I Want to Write a Review on Sandhiils Emergency Physicians
PLoS 1. 2019; 14(iv): e0215231.
The Dedicated Emergency Physician Model of emergency care is associated with reduced pre-hospital transportation fourth dimension: A retrospective study with a nationwide database in Japan
Hidenori Higashi
1 Department of Emergency and Disquisitional Care Medicine, Japanese Red Cross Wakayama Medical Heart, Wakayama Metropolis, Wakayama, Japan
Reo Takaku
2 Establish for Health Economics and Policy, Minato-ku, Tokyo, Japan
Atsushi Yamaoka
3 Kinesthesia of Economics of Kobe Academy, Nada-ku Kobe Urban center, Hyogo, Nippon
Alan Kawarai Lefor
4 Department of Surgery, Jichi Medical Academy, Shimotsuke, Tochigi, Japan
Takashi Shiga
5 Department of Emergency Medicine, International University of Wellness and Welfare, Minato-ku, Tokyo, Japan
Wisit Cheungpasitporn, Editor
Received 2019 Jan vii; Accepted 2019 Mar 28.
- Data Availability Statement
-
The database is available to researchers approved by the Japanese authorities through multiple processes to ensure data security, consequent with the laws of Japan. In add-on, at that place are restrictions on the availability of data due to consent agreements for data security as well as IRB approval, which allow access only to external researchers for inquiry monitoring purposes. The Japanese authorities owns the data and interested researchers can contact Ministry building of Internal Affairs and Communications Fire and Disaster Management Agency Ambulance Service Planning Office. telephone number: +81-iii-5253-7529.
Abstruse
In Nippon, the increasing number of patients needing emergency medical care due to population aging is a major public wellness problem. Recently, emergency medicine in Nippon has seen a growth in the number of Defended Emergency Physician Model fashion departments. We aimed to decide whether at that place is an association between Dedicated Emergency Physician Model emergency intendance and pre-hospital transportation time. We conducted a secondary analysis of a Japanese national pre-hospital database from 2010 to 2014. Three regions (group 1: Urayasu city and Ichikawa city in Chiba prefecture, group 2: Kamakura city, Chigasaki city, Fujisawa city and Zushi city in Kanagawa prefecture, and group iii: Fukui urban center in Fukui prefecture) were evaluated as Dedicated Emergency Medico Model emergency medicine areas. We compared transportation times in these areas with all municipalities in the same prefectures, and with a nearby surface area using multivariate linear regression with cluster adjustment. The variables used for adjustment are the fourth dimension from Emergency Medical Services activation to the scene, month, day of the month, twenty-four hours of the week, time of mean solar day, age, gender, type of injury, severity, and location of telephone call. Compared with all municipalities in each prefecture there were significant reductions in pre-hospital transportation time: iv.two minutes (95% confidence interval, 0.9 to 7.v, p<0.05) in Group 1, half-dozen.2 minutes (95%CI, two.9 to 9.vi, p<0.01) fin Group ii and 7.v minutes (95%CI, six.0 to nine.0, p<0.01) in Group 3. Compared with nearby areas, in that location were statistically significant reductions in transportation fourth dimension in Grouping i, 6.8 minutes (95%CI, 0.7 to 12.viii, p<0.05) and in Grouping 2, half dozen.viii minutes (95%CI, iii.seven to 9.9, p<0.05). There was a trend for reduced transportation time in Grouping 3, 2.3 minutes, (v.3 to -0.half dozen, p<0.1). Areas with a Dedicated Emergency Doctor Model are associated with reduced pre-hospital transportation fourth dimension.
Introduction
An increasing number of emergency medical care patients due to population aging is a major public health trouble in many nations [i–5]. In Nihon, the number of ambulance transports per 10,000 population doubled and transportation time from Emergency Medical Services activation to infirmary arrival increased nigh one.5 times (24.4 minutes to 39.iii minutes) in the past 2 decades [6]. 1 of the reasons for transportation delay is an imbalance of supply and demand.
