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- Taylor & Francis
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- © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China.
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- 1346-7581
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- 10.1080/13467581.2022.2074018
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Abstract
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING https://doi.org/10.1080/13467581.2022.2074018 Spatial distribution of patient traffic volume in outpatient buildings of large general hospitals in China Malu Zhang and Ning Yang School of Design and Architecture, Zhejiang University of Technology, Hangzhou, Zhejiang Province, China ABSTRACT ARTICLE HISTORY Received 25 November 2021 The spatial distribution of patient traffic is important for hospital building design, but there is Accepted 2 May 2022 still insufficient targeted discussion for Chinese outpatient buildings. To obtain reliable evi- dence, this study examined the outpatient traffic spatial distribution in a large Chinese general KEYWORDS hospital by describing it as a set of inter-department patient traffic frequencies (q), which Outpatient building; patient means the ratio of the number of patient trips within a pair of department units to the traffic volume; spatial hospital’s total outpatient visits. Through three hospital samples and 2443 patient samples, distribution; inter- three main findings were obtained: (1) The “q” value sets of each hospital sample and their department traffic frequency; evidence for average value set were obtained, and the idea of using them as evidence in outpatient building architectural design design was presented; (2) Outpatient traffic distribution was similar among hospitals and was characterized by clustering among certain departments: 38 out of 150 traffic sections created 90% of outpatient traffic, and four public departments’ outpatient traffic presented directivity; (3) There was an indicated slight variation among the samples; therefore, more precise evidence for specific cases required “q” values generated by themselves, which could be obtained conveniently through methods presented in this study. Subsequently, both evidence and methods are provided. 1. Introduction for a long time (Yang and Guo 2013), which signifi - cantly reduces efficiency (Vos, Groothuis, and van Outpatient buildings usually occupy approximately Merode 2007) and patient satisfaction (Parente, Pinto, 20% of the total building area of large general hospi- and Barber 2005). To shorten the travel distance in tals in China (National Health Commission of the outpatient building design, outpatient flow should be People’s Republic of China, 2021, 6–7) and are visited considered carefully in terms of reasonability frequently by large numbers of patients. Consequently, (Tzortzopoulos et al. 2009; Long, Zhang, and Ma outpatients must often walk long distances and wait 2016), which depends on meeting the corresponding Correspondence to Yang Ning 1285910860@qq.com School of Design and Architecture, Zhejiang University of Technology, Xihu District, Hangzhou, Zhejiang Province 310023, China © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 Z. MALU AND Y. NING functional demands and requires reliable evidence measures for the units to be evaluated, and the result (Ulrich 2006). To this end, the spatial distribution of was referred to as the “Yale Traffic Index.” (Pelletier and patient traffic volume, as a comprehensive reflection of Thompson 1960, cited by Delon and Smalley 1970). outpatient medical flow and functional requirements, Subsequently, scholars proposed data evidence should be important evidence. However, evidence on (Zadeh, Shepley, and Waggener 2012) and quantitative outpatient buildings in China is still insufficient. models to evaluate the transportation efficiency of A Chinese book Modern Hospital Building Design nursing units, such as the MPA/BTA Nursing Unit proposed the concept of “traffic intensity and fre- Analysis Model (Kobus et al. 2000, pp: 138–139). For quency” (hereinafter “traffic frequency”) at the begin- circulation among hospital departments, Delon and ning of this century to describe the frequency of trips Smalley (1970) took the number of hospital staff trips between departments (Luo 2009, 21). It deconstructs as the basic indicator to measure the relationships the internal traffic of a general hospital into a network between pairs of hospital departments. They recorded and investigates the traffic frequency between each hourly traffic frequencies of the day shift during six pair of departments. The higher the frequency, the weeks, by personnel classification and origin and des- higher the aggregation of traffic volume between this tination, with separate forms used for incoming and pair of departments. In this way, the concept evaluates outgoing trips. Based on the analysis of collected infor- traffic frequency of people and goods between mation, they pointed out that 55% of all hospital Chinese hospital departments into four levels, “高强 departments accounted for 75% of the incoming and 紧密 [high strength, closely],” “反复多次 [repeatedly],” outgoing personnel traffic of nursing units, and the “不连贯 [incoherently],” and “极少 [very few],” and number of trips between departments whose medical these are presented in Table 1. flow was determined could be predicted according to While Luo’s (2009, 21) work has long been adopted the medical flow. as the main relevant research, it uses vague terms to Existing quantitative empirical studies on traffic dis- define the four levels without specifying their assess- tribution in hospitals have mainly focused on staff flow, ment criteria and the method. This makes it conceptual whereas patient flow is more important in outpatient guidance that cannot support current research regard- building design. However, Chinese hospitals have been ing accuracy, reliability, and real-time performance. For omitted from existing research, and the results cannot example, it was used as a quantitative indicator for be directly adopted in China due to different demand calculating the “organizational efficiency ” of hospital (Cai and Zimring 2019). Additionally, data collection in buildings by Bai (2011, 71) but its lack of accurate data existing studies required daily on-site observations for limited the accuracy of Bai’s results. Additionally, Luo’s weeks or longer. This amount of work causes difficulty in study treats the “outpatient department” as one obtaining samples over a longer period and applicating department without describing the traffic distribution to other cases, which is unfriendly to obtaining self-data within an outpatient building. in specific case study for more reliable results (Long and As medical procedures become more precise and Kuang 2014; Ulrich 2006). complex, hospital buildings need to become more Therefore, to obtain more accurate and reliable evi- sophisticated. Consequently, architects are constantly dence for Chinese outpatient building design and looking for more accurate and reliable evidence for research, and considering the insufficient targeted dis- hospital design. The theory of evidence-based design cussion in existing studies (Table 2), this study focused (Ulrich 1984) argues that decisions about the built on outpatient buildings of large general hospitals in environment should be based on sound research China and explored the general characteristics and results (Hamilton 2004). rules of the spatial distribution of their patient traffic Early empirical research on traffic distribution in volume empirically and quantitatively. To support case- medical buildings focused mainly on the circulation specific data obtaining and consider existing methods’ of medical staff in nursing units. Based on the concept flaws (Table 2), we also developed a data collection of functional efficiency, Pelletier and Thompson (1960) method for longer periods and easier operation. identified 16 areas on a typical nursing ward and recorded the number of trips between each pair of 1.1. Theoretical definition areas, which they labeled the “link.” They found that 14 links accounted for more than 91% of nurse traffic in The “large general hospital” referred to in this study is the nursing unit, which was the prime determinant of a “Grade 3” hospital following China’s “The measures unit efficiency. The weights (relative trip frequencies) for the administration of hospital grades,” in which of these 14 links were combined with distance “Grade 3” is the highest grade (National Health In Bai’s study (Bai 2011), “organization efficiency” was used to evaluate the spatial interrelations among units in the contribution to the overall function of hospital buildings. MPA: Medical Planning Associates. BTA: Bobrow/Thomas & Associates. