Fact-Finding Survey on the Operational Status of Electronic Medical Record Systems in Japan

Citation

Kuroda, Y., Fukuda, K., Yamase, H., Seto, R., Ito, M., Shimomai , K., Furukawa, H., Tatsuno, J. & Tado, A. (Feb, 2017). Fact-Finding Survey on the Operational Status of Electronic Medical Record Systems in Japan. Online Journal of Nursing Informatics (OJNI), 21(1), Available at http://www.himss.org/ojni

Abstract

We aimed to identify the present operational status of Japanese electronic medical record (EMR) systems and the extent of computerized nursing record adoption in nursing departments. Using a descriptive study design, we conducted a postcard survey targeting all 5,328 hospitals in Japan registered as having ≥100 beds in February of 2015. Survey items concerned hospital demographic variables, the operational status of any EMR system in a hospital nursing department, and (when operational), its capabilities for nurses to input data and formulate nursing care plans. Over half of 1,235 analyzed hospitals (55.1%) had operational EMR systems. EMR operational status was associated with the number of beds and the governing body of the hospitals but not with their locations. Hospitals that had introduced an EMR system with which nurses could enter patient information, nursing care plan formulations, nursing records, and nursing assessments accounted for about half of respondents for each data-entry category. Few hospitals used nursing assessment frameworks and standardized nursing practice taxonomies when formulating nursing care plans.

We found that a high proportion of hospitals have operational EMR systems with (55.1%), especially medium- and large-scale hospitals. In contrast, many hospitals with fewer than 300 beds are still not planning to introduce EMR systems, or the introduction is only in the planning stage. The nursing departments of about half of the hospitals had EMR systems that allowed nurses to input data, but the great majority of hospitals did not.

Introduction

Systems to manage hospital information electronically, or hospital information systems, were introduced in Japan in the first half of the 1970s, leading hospitals to use computers for the clerical processing of medical tasks, hospital management, and other responsibilities. The 1980s saw the establishment of various medical databases (Inada, 1996) and the emergence of computer physician order entry (CPOE). In the 1990s, the electronification of medical records advanced even more rapidly (Honda & Yamanobe, 2004). Under the grand design of the e-Japan strategy (Committee for Healthcare Information System, 2001; IT Strategic Headquarters, 2001), government subsidies were granted for the adoption of electronic medical record (EMR) systems, accelerating the computerization of medical records, mainly at large-scale hospitals (Koide, Asanuma, Naito, Igawa, &Shimizu, 2006).

Nursing departments are no exception to the spread of computerized medical records in Japan where the computerization of nursing records began in 2002 following the publication of “Grand Design for the Development of Information Systems in the Healthcare and Medical Fields” by the Ministry of Health, Labour and Welfare (Ministry of Health, Labor and Walfare, 2003). During this period, hospital nursing departments throughout the country explored ways to support computerization even as they coped with institution-specific compatibility issues. Ensuring the quality of nursing practice was essential in this transition period; nursing professionals were obligated to proceed with the electronification of medical records without compromising nursing practice quality.

Accordingly, the authors conducted a preliminary study in 2004 to explore the relatedness of computerization in the healthcare field with nursing care outcomes. Focusing on the adoption of EMR systems in Japan, we restricted analysis to the nursing departments of approximately 2,000 hospitals with ≥100 beds (from an initial study population of around 5,000 medical institutions), revealing the state of their nursing support systems at the time (Kuroda, et al., 2007). The investigation found that 103 hospitals, a mere 5.9%, had operational EMR systems, and 947 (54.2%) were “not planning” to introduce one, thus the majority of respondents were not yet running operational EMR systems. While the electronification of EMR systems had proceeded in some sense, the extent of their adoption in Japan in 2004 was clearly and indisputably inadequate (Kuroda, et al.). Nevertheless, we also found that 25.1% of hospitals were “preparing” to introduce an EMR system with launches projected to peak in 2006 and that 10.8% were “planning” to do so eventually. Based on this finding, we expected EMR systems use to continue to  spread and adoption become common. About 10 years have elapsed since that study; we expected that the incidence of EMR systems at Japanese hospitals has likely risen in the interim. In the present study, we explored the current state of EMR systems in terms of their operational status as well as the adoption rates and characteristics of nursing support systems.

