Discussion Paper No.442

Estimating Medical Cost Savings
from Supporting Health Behavior
Using AI and ICT in Japan

Fusae OKANIWA, Hiroshi YOSHIDA

December, 2020TOHOKU ECONOMICS RESEARCH GROUPGRADUATE SCHOOL OF ECONOMICS AND
MANAGEMENT TOHOKU UNIVERSITY
27-1 KAWAUCHI, AOBA-KU, SENDAI,
980-8576 JAPANEstimating Medical Cost Savings from Supporting Health Behavior
Using AI and ICT in Japan

Fusae OKANIWA†
Hiroshi YOSHIDA‡

ABSTRACTThe fields of artificial intelligence (AI) and information and communication technology(ICT) are currently witnessing remarkable progress, and they are now being applied tosystems that encourage people to engage in voluntary health promotion behaviors. In the
future, health intervention systems will likely be replaced by AI. The development of such AIbased health intervention systems is still in its infancy, however, and there is little evidence of
their effectiveness. The purpose of this study is to estimate the macro health care cost savings
that would result from the widespread use of AI-based health intervention systems. This study
to estimates the healthcare cost reductions that could be achieved if AI interventions were to
spread and the number of obese people were to decrease, based on the estimates of Okaniwa
and Yoshida (2020). It was estimated that AI interventions could reduce medical costs fordiabetes and hypertensive diseases by 8.2% and 7.2%, respectively. The findings of this paper
could contribute to providing preliminary material for new developments in future researchin this field.

JEL classification: D91, H51, I10
Keywords: AI, ICT, medical cost savings, health intervention, BMIThis paper has partially revised a report (Okaniwa and Yoshida, 2020) of the Japan Institute of

Public Finance (JIPF) on October 2020. The previous version is available on the JIPF members
page (https://service.gakkai.ne.jp/society-member/auth/abstract).
†

Assistant Professor, Graduate School of Economics and Management, Tohoku University, 27-1

Kawauchi, Aoba-Ku Sendai 980-8576, Japan. E-mail: fusae.okaniwa.b4@tohoku.ac.jp‡

Professor, Graduate School of Economics and Management, Tohoku University, 27-1 Kawauchi,

Aoba-Ku Sendai 980-8576, Japan. E-mail: hiroshi.yoshida.e2@tohoku.ac.jp

11. INTRODUCTION
Obesity is a public health threat in developed countries. In Japan, lifestyle-related diseases,
including obesity, account for more than 30% of medical costs (Ministry of Health, Labourand Welfare, 2018a). Medical costs are 22.3% higher in obese groups than in non-obese
groups (Kuriyama et al., 2002), and maintaining a 10% weight loss reduces one’s lifetime
medical costs by US$2,200-5,300 (Oster et al., 1999). Asians, in particular, are more prone
to diabetes following even a small increase in Body Mass Index (BMI), compared to otherethnic groups (Sone et al., 2004).
In this context, the Japanese government is stepping up its assistance for research into
the development of new service models and platforms for data management and utilization
for serious lifestyle-related diseases and nursing care prevention. In recent years, thewidespread use of cloud computing and mobile devices (e.g., smartphones) has made it
possible to utilize personal health records (PHRs), which comprise the medical, nursing, andhealth data of individuals, for various services, with the consent of the individual. In addition,
information and communication technology (ICT) and artificial intelligence (AI) are now
being considered in the context of providing health interventions. Using ICT and AI could
potentially reduce the costs of traditional direct human interventions, and could reach a large
number of people. However, there is little evidence regarding the effectiveness of AI-based
health intervention systems.
Okaniwa and Yoshida (2020) conducted an intervention experiment1 and tested theeffectiveness of AI-based health interventions. In the experiment, they used a smartphonebased diet management application (app) for the intervention. Users took photos of theirmeals with their smartphone’s camera, and the app analyzed the photos, determined thenutrients and calories in their meals, and calculated a meal balance score. The app alsodelivered advice on balanced meals, supervised by nutritionists, and created by the AI's
algorithm. The analysis revealed that the intervention from the AI-delivered text advice
significantly lowered the participants’ BMIs by 2.6%. This paper uses the results from thisprevious study to estimate the effect such an AI-delivered service on reducing medical costs.
In other words, this study estimated the macro medical cost savings of AI-delivered text
message interventions becoming more widespread (and thus the prevalence of obesity
decreasing). This paper will contribute to providing preliminary material for new
developments in future research in this field.
1

