Study on Sustainable Development Oriented Community Public Hospital in China Based on Optimal Decision Making Model for Environment Renovation

Abstract

Community public hospital provides public health and basic medical services, and their construction environment has an important impact on the health and well-being of the residents. Due to the long construction period of most community hospitals, the population expansion and the change in age structure have led to a mismatch between the needs of patients and the current situation, which is in urgent need of renovation. This paper aims to support the government and hospitals in making decisions by eliminating the blind spots in capital investment and benefit evaluation. In this study, environmental modification design variables are first established, and on this basis, the fuzzy Delphi method is used to understand the willingness for renovation on the part of patients, accompanying personnel, staff, healthcare workers as well as hospital administrators. Besides, the I-S model is used to identify the renovation projects that would maximize user satisfaction. Furthermore, the differences between patients and medical staff are compared. The ODM (Optimal Decision-making) model is used to evaluate the cost investment and benefits, which provides support for the decision-making of government and hospitals. The results clarify the key renovation design variables that can significantly improve user satisfaction and extend them into renovation strategies that can be landed, and identify renovation strategies with larger improvement scores and the range of renovation funds that maximize benefits to promote the sustainable development of community public hospitals. It can be seen that the ODM model can also be well applied to the renovation of community healthcare services. In addition, the reference classification and design variables should be adjusted according to the characteristics of China, taking into consideration the specificity of individual hospitals.

1. Introduction

In recent years, the transformation of the disease spectrum, the demand for the healthcare environment [1,2,3], the transformation of the nursing model [4,5], the improvement of the management level [6], and other factors have led to the need for reconstruction and expansion of many community public hospitals to keep the sustainability [7].

Current studies focus on large hospitals, with research on the following. (1) Fields that could be easily quantified [8,9], such as environmental color, lighting effects, background music, and construction materials. (2) Environmental variables affect the degree of satisfaction of patients and employees [10,11,12]. (3) Key departments include ICU, emergency [13], and operation rooms [14]. But, there are few pieces of research on community public hospitals. Compared to large-scale hospitals with 2000–3000 beds, Chinese community public hospitals might only have 200–300 beds and provide close and quality community medical services. The major responsibility of community-based medical services is to provide basic medical services, including treatment and care for nearby residents on common diseases, frequently occurring diseases, and general chronic diseases. Therefore, the staffing, the selection of medical equipment, and the space design of the hospital should be carried out depending on their characteristics. It is necessary to start targeted research instead of applying the results from large hospitals simply.

On the other hand, China has also proposed requirements for hospital reconstruction. Chinese community public hospitals provide public health and basic medical services, conduct health education, prevention, healthcare, rehabilitation, family planning technical services, and diagnosis and treatment of common and frequently-occurring diseases [15]. With the change in population structure [16] and disease spectrum, China has been promoting the positioning and functional reform policy of community public hospitals. In 2015, the government suggested setting up rehabilitation wards in community public hospitals [17]. Next, the proposal about adding medical and nursing service facilities was submitted in 2019 [18]. Under COVID-19, related departments further proposed strengthening the setting of fever clinics in primary medical and health institutions in 2020 [19]. The above governmental policies or suggestions provide guidance for the future reconstruction of community public hospitals.

After years of exploration and reform, researchers have obtained some results. Firstly, the pre-diagnose system for grading is approaching perfection [20]. It pulls the community public hospitals and the residents closer. The private doctor system enables doctors in community public hospitals to make home visits. Secondly, the internet systems helped establish communication channels between residents and hospitals during the study. Further, according to statistics, the number of community public hospitals steadily rose from 2019 to 2021. These new hospitals had better clinical settings, equipment, and services, making positive progress [21].

However, construction and development problems still not be solved. In particular, the scarce land and environmental resources in the crowded major cities. Many small and medium-sized community public hospitals are facing urgent indoor renovation, including changes in the size, location, and function of departments, as well as the redesign of interior decoration, heating, and ventilation, to improve positioning and functional accuracy, space utilization, and environmental quality [22].

The optimal decision-making (ODM) model could establish the relationship between each decision variable, making the complicated issue clearer using mathematical tools. It showed a broad application and a promising future in solving issues in the medical area. Residents expect much from community public hospitals, but the renovation funds are limited. How to find out a program that can most improve the users’ satisfaction and make a priority list is the primary issue. ODM model can help to find and select the optimal solution. In addition, the ODM model was used for the study of resource allocation in public hospitals, including the optimal budget allocation under the constraint of multi-purpose [23], optimized distributed function system allocation [24], and personnel allocation [25]. Further, it was applied in hospital renovation to evaluate the current environment of hospitals to be renovated and suggest the optimal solution for sustainable renovation [26]. In terms of environment, the model was adopted to study the outdoor environment reform in old communities [27], as well as to guide the cost-efficiency decision in the hospital environmental design [13].