Methods

Research design
This study uses a quantitative descriptive design. The current state of EMR systems and nursing support systems were explored through a postage-paid postcard survey.

Subjects
The study population was made up of all 5,328 hospitals throughout Japan registered as having ≥100 beds in February 2015.

Procedure and data collection
The survey was conducted from February 27 to March 21, 2015. Postage-paid postcards were addressed to the heads of nursing departments at all hospitals with ≥100 beds. On the postcard was a request that survey items be completed by the nursing department’s health information administrator or a related party. If no such party was present, a member of a committee concerned with nursing records or a deputy head of the nursing department charged with those responsibilities was also  appropriate. In addition, the study purpose, methodology, and ethical considerations were described with a request for replies only from respondents who freely gave their consent to participate. A note was also included that expressed that receiving the reply would be considered equivalent to obtaining consent. The reverse of the addressee side of the card included requested responses to survey items regarding hospital demographic variables and information concerning EMR system adoption at the addressee’s nursing department.

The question items were reviewed through discussions with co-researchers and researchers working in related fields. To determine the validity of the content of the questions, each question was evaluated two or more times to ensure that they were easily understood and to determine whether the question covered its intended topic. Hospital demographic variables consisted of eight items: the respondent’s position and prefecture; the institution’s governing body; the total number of beds, full-time nursing personnel, and hospital departments; and whether or not direct primary care had been introduced there. There were five items regarding the EMR system operational status of the nursing department. These were operational status of the EMR system and, if currently operational, the following four items: the system’s rollout date and update frequency, its data-entry functions available to nurses, its nursing practice taxonomy for the formulation of nursing care plans, and its nursing assessment framework.

Statistical analysis
Descriptive statistics were calculated for number of beds, governing body, and hospital location. In addition, the associations of these variables with EMR system operational status were analyzed through chi-square testing. The statistical software used was SPSS v.21.0 (for Windows). Statistical significance (two-sided) was set at <0.05.

Ethical considerations
We performed this study after obtaining approval from the ethics committees of our affiliated institutions. We considered reply mail as indicative of cooperation only when the head of the corresponding nursing department had freely provided consent. Anonymity was maintained for all data; data were processed without specifying hospitals.

Results
Replies were received from 1,622 hospitals out of the 5,328 hospitals registered as having ≥100 beds to which postage-paid postcards were sent in February 2015 (response rate: 30.3%). The analysis population consisted of 1,235 hospitals after excluding those with 100 or fewer beds, unknown number of beds, unknown number of diagnosis and treatment department, and only one diagnosis and treatment department (final response rate: 23.2%) 

Bed Number, Governing Body, Location, and Operational Status of Respondent Hospitals

Bed number
The number of hospital beds ranged from 101 to 1,225 with a mode of 199 and a median of 225. The quartile deviation was 225 [IQR: 154.00, 372.00].

Governing body
Medical corporations were the governing bodies for 772 hospitals, comprising nearly half of respondents (44.3%), followed in dominance by local governments (236 hospitals; 19.1%). Other public institutions served as the governing bodies for 167 hospitals (13.5%).

Location
Hospitals were divided into two groups according to location: a city group for hospitals from areas with populations of 1 million people and over and a regional group for those from areas with fewer than 1 million people. Most respondent hospitals (1,123; 90.9%) fell into the city group while 74 belonged to the regional group.

EMR system operational status
Over half of the hospitals (681; 55.1%) responded that their EMR system was “operational.” The next most common response was “no introduction planned,” accounting for 306 hospitals (24.8%); 147 (11.9%) and 70 (5.7%) hospitals responded that their EMR systems were in the “planning” and “preparation” stages, respectively (Table 1).