The experiment was conducted for three months, from February to April 2020. The subjects

were recruited from 102 users of asken Inc.'s diet management app. For details of the experiment,
please refer to Okaniwa and Yoshida (2020).

22. DATA AND METHODS
Okaniwa and Yoshida (2020) found that an AI-delivered text advice intervention significantly
lowered participants’ BMIs by 2.6% in three months. In this study, this estimate was used to
examine the medical cost savings that could be achieve if people's health were to be improved
using these interventions. Obesity increases the risk of diabetes, hypertensive diseases, andhyperlipidemia (Kadowaki et al., 1984; Ohnishi et al., 2006; Kadowaki et al., 2006) and causes
atherosclerosis (Iwabu et al., 2010). Obesity interventions have been found to prevent theonset of these illnesses (Kosaka et al., 2005). If AI and human interventions, such as thoseexamined in our study, were to become widely used, people's BMIs might decrease. This could
reduce the prevalence of obesity in the population, and therefore the number of people withobesity-related diseases. Thus, medical costs could be reduced.
Estimating how many people would no longer be obese following such an intervention
requires data on the BMI distribution of the population. BMI is calculated in units of kg/m2,
where kg is a person's weight in kilograms and m2 is their height in meters, squared. According
to the Japan Society for the Study of Obesity (JSSO, 2011), obesity comprises by a BMI of 25
or higher and a visceral fat area ≥100 cm2 (as measured by computed tomography). In thispaper, individuals with a BMI of 25 or higher were considered to be obese, and the numberof people who could have their BMI reduced to below 25 by the intervention was estimated.
The population’s BMI distribution was obtained from the National Health and Nutrition
Survey (NHNS, Kokumin Kenkou Eiyou Chousa) from 2018. The distributions of each sample
are shown in Figure 1. The BMI distribution at the time of the NHNS survey is represented
in blue, with 25.8% of the total population being obese. The distribution in red shows aprojected 2.6% decrease in BMI after the AI health intervention. At this time, the percentageof obese people in the total population would be 17.9%, representing a 30.3% decrease in thenumber of obese people, compared to before the intervention.
Figure 1: Distribution of BMI

Source: created by the author, based on the Ministry of Health, Labor, and Welfare (2018b)
National Health and Nutrition Survey.

3

Finally, the effects of reducing the number of obese people by approximately 30% on
medical costs was estimated. The medical cost-saving rate was calculated as follows:

𝑀𝑒𝑑𝑖𝑐𝑎𝑙 𝐶𝑜𝑠𝑡 𝑆𝑎𝑣𝑖𝑛𝑔 𝑅𝑎𝑡𝑒 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑠 =(𝑀𝑖 × 𝜌𝑖 × 𝜎)𝑀𝑖・・・(1)

𝑀𝑒𝑑𝑖𝑐𝑎𝑙 𝐶𝑜𝑠𝑡 𝑆𝑎𝑣𝑖𝑛𝑔 𝑅𝑎𝑡𝑒 𝑓𝑜𝑟 𝑡𝑜𝑡𝑎𝑙 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑠 =(𝑀𝑖 × 𝜌𝑖 × 𝜎)
∑122
𝑖=1 𝑀𝑖・・・(2)