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 3 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Table 1. Traffic intensity and frequency analysis chart of hospital departments (translated from modern hospital building design). Obstetrics Admission Central Internal Plastic & Emergency Operating Delivery Clinical Nuclear and Rehabilitation Blood Sterile Meal Medicine Surgery Surgery Gynecology Pediatrics Room ICU Theater Room Lab Radiology Medicine Pathology Outpatients Discharge Physiotherapy Radiotherapy Bank Pharmacy Supply Preparation Laundry Internal ◔ ◔ ◔ ◔ ◑ ◔ ◕ ◕ ◑ ◔ ◑ ◑ ◑ ◑ ◑ ◕ ◔ ◑ ◑ Medicine Surgery ◔ ◑ ◑ ◕ ◔ ◕ ◕ ◑ ◑ ◑ ◑ ◑ ◑ ◕ ◑ ◑ ◑ Plastic ◔ ◑ ◑ ◕ ◕ ◕ ◑ ◑ ◑ ◔ ◕ ◑ ◕ ◑ ◑ ◑ Surgery Obstetrics & ◔ ◑ ◑ ◑ ◕ ◕ ◕ ◕ ◑ ◑ ◑ ◑ ◑ ◕ ◑ ◑ ◑ Gynecology Pediatrics ◔ ◑ ◑ ◔ ◑ ◕ ◕ ◕ ◔ ◔ ◑ ◑ ◑ ◔ ◑ ◕ ◑ ◑ ◑ Emergency ◑ ◑ ◑ ◑ ◑ ◕ ◕ ◕ ◕ ◕ ◔ ◕ ◕ ◕ ◕ ◑ ◑ ◑ Room ICU ◔ ◑ ◑ ◑ ◔ ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◑ ◑ ◑ Operating ◕ ◕ ◕ ◑ ◕ ◕ ◑ ◕ ◕ ◑ ◑ ◕ ◕ ◕ ◑ Theater Deliver ◔ ◕ ◕ ◕ ◕ ◑ ◕ ◑ ◔ ◕ ◕ ◕ ◑ Room Clinical ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◑ ◑ ◕ ◕ ◕ ◑ ◔ ◔ Lab Radiology ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◑ ◑ ◕ ◕ ◔ ◕ ◔ ◔ Nuclear ◑ ◑ ◑ ◑ ◔ ◔ ◑ ◕ ◕ ◑ ◕ ◑ ◕ ◔ ◔ Medicine Pathology ◔ ◑ ◑ ◑ ◔ ◕ ◕ ◑ ◔ ◕ ◕ ◕ ◑ ◑ ◑ ◔ Outpatients ◑ ◑ ◑ ◑ ◑ ◑ ◕ ◕ ◑ ◑ ◑ ◔ ◕ ◑ ◑ Admission ◑ ◑ ◔ ◑ ◑ ◕ ◑ ◔ and Discharge Rehabilitation Physiotherapy ◑ ◑ ◕ ◑ ◔ ◑ ◔ ◑ ◑ Radiotherapy ◑ ◔ ◕ ◕ ◕ ◑ ◔ Blood ◑ ◑ ◑ ◑ ◑ ◕ ◕ ◕ ◕ ◕ ◑ ◑ ◔ ◑ ◑ ◑ ◔ Bank Pharmacy ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◕ ◑ ◕ ◑ ◕ ◔ ◑ ◑ ◔ Central ◔ ◑ ◑ ◑ ◑ ◑ ◑ ◕ ◕ ◔ ◔ ◔ ◑ ◑ ◑ ◑ ◕ Sterile Supply Meal ◑ ◑ ◑ ◑ ◑ ◑ ◑ ◑ ◑ Preparation Laundry ◑ ◑ ◑ ◑ ◑ ◑ ◑ ◑ ◑ ◔ ◔ ◔ ◔ ◑ ◔ ◑ ◔ ◔ ◔ ◕ ◑ ◕高强紧密 [high strength, closely]; ◑反复多次 [repeatedly]; ◔不连贯 [incoherently]; ○极少 [very few]. 4 Z. MALU AND Y. NING Table 2. Comparison of existing studies on hospital traffic outpatient building by the “inter-department patient volume with problems dealt with in this study. traffic frequency” value set and defined the channel Problems dealt with in this study Type of connecting a pair of department units as a “traffic sec- existing tion,” with some overlapping in space (Figure 1). related study Quantitatively, the number of patient trips is gen- I II III erally directly proportional to the number of outpati- Chinese hospitals’ medical flow Yes No No ent visits. Therefore, to eliminate the influence of the Focus on patient traffic in outpatient building No No No visit scale, we defined “inter-department patient traffic Focus on the traffic between departments Yes No Yes frequency (q)” as the ratio of the annual number of Adopt quantitative empirical method to make No Yes Yes patient trips (hereinafter “annual patient traffic the results clear and reliable The data collected covered no less than a year, and the No No No volume”) passing through a traffic section (Q) to the method should be easy to apply in general projects. annual number of outpatient visits to this outpatient “Type I” is represented by Luo’s study (Luo 2009); “type II” is represented building (v). In this way, the set of “q” values of an by Pelletier and Thompson’s (1960); “type III” is represented by Delon & Smalley’s study (Delon and Smalley 1970). outpatient building can reflect the relative volume of patient traffic in different traffic sections, which is only Commission of the People’s Republic of China 1989). determined by the corresponding outpatient medical As “department” is the basic functional unit of an out- flow and department type. patient building, we used patient traffic between q ¼ Q=v (1) department units (hereinafter “inter-department patient traffic”) in this study. In the above equation, the values of “Q” and “v” could According to the aforementioned studies, the spa- be obtained mainly from a hospital survey. Taking tial traffic distribution in hospitals can be measured by Figure 1 as an example, suppose we learned from the the number of trips, which is also suitable for this investigation that the “Q” values of the traffic sections study. Considering that the visit scale of different out- [A–B] (the blue line), [A–D] (the red line), and [C–D] (the patient buildings varies greatly in China, using the yellow line) were “X,” “Y,” and “Z,” and the “v” value of number of trips as the eigenvalue may reduce the this hospital was “N.” According to equation (1), the “q” research results’ universality. Therefore, we converted values of section [A–B], [A–D] and [C–D] were respec- the number of trips into an indicator with a similar tively “X/N,” “Y/N” and “Z/N.” meaning, but the impact of the scale was ruled out, The results in the above form provide direct evi- which could be described as “traffic frequency” men- dence for department layout design. For the local tioned by Luo’s (2009, 21). We measured the spatial space design of the corridor, the “q” values of the distribution of patient traffic volume in the Chinese corridor were required instead of the traffic section, Figure 1. Schematic diagram of “traffic section”. Patients leaving Charge Charge Charge Patients arriving JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 5 in which the overlapping part should be further calcu- (Figure 3), signs in the outpatient lobby, and related lated. For example, the “q” value of the overlapping studies (Jiang 2005, 28; Yang and Guo 2013; Hu 2016, part of [A–B] and [A–D] in Figure 1 should be the sum 43; Zhang 2019, 86). of the “q” values of [A–B] and [A–D]. After arriving at the outpatient building, patients register (manual service windows or self-service machine), choose appropriate clinical departments, and the registrar or nurse at the information desk 1.2. Types of traffic sections provides advice on the department selection. The The origin and destination of the traffic sections were patients then see doctors at a specialized department. both department units, so the type of connected units Some patients may need to go to public departments identified the type of traffic section. China’s policies have for examinations and then return to the specialized strict regulations (National Health Commission of the department with their examination reports, which People’s Republic of China 2016, 1–2) for departments in might be repeated several times before the doctors’ large general hospitals (grade 3 hospitals). Therefore, the final diagnosis. Finally, some patients can leave, while department settings are the same in most large general other patients must go to a public department for hospital outpatient buildings in China, including outpati- treatment (including pharmacy) and then leave. ent, emergency, and related medical technology depart- Moreover, patients are required to pay fees before ments. Subsequently, combined with the routine registration, examinations, and treatment (including separation of outpatient buildings (Zhang 2019), we listed pharmacy); however, as mobile payments have the routine department units related to outpatient trans- become common in Chinese general hospitals, and portation in a typical large general hospital outpatient the medical payment flow does not require additional building in China and grouped them according to their patient movement, it was not considered in this model. role in the outpatient medical flow of China (Table 3). In addition to the above arterial medical flow, some Most large hospitals in China are public (National branch medical flows are affected by complex subjective Health Commission of the People’s Republic of China and objective factors and present great uncertainty. In 2019), so they share similar outpatient medical flow the outpatient medical flow between “specialized depart- (Figure 2), and similar instructions can be found on ments” and “public department for examinations,” as most Chinese general hospitals’ official websites some examination items may need appointments and Table 3. Department units in outpatient buildings in large general hospitals in China. Medical department Public department Unit Supporting type Specialized department Treatment Examination department Unit Emergency, Physical Examination Center, Internal Medicine, TCM* Pharmacy, Clinical Lab, Radiology, Registration name Surgery, Obstetrics & Gynecology, Pediatrics, Ophthalmology, Pharmacy, Ultrasonography, ECG & EEG, E.N.T., Stomatology, Dermatology, Psychiatry, Infectious Ambulatory Endoscopy, Nuclear Medicine Diseases Dept., Oncology, Rehabilitation Medicine, Aches and Surgery Pains Clinic, TCM* “TCM” = “traditional Chinese medicine” Registration Diagnosis Treatment In public In registration In specialized department for department department Patients treatment move Examination In public department for examination Medical staff move Resuscitation Observation In resuscitation room In observation room Figure 2. Outpatient medical flow in China. According to the China Health Statistical Yearbook 2019, in 2018, 2263 of China’s large hospitals (Grade 3 hospitals) were public, while only 285 were not. 6 Z. MALU AND Y. NING Figure 3. Outpatient medical flow from a Chinese general hospital’s official website (translated and analyzed from http://www. byytfy.com/mzfw/jyxz/). are not carried out on the day of the patient’s visit or the department → examination department → specia- examination report is not immediately available, the lized department” in the arterial medical flow. This patients’ traffic behavior could be “specialized depart- depends more on personal subjective factors than ment → examination department → entrance of build- the former medical flow branch. Therefore, ing → registration department (examination whether and how this medical flow branch occurs department) → examination department (registration could be uncontrollable and unpredictable. department) → specialized department” instead of “spe- Additionally, China’s current medical policy cialized department → examination department → spe- requires hospitals to control the proportion of cialized department” in the arterial medical flow. medical technology examinations as much as pos- However, whether and how this medical flow happens sible, so this medical flow branch is less likely to depends on individual regulations, which differ among occur. Therefore, this medical flow branch was not hospitals. As we want to obtain universal results in this included in our model. study, this medical flow branch was omitted in our In summary, in a typical outpatient building in model. China, outpatients mainly travel between 150 pairs Similarly, in some cases, outpatients might be of departments in two types in the model, which required to visit different departments for several are also traffic section types: examinations at once, and the patients’ traffic 1) [specialized department–public department], totally behavior in this part could be “specialized depart- 135 pairs; ment → examination department A→ examination department B→ examination department C → . . . 