In summary, it has been found that the functions of many community public hospitals located in urban centers in China have been continuously adjusted to meet the basic medical needs of local residents after a period of development. However, the matching degree with the existing space needs to be improved, and there is a large demand for renovation. Therefore, this study first focused on small and medium-sized community public hospitals, filling the gap in research on small and medium-sized hospitals in medical facility studies. And then, this manuscript innovatively establishes the environmental renovation design variables for community public hospitals and introduces the ODM model [27] to the environmental renovation of medical facilities. The design variables and renovation strategies of Dongshan community public hospital are studied and discussed, which will help government investors and hospital managers quickly find renovation objects to improve satisfaction. Meanwhile, the renovation cost estimation and benefit evaluation are conducted to eliminate the investment blind spots and thus support government and hospital decision-making in the environmental design. In addition, the service life of buildings is extended through renovation to promote sustainable development of facilities. Moreover, the limitations of this study and future research topics are proposed.

2. Method

2.1. Research Framework

The study aims to introduce the ODM model for the environmental renovation of community public hospitals in major cities, thereby helping government investors and hospital managers to target renovation objects that could improve satisfaction and evaluate the cost-effectiveness after renovation. The research framework of the paper is shown in Figure 1.

Based on two papers by Roger Ulirich, this paper first created a set of draft design variables for the environmental renovation of community public hospitals that were suitable in China. Through the expert Delphi method, experts such as the government, architects, and hospital management were consulted to jointly modify and form a set of initial design variables for the environmental renovation of community public hospitals. After that, this paper used the optimal decision-making (ODM)model for reference, and DS (Dongshan) community public hospitals to be transformed were selected as experimental objects. At the initial stage of hospital environmental renovation, multiple stakeholders were involved in formulating design variables, including government, architects, hospital management, doctors, nurses, and caregivers. Next, the Fuzzy-Delphi Method (FDM) [28] was used to invite experts and others to rate the importance of environmental design variables to be improved in community public hospitals. Then, this paper used the Importance-Satisfaction (I-S) model [29] to screen out environmental design variables and specific recommendations to be optimized that were valued by patients and medical staff. Meanwhile, researchers calculated the total improvement score of specific transformation strategies and the benefit evaluation of cost and capital investment corresponding to the design variables through the Zero-one integer programming (ZOIP) [30], helping decision-makers identify key transformation targets and use government funds most effectively. Finally, the role and suitability of the ODM model in the environmental renovation of community public hospitals are summarized and discussed.

2.2. The Formulation of Initial Design Variables for Environmental Renovation

This study drew on the three major categories and subcategories of health outcomes (R. Ulirich, 2008) [31] in

Table 1. Classification table of healthcare outcomes based on the paper of R. Ulirich (2008) [31].

Category	Healthcare Outcomes
Patient safety	Reduced hospital-acquired infections; Reduced medical errors; Reduced patient falls.
Other patient outcomes	Reduced pain; Improved patient sleep; Reduced patient stress; Reduced depression; Reduced length of stay; Improved patient privacy and confidentiality; Improved communication with patients & family members; Improved social support; Increased patient satisfaction.
Staff outcomes	Decreased staff injuries; Decreased staff stress; Increased staff effectiveness; Increased staff satisfaction.

to set up the initial environmental renovation design variables framework, as shown in

Table 2. Initial design variables for environmental renovation in community public hospital.

