Millennial Medical Record Project

Millennial Medical Record Project
Toward establishment of authentic Japanese version EHR and secondary use of medical
data
YOSHIHARA Hiroyuki

1, 2

1 Kyoto University, Graduate School of Medicine, the EHR Research
2 University of Miyazaki Hospital, the Center for EHR

The beginning of EHR (Electronic Health Record) can be traced back to the start of MML (Medical Markup
Language) studies in 1995. In 2001, EHR which structured MML as a database were developed, andexpanded to Kumamoto, Miyazaki, Tokyo, and Kyoto (Dolphin Project). Subsequently the need for medical
data management on the national level was recognized. For this reason and due to the needs for secondary
use of medical data, the Millennial Medical Record Project was started in 2015 as a national version of theDolphin Project. During the 4 years up through FY 2018, the number of connected hospitals increased
sufficiently and the stage was set for the secondary use of medical data beginning from FY 2019. The
objective is to achieve independent profitability for the EHR divisions based on income from secondary use
and without depending on subsidies.
EHR, Electronic Health Record, Millennial Medical Record Project, regional cooperative medicine, medical
record disclosure, medical data, secondary use, data use
Manuscript received Nov. 21, 2017
Journal of Information Processing and Management, 2018, Vol. 60, No. 11, p. 767-778. doi: https://doi.org/10.1241/johokanri.60.767

1.

Introduction

The Next Generation Medical ICT Task Force1(Cabinet Secretariat)* was established in March
2014, and began studies related primarily to thesecondary use of medical data. The first Next
*2
Generation Medical ICT Infrastructure Conference(hereafter referred to as “the Conference”) was heldon April 2, 2015. This is one organization under the
Headquarters for Healthcare and Medical Strategy
Promotion, which is led by the Prime Minister. Oneof the projects launched by the Conference was theMillennial Medical Record Project, which was basedon the theme of “large-scale collection and use of3
health and medical data”* .
In September 2015, this project was officiallyadopted as a research announcement of the Japan
Agency for Medical Research and Development(AMED) (research subject: national collaborative
research related to collection and use ofinternationally standardized health and medicaldata). This research aims for commercialization
beginning from FY 2019. Starting from 2002, it hascollected the separately operating EHR (Electronic
4
Health Record)* websites in Kyushu, Tokyo, Kyoto,

and other regions (Dolphin Project) at a newly
developed and established collaborative EHR
Center. With participation by large numbers of
medical institutions in other regions as well, thecollaborative use of this data center aims to ensure
data safety and reduce operating expenses.
Although the data standards of the data output by
the medical institutions vary widely, the data isfinally collected at the data center using
5
ISO13606* . Secondary use of this data will be
carried out fairly and safely in accordance with laws,
and the income used to operate the EHR and
ensure the sustainability of the project (ending thedependence on subsidies and research expenses)(Figure 1).
In accordance with the Next Generation Medical
Infrastructure Act (Act Regarding Anonymized
Medical Data Contributing to R&D in the Medical
Field) that is scheduled to take effect in FY 2018,
two business entities (operating agency for primary
EHR use, and certified operators who create
anonymized medical data) will be established andoperated. This project will result in full-scale
operation of a medical database that has been an
outstanding issue since the 1990s, and insecondary use of the data. It is expected that theresults will be applied to cooperative health care,

Journal of Information Processing and Management, 2018.2 Vol. 60 No. 11767https://jipsti.jst.go.jp/johokanri/Figure 1: Overview of Millennial Medical Records

clinical research, drug development, public health,
government, and other areas. This paper provides
an overview of the future issues, including legal
problems, facing the Millennial Medical Record
Project system.

2.

History of EHR in Japan

2.1

Dolphin Project

Private use of the Internet first became possible in
1993. At that time, young medical data researchers
and business representatives came together todiscuss the functions which should be included infuture electronic health records. In 1995, the firstgathering was held in Miyazaki, and was named the“Seagaia Meeting” for the venue where it was held.
These meetings have been held each year up to6
the present* .
The objective of electronic health records is“connectivity”. When a patient is seen at a new
hospital, the electronic health record should display
all of that person’s past medical history. There arethree issues to achieving this in an environment
where electronic health records from multiple
different vendors are used. These are data location,
data compatibility, and access control (Figure 2).