Table 1: EMR system operation status

Year of EMR system rollout

The rollout year for hospitals with operational EMR systems exhibited an increasing trend starting in 2001 that peaked in 2014 (Table 2). Update frequency ranged from 0 to 20 times (SD ± 1.09). The most common response was no times (895 hospitals; 75.8%) followed by one time (205; 17.4%) and two times (53; 4.5%).

Table 2: Year of operational EMR system rollout

Association between EMR system operational status and bed number
Table 3 is a cross-tabulation table of EMR operational status versus bed number group. Chi-square testing demonstrated an association between EMR operational status and bed number group (Χ2 = 203.665, p = 0.000). In brief, hospitals with 100–299 beds accounted for the highest percentage (89.2%) of “no introduction planned” responses while the single greatest response from the 300–599-bed, 600–899-bed, and ≥900-bed groups was “operational.”

Table 3: A cross-tabulation of the EMR system operational status and bed number groups

Association between EMR system operational status and governing body
Chi-square test results indicated that EMR system operational status was associated with governing body (Χ2 = 218.390, p = 0.000) (Table 4). Having an “operational” EMR system accounted for the highest percentage of responses at hospitals receiving heavy national subsidies, i.e., medical corporations, local governments, and other public institutions. “No introduction planned” accounted for the highest percentage of responses from private hospitals.

Table 4: A cross-tabulation of the EMR system operational status and governing body

Association between EMR System Operational Status and Location
Table 5 shows a cross-tabulation of EMR system operational status versus the two aforementioned location groups. Chi-square testing showed EMR system operational status was not associated with location group (Χ2 = 8.738, p = 0.068).

Table 5: A cross-tabulation of the EMR system operational status and location

Data-entry capabilities for nurses with operational EMR systems
In terms of the data-entry capabilities for nurses allowed by EMR systems, 670 hospitals (54.8%) responded “Yes” to the item “Can nurses input patient information?” Likewise, 657 hospitals (53.3%) responded “Yes” to the item “Can nurses formulate nursing care plans?” and 657 hospitals (53.3%) responded “Yes” to the item “Can nurses describe nursing records?” Further, 539 hospitals (44.1%) responded “Yes” to the item “Can nurses input nursing assessments?” In other words, nearly half of the hospitals surveyed responded affirmatively to the four items regarding specific data-entry capabilities afforded by EMR systems to nurses (Table 6).

Table 6: Data-entry capabilities for nurses with operational EMR systems

In addition, chi-square test results for these items by bed number group found the two measures to be significantly related (ᵡ2 = 195.403, p = 0.000). Specifically, whether small-, medium-, or large-scale, high proportions of hospitals had introduced all four capabilities (49.2% of hospitals with 300–599 beds, 61.9% of hospitals with 600–899 beds, and 63.2% of hospitals with ≥900 beds) while low proportions of hospitals had introduced one to three of them (Table 7).

Table 7: A cross-tabulation of SUM of function for nurses with the EMR system operational status and bed number groups

Nursing practice taxonomies at hospitals with operational EMR systems
Regardless of whether they had an operational EMR system, 246 (20.9%) and 929 (79.1%) hospitals, respectively, used or did not use the North American Nursing Diagnosis Association-International (NANDA-I) as their nursing practice taxonomy when formulating nursing care plans in the EMR system. In addition, 168 (14.3%) used NANDA, while 48 (4.1%) used Gordon’s Nursing Diagnosis (Table 8). NANDA-I and NANDA were each used as nursing practice taxonomies in over 10% of hospitals; every other option, on the other hand, was used by only a few percent of hospitals. As well, 16.5% of hospitals used “other” nursing practice taxonomies; the accompanying free descriptions suggested that these hospitals utilize both standardized and institution-specific nursing care plans. 