2) [Supporting department (registration)–specialized → specialized department” instead of “specialized department], totaling 15 pairs. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 7 Table 4. Factors considered in the selection of hospital samples patients. Therefore, patients and their companions in this study. are both components of travelers, and the “Q” value Factors Reason Measure in equation (1) includes two parts: the annual traffic Location Due to cultural and social Chose samples from east, volume generated by the outpatients themselves (p) differences, doctors and middle, and west China and the annual traffic volume generated by the out- patients in different areas separately of China may have patients’ companions (c): different medical preferences, leading to Q ¼ pþ c (2) regional differences in research results The above “p” value was obtained directly from a field Annual Although excluding the Most of the samples were number of direct effect of the visit consistent with the mean survey. Meanwhile, as the above “c” value is roughly outpatient scale was considered in value* among Chinese directly proportional to the patient volume according visits equation (1), it might still Grade 3 hospitals, and at to existing studies, we counted the ratio of the com- affect the results in other least one sample was ways. For example, with a large difference panion volume to the patient volume through smaller outpatient from it for comparison a sample survey, called the “accompanying rate” (r), buildings may have fewer medical and then multiplied the “r” value by the “p” value from equipment, resulting in our survey to obtain the companion volume (c) in lower traffic volume associated with medical equation (2): technology examination Number of This represents the c ¼ r� p (3) beds hospital’s size, and it is generally believed that it Owing to the different disease types, the accompany- highly correlates with the ing rates might differ among specialized departments, “annual number of outpatient visits.” We which were also different among traffic sections. considered it for the Meanwhile, the accompanying rate might differ same reason as the “annual number of among different regions due to different habits and outpatient visits” customs. Moreover, the period patients choose to visit Building To prepare for the analysis Chose samples with “Single layout of issues related to gallery,” “Street,” and might reflect disease progression, so the correspond- (secondary) architectural design “Centralized” patterns ing accompanying rate might differ. Therefore, accom- strategy in the follow-up separately, the main panying rate might need to be grouped according to research types of outpatient building layout in China these factors in the calculation. Outpatient To prepare for the analysis Chose samples with For example, assume a traffic section connected building of issues related to building areas “higher area architectural design than,” “lower than,” and specialized department A and examination depart- (secondary) strategy in the follow-up “close to” the provisions ment B. Its “q” value was obtained as “X,” and the research on the construction standards* separately accompanying rate of specialized department A (with 1) “Secondary” factors are not the requirements that need to be strictly corresponding time and location) was “Y.” Then, followed in the sample selection but should be met as far as possible according to Equations (2) and (3), the “Q” value of and were not analyzed deeply in this study. 2) For the “location measure,” Chinese regions are usually divided into west, this traffic section should be “X + X * Y.” Additionally, as middle, and east in China’s official statistics (National Health Commission of existing research on accompanying rates ignores these the People’s Republic of China 2019). 3) The “mean values*” were calculated using the China Health Statistical differences, they were investigated further in this Yearbook 2019 (National Health Commission of the People’s Republic of study. China 2019): annual outpatient visits: 1,854,787 thousand/ 2548 = 727,938.38; number of beds: 2,567,138/2548 = 1,007.51. 3. “The construction standards*” is “General hospital construction standards” (National Health Commission of the People’s Republic of China 2021), 2. Materials and methods which defines the area standard of buildings in general hospitals according to number of beds and other factors. We conducted this investigation as follows (Figure 4): None of the authors had access to identifiable patient information, and the patient transfer data ana- lyzed in this study were exported from hospitals with- 1.3. Traveler categories out any identifiable information. Additionally, the transferred dataset contained no potentially identifi - Outpatients are the major traveler category who gen- able information. erate patient traffic volumes in outpatient buildings. Meanwhile, in China, outpatients were usually accom- panied by family members or friends during the visit 2.1. Patient traffic volume (p) and outpatient visit (Luo 2009, 107), and there were 1.15 ~ 2 companions volume (v) for each patient on average (Zhang 2013, 17; Jiang and Ge 2021). Companions always travel with patients in In this section, “patient traffic volume” is the annual the entire medical flow among departments and pro- traffic volume generated by patients, or the “p” value vide psychological and behavioral assistance to in equations (2) and (3), while the “outpatient visit 8 Z. MALU AND Y. NING Figure 4. The framework of this investigation Figure 4 The framework of this investigation. volume” is the annual number of hospital outpatient Previous studies collected data mainly by manual visits, or the “v” value in equation (1). The corre- field recording of each traveler’s origin and destination sponding investigation steps are illustrated in (Pelletier and Thompson 1960; Delon and Smalley Figure 5. 1970). Due to its deficiencies mentioned above, Although a previous similar study sampled data instead, we used existing data which could be found from only one hospital over six weeks (Delon and in most Chinese hospitals to remove the manual data Smalley 1970), we expected larger sample size. Based collection work, so the method can be used in most on the reasons from Table 4, we identified four large cases and extends the time coverage of data. general hospital samples according to location, Driven by China’s medical policies, the hospital annual outpatient visits, and the number of beds, information system (HIS) is widely used in Chinese and they agreed to participate in the study by pro- general hospitals to record basic patient visit infor- viding informed consent. Then, data covered mation (Hu 2013) with similar forms and contents to one year were collected successfully in three while meet the above requirements, which was confirmed abortively in one. The three valid hospital samples in the three samples. Among the HIS data, the still met the above requirements, and their basic patients’ medical/charge records, indicating the cor- information is presented in Table 5 and Figure 6. respondence between patients’ origin and destina- According to site observations combined with infor- tion departments, could reflect patient flow and be mation from their official websites, the three hospi- used to predict the number of corresponding tals’ outpatient medical flow and department patient trips (Table 6). Similar ideas have been settings were all typical (Table 3, Figure 2). used in hospital management studies (Carrara, JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 9 Figure 5. The investigation framework for patient traffic volume and outpatient visit volume. Kalay, and Novembri 1986; Eastman and Siabiris From Table 7, the basic forms of the three samples’ 1995; Ekholm and Fridqvist 1996; Simeone and original data were the same, and thus, their processing Kalay 2012; Bean, Taylor, and Dobson 2019). of calculating the “v” and “q” values from the raw files Therefore, we obtained relevant data from the was similar. The details were collated in Table 8 and three hospitals’ HISs (Table 7). Table 9, citing Hospital 1 as an example. Table 5. Basic information of “general hospital” samples. Sample No. Hospital 1 Hospital 2 Hospital 3 Location West China Middle China East China (Chongqing) (Yichang) (Yantai) Annual number of outpatient 831,121 189,916 758,527 visits Number of beds 1856 1000 1298 Outpatient building area 28,461 13,328 61,777 (Square meter) (Close to the construction (Lower than the construction (Higher than the construction standard) standard) standard Building layout Centralized pattern Single gallery pattern Street pattern The “east/ west /middle” divisions are from the National Bureau of Statistics of China (2011). 