Current Status of Databases in Japan



Similar documents
Asian Data Resources. October 24, :30-12:30 Using pharmacoepidemiology database resources to address drug safety research

Fujitsu Healthcare Business Overview

1/16/2015 HOW CLINICAL EDUCATORS CAN DISCLOSURE LEARNING OBJECTIVES MAKE MEANINGFUL USE MEANINGFUL. We have no financial disclosures

Speaker Second Plenary Session ELECTRONIC HEALTH RECORDS FOR INFORMED HEALTH CARE IN ASIA- PACIFIC: LEARNING FROM EACH OTHER.

How To Improve The Pharmaceutical Industry In Japanese

Risk Management Plan (RMP) Guidance (Draft)

Application for Accreditation of Foreign Manufacturers

Secondary Uses of Data for Comparative Effectiveness Research

Meaningful Use Objectives

A method for handling multi-institutional HL7 data on Hadoop in the cloud

Public Health and the Learning Health Care System Lessons from Two Distributed Networks for Public Health

Meaningful Use: Stage 1 and 2 Hospitals (EH) and Providers (EP) Lindsey Mongold, MHA HIT Practice Advisor Oklahoma Foundation for Medical Quality

Joint Position on the Disclosure of Clinical Trial Information via Clinical Trial Registries and Databases 1 Updated November 10, 2009

Environmental Health Science. Brian S. Schwartz, MD, MS

Guide To Meaningful Use

MEANINGFUL USE STAGE 2 REQUIREMENTS FOR ELIGIBLE PROVIDERS USING CERTIFIED EMR TECHNOLOGY

Secondary Uses of Health Data IMPAC s Oncology Data Alliance Program

Key Learnings Pharmacy Access Project. Key Learnings and Applications. Changes in Medication Adherence Rates. Jack Mahoney, MD Medical Director

Enabling Patients Decision Making Power: A Meaningful Use Outcome. Lindsey Mongold, MHA HIT Practice Advisor Oklahoma Foundation for Medical Quality

MEANINGFUL USE STAGE FOR ELIGIBLE PROVIDERS USING CERTIFIED EMR TECHNOLOGY

Health Information Technology and the National Quality Agenda. Daphne Ayn Bascom, MD PhD Chief Clinical Systems Officer Medical Operations

Strengthening the HCV Continuum of Care

Risk Management Plan (RMP) on Biologicals and NCE

HEALTH CARE DATA IN QATAR

gehr Project: Nation-wide EHR Implementation in JAPAN

Meaningful Use. Medicare and Medicaid EHR Incentive Programs

ELECTRONIC MEDICAL RECORDS (EMR)

Information for patients and the public and patient information about DNA / Biobanking across Europe

Electronic Medical Records

Manitoba EMR Data Extract Specifications

MICROMD EMR VERSION OBJECTIVE MEASURE CALCULATIONS

Electronic Health Records - An Overview - Martin C. Were, MD MS March 24, 2010

Electronic health records to study population health: opportunities and challenges

Custom Report Data Elements: IT Database Fields. Source: American Hospital Association IT Survey

Meaningful Use of Certified EHR Technology with My Vision Express*

IMS Meaningful Use Webinar

CRITICAL ILLNESS CLAIM FORM

Meaningful Use: Registration, Attestation, Workflow Tips and Tricks

STAGES 1 AND 2 REQUIREMENTS FOR MEETING MEANINGFUL USE OF EHRs 1

Use of routinely collected electronic healthcare data: Lessons Learned

Risk Adjustment: Implications for Community Health Centers

Current Status and Problems of Breast Cancer Screening

Meaningful Use Rules Proposed for Electronic Health Record Incentives Under HITECH Act By: Cherilyn G. Murer, JD, CRA

CONSENT FOR MEDICAL TREATMENT

HL7 & Meaningful Use. Charles Jaffe, MD, PhD CEO Health Level Seven International. HIMSS 11 Orlando February 23, 2011

Stage 2 Meaningful Use What the Future Holds. Lindsey Wiley, MHA HIT Manager Oklahoma Foundation for Medical Quality

Population Health Management A Key Addition to Your Electronic Health Record

Agenda. What is Meaningful Use? Stage 2 - Meaningful Use Core Set. Stage 2 - Menu Set. Clinical Quality Measures (CQM) Clinical Considerations

VIII. Dentist Crosswalk

EHR Meaningful Use Incentives for School-Based Health Clinics

Webinar #1 Meaningful Use: Stage 1 & 2 Comparison CPS 12 & UDS 2013

The Japan Generic Market Drivers and Obstacles for Change. Matt Heimerdinger Anterio Inc.

