Welcome to

Pay & Research is a platform tailored to facilitate access to Leumit Health Services (HMO) medical data, enhancing research, development, and feasibility studies. It serves healthcare startups, entrepreneurs, academic entities, and research institutions, aiming to address and simplify common obstacles faced in healthcare innovation.

 

Changing the Rules
of Engagement

Reduced Costs

Accessing medical data for research and development can be expensive.
Our innovative engagement model enables Leumit Start to give you fast, easy and significantly cheaper access to our comprehensive synthetic medical records for 2,500 Euro + VAT.

Reduced Time

The process of signing a data sharing agreement might take months.
However, in Pay & Research, our online agreement streamlines the typically long legal procedures both parties go through for such collaborations, accomplishing it within just two working days.

Reduced Bureaucracy

Signing collaboration agreement and obtaining IRB approval (Helsinki) consist of a lot of paper-work.
By signing online on a generic agreement, and by using Synthetic data we reduced the bureaucracy to minimum.

01

Register

Read carefully and understand our general Terms & Conditions and the Term of Use Agreement, explore “The Data” description page and make sure we have the data you’re looking for.

02

Sign & Pay

Accept the Term of Use Agreement, after which we’ll call you to schedule a video meeting to verify your identity.

Upon completion of this procedure, you’ll receive an email containing a link that will allow you to sign-in and purchase a Virtual Lab Room.

 

03

Get Access

You will be provided with guidance on connecting to the Virtual Lab Room, where you can proceed to install your analysis tools.

Following the installation, we will upload the synthetic data set to your Lab Room, enabling you to begin your work.

About the
Data set

The data intended for sharing is a “one size fits all” synthetic data, based on real-world, raw data from Leumit’s EMR.

The synthetic data represents patients’ information from 2003 until June 2018 but due to the synthesis process the data will be spread beyond this range (for computational reasons only).

The data contains information on approximately 130,000 synthetic patients in a distribution close to reality in Leumit. The data however, does not claim to be a full representation of the population. For a more in-depth explanation, please refer to the attached file below.

This cohort is randomly selected and is a representative sample of the population in terms of demographic, medical condition and other aspects. The synthetic data is structured in such a way that it simulates the statistical behavior of the real population and enables a reliable analysis and a very close approximation to the real-world data.

 

The data will be accessible in CSV format files and contains the following types of information:

  • Customer Details: Gender, demographics, customer status, etc.
  • Provider’s Treatments: Data regarding hospitalizations, ERs, and other treatments (treatment code, date, etc.) as reported by external providers.
  • Physician’s/Nurse’s Procedures: Procedures performed by the Physicians/ nurses in the HMO clinics (Procedure code, date, etc.).
  • Medication Consumption: Data regarding medication consumption (Date, Drug ATC7 code, Quantity, etc.).
  • Medication Prescriptions: Data regarding prescriptions for medications given by the Physician (Date, Drug ATC7 code, Dose, etc.).
  • Physicians’ Details: License number (deidentified token), specialty.
  • HMO Clinic Visits: Physician license (deidentified token), Customer token, date & time.
  • Diagnosis – HMO: Diagnosis given by the HMO’s physicians encoded by ICD9 code (Physician license deidentified token), Customer token, date & time, ICD9, status, etc.).
  • Diagnosis – Hospitals: Diagnosis given by the hospital’s physicians encoded by ICD9 code (Hospital, Date, ICD9) (data from 2007, gradually added).
  • Medical Measures: Height, weight, heartbeat, blood pressure, etc.
  • Sensitivities: Medications, allergies, food, etc. (Date, type, description, status, etc.).
  • Laboratory Tests: Demands, Orders, Results (Date, type, name, Physician license (deidentified token), results, etc.).
  • Medical Assessments: Data regarding estimations/ Assessments performed mainly by nurses (Date, assessment code, Nurse details, result, question & answer code, etc.
  • Risk Factors: Such as smoking, drinking, drugs, etc. (risk code, date, value, etc.).
  • Insurance Details: Insurance type, date start/end

In the table below, we have gathered data on the prevalence of a few common clinical conditions in the dataset that are often subjects of research (based on documented diagnoses). For detailed information on these conditions and the diagnoses that were included.

Diagnosis
Cohort size
Colorectal cancer
561
Lung cancer
909
Breast cancer
1075
Skin cancer
2636
Diabetes
10236
CKD
2052
COPD
3488
Asthma
29258
Stroke
1817
Dementia syndromes
2146
NAFLD
2784
Cardiovascular
10092

Limitation in the dataset:

  • The data does not contain any prices and costs.
  • The data does not contain the names of locations (towns, cities etc.), providers and hospitals.
  • Very rare occurrences are not fully reflected in the synthetic data.
  • The synthetic data is not suitable for research related to seasonality.
  • Medication names are based on the generic name and not the commercial name.

For more information about the synthetic data, please read carefully these two files:

MDClone – Leumit Synthetic Data solution
Leumit- The complete Data Catalog

Frequently Asked Questions

In order to accelerate research & development in data while keeping patient privacy, an innovative approach to data anonymization has developed in the past few years.

This approach is dealing with generating artificial patients’ records that reside on real patients’ data with high fidelity to real patients.