10 Z. MALU AND Y. NING Figure 6. Intermediate floor plans of the outpatient buildings of the three samples. Thus, we determined the annual outpatient visit Based on equation (3), accompanying rate was volume of each sample, which was used as the “v” required other than the calculated “p” values. We verified values in equation (1), and the patient traffic volume whether “visit time (morning/afternoon)” and “location value of each traffic section separately, which were (west/middle/east China)” were the basis for grouping used as the “p” values in equations (2) and (3). and then calculated the results. The investigation was divided into three stages: 1) As “visit time” was likely to be less influential than “location”, we first analyzed the 2.2. Companion traffic volume (c) influence of “visit time” and “specialized department” on accompanying rate using the two-factor variance The “Companion Traffic Volume” was generated by method; 2) from the stage 1 results, we further analyzed patient companions, and the investigation steps are that of “location” and “specialized department” in the illustrated in Figure 7. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 11 Table 6. Use of HIS data in this study. Related data in HIS Role in this investigation Charge/ Item “patient ID,” “time/ Used to distinguish between different Used to calculate the patient traffic medical record of each date” records volume of traffic sections “specialized patient Item “billing The department where clinical doctors department–public department” (mandatory) department” issue examination or treatment orders represent the “specialized department” in traffic sections “specialized department–public department” Item “executing The department where patients are department” examined or treated representing the “public department” in traffic sections “specialized department–public department.” Number of outpatient visits of each specialized department to Equal to patient traffic volume of “each specialized department → this public a certain public department (optional) department” (Could be replaced by the “charge/medical record of each patient”) Number of outpatient visits of each specialized department Equal to patient traffic volume of “registration → each specialized department” (optional) (Could be replaced by the item “billing department” in “charge/medical record of each patient”) Total number of outpatient visits of the hospital (optional) Equal to the “v” value in equation (1). (Could be replaced by the “total number of outpatient visits of each specialized department” or “charge/medical record of each patient”) value of their requirements, which was the stage 3 sample Table 7. Types of data collected from the three samples. size. It was calculated according to the statistical formula, Annual number of outpatient visits of and the result was 1485, which was considered the mini- Charge/ Annual number of each specialized mum standard. Within the planned time, we tried to Data treatment outpatient visits department to a type record of of each certain public expand the sample size and balance the sample distribu- Sample each patient specialized department tion. Totally, 2470 samples were obtained, of which 2443 No. in one year department were valid. Their distributions are illustrated in Table 11. Hospital 1 ✓ ✓ ✓(Ambulatory surgery) Hospital 2 ✓ ✓ ✓(ECG & EEG, For each traffic section, we substituted its “p” value Endoscopy, Nuclear and the “r” value corresponding to its “location” and Medicine, “specialized department” into equation (3) to calculate Ambulatory surgery) Hospital 3 ✓ ✓ ✗ its companion traffic volume, which was the “c” value in equation (2). same way; and 3) grouped the samples from the above 2.3. Inter-department patient traffic frequency (q) analysis and calculated the accompanying average rates For each traffic section separately, we substituted within each group, which were used as the “r” values in the “p” and “c” values into equation (2) to calcu- equation (3). late its annual patient traffic volume (Q). Then, In this process, the patient samples were obtained each traffic section’s “Q” value and corresponding from three sources: on-site observations, on-site question- “v” value were substituted into equation (1) sepa- naire surveys, and online questionnaire surveys, as illu- rately to calculate its inter-department patient traf- strated in Table 10 and Figure 8. Accordingly, combined fic frequency (q). For each hospital sample, the set with the research needs of each stage, we selected the of its traffic sections’ “q” values represented its patient samples. spatial distribution of the patient traffic volume. In stage 1, sample information on “time” and “specia- Finally, we calculated the average “q” value set. lized department” was required, so on-site observations On this basis, we further analyzed their spatial were appropriate. Then, we used G*Power to calculate distribution characteristics through the average the sample size and got the result of “448.” After making value set, identified their differences among hospi- a 20–30% upward float, we collected 580 patient samples tal samples, and clarified the influencing factors at Hospital 1, of which 570 were valid. In stage 2, samples’ other than the “traffic section” through each “q” location coverage as wide as possible were required with- values (Table 12). out “time” information, so on-site and online question- naire surveys were both appropriate. They alternated to balance the sample distribution and expand the sample 3. Results size. The requirements for samples in stage 3 were the 3.1. Accompanying rate same as stage 2, albeit the different purposes, so the samples could be shared. Therefore, we determined the The statistical characteristics of patient samples’ sample size of stages 2 and 3 according to the higher accompanying rates are illustrated in Table 13. 12 Z. MALU AND Y. NING Table 8. Calculation of “v” and “q” values from hospital 1ʹs raw files of HIS data. Raw files Calculation Number of Contents records Data items Annual outpatient visits by specialized 1 Specialized department name, “v” value: sum all the values in it. department annual number of visits of each “p” values of [registration–specialized department]: specialized department directly obtained from it. Annual number of ambulatory surgeries by 1 Specialized department name, “p” values of [ambulatory surgery (public department annual number of ambulatory department)–specialized department]: directly surgeries of each specialized obtained from it. department Charge records with the “executing 392,668 Patient ID, time, billing department “p” values of the other traffic sections: department” referring to Radiology, code, executing department code 1) Pretreat data (Table 9). Ultrasonography, ECG & EEG, Endoscopy, 2) Count the processed files according to the Nuclear Medicine combination of the “billing department” and Charge records with the “executing 690,202 “executing department,” respectively. For example, department” referring to TCM Pharmacy the “p” value between specialized department and Pharmacy A and public department B equals the number of Medical records of outpatients visited Clinical 756,246 Medical record number, time, billing all records with billing department “A” and Lab department code executing department “B.” 3)* Multiplied the count results related to examination department by 2. 1) The “v” value is the annual number of outpatient visits of the hospital, and “q” value means annual inter-department traffic volume of a traffic section generated by patients. 2) * As a patient trip between the specialized department and the examination department is characterized as a round-trip, we multiplied the corresponding count results by 2. For stage 1, a two-factor ANOVA result is illustrated in 3.2. Inter-department patient traffic frequency Table 14, in which the P-value (Sig.) of “specialized The inter-department patient traffic frequency (q) department” was “0.000” < 0.05, indicating that it had values for each traffic section in each hospital sample a significant impact on the “accompanying rate,” while and their average values are illustrated in Table 17. the P-value (Sig.) of “visit time” was “0.090” > 0.05, indi- Based on the “q” values of hospitals 1, 2, and 3 in cating that it had no significant impact on the “accom- Table 17, the coefficients of variation for each traffic panying rate.” In other words, the “visit time (morning/ section among samples are illustrated in Table 18, afternoon)” could be disregarded in the grouping. which were between 6.07% and 141.42%, indicating For stage 2, the results of the two-factor ANOVA are “q” values differed among hospitals. Therefore, we illustrated in Table 15, in which the P-value (Sig.) of ranked the traffic sections within each hospital sample “specialized department” and “location” were both by their “q” values and calculated the coefficients of “0.000” < 0.05, indicating that they both had significant variation of each traffic section’s rankings among the impacts on the “accompanying rate.” In other words, three hospitals. Then, we ranked the 150 traffic sections “department” and “location” should be considered the according to their coefficients of variation. The results grouping standards. are listed in Table 19, in which there were only 15 traffic Finally, in stage 3, we obtained the results of the sections with coefficients of variation greater than 50%. accompanying rate grouping by their “specialized Based on the “average values” in Table 17, we department” and “location” as follows (Table 16), ranked the traffic section’s “q” values (Table 20). which would be used as the “r” values in equation (3). The mean value, standard deviation, and Table 9. Data pretreating for patient traffic volume investigation of hospital 1. Purpose Measure Explanation Step 1: clear Made each record Deleted the “duplicate records” according to their When a patient receives several different duplicate and corresponding to each “patient ID,” “date/time,” “executing examination/treatment items in one abnormal patient trip department,” and “billing department” department, each item generates an records independent charge record in HIS, and they all belong to the same trip of this patient, which were “duplicate records” in this study Cleared the other abnormal Deleted abnormal records with incomplete data Unknown records items and significant abnormal values Step 2: data Made the “department” in Assigned all department codes in original records Some different “department codes” in the original generalization the data corresponding into the department units listed in Table 3 and records belonged to the same department to the department unit marked each piece of record’s “billing units, such as different clinics in the same in space department” and “executing department” in department, which were the same origin/ a new way destination of the inter-department traffic section JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 13 Figure 7. The investigation framework for companion traffic volume. coefficients of variation of these “q” values were there was only one sample in each “location” 0.0361, 0.0688, and 190.77 %, respectively, indicat- group, this impact needed further confirmation. ing that they were highly discrete. Out of 150 In Table 23, the P-value (Sig.) between patient traffic sections, 38 traffic sections, 11 traffic sec- traffic frequency and outpatient patient visit scale tions, and four traffic sections accounted for was “0.818” > 0.05, the P-value (Sig.) between approximately 90%, 50%, and 25% of the patient patient traffic frequency and the number of beds traffic volume, respectively. was “0.225” > 0.05, indicating that both had no According to Table 21, grouping by their public significant correlation. The P-value (Sig.) between (registration) departments, four traffic sections outpatient visits and the number of beds was accounted for more than 50% of “q” values within “0.000” < 0.05, indicating a significant correlation. the group: [Endoscopy–Internal Medicine], [TCM Pharmacy–TCM], [Ambulatory Surgery–Obstetrics 4. Discussion & Gynecology], and [ECG & EEG–Internal Medicine]. This indicated that the related four pub- Patient traffic distribution is affected by many factors, lic department units presented clear directivity. such as medical flow, the proportion of patients with To understand the effect of “location,” “number different diseases, and their preference for diagnosis of beds in the hospital,” and “outpatient visit and treatment mode (Wanyenze et al. 2010; Yang and scale” on the “q” values, we performed Guo 2013; Palmer, Fulop, and Utley 2018; Dhar, Michel, a univariate ANOVA and a correlation analysis on and Kanna 2011). These factors are mainly determined the “q” values of each hospital sample (illustrated by policies, management, and medical philosophy in Table 17 A)–C)). The results are illustrated in (Wanyenze et al. 2010; Belson, Scott, and Overton Tables 22 and 23, respectively. 2010; Vilkko et al. 2021). In China, large general hospi- In Table 22, the P-value (Sig.) of the location tals are generally similar in these aspects; hence, their was “0.041” < 0.05, indicating that it had outpatient traffic distribution is also similar, but with a significant impact on “q” values. However, as some differences. 14 Z. MALU AND Y. NING Table 10. Details of the three patient sample sources. A) Measures Information collected from each “patient” Sample source Measure sample On-site observations Randomly selected about 40 patients in each specialized department and Location, specialized department, Number of recorded their information separately companions (accompanying rate), visit time (morning/afternoon) Questionnaire surveys Randomly distributed questionnaires are illustrated in Figure 8 Location, specialized department, Number of companions (accompanying rate) B) Characteristics Sample source Characteristics On-site observation Questionnaire survey On-site Online Information on “Visiting Available and controllable Unavailable Unavailable time” Information on “Specialized Available and controllable Available but sometimes Available but department” uncontrollable uncontrolled Information on “Location” Available and controllable Available and controllable Available but uncontrolled Coverage of sample Limited to a hospital Limited to several cities Across the “Location” country Investigator and survey The most among the three Somewhere in between The least among time requirements the three Support from hospital Necessary As far as possible Not need administrators 1) In the Internet questionnaire survey, we identified the respondent’s location by their IP address. 2) The locations were recorded as “east,” “middle,” and “west” China. Table 11. Basic information of “patient” samples. Classification standard Number of valid samples Proportion By acquisition source On-site observation from hospital 1 570 23.33% On-site questionnaire surveys from other hospital sites 861 35.24% Online Internet questionnaire surveys 1012 41.42% By sample location West China 717 29.35% East China 1081 44.25% Middle China 645 26.40% Total 2443 100.00% Figure 8. Questionnaire design for the accompanying rate. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 15 Table 12. Methods used in the analysis of the inter-department outpatient traffic frequency results. Objective Methods Clarify the spatial distribution The aggregation of outpatient traffic frequency in Sorted the “q” values of each traffic section and calculated their characteristics of outpatient different traffic sections proportion respectively, and compared them traffic frequency The directivity of patient traffic associated with Grouped traffic sections by public departments, calculated the public department units proportion of their “q” values within the group and verified whether the highest proportion in each group exceeded 50% Identify differences in spatial outpatient traffic distribution among the three hospital Calculated the standard deviations and coefficients of variation samples of each traffic section’s “q” value among the three hospitals Clarify the Influencing factors The effect of “location” on the inter-department Performed a univariate ANOVA on the results of each traffic other than “traffic section” patient traffic frequency (q) values section of each hospital sample The effect of “outpatient visit scale” and “number Performed a correlation analysis on the results of each traffic of beds” on the inter-department patient traffic section of each hospital sample frequency (q) values Table 13. Statistical characteristics of patient samples’ accompanying rate. A) General Average value Standard deviation Coefficients of variation B) Accompanying rate distribution (The total samples) Accompanying rate 0 1 2 3 4 5 6 7 Total The total samples 1.0765 0.9822 91.23% Samples used in stage 1 0.9000 0.8779 97.54% Table 14. Analysis result of the factors of “department” and “time” on accompanying rate by Two-factor ANOVA (exported from SPSS analysis). Tests of Between-Subjects Effects Dependent Variable: accompanying_rate Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 104.346 27 3.865 6.254 .000 Intercept 266.861 1 266.861 431.816 .000 specialized_department 94.228 14 6.731 10.891 .000 time 1.786 1 1.786 2.890 .090 specialized_department * Time 14.440 12 1.203 1.947 .027 Error 334.954 542 .618 Total 901.000 570 Corrected Total 439.300 569 a. R Squared = .238 (Adjusted R Squared = .200) 4.1. General characteristics the main auxiliary basis for the diagnosis; therefore, According to the average “q” values illustrated in clinical labs were visited the most frequently among Table 17 and Table 20, most patient traffic was the examination public departments. Meanwhile, concentrated between a few department units: the specialized departments of internal medicine, [Registration–Internal Medicine], [Pharmacy–Internal surgery, pediatrics, emergency, and obstetrics & Medicine], [Registration–Pediatrics], [Registration– gynecology are the most frequently visited accord- Emergency], [Clinical Lab–Obstetrics & Gynecology], ing to China’s official statistics (National Health [Registration–Surgery], [Pharmacy–Pediatrics], Commission of the People’s Republic of China [Registration–Obstetrics & Gynecology], [Clinical 2019), which also attracted a lot of outpatient Lab–Emergency], [Clinical Lab–Pediatrics], and traffic. [Clinical Lab–Internal Medicine], accounting for A public (or registration) department unit with more approximately 50% of outpatient traffic. than 50% of its outpatients coming from the same Among the above department units, the registra- specialized department unit was considered clear tion unit was the first station all outpatients had to directivity in this study. It reflects the spatial distribu- visit in the outpatient medical flow (Figure 2), which tion of public departments’ service objects. According attracted a lot of outpatient