Journal of the National Institute of Public Health2013 Vol.62 No.1 p.68−80

Topics：Strategic management of evidence-based health and medical care policy: How to use new Digital Big Data in health care system＜Review＞
Management of medical staff utilizing sensing technology:
Development of a nursing activities measurement method withultrasound positioning
Daisuke MATSUSHITA１），Toshiro KUMAKAWA２），Ikue ICHIKAWA３），Ryouko ODAWARA４），
Etsuko ISOKAWA４），Tetsuro YAMASHITA５）
１）

Department of Architecture, Graduate School of Engineering, Okayama University of Science
Department of Health and Welfare Service, National Institute of public Health３）
Department of Nursing Service, Showa University
４）
Department of Nursing, Showa University Hospital
５）
Department of Architecture, School of Architecture, Kogakuin University
２）

Abstract
The ultrasound positioning technology used in this investigation makes it possible to collect data on
nursing activities consecutively for 7 days in the respiratory disease center of Showa University
Hospital. The total of 142 person-days and 1,464 person-hours of positioning data within the hospitalward floor were recorded; meanwhile, follow-up records on the nursing details of every nurse on duty
were also collected. Nursing activities were visualized in motion pictures, and nursing movement
patterns were visualized in charts and network models. The characteristics of nursing activities
regarding the duration of stay in the nurses’ station, patient rooms, corridors, and other locations; thefrequency of visiting patient rooms; the frequency of traveling between rooms; and the length of direct
nursing time were discussed. For nurses in different roles, the characteristic patterns of nursing
activities were observed.
keywords: human behavior, nursing traffic line, nursing staff management, sensing technology, artifact
link-chain(accepted for publication, 17th January 2013)

1.

I.

Introduction

This paper first provides a brief introduction on the trendof sensing technology applied in human activities and its
application on improvement of daily living. Afterward, theauthors’ research will be described in detail.

Motion sensing of human activities and its
application(a) “The weakest link”
Sensing technology has been applied to the investigationof human motion for research in IT, robotics, and otherleading-edge fields. The extracted patterns are also
important evidence for developing new measures tosupport daily activities. In a paper titled “Human, theWeakest Link,” Kanade [1] claimed that no matter how the

Corresponding author: Daisuke Matsushita
1-1 Ridai-cho, Kita-ku, Okayama-shi, Okayama-ken, 700-0005, Japan.
T e l: 086-256-9532
Fax: 086-256-9532
E-mail: matsushita@archi.ous.ac.jp

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Management of medical staff utilizing sensing technology: Development of a nursing activities measurement method with ultrasound positioning

artificial elements correlated with each other to strengthena designed system for human manipulation, the humanfactor is the weakest link to the other artificial elements tomake the system operate well. The strength of links
between every pair of elements within the connected
system holds the key of the overall strength of such asystem. That is to say, the weakness of connecting to thehuman factor is dragging down the design that is supposed
to be supportive of human daily life. Here, for a humanmanipulated system, the smoother the human factor could
be incorporated into such a system, the better the
performance that could be achieved.
In the research on designing a network of artificial
elements, taking the connection of the human factor as thelast frontier to success, various efforts has been made on
sensing human actions for the innovation of artificial
elements supportive of human-manipulated systems. For
example, in automotive or aircraft design, erroneous driveror pilot manipulation can be detected in advance to forestallsevere mishaps. Sensing technology is applied not only towarn drivers simply for leaving a door unlocked or aseatbelt unbuckled, but also adjustment of the operating
program for safe driving by, say, detecting the driver’s
habits of stepping on the gas for optimal acceleration,
forecasting a crash and braking in advance, and reading thedriver’s eyelid movement to sense drowsiness. Meanwhile,in the authors’ field of concern, architectural elements suchas a habitually occupied space have only slight sensing
knowledge about when, where, or what its inhabitants areor how they behave within. As compared to the average
usage of an automobile in tens of minutes per day, people
spend much longer time inside specific built spaces.
Despite the difference, there is obviously not a balanced
effort to make daily habitual spaces more adjustable totheir long term users. We might have been overlooking theimportance of designing better habitual spaces that cansense and adjust themselves to fit human needs.
(b) Mortality statistics in home accidents and trafficrelated deaths
In 2005 statistics show that home accidents caused thedeaths of 12,781 people, a number that has been rising inrecent years. Meanwhile, in 2012, traffic-related deaths
stood at 4,612, which has been decreasing year by year.
Mortality at home has been surpassing that caused on theroad, showing the trend of a broadening gap.
The habit of taking baths may be the cause of the highdrowning death rate in Japan. Around 7,000 people per year
are found dead inside bathtubs. Inside a closed bathroom, it
is difficult for a second person on the outside to confirm thebather’s status or sense signs of an accident in time.
Japan has entered a period of a super-aging society, thelikes of which no country has ever experienced with suchspeed and context. According to the Statistics Bureau ofJapan, the projected aged population, those age 65 and over,will reach the 34 million range; every 2 people in theworking-age group, ages 15 to 64, will be supporting the lifeof one person in the aged group. The number of the elderlywith dementia will reach 3.2 million [15]. The number of
elderly households will reach 18.43 million, 37% of the total
number of households, within which 6.8 million, or 13.5% of
all households, will be single-person elderly households[16]. The risk of home accidents is rising as the elderly
losing their physical and cognitive functions. Similarly,
there exists an increasing probability of unnoticed deaths
when bathing at home among the elderly who live alone.
(c) The Potential of sensing technology
If a built space is designed with the function to observe
human motion and sense abnormalities, it is possible tohelp prevent everyday home accidents and help provide
inhabitants with more efficient environmental controls forsafer and easy-to-control daily living.
Taking the bathing case by way of explanation, if there
are sensors to detect abnormal drowning movements
within the bathtub, or sensors to continually check thebather’s heartbeat, bathing time versus water temperature
for the adjustment of a safer bathing environment, it ispossible to reduce the risk of death caused by unnoticed
drowning, heart attack, or other accidents.
In the case of an unexpected disaster like an earthquakeor tsunami, it is believed that motion sensing technology
would be of great assistance to those people with mobility
disability who come to top priority.
Last summer, the worldwide heat wave led to the rise ofheat-related deaths around the world. The global warming
trend continues. By the end of the 21st century, a further
rise of 6 degrees in the average temperature is predicted.
For human beings inhabiting the earth, an efficient indoor
environment-controlling system to provide habitable spaceagainst severe climate changes will become necessary.
Crime in multistory apartments has been increasing. The
automatic dweller identification innovation is one possible
crime-prevention measure.
In northern Europe, where services for the seniorsociety are advancing, the shift of terminal care in hospices
to care at home has been observed. Home-care technology
plays another important role in supporting patient care andthe family’s needs.
(d) Nursing activities in hospital acute care wards
The social issues of an aging population, changes indisease structure, patients’ attitudes toward medical care,
and advancing medical technology make nurses’ daily
duties complicated and busy. In today’s turbulent healthcare environment, there is rising concern about the qualityof care and patient safety, particularly in demanding acutecare wards. The strain of the current situation is believed to