Table 8: Nursing practice taxonomies with operational EMR systems

In addition, chi-square testing of each nursing practice taxonomy by bed number group found the following to be associated with hospital bed number: NANDA (ᵡ2 = 19.244, p = 0.004), NANDA International (NANDA-I) (158.091, 0.000), Carpenito’s Nursing Diagnosis (15.491, 0.001), Nursing Interventions Classification (NIC) (58.498, 0.000), and the Nursing Outcomes Classification (NOC) (67.411, 0.000). Cross-tabulations of nursing practice taxonomies for which these associations were observed versus bed number group are shown in Tables 9–13.

Table 9: A cross-tabulation of nursing taxonomy “NANDA” and bed number groups

Table 10: A cross-tabulation of nursing taxonomy “NANDA-I” and bed number groups

In the crosstab data, the proportion of medium-scale hospitals with 300–599 beds using the NANDA was 20.7%; it was around 10–15% for hospitals of other sizes. The proportion of hospitals using the NANDA-I rose with increasing hospital size, measuring 10.9% of hospitals with 100–299 beds, 44.3% of hospitals with 300–599 beds, and 59.4% of hospitals with ≥600 beds. Similarly, the proportions of hospitals using Carpenito’s Nursing Diagnosis, the NIC, and the NOC also rose with increasing hospital size. That being said, the proportion of total hospitals using Carpenito’s Nursing Diagnosis was extremely low at 1.3%.

Table 11: A cross-tabulation of nursing taxonomy “Carpenito’s Nursing Diagnosis” and bed number groups

Table 12: A cross-tabulation of nursing taxonomy “NIC” and bed number groups

Table 13: A cross-tabulation of nursing taxonomy “NOC” and bed number groups

Nursing assessment frameworks at hospitals with operational EMR systems
Regardless of whether they had an operational EMR system, 326 (26.4%) and 731 (59.2%) hospitals, respectively, used and did not use the 13 domains of NANDA-I as the framework when inputting nursing assessments in their institution’s EMR system. Fifty-three hospitals used Henderson’s nursing need theory (3.5%; Table 14). The 13 domains of NANDA-I as a nursing assessment framework are used in about a quarter of hospitals with operational EMR systems with other frameworks used at a mere few percent of hospitals.

Table 14: Nursing assessment frameworks with operational EMR systems

In addition, chi-square testing of assessment frameworks by bed number group found the 13 domains of the NANDA-I and Orem’s self-care theory to be associated with hospital bed number (ᵡ2 = 123.461 and 11.067, p = 0.00 and 0.011, respectively). Cross-tabulations of assessment frameworks for which these associations were observed versus bed number group are shown in Tables 15–16. In the crosstabulation data, the proportion of small-scale hospitals with 100–299 beds using the 13 domains of the NANDA-I was low at 19.0%; it was even lower for hospitals with 300–599 and ≥600 beds at around 5% for each. Around 1% each of hospitals with 300–599 beds and those with ≥600 beds had introduced Orem’s self-care theory; the proportion of total hospitals using it was extremely low.

Table 15: A cross-tabulation of nursing assessment framework “the 13 domains of NANDA-I” and bed number groups

Table 16: A cross-tabulation of nursing assessment framework “Orem’s self-care theory” and bed number groups

Discussion

According to a report published by the Ministry of Health, Labour and Welfare, over 90% of the 8,670 hospitals nationwide have fewer than 600 beds (Ministry of Health, Labor and Welfare, 2013). Our findings showed similar results, with 93.1% of hospitals having fewer than 600 beds. In terms of the governing bodies of the hospitals analyzed in this study, 50% were medical corporations, including independent administrative agencies, which receive the greatest subsidies from the national budget. This exceeds 70% after adding local governments and social insurance-related organizations, confirming the ministry’s published percentages of hospitals according to establishing organization (Ministry of Health, and Welfare). Furthermore, as for the locations of the hospitals analyzed in our study, 90.9% of hospitals belonged to areas with populations over 1 million people (i.e., the city group). This percentage is similar to the reported Ministry statistics. Based on the above, we conclude that the hospitals analyzed in this present study are generally representative of medical institutions in Japan overall.