In Nippon, the emergency care system is traditionally different from that in another countries. In the majority of primary and secondary clinics and hospitals, emergency care is provided either by on-phone call physicians trained in any of a broad range of medical specialties or moonlighting physicians. These physicians oftentimes have no full general skills for emergency care. In a few areas in Japan, care is provided co-ordinate to a Defended Emergency Physician model and plays an important office to meet the increasing demand.
Multiple descriptive studies advise that patient characteristics are related to ambulance acceptance or overuse of Emergency Medical Services [7–11]. An interventional report reported that information and advice technology shortened transportation time [12]. In contrast, the show for the issue of Dedicated Emergency Physician Model emergency intendance to reduce transportation fourth dimension remains deficient.
To accost the knowledge gap in the literature, we conducted a secondary analysis of a nationwide pre-hospital database to determine whether at that place is an association between Defended Emergency Physician Model care and reduction in transportation time.
Methods
Dedicated Emergency Physician Model emergency care in Japan
Currently, institutions in Japan adopt two dissimilar models of emergency medical care, the critical care model and the Dedicated Emergency Physician Model. The critical care model focuses on third-level patients and is responsible for fewer than 5% of all emergency patients [thirteen]. Traditional Japanese emergency physicians appoint in the care of critically ill patients merely. The bulk of emergency care is provided either by on-phone call physicians trained in any specialty or moonlighting physicians. These physicians frequently accept express skill for emergency care. In this organisation, a junior resident or nurse sees the patient first and and so assigns the patient to a particular department. Currently, in Nippon, this is the nigh mutual model of practice. Issues arise when the patients are misdiagnosed and mis-assigned, or when a patient has multiple problems that involve several departments, every bit is often seen in patients with traumatic injuries.
The Defended Emergency Physician Model emergency care is a organization where emergency physicians dedicate themselves to emergency patient care and they ever work in shifts. This model of care is in widespread use in Northward America. Physicians have care of patients regardless of a patient's condition or historic period. They are non involved in inpatient care and are involved in diagnosis, initial care, and avant-garde triage (disposition).
In Japan, the number of emergency physician is less than in the Usa of America. The average number of emergency physicians is but 3,413 from 2010–2014 in Japan. The number of emergency doctor per 10,000 populations is one.24 in Us and 0.26 in Nippon. In addition, Dedicated Emergency Md Model physicians comprise less than 10% of the total emergency doc in Japan [14]. Therefore, a Defended Emergency Doctor Model emergency medicine is provided in only express areas.
Emergency medical service system in Japan
When emergency patients call for Emergency Medical Services, on-scene Emergency Medical Services personnel make up one's mind the appropriate hospital in the area that is best able to care for the patients according to their symptoms and weather condition. The Emergency Medical Services personnel then ship the patient to the selected infirmary later obtaining the hospital staff'southward agreement. Due to the emergency intendance models of care mentioned higher up and no laws controlling emergency intendance with negative sanctions such as the Emergency Medical Handling and Active Labor Deed (The states), difficulty in transporting the patient to an accepting infirmary can occur at the scene in Nihon. As a consequence, the transportation fourth dimension from Emergency Medical Services activation to hospital arrival lengthens and delays the initiation of emergent treatment, which might lead to a worse patient outcome [6].