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Daisuke MATSUSHITA，Toshiro KUMAKAWA，Ikue ICHIKAWA，Ryouko ODAWARA，Etsuko ISOKAWA，Tetsuro YAMASHITA

be expanding rather than reducing. As one strategy toeffectively distribute the limited medical resources, we areattempting to search for improving solutions in regard toward floor planning and the nurse staffing model. There isexisting research that has attempted to visualize thenursing workload for efficient staffing. As mentioned above,
we succeeded in developing a research methodology thatutilizes sensing technology for tracing nurses’ movement
tracks in real time for further analysis. We aim to establish
a model for optimal nurse staffing on the wards.

II. Background and the aim of the research
1.

Literature review and the importance of theresearch
Numerous studies have shown many findings in relation
to nurses’ duties and path tracing within and outside Japan
since the 1950s [2-14]. Most of these findings provide
evidence aiming for quality care and efficiency. To achieve
similar aims, there is research related to architectural floor
planning for patient wards. These findings have contributed
to planning strategies such as shortening traveling
distances between patients and nurses, improvement ofaccessibility to nursing care, and increasing direct nursing

Table 1 Summary of precedent studies.
Methodology
Yoshitake,etc.
(1952) [2]
Tomokiyo,etc.
(1976) [3]Nagasawa(1983) [5]Fixed point
observation
Fixed point
observation

Follow-up survey

Yanagisawa, Self-record, Fixedetc.
point observation,(1984) [10] Follow-up survey
Taniguchi,
Follow-up survey
etc.
(1984) [11]Nagasawa(1986) [6]

Research
Duration
1 day(Day shift
8:00〜17:00)
1 day(Day shift
8:00〜17:00)

Objects
2 hospitals
14 + 7 ppl
2 hospitals
19 + 12 ppl

1 day(Night shift
16:00〜9:00)

5 wards,
Night shift
5〜8 ppleach

1 day(00:00〜
24:00)

Hospitalstaffs

6 wards
Day shift 6〜11 ppl1 day
10 wards
Self-record, Fixed (from start of Day 13〜17
point observation, day shift to thepplFollow-up survey end of nightNight 6-8
shift)ppl
1 day

Zhou, etc.
(2004) [6]Fixed point
observation,
Follow-up survey(One day each)1 day
Day 2 ppl
(from start of Early Nightday shift to the
2 ppl
end of night
Mid-night 2
shift)
ppl

Toriyama,
etc.
(2007) [7]

Follow-up survey(non-participant
observation)

1 day(from start ofday shift to theend of night
shift)