This approach is called Synthetic Data. Synthetic data is non-reversible, artificially created data that replicates the statistical characteristics and correlations of real-world, raw data. Utilizing both discrete and non-discrete variables of interest, synthetic data does not contain identifiable information because it uses a statistical approach to create a brand-new data set.

The reason we use Synthetic Data in Pay & Research is the ability to deliver data for research in days instead of months.

We use Mdclone’s technology to generate the Synthetic Data. 

Following signing the agreement you’ll get access to a Virtual Lab Room, where you’ll be able to upload and install any analysis tool you think you might need (Windows OS environment).

After confirming you have finished installing your research tools, we will take two steps:

1. We will disable Internet access in the Virtual Lab Room.

2. We will upload the synthetic data to the Virtual Lab Room.

After performing these two steps you’ll be able to log-in to the Virtual Lab Room and start your work on the Data.

Please notice that you should use tools that don’t need any on-line access to the Internet.

Virtual Lab Room is a concept in which the data research is done on a dedicated computer that the researcher connects to, and contains the Data itself and the research tools for the study.

In this way the data never leaves the organization, and the researcher can work from wherever he/she likes.

In our solution the Virtual Lab Rooms are hosted on Microsoft Azure machines running Windows OS environment.

you’ll connect to the machine using VPN.

Access to the Virtual Lab Room is configured for single-user access. However, access can be extended for an additional fee by prior arrangement with the support team.

After signing the agreement and purchasing the Pay & Research service, we’ll provide you access to the Virtual Lab Room in 2 working days.

From that point you’ll have the ability to upload your analysis tools to the Virtual Lab Room.

When you confirm you finished installing the analysis tools, we’ll disconnect the Lab Room from the internet and upload the synthetic data set to the Lab Room.

This usually takes less than 2 working days.

We’re here to assist you with any problem or concern.

You’ll need to contact our support team and we’ll get back to you shortly, and not more than one working day.

Your data is securely stored in your personal Virtual Lab room.

Only you and our support team can access this data.

In this case you’ll have to contact our support team and we’ll help you upload any tool or file you might need.

Please make sure to upload any tool you might need in advance.

Unfortunately, we can’t allow any raw data to be downloaded from the Virtual Lab Room.

You’ll be able of-course to download any analysis material and products based on the synthetic data you were working on (but not the raw data itself).

When you confirm you’ve finished your work, you’ll be asked to make sure that all relevant files are moved to a specific “export” folder, then we’ll organize your data to be downloaded without the raw synthetic data set.

Unless you specifically ask to end the subscription, the subscription is automatically renewed for another period of 30 days, at a cost of 2,000 NIS + VAT. We will inform you by email before the subscription period ends.

 

· The data contains information from 2003 to June 2018.

· The data does not contain any prices and costs.

· The data does not contain the names of locations (towns, cities etc.), providers and hospitals.

· Very rare occurrences are not fully reflected in the synthetic data.

· The synthetic data is not suitable for research related to seasonality.

· Medication names are based on the generic name and not the commercial name.

Our platform runs on Microsoft’s Azure, and these are the specs of a standard Virtual Lab Room :

· Compute power: 4 cores CPU with 16 GB of RAM (VM size model in Azure: “Standard_D4s_v3”).

· Disk storage: 127 GB + dedicated 30GB storage for the RAW data.

· Operating System: Windows 11.

According to our experience, a standard Virtual Lab Room machine suits most cases regarding the analysis of structured data. However, by contacting us, you can upgrade to a more powerful machine with an additional payment, according to the costs in Azure, covering the difference from the standard machine.

According to our experience, a standard Virtual Lab Room machine suits most cases regarding the analysis of structured data. However, by contacting us, you can upgrade to a more powerful machine with an additional payment, according to the costs in Azure, covering the difference from the standard machine.

Access to the Virtual Lab Room is configured for single-user access. However, access can be extended for an additional fee by prior arrangement with the support team.

You can contact us on the ‘Contact Us’ page or by email at innovation@leumit.co.il. Our office address and phone number are: Leumit Laatid, Harba 18/A, Tel-Aviv, P.C. 6473918, +972 (3) 6913775.

Unlock Instant Access to the Data You Need

Start your medical research journey today! Leave your name and email below to begin the process digitally and receive everything you need to access Leumit Start’s synthetic medical data platform.

Once you submit, you’ll immediately enter the streamlined process—getting you the data you need in just two working days.

 

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Sign up now to get started and simplify your research with fast, reliable healthcare data. The process begins as soon as you click “Submit”!

Pay & Research

Leumit Start is Leumit Health Services innovation and digital health unit. We aim to encourage Innovation in the HealthTech industry by supplying responsible and reasonable access to de-identified high quality clinical data, clinical consultancy, clinical trials and more. Leumit Start operates under Leumit La’atid Ltd.

Welcome to P&R!

Your application has been sent for human review.

Please set a meeting via this link for a video call to verify your identity.

Please ensure you have a valid ID with you for the call.
Once we have confirmed your identity, you will be able to proceed to the payment stage and get access to the Virtual Lab Room.

Thank you,Pay & Research,
Leumit Start Team