Written report design, population, and setting
This is a retrospective observational report based on a Japanese national database from 2010 to 2014 [15]. All emergency patients who called ambulances and were transported to hospitals were registered in our study. We selected three medical care areas (group ane: Urayasu urban center and Ichikawa city in Chiba prefecture, group 2: Kamakura metropolis, Chigasaki city, Fujisawa metropolis and Zushi city in Kanagawa prefecture, and group 3: Fukui metropolis in Fukui prefecture) every bit Defended Emergency Doc Model emergency medicine areas. Each of these three areas has some Dedicated Emergency Physician Model emergency medicine hospitals (grouping ane: Tokyo Bay Urayasu Ichikawa medical eye, group 2: Shonan Kamakura general hospital, group three: Fukui prefectural infirmary). These hospitals have Defended Emergency Doctor Model emergency departments which practise non pass up emergency patients and accept more 15 Dedicated Emergency Physician Model emergency physicians including senior staff and senior residents. For each Dedicated Emergency Physician Model area, we selected a nearby area, geographically next to the Dedicated Emergency Physician Model area and comparable in terms of population size, geographical size, and geographical location, for comparison. The Defended Emergency Doctor Model areas were compared with all other municipalities in the same prefecture and with the nearby expanse. The nearby comparison area is Funabashi city for group 1, Odawara city, Isehara city, Hatano city and Sagamihara city for group 2, and Echizen city, Sabae city for group 3. Records with missing data were excluded from the analysis. Ambulance records are considered administrative records, and the requirement of obtaining patients' informed consent was waived because the data are anonymous. This report was approved by the Ethics Commission of the Japan Red Cross Wakayama Medical Middle (Approving Number: 570).
Information drove and quality control
Information were uniformly nerveless using specific data drove forms and included age, gender, location of telephone call, chronological factors such every bit time of twenty-four hour period or day of week, time course of transport such every bit time of emergency call, time spent in contact with the patient, and time of hospital arrival, blazon of injury, and severity. The physicians who care for the patients subjectively evaluate their severity at the fourth dimension of hospital arrival. Ems personnel consummate a data course upon receiving a request for EMS transportation. Subsequently they record patient data obtained during ship. Upon arrival at the receiving hospital, a physician assessment is provided to the European monetary system team to complete the data grade. Each data course is double checked past peer EMS personnel to ensure data accuracy. Finally, a designated supervising officer at each fire station assures the completeness of ship information. These data are an authoritative record by the fire departments which do not crave or connect to patient medical records.
Issue measures
The primary event measure out is transportation fourth dimension from arrival at the scene to arrival at the hospital in geographic areas using the Dedicated Emergency Physician Model for emergency care and control groups.
Statistical analysis
Multivariate linear regression analysis was used to investigate the association between the Dedicated Emergency Physician Model of emergency intendance and a reduction in pre-hospital transportation time. The standard errors were clustered at the city level. As covariates, the time from Emergency Medical Services activation to the scene, month, twenty-four hours of the calendar month, day in the week, time in the day, age, gender, type of injury, severity, and location of call were controlled for, based on a priori noesis [vii, 8]. To examine the multicollinearity of the models, nosotros calculated a variance aggrandizement factor for each model. We also divided the written report population into two groups, high severity (includes patients classified equally severe and dead) and low severity (includes patients classified equally mild and moderate) and performed subpopulation analyses for each group. Information were analyzed using Stata version 14 (College Station, TX). All tests were two-tailed, and p values <0.05 were statistically significant.
Results
During the study period, a total of 24,829,932 emergency patients were documented in the national database. A total of v,087,817 were excluded for missing data. In the target area of this written report, a total of 2,508,691 were enrolled. Of these, the number of emergency patients in each expanse is 529,094 in Grouping i, 1,864,321 in Group ii and 115,276 in Grouping three. A total of 645,869 patients were excluded for missing data in the target area. (Fig 1).
Patient flow.
EMS, emergency medical services.
Baseline characteristics of written report patients in each expanse are shown in Table 1. Medical illness is the most mutual reason for transportation and natural disasters is the least common in each area. More than than one-half of all patients were judged to be mild to moderate severity in each area. More than half of the calls were from the patient's home. About one-half of the calls are during nighttime.
