EndlessMedical API supports 2000+ clinical data points grouped into 830+ features and over 180 diseases.
EndlessMedical API is one of the most advanced AI clinical diagnosis systems. It supports symptoms, signs, results of blood work, imaging, and physical examination findings - just to mention few categories of features.
EndlessMedical API technology
Patent pending methods create synthetic patient level data, which can be later used for AI/ML modeling.
EndlessMedical API doesn’t allow to input any patients identifying data, like date of birth, or name
With synthetic data there are significantly fewer concerns about patients’ privacy.
Our technology only processes de-identified numbers for numerical features (i.e. age, hemoglobin level) and labels’ identifying categories for categorical features.
EndlessMedical API, at any point, can suggest the next "best step” on diagnostic pathway
It may be next physical examination maneuver, next question to the patient, next blood test or imaging study to be done, which will most efficiently narrow down the list of likely diagnoses, recommended tests, and additional recommendations.
EndlessMedical API will produce explanation on why this "next step” is recommended
This feature of our modelling can be used to optimize the time of care by guiding providers through interviews, physical examination sessions, workup, and treatment processes in the most efficient way.
We hope this will allow providers and patients to avoid unwanted adverse events from unnecessary testing and save resources spent on unnecessary testing.
Diagnoses are flagged by, including “life-threatening emergency”, “high risk”, or otherwise.
This feature will allow providers to triage patients and suggest preparing billing and coding consistent with E/M guidelines.
Flags will allow call-center triaging and in person triaging the patients to right setting (office, telehealth, ER, urgent care) and correct specialist.
Avoid being misled by big-data
How can we trust AI/ML modeling built on big-data when the research and experience repeatedly show that big data electronic medical record data is not trustworthy?
This especially matters when it comes to rare diseases. Our technologies can be used to model rare (i.e. orphan) diseases.
Endless Medical API uses patent-pending technologies (USPTO # 20200118691, PCT/US2019/055747) to generate a knowledge database containing synthetic, individual patient- level data based on research, clinical experience and literature.
We encourage everybody to review the patents information to make sure they understand the advantages and disadvantages of EndlessMedical API technologies.
EndlessMedical API uses synthetic, individual patient-level data
EndlessMedical API does not use "big-data". Big data is inaccurate, has errors, typos, and biases, brings security, privacy and data ownership concerns.
We are using synthetic patient-level data for AI/ML modeling to overcome some of the big-data issues.
We host our products on HIPAA compliant, HITECH, SOC2 and SOC3, NIST and PCI certified hosting.
The EndlessMedical API's knowledge database and models are created only by Lukasz Kiljanek MD and his review of literature when deemed necessary. It can, on-demand, be transposed, to a different clinical setting, like an intensive care unit or an emergency department.
In the future, data sourced from multiple experts or data sources (i.e., research papers) will be merged to increase the robustness of the medical knowledge database and thereof sensitivity and specificity of AI/ML models.
Big-data under-represents rare diseases and tends to over-represent billable diagnoses and pertinent positive findings in physical examination and history, ignoring other normal and negative findings.
Big-data acquisition for research or commercial use requires costly and time-consuming IRB approvals.
Synthethic patient-level data is not perfect either, but maybe an alternative to big-data for some usages. For example, Endless Medical API uses these data for AI/ML modeling.
Endless Medical API technologies, allow creating synthetic databases echoing the true prevalence of diseases and clinical findings in a given environment.
For example, the incidence of strep throat in general practice office is different than the incidence of strep throat in nursing homes. Historically, AI and ML models trained on data from one environment can not be effectively used in another. EndlessMedical technologies would allow us to transpose the data from one environment to another.
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