Development of Analysis System Connecting Healthcare and ......Policy Research Institute, Ministry...

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Development of Analysis System Connecting Healthcare and Long Term Care Insurance Claim Data and Specified Health Checkup Data

Shinya MatsudaProfessor, Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health

Yoshihisa FujinoAssociate Professor, Department of Preventive Medicine and Community Health, School of Medicine, University of Occu-pational and Environmental Health

AbstractIn order to provide high-quality healthcare and long-term care services in a comprehen-

sive manner despite constraints such as the aging of society coupled with the low birthrate and weak economic growth, it is essential to collect comprehensive information that enables systematic provision of such services. From this perspective, in Fukuoka Prefecture and oth-er local governments in Japan, the authors have been building a system for analyzing claim data for national healthcare insurance (healthcare services and prescription), the healthcare system for people aged 75 or older (healthcare services and prescription), claim data for long-term care benefits and specified health checkup data by linking them with each other with regard to each insured individual. As example cases of use of this system, this paper in-troduces readers to a structural analysis of the costs of healthcare and long-term care bene-fits, the economic benefits of the use of generic drugs in place of patented drugs, the appli-cation of specified health checkup data to marketing and a cost-benefit analysis of a pneumococcus vaccine.

Claim data submitted to insured persons by medical and long-term care institutions are excellent sources of medical information. As of April 2014, 99.9% of prescription claims were computerized. The computerization rate of medical service claim data came to 98.9% at hospitals and 96.9% at clinics. Claim data can be used as a source of big data containing detailed information concerning medical practices conducted in medical consultations and prescribed medication. This constitutes an important infrastructure that is useful when we consider the future of healthcare in an aged society, so there are high expectations for appro-priate use of it.

I. Introduction

Along with the ageing of society, social security dispenses are rapidly growing in Japan. The Ministry of Finance (MOF) is increasing its concern over the sustainability of the social security system. However, from an international viewpoint, Japan’s health expenditure com-pared to GDP is rather low. Based on this fact, medical organizations such as the Japan

Policy Research Institute, Ministry of Finance, Japan, Public Policy Review, Vol.11, No.5, October 2015

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Medical Association (JMA) require more public financial support in order to prevent a crisis caused by underfinancing1). Some Input-output analyses have indicated that investment in the medical sector would lead to an equal or greater effect on the general economy than would investment in the civil engineering and construction sectors2),3). However, the health sector is a safety net in itself, so there are doubts about its potential as a social engine for economic development. The Japan Revitalization Strategy (JRS) regards the medical sector as a growing domain which requires more deregulation for its industrialization, such as per-mitting for-profit companies to manage hospitals and permitting mixed billing of medical services. The authors do not wholly agree with JRS members’ viewpoints. They regard med-ical services as commodities that must be treated the same as other market ordinaries. How-ever, it is certain that the current social security system is not sustainable without drastic re-forms.

The Ministry of Health, Labour and Welfare (MHLW) introduced a disease management program targeting for metabolic syndrome in 2008 in order to realize health promotion and cost containment. We are skeptical of the idea of regulating health expenditures through health promotion activities4). However, we have much interest in new health movements among European countries—so-called “Fitness for work,” which tries to medically support workers in continuing to work5-9). This idea is very informative for the Japanese government, which intends to realize an ageless society. We think that the Japanese disease management program for metabolic syndrome must be re-defined as part of the basis for the realization of an ageless society. From this perspective, our department has been conducting several field studies in order to accumulate evidence.

In order to evaluate the validity of this program, we need to analyze claim data from be-tween participants and non-participants of the DM program. Considering that the DM pro-gram would influence the incident rate of morbidity covered by the health insurance scheme for the aged (HISA) and of dependency covered by Long Term Care Insurance (LTCI), the claim data includes all health insurance and LTCI.

Actually, these insurance schemes are organized independently, making it rather dif-ficult to evaluate the total life-long health cost for each individual. This situation makes it difficult to develop integrated health programs. In order to overcome this difficulty, we have developed an integrated claim data analysis system that can combine the claim data of na-tional health insurance, the health insurance scheme for the aged, and LTCI 10-12). In this arti-cle, we will explain the details of this system and give some examples of output.

II. Data and methods used

II-1. Data used

We used NHI and HISA claim data (medical services and pharmaceuticals), LTCI claim data and lab-data from the disease management program of 15 municipalities of Fukuoka prefecture from April 2012 to March 2014.

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II-2. Methods

The upper chart of figure 1 shows the format of the current e-claim. This format is not normalized, making it difficult to process. Therefore, it is necessary to normalize it. We de-veloped an SQL program to change the format to one like that under the chart of figure 1. In this way we have constructed a dataset that combines all of the above data on an individual basis.

For the diagnosis, we used the first principal diagnosis that was registered in each e-claim. The diagnosis was converted to the corresponding ICD code and then to the Major Diagnosis Category (MDC) of the Japanese Casemix system, so-called DPC (table 1).

For the pharmaceutics, the claim data from pharmacies were individually connected with the corresponding medical facilities that issued the prescriptions. In this way, the total medical expenditure of one episode can be calculated. Using this system we can calculate the total expenditure for each diagnosis, combining healthcare and LTCI claim data. How-ever, for LTCI users, health insurance claim data was available only for persons who used health care at the same time. Because LTCI claim data does not include diagnosis, it is not possible to calculate the disease-based total expenditure for persons who used only LTCI services. This is one of the limitations of our system.