Our investigation revealed that EMR systems have already been introduced and are operational in a majority of hospitals today. In our 2004 study (Kuroda, et al., 2007), over half of the hospitals studied had “no plans” to introduce an EMR system, and only 10% or so had “operational” or “partially operational” EMR systems. Compared with these values, the proportion of hospitals having operational EMR systems today is overwhelmingly high. This is likely a result of those hospitals that were “planning” or “preparing” to introduce EMR systems in the 2004 survey did so. In addition, the peak rollout year for EMR systems in the 2004 survey was 2006 with a rising trend from 2002 already apparent. This trend continued in our current survey; the number of hospitals rolling out EMR systems continued to increase each year from 2004 onwards with a peak of 92 hospitals in 2014.

The association between EMR operational status and bed number also bears consideration; the high proportion of “operational” responses from medium- and large-scale hospitals with ≥600 beds demonstrates that the introduction of EMR systems has primarily been promoted at such institutions. In other words, we can conclude that, as far as medium- and large-scale hospitals are concerned, EMR systems are adopted and well established. One U.S. survey conducted in 2011 by the National Center for Health Statistics reported that electronic health record systems have been adopted by 57% of hospitals nationwide (Chun-Ju, Esthen, Thomas, & Bill Cai, 2011). While our survey concerns the spread of EMR systems in Japan, our adoption rate is almost the same as in the U.S. In addition, 199 of the 276 hospitals who responded saying they were “preparing” or “planning” to introduce an EMR system were small-scale hospitals with 100–299 beds. This finding suggests that small-scale hospitals hold the key to further adoption of EMR systems. On the other hand, the proportion of hospitals that had “no plans” to introduce an EMR system was highest among small-scale hospitals, mirroring similar results in our 2004 survey. This was probably a result of the financial challenges small-scale hospitals face in trying to introduce CPOE (Ohsfeldt, et al., 2005). Of particular note is the fact that Japan is characterized by its large number of small-scale hospitals; such institutions would find it difficult to secure a return on investment in an EMR system. Information technology (IT) adoption was promoted both in the e-Japan Strategy published in 2001 and in the following New IT Reform Strategy published in 2007 by the Japanese government (IT Strategic Headquarters, 2006).
In the New IT Reform Strategy, the government declared it would complete IT reforms by 2010; as a part of this pledge, it additionally promised to introduce EMR systems to nearly all hospitals with ≥200 beds over the same period. The results of our survey, however, revealed that, while over half of the hospitals indeed have operational EMR systems, high proportions of medical institutions with fewer than 300 beds either are not planning to introduce an EMR system or else are only at the planning stage of the process. Considering these findings, we find it hard to say that the goals of the New IT Reform Strategy have been achieved in full.

When investigating the extent to which medical records have become computerized in Japan, it is essential to examine the conditions in nursing departments at the same time. Our questionnaire also contained items regarding the data-entry capabilities that EMR systems provided to nurses in medical institutions. We found that four kinds of data—patient information, nursing care plan formulation, nursing records, and nursing assessment—could be entered by nurses using the EMR system in about half of respondent hospitals. This finding seems to lend support to the notion that all four of these items are typically introduced together whenever a nursing support system is introduced. In the remaining half of hospitals, on the other hand, it is highly unlikely that nursing records have been computerized. If nursing records are not computerized (i.e., if they remain paper-based), we fear that nurses will be unable to use hospital information systems to exchange or share information with other medical departments even if an EMR system is introduced. Further adoption of computerized nursing records is desirable in order to avoid this. In addition, we must continue to try and describe in detail the pros and cons resulting from the shift to computerized nursing records, as well as the relationship of this trend with quality of nursing care.

We also examined associations between the number of data-entry capabilities afforded to nurses versus hospital bed number. Two characteristics were evident in the results: the proportion of hospitals that had introduced all four examined capabilities was greatest for medium- and large-scale hospitals, and this proportion rose with increasing hospital size (49.2% of 300–599-bed, 61.9% of 600–899-bed, and 63.2% of ≥900-bed hospitals). In comparison, this proportion for small-scale hospitals with 100–299 beds was 25%, clearly lower than medium- and large-scale hospitals. Therefore, as with the adoption of EMR systems, the adoption of nursing support systems in the future must focus on small-scale hospitals.