768

Figure 2: Technology elements required for
EHR operation
The above issues were resolved in the 1990s, andthe development and operation of EHR (here themedical data exchange infrastructure) which
7, 1
structured MML (Medical Markup Language)*
into a database was started in 2001 with a researchsubsidy from the Ministry of Economy, Trade and2 -4
Industry (Dolphin Project) . Through the Dolphin
Project, regional medical data centers (hereafter,“data centers”) to manage patient medical data
were created in each region. The project serves asa mechanism which uses these data centers as
hubs for cooperative medicine and disclosure ofelectrical medical records (Figure 3).

Millennial Medical Record Project

Figure 3: Basic concept of the Dolphin Project

The goal of the Dolphin Project was to provide an
infrastructure that enabled efficient connections
between hospital data systems in different regions.
Medical data that is stored on a central server isstored under designated security conditions, andmedical practitioners can centrally view the medical
data of patients who have concluded a treatment
agreement. This not only makes cooperative
medicine possible, but also allows the patient toview his or her own medical data (electronic healthrecord disclosure) and also to record symptoms orother information in the record.
In order to make this possible, it was necessary toestablish data centers and connect them to clinics,
hospitals, testing centers, pharmacies, and homenursing care stations. Progress records, test results,
referrals, discharge summaries, and other data issent and collected in an integrated manner for eachpatient. This data is used for medical data sharing
within the region, and also as backup medical
records for each medical institution.

2.2

Mechanism for linking data centersand electronic medical records

Data centers include an interface for receiving data,and can accept medical data that is recorded informats such as MML or HL7 (Health Level 7). The
medical data in the electronic medical records ateach medical institution is converted to MML or
other format before it is sent. It is then received by
the data center and stored in the database. The

data can be viewed by medical institutions, patients,and other parties.
Data centers for each region were established andbegan operating in sequence. Higo Med began
operating in Kumamoto in 2001, Haniwa Net in
Miyazaki in 2002, HOT Project in Tokyo in 2003,
and Maiko Net in Kyoto in 2006. As of 2017, thecenters in Miyazaki and Kyoto are still operating.

2.3

Opportunities
consolidation

for

data

centerAlthough operation of the regional data centersvaried depending on the regional project, a verylarge human and financial burden is involved withmatters such as installation at a university hospitalor other site, posting to major data centers, andserver purchase, management, and replacement
every 5 - 6 years. For this reason, the idea wasborn of reducing installation and operating costs asmuch as possible by creating large data centers inJapan to contain the regional servers, and by
operating EHR services as SaaS (Software as a8Service)* . This was started from around 2014, andunder the government’s next generation medical
ICT infrastructure construction plan, this led toconstruction of an EHR infrastructure on thenational level.

Journal of Information Processing and Management, 2018.2 Vol. 60 No. 11769https://jipsti.jst.go.jp/johokanri/

3.

Millennial Medical Record Project

As described in the previous section, in order toreduce the heavy burdens involved in operating
data centers for each region in the Dolphin Project,
the Millennial Medical Record Project designed asingle EHR Center to concentrate the data centers
and allow distributed use of the functions in eachregion via the Internet (Figure 1).
In the first year (FY 2015), the EHR Center
infrastructure (databases, etc.) was constructed. At
the same time, the hospitals (11 facilities) which
had been connected to the old EHR in Kyoto andMiyazaki were connected to the EHR Center.
Continuing coordination resulted in the addition of
23 connected hospitals in FY 2016, with 40 or more
new connections expected in FY 2017. The plan is
to also connect 40 or more hospitals, pharmacies,
and other facilities during FY 2018, the final year of
the AMED research period. The final objective calls
for around 150 facilities in the large hospital class(Figure 4).
As shown in Figure 5, the Millennial Medical
Record Project is first prioritizing construction of the
EHR system shown in the center of the figure(through FY 2018) and providing EHR services. It is
expected that newly certified operators who create
anonymized medical data will begin operating after
the Next Generation Medical Infrastructure Act

takes effect in 2018. The operating agency forprimary EHR use will continue to be the existing
NPO Japan Medical Network Association.