Figure 2 shows an overview of the system, and figure 3 is the face sheet. Figure 4 shows

Figure 1Format of e-claim and data format for analysis


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Table 1Major Diagnosis Categories

Figure 2Total health expenditure (K town, Fukuoka Prefecture, September 2009)


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Figure 3Face sheet of the system

Figure 4Structure of analysis sheet


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the structure of analysis sheet. Using the list box we can choose the analytic viewpoint and results are shown by tables and charts. For the visualization of results, we have employed Clikview.

III. Results : Some examples derived from the system

In this section we will show some results derived from the system.

III-1. Analysis of healthcare expenditure

Figure 5 shows NHS in-patient expenditure stratified by each primary diagnosis (MDC expression) of a municipality of Fukuoka prefecture (October, 2011). Total in-patient expen-diture was 383.5 million Japanese yen (JPY). The most important expenditure was that of mental disorders (89.0 million JPY, 23.2%), followed by neuro-system disorders (62,2 mil-lion JPY, 17.7%), and digestive system, hepato-biliary system and pancreas disorders (60.2 million JPY, 16.2%).

Figure 6 breaks down in-patient expenditure for neuro-system disorders. The total was 62.2 million JPY, of which 16.4 million JPY (26.4%) was due to stroke, followed by non-traumatic brain hemorrhage (11.3 million JPY, 18.2%) and dementia (7.6 million JPY, 12.2%).

Figure 7 shows out-patient expenditure stratified by each primary diagnosis (MDC ex-pression). Total out-patient expenditure was 440.3 million JPY. The most important expen-

Figure 5NHS expenditure stratified by each primary diagnosis (in-patient, Oct. 2011)


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diture was that of circulatory disorders (76.1 million JPY, 17.3%), followed by digestive system, hepato-biliary system and pancreas disorders (47.9 million JPY, 10.9%) and kidney, urinary tract and male reproductive system disorders (31.8 million JPY, 7.2%). Compared with in-patient data, out-patient data contains more that is not coded for diagnosis (13.3%). This is due to the fact that not all medical facilities follow the MHLW master table of stan-dardized diagnosis.

Figure 8 breaks down out-patient expenditure for kidney, urinary tract and male repro-ductive system disorders. The total was 31.8 million JPY, of which 17.0 million JPY

Figure 6NHS expenditure stratified by each primary diagnosis

(detail of MDC01, in-patient, Oct. 2011)

Figure 7NHS expenditure stratified by each primary diagnosis (out-patient, Oct. 2011)


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(53.4%) was due to chronic nephritic syndrome, chronic stroma-related nephritis and chron-ic kidney failure.

III-2. Analysis of LTCI expenditure

Figure 9 shows LTCI expenditure for assistance required level 2 stratified by covered services of three municipalities of Fukuoka prefecture (October, 2011). For the service pro-

Figure 8NHS expenditure stratified by each primary diagnosis

(detail of MDC10, out-patient, Oct. 2011)

Figure 9LTCI expenditure stratified by covered services and municipalities

(assistance required level 2, Fukuoka prefecture, Oct, 2011)


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file, there were clear differences among them: City A used more preventive ADL care at specified facilities (21.1% of the total), City B used more preventive home help services (39.4%) and City C used more preventive day services (43.5%). As the objective of “pre-ventive services” is to prevent the aggravation of dependency levels among frail elderly, we can evaluate the effectiveness of these services based on chronological changes in depen-dency level and/or LTCI expenditure at each level—municipal, service provider and indi-vidual. It is important to recognize that this kind of comparison must be possible via our system.

III-3. Analysis of combined medical and LTCI data

Figure 10 shows total expenditure (health insurance and LTCI) for aged persons, be-tween 65 and 74 years old, stratified by dependency level (data from one municipality of Fukuoka prefecture, October, 2011). It should be noted that the data only includes those who used both services covered by health insurance and LTCI. It is interesting to note that LTCI expenditure was larger than that of health insurance. This result indicates that the LTCI scheme might have a complementary effect to the health insurance scheme and that it is in-dispensable in evaluating total expenditure for appropriate evaluation of the effect of ageing on the social security system.

Using this system we can follow up the chronological profile for each individual insured. Figure 11 shows an example. This insured (dependency level 5) individual was hospitalized in an acute care hospital up to May 2011 because of a non-trauma brain hemorrhage. After discharge this patient received rehabilitation care at a rehabilitation hospital up to November 2011 and then received home care services. The details of home-based care were as follows: day rehabilitation service at the rehabilitation hospital, assistance device rental services in

Figure 10Analysis of total expenditure (health insurance and LTCI) of aged between 65 and 74 years

stratified by dependency level Oct, 2011)


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November 2011, and day service, assistance device rental services, health guidance for home care and home help service after December.

III-4. Analysis of pharmaceutical expenditure

Under difficult financial conditions, cost containment has become an emergent require-ment for all insurers. At present the most available and quickly effective strategy is to pro-mote the use of generic pharmaceuticals. It is possible for insurers to implement a proper program for the promotion of generic drug use via analysis of pharmaceutical claim data. In fact, many insurers are doing this analysis and giving reports of pharmaceutical use for each individual insured by using outside organizations for this task.