Table one
Patient characteristics.
| Chiba- Grouping 1 | Kanagawa- Group 2 | Fukui- Group iii | |
|---|---|---|---|
| Observations | 529,094 | 1,864,321 | 115,276 |
| Historic period, median (IQR) | 65 (39–79) | 65 (39–80) | 71 (47–82) |
| Female, n (%) | 250,113 (47.3) | 883,146 (47.4) | 54,778 (47.v) |
| Time from emergency telephone call to the scene, hateful (min) | 9 | viii | 7 |
| Send reason, n (%) | |||
| Burn down accident | 667 (0.ane) | 2,003 (0.1) | 162 (0.1) |
| Natural disaster | 65 (0.01) | 345 (0.02) | 18 (0.02) |
| Water accident | 153 (0.03) | 568 (0.03) | 148 (0.1) |
| Traffic accident | 53,527 (x.1) | 166,798 (eight.ix) | 15,071 (xiii.1) |
| Industrial accident | v,128 (1.0) | 14,845 (0.8) | i,116 (1.0) |
| Disease and injury during sports | iii,357 (0.6) | 13,044 (0.seven) | 964 (0.8) |
| Other injury | 75,446 (14.3) | 291,332 (xv.6) | sixteen,461 (14.3) |
| Assault | iii,713 (0.7) | 13,449 (0.7) | 348 (0.3) |
| Cocky-induced trauma | iv,184 (0.8) | 16,012 (0.9) | 796 (0.seven) |
| Medical affliction | 334,236 (63.2) | i,223,355 (65.half-dozen) | 68,027 (59.0) |
| Transfer to a different hospital | 46,577 (viii.8) | 119,943 (half dozen.4) | 12,106 (x.5) |
| Severity, due north (%) † | |||
| Death | 6,892 (1.3) | 23,112 (ane.2) | 2,115 (1.8) |
| Severe | 38,474 (7.3) | 157,300 (8.iv) | 17,654 (xv.three) |
| Moderate | 216,686 (41.0) | 719,351 (38.vi) | 51,529 (44.7) |
| Mild | 267,042 (50.5) | 964,558 (51.7) | 43,978 (38.2) |
| Location, due north (%) | |||
| Home | 307,345 (58.i) | 1,111,088 (59.6) | sixty,809 (52.8) |
| Public infinite | 127,450 (24.1) | 428,025 (23.0) | thirty,191 (26.2) |
| Workplace | 12,519 (ii.4) | 41,772 (2.2) | ii,780 (2.4) |
| Route | 75,813 (xiv.three) | 264,023 (xiv.2) | 18,492 (16.0) |
| Others | 5,967 (1.i) | nineteen,413 (1.0) | three,004 (2.vi) |
| Nighttime, north(%)‡ | 266,644 (50.4) | 961,797 (51.6) | 56,101 (48.7) |
Results of multivariate linear regression analysis and the effects of covariate factors are shown in Table 2.
Table 2
Results of multivariate linear regression analysis.
| Chiba- Grouping 1 | Kanagawa- Grouping 2 | Fukui- Grouping 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Comparison grouping | All other areas | Other cities | Nearby area | All other areas | Other cities | Nearby area | All other areas | Other cities | Nearby area |
| Observations | 529,094 | 427,720 | 103,241 | 1,864,321 | 1,793,711 | 465,817 | 115,276 | 46,223 | 60,527 |
| Departure in transportation time (min)† | -four.17** | -3.2 | -2.34* | -6.25*** | -half-dozen.06*** | -6.76** | -7.54*** | -11.26** | -half-dozen.78** |
| [-7.48–-0.87] | [-seven.42–1.03] | [-5.26–0.58] | [-nine.60–-two.89] | [-9.58–-two.54] | [-12.79–-0.73] | [-9.03–-half-dozen.05] | [-18.51–-4.00] | [-9.87–-three.68] | |
| Effect of time from emergency call to the scene‡ | 1.37*** | 1.38*** | i.32*** | 1.39*** | 1.forty*** | 1.43*** | 1.63*** | 1.52*** | one.55*** |
| [1.24–ane.49] | [1.25–ane.51] | [1.17–i.46] | [i.23–1.55] | [1.23–one.58] | [1.28–1.57] | [1.42–i.85] | [1.25–1.79] | [1.xx–1.87] | |
| Adapted R-squared | 0.xi | 0.102 | 0.139 | 0.153 | 0.155 | 0.203 | 0.292 | 0.318 | 0.316 |