70

4 wards
Day 5〜7
ppl
Night 3 ppl

care hours.
The related study began with a series of precedentstudies outside Japan. In the Japanese context, it was led byresearch by Yoshitake [2] in which nursing duties were firstobserved and basic information was collected. Other
observational research followed that focused on the duties
of night-shift nurses, recorded consecutively by one-on-one
researchers [5]. Another study collected data by nurses
completing forms themselves and the researchers
simultaneously recording the frequency of patient ward
visits and the extent of patient care [6]. The characteristics
of nurses’ traveling paths were analyzed in another paper ina follow-up survey on the progression of nurses’ duties [7].
In a similar follow-up survey on other research, it was
shown that the “personal nursing base” strategy has aneffect on nurses’ duty routines [8, 9]. Prediction of nurses’
traveling paths was also tentatively attempted in paststudies [10, 11] by recording the actual tracks traveled.
The methodology, duration of survey, survey objects, andother research conditions of each of the above researchstudies are sensitively tailored for the respective aims, typeof wards, nursing duties, staffing models, patient care level,and so on; and most were conducted in one day’s time on alimited number of target nurses. There is a limitation
shown in the traditional observation methods used tocollect consecutive data of longer duration and a larger
number of survey objects. Here, our group shows thepracticability of an advancing survey method to collectnursing activity records for all the nurse members in one
care ward for a time period of 7 consecutive days. The scaleof the amount of data being collected has not been achieved
before in any hospital under full operation [*1]. In theoverall project, we aimed to accumulate a series of basic
data in a consecutive period of time for further investigationof the nurse staffing model and floor planning for hospitalwards. This report, as part of the project, is aimed to focus
on the affirmation of the practicability of recording realtime nurses’ duty movements for comparatively long
duration, in a format that is technically comprehensible andeasy to revisualize.

III. Methodology of the survey on nursing
activities
1.

Survey target and duration
The targeted hospital (see table 2) has been actively
undergoing reforms to continually strive for better nursing
services. We focused on the nurse staff, including
registered nurses, assistant nurses, clerks, and on-demandnurses from other wards (here called “relief nurses”) who
are working in the most demanding ward in the hospital. If
it is possible to accurately record nursing activities in thisdemanding and complicatedly staffed ward, it is proved thatthe survey method is also applicable to other situations.

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Management of medical staff utilizing sensing technology: Development of a nursing activities measurement method with ultrasound positioning
Table 2 Data of hospital ward under investigation.
Ward Detail

Building Detail

The Respiratory Disease Center, 11/F, theShowa University Hospital
17 floors above ground +2 floors
underground, Gross floor area㎡

No. of bed

55 beds(Single bed: 5 rooms; Double: 5; 3
beds: 2; 4 beds: 1; 6 beds: 5)

Nurse Staffing
Model

2 shifts, patients assigned to 2 teams inchargeThe one-week survey, which included the weekend, lasted
from the day shift that started at 8:30 a.m. on Thursday,
January 19, 2012, until the night shift that ended at 11 a.m.
on Wednesday, January 25, 2012.

Fig. 1 The structure of ultrasound positioning system.

2. Survey methods(a) Nursing activities traced by an ultrasoundpositioning system
Ultrasound positioning technology uses ultrasoundtransmitting tags to emit a real-time 40 kHz signal toultrasonic receivers fixed in various locations. The positionof a specific tag is determined by the trilateration algorithm
calculation of the distance data based on the time delays of
the ultrasound signals reaching the 3 nearest receivers (fig.
1). Here we use the name “tag transmitters” (fig. 2), which
are attached to each nurse on duty, and “receivers” (fig. 3),
which were attached in advance below ceiling boards invarious strategic locations within the targeted ward (fig. 4)[*2]. The ultrasound signal has a few merits over light andradio waves. The transmission speed of ultrasound is slow,
and therefore the time delay for the signal to reach multiple
receivers is obvious, and a more accurate position can becalculated. Furthermore, the weak ultrasound waves do nothave adverse effects on the human body or medical
equipment.
(b) Nursing activities record by follow-up survey
In addition to the ultrasound positioning tracing survey, afollow-up survey was conducted simultaneously by having
every nurse followed by an individual surveyor to record
his or her nursing activities in detail. There are a couple ofpurposes for conducting the follow-up survey. First, thelocation and time data in the written follow-up records areused to counter-check against the ultrasound positioning
data in order to verify the accuracy of the data retrieved by
the ultrasound positioning system and calculations. Second,in addition to the movement paths, the record details (seetable 3) can help ascertain the content and progression ofnursing activities.
(c) Survey procedures
The survey was conveyed in the following procedures:(i) Nursing activities tracing by ultrasound positioning

Fig. 2 Transmitter (TAG)

Fig. 3 Receiver (RDR).

Fig. 4 Locations of the ultrasound receivers and otherdevices on the ward floor plan.

Table 3 Details of nursing activities record.
1.