However, as it is necessary to consider both effectiveness and efficacy for the use of ge-neric drugs. It is important to know that efficacy can vary according to pill type even for those with the same constituents. So, it will be more plausible for insurers to give the profil-ing data of pharmaceuticals to the local pharmacist’s association and ask them to choose ap-propriate generic drugs giving respect to the effectiveness and efficacy. With such support, it is certain that the use of generics is more accepted by the general population. However, it might be rather psychologically difficult for patients to directly ask their physicians to sub-

Figure 11Chronological profile of service use for each individual

(Example: Dependency level 5, non-traumatic brain hemorrhage, April 20011 to Feb. 2012)


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scribe the generics. This is why we developed an information system with which each insur-er can analyze the details of pharmaceutical use of their insured and act as an advocate. Us-ing this system, the insurer can estimate the maximum and minimum financial effect of substitution based on the master table that groups pharmaceuticals with the same effect. This master table is renewed annually.

Table 2 shows the maximum expenditure possibly reduced by generics stratified by me-dicinal virtues (total expenditure including in- and out-patient) of a municipality of Fukuoka prefecture (October, 2012). The most important reduction was observed for cardio-vascular drugs. The actual expenditure was 25,881,306 JPY and the percentage of generic use was 11.0% (2,813,940 JPY). The minimum possibly reduction would be 4,284,846 JPY (17%) and the maximum 5,996,873 JPY (23.0%). In fact, it is rather difficult for pharmacists to carry out drug substitution without more detailed information. In order to respond to this re-quirement, our system can break down the classification of pharmaceuticals. Table 3 shows an example. The table shows that coronary vasodilators would have the most important re-duction possibility among the cardio-vascular drugs. Total expenditure on coronary vasodi-lators was 4,303,384 JPY, and the possible reduction would be 2,015,668 JPY (47%) at most and 1,087,871 JPY (25%) at minimum.

The generalization of separation of medical practice and drug dispensation has reduced psychological resistance among physicians against drug substitution by pharmacists. There-fore, this kind of information service for local medical associations and local pharmacist as-sociations would make it possible to promote cost containment at the local level. The role of

Table 2Analysis of pharmaceutics use

(Analysis of generic use stratified by medicinal virtues: Oct. 2011)


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pharmacists is especially important.One problem to be solved is the lack of diagnosis in the prescription. In Japan, the offi-

cial prescription sheet from physician to pharmacist does not include the diagnoses of the patient. In order to safely do drug substitution, it is preferable that physicians and pharma-cists share clinical information. In the case of multi-morbidities patients, it is preferable to share information between pharmacists of different pharmacies. In France, they have been able to carry out comprehensive monitoring of prescribed drugs via an IC card in which each pharmacist registers information. In the same way we will be able to realize such a sys-tem of sharing prescription information by introducing electronic drug notebooks. A positive response to this proposal is strongly recommended.

III-5. Analysis of Specified Health Checkup Data

Currently there are several problems to be solved in the Specified Health Checkup pro-gram, i.e., low utilization rate among NHS insured and difficulty of data matching between healthcare and Specified Health Checkup data because of unsystematic ID coding. As there has not been any evidence that clarifies the effectiveness of the Specified Health Checkup program in cost-containment of health insurance finance, the authors are skeptical of the cost reducing effect of the program at the moment. However, we also accept its value as a social experimentation in order to test the hypothesis4). This requires a tool for proving it, and it is indispensable in modifying the program if necessary.

There is no doubt that an overly complicated information format and its transfer system were major factors for delay of implementation at the starting phase. During this confusing

Table 3Analysis of pharmaceutics use (Detailed analysis of generic use stratified

by medicinal virtues:cardio-vascular drugs, Oct. 2011)


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initial phase, many disease management organizations started their business without mutual harmonization, which has caused a negative effect on the standardization of information for-mats. If the standardization and clarification of information formats had been realized at the initial phase of introduction of the program, the actual situation would be better. In consid-eration of this situation, we have paid much attention to the standardization of data and the simplification of data processing in order to realize a PDCA cycle of business development. At the moment the data volume is not sufficient for evaluating the effectiveness of the pro-gram. In the near future we will evaluate metabolic syndrome-related clinical events (i.e., coronary events) and costs between DM project participants and non-participants using our database.

In this article we explain some results that can be analyzed based on the current data (short-term data). Figure 12 shows the analytic sheet of the Specified Health Checkup pro-gram. The program employs a standardized questionnaire of life-style that might be associ-ated with the occurrence of metabolic syndrome, e.g., “10 kg or more increase of body weight since 20 years old”, “more than twice-weekly frequency of physical activity (more than 30 minutes), continued for more than 1 year”, and “shorter eating time than other per-sons”. The right-upper table of figure 12 shows the prevalence of metabolic syndrome for the total population and the lower table shows the prevalence among the population who are “10 kg or more increase of body weight since 20 years old: yes” and “current physical fit-ness activity: no”. The percentage of “metabolic syndrome” + “metabolic syndrome sus-pected” of the former group and the latter group were increased from 25.2% to 55.6% in 40’s, 28.5% to 63.8% in 50’s, 32.2% to 66.7% in 60’s and 35.2% to 66.0% in 70’s, individu-

Figure 12Analysis sheet for specific health check up


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ally. Insurers can use these kinds of results for promotion of the program. As one of the rou-

tine tasks, each insurer disseminates an invitation message in their city’s newsletter saying “Please get involved in the Specified Health Checkup program”. However, most citizens do not pay much attention to such messages as they do not feel that such is related to their cur-rent condition and the future risks. If the message contains information such as the follow-ing, “according to last year’s results of the DM program for our city, among persons who experienced “10 kg or more increase of body weight since 20 years old” with no “current physical fitness activity”, more than 60% were diagnosed as “metabolic syndrome” or “met-abolic syndrome suspected”. Metabolic syndrome can cause acute myocardial infarct and cerebro-vascular attack. Therefore it is better to take preventive measures as early as possi-ble. If you meet the criteria, it is strongly recommended that you get involved with the Spec-ified Health Checkup program.” This kind of detailed information would be more influential for the general population’s acceptance and would improve the participation rate. This type of dissemination of information is one of the most popular promotion strategies in market-ing theory.

