&vq=large

Medloom PDFs

Clinical ValidationIndex_files/Clinical%20Validation,%20June%202011.pdf
Case StudyIndex_files/Medloom%20Case%20Study%201.pdf
Drug SafetyIndex_files/Medloom,%20the%20Ultimate%20Source%20for%20Drug%20Safety.pdf
Medloom for Dummieshttp://youtu.be/deKjmTCDxYE
Medloom for Pharmahttp://youtu.be/5NuIYLHCwco
‘Medloom for Me’ for the iPhonehttp://www.MedloomForMe.com
‘Medloom in an HIE’ for Dummieshttp://youtu.be/fvh_147sRng

Why we’re different

Medloom’s HL7 integration enginehttp://www.maddash.net/videos/intersystems/ensemble/hl7/
Medloom: A live demonstrationhttp://www.youtube.com/watch?v=9xWY2y-gGpI&feature=g-upl
Medloom: What it’s for and how it workshttp://youtu.be/5ukfx-lmsyE