THE EYE INSTITUTE. Dear Patient:

Custom Report Data Elements: 2012 IT Database Fields. Source: American Hospital Association IT Survey

Disclosures. Real World Data Sources

Using the Taiwan National Health Insurance Database to Design No Claim Discount in Hospitalization

Taking Part in Research at University Hospitals Birmingham

A Greater Understanding. A Practical Guide to Clinical Trials

The global classroom: Training the world s healthcare professionals

I-STOP: What You Need to Know. Lyuba Konopasek, MD Designated Institutional Official NewYork-Presbyterian Hospital August 26, 2013

Integrated Healthcare Information System. Darko Gvozdanović, M.Sc.E.E. Department manager e-health

How Big Data is Shaping Health Care Decisions Pat Keran Sr. Director of Innovation Optum Technologies

Tuberculosis Treatment in Japan: Problems and perspectives

Disease Prevention to Reduce New Hampshire Healthcare Claims and Costs: A Data Mining Approach

Natalia Olchanski, MS, Paige Lin, PhD, Aaron Winn, MPP. Center for Evaluation of Value and Risk in Health, Tufts Medical Center.

3/11/15. COPD Disease Management Tackling the Transition. Objectives. Describe the multidisciplinary approach to inpatient care for COPD patients

Treatment of Low Risk MDS. Overview. Myelodysplastic Syndromes (MDS)

Safety Risk Management Company Perspective

Maximizing Limited Care Management Resources to Improve Clinical Quality and Ensure Safe Transitions

Image Retention in the PACS Era

Big Data Analytics in Healthcare In pursuit of the Triple Aim with Analytics. David Wiggin, Director, Industry Marketing, Teradata 20 November, 2014

Survey of Medical Care Activities in Public Health Insurance

Transcription:

Current Status of Databases in Japan 2012.03 Kiyoshi Kubota, MD, PhD, FISPE Department of Pharmacodpiemiology, Graduate School of Medicine, University of Tokyo Kubotape-tky@umin.ac.jp NPO Drug Safety Research Japan

Small Databases

Databases in Japan Small databases (including commercially available databases) posted in the web page of Japanese Society for Pharmacoepidemiology (JSPE) http://www.jspe.jp/mt-static/fileupload/files/jspe_db_tf_e.pdf Electronic Health Record (EHR)-type DB Claims data From Insurers Claims data From Pharmacies ~2million people ~1million ~0.6million ~7million

EHR-type DBs Merits (Some) labo-data results are available Patients covered by different-types of insurance are included Almost complete data for inpatients Demerits Source population cannot be well defined Individual hospitals have no clear catchment area in Japan not quite representative of the whole patients Follow-up (particularly long follow-up) is difficult All Japanese patients are allowed to make his/her own decision on the medical institution to visit.

Claims data from insurers Merits Relatively long follow-up is possible The insured can be followed unless he/she changes the insurance union (~ around job change). The records of two or more clinics/hospitals he/she uses at the same time are available. Demerits No labo-data are available not representative of the whole nation Age distribution is different from the whole population. Data are those of workers and their family

Claims data from pharmacies Merits Records for long period (more than 10 years) Data come from many medical institutions probably more representative as compared to other 2. Demerits Source population cannot be defined No data on diagnosis, labo results and procedures Follow-up of individual patients is difficult

Problems common to all DBs Linkage to the data source outside the DB is currently very difficult partly due to the lack of the Social Security Number or other identifier used in Health-Care

Two emerging large Databases [A] Medical Information Database* mainly for Japanese Sentinel * No formal English term is available just beginning EHR-type database MHLW Pharmaceutical and Food Safety Bureau (Safety Division) + PMDA (Pharmaceuticals and Medical devices Agency) [B] National Database (NDB) Claims database (plus some data from health screening) Health Insurance Bureau in the pilot phase for the secondary use