On the other hand, one of the most effective countermeasures for the short-term is to persuade high risk groups to receive a consultation from a physician. Under the current scheme, a person who has problems with hypertension, hyperglycemia or dyslipidemia is recommended to consult a physician as early as possible. It is important to ensure that such high-risk persons receive medical consultation. Our system supports this kind of patient management activity. Figure 13 shows the monitoring system. Our system is equipped with a master table of drugs for hypertension, diabetes mellitus and dyslipidemia, and can moni-tor the amount of use of those drugs for every individual monthly. If a person who showed any high abnormalities of blood pressure, blood sugar or blood lipid, and therefore was rec-ommended to consult a physician, it would be very possible that this person would be pre-scribed some sort of drugs for these abnormalities after the health checkup. If not, it is high-ly possible that this person will not have received any consultation regardless of recommendation.

As persons in the frame of the lower part of figure 13 had abnormalities in blood sugar, blood pressure and blood lipids, and received some drugs for these abnormalities, they are evaluated as having received medical consultation. On the other hand, persons in the frame of the upper part of figure 13 had not received any drugs for these abnormalities. It is very possible that they have not received any medical consultation. For this case, a public health nurse of the insurer in charge of the health checkup program can make contact with them by e-mail or telephone in order to ascertain the actual situation and to advise them on receiving medical consultation if necessary.

As shown in figure 8, this municipality (NHS insurer) pays a lot of money for the treat-ment of chronic nephritic syndrome, chronic stroma-related nephritis and chronic renal fail-ure. One of the most important causes of these disorders is diabetes mellitus, and therefore it is very important to ensure that this high-risk group receives proper medical control in order


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to secure their quality of life (QOL) and to realize cost control for healthcare expenditure. Kure city (Hiroshima prefecture) and Amagasaki city (Hyogo prefecture) have been imple-menting such a monitoring system under collaboration with private companies. Considering the financial difficulty, it is not easy for local municipalities to consign these programs to private companies. Our system can be one of the better cost-effective substitutes.

III-6. Application for health policy evaluation

One of the purposes of developing the current system was to apply it in health policy evaluation. There will be tacit consent that the public welfare level will improve by the in-clusion of a new medical technology for the official tariff schedule. Of course it is the prem-ise that a medical technology evaluation will be carried out on the occasion of such a techni-cal introduction, and the effectiveness will be confirmed. However, for most cases, such pre-inclusion clinical trials are conducted using a relatively homogenous group of limited sample size. Therefore, the government requires post-marketing clinical trials. Considering that our society employs a public social security scheme, such a post-marketing clinical trial is necessary both from the viewpoint of health economics and of clinical effectiveness (or efficacy). In some cases, cost accounting research is planned, however, it is rather difficult to obtain consensus for the results of such “strict research” because of differences in meth-odology in regard to allocating indirect costs and calculating opportunity cost.

In our system, we used the claim data for the calculation of charged cost as a substitute

Figure 13Example of follow up system for high risk insurers


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for real cost in order to avoid such confusion. In order to implement prevention programs, i.e., vaccination and health promotion, it is required to show evidence with numerical ratio-nality. In the following part, we would like to show an example of the health policy evalua-tion program which evaluated the cost-benefit of pneumococcus vaccination based on the NHS and SHIA claim data of a municipality of Fukuoka prefecture.

III-6-1. Population studiedData used was the claim data of 3,894 insured (60 years and over) who received an invi-

tation for pneumococcus vaccination from April 2013 to March 2014. In this municipality, the program is organized at each sub-area and all insured 60 years and over will be covered within 3 years.

III-6-2. MethodsAmong the 3,894 persons, 1,134 received vaccination and the other 2,760 did not. We

conducted a survival analysis based on the claim data of above mentioned 3,894 persons. We calculated the person-months from the vaccination to the occurrence of pneumonia (or end of observation period: March 2014) for those vaccinated and the person-months from April 2012 to the occurrence of pneumonia (or end of observation period: March 2014) for non-vaccinated. Then we compared the incident rate of pneumonia and the pneumonia-asso-ciated healthcare costs between the two groups. Finally, we conducted a cost benefit analysis for the pneumococcus vaccination program. For this analysis, the cost was calculated based on claim data from the viewpoint of the insurer, and did not include any opportunity cost for the insured (e.g., transportation costs, absence from work, etc.).