| Transportation time, mean (min) | 44.32 | 43.05 | 42.14 | 38.77 | 38.66 | 36.27 | xxx.89 | 27.65 | 28.34 |
Compared with all other areas, there were reductions in transportation fourth dimension in the areas served past the Defended Emergency Doctor Model: 4.two minutes (95% confidence interval, 7.v to 0.9, p<0.05) in Group 1, vi.2 minutes (95% CI, 2.9 to 9.vi, p<0.01) in Group 2 and seven.5 minutes (95%CI, six.0 to 9.0, p<0.01) in Grouping iii. When compared with nearby comparison areas, in that location were reductions in transportation fourth dimension with statistical significance in Group two past six.8 minutes (95%CI, 0.7 to 12.8, p<0.05) and in Group 3 by 6.8 minutes (95%CI, 3.vii to 9.nine, p<0.05). There was a trend toward a reduction in transportation time in Group i of two.3 minutes, (5.iii to -0.six, p<0.i). For each model, nosotros calculated variance inflation factors. There is no multicollinearity because the variance inflation factor of each model is less than 10.
The results of subgroup analyses are shown in Tabular array 3. In both the high and low severity groups, in that location were reductions in transportation time with statistical significance compared with all other areas.
Table iii
Results of subgroup analysis by severity.
| Chiba- Grouping ane | Kanagawa- Group 2 | Fukui- Group 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| All other areas | Other cities | Nearby area | All other areas | Other cities | Nearby area | All other areas | Other cities | Nearby surface area | |
| Severe or dead | |||||||||
| Observations | 45,366 | 32,933 | viii,308 | 180,412 | 173,164 | 44,420 | 19,769 | vi,746 | x,040 |
| Difference in transportation time (min)† | -3.50** | -1.52 | -4.41* | -2.86** | -2.52* | -4.66* | -ten.31*** | -15.20** | -7.83** |
| [-vi.57–-0.43] | [-4.81–1.76] | [-9.46–0.64] | [-five.46–-0.26] | [-5.xiv–0.x] | [-nine.74–0.41] | [-12.87–-7.75] | [-27.21–-3.19] | [-15.xi–-0.55] | |
| Adapted R-squared | 0.111 | 0.106 | 0.143 | 0.126 | 0.13 | 0.16 | 0.26 | 0.337 | 0.295 |
| Transportation time, hateful (min) | 45 | 42 | 44 | 38 | 38 | 37 | 33 | 29 | 29 |
| Mild or moderate | |||||||||
| Observations | 483,728 | 394,787 | 94,933 | 1,683,909 | one,620,547 | 421,397 | 95,507 | 39,477 | fifty,487 |
| Departure in transportation time (min)† | -four.26** | -iii.38 | -2.xv* | -half dozen.59*** | -6.42*** | -6.96** | -seven.12*** | -10.28** | -six.57*** |
| [-7.71–-0.80] | [-vii.82–1.07] | [-four.89–0.lx] | [-10.05–-3.12] | [-ten.06–-ii.77] | [-13.10–-0.81] | [-8.60–-five.63] | [-16.95–-iii.sixty] | [-8.86–-4.29] | |
| Adjusted R-squared | 0.114 | 0.106 | 0.144 | 0.xvi | 0.162 | 0.213 | 0.318 | 0.323 | 0.328 |
| Transportation time, mean (min) | 44 | 43 | 42 | 39 | 39 | 36 | 30 | 27 | 28 |
Discussion
In this retrospective observational study of over 2,500,000 pre-hospital transports, we establish an association between use of a Defended Emergency Physician Model of intendance with reduced transportation time. This observation suggests that the Dedicated Emergency Physician Model of emergency care plays an important function to answer to the increasing number of patients requiring transportation past ambulance.