where 𝑀𝑖 is the medical cost of each disease (𝑖 = 24:diabetes, 𝑖 = 51: hypertensive diseases).
Data for each health insurance association were obtained from the Fact-finding Survey on
Medical Benefits (Iryou-Kyuuhu Jittai Chousa) in 2018. Diabetes and hypertensive diseases
are highly associated with obesity, but not all patients treated for these diseases are obese.
Therefore, data on the proportion of obesity in these diseases (= 𝜌𝑖 ) were needed. Furukawa
and Nishimura (2007) found that had 27.1% of diabetic patients and 23.6% of hypertensive
disease patients are obese, respectively, using micro data from the NHNS. The medical costs
caused by obesity before the intervention (= 𝑀𝑖 × 𝜌𝑖 ) were calculated by multiplying thesenumbers by each disease’s medical costs. As mentioned above, the number of obese people
was estimated by the distribution of BMI reported by the NHNS, and the estimates of
Okaniwa and Yoshida (2020) were used to calculate the percentage reduction in obesity due
to the intervention (𝜎 ). Here, the rate of decrease in the number of obese people wasmultiplied by each medical cost caused by obesity before the intervention (= 𝑀𝑖 × 𝜌𝑖 × 𝜎) to
calculate the medical cost savings rate of each cause of obesity after the intervention. Equation(1) represents the rate of reduction in each medical cost for diabetes or hypertensive diseases.
In addition, equation (2) represents the rate of reduction in total medical costs. Figure 2 showsan outline of the estimation.
Figure 2: Outline of the estimation of medical cost savings

Source: created by the author.

43. ESTIMATION RESULTS
Table 1 shows the estimates based on the BMI distribution data from the NHNS. This section
focuses on diabetes and hypertensive diseases. First, column (1) shows the medical costs foreach health insurance association, collected from the Medical Benefits Survey (2018). The
total medical expenses for all associations were 1.01 trillion yen for diabetes and 1.59 trillion
yen for hypertensive diseases. Of these, medical expenses due to obesity are shown in column(2). These values were calculated by multiplying the medical cost in column (1) by 𝜌𝑖 , basedon Furukawa and Nishimura (2007), as described above. Column (3) shows the reduction rateof obese people resulting from AI intervention. The medical cost reductions after theintervention (shown in column (4)) were calculated by multiplying columns (2) and (3). The
results show that AI intervention by AI could reduce medical costs by 83 billion yen fordiabetes and by 114 billion yen for hypertensive diseases. As shown in column (6), theestimated effects of AI intervention could therefore reduce medical costs by 8.2% for diabetes
and 7.2% for hypertension. Column (7) shows these cost reductions as a percentage of thetotal medical cost. The predicted largest reductions in total medical costs were seen in the
National Health Insurance Association (NHI) for diabetes and in the medical care system forelderly individuals in the latter stage of life for hypertensive diseases.

5

6

1,014,147,332
169,615,856
51,042,420
7,914,165
336,558,610
449,016,281
1,594,677,453
216,395,278
64,811,753
10,240,136
412,817,131
890,413,155

Benefits(2018)

on Medical

Fact-finding Survey=Mieach disease

Medical cost of

(2)

30.3%

(2020)

Okaniwa and YoshidaNishimura(2007)
274,833,927
45,965,897
13,832,496
2,144,739
91,207,383
121,683,412
376,343,879
51,069,286
15,295,574
2,416,672
97,424,843
210,137,505

NHNS(2018) andCalculated besed on=σpeople by intervention

Decrease rate of obesity(3)

Furukawa and=Mi*ρobesityeach disease caused

Medical cost of(4)

83,274,680
13,927,667
4,191,246
649,856
27,635,837
36,870,074
114,032,195
15,473,994
4,634,559
732,252
29,519,727
63,671,664=Mi*ρ*σintervention

saving by AI

Medical cost

26,875,248,488
4,788,672,926
1,882,990,969
356,871,793
7,718,579,168
12,128,133,631

26,875,248,488
4,788,672,926
1,882,990,969
356,871,793
7,718,579,168
12,128,133,631