III-6-3. ResultsTable 4 shows the results. For the non-vaccinated, total observation person-month was

30,456 and 424 pneumonia cases were observed (incident rate is 0.01392), and in the case of vaccinated these figures were 7,983 and 89 (0.01115). The pneumonia related costs calcu-

Table 4Example of cost benefit analysis (Effectiveness of Pneumococcus vaccine)


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lated from claim data were, respectively, 1,179,856 JPY (SD: 1,758,259 JPY) and 350,978 JPY (SD: 613,619) per person. The vaccinated persons showed a statistically significant low incident rate (p<0.01, t-test) and low pneumonia-related costs (p<0.01, t-test). These results indicate the effectiveness of pneumococcus vaccination for prevention and cost contain-ment.

Figure 14 shows the results of the Kaplan-Myer method. The vaccinated group showed statistically lower incident rate than the non-vaccinated group (p<0.01; Log rank test).

The lower part of table 4 shows the results of cost-benefit analysis. If 10,000 persons are recruited, the total cost of treatment for non-vaccinated was estimated at 164,256,315 JPY=10,000 persons x 0.01392 (incident rate) x 1,758,259 JPY (cost per pneumonia case), and that of vaccinated was 39,129,425 JPY=10,000 persons x 0.01115 x 350,978 JPY. The difference of 125,126,890 JPY was estimated as the cost-containment effect of pneumococ-cus vaccination for 10,000 persons. This municipality pays 7,000 JPY for each vaccination. Therefore the net cost-containment effect would be 125,126,890–70,000,000=55,126,890 JPY (or 5,512.7 JPY per person). This result suggests that there would be a statistically sig-

Figure 14Example of cost benefit analysis (Analysis of effectiveness of pneumococcus vaccination)


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nificant cost-containment effect in pneumococcus vaccination. There are several limitations for this analysis. First, this analysis was not planed a prio-

ri. It used already existing data, and thus was not a sophisticated study design for proper comparison. According to the current design, observed person-months became longer for the non-vaccinated than the vaccinated group. This uneven arrangement might cause a bias for more pneumonia cases detected from the non-vaccinated group. This bias might overes-timate the effectiveness of vaccination. Secondly, the definition of pneumonia must be re-considered. In this study, we adopted a wide definition for pneumonia that includes not only pneumonia but also broncho-pneumonia and bronchitis based on diagnoses in claim data. The percentage of 7.8% (89 cases per 1,134) for vaccinated and 15.4% (424 cases per 2,760) for non-vaccinated might be too much even though these data were derived from the aged population. In order to obtain more reliable evidence, we have to employ a more sophisticat-ed study design including sensitivity analysis.

It is true that the current system has several problems to be solved. However, it is im-portant to recognize that we could develop the basis for health policy evaluation with the current integrated claim data analysis system.

IV. Discussion and conclusion

IV-1. Characteristics of Japanese claim data and its usability

Japanese claim data is excellent medical information. According to the MHLW report, as of April 2014, 99.9% of prescription claims were computerized. The computerization rate of medical service claim data came to 98.9% at hospitals and 96.9% at clinics. Japanese claim data contains detailed service profiles such as medical procedures, drug use and fre-quency and volume and date of service. Claim data from pharmacies has the ID code of the health facility that issues the prescription. This makes it possible to analyze the total care pathway, combining clinic and pharmacy. From the international point of view, there are few countries that have similar health data at this level of detail. Thus, Japanese claim data is a very important intellectual property. In addition to medical claim data, LTCI claim data has become almost perfectly computerized since its introduction in 2000. Although there are several points to be ameliorated, it is important to recognize that Japan has an integrated health claim data system (medical insurance, disease management and LTCI).

However, this precious health data was not fully used for health policy making before the establishment of the National Database (NDB). NDB is a database through which the MHLW gathers all health care insurance claim data according to the Law on the medical se-curity of elderly persons. Originally this database was constructed in order to analyze health expenditure at a national level. Today its use is also permitted for prefectural governments and more importantly for various academic research projects. Data collection started in April 2009 and today more than 170 million health insurance claim, 25 million specified health checkup and 840 thousand health guidance data is collected annually. As shown in


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figure 15, data collection is included into the daily routine work of claim data processing. This database is constructed on the basis of the individual insurer. The ID number is treated by the Hash function conversion twice in order to make it anonymous but is still usable for combining all data for the individual insured.

It is the premise that information input should be formatted on the occasion of these da-tabase compilations, but it has already been reported that the matching rate among these data was rather low at 14.9% in 2013. The reason for this failure was very simple—a for-matting error. The same problems happened with our system. In order to overcome this problem we corrected the data manually. This kind of correction is impossible in the case of Big Data such as NDB. Thus it is necessary to consider ways to assure correct ID manage-ment in each medical facility.

IV-2. Use of claim data for health policy making and evaluation

As explained in this article, claim based information is very useful data for health policy making and evaluation. Using this data, we can summarize local health conditions and then establish health programs based on the evidence derived.

For past medical and LTCI-related plans, the national government published a series of planning manuals, and most local governments have sincerely followed the official manuals.

Figure 15Claim data collection pathway of National Database


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This process has resulted in the mass production of similar plans that only applied a series of data in accordance with the template. This kind of uniform planning process is not useful because of large socio-economic differences among the local governments. Considering the fact that even in the same prefecture there are wide variations of socio-economic situations among the municipalities, it is preferable for each municipality to have the ability to carry out data analysis to some extent for planning.

In this article, we showed some examples of data analyses, i.e., in the cost-containment effect of generic use and in the pneumococcus vaccination program. From the academic viewpoint, there must be more sophisticated analyses. However, these kinds of “rough anal-yses” should be sufficient for the daily management of local policy. With these evidences, it will be easier to obtain accord from other sections of local government and the local assem-bly.