A previous report demonstrated that pre-infirmary factors such as being elderly, foreigners, loss of consciousness, holiday/weekend, nighttime, gas poisoning, trauma by assault, cocky-induced drug/gas abuse poisoning, and self-induced trauma were positively associated with difficulty in hospital acceptance [seven]. Another study showed that patients with traumatic injuries, pediatric patients, male gender, moderate to astringent grade trauma, holidays and weekends and dark were positively associated with difficulty in hospital acceptance [eight]. These studies examined patient characteristics. The present written report is unique it focuses on the deviation the system of care, especially the effect of having a Dedicated Emergency Medico Model of emergency care.
The effect of the Dedicated Emergency Physician Model to result in reduced transportation time, could be explained by strengths of emergency medicine good according to the Dedicated Emergency Md Model. Each hospital which uses this model of care has a training program to teach this model of emergency medicine and physicians can manage emergency patients regardless of their status or severity and tin concentrate on initial emergency care. In addition, they tin can work with fewer burdens considering of the shift work organization. They accept a greater capacity to accept multiple emergency patients. We suggest that these factors contribute to the observed reduction in transportation time.
This study has important implications for emergency medical systems. Previously, whether the Dedicated Emergency Dr. Model of emergency intendance affects transportation time had not been emphasized. If this model becomes more accepted, transportation time will be reduced in many regions and it may contribute to improved prognosis. In add-on, the Defended Emergency Medico Model might facilitate amend medical care in communities with express medical resource.
The nowadays report has some limitations. Outset, the outcome measure of this study is the average pre-infirmary transportation time. Due to this, the results do not reflect in-hospital outcomes. In-hospital data would be needed in a future study. Second, we demand to be specific regarding the definition of the Dedicated Emergency Physician Model. In this study, nosotros strictly divers the Dedicated Emergency Physician Model every bit a department which does not refuse emergency patients and has more than 15 emergency physicians. However, smaller hospitals in command groups may accept mixed models of care such as a daytime Dedicated Emergency Physician Model and a nighttime traditional care model. Third, pre-hospital transportation time is partially dependent on the geographic distance from fire-station to the scene or from the scene to the hospital. We tried to control this cistron past defining pre-hospital transportation time every bit from the scene to the hospital as well as adjusting for the time from the emergency call to the scene every bit ane of the covariates. Fourth, these results may not be generalized considering the present study is based on the Japanese national pre-hospital database and Japanese Emergency Medical Services systems and ER systems are dissimilar from other countries. Fifth, because of the nature of an observational study, nosotros need to consider the possibility of unknown confounding factors that influenced our results. Sixth, we checked the number of physicians per 1000 person in 2010 (Japan Medical Association Inquiry Institute 2013) since transportation time may be shorter in the areas with more physicians, even in the absence of a Dedicated Emergency Medico Model emergency organization. Even so, we did not observe big differences in all sites, though the number of physicians in Fukui metropolis is twice as large equally the optimum comparing group (i.e. Sabae and Echizen urban center).
Conclusion
In this retrospective written report using a big national database, areas with hospitals that employ a Dedicated Emergency Physician Model of emergency care are associated with reduced pre-hospital transportation time.
Acknowledgments
We would like to thank all of the emergency medical services personnel.
Funding Statement
The authors received no specific funding for this work.
Data Availability
The database is available to researchers canonical by the Japanese authorities through multiple processes to ensure data security, consistent with the laws of Nippon. In addition, there are restrictions on the availability of data due to consent agreements for data security as well as IRB approval, which allow access just to external researchers for inquiry monitoring purposes. The Japanese regime owns the data and interested researchers tin can contact Ministry of Internal Diplomacy and Communications Fire and Disaster Management Bureau Ambulance Service Planning Office. phone number: +81-3-5253-7529.
