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1 The Relationship Between ``Overall Rating´´ and Survey Response Rate

1 The Relationship Between ``Overall Rating´´ and Survey Response Rate

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Exploratory Analysis of Factors of Patient Satisfaction in HCAHPS Databases


study, communication items were not grouped with “overall rating” in all the

response sizes, an observation that suggests that although communication with

nurses and doctors is highly valued, they receive assessments that differ greatly

from overall satisfaction. Patients do not differentiate communication with nurses

from that with doctors. The communication factor as a part of the structure of

patient satisfaction is not influenced by hospital characteristics.

Pain management has also been reported as an important determinant of overall

satisfaction [31, 32]. This observation was supported in the present study, with

“pain management” showing proximity to “overall rating” in all response sizes.

MDS demonstrated that “staff responsiveness” also showed proximity to “pain

management” and “overall rating” in all the response sizes. This observation

suggests that these three items have a similar basis and describe needs that are

common to all patients irrespective of the hospital size. MDS also shows that

“medication information” received quite a different and lowest assessment of all.

This result suggests that patients need information about medications much more

strongly than what the medical staff think. These three aspects of patient satisfaction were not strongly influenced by hospital characteristics.

In response size 300 and above, “medication information”, “staff responsiveness” and “quietness” were grouped in one cluster with lower evaluation, though

those three were grouped with other items in the other response sizes. Because this

response size probably corresponds with large hospitals, given that most of the

hospitals with a response size of 300 and above were acute-care hospitals with

greater capacity for medical treatment, this finding suggests that the different and

lower staff responsiveness evaluation is accounted for by the likelihood that

patients receive invasive treatment. The OECD reports that the average lengths of

stay in the US is 4.8 days, in contrast to those in other countries, such as 5.6 days in

France, 7.0 days in U.K. and 17.5 days in Japan [33]. Many inpatients in the US

may still suffer from illnesses or injuries and need help from the hospital staff,

accounting for the lower evaluation of quietness, staff responsiveness and especially requirement for providing more information about medications. These are

more difficult to provide as part of a patient-centered approach, owing to the

characteristics of health care service in acute care.

Patients give higher assessment of communication, which represents the evaluation of approaches by doctors and nurses in the HCAHPS patient survey, but such

communication may not fulfill the patients’ actual needs. This finding suggests that

even if pain is well controlled and patients are, in general, relatively well satisfied

with the communication they receive, they need swifter, more individualized

attention from medical staff and a quieter environment, and especially medication

information. Donabedian suggested that two elements of the performance of medical practitioners are technical performance and interpersonal performance [34] and

that they should be provided together in a more patient-centered manner. The

structure of patient satisfaction may differ by hospital characteristics.

In Japan, a different structure of patient satisfaction was reported, in which

overall rating, communication and environmental variables formed the same cluster

despite differences in the lengths of stay [8]. The structure was observed especially


M. Okuda et al.

among elderly people. This observation could result from the relatively long

hospitalization in Japan because, with longer hospital stays, patients may expect

more comfort in both their environment and human relations.


To Improve Quality of Care

Hospitals with low survey response rates should seek the reasons. Improving staff

responsiveness and pain management will have a direct effect on overall rating, but

more attention should be paid to providing medication information and a quieter

environment with more individualized interpersonal skills. Education in attending

to personal needs should be more focused.



The response size may not indicate the actual hospital bed size. Non-respondents’

assessments were not reflected. Further stratification by hospital characteristics is

needed. Other factors of patient satisfaction should be considered to identify the

structure of patient satisfaction.


Future Work

Another data mining activity is to build models and procedures/techniques and

assess the predictive accuracy of those models when applied to new data [12].

Constructing models of the structure of patient satisfaction by distance scaling

using the dataset of “structure”, “process” and “outcome” will contribute to the

improvement of quality of care, focusing on patient-centered approach.

6 Conclusions

As patient satisfaction falls, survey response rates fall exponentially. The structure

of patient satisfaction differs by hospital size and hospital specialties. Some factors

do not change the relationships within a given cluster but with other clusters. To

improve overall patient satisfaction, more attention should be paid to the relationships among the factors of patient satisfaction and their changes.

