We support pharmaceutical/medical device companies by taking advantage of our strengths in possessing raw data and having deep knowledge in data analysis.
Ad hoc service
1. Raw data services
We provide raw data related to all disease areas.
|Patient ID||Medical institution ID||Claims ID||Claims ID||Claims ID||Claims ID||Claims ID|
|Patient date of birth||Number of beds||Patient ID||Claim type||Claim type||Claim type||Claim type|
|Patient sex||HPGP classification||Claim type||Patient ID||Patient ID||Patient ID||Patient ID|
|Individual’s own family||Medical department major classification||Date of medical care||Date of medical care||Date of medical care||Date of medical care||Date of medical care|
|Start date of medical observation||Medical department intermediate classification||Medical department major classification||ICD10 major classification code||Item ID||Date performed||Date used|
|End date of medical observation||Management body||Medical department intermediate classification||ICD10 major classification term||ATC major classification code||Item ID||Item ID|
|Reason for end of medical observation flag||Home care support clinics||Number of days for medical procedure||ICD10 intermediate classification code||ATC major classification term||Medical procedure category intermediate classification term||Major category term|
|Community health support hospital||Hospitalization date||ICD10 intermediate classification term||ATC intermediate classification code||Medical procedure category minor classification term||Intermediate classification term|
|Designated cancer hospital||Discharge date||ICD10 minor classification code||ATC intermediate classification term||Medical procedure category sub-classification term||Medical procedure points quick reference table classification code|
|Medical institutions that have adopted DPC||Points||ICD10 minor classification term||ATC minor classification code||Medical procedure points quick reference table classification code||Standardized specimen code|
|Advanced treatment hospitals||ICD10 sub-classification code||ATC minor classification term||Standardized medical procedure ID||Standardized specimen term|
|ICD10 sub-classification term||ATC sub-classification code||Standardized medical procedure term||Unit Chinese character term|
|Standardized injury/disease code||ATC sub-classification term||Frequency||Frequency|
|Standard disease term||WHO-ATC code||Simultaneous ID||Simultaneous ID|
|Suspected flag||WHO-ATC term||Medical procedure category||Medical procedure category|
|Start month of medical procedure||Component name||Points||Specimen price|
|Pharmaceutical product name|
|Generic drug flag|
|Daily dose per prescription|
|Number of dosing days per prescription|
|Dosage form major classification term|
|Dosage form intermediate classification term|
|Dose to be taken only once flag|
|Medical procedure category|
|Body mass index (BMI)||Hemoglobin A1c (HbA1c)||Smoking|
|Abdominal circumference||Urine sugar||Exercise habits|
|Systolic blood pressure||Urine protein (qualitative)||Food consumption habits 1 (eating fast, etc.)|
|Diastolic blood pressure||Serum uric acid||Food consumption habits 2 (eating before bed)|
|Blood sampling time (after meals)||Serum creatinine||Food consumption habits 3 (eating at night/between meals)|
|High density lipoprotein (HDL) cholesterol||Hemoglobin||Alcohol consumption habits|
|Low density lipoprotein (LDL) cholesterol||Red blood cells||Volume of alcohol consumed|
|Glutamic-oxaloacetic transaminase (GOT) [Aspartate aminotransferase (AST)]||Electrocardiograms (ECG) [presence/absence of findings]||Sleep|
|Glutamic-pyruvic transaminase (GPT) [Alanine aminotransferase (ALT)]||Fundus examination (Keith-Wagner classification)|
|Gamma-glutamyltransferase (γ-GTP)||Fundus examination (Scheie classification: H)|
|Fasting blood glucose||Fundus examination (Scheie classification: S)|
|Casual blood glucose||Fundus examination (SCOTT classification)|
|Smoking||At present, I regularly smoke cigarettes
(1: Yes, 2: No)
|Exercise habits||I exercise lightly enough to sweat for at least 30 minutes a day at least twice a week for at least 1 year
(1: Yes, 2: No)
|Food consumption habits 1 (eating fast, etc.)||Compared to others, I eat fast.
(1: Fast, 2: Normal, 3: Slow)
|Food consumption habits 2 (eating before bed)||I eat dinner at least 3 times a week within 2 hours before bedtime.
(1: Yes, 2: No)
|Food consumption habits 3 (eating at night/between meals)||I eat snacks between meals (an evening snack other than the 3 meals) at least 3 times a week after dinner.
(1: Yes, 2: No)
|Dietary habits||I skip breakfast at least 3 times a week.
(1: Yes, 2: No)
|Alcohol consumption habits||Frequency of alcohol (sake, shochu, beer, liquor, etc.) consumption
(1: Every day, 2: Sometimes, 3: Rarely)
|Volume of alcohol consumed||Volume of alcohol consumed per day on a drinking day
(1: <1 go (1 go ＝ <0.18 L), 2: 1 to 2 go, 3: 2 to 3 go, 4: ≥3 go)
|Sleep||I get enough restful sleep.
(1: Yes, 2: No)
2. Data aggregation services
Analyses are custom-designed according to research questions.
Flow of data aggregation services
Full data access licenses
Annual full access licenses for JMDC's Payer-based DB/Hospital-based DB will be provided. Users can freely access the database during the contract period.
This is a database that focuses on medical institution targeting and area marketing based on open data.
Refer to the following information for further details.
JMDC Pro is a new real world data (RWD) analysis tool based on business intelligence (BI) tools from the Google group company, Looker Inc. (United States), which was developed using JMDC's deep knowledge on RWD.
Features of JMDC Pro
This analyzes the monthly continuation/new/addition/switch (in, out)/dropout patient share for any drug. An accurate picture of the acquired share of one’s own drugs among new patients and the drugs from which these patients were acquired or dropped out can be obtained.