3.1
3.1.1

Databases
Databases (engine)

The database engine for Millennial Medical
Records will be based on the Hadoop Eco System
which is an open source large-scale distributed filesystem. Of the technological elements which
compose the Hadoop Eco System, the primary
technological elements that are used here (Figure6) are explained below.
(1) Hadoop
The Millennial Medical Records contain electronic
medical record data that is sent from participating
medical institutions across Japan. Because thedata which should be stored increases according to
the number of participating institutions, a flexible
approach to data processing capacity and storage
capacity is needed. Generally there are two
approaches to increasing processing and storage
capacity. These are known as “scale up” and “scale
9out”* . Apache Hadoop can be expanded by scalingout, and it is possible to expand while continuing toprovide services. Because data is replicated and10
stored on multiple servers* , this ensures a highlevel of failure resistance.

Figure 4: Millennial Medical Records implementation plan

770

Millennial Medical Record Project

Data is collected from medical institutions and other entities in the EHR system that is operated by the
NPO Japan Medical Network Association (lower half of figure), and a secondary use operating agency
that is certified under the new law anonymizes the identifying data that was provided from the EHR
Center, and provides the data to researchers for a fee (upper left half of figure). The hospitals or life
care industry conclude individual agreements with patients, and the non-anonymous data operating
agency provides services which add value to medical information.

Figure 5: Overview of the Millennial Medical Record Project(2) Hive
The medical record data that is sent from each
medical institution is sent in the MML format that isprescribed by the MedXML Consortium. After it isconverted to a format known as JSON, it is stored
in the Millennial Medical Records. Because the data
may be sent from the medical institutions at any
time, a mechanism which can process a vast
number of data items is necessary.
Apache Hive is a DWH (Data WareHouse) productthat supports large-size data which is difficult tohandle with a RDB (Relational Data Base). The
processing that is executed through Hive isconverted according to a distributed processing
framework known as YARN for distributed
execution.

(3) HBaseIn addition to the above MML format data, Millennial
Medical Records also contain ISO13606 format
data. ISO13606 features a flexible data model
structure using archetypes, and a storage methodthat is highly compatible with this feature isnecessary. HBase is an open source large-scale
distributed storage system. A KVS (Key-ValueStore) system is used for the data structure. An
ordinary RDB requires highly precise definitions(schema) related to the data structure, however
HBase does not require schema (schema-less) andis capable of flexible data management.

Journal of Information Processing and Management, 2018.2 Vol. 60 No. 11771

https://jipsti.jst.go.jp/johokanri/Figure 6: Millennial Medical Record data flow and relationship of technological elements(4) Impala
An ordinary EHR is used only by the medical
institution that creates it, and even a regional EHR
is used only by medical institutions within thedesignated region. However Millennial Medical
Records contain data used by medical institutions
across Japan, and the volume of data is vast. The
Millennial Medical Records allow the medical record
data to be viewed by the patient and medical staff
on a web screen, and special consideration mustbe given to online search performance.
Impala is an open source distributed SQL query
engine, and is capable of high-speed searches ofdata stored on an HDFS. Because the data can beshared with the Hive described above, Millennial
Medical Records utilize Hive for batch processing
and Impala for online processing in order to makebest use of the characteristics of each technology.

MML while increasing development convenienceand adding modules for vital signs, bodytemperature chart, prescriptions, and injection
records. This was adopted as one of the interfaces
for this project, and a supporting logical model was
5
designed and mapping tables were created .
6
Because a data format which conforms to HL7
CDA Rel2 defined by HL7 Japan is widely used for
health screenings, in the same way an archetypebased on this schema was used to design a logicalmodel and make it possible to receive data fromhealth screening facilities as well.
For HL7 and vendors that are not accustomed to
MML development, uploaders that support JSON (asimplified version of MML) and original formats
were also prepared. The data is acquired by logicalmodels that also support these data formats.