Category of Healthcare Outcomes	Healthcare Outcomes	Category of Environment Design Variables	Environment Design Variables	No.
Patient safety	The reduction of hospital infection	Layout	Reducing the intersection of flow lines between healthy and unhealthy groups (such as independent entrances in public health areas)	A1
			The vaccination space (such as the flow line and space for registration, waiting, and watching of a certain number of people at the same time)	A2
			The reserved space for COVID-19 inspection (including parking space for social vehicles coming for inspection)	A3
			The fever clinic is designed according to the standard of three zones and two channels (community public hospitals with infectious disease department and fever clinic)	A4
			The reasonable arrangement of nurse stations (the convenience for monitoring and nursing patients)	A5
		Air	The optimization of the air filtration and circulation system (such as adding natural ventilation and an auxiliary fresh air system)	B1
			The installation of barriers (such as screens or baffles in the infusion area)	B2
			The corresponding HVAC design for rooms with different functions (for example, the dental air supply should be set as an independent system)	B3
		Water	The optimization of rainwater and sewage diversion system	C1
			The sewage treatment for key departments (such as sewage pretreatment of the infectious department and oil removal treatment of kitchen sewage)	C2
			The reuse system of reclaimed water (such as collection and utilization of roof rainwater)	C3
			The optimization of water facilities (for example, each independent consulting room and treatment room is equipped with a hand wash basin and a water terminal with a non-manual switch)	C4
	The reduction of medical accidents	Noise	The installation of high-performance sound-absorbing materials	D1
			The adoption of a noiseless paging system	D2
		Daylighting and lighting	The site selection and orientation of buildings (for example, some consulting rooms and wards are arranged along a better orientation)	E1
			The increase of the lighting and light intensity in the working area (for example, the working area of the medical staff shall be arranged as close to the external wall as possible)	E2
		Medical contact	The installation of the alcohol hand sanitizer dispenser	F1
			The optimization of contact surface materials (such as ground materials that are easy to clean and skid resistant)	F2
			The proper increase in the single ward	F3
			The optimization of toilet design (the distinguishing between public and private toilets)	F4
			The proper place of handrails (reducing the risk of falls)	F5
		Nursing process	The nursing process adapted to the urgency of the disease (such as severe, acute, convalescent, convalescent, etc.)	G1
Other patient outcomes	The reduction of pain, stress, and depression; sleep promotion; the shortening of hospital stay		The window area (good lighting and view)	H1
			Providing natural or stimulated-natural window vision (such as in operating space, treatment room, and waiting area)	H2
			Outdoor landscape area (such as healing garden and sunken square)	H3
			Indoor landscape area (such as green plants or landscape sketches)	H4
			Public communication space	H5
			Art placement (such as painting, sculpture, etc.)	H6
			Multimedia devices (such as display, music player, etc.)	H7
			Comprehensive rehabilitation site and equipment	H8
	The shortening of medical procedures		The optimization of the outpatient processes and plans (such as guiding and separating the outpatient service, dynamic and static zoning, adding floor registration and charging windows, etc.)	J1
			The optimization of emergency processes and plans (such as distinguishing from emergency streamlines)	J2
	Wayfinding system		The entrance is close to the main corridor	K1
			Improving the availability of map information on the network	K2
			The sign system with logic (such as distinguishing floors by color and setting maps and signs along the way)	K3
			The reasonable layout of the information desk	K4
	Privacy protection		Private reception rooms (e.g., near waiting and reception areas)	L1
			Soft lighting in the consulting room (promoting communication)	L2
			Lounge, day lounge, and waiting room	L3
	Social support (promoting communication with family members and social support)		Family visiting area (such as the entrance of the inpatient department)	M1
			Additional single-room furniture (such as sofa bed, storage cabinet, movable furniture, etc.)	M2
			The increase in consumer services (such as cafeterias, snack bars, etc.)	M3
			The increase in parking space (availability and proximity)	M4
Staff outcomes	The reduction of employee injuries and stress	Function configuration	The optimization of employee dressing and duty space (such as adding personal storage space)	N1
			Additional small assembly space	N2
			Additional staff lounge	N3
			The optimization of the design and configuration in the operating room	N4
			The optimization of the configuration in the care unit (such as the availability of furniture and equipment)	N5
		Streamline optimization	The optimization of dosage streamlines	O1
			The shortening of nursing moving distance	O2
Priority transformation department			Outpatient area (reasonable layout to avoid personnel confusion)	P1
			Emergency department (such as distinguishing between first aid and emergency streamlines)	P2
			Medical technology (such as placing an X-ray room, CT room, MRI room, etc., on the first floor as far as possible)	P3
			Inpatient department (such as optimizing the allocation of nursing stations and beds)	P4
			Maternal and child healthcare (such as area expansion and the independent setting of maternity and child care)	P5
			Logistics (the increase in the staff canteen and other logistics rooms)	P6

, and applied them to the first two columns. Researchers used The nine major categories and specific design variables (R. Ulirich, 2010) [32] of Environmental Design Variables in

Table 3. Classification of environmental design variables in the paper of R. Ulirich (2010) [32].