We would like to show another example of health policy evaluation based on our LTCI claim data analysis system. Through the modification of LTCI law in 2006, a prevention program has been officially introduced in order to contain LTCI costs. This modification in-troduced frailty screening and organized a series of prevention programs, e.g., physical fit-ness, oral function and hygiene amelioration and nourishment improvement programs. Since 2015, these programs have been integrated into the daily life support program through mod-ification of LTCI law. Before implementing the new program, we have to reconsider why our LTCI scheme has decided to cover the frail aged who were evaluated as “assistance re-quired”. In similar schemes in other countries, e.g., Germany and France, the slightly frail aged are not covered by such schemes.

According to the LTCI law, an “assistance-required frail aged person” is defined as fol-lows: a frail elderly person who does not require ADL care assistance but is highly likely to become dependent without proper prevention activities. So, the services provided for “assis-tance required elderly” must be preventive. There is a question whether the services provid-ed for these elderly have proven usefulness in prevention. In fact, there have not been any evaluations of the effectiveness of preventive services for this category of frail aged at the national level.

It is estimated that the average monthly premium of LTCI will be about 6,000 JPY in 2015. If the current increasing trend continues, it will be difficult to maintain the LTCI scheme for most municipalities. Cost-effectiveness is required in order to distribute resourc-es to the appropriate services. This requires developing an integrated analysis system for LTCI data for appropriate policy management.

Actually, MHLW is developing an “LTCI Visualizing Program” that summarizes LT-CI-related data (assessment and claim data) at various regional levels (prefecture, local gov-ernment, daily life zone). When this system is constructed, each local government will be able to grasp the problems to be solved with concrete data. This situation gives more incen-tive for each local government to efficiently manage the LTCI scheme.

In order to implement more effective and concrete programs for better management of the LTCI scheme, it is necessary to equip them with analytic tools such as our system in ad-


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dition to the above mentioned visualization system. For example, one local government has clarified the fact that LTCI beneficiaries and expenditures sharply increase among the in-sured 85 years or older. This evidence suggests the importance of programs that make it possible for the more aged (over 75 years old) to maintain independency level in order to enable cost-control for the LTCI scheme.

Another local government using our system clarified the following facts:- There were many dependent cases requiring ADL assistance among the elderly who had

been evaluated as not needing it by the frailty screening program.- ‌‌ Frail elderly who participated in health promotion classes organized by the community

used LTCI services less often than non-participants.These results suggest the effectiveness of prevention programs targeting high risk groups

and at the same time the necessity of population strategy for all aged people regardless of the dependency level evaluated by official assessment and screening tools. It will be possi-ble for the local government to organize mixed strategies for high risk and population ap-proach and evaluate the effectiveness of such strategies via the PDCA approach based on data analyzing systems like our own.

The problem of frail elderly is commonly very complex and wide ranging, encompass-ing medical, physical, psychological and social aspects. Needs of such elderly persons are to be comprehensively covered by medical and LTCI services in most cases. Therefore, the economic effectiveness of preventive programs should be evaluated based on both medical and LTCI schemes. In fact, our system has clarified that frail elderly participating the LTCI prevention program used less medical care and LTCI services.

IV-3. Application of claim data for Regional Health Care and Integrated Community Care plans

In order to reorganize health care delivery systems adapted to a highly aged society, im-portant health service research based on NBD data has been on-going under the Health La-bour Sciences Research Grant. The main purpose of this research is to analyze the actual disease structure and the current situation of the health service delivery system and to fore-cast future service needs. Using the NDB data, we analyze the disease structure and the re-source allocation to intensive, acute, subacute and chronic in-hospital services independent-ly for each secondary level of health care region, currently and for in the future (2025 and 2040)13-17). In this research, we provide a forecasting tool by which each prefectural govern-ment can estimate future resource requirements and realize affordable health policies through collaboration with health service providers. Use of claim data in this way will have the effect of facilitating more active use of claim data itself. This requires a basis for infor-mation security. Actually, the government organizes a series of committees for this purpose. Our system will offer valuable materials for the discussion.


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IV-4. Issues to be solved for better use of claim data

As explained in this article, it is possible to formulate various useful data for health poli-cy making and evaluation by integrating claim data that has been accumulated by different insurers. The Japanese e-claim uses a standardized format in principle where it is possible to semi-automatically construct and revise the dataset once the SQL for processing is estab-lished. Of course there are some issues to be solved or modifications to be made in order to realize such a system, as discussed below.

IV-4-1. Necessity of unique social security numberAs Japan does not use a unique social security number, a citizen receives different types

of social security numbers that are “unique” for each regime. Therefore this situation re-quires the combination of differently formatted data for each individual (i.e., NHS and LTSI) for the construction of an integrated database. In the case of NHS, a social security number is allocated on a per-family basis, so we have to distinguish each insurer by birth date, sex and name. As employment fluidity is progressing in Japan, it is becoming very common that a person frequently changes their social insurance affiliation. This situation causes problems in traceability for the individual. In light of this situation, it is necessary to introduce a unique social security number. Considering that other developed countries such as France and Nordic countries have already introduced such a unique social security num-ber, it would not be so difficult for Japan to adopt such a system. Of course, as the claim in-formation is very sensitive, privacy protection must be highly secured. In respecting the re-quirements of the Act on the Protection of Personal Information, the government must list up the issues to be solved as early as possible.