Distance scaling has the potential to identify different aspects of patient


Exploratory Analysis of Factors of Patient Satisfaction in HCAHPS Databases



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Part IV

Public and Urban Services

One Cycle of Smart Access Vehicle Service


Hideyuki Nakashima, Shoji Sano, Keiji Hirata, Yoh Shiraishi,

Hitoshi Matsubara, Ryo Kanamori, Hitoshi Koshiba, and Itsuki Noda

Abstract Under JST RISTEX S3FIRE program, we are trying to implement Smart

Access Vehicle (SAV) Service in Hakodate. The project adopts the method of

service science loop – the repeated cycle of observation, design and implementation. In this paper we report the completion of its first cycle, and discuss how the

cycle improved our initial design. We first conducted person trip research in

Hakodate. We chose 20 candidates of various age and occupation, and recorded

their everyday movements for 4 months. We then analyzed the result and made a

person trip model. The model was then fed into our multi-agent simulator for

Hakodate public transportation system. We conducted a small field test with five

vehicles for 1 week. The most significant achievement is that we confirmed that our

design of SAV system works. We succeeded in automatically dispatching five

vehicles for 11 h without any significant trouble or human supervision.

Keywords Smart access vehicle • Hakodate • Demand responsive transit

1 Introduction

We initiated the project named “Smart City Hakodate” in 2009 as an envelope

project, without attachment to any particular funding. Various activities followed.

Future University Hakodate (FUN hereafter) signed mutual agreement on research

collaboration with IBM in 2009. An NPO Smart City Hakodate was founded in

H. Nakashima (*) • S. Sano • K. Hirata • Y. Shiraishi • H. Matsubara

Future University Hakodate, Hakodate, Japan

e-mail: h.nakashima@fun.ac.jp

R. Kanamori

Nagoya University, Nagoya, Japan

H. Koshiba

National Institute of Science and Technology Policy, Tokyo, Japan

Future University Hakodate, Hakodate, Japan

I. Noda

National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

© Springer Japan 2016

T. Maeno et al. (eds.), Serviceology for Designing the Future,

DOI 10.1007/978-4-431-55861-3_17



H. Nakashima et al.

Fig. 1 Design-service loop


2010. FUN provided special research funding. And finally we were able to obtain

JST RISTEX funding for “IT-enabled Novel Societal Service Design”.

As a part of Smart City Hakodate project, we are designing and implementing a

new transportation system named Smart Access Vehicle System (SAVS). The key

is extensive use of information technology (IT). IT should not be regarded as just a

replacement mechanism for traditional mechanical ones. Just as the invention of

steam engine changed the whole societal structure, toward “industrial era” in

sixteenth century, IT will open up a new “information era”.

Proper design is essential for IT to be effectively used in the societal system. We

use the implementation methodology induced by Serviceology. We claimed that

service provision essentially goes through a design-service loop [1], and our project

is designed as such (Fig. 1). Although service, or field test, at Hakodate is the main

process, the modeling of person trip in Hakodate and design of the algorithm for the

service are also essential to the implementation of SAVS. We plan to repeat the

cycle several times. The purpose of this paper is to report its first cycle taking closer

look at those each steps.

2 Smart Access Vehicle System

Smart Access Vehicle System (hereafter, we use SAV for each vehicle and SAVS

for the whole system/service) is a new public transportation service that unifies bus

and taxi services (Fig. 2).

SAVS falls into a category called Demand Responsive Transportation (DRT)

system, which is further classified into the following:

1. Detour/free stop

Fixed route + {detour/stop} on demand


Examples: many rural cities

One Cycle of Smart Access Vehicle Service Development


Fig. 2 The basic concept of SAVS

2. Flex-routing

Fixed stops with on-demand routing


Examples: EU project DRT’s [2], Soja city, Todai combinicles [3]

3. Full-demand

3.1 Low demand areas (mainly pre-scheduling)

3.1.1 Full-demand bus

Example: Nakamura (Shimanto) city bus

3.1.2 Shared taxi

Example: SAVS

3.2 Urban areas (real-time scheduling)

SAVS is classified as 3.2 above, and is designed to replace current urban local

public transportation systems. As far as we know of, SAVS is the only DRT

designed for urban areas.

From users’ point of view, the process of calling a SAV is very similar to

reserving a demand bus:

1. A user contacts the system with the demand (the current location and the



H. Nakashima et al.

2. The system searches for a best vehicle considering their current position and

future route.

3. The system tells the user the pickup point, the estimated time of pickup, and the

estimated time to the destination (with a small margin of delay). The user has a

choice to either accept or decline the service.

The differences are:

1. SAV’s operate in real time (reservation is optional). A user may call a SAV

when the actual demand emerges.