Benefits(2018)

on Medical

Fact-finding Survey=ΣMi

Total medical cost

(5)

(6)

(7)

total medical costsaving rate ofMedical cost

7.15%

8.21%

0.31%
0.29%
0.22%
0.18%
0.36%
0.30%
0.42%
0.32%
0.25%
0.21%
0.38%
0.52%

=Mi*ρ*σ / Mi =Mi*ρ*σ/ΣMieach diseasesaving rate ofMedical cost

6

Source: Estimations are based on the Ministry of Health, Labour and Welfare (2018c) Fact-finding Survey of Medical Benefit, the Ministry of
Health, Labour and Welfare (2018b) National Health and Nutrition Survey, Furukawa and Nishimura (2007), and Okaniwa and Yoshida (2020).

Total
Japan Health Insurance Association
Diabetes
Health Insurance Societies(ρ=0.271) Multual Aud Associations
National Health Insurance
Medical care system for elderly in the latter stage of life
Total
Japan Health Insurance Association
Hypertensive
Health Insurance Societies
diseaseMultual Aud Associations(ρ=0.236)
National Health Insurance
Medical care system for elderly in the latter stage of life

Data Source

JPY one thousand(1)

Table 1: Estimated Medical Cost Savings

4. CONCLUSIONS AND DISCUSSION
The purpose of this paper is to provide a preliminary analysis of the impact of AI healthintervention systems on reducing health care costs, if they were to become widespread in the
future. Using the BMI distribution data based on the NHNS, here it is predicted that AI
interventions would reduce obesity in the population by 30.3%. AI interventions are also
predicted to reduce medical costs by 7.15-8.21% for diabetes and hypertensive diseases.
Currently, however, there are few studies into the effects of AI health interventions, andthere is insufficient evidence to evaluate their impact. Due to this lack of data, only diabetes
and hypertensive diseases were to be caused by obesity in this study. However, obesity cancause many other diseases. It is also likely that people with a BMI of 25 or less include people
with diabetes and hypertensive diseases, but these people were excluded from the estimates
made in this paper. Therefore, the medical cost savings in this study are likely
underestimations.
Moreover, although this study focused on BMI, it is also necessary to validate the bodyfat percentage (BFP), which represents body composition. BMI is a measure of nutritional
status in adults. Though it is a simple indicator, which can be calculated from height andweight alone, it is not a complete indicator of physical status. This is because it does notconsider other factors, and does not accurately predict BFP (Barba et al., 2004; Moreno et al.,2006). On the other hand, BFP is more reliable as a health indicator than BMI, because it
calculates fat as a percentage of body weight, and therefore actually represents bodycomposition. The American College of Physicians has stated that BFP is more important than
BMI for assessing a patient's health and mortality risk (Padwal et al., 2016). However, it was
not possible to obtain the distribution data of BFP from other national surveys in Japan. BMI
can be calculated automatically using height and weight information, whereas BFP is
measured using an instrument that detects fat in the body. This difference in their measureprocesses may explain why the distribution data of BFP is not available.
Furthermore, this study focuses on medical costs, but in the future, both the costs andeffectiveness of AI must be examined, including development costs. Furthermore, medical
costs are disproportionately spent on those who are seriously ill (Kazawa, 2020). In terms of
reducing medical costs, therefore, a greater effect may be achieved by focusing on those whoare severely ill. Interventions that reduce the number of people who are likely to become
severely ill in the future, along with improving the symptoms of those who are already severely
ill, would be more effective in reducing health care costs. These are the limitations of thisstudy, and in the future, other associated analyses should be undertaken to further examine
these issues.

7

ACKNOWLEDGEMENTS
We express gratitude to Professor Shuzo Nishimura of Kyoto University of Advanced Science
for many helpful suggestions at the Japan Institute of Public Finance. We also thank AssistantProfessor Chen Fengming of Tohoku University for his very useful comments.

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