Time

Record in 1 minute interval

2.

Location CodeThe place or the patient room where thenursing act was taken place

3.

Patient name

Full name of patient whom was receiving
the nursing care

4.

Nurse CallContent of each nurse call5.

Activities

Technical term of nursing act

6.

Activity Code

Code Nursing act, categorized according
to the Table of nurse act [*3]7.

RemarkRecord of exceptional situation

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system
1. Nurse duty begins. Surveyor puts the tag on thenurse, records the tag number and starting time.
2. Check the real-time position of the nurse against thepositioning data shown in the system while on duty.
3. Nurse duty ends. Surveyor takes off the tag, records
the tag number and ending time.
(ii) Nursing activities record by follow-up survey
1. Document distribution to surveyor, briefing, Q&A
session
2. Synchronize surveyor’s timing device
3. Allocation of targeted nurse. Introduce surveyor todesignated nurse. Recording starts.

IV. Results
1. Data properties(a) Daily nurse staffing and patients conditions inthe survey period
Table 4 shows the summary of nurse staffing andpatients’ conditions in the survey period. From the table, it
is obvious that according to changing patients’ conditions,nurse staffing was flexibly adjusted daily for the optimal
provision of nursing care through the addition of assistantnurses and relief nurses.

(b) Visualization of nursing trips within the hospitalward
The tracing data of the group of nurses is organized in atimeline for visualization by a program development
environment [*4]. The nurses’ movement is animated over
the ward floor plan (fig. 5). The post of each nurse isrepresented with a code for recognition. The overall
perspective of the temporal nurse activities within the ward
can clearly be visualized.
(c) Correction of raw positioning data for analysis
Positioning data is recorded in text tables in the formats
shown below in table 5. Visualization of the overlapping of aseries of raw positioning data for a specific nurse inconsecutive intervals of time on the floor plan is shown infigure 5.
It shows a discrepancy in the positioning data that trips
oscillate in straight lines between the rooms over thepartitions without accordingly passing along the corridor.
Theoretically, the receiver catches ultrasound signals andtherefore records positioning data for each nurse inapproximately 2-second intervals. In the actual situation,
the tag signals may be blocked by unexpected obstacles, orthey may transmit to multiple receivers when the nurse,
wearing the tag on the shoulder, turns his or her body only
slightly. Therefore, the tag signals reach the receivers inintervals that vary from a couple of seconds to a few

Table 4 Summary of the nurse staffing and the patients condition on each survey date.
Date

Day Shift

Jan 19Thu

17 pplJan 20Fri

18

Jan 21
Sat11

Jan 22Sun10

Jan 23Mon

18

Jan 24
Tue

18

Jan 25
Wed

17

Total

109 man day

Day Staffing (Post : ppl)

Night Shift

Charge Nurse(S):1, Clerk(C):1, Assistantnurse(H):3, Team 1: 7 (Chief Leader (T):1),
Team 2: 5 (Leader(L):1, #EarlyLeave:1)
S: 1, Senior Nurse: 1, C: 1, H:3,
Team 1: 8 (T:1)
Team 2: 4 (L:1)
H: 1, Relief Nurse(R): 1,
Team 1: 6 (L:1),
Team 2: 3 (T:1)
Team 1: 5 (L:1)
Team 2: 4 (T:1), R: 2
S: 1, C: 1, H: 3,
Team 1: 6 (T:1)
Team 2: 7 (L:1)
S: 1, C: 1, H: 3,
Team 1: 8 (T:1)
Team 2: 5 (L:1)
S: 1, C: 1, H: 3,
Team 1: 6 (T:1)
Team 2: 6 (L:1)

4 ppl

4
4
Relief: 2
4
Relief: 2
4
Relief: 1
4

4

Patients’ Condition (State : ppl)
Admission: 2, Discharge: 2, Fr other hospital:
1, Room Change: 6, Extra Care: 4, Observe:
16, Stay-out:1
Admission (Emergency): 1, Discharge: 3, Frother ward: 1, Room Change: 4, Extra Care:5, Observe: 17, Stay-out: 4
Fr other hospital: 1, Discharge:5, RoomChange: 4, Extra Care: 4, Observe: 16, Stayout:4
Severe: 1, Extra Care: 4, Observe: 19, Stayout: 4
Admission: 1, Discharge: 1, To other ward: 1,
Room Change: 3, Severe: 3, Extra Care: 3,
Observe: 20, Operation: 1, Stay-out: 1
Admission (Emergency): 1, Discharge: 3, Frother ward: 2, Room Change: 6, Severe: 2,Extra Care: 3, Observe: 18
Admission (Emergency): 1, Discharge: 3, Frother ward: 1, Room Change: 3, Severe: 1,
Extra Care: 6, Observe: 21

3 man day

Table 5 Format of positioning data.