3.2
3.1.2

Databases (logical structure)

Based on the above decisions, the initial data
sources for the Millennial Medical Record Project
were the MML Version 2 and 3 data that was output
to the old EHR. In order to increase the number ofconnected hospitals and increase the types of
supported data, it became necessary to support not
only MML but also HL7 or original hospital orvendor formats. Therefore the logical structure
inside the database uses an archetype model
11
based on ISO13606 openEHR* , which is flexible
and provides excellent search performance. The
use of the archetype model makes it possible toflexibly acquire data from multiple data sources andperform cross-sectional data searches.
In addition, MML Version 4 was released, which
maintains backwards compatibility with existing772

Access control

Previously, access control was designed for strict 1to-1 control that decides who the patient data canbe shown to. However because this puts a large
load on hospital operations, it was almost neverused. This project defined the following three
entities as stakeholders: the facility creating thedata, cooperating hospitals, and the patient.
(1)

Establishing a policy for disclosure by
medical institutions
The medical data sharing range and access rights
for medical institutions are set.
Ordinarily, the medical data that is subject toaccess controls is classified according todepartment, physician, document type, and item. In
this project, it was assumed from the start thataccess rights would be set for each document typeand department, without control for individual

Millennial Medical Record Project

Figure 7: Access control for each medical document and department, set for each medical institution

physicians and items. Using the 18 documents thatare defined by the MML standard convention as theunits, access rights to each of them can be set forpatients and cooperating facilities (Figure 7).
(2) Patient opt-out
The patient selects the medical institutions from thehistory of examining medical institutions which heor she does not want the medical information
shared with.
The patient intentions are given priority, so
information which a patient refuses to share with a
medical institution is not shared even if the medical
institution permits sharing with cooperating
hospitals.
(3) Rules for joint care
Only medical information which was permitted byboth the medical institution which created the
medical information and the patient who was
examined by that medical institution is shared, andis shared only with cooperating hospitals listed in
the patient’s examination history. For example,
information is not shared with a hospital that has
not examined the patient even if the hospital isparticipating in this project.

3.3

Service functions

The Millennial Medical Record Project classifies theservices provided to medical institutions, patients,and others into the following categories: zero-orderuse (remote backup of diagnosis result data),
primary use (sharing of information for joint careand disclosure to the patient), 1.5-order use(automatic discovery of medical risks in the EHR),
and secondary use (use of anonymous or nonanonymous medical information for research).

3.3.1

Zero-order data use

Disaster countermeasures by medical institutions
are generally carried out following the most recent
earthquake. The ideal of this project is topermanently store the lifetime medical records ofindividuals as an asset for humanity, and medical
institutions can be described of as a mechanism
which allows the most recent medical information to
be continually stored. EHR provides the mostrecent patient information during the period until theelectronic medical records are restored following adisaster.
Restoring electronic medical record data from abackup (all data including diagnosis results data,and operational data such as orders and medical
business data) requires power, communications,
local hardware, and restart of operations. As aresult, performing recovery immediately after adisaster occurs is difficult. However because the
EHR Center is able to provide medical data as longas it has power and communications, it cantherefore promptly provide the most recent test
results, prescriptions, and other information that isneeded immediately following a disaster. By
providing this information to the patients via EHR inadvance, an individual patient can refer to theinformation using the smartphone which the patientcarries with him or her following a disaster, allowing
medical treatment to continue.

Journal of Information Processing and Management, 2018.2 Vol. 60 No. 11773https://jipsti.jst.go.jp/johokanri/Figure 8: Millennial Medical Record web interface

3.3.2

Primary data use

There are four primary uses of the data that isstored in EHR: data viewing, Electronic Data
Capture (EDC), alert system, and business index
analysis.
(1)

Data viewing: Viewer for the patients andmedical staff
PC (web browser) applications and smartphone
applications (iOS, Android) have been created forthe user viewers. Figure 8 shows the PC
application screen. After logging in with an ID andpassword, tests, prescriptions, reports, and othermedical record items are displayed in time series
lists in the left window (bottom of Figure 8), withthe most recent data at the top. Clicking displays
the details in the right window. For test results andother items, clicking multiple items will display themin a line graph. For details, see the YouTube
12
video* .
Figure 9 shows the smartphone application screen.
This is for patients only, and not for physicians.
Applications have been created for iOS and forAndroid, and can be downloaded for free from theApple Store or other site.
After logging in with an ID and password, at first thefull patient medical history is downloaded. Although
it depends on the size of the history, 4,000 records
can be downloaded in around 25 minutes. In thecase of a PC, the data is erased when the viewer is774

closed. However with a smartphone the dataremains recorded. Only the new data is
downloaded the next time that access occurs. Thismeans that the patient’s entire medical history is
stored on the smartphone. Power outages andnetwork interruptions are to be expected following adisaster. However when the patient has asmartphone, then it is possible to check the mostrecent prescriptions, tests, and other data.