traffic. As most out- to Table 21, four public department units presented patient treatments require patients to take medica- clear directivity: [TCM Pharmacy–TCM], [Ambulatory tions, pharmacy units are visited the most Surgery–Obstetrics & Gynecology], [ECG & EEG– frequently among treatment public departments. Internal Medicine], and [Endoscopy–Internal Similarly, examination reports from clinical labs are Medicine]. This demonstrates that, based on the 16 Z. MALU AND Y. NING Table 15. Analysis result of the factors of “specialized department” and “location” on accompanying rate by Two-factor ANOVA (exported from SPSS analysis). Tests of Between-Subjects Effects Dependent Variable: accompanying_rate Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 445.674 47 9.482 11.884 .000 Intercept 909.874 1 909.874 1140.311 .000 specialized_department 236.863 16 14.804 18.553 .000 location 16.455 2 8.228 10.311 .000 specialized_department * location 152.125 29 5.246 6.574 .000 Error 1911.012 2395 .798 Total 5188.000 2443 Corrected Total 2356.686 2442 a. R Squared = .189 (Adjusted R Squared = .173) Table 16. Accompanying rate of each department and location. Location Department West China Middle China East China Average value Physical Examination Center 2.000 0.455 0.300 0.918 Emergency 2.143 1.474 1.630 1.749 Internal Medicine 0.944 1.167 0.858 0.989 Surgery 0.861 1.600 0.810 1.091 Obstetrics & Gynecology 1.000 1.500 1.024 1.175 Ophthalmology 1.077 1.750 1.611 1.479 Stomatology 1.100 1.083 0.731 0.971 Pediatrics 2.441 1.682 1.804 1.976 E.N.T. 1.222 1.286 0.974 1.161 Dermatology 0.857 0.918 0.964 0.913 Oncology 0.952 3.333 0.696 1.660 TCM 0.886 1.129 0.730 0.915 Rehabilitation Medicine 0.667 0.667 1.031 0.788 Psychiatry 0.667 0.500 0.591 0.586 Infectious Diseases Dept. 0.600 0.000 0.600 0.400 Average value 1.161 1.236 0.957 1.118 treatment characteristics of different diseases, the the sample size, the traffic sections with the coeffi - above public departments mainly served correspond- cients of variation greater than 50% were consid- ing specialized departments. ered significantly different among hospitals. In this Compared with Luo’s (2009, 21) study (Table 1), the way, only 15 out of 150 traffic sections demon- distribution characteristics of traffic frequency strated significant differences, which included between specialized and public departments pre- [Pharmacy–Internal Medicine], [Clinical Lab–Physical sented in this study (Table 17) were similar by visual Examination Center], [Registration–Internal observation. As the “specialized departments” in Medicine], [Ultrasonography–Obstetrics & Table 1 were inpatient while in Table 17 were out- Gynecology], [Pharmacy–TCM], [Registration–TCM], patient, this indicated that the distribution of inter- [Pharmacy–Stomatology], [Radiology–Emergency], department patient traffic volume was largely deter- [Ambulatory Surgery–Obstetrics & Gynecology], mined by the composition of different diseases in the [Clinical Lab–Emergency], [Ultrasonography– population and the common treatment methods, Emergency], [Registration–Pediatrics], [Registration– which were similar in outpatient and inpatient depart- Surgery], [Registration–Obstetrics & Gynecology], ments in China. and [Pharmacy–Emergency]. In the above traffic sections, public departments of pharmacy and clinical lab appeared most fre- 4.2. Differences and variations quently. Although oral medication and examination According to Table 19, the spatial distribution of reports from clinical labs were the most popular outpatient traffic volume was roughly similar but treatment methods and diagnosis bases, the prob- varied slightly across the hospitals to an extent. ability of using these two varied greatly among hos- Each traffic section’s “coefficients of variation” in pitals based on their doctors’ work preferences, Table 19 reflected the difference of the patient hospital operations, and equipment. Meantime, the traffic distribution among hospitals. Considering specialized departments of emergency and internal JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 17 Table 17. Results of inter-department patient traffic frequency. A) Hospital 1 Specialized Public/Support department department Registration TCM Pharmacy Pharmacy Clinical Lab Ambulatory Surgery Radiology Ultrasonography ECG & EEG Room Endoscopy Nuclear Medicine Infectious Diseases Dept. 0.0396 0.0001 0.0295 0.0530 0.0000 0.0060 0.0130 0.0005 0.0005 0.0053 Physical Examination Center 0.0132 0.0000 0.0000 0.2452 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Internal Medicine 0.6083 0.0003 0.6140 0.2782 0.0000 0.1590 0.1053 0.1272 0.0953 0.0732 Surgery 0.2089 0.0001 0.1015 0.0796 0.0085 0.0813 0.0916 0.0058 0.0104 0.0133 Obstetrics & Gynecology 0.1733 0.0000 0.1038 0.2441 0.0080 0.0019 0.0441 0.0228 0.0001 0.0934 Pediatrics 0.2795 0.0000 0.2385 0.1991 0.0000 0.0393 0.0245 0.0092 0.0007 0.0059 Oncology 0.0352 0.0009 0.0239 0.0351 0.0000 0.0050 0.0035 0.0006 0.0002 0.0055 Dermatology 0.1021 0.0000 0.0908 0.0153 0.0040 0.0001 0.0012 0.0000 0.0000 0.0005 Ophthalmology 0.0984 0.0000 0.0748 0.0011 0.0028 0.0004 0.0018 0.0001 0.0000 0.0001 E.N.T. 0.1230 0.0000 0.0956 0.0067 0.0016 0.0067 0.0016 0.0013 0.0002 0.0004 Stomatology 0.1071 0.0000 0.0001 0.0029 0.0027 0.0002 0.0001 0.0027 0.0000 0.0012 TCM 0.0713 0.0358 0.0549 0.0306 0.0003 0.0085 0.0060 0.0039 0.0017 0.0021 Rehabilitation Medicine 0.0247 0.0000 0.0157 0.0034 0.0000 0.0136 0.0028 0.0005 0.0001 0.0003 Psychiatry 0.0046 0.0000 0.0045 0.0008 0.0000 0.0001 0.0000 0.0000 0.0000 0.0001 Emergency 0.3007 0.0001 0.0823 0.3206 0.0053 0.2551 0.1466 0.0117 0.0046 0.0480 B) Hospital 2 Specialized Public/Support department department Registration TCM Pharmacy Pharmacy Clinical Lab Ambulatory Surgery Radiology Ultrasonography ECG & EEG Room Endoscopy Nuclear Medicine Infectious Diseases Dept. 0.0023 0.0000 0.0013 0.0021 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 Physical Examination Center 0.0898 0.0006 0.0054 0.0848 0.0000 0.0003 0.0253 0.0184 0.0007 0.0000 Internal Medicine 0.4090 0.0524 0.2827 0.1271 0.0000 0.0320 0.0148 0.1839 0.0050 0.0000 Surgery 0.2922 0.0119 0.0976 0.0675 0.0246 0.1235 0.0537 0.0033 0.0047 0.0010 Obstetrics & Gynecology 0.2160 0.0117 0.0772 0.2085 0.0995 0.0019 0.1684 0.0263 0.0008 0.0000 Pediatrics 0.2226 0.0004 0.1752 0.1756 0.0000 0.0105 0.0079 0.0129 0.0002 0.0000 Oncology 0.0441 0.0005 0.0097 0.0110 0.0000 0.0034 0.0094 0.0000 0.0002 0.0000 Dermatology 0.1533 0.0221 0.0909 0.0410 0.0035 0.0012 0.0009 0.0000 0.0000 0.0000 Ophthalmology 0.1146 0.0000 0.0554 0.0053 0.0007 0.0019 0.0009 0.0000 0.0000 0.0000 E.N.T. 0.1130 0.0007 0.0498 0.0075 0.0009 0.0182 0.0059 0.0000 0.0003 0.0000 Stomatology 0.0700 0.0000 0.0273 0.0018 0.0000 0.0002 0.0002 0.0000 0.0000 0.0000 TCM 0.2682 0.1895 0.1761 0.0177 0.0000 0.0026 0.0032 0.0000 0.0002 0.0000 Rehabilitation Medicine 0.0162 0.0001 0.0062 0.0004 0.0000 0.0049 0.0005 0.0000 0.0000 0.0000 Psychiatry 0.0103 0.0001 0.0072 0.0009 0.0000 0.0001 0.0001 0.0012 0.0000 0.0000 Emergency 0.2672 0.0025 0.2056 0.1301 0.0112 0.0841 0.0104 0.0677 0.0018 0.0000 C) Hospital 3 Specialized Public/Support department department Registration TCM Pharmacy Pharmacy Clinical Lab Ambulatory Surgery Radiology Ultrasonography ECG & EEG Room Endoscopy Nuclear Medicine Infectious Diseases Dept. 0.0042 0.0000 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Physical Examination Center 0.0538 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Internal Medicine 0.1447 0.0000 0.1145 0.1405 0.0000 0.0401 0.0252 0.0248 0.0220 0.0000 Surgery 0.1199 0.0003 0.0515 0.0421 0.0004 0.0783 0.0438 0.0022 0.0041 0.0000 Obstetrics & Gynecology 0.2029 0.0000 0.0690 0.2359 0.0000 0.0014 0.1822 0.0325 0.0000 0.0000 (Continued) 18 Z. MALU AND Y. NING Table 17. (Continued). Pediatrics 0.3390 0.0000 0.1837 0.2014 0.0000 0.0215 0.0280 0.0135 0.0005 0.0000 Oncology 0.0067 0.0001 0.0056 0.0055 0.0000 0.0017 0.0025 0.0003 0.0001 0.0000 Dermatology 0.0405 0.0001 0.0237 0.0035 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 Ophthalmology 0.1091 0.0000 0.0762 0.0043 0.0000 0.0010 0.0063 0.0036 0.0000 0.0000 E.N.T. 0.0517 0.0001 0.0276 0.0024 0.0000 0.0062 0.0024 0.0005 0.0001 0.0000 Stomatology 0.0301 0.0000 0.0145 0.0012 0.0000 0.0048 0.0013 0.0001 0.0000 0.0000 TCM 0.0174 0.0139 0.0008 0.0009 0.0000 0.0001 0.0003 0.0001 0.0001 0.0000 Rehabilitation Medicine 0.0111 0.0002 0.0088 0.0017 0.0000 0.0093 0.0009 0.0002 0.0000 0.0000 Psychiatry 0.0034 0.0000 0.0048 0.0002 0.0000 0.0001 0.0000 0.0002 0.0000 0.0000 Emergency 0.1780 0.0000 0.1193 0.1388 0.0000 0.1337 0.0493 0.0130 0.0017 0.0000 D) Average values Specialized Public/Support department department Registration TCM Pharmacy Pharmacy Clinical Lab Ambulatory Surgery Radiology Ultrasonography ECG & EEG Room Endoscopy Nuclear Medicine Infectious Diseases Dept. 0.0153 0.0000 0.0103 0.0184 0.0000 0.0020 0.0043 0.0002 0.0002 0.0018 Physical Examination Center 0.0523 0.0002 0.0018 0.1100 0.0000 0.0001 0.0085 0.0062 0.0002 0.0000 Internal Medicine 0.3873 0.0176 0.3371 0.1819 0.0000 0.0770 0.0484 0.1120 0.0408 0.0244 Surgery 0.2070 0.0041 0.0835 0.0631 0.0112 0.0944 0.0630 0.0038 0.0064 0.0048 Obstetrics & Gynecology 0.1974 0.0039 0.0833 0.2295 0.0358 0.0017 0.1316 0.0272 0.0003 0.0311 Pediatrics 0.2804 0.0001 0.1992 0.1920 0.0000 0.0238 0.0201 0.0119 0.0005 0.0020 Oncology 0.0287 0.0005 0.0131 0.0172 0.0000 0.0034 0.0052 0.0003 0.0002 0.0018 Dermatology 0.0986 0.0074 0.0685 0.0199 0.0025 0.0004 0.0007 0.0000 0.0000 0.0002 Ophthalmology 0.1074 0.0000 0.0688 0.0036 0.0012 0.0011 0.0030 0.0012 0.0000 0.0000 E.N.T. 0.0959 0.0003 0.0577 0.0055 0.0008 0.0104 0.0033 0.0006 0.0002 0.0001 Stomatology 0.0691 0.0000 0.0139 0.0019 0.0009 0.0017 0.0005 0.0009 0.0000 0.0004 TCM 0.1190 0.0797 0.0772 0.0164 0.0001 0.0037 0.0032 0.0013 0.0007 0.0007 Rehabilitation Medicine 0.0173 0.0001 0.0102 0.0018 0.0000 0.0093 0.0014 0.0002 0.0000 0.0001 Psychiatry 0.0061 0.0000 0.0055 0.0006 0.0000 0.0001 0.0000 0.0005 0.0000 0.0000 Emergency 0.2487 0.0009 0.1357 0.1965 0.0055 0.1576 0.0688 0.0308 0.0027 0.0160 The rows and columns of the cells represent the origin/destination of the traffic section. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 19 Table 18. Coefficients of variation of each traffic’s inter-department patient traffic frequency among the three samples (%). Public/Support department Specialized TCM Clinical Ambulatory ECG & EEG Nuclear department Registration Pharmacy Pharmacy Lab Surgery Radiology Ultrasonography Room Endoscopy Medicine Infectious Diseases 111.87 103.97 131.95 133.26 / 136.72 141.35 141.42 141.42 141.42 Dept. Physical 59.82 140.78 138.16 92.42 / 132.92 141.27 141.18 137.22 / Examination Center Internal Medicine 49.02 140.01 61.58 37.52 / 75.36 83.53 58.77 96.04 141.42 Surgery 33.98 135.07 27.21 24.73 90.04 21.89 32.68 40.42 44.76 127.37 Obstetrics & 9.06 140.04 17.85 6.64 125.91 13.04 47.23 14.76 120.07 141.42 Gynecology Pediatrics 16.95 121.87 14.08 6.07 / 49.89 43.43 16.37 44.58 141.42 Oncology 55.76 66.01 59.98 74.48 / 40.50 59.50 84.20 46.84 141.42 Dermatology 46.79 139.95 46.21 78.45 71.13 117.91 66.79 71.65 / 141.42 Ophthalmology 6.25 141.42 13.80 50.32 101.01 52.92 78.08 135.98 141.42 141.42 E.N.T. 32.90 116.95 49.11 40.23 80.03 53.35 56.21 90.89 47.52 141.42 Stomatology 45.52 141.42 79.69 37.06 138.00 125.58 101.32 135.16 141.42 141.42 TCM 90.60 98.05 94.90 74.02 141.42 94.42 73.16 133.99 107.93 141.42 Rehabilitation 32.33 72.71 39.22 65.46 / 37.87 70.13 86.54 84.92 141.42 Medicine Psychiatry 49.24 137.20 22.19 44.54 / 6.52 57.96 112.32 141.42 141.42 Emergency 20.82 131.02 38.06 44.70 83.32 45.55 83.30 84.70 49.24 141.42 1) The rows and columns of the cells represent the origin/destination of the traffic section. 2) ”/” means that the average value of this traffic section was “0,” so the denominator of the coefficients of variation was “0,” which was meaningless. Table 19. Results of sorting traffic sections by coefficients of variation of their “sequence number” among the three samples. Rank in order of “q” * Traffic section Hospital 1 Hospital 2 Hospital 3 Coefficients of variation 1 [Pharmacy–Internal Medicine] 1 3 14 95.26% 2 [Clinical Lab–Physical Examination Center] 8 26 100 89.13% 3 [Registration–Internal Medicine] 2 1 8 84.31% 4 [Ultrasonography–Obstetrics & Gynecology] 38 15 6 68.51% 5 [Pharmacy–TCM] 35 12 70 61.15% 6 [Registration–TCM] 34 4 36 59.34% 7 [Pharmacy–Stomatology] 120 39 37 59.18% 8 [Radiology–Emergency] 7 27 11 57.61% 9 [Ambulatory Surgery–Obstetrics & Gynecology] 61 22 114 57.42% 10 [Clinical Lab–Emergency] 3 17 10 57.15% 11 [Ultrasonography–Emergency] 15 55 22 56.88% 12 [Registration–Pediatrics] 5 6 1 54.01% 13 [Registration–Surgery] 11 2 12 53.96% 14 [Registration–Obstetrics & Gynecology] 13 7 3 53.60% 15 [Pharmacy–Emergency] 29 9 13 50.83% 16 [Nuclear Medicine–Obstetrics & Gynecology] 26 112 114 48.83% . . . . . . . . . . . . . . . . . . 150 [TCM Pharmacy–Infectious Diseases Dept.] 114 111 114 1.25% The “Rank in order of ‘q’” in this table represented the rank of the patient traffic frequency of a traffic section among all traffic sections of this hospital. Table 20. Results of ranking traffic sections by patient traffic frequency values. Rank Traffic section Patient traffic frequency Proportion Cumulative proportion 1 [Registration–Internal Medicine] 0.387342 7.16% 7.16% 2 [Pharmacy–Internal Medicine] 0.337060 6.23% 13.39% 3 [Registration–Pediatrics] 0.280353 5.18% 18.57% 4 [Registration–Emergency] 0.248658 4.60% 23.17% 5 [Clinical Lab–Obstetrics & Gynecology] 0.229491 4.24% 27.41% 6 [Registration–Surgery] 0.206988 3.83% 31.24% 7 [Pharmacy–Pediatrics] 0.199168 3.68% 34.92% 8 [Registration–Obstetrics & Gynecology] 0.197402 3.65% 38.57% 9 [Clinical Lab–Emergency] 0.196466 3.63% 42.20% 10 [Clinical Lab–Pediatrics] 0.192035 3.55% 45.75% 11 [Clinical Lab–Internal Medicine] 0.181936 3.36% 49.12% 12 [Radiology–Emergency] 0.157617 2.91% 52.03% . . . . . . . . . . . . . . . 37 [Ambulatory Surgery–Obstetrics & Gynecology] 0.035840 0.66% 89.78% 38 [Nuclear Medicine–Obstetrics & Gynecology] 0.031149 0.58% 90.35% . . . . . . . . . . . . . . . 150 [Nuclear Medicine–Physical Examination Center] 0.0000 0.00% 100.00% Average value 0.0361 / / Standard deviation 0.0688 / / Coefficients of variation 190.77% / / 20 Z. MALU AND Y. NING Table 21. Traffic section with the highest “q” value within each group and their “q” value proportions within the group (grouping all traffic sections by public/support department). Group (Public/support department) The traffic section with the highest “q” value The highest “q” value proportion TCM Pharmacy [TCM Pharmacy–TCM] 69.38% Pharmacy [Pharmacy–Internal Medicine] 28.91% Clinical Lab [Clinical Lab–Obstetrics & Gynecology] 21.68% Ambulatory Surgery [Ambulatory Surgery–Obstetrics & Gynecology] 61.74% Radiology [Radiology–Emergency] 40.74% Ultrasonography [Ultrasonography–Obstetrics & Gynecology] 36.34% ECG & EEG [ECG & EEG–Internal Medicine] 56.82% Endoscopy [Endoscopy–Internal Medicine] 78.25% Nuclear Medicine [Nuclear Medicine–Obstetrics & Gynecology] 37.34% Registration [Registration–Internal Medicine] 20.07% Table 22. Analysis result of the factor of “location (west/middle/east China)” on patient traffic frequency by univariate ANOVA (exported from SPSS analysis). Tests of Between-Subjects Effects Dependent Variable: patient_traffic_frequency Source Type III Sum of Squares df Mean Square F Sig. Corrected Model .038 2 .019 3.215 .041 Intercept .585 1 .585 99.266 .000 location .038 2 .019 3.215 .041 Error 2.635 447 .006 Total 3.258 450 Corrected Total 2.673 449 a. R Squared = .014 (Adjusted R Squared = .010) Table 23. Analysis result of the correlation among “annual outpatient visits,” “patient traffic frequency,” and “number of beds” (exported from SPSS analysis). Correlations patient_traffic_frequency outpatient_visit_scale number_of_beds patient_traffic_frequency Pearson Correlation 1 −.011 .057 Sig. (2-tailed) .818 .225 N 450 450 450 outpatient_visit_scale Pearson Correlation −.011 1 .829** Sig. (2-tailed) .818 .000 N 450 450 450 number_of_beds Pearson Correlation .057 .829** 1 Sig. (2-tailed) .225 .000 N 450 450 450 **. Correlation is significant at the 0.01 level (2-tailed). medicine appeared the most frequently. This might as the “visits-beds ratio” in medical management, be due to the large variation in their proportion in which was considered a reasonable value of 3:1 outpatient visits among the hospitals. (National Health Commission of the People’s According to Table 22, location (west/middle/east Republic of China 2021). China) might be the main factor contributing to the above differences. However, as the sample size was 4.3. Reproducibility of methods limited, this result was not yet conclusive. According to Table 23, the annual outpatient visit scale and In addition to providing the above direct general evi- number of beds in a hospital were proven to have dence, to further support case-specific evidence obtain- no significant effect on the spatial distribution of ing, the reproducibility and convenience of the methods outpatient traffic frequency. This demonstrated that were considered. As the accompanying rate is not the influence of these two factors through “other affected by individual hospital related factors, which ways” could be ignored, which was supposed in does not have to be case-specific, and the method’s the hospital sample selection (Table 4). Meanwhile, reproducibility was primarily considered in “p” value eliminating the influence of the outpatient visit scale investigation. in equation (1) was feasible. Additionally, the number Firstly, due to the reasons stated in the of beds significantly correlated with the number of Introduction, the of department unit types and outpatient visits, which was due to the potential main medical flows are the same among outpatient associations between these two. The ratio of the buildings of large Chinese general hospitals, which outpatient visits and number of beds was mentioned makes the theoretical model and definition JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 21 established in this study applicable to most other based on travel costs (Yang et al. 2018; Silalahi et al. 2020), similar outpatient buildings. Subsequently, we and their model can be applied to outpatient buildings chose the existing hospital data as the research with traffic frequency as the weight. Compared to material and limited the data source to HIS, which Pelletier and Thompson’s study, they further described almost all the hospitals in China use, so this method the road network through the OD matrix and incorpo- did not require too much additional data collection. rated more complex factors into the evaluation model Among the operation records in HIS, the (Jin et al. 2020). Therefore, outpatient buildings can be charge/medical records of patients in China all evaluated by their layout patterns (Shi et al. 2021). include “patient ID,” “time,” “billing department,” To calculate the layout position of each department and “executing department,” which were used as unit in the outpatient building, the quantitative mod- mandatory information for calculating the traffic els used in the facility’s location issues can serve as section “p” values and the hospital “v.” Therefore, references (Yang and Guo 2013). For example, through the method we used to calculate “p” and “v” a model for median-based problems (Ahmadi-Javid, values, presented in Table 6, can also be extended Seyedi, and Syam 2017), layout results can be obtained to other cases. For the same reason, the data pre- to minimize the weighted average distance. medical processing method we used (Table 9) can Researchers are already using these models to calcu- be replicated in other cases. late room layouts in emergency departments (Ma et al. Furthermore, as the original materials used in this 2016), in which the patient flow volume was also method are all digital, the data extraction, data proces- a basic parameter. sing, and calculating the “q” values according to equa- Another way to minimize the average trip distance/ tions (1), (2), and (3) can be programmed into computer time in the outpatient building layout is splitting the program scripts to further improve the convenience. public department with large patient visits and low directivity (such as clinical lab and radiology) into sub- departments, and setting them near specialized 4.4. Implications for application departments with frequent contact. This method has been applied in some constructions of large general The “average values” presented in Table 17 represent the hospitals in China (Su 2013); however, it could signifi - general situation of the spatial distribution of patient cantly increase the traffic burden of staff and take up traffic volume in China’s outpatient buildings, which can more floor area. Therefore, it should be considered be used as direct evidence of outpatient buildings design carefully before adoption. in China without requiring highly precise results or with- The second principle is mainly associated with out access to further information. However, for more local space of corridor. Theoretically, the width of precise and reliable results of a specific case, the differ - the corridor should be directly proportional to the ences among hospitals should be considered, and the “q” traffic volume; however, the width of a corridor in values of the specific case should be obtained. The inves- an outpatient building is generally uniform in dif- tigation methods adopted in this study were considered ferent parts, while it might be superimposed with as convenient and reproducible as possible so that they different traffic sections; therefore, the traffic can be used. In the targeted investigation, the 15 traffic volume distribution is not uniform. Space might sections that exhibited significant differences were the be wasted if corridor widths are designed to most important. match the maximum traffic volume. Thus, in archi- Subsequently, there are two main principles in out- tectural design, the traffic volume of patients super- patient building design: 1) the higher the frequency of imposed in different parts of the same corridor patient traffic between departments, the closer the should be as uniform as possible, mainly deter- traffic distance between them should be (Jiang 2005, mined by the types of departments in the corridor. 29), and 2) the higher a corridor bears patient traffic Furthermore, for corridors whose width was greater frequency (considering all traffic sections within it), the than the traffic demand, rest areas, waiting areas, more spacious it should be (Jiang and Ge 2021). and other facilities could be set up to use the space The first was associated with the outpatient building fully. layout. Consequently, the idea of “minimizing the aver- age trip distance/time of staff” and “considering traffic frequency as the weight of the corresponding traffic 5. Conclusion section” were adopted in evaluating nursing unit effi - ciency in previous studies (Pelletier and Thompson In summary, based on the existing research, this study 1960; Kobus et al. 2000, 138–139), which are also applic- discussed the spatial distribution of patient traffic in out- able to outpatient buildings. Moreover, at the city level, patient buildings of large general hospitals in China by there are similar but more accurate quantitative evalua- quantitatively describing the inter-department patient tion methods, usually called accessibility evaluation. They traffic frequencies within each pair of department units measured the accessibility of city or regional networks and defining “inter-department patient traffic 22 Z. MALU AND Y. NING frequency(q)” as the ratio of the annual number of patient be considered simultaneously. These limitations will trips passing through a traffic section (Q) to the annual be further addressed in follow-up research. number of outpatient visits of this outpatient building (v). Based on the theoretical model and investigating three hospital samples and 2443 patient samples, we obtained Acknowledgments the “q” value set of each hospital sample and their aver- The authors would like to express their gratitude to Dr. Long age values among the three samples (Table 17). Hao, Professor at the School of Architecture and Urban By analyzing these data, we found that the spatial Planning, Chongqing University, for his assistance in organizing distribution of outpatient traffic volume in China was the participation of hospitals 1 and 3 in this study. The authors also thank the reviewers for their valuable comments and roughly similar, with two main characteristics: 1) outpati- suggestions. ent traffic frequency was unevenly distributed in the out- patient building, and out of a total of 150 traffic sections, 38, 11, and four sections accounted for 90%, 50%, and Disclosure statement 25% of the patient traffic, respectively (Table 20), and four public department units presented obvious directivity No potential conflict of interest was reported by the (Table 21). author(s). However, the results varied slightly across the three hospital samples. Out of a total of 150 traffic sections, 15 presented significant differences in “q” value Funding among the three samples, which could be generated This work was supported by the Research Project of Zhejiang by their location difference (Table 22), but more evi- Provincial Education Department under Grant No. dence is required. Furthermore, the outpatient visit Y202146806 and the Humanities and Social Science scale and number of beds in hospital did not signifi - Research Foundation of Zhejiang University of Technology under Grant No. SKY-ZX-20210236 and the National Natural cantly affect the “difference” mentioned above Science Foundation of China under Grant No. 51778074. (Table 23). Therefore, the results of this study support Chinese outpatient building design and research from two perspectives. Notes on contributors Without requiring highly precise results or access to further information, the above results, in particular Malu Zhang received the PH.D. degree in Architecture from Table 17, can be used as direct evidence. For more Chongqing University, Chongqing, China in 2019. She is currently a lecturer at Zhejiang University of Technology, precise and reliable results in the design or research of Hangzhou, China. Her main research area is medical building a specific case, the method in this study, whose reprodu- design. cibility and convenience of operation were fully consid- Ning Yang received the PH.D. degree in Urban and Rural ered, can be used to support the targeted investigation Planning from Chongqing University, Chongqing, China in of the special distribution of patient volume in specific 2020. He is currently a lecturer at Zhejiang University of cases. Technology, Hangzhou, China. His research interests include The “q” value set can be used to evaluate the func- healthy urban design and urban planning, etc.. tional efficiency of the building layout and accurately calculate the location and layout of outpatient depart- ment units and corridor width. Relevant studies in ORCID urban and rural planning and geography can also be Malu Zhang http://orcid.org/0000-0001-9261-7208 used as references for the calculation model. Ning Yang http://orcid.org/0000-0003-2600-7043 In terms of limitations, we obtained only three hospital samples on the premise of meeting the requirements mentioned above, which made it Data availability statement impossible to analyze the influence of location fac- Due to the nature of this research, participants of this study tors on the results fully. As there was no direct did not agree for their data to be shared publicly, so support- impact on the results of this study, we did not dis- ing data is not available. cuss the medical flow branch mentioned in the Introduction, which reduced the replicability of the method in this study to some extent. The main References purpose of this study was to provide evidence for Ahmadi-Javid, A., P. Seyedi, and S. S. Syam. 2017. “A Survey of the architectural design, so we did not present Healthcare Facility Location.” Computers & Operations details on how to use them in the design. 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Journal
Journal of Asian Architecture and Building Engineering – Taylor & Francis
Published: May 4, 2023
Keywords: Outpatient building; patient traffic volume; spatial distribution; inter-department traffic frequency; evidence for architectural design