72

1/Date2/Time3/Tag Number

4/Coordinates

5/Position Parameteryyyy/mm/dd

hh:mm:ss.000

4 digit ID(X,Y,Z)

Parameter

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Management of medical staff utilizing sensing technology: Development of a nursing activities measurement method with ultrasound positioning

Fig. 5 A single shoot of the animated nursing paths on thehospital ward floor plan.

Fig. 6 The visualization of the positioning data of certain
nurse in a certain interval of time

Fig. 7 The estimated network of travelling routes.

minutes, and consecutive coordinates are not spatiallycontinual.
The aim is to extract from the positioning data for further
analysis some basic movement properties such as the
distance of travel, duration of stay in patient rooms, andfrequency of visits to patient rooms. Nonconsecutive
positioning data must therefore be processed to rectify the
missing trips between rooms. That is to say, when the raw
positioning data shows that a specific nurse moved fromone point to another, the path and the distance between thetwo points are not necessarily represented by a straight line
linking the two points. It is therefore necessary to assign
the possible trip for travel between specific pairs of points.
The estimated routes of traveling within the ward aredefined and shown in figure 7.
When the data shows that a certain nurse moved fromone point to another, it is assumed that she or he had
walked in or out of the room through the door opening,
passing through the middle of the corridor to reach thedestination. The coverage of each room or each section ofcorridors is defined by a range of coordinates. The travel
distance from one of these locations to another is definedby the shortest path calculated from the route along the
network, and the distance matrix is set up and shown infigure 8.
2. Data analysis(a) Accuracy of ultrasound positioning data
The accuracy of the ultrasound positioning data isverified by comparing the ultrasound data with therecorded locations in the follow-up survey. The length oftime when the position does not match is extracted, assummarized in table 6.
There are human mistakes simply due to the recordingof the wrong room number in the follow-up records. These
kinds of human mistakes are corrected manually and are
not considered positioning mistakes caused by the systemsetup. It can be summarized that the ultrasound positioning
data mismatches or repetitive position changes (position
jumping repetitively among 2 or more locations within theinterval of a couple of seconds) mostly occurs between thenurses station (NS) and its nearby corridor areas, as wellas the patient rooms and the adjacent corridor area. It is

Table 6 Total duration when positioning error occurs

Fig. 8The distance matrix among the defined locations.

Basic data

Duty
duration

Duration withpositioning errors% of error

Jan 19, Day
Shift, Tag ID:3

9 hours
31 mins

20 mins 17 sec

3.5%

Jan 19, Day
Shift, Tag ID:4

9 hours
16 mins

23 mins 24 sec

4.2%

Jan 19, Day
Shift, TagID:10

9 hours
15 mins

24 mins 40 sec

4.4%

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Daisuke MATSUSHITA，Toshiro KUMAKAWA，Ikue ICHIKAWA，Ryouko ODAWARA，Etsuko ISOKAWA，Tetsuro YAMASHITA

predicted that rapid changes in positioning data werecaused by the change of orientation of the name tag
attached to the nurse’s shoulder, when the nurse might
have been, for example, sitting near the corridor inside theNS and turned her body around to take a file behind herseat, even when the nurse who was wearing the tag did notleave the room at all. When observing the positioning data,
it is obvious when there is a consecutive series of positionchanges within a few seconds. To rectify this kind of
discrepancy, whenever there is position data showing lessthan 5 seconds duration of stay, the position change isignored and the duration of stay is counted with theprevious position.
(b) Proportion of duration of stay in all locations
The overall duty duration of all of the nurses within thecomplete survey period and the accumulation in each of thespecified locations are summarized, and the proportion ofduration of stay in each specified location is shown in figure
9. The one exception is the duration of stay recorded in “EV
Hall,” which includes the duration of time when the tagged
nurse was leaving the ward floor for other duties outside
the survey ward. The following findings were observed:
 40.9% of the overall duty duration of all nurses wasspent in the NS.
 12.6% of the overall duty duration was spent in thecorridor areas.
 34.5% of the overall duty duration was spent in thepatient rooms.
 Severely ill patients’ rooms, namely 1103, 1121, and1122, are all spatially close to the NS. The respective

proportion of duration of stay in these rooms isrelatively higher than for other patient rooms.
(c) Duration of stay in patient rooms and travel
distance of each nurse each survey day
The statistics of duration of stay in patient rooms [*5],durations of stay outside patient rooms, and the travel
distances of every nurse each day of the survey isvisualized in the following charts (figs. 10-16) for the
purpose of clarifying the variation in workload of every typeof nurse each survey day during the whole period ofinvestigation.

Fig. 10 The bar chart of duration of stay in patient rooms andtravel distance of each nurse on Jan 19.

Fig. 11 Bar chart of each nurses on Jan 20

Fig. 9 The Proportion of the duration of stay in all locations
where nursing activities were taken place.