Figure 9: “Millennial Medical Record”
smartphone app (two on left = iOS, on right =Android)(2) Electronic Data Capture (EDC)
Regarding use for EDC, some electronic medical
records include a mechanism for sending data to
the EHR Center when template data is input.
The objective is to allow data to be collected easily
without introducing a whole new EDC system whenMillennial Medical Record Project

it is necessary for a particular project to record
more detailed data items than those listed in theelectronic medical record. Because data is
exchanged between medical institutions usingroutes that are equivalent to a dedicated line
between the EHR Center and medical institution, it
is not necessary to secure a safe route for eachproject.
(3) Alert system, business index analysis
Functions will be installed which will combine thedata analysis results from 1.5-order use (describedlater) with the medical data display screen. They
detect changes in particular test values of aparticular patient, and display an alert whennecessary. This will produce a significant
improvement in clinical safety, particularly among
patients who are examined regularly for chronic
ailments, by analyzing sudden changes in irregularvalues and providing feedback to the physician.
Previously there were cases where an alert system
was installed on electronic medical records within a
hospital, however this required a large burden on
the electronic medical record vendor or hospital
operations division. With this project, this data
analysis and alert generation is performed centrally
by the EHR Center, placing no burden on theindividual medical institutions and allowing a
reduction in overall medical costs.
In addition, information such as portfolios andbenchmarks will be returned to the management of
medical institutions which provide medical business
data.

3.3.3

1.5-order data use

One of the objectives in constructing EHR is toprovide diagnosis support based on the collected
13
data* . With this project, data use that is provided
to the medical institutions and patients is
considered primary use, and use of the data fordiagnosis support is considered 1.5-order use.
Already there is a report of improved prognosis
resulting from the application of AI to EHR data for7early detection of acute renal insufficiency . This
project is also conducting a feasibility study for thispurpose.
In Sweden, it was reported that when anticoagulant
therapy
conforming
toguidelines
wasrecommended to physicians treating patients withatrial fibrillation based on EHR data that utilizes
archetypes as the internal logical model in thesame way as this project, an improvement in
8compliance was seen . The mechanism that wasused in Sweden to provide greater diagnosis

support from the EHR described in the paper has
been made available as open source software andthis project intends to make use of if.

3.3.4

Secondary data use

Data secondary use, which was difficult before,
became possible with the enactment of the Next
Generation Medical Infrastructure Act. Under thislaw, national certification will be carried out for thecertified operators who create anonymized medical
data. These agencies are permitted to receive data
bearing patient names from EHR agencies,
hospitals, and other entities. In general, the data iscollected and is anonymized without the consent of
individual patients (opt-out). This data can beprovided (for a fee) to researchers for the purposeof medical research. Because the data is collectedunder the actual patient names before it isanonymized, it is possible to trace the individualwith an anonymous ID. Use of the data for
purposes such as clinical research and postmarketing surveys is expected.

4.

Issues to be resolved

4.1

Data
commonization
standardization

andWhen data from different vendors is stored in thesame database, data commonization is an essential
requirement. Beginning from the 1990s, common
standards for medical information in countries andregions around the world were developed, andeach country adopted its own standard. Examples
14
include HL7 (Health Level Seven International* ) in
the United States, openEHR and openEHR
foundation in Europe, and MML (MedXML
15
Consortium* ) in Japan. In fact, both in Japan andoverseas, a variety of different standards are in use
and method of mapping is used to store them in thesame database. Even now as specialist mapper
companies(example:
Orion
Health,https://orionhealth.com/) have taken the world bystorm, the achievement of a single global standard
is clearly not practical.
In the 2010s, a standard derived from Europe
openEHR was codified as ISO13606. An
investigation of the present relationship with HL7
and MML confirmed that mapping to ISO13606 is
possible. Therefore in the Millennial Medical Record
Project, the various standards (MML, HL7, originalindustry standards, etc.) of data output by hospitals,
pharmacies, health screening centers, sample

Journal of Information Processing and Management, 2018.2 Vol. 60 No. 11775

https://jipsti.jst.go.jp/johokanri/

testing companies, and others are mapped toISO13606 before they are stored.