References
i. Hasegawa K, Tsugawa Y, Camargo CA Jr., Hiraide A, Brown DF. Regional variability in survival outcomes of out-of-hospital cardiac arrest: the All-Japan Utstein Registry. Resuscitation. 2013;84(8):1099–107. x.1016/j.resuscitation.2013.03.007 [PubMed] [CrossRef] [Google Scholar]
2. Clark MJ, FitzGerald G. Older people's use of ambulance services: a population based analysis. Journal of blow & emergency medicine. 1999;xvi(2):108–11. [PMC free article] [PubMed] [Google Scholar]
iii. Lowthian JA, Jolley DJ, Curtis AJ, Currell A, Cameron PA, Stoelwinder JU, et al. The challenges of population ageing: accelerating demand for emergency ambulance services by older patients, 1995–2015. The Medical journal of Australia. 2011;194(11):574–eight. [PubMed] [Google Scholar]
4. Platts-Mills TF, Leacock B, Cabanas JG, Shofer FS, McLean SA. Emergency medical services use past the elderly: analysis of a statewide database. Prehospital emergency care: official periodical of the National Association of Ems Physicians and the National Clan of State European monetary system Directors. 2010;14(3):329–33. [PubMed] [Google Scholar]
5. Wrigley H, George S, Smith H, Snooks H, Glasper A, Thomas Eastward. Trends in need for emergency ambulance services in Wiltshire over nine years: observational study. BMJ (Clinical research ed). 2002;324(7338):646–vii. [PMC complimentary article] [PubMed] [Google Scholar]
7. Katayama Y, Kitamura T, Kiyohara K, Iwami T, Kawamura T, Hayashida S, et al. Factors associated with the difficulty in hospital acceptance at the scene past emergency medical service personnel: a population-based study in Osaka City, Japan. BMJ Open up. 2016;vi(10):e013849 10.1136/bmjopen-2016-013849 [PMC costless article] [PubMed] [CrossRef] [Google Scholar]
8. Katayama Y, Kitamura T, Kiyohara One thousand, Iwami T, Kawamura T, Hayashida Southward, et al. Evaluation of factors associated with the difficulty in finding receiving hospitals for traffic blow patients at the scene treated past emergency medical services: a population-based study in Osaka Metropolis, Nippon. Acute medicine & surgery. 2017;four(4):401–7. [PMC free commodity] [PubMed] [Google Scholar]
ix. Matsuyama T, Kitamura T, Katayama Y, Kiyohara K, Hayashida S, Kawamura T, et al. Factors associated with the difficulty in infirmary acceptance among elderly emergency patients: A population-based study in Osaka City, Nihon. Geriatrics & gerontology international. 2017;17(12):2441–8. [PubMed] [Google Scholar]
10. Sandoval E, Smith Due south, Walter J, Schuman SA, Olson MP, Striefler R, et al. A comparison of frequent and infrequent visitors to an urban emergency department. The Journal of emergency medicine. 2010;38(2):115–21. 10.1016/j.jemermed.2007.09.042 [PubMed] [CrossRef] [Google Scholar]
eleven. Markham D, Graudins A. Characteristics of frequent emergency department presenters to an Australian emergency medicine network. BMC Emerg Med. 2011;eleven(21):21. [PMC costless article] [PubMed] [Google Scholar]
12. Yamada KC, Inoue South, Sakamoto Y. An constructive support organisation of emergency medical services with tablet computers. JMIR mHealth and uHealth. 2015;three(i):e23 10.2196/mhealth.3293 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
13. Hori Southward. Emergency Medicine in Japan. Keio J Med. 2010;59:131–9. [PubMed] [Google Scholar]
xiv. Seikei Hibino SH. Emergency Medicine in the US and the US model Emergency Medicine in Nippon. Journal of Japanese Association for Acute Medicine. 2010;21:925–34. [Google Scholar]
15. Takaku R, Yamaoka A. Payment systems and hospital length of stay: a bunching-based testify. Int J Health Econ Manag. 2018. [PubMed] [Google Scholar]
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