Category	Environmental Design Variables
Audio Environment	Environmental surface finishes: sound-absorbing vs. sound-reflecting (ceiling, walls, flooring); Equipment noise (alarms, paging, monitors, carts); Acoustic walls; Music
Visual Environment	Windows (natural light & nature views); Siting and orientation of building; Art; Visual stimuli on the ceiling; Gardens and plants; Video games; Internet access; Television.
Safety Enhancement	Location of alcohol gel hand rub dispensers; Location of hand washing sinks; Air quality and ventilation; Staff visual access to patients; Easy-to-clean surfaces; Optimized water systems; Ceiling hoists for lifting patients; Brighter task lighting levels in staff work areas; Levels of interruptions and distractions in medication dispensing and other work areas; Appropriately placed handrails and non-slippery floor coverings.
Wayfinding System	Building entrance; Signage; Floor plan; Information desk; Consumer services (e.g., cafeteria)
Sustainability	Building mass/shape; Building materials; HVAC system; Energy efficiency measures; Waste management; Water treatment system.
Patient Room	Single vs. multi-bed rooms; Private vs. shared toilets; Hard wall partitions vs. curtains (e.g., in EDs, post-anesthesia recovery); Acuity-adaptable single rooms; Same-handed rooms; Convenient control of light, and temperature; Patient choice of art and decorations.
Family Support Spaces	Comfortable waiting rooms (movable seating, quiet, uncrowded); Convenient access to toilets; Access to food; Overnight bed in the patient room; Personal storage; Computer/work space; Internet access; Private meeting room; Gardens; Availability and proximity of parking.
Staff Support Spaces	Quality of workstation; Centralized vs. decentralized nurse stations; Nursing floor layout; The proximity of supplies, storage; The proximity of medications; Quality of spaces for meetings, handoffs, and other communication; Quality and accessibility of break areas; Availability and proximity of parking.
physician Support Spaces	Availability and proximity of parking; The proximity of offices; Quality of break area; Quality and location of workstation; Quality of meeting spaces; Acoustics of operating rooms (noise, distractions, music); The air quality of operating rooms; Task lighting.

for reference. Meanwhile, researchers applied the category of environmental design variables and environmental design variables in Table 2 to establish a framework for initial environmental improvement design variables, targeting the indoor renovation and outdoor landscape parts. After that, researchers invited a team of experts to use the Delphi method to improve the draft framework. In detail, researchers conducted a consultation in the form of questionnaires and unified their opinions. The expert team consisted of 5 people, including 1 scholar from the University College of Architecture, 1 architect, 2 community public hospital managers, and 1 expert from the governmental construction planning department. All of them worked locally and were familiar with the situation of local community public hospitals and had an undergraduate degree or above and experience in community public hospital construction projects from different perspectives, so they were invited to this research (

Table 4. Statistical Table of Expert Information.

Expert No.	Age	Workplace	Profession	Education
Scholar 1	40–50	School of Architecture, Nanjing University of Technology	Professor	Doctor
Architect 1	40–50	Jiangsu Provincial Institute of Architectural Design and Research	Designer	Master
Hospital Manager 1	40–50	President of Nanjing Xiaohang Hospital	Dean	Doctor
Hospital Manager 2	30–40	Dongshan Hospital Infrastructure Department	Director	Undergraduate
Government Official 1	30–40	Nanjing Qinhuai District Health Commission	Director	Undergraduate

).

In the process of unifying the opinions of experts through the Delphi method, the researchers first provided the expert team with the draft framework of environmental optimization design variables and asked them to express their own opinions. Each category and design variable would have three choices, including Agreement, Revision, and Supplementation. After the first round of feedback collection, the researchers improved the draft based on the opinions of experts and then distributed the revised version of the draft to the experts for the second round of opinion collection. This cycle would continue until the experts agreed on the improved design variables, which meant the signed agreement. The Delphi method conducted four rounds of opinion collection and three revisions, which lasted one month from July 2022. In the first round of revision, the excessive repetition of design variables in many different categories and the excessive frequency of some repetition confused scoring experts. Therefore, researchers simplified the number of design variables from 136 to 78, ensuring that each design variable only appeared once and remained in the category with the greatest impact. In the second round of amendments, design variables for medical waste treatment, HVAC, water supply, and drainage were added. The third round mainly ruled on controversial design variables such as self-service terminal logistics systems, intelligent medical information, and the number of elevators. Furthermore, this paper retained design variables with many agreements. Since there was no significant difference, no centralized meeting was held this time. Finally, the initial design variables of environmental transformation, representing the opinions of the expert group, were formed, with a total of 56 design variables in 15 categories, as shown in Table 4 above.

2.3. Case Study

DS Community Public Hospital is located in Nanjing, Jiangsu Province. It was completed in 2010. The current service population is 240,000, which belongs to a secondary hospital with the function of a primary hospital. In 2021, outpatient visits reached 330,000, of which the elderly and children accounted for about 50%. At present, the average number of COVID-19 daily examinations is 2000. See

Table 5. Construction data for 2010.