4.2

Insufficient structuring of electronic
medical record data

Although the data output from electronic medical
records is output using a variety of standards suchas HL7, MML, or original industry standard CSV,
mapping can be used to integrate this data.
However in many cases the data remains on thesubsystems and cannot be output in the first place.
Even if the data is somehow sent to the electronic
medical record, it is generally in PDF, JPEG, orother unstructured format. This data may be humanreadable, but it is not machine readable andtherefore secondary use is not possible. There is no
package that is equipped as standard with afunction for data output in a common format, and inorder to achieve the secondary use that is a
primary objective of the government, it will benecessary to reconsider the handling of data inelectronic medical records on an industry level.

4.3

Legal problems

Because the revised Act on the Protection of
Personal Information that was enacted in May 2017is a general statute, it does not give any
consideration to the special case of medicine. As aresult, extreme abnormal values and similar data isclassified as sensitive information, and an

exception must be created for it. This is a fatal
obstacle to medical research. In addition, when
medical data is collected in the course of joint
research conducted by multiple facilities, it isnecessary to perform the anonymization process
for each hospital. As a result, the data for the same
individual when examined by multiple medical
institutions must be handled as data for different
persons. In other words, it is not possible to trace
the individual using the anonymized data. In manycases,
ordinances
establishedby
local
governments require that medical information be
handled at a higher level of confidentiality that isrequired by the Act on the Protection of Personal
Information, and this is also a large obstacle to16
EHR and secondary use (2000 Problems)* .
The Next Generation Medical Infrastructure Act thatwas enacted in May 2018 is positioned as a law
which supersedes these problems. As shown in
Figure 10, the newly certified agencies are
permitted to receive data bearing patient names
from EHR agencies, hospitals, and other entities.
This make it possible to collect and anonymize data
without the consent of individual patients (opt-out),
and because the data is collected under the actual
patient names before it is anonymized, it is possible
to trace the individual with an anonymous ID. The
establishment of this law is expected to produce
dramatic advances in medical research.

Figure 10: Handling of medical information under the Next Generation Medical Infrastructure Act

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Millennial Medical Record Project

4.4

Japan EHR system in comparison
with other countries

We began EHR research in 1995. Based on theseresults, EHR were implemented as a social system
in 2001. Although Japan was leading the world atthat time, over the last 15 years or so, Japan has
fallen behind other countries. In several advanced
countries (such as Europe, Canada, and Oceania),
EHR are implemented under the direction of thenational government without exception. The budget
used for this purpose is in all cases approximately
50 dollars (approximately 5,000 yen) per citizen.
Considering the population of Japan, that works out
to 700 - 800 billion yen. Looking back, the amountthat Japan has invested in EHR is much less thanthis, and the manner of use has been
uncoordinated and inefficient. This project did not
require such a large budget, and we felt clearly theneed to formulate a full-scale plan that is focusedon the future. It is expected that if EHR covered all
medical institutions, the reduction in redundant
tests, prescriptions, and other treatment would beapproximately 5%. That represents savings of 2
trillion yen, and would allow more effective medical
investment (such as investment in equipment). We
are hopeful for a governmental and politicaldecision.

5.

Hiroyuki Yoshihara (hiroyuki.yoshihara@gmail.com)
Advanced to the Faculty of Medicine at Miyazaki
University Hospital after graduating from the Departmentof Chemistry in the Osaka University Faculty ofEngineering Science in 1973 and working at a privatecompany. After graduating in 1995, he entered the fields
of surgery and then medical informatics. He worked as aprofessor for a total of 18 years, employed at the Facultyof Medicine at Miyazaki University Hospital in 1995,
Kumamoto University in 2000, and Kyoto University(Medical Informatics) in 2003 before retirement in March
2013. He launched the Kyoto University EHR Joint
Research Project in April 2013. He served as reagent
and hospital director at Miyazaki University from April
2014 to March 2016. From April 2016, he has served
concurrently at Kyoto University and Miyazaki University(director of the Kyoto University EHR Joint Research
Program / distinguished professor at The Center for EHR,
Miyazaki University Hospital). He is professor emeritus at
Kyoto University and Miyazaki University. EHR andmedical DB are his life’s work.