74

Fig. 12 Bar chart of each nurse on Jan 21

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Management of medical staff utilizing sensing technology: Development of a nursing activities measurement method with ultrasound positioning

Fig. 13 Bar Chart of each nurses on Jan 22

Fig. 15 Bar Chart of each nurses on Jan 24

Fig. 14 Bar Chart of each nurse on Jan 23

Fig. 16 Bar Chart of each nurse on Jan 25

Fig. 17 Comparison of the characteristics of the durations of stay in each specified locations among each nurse types(d) Characteristics of proportion of duration of stayand travel distance for each type of nurse
After clarification of the daily characteristics of nursing
activities for each nurse, the characteristics of working
modes of each type of nurse have been analyzed throughthe respective summation of their duration of stay in eachspecified location and their average travel distance forcomparisons (see table 7 and figs. 17-19).
 Lead nurses of the day-shift teams and night-shift teamhave similarly longer working hours as the teammembers within the NS, but shorter duration of stay inthe patient rooms (see table 7 and fig. 17).
The night-shift team (including the lead nurse andmember nurses) shows a longer duration of stay in thepatient rooms than those of the day-shift teams.
The relief nurses, who work in the ward on demand,
have the longest duration of stay in the patient rooms.
The nurses’ assistants move around the most, working

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mostly in the corridor areas or function rooms otherthan the patient rooms.
Among the day-shift teams, the members have a longer
the average travel distance per day and per hour thanthe lead nurse (figs. 18 and 19).
The average travel distance per day and per hour oflead nurses of the night-shift team are both much
longer than the night-shift member nurses. The leadnurse’s working time is longer than that of the member
nurses.
From the travel distance statistics, it is found that thenight-shift lead nurse travels 1.4 times more per hourand 2.5 times more per day compared to the day-shift
lead nurse. It is evident that the night-shift lead nurse
is busier on that shift.



The members of both the day-shift and night-shift
teams have similar travel distance per hour. However,considering the longer working hours per day for thenight-shift member nurses, on average the night-shiftmember nurses travel 1.5 times more in one day’s
work than that of the day-shift member nurses.
The nurses’ assistants and the relief nurses both havethe relatively longer distance of travel per hour.
Considering the short shift hours, it is evident thatthey move around relatively more for various tasks
during their working hours.

(e) The Frequency of visits of each type of nurse
It has been clarified that for nurses in different work
positions, there are different patterns of duration of stay inFig. 18 Average travel distance per man day

Fig. 19 The average travel distance per man hour

Table 7 The proportions of the durations of stay in each location characterized by each nurse types% ofduration of
stay

Nurse
Station(NS)

Day Shift
Leader67.0

1.9

Day Shift
Member

35.3

Night Shift
LeaderDay
room

Patient
rooms

Bath

WC

Sanitary

Day
room

ＥＶ
Hall

0.6

6.2

17.3

0.1

0.6

0.3

0.2

1.0

1.8

2.8

0.1

0.5

2.2

5.1

40.5

0.5

2.1

0.7

0.8

2.5

4.4

4.4

0.9

48.4

0.6

1.7

6.8

31.1

0.0

2.1

0.6

0.3

0.3

5.3

2.4

0.3

Night Shift
Member

39.5

0.5

1.4

5.4

41.4

0.0

2.8

0.6

0.2

0.5

2.8

4.7

0.2

Assistantnurses

18.6

9.5

0.3

0.1

24.2

0.1

2.5

5.9

1.0

16.7

15.0

4.8

1.2

Relief Nurse

34.0

0.1

0.7

0.0

43.9

0.0

2.3

4.1

0.3

2.1

4.5

7.7

0.3

76

NS
Conference(ICU1)J. Natl. Inst. Public Health, 62（1）: 2013

North
South
East
corridor corridor corridor

Management of medical staff utilizing sensing technology: Development of a nursing activities measurement method with ultrasound positioning

various specified locations (fig. 17). So it is worthwhile tofurther clarify the characteristics of their place-visiting
pattern in the respective locations. For each type of nurse,
the pattern of the frequency of visiting the various locationsin the ward is visualized in a separate network graph (figs.
20-24). Each node represents each location of a visit. The
radius, and therefore the size of each node in the graph, is
proportional to the average numbers of visits per eachnurse in one day (per person-day). The thickness of the link
between each pair of nodes represents the number of direct
trips between the two locations. Bigger nodes show that thelocation is more visited on average. The thicker links showthat it is more usual that that type of nurse tends to travelback and forth between the two locations. Other
observations are summarized below:
Fig. 22 Network graph of all night shift nurse members.

Fig. 20 The average times of visit to every room of the day
shift nurse members
Fig. 23 Network graph of all night shift nurse leaders.

Fig. 21 The average times of visit to every room of the day
shift nurse leaders.

Fig. 24 Average by all nurse assistants (H).