Two different bureaus for Two large DBs and [A] Medical Information Database (Japanese Sentinel) [B] National Database (NDB)

Medical Information Database (for Japanese Sentinel)

Medical Information Database (for Japanese Sentinel) By 2015, EHR-type data are collected from hospital groups in 5 area in Japan aiming database covering 10 million people PMDA http://square.umin.ac.jp/~helics/files/event_20111120/symposium_20111120-04.pdf

Medical Information Database (for Japanese Sentinel) Hospital A Data mapping of local code to standardized code may be needed Query PMDA Hospital B Data Center Data for individual patients, after deleting ID info, will be collected by data center, analyzed and then deleted for each query SS-MIX: storage of standardized information http://www.pmda.go.jp/chotatsu/bid/file/23/20111013_3nyusatsu_si.pdf

Medical Information Database (for Japanese Sentinel) Merits and Demerits: the same as that for HER-type data Merits (Some) labo-data results are available Almost complete data for inpatients Demerits Source population cannot be well defined not quite representative of the whole patients Follow-up (particularly long follow-up) is difficult

Data in data center (Medical Information database) Data in data center will be not allowed to be linked with other data source at least for the time being. For each query, data of individual patients, after deleting ID information, are collected from individual hospitals to the data center, analyzed and then deleted. However, in the future, it may be not impossible to link the data with other data sources because the original data in each hospital have the original ID information

National Database (NDB) in Japan

National Database (NDB) 2010.10.5 First advisory committee for MHLW 2011.03.31 Guidelines for use of NDB during the pilot period (2011.04 to 2013.3) issued by the MHLW 2011.08 MHLW received 43 applications for the secondary use of NDB (first application) Governmental bodies Universities National cancer centers and other centers with research activity Drug companies are not allowed to make application (directly) 2011.11.10 The advisory committee approved 6 of 43 applications 2012.04 Second application will be received by the MHLW (only those who failed in the first application can make application)

NDB(1) Data of claims (from 2009.04) plus Data of the results of specific health screening Data of about 20 million subjects, 38% (i.e., selective?) of 50-million target (by law) population of 40-74 of age (data on BP, serum lipid, FBS, HbA1c and smoking status) Claims data Health screening data

NDB(2) New data are added to the old data by using the ID which is undecodable hash New Data Old claims data hash Key from hash Old health screening data

NDB(3) [1] number for insured (two kinds) DOB Gender [2] Name DOB Gender 2 kinds of hash will be made to match old and new data [1] may work when family name is changed (e.g., marriage) [2] may work when moving to different insurance union (e.g., job change)

NDB(4) It will be very difficult or actually impossible to link ID (hash) used in NDB with ID in the data source outside NDB: i.e., Data in NDB cannot be linked with demographic data cannot be linked with disease registry cannot be validated by the original medical record as long as the current policy to use hash is maintained

SS-MIX may provide a key function in the future Pharmacoepi study in Japan Standard Structured Medical Information exchange Methods Inf Med. 2011;50:131-9.

Those in individual hospitals can use SS-MIX data in the own hospital Hospital A Hospital B Data Center SS-Mix: storage of standardized information The data in each hospital can be used with its original ID information inside the hospital. The data in SS-MIX is all standardized

Current status of SS-MIX storage SS-MIX storage can work when introduced to the hopital with the data in standardized format (HL7 and DICOM) About 700 large hospitals have now Hospital Information System with the data in standardized format but only a few hospitals have introduced SS- MIX storage so far. Cost is about 200 thousand USD for introducing SS- MIX storage to each hospital. not very expensive but not very cheap some incentive needed for more hospitals to introduce SS-MIX

SS-MIX, when introduced into hundreds of hospitals, can identify cohorts of new users of two drugs which may be compared with each other, can retrieve all drugs, labo data and diagnostic codes for those cohorts automatically, so that physicians who participate in the study in each hospital may just give information on the outcome to complete individual case report and may be an important tool in particular because the record linkage is difficult with NDB.