This shows the rate of continuation of prescription, ongoing concomitant drugs, and prescription after dropout from the treatment. By comparing these over time, verification of the effectiveness of measures becomes feasible.
Analysis examples：Treatment flow
This analyzes the flow of prescription patterns. With an actual prescription-based analysis that does not rely on memory, an accurate picture of clinical phenomena, such as the timing of prescribing one’s own products and concomitant medications, can be obtained.
JMDC Data Mart
JMDC Data Mart(JDM) is an online tool that connects the JMDC database and clients, enabling a speedy and intuitive analysis.
Features of JDM
JDM can be used in various situations from the drug discovery stage to the post-marketing stage.
Enables comprehensive market analyses, such as estimating the number of patients, number of days of administration, dose, and pattern of concomitant medications as per the disease.
Patient reported outcome (PRO) surveys
Acquiring real-world PRO data is feasible through the health portal site for insured individuals. In addition, valuable research and surveys can be conducted by combining this with health data.
Can be utilized as a new tool for patient surveys and as an extension of web-based clinical research.
PRO survey positioning
Digital and medical/healthcare data self-evaluation enables research and surveys to be conducted in a short period of time at a lower cost with a quality exceeding that of a survey panel.
For example, the following surveys are feasible.
We provide total support for activities related to database research, from consulting to analyses, for setting research questions.
|What is required for database research?||JMDC|
|Setting of research questions/consultation||◯|
|Environment to use various databases||△*|
|Creation of concept sheets||◯|
|Preparation of analysis plan||◯|
|Statistical analysis activities||◯|
|Presentation at scientific/academic conferences||◯|
|Management of Key Opinion Leader (KOL) meetings||Under preparation|
*Research using data from other companies, website surveys, etc. can also be conducted. Please consult us for more details.
*A portion of activities will be outsourced.
Example of deliverables
|Deliverables (example)||Details of activities|
|Feasibility survey and research consultation||Confirmation of feasibility depending on research questions (prior to purchase of databases)
Consultation prior to development of research study protocol
|Concept sheets||Create concept sheets at the time of research study planning|
|Research study protocol||Prepare draft study protocols based on concept sheets|
|Analysis plan||Prepare analysis plans based on study protocols|
|Output plan||Prepare output plans based on study protocols and analysis plans|
|Data set structure definition document for analyses
Analysis data sets
|Prepare and provide support in instances where analysis is performed in-house, etc.|
|Analysis activities (output results)||Analysis|
|Analysis reports||Prepare analysis reports at the completion of analyses|
|Quality control (QC) records||Prepare QC records of analysis activities|
|Clinical study report (CSR)||Prepare CSRs|
|Publication activities||Prepare manuscripts and materials for presentations at academic conferences|
*Deliverables vary depending on contract details.
Published articles from research outsourced to JMDC’s analysis team
|Prevalence, disease burden, and treatment reality of patients with severe, uncontrolled asthma in Japan.||Allergology International 2019. pii:S1323-8930(19)30077-2.
|Burden of Herpes Zoster in the Japanese population with immunocompromised/chronic disease conditions: Results from a cohort study claims database from 2005-2014.||Dermatology and Therapy 2019;9(1):117-133.|
|The prevalence, characteristics, and patient burden of severe asthma determined by using a Japan health care claims database.||Clinical Therapeutic 2019. pii: S0149-2918(19)30418-7.|
|Disease severity and economic burden in Japanese patients with systemic lupus erythematosus: A retrospective, observational study.||International Journal of Rheumatic Diseases 2018;21(8):1609-1618.|
|Risk of herpes zoster in the Japanese population with immunocompromising and chronic disease conditions: Results from a claims database cohort study, from 2005 to 2014||The Journal of Dermatology. 2020 Mar;47(3):236-244.|
|Patients with asthma prescribed once-daily fluticasone furoate/vilanterol or twice-daily fluticasone propionate/salmeterol as maintenance treatment: Analysis from a claims database.||Pulmonary Therapy 2018;4(2):135‒147.|
|Impact of body mass index and metabolic phenotypes on coronary artery disease according to glucose tolerance status.||Diabetes & Metabolism 2017;43(6):543-546.|
|Impact of glucose tolerance status on the development of coronary artery disease among working-age men.||Diabetes & Metabolism 2017;43(3):261-264.|
|Treatment patterns and health care costs for age-related macular degeneration in Japan: An analysis of national insurance claims data.||Ophthalmology 2016;123(6):1263-8.|
|Association between severe hypoglycemia and cardiovascular disease risk in Japanese patients with type 2 diabetes.||Journal of the American Heart Association 2016;9;5(3):e002875.|
|Adherence to dipeptidyl peptidase-4 inhibitor therapy among type 2 diabetes patients with employer-sponsored health insurance in Japan.||Journal of Diabetes Investigation 2016;7(5):737‒743.|
|Short-term impacts of sodium/glucose co-transporter 2 inhibitors in Japanese clinical practice: considerations for their appropriate use to avoid serious adverse events.||Expert Opinion on Drug Safety 2015;4(6):795-800.|
|Use of the Japanese health insurance claims database to assess the risk of acute pancreatitis in patients with diabetes: comparison of DPP-4 inhibitors with other oral antidiabetic drugs.||Diabetes, Obesity and Metabolism 2015;17(4):430-4.|
|Intussusception in Japanese infants: Analysis of health insurance claims database||Open Journal of Pediatrics 2013;3(4):311-316.|
Post-marketing database surveys
We have developed a database for post-marketing database surveys.
The system/procedures are also in place. Please consult us if you are interested to use it.
As a data expert, JMDC provides consulting services to help companies maximize the potential of various data.
Our strengths are in the retention of raw data, deep knowledge in data analysis, and access to diverse human resources, such as strategic consultants and machine learning engineers.