J. Natl. Inst. Public Health, 62（1）: 201377

Daisuke MATSUSHITA，Toshiro KUMAKAWA，Ikue ICHIKAWA，Ryouko ODAWARA，Etsuko ISOKAWA，Tetsuro YAMASHITAThe nurse members make a greater number of visits toevery location than the lead nurses.
 It is evident that there are more trips between thepatient rooms accommodating severely ill patients andthe NS, probably because these patients need more
frequent nursing care than others.
 The night-shift nurses visit every location more
frequently than those of the day-shift nurses on
average.
 The day-shift members, having the NS as their base,tend to travel directly between the NS and the patient
rooms.
 As compared to the night-shift members, other thantraveling more between respective patient rooms, theyalso tend to travel between patient rooms more
frequently, thus resulting in a more interwoven
network graph.
 Similarly, the assistant nurses, who are not necessary
based at the NS, travel over places in the ward todeliver nursing services. Their network graph is themost interwoven among the types of nurse.

V.

Summary

1.

The Validity of the ultrasound positioning method
With less than 5% system error, it can be concluded thatthe ultrasound positioning method is feasible for themeasurement of trips for nursing activities in a busy
hospital ward for proper analysis.
For achieving higher accuracy of measurement, it issuggested to: (1) increase the number of receivers in blackspots where discrepancy frequently occurs, or (2) relocate
the receivers into a strategic location for better signal
reception.
2.

The reality of nursing activities reviewed in the
Analysis(a) The proportion of duration of stay in locations
where nursing activities took place
Through simple analysis, the statistics of where nursing
activities took place, for how long activities took place intotal, how far the nurse traveled, and therefore thecharacteristics of the activities can be visualized.
The working hours in the nurses’ station (NS) are thelongest among all the locations. It is believed that
improving accessibility of the NS, where miscellaneoustasks like recording, preparation, meetings, and discussions
take place, would be strategically beneficial for increasing
the efficiency of daily nursing activities.
In this project, patients in severe condition are allocatedto rooms located near the NS. The duration of stay and thefrequency of visits to these rooms are proportionallyhigher. This planning of special rooms to be adjacent tohubs such as the NS has been proved in preceding studies

78

to be beneficial in saving staff resources.
(b) The working characteristics of different types ofnurse(i) Proportion of working hours in various locations
The lead nurses of both day and night shifts have a large
portion of working hours in the NS than the teammembers, but have a shorter duration in visiting patient
rooms. For both lead nurses and other team members, thenight-shift teams have a higher proportion of duration ofstay in the patient rooms. The relief nurses have the largest
proportion of working hours in the patient rooms. The
assistant nurses travel and stay more along the corridors,
go outside the wards through the EV Hall, and travel
among function rooms other than the patient rooms forcarrying out nursing activities. The relief nurses and thenight-shift teams have the longest working hours, and it isevident that they are particularly busier in direct nursing.
(ii) Travel distance
Regarding the day shift, the team members have a longer
daily and hourly travel distance than the lead nurses do.
Regarding the night shift, the lead nurses travel a longer
distance daily, while the member nurses travel a longer
distance hourly. On the night shift, the lead nurses have
longer working hours than the member nurses. The nightshift lead nurses, as compare to the day-shift lead nurses,
travel 1.4 times more in an hour, and 2.5 times more in aday. It is evident to conclude that the workload of the nightshift lead nurses is extremely heavy. The night-shiftmember nurses have similar hourly travel distances as theday-shift member nurses do. However, considering that thenight shift requires longer working hours, the night-shiftteam members travel 1.5 times more daily than the day-shiftteam members. The assistant nurses and the relief nurses
have a relatively long hourly travel distance. They work fora shorter time, but they travel intensively within theirworking hours in order to share the ward s workload in thepeak hours.
(iii) Frequency of visits to patient rooms and other locations
In relation to the duration of stay and travel distancesummarized above, it is evident that the lead nurses work in
the NS as a base, while the member nurses work moreclosely with the patients, who stay in the patient rooms toreceive nursing care. The night-shift teams have a higher
frequency of visits than the day-shift teams. The lead nursesof the night shift have more visits to the patient rooms andother locations than the day-shift member nurses.
Meanwhile, the assistant nurses do not necessarily makethe NS their base for providing nursing services. Instead,they move around from room to room, place to place torelieve the heavy workload carried by the regular teammembers.