IV-4-2. Strict application of standard format of e-claimOne of the difficulties we experienced during the development of the current system was

the existence of e-claims in an unstandardized format. For example, the mixture of one and two byte characters made individual identification difficult after the Hash transformation. It is strongly recommended that strict regulations are applied for claim data preparation under the social security scheme. As major parts of this problem are caused by the computer sys-tem of each medical facility, the strict application of general rules for the associated supplier are required.

In the case of France where big size claim data is widely used for health policy making, the government prepares various master tables and corresponding manuals. The suppliers are then required to use these materials in an official registration system. In Japan, MEDIS applies a similar rule; however, there is no obligation for suppliers to follow this standard. The initiative of the government in regard to the observance of common rules is necessary.


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IV-4-3. How to treat multi-morbiditiesIt is often required to estimate diagnosis-specific expenditures. However, it is very com-

mon that there are multiple principal diagnoses within one e-claim. Furthermore, under the current e-claim format, it is rather difficult to combine each procedure of a specific diagno-sis. This makes it difficult to estimate disease-specific expenditure. In order to evaluate the effectiveness of a specified health checkup program, it is preferable to be able to estimate the disease specific expenditure, such as diabetes mellitus-associated expenditure. This will require the modification of the e-claim format, e.g., limiting the number of principal diagno-ses in a claim to three. As this problem is related with the peer review process, further dis-cussion among the related organizations will be necessary.

Actually, there are several methods of estimating disease-specific expenditure that have been developed by researchers. However, most of them are already used by business agents and protected by patents. This situation makes it difficult to generalize any of methods. It is desirable for the government to prepare a standard method.

IV-4-4. Diseases and disorders responsible for dependencyUnder the current system, LTCI claim data does not have any information about diseases

or disorders that have caused dependency. During the eligibility assessment process, the family doctor’s opinion sheet is required for determining dependency level. This sheet indi-cates diagnoses related to dependency for each applicant. This sheet is simply formatted, and rather easy to analyze compared with the medical e-claim. Within the current project, we could not use this information, because it is not digitized. Most physicians create opinion sheets via computer programs, e.g., “Iken-sho,” which is provided by the Japan Medical As-sociation. Thus it is not difficult to integrate this information into the current system. It is recommended that the associated organizations positively consider the use of Doctor’s opin-ion sheets.

IV-4-5. Treatment of un-coded diagnosesCurrent e-claim data has the problem of un-coded diagnoses. For the current project, we

used a diagnosis master table that was developed through the DPC project. Using this mas-ter table, we transformed the diagnosis written in Japanese into ICD-10 code. In the case of the Japanese diagnosis without corresponding ICD-10 in the master table, we coded it man-ually. This work required extensive labor. For some cases, the quality of diagnosis was ques-tioned. It is necessary to implement a quality check for diagnosis on site. Although there are several systems that automatize the allocation of diagnosis based on the procedures and drugs provided, it is preferable to make it obligatory for each medical facility to select prop-er diagnoses with correct ICD-10 code.

IV-4-6. Packaging of the systems construction and organization of supporting equipmentIn our system, we batch-process the fixed-period data of local governments (usually 6

months’ worth), which requires about 4 weeks (2 weeks for data processing, 2 weeks for


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system construction or modification). As this process requires SQL skill for processing, it is rather difficult for the staff of local governments. So, it requires an outside support team for data processing and analyses. Although the base system is common, each local government has its own health program that requires some kind of customization. Therefore it is prefera-ble that the prefecture and/or academics support them and that the national government de-fines the requirements of system construction. This prevents the enclosure of systems by particular vendors and makes it possible to construct a universal system with a cheaper cost.

Standardizing the treating process for basic information such as NHS and LTCI claim data is indispensable. Actually, the All Japan Federation of National Health Insurance Orga-nization is developing an integrated database combining NHS and LTCI (the so-called, Kokuho Database: KDB). If this database is constructed, it will be possible to extract the necessary data for the target community at rather low cost. Our system is also constructed to be compatible with the KDB format.

The development of our system requires a relatively low budget—at most 2 million JPY. Of course, as our system was constructed for research use, it does not include further costs required for business development, such as renovation, staff training and personnel costs. So, it will require some extra money in order to do business using this system. Today this field attracts the interest of many IT developers. If there is no standard for data transfer in-terface, mutual communication among the different systems will be expensive. That has happened for electronic medical records in Japan. In order to avoid such a situation, it is strongly recommended that a national standard for data sharing is established as early as possible.

IV-4-7. Capacity building of usersEven though the system is excellent, it cannot be used without the capacity building of

users. Tsutsui classifies the ability of insurers into three categories: routine work function (management of insures, reimbursement), development function and strategic function. She criticizes the fact that most Japanese insurers do not serve to provide even the most routine work functions18). Today, workload associated with social security is increasing at local gov-ernment level along with the ageing of society. Tasks required are also becoming more com-plex. Especially in the case of the medical insurance matter, staff are sometimes required to have a deep understanding of medical services. In order to respond to these requirements, we have organized a series of seminars where participants can master use of the system and learn how to apply data derived from the system for the establishment of the Regional Health Care Plan. During this process, we were surprised to learn that a significant number of participants from local governments did not know how to use basic software such as MS-Excel. Today the total amount for Special accounts of local government, such as NHS and LTCI, occupies a large part of its total budget and expenses. As the ageing of society will make social security-related management more complex and important at each local government level, they must pay more attention to capacity building for staff in charge of this task.