2. Many (in the order of 1000 or more) vehicles are involved so that the operation is


The system knows the locations and routes (destinations of passengers on board)

of all vehicles. When a new demand arrives, the system searches for a vehicle that

can pick up and deliver the passenger with minimum detour. Even when a vehicle is

very close to the request point, it may not be selected if it is heading toward the

wrong direction or if a large detour, beyond the limit of promised tie of delivery for

already on-board passengers, is required. If the system cannot find any vehicle, it

must decline the request. However, we are hoping that denial of service is very rare

case such as a large accident or wide area disaster (including heavy storm or snow)

as long as sufficient number of vehicles exist in the system.

The central dispatch system runs on MA simulation of the city traffic. If we aim

for the best solution, the computation may be too heavy. We use near-optimum

solution (see Sect. 5 for more detail).

3 SAVS Project as Serviceology

Implementation of a novel transportation system such as SAVS encounters several

practical problems, which supplies several interesting issues for Serviceology.


U-Shape Transition

The first issue is what we call the U-shape transition of the service (Fig. 3). When

we gradually introduce a full-demand bus system mixed with the traditional bus

system, the total efficiency drops initially. Actually, after several field test of the

full-demand-responsive transportation (DRT) system with a few number of vehicles, it is commonly accepted that the full-demand bus system is inefficient in highdemand areas; Full-DRT is suitable only for low-demand areas [2]. However, using

MA simulation, we found that it is not necessarily the case: Full-DRT, operated

fully, becomes more and more efficient as the demand increases [4]. Therefore, we

have to find a practical tactics to jump over the U-shape valley.

One Cycle of Smart Access Vehicle Service Development


Fig. 3 U-shape Valley

Note: This U-Shape valley is conceptually different from the “death valley” that

lies between research and industrial development. The latter is the problem of

development risk, and the former is an inherent property of our proposed system.

However, we believe “U-shape” problem itself is universal to most of innovational

systems, since innovation is a jump.


Value Co-creation

Service in general should be viewed as value co-creation of the provider and the

user [5]. SAVS should be a good example of value co-creation in service. Since

SAVS provides the transportation infrastructure for urban life, it should have a large

impact on the life style of the users. Therefore, it is expected that the introduction of

SAVS changes the trip pattern of people living in the area. People may give up

using their private cars within the city. It will push up the importance of SAVS. It

may add new value to the public transportation. Therefore, value co-created by

SAVS and its users is expected to be quite large and unpredictable.

At the same time that new values are created, new requirements for public

transportation may emerge. Design of SAVS should be kept changing as depicted

in Fig. 1.

Unfortunately, the above scenario of value co-creation is not realized yet. So far,

we could try only small-scale operation tests for short periods – far from enough to

change life style of the users.

However, without such expectation of change, taxi companies would not have

joined the project. If we assume that the same number of people use public

transportation with the same life-pattern, introduction of SAVS just decreases

income of taxi companies, because the cost for users should be smaller than the

current taxi system. Expectation of increased number of users is mandatory.

In short, value co-creation is not happening yet, but it is already an essential part

of the plan.



H. Nakashima et al.

User Involvement

Another issue is involvement of users during the design phase (inclusive design).

Since no one has ever experienced SAV system, which we regard as one implementation of full-DRT, we do not know the best service parameters. There are

several parameters yet to be decided:

the size of the vehicles (passenger capacity),

the number of vehicles per area or per population,

type of stops (predetermined or free),

prior reservation,

fare, and

special services (such as priority delivery).

These parameters are to be decided while the service is carried out.


Law Issues

The third issue is the law restrictions. Currently in Japan, bus and taxi systems are

strictly divided by the law to protect niches of each system. Taxis are not allowed to

pickup multiple group of passengers (with only a few exceptions), and buses are not

allowed to run free routes but have to load and/or unload passengers only at

predetermined bus stops. Vehicles that carry up to 9 people are defined to be

taxis and vehicles that carry more than 9 people are defined to be buses. Since

our SAV system unifies both of them, it cannot be operated under the current law.

We need a special designated area for SAV operation.

4 Person Trip Model


Acquisition of Person Trip Data

Figure 4 is the result from our previous work [6] to show the superiority of the fulldemand bus system over conventional fixed-route fixed-timetable bus systems.

Horizontal axis is number of vehicles (increases proportional to population). The

vertical axis is average trip time to the destination. As the population – therefore

number of vehicles – increases, the average trip time decreases. Conventional bus

system, plotted with “x” marks and thicker lines, becomes more efficient as the

population glows – buses in large cities are more convenient than buses in rural

districts. Full demand bus system is less efficient when the population is small, but

quickly become more efficient than fixed route buses as the population grow larger.

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