J. Natl. Inst. Public Health, 62（1）: 2013

Management of medical staff utilizing sensing technology: Development of a nursing activities measurement method with ultrasound positioning

VI. Afterword

References

Our group has succeeded in carrying out a comprehensive
survey in a very busy acute respiratory ward. The nursing
activities of all the nurse members on the ward floor wererecorded in the form of positioning data in correspondingtime for a 7 consecutive days, and an acceptable level ofaccuracy was achieved.
As the first group to attempt this, we have successfully
revisualized the reality of nursing activities and quantitatively
ascertained the characteristics of the nursing activities ofeach type of nurse, namely the day-shift and night-shift
nurses, lead nurses and member nurses, regular teammembers and helpers.
For comparison of the characteristics among each type ofnurse, a certain quantity of data, obtained through a certain
period of time and obtained from a certain number ofsamples, is needed for analysis. In this project, we haveattempted to introduce a new data-acquiring method?anultrasound positioning system and we displayed evidentiallythe variations in the characteristics of nursing activities
carried out by different roles of nurses on the same ward.
To review the feasibility of applying the on-demand nursestaffing model adopted in Western countries in the context
of Japan, it is necessary to develop a close-to-reality modelof the present nursing services environment to create acost and services simulation. However, the provision ofproper nursing services to fit the demands of the everchanging conditions of the patients is a complicated issue,
not to mention other substantial support for daily living or
mental health care. Drawing conclusions on thecharacteristics of travel distances or duration of nursing
care alone would not be sufficient to provide optimal
adjustment for easy access to direct patient nursing.
From now, further efforts are needed to refine our skill
and equipment for conducting quantitative surveys in
hospital wards, for the purpose of accumulating data on
nursing activities for further clarification of thecharacteristics of nursing services in the field. We areaiming at the development of a nursing service model for
simulation and adjustment of nurse staffing for better costand services performance.

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[3] Tomokiyo T, Aoki M. Verification of the planning ofmultiple corridors in hospital ward No.1. AIJ Kyushu
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[5] Nagasawa Y. Activity analysis of ward nurses during
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[8] Toriyama A, Watanabe R, Nakayama S, Kakehi A,
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T. A study on prediction of staffs’ trips in hospital.
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Acknowledgements
This research was funded by the Grant-in-Aid for
Scientific Research (KAKENHI) of Japan (Grant No.
23249029). Appreciation goes to those who contributed tothe survey: The nurses of the Showa University Hospital,
the doctors of the Respiratory Disease Center of thehospital, the undergraduates of Okayama University of
Science as the surveyors, and many other related
individuals.

J. Natl. Inst. Public Health, 62（1）: 201379

Daisuke MATSUSHITA，Toshiro KUMAKAWA，Ikue ICHIKAWA，Ryouko ODAWARA，Etsuko ISOKAWA，Tetsuro YAMASHITA

2012;E-1 (5308):657-8.
[15] Statistics. Health and Welfare Bureau for the Elderly of
the Ministry of Health, Labour and Welfare of Japan.
[16] Number of Household Projection for Japan. National
Institute of Population and Social Security Research ofJapan.

Note[*1] The content of this research project is approved by theShowa Hospital Ethics Committee.
[*2] ZPS-3D ultrasound positioning system setup by the
Furukawa Sangyo Kaisha, Ltd. is used in this project.
[*3] The Table of Codes of Nursing Activities specified by

the Showa University Hospital.
[*4] A Java-based development language specialized for CG
production, called “Processing”, is used.
[*5] The “duration of stay in the patient rooms” is consideredalmost equivalent to the “direct care duration”. In thispaper, for analysis purpose the nursing activities are
not categorized according to its contents which can be
finely defined into direct care, indirect care and others.
Therefore, in this paper, the term “duration of stay inpatient rooms” is used to represent the direct nursing
duration.
[*6] This report is revised from result presented in theprecedent technical paper [14].

センシング技術を活用した医療スタッフのマネジメント
抄録
高齢化，疾病構造の変化，受療者の意識変化，医療技術の進歩などに伴い，急性期病棟の看護業務は繁忙で複雑化しており，患者の安全確保が困難な状況にある．このような状況は今後一層顕著となるとされており，限られた医療資源を適正に配分するために，病棟計画や看護配置計画の改善を続けることが求められている．看護業務量を把握して適正化を目指す研究は様々に行われてきた．筆者らはセンシング技術の一つである超音波測位により昭和大学病院呼吸器センターにおいて７日間継続的に全看護スタッフの動線を計測した．１
４
２人日，１４, ６４人時間の看護動線が計測された．看護スタッフの移動経路を把握するためにネットワークモデルを作成した．看護動線は病院平面上に動画により可視化された．看護行為のパターンがグラフやネットワークモデルにより表された．得られたデータの集計により，看護スタッフの各場所での滞在時間，室訪問回数，動線距離などが把握され，看護ス
タッフのシフトや職能の属性による特徴が明らかになった．
キーワード：人間行動，看護動線，看護配置，センシング技術，人工物連環松下大輔１），熊川寿郎２），市川幾恵３），小田原良子４），磯川悦子４），山下哲郎５）
岡山理科大学工学研究科建築学専攻
２）
国立保健医療科学院医療・福祉サービス研究部
３）
昭和大学統括看護部
４）
昭和大学病院看護部
５）
工学院大学建築学部建築学科
１）

80

J. Natl. Inst. Public Health, 62（1）: 2013