Additionally, we discussed the matters below based on the survey results for nursing practice taxonomies and nursing assessment frameworks at institutions where nurses were able to formulate nursing care plans in their EMR systems. First, few hospitals had introduced any standardized nursing practice taxonomy (NANDA, NANDA-I, Gordon’s Nursing Diagnosis, or others) with the most common taxonomy, the NANDA-I, used in just over 20% of cases. The NANDA-I is the most widespread taxonomy in the USA followed by the NIC and the NOC. It has been suggested that nurses are more comfortable with these three than with other standardized nursing practice taxonomies (Schwiran, &Thede, 2011). The NANDA-I is the most widely used taxonomy in Europe as well, followed, again, by the NIC and the NOC (Thoroddsen, Ehrenberg, Sermeus, &Saranto, 2012).

Adoption trends similar to those in the U.S. and Europe were observed in the results of the present study with the NANDA-I most widely used (in 168 hospitals) followed by the NIC and the NOC (in 91 and 86 hospitals, respectively). The American Nursing Association recognized 12 different standardized nursing practice taxonomies, including the NANDA, NIC, NOC, ICNP, and Omaha System (American Nurses Association, 2006b; Weatra, Connie, Konicek, &Keenan, 2008). Of these twelve, besides the NANDA-I, NIC, and NOC, Japanese language editions have been only issued for the Gordon’s Nursing Diagnosis and Carpenito’s Nursing Diagnosis frameworks which are updated to the latest version on a regular basis. However, the ICNP has not been translated since its first edition and has hardly been used in Japan at all as a result. In short, the results of the present study showed that hospitals that have introduced standardized nursing practice taxonomies appeared to select ones that are updated on a continual basis. We thus concluded that translating standardized nursing practice taxonomies into local languages and updating them to the newest editions is essential to their spread. Furthermore, the results of our examination of associations between standardized nursing practice taxonomies and hospital size revealed that, the larger an institution is, the more likely it is to use a standardized nursing practice taxonomy. This characteristic was more prominently reflected for Carpenito’s Nursing Diagnosis than for the NANDA-I, NIC, or NOC, given the extremely low proportion of hospitals that had implemented it (1.3%).

The next point of discussion regards nursing assessment frameworks. The overwhelming majority of hospitals had instituted neither the 13 domains of the NANDA-I, Roy’s adaptation model, Orem’s self-care model, nor Henderson’s nursing need theory as a nursing assessment framework. However, the 13 domains of the NANDA-I had been introduced in about 20% of hospitals and in about half of medium- and half of large-scale hospitals, whose EMR systems allowed the formulation of nursing care plans. The above findings revealed that few hospitals require nurses to use some kind of nursing assessment framework or standardized nursing practice taxonomy when formulating nursing care plans. The use of standardized nursing languages has many advantages for the direct care/ bedside nurse (Rutherford, 2009). That is, the use of nursing assessment frameworks or standardized nursing practice taxonomies may increase visibility of nursing interventions, improve direct care, and facilitate nursing assessment. That being noted, nursing assessment frameworks and standardized nursing practice taxonomies were more widely used in medium- and large-scale hospitals than in small-scale hospitals.

Within these frameworks and taxonomies, the 13 domains of the NANDA-I, NANDA-I, NIC, and NOC tended to be the most widely used. In addition, 15.5% of hospitals responded that they used “other” standardized nursing practice taxonomies, meaning some hospitals used institution-specific nursing care plans in addition to standard ones. Moreover, just slightly over 20% of hospitals use the NANDA-I, and just slightly over 10% use the NIC or the NOC. In consideration of these rates, we would expect that, while nursing diagnoses are sometimes made using the NANDA-I, nursing care plans appear to rarely be formulated using standardized taxonomies specifically for nursing care.