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In this article we have explained the details of the integrated claim data analysis system that we have developed. In Japan we have a set of claim data under universal coverage. This information is very detailed compared with that of other countries. The establishment of NDB and its application for the regional Health Care Plan and the Data Health program (use of health data for realization of healthy companies) will further advance the use of health data. However, it requires several problems to be solved: the reorganization of information bases, balancing the use of data and privacy issues. Today many people talk about “Big Health Data” in a loud voice. We must keep cool heads when dealing with this issue. The priority is to ameliorate the national health level from the public point of view. We need an appropriate philosophy for the use of “Big Health Data”. As this is very new for those in Japanese society, it is preferable to start the discussion based on social experimentation. Our project is one such social project. We will open the source code for our system in the near future in order to share our experiences with others. It will be a great pleasure if our experi-ences contribute to improving the welfare level of our country.

AcknowledgementThis paper was written based on the reports of two studies: (1) research on the strategic

management of community-based health promotion activities for the prevention of frailty, supported by a Health Labour Sciences Research Grant 2013 (Comprehensive Research on Aging and Health, H25-006) for development of integrated claim data analysis systems, and (2) research on the area ward’s function and the allocation of human resources in hospitals, Health Labour Sciences Research Grant 2013 (Comprehensive Research on Aging and Health, H26-medicine-general-001), for cost-benefit analysis of pneumococcus vaccination.

Authors would like to express special gratitude to the staff of Fukuoka prefectures and local governments for their assistance in this study.

V. Literature

1) Japan Medical Association (2009): Grand design 2009—Medical systems to support the wellbeing of citizens, Tokyo: JMA, February, 2009. (in Japanese)

2) Matsuda S, Murata H and Funatani F (1997): Input-output analysis of investment in the health sector of Kitakyushu city, Japanese Journal of Health Economics & Policy, Vol.4: 51–70. (in Japanese)

3) Institute for Health Economics and Policy (2004): Input-output analysis of investment in health and welfare sector, Tokyo: IHEP. (in Japanese)

4) Matsuda S (2009): Situation analysis of specified health checkup and guidance program, Ten days report of social security (Syakai Hoken Junpo), No.2256: 182–15 (in Japanese).

5) Matsuda S (2013): Health system reform in the United Kingdom, Journal of UOEH, Vol.35 (4): 279–289. (in Japanese)

6) Fujino Y, Kubo T, Muramatsu K, Watase M, Matsuda S (2013): General background and practical implementation of fitness for work statement in the UK, Journal of UOEH,


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Vol.35(4): 291–297. (in Japanese)7) Kubo T, Fujino Y, Muramatsu K, Matsuda S (2013): Occupational physicians system in

the United Kingdom and fit note to promote access to occupational health services, Jour-nal of UOEH ,Vol.35(4): 299–303. (in Japanese)

8) Muramatsu K, Kubo T, Fujino Y, Matsuda S (2013): Employment-related benefit system in the UK, Journal of UOEH , Vol.35(4): 305–311. (in Japanese)

9) Muramatsu K, Kubo T, Fujino Y, Matsuda S (2013): Industrial injury insurance system in the UK, Journal of UOEH , Vol.35(4): 313–316. (in Japanese)

10) Matsuda S (2005): Necessity of integrated health database for health promotion pro-gram, Ten days report of social security (Syakai Hoken Junpo), No.2256, 18–24. (in Japanese)

11) Matsuda S (2013): What is the problem of health system—the Japanese health system for the highly aged society—, Tokyo: Keiso shobo. (in Japanese)

12) Matsuda S (2013), Report of “Research on strategic management of community based health promotion activity for the prevention of frailty”, Health Labour Sciences Re-search Grant 2013, March, 2014. (in Japanese)

13) Matsuda S, Muramatsu K, Fushimi K, Fujimori K, Ishikawa BK (2014): Data oriented regional health care system planning (1) —Emergency medicine and cancer medi-cine—, Ten days report of social security (Syakai Hoken Junpo), No. 2583: 10–18. (in Japanese)

14) Matsuda S, Muramatsu K, Fushimi K, Fujimori K, Ishikawa BK (2014): Data oriented regional health care system planning (2) —Planning by the estimation of future disease structure—, Ten days report of social security (Syakai Hoken Junpo), No. 2584: 32–38. (in Japanese)

15) Matsuda S, Muramatsu K (2014): Data oriented regional health care system planning (3) —Comprehensive analysis of health care insurance and LTCI claim data—, Ten days report of social security (Syakai Hoken Junpo), No. 2585: 18–24. (in Japanese)

16) Matsuda S, Muramatsu K (2014): Data oriented regional health care system planning (4) —Regional health care vison—, Ten days report of social security (Syakai Hoken Jun-po), No. 2586: 18–24. (in Japanese)

17) Matsuda S (2014): Report of research on the ward’s function and allocation of human resources of hospital, Health Labour Sciences Research Grant 2013 (No. H26–medi-cine-general-001), March, 2014. (in Japanese)

18) Tsutsui T (2014): Science of integrated community care—the theory and practice of in-tegrated care—, Tokyo: Syakai Hosyo Kenkyujo. (in Japanese)


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