Conclusions

This paper has provided an overview of the history
and current state of EHR in Japan. The programs
carried out in Japan over the past 3 years havebeen very ambitious and are expected to deliver
results. Aiming to make up for the lost 15 years,
Japan is putting every effort into constructing andoperating a system for EHR and secondary use.

Acknowledgments
I would like to express my thanks to members of
the Millennial Medical Record Project Naoto Kume,
Shinji Kobayashi (EHR Joint Research Program,
Kyoto University Graduate School of Medicine), andMasao Sueyoshi (Stellaplace, Inc.) for their great
help in preparing this paper.

Journal of Information Processing and Management, 2018.2 Vol. 60 No. 11777

https://jipsti.jst.go.jp/johokanri/Notes from text

*1

Next Generation Medical ICT Task Force: http://www.kantei.go.jp/jp/singi/kenkouiryou/jisedai/kaisai.html*2

Next Generation Medical ICT Infrastructure Conference:https://www.kantei.go.jp/jp/singi/kenkouiryou/jisedai_kiban/kaisai.html*3Millennial Medical Record Project: www.gehr.jp*4

EHR in Japan refers to the collection of data in EMR (Electronic Medical Record) on the regional, national,
or other level. PHR (Personal Health Record) refers to an EHR composed of data from a hospital, to whichdata (such as body weight, blood pressure, walking distance, and workplace health examinations) that was
generated from a non-hospital source (the individual, workplace, etc.) was added. The website of the Japan
Medical Network Association (http://www.ehr.or.jp/) contains links to the Dolphin Project and Millennial
Medical Record Project (http://www.gehr.jp/).

*5

ISO13606-1:2008 Health informatics -- Electronic health record communication -- Part 1: Reference model.
https://www.iso.org/standard/40784.html*6

Seagaia Meeting: http://www.seagaia.org/*7

MML: Standard developed for correctly exchanging medical data between different medical institutions(electronic
medicalrecordsystems).
Description
fromthe
MedXML
Consortium(http://medxml.net/?page_id=9).

*8

SaaS: Refers to software that allows the necessary functions to be used at the necessary times and inexactly the amount necessary as services provided over the Internet or other network, or to the provision ofservices in this form.

*9

Scale-up, scale-out: Scale-up is a method of increasing processing performance by increasing thecapabilities of hardware such as servers and memory. Scale-out is a method of increasing processing
performance by increasing the number of servers.

*10

Replication: Copying data on a server to a remote site in real time*11

The openEHR Foundation: http://www.openehr.org. Japan openEHR Association: http://www.openehr.jp/*12

Millennial Medical Record viewing service for patients: https://youtu.be/ShKchgsnD7Q*13

Health informatics -- Requirements for an electronic health record architecture, ISO18308:2011.
https://www.iso.org/standard/52823.html*14

Health Level Seven International: http://www.hl7.org/*15

MedXML Consortium: http://medxml.net/*16

2000 Problems: Excessive personal information protection required by 2,000 ordinances from local
governments across Japan. The new law is positioned to supersede the information protection provisions ofthese ordinances.

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Millennial Medical Record Project4

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Author Abstract

The beginning of EHR (Electronic Health Record) can be traced back to the examination of the medical
information common standard in 1995 (MML: Medical Markup Language). In 2001, EHR with database structureof MML was developed and expanded to Miyazaki, Kumamoto, Tokyo, Kyoto (Dolphin Project). After that, thenecessity of medical information management at national level and the importance of secondary use of medical
information came to be recognized. In 2015 the country level version of the Dolphin Project "The Millennial
Medical Record Project" began. We will increase the number of connected hospitals in 4 years until 2018 andprepare for secondary use of medical information starting from 2019. We are aiming for independent profit
including EHR department by revenue of secondary use of data without relying on government subsidies.

Key words

EHR, Electronic Health Record, Millennial Medical Record Project, collaborative medicine in the community,
disclosure medical records to patients, medical data, secondary use, data use

Journal of Information Processing and Management, 2018.2 Vol. 60 No. 11779