Limitations and recommendations for the future research

Hospitals which did not plan on using an EMR would be more likely not to reply at all; there was only a 30.3% response rate to the postcards in this survey. Thus, a better understanding of the EMR system operational status is one of our objectives for future research.

In this fact-finding survey, we did not investigate the details surrounding what kinds of nursing support system(s) these hospitals use. In the future, it will be necessary to conduct detailed investigations focused on specific nursing support systems and their relatedness with nursing outcomes.

At the same time, as has been said previously (Müller-Staub, 2009), nursing education is essential to the adoption of standardized nursing taxonomies like the NANDA-I, NIC, and NOC within EMR systems; it will thus be necessary to examine the education provided to nurses as well.

Conclusion

This study focused on the state of current EMR systems and nursing support systems in Japan. We found that a high proportion of hospitals have operational EMR systems especially in medium- and large-scale hospitals. Many hospitals with fewer than 300 beds are still not planning to introduce EMR systems, or the introduction is only in the planning stage. The nursing departments of about half of the hospitals had EMR systems that allowed nurses to input data, but the great majority of hospitals use neither specific frameworks nor nursing practice taxonomies for data entry.

Hospitals which did not plan on using an EMR would be more likely not to reply at all; there was only a 30.3% response rate to the postcards in this survey. Thus, a better understanding of the EMR system operational status is one of objective for the future research. In addition, we did not investigate in this fact-finding survey the details surrounding what kinds of nursing support system(s) these hospitals use. In the future, it will be necessary to conduct detailed investigations focused on nursing support systems and their relatedness with nursing outcomes. At the same time, nursing education is essential to the adoption of standardized nursing taxonomies like the NANDA-I, NIC, and NOC within EMR systems; it will thus be necessary to examine the education provided to nurses as well.

Acknowledgment

The authors wish to acknowledge the cooperation of the people in the nursing departments at all of the participating hospitals. This study was part of a research project funded by a Grant-in-Aid for Scientific Research (B) (Grant Number 23390496).

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Author Bios:

Yuko Kuroda is a professor at the graduate school of nursing at Tokushima Bunri University of Japan. She has a doctoral degree from the graduate school of nursing science at St. Luke’s International University. Her current research and interests involve nursing practice in critical care and nursing support systems.

Mitsumi Masuda is an assistant professor at the school of nursing/graduate school of nursing at Nagoya Citi University. She has a doctoral degree from the graduate school of health care science at Tokyo Medical and Dental University. Her current research and interests involve nursing practice in critical care.

Kazuaki Fukuda is a professor at the school of nursing at Tokushima Bunri University of Japan. He has a doctoral degree from the graduate school of nursing science at Kitasato University. His current research and interests involve nursing practice in critical care.

Hiroaki Yamase is a professor at the graduate school of medicine at Yamaguchi University of Japan. He has a doctoral degree from the graduate school of medicine at Yamaguchi University. His current research and interests involve nursing practice in critical care.

Ryoma Seto is a lecturer at the school of healthcare at Tokyo Healthcare University of Japan. He has a doctoral degree from the graduate school of health and welfare service administration at International University. His current research and interests involve medical informatics and electronic medical records.

Misae Ito is a professor at the school of medical welfare at Kawasaki University of Medical Welfare of Japan. She has a doctoral degree from the graduate school of medicine at Yamaguchi University. Her current research and interests involve nursing support systems and nursing education.

Kimiyo Shimomai is a professor at the school of nursing at Kansai University of Nursing and Health Sciences of Japan. Her current research and interests involve cancer nursing and end-of-life care.

Hidetoshi Furukawa is an assistant professor at the school of nursing at Kansai University of Nursing and Health Sciences of Japan. He has a master’s degree from the graduate school of nursing at Chiba University. His current research and interests involve gerontological nursing.

Junko Tatsuno is a certified nurse specialist for critical care nursing at Kokura Memorial Hospital of Japan. Her current research involves family nursing in critical care.

Asami Tado is an associate professor at the graduate school of medicine at Yamaguchi University of Japan. Her current research and interests involve nursing practice in critical care.