Service Population (10,000 People)	Building Area (10,000 m2)	Building Floor Area (10,000 m2)	Number of Beds (Beds)	Total Parking Spaces (Nr.)
10	1.86	0.21	200	95

for details.

Nowadays, the hospital has 70 doctors and 88 nurses, mainly providing general medical outpatient service and small general surgery for common and frequently-occurring diseases. There are 4 wards, including 2 rehabilitation wards, 1 comprehensive ward, and 1 medical and nursing center. The current main issues are the overloaded outpatient service, the intersection of flow lines between healthy people and unhealthy groups, the outdoor and indoor air quality, not enough fever clinics, and the powerless atrium air conditioning system, the intersection of flow lines between healthy and unhealthy groups, low indoor and outdoor air quality, lack of fever clinics. The research period was from August 2022 to October 2022.

2.4. ODM Model

2.4.1. FDM (Fuzzy-Delphi Method)

The FDM method used in this paper applied the fuzzy set theory [33] to unified experts’ opinions, which was formed by asking respondents to give three-point estimations (i.e., conservative, expert value, and optimistic value) and triangular fuzzy numbers (TFNs). Then researchers calculated these “group averages” and described them as two TFNs: one was a conservative TFN (C_L, C_M, C_U), and the other was an optimistic TFN (O_L, O_M, O_U), as shown in Figure 2. The intersection of expert fuzzy opinions (gray triangle area) represented the consensus consistency or convergence. Finally, the consensus value (G_i) of the investigation project or question could be obtained by calculating the geometric mean of the conservative value, expert value, and optimistic value [34]. This study collected the opinions of experts from a questionnaire and also created TFN as follows [27]:

$$\mathrm{Triangular} \mathrm{fuzzy} \mathrm{number}\left(\mathrm{TFN}\right)=\left(C^i,G^i,O^i\right)$$

(1)

$$C^i=\min \left(X_{ij}\right)$$

(2)

$${\mathrm{G}}^i=\sqrt[n]{{\displaystyle \prod }_{j=1}^{n}Xij}$$

(3)

$$O^i=\max \left(X_{ij}\right)$$

(4)

where i is the number of factors; j is the number of experts; Cⁱ is the bottom of the evaluation values of factor i by all experts; Oⁱ is the upper limit of the evaluation value of factor i by all experts; Gⁱ is the geometric mean value of evaluation values of factor i; Xij is the evaluation value of factor i by the jth expert.

2.4.2. I-S (Importance-Satisfaction) Model

The Importance-Satisfaction rating is based on the concept that public agencies will maximize overall customer satisfaction by emphasizing improvements in those areas where the level of satisfaction is relatively low and the perceived importance of the service is relatively high [35]. This method is easy to understand and has high popularity, so it is widely used as an important tool for evaluating and judging the satisfaction of the masses. In this model, the horizontal and vertical dimension represents the importance and satisfaction of quality attributes, respectively. The coordinates are divided into four regions using the average of the importance and satisfaction of quality attributes [27], as shown in Figure 3.

To measure the urgency of renovation for each fourth quadrant design variable, the improvement coefficient was calculated using the following formula [36]. The higher the absolute value of the score, the higher the priority of renovation.

$$\mathrm{Improvement} \mathrm{score}=\frac{\left(Satisfaction level-Importment degree\right)}{Importance degree}$$

(5)

2.4.3. ZOIP (Zero-One Integer Programming)

ZOIP represents variable x that only takes a value of 0 or 1. Note that x is called the 0-1 variable, or binary variable. In practical problems, if the 0-1 variable is introduced, the linear programming problems that need to be discussed separately in various situations can be discussed in one problem [30]. The purpose of using ZOIP in this study was to select strategies within the limits of intervention funding, achieving the highest overall score on users and experts aspects. The ZOIP model can be described by Equations (6) and (7).

$$\mathrm{Maximize} \mathrm{Z}={\displaystyle \sum }_{j=1}^n{C}_j{X}_j={\displaystyle \sum }_{j=1}^n\left(i_j+e_j\right)X_j$$

(6)

$${\displaystyle \sum }_{j=1}^n{a}_j{X}_j\le b X_j=0 or 1 \left(j=1,2,\dots n\right)$$

(7)

where 1 indicates it has been selected and 0 indicates it has not been selected. C_j is the comprehensive score of the jth decision variable, including the adaptability scores by users and experts, and a_j is the cost of the jth strategy. i_j is the improvement coefficient, and e_j is the expert adaptability score. Z is the standard total value, and b is a limited fixed budget. x_j is the decision variable [27].

2.4.4. Budget Sensitivity Analysis

Sensitivity analysis [27] is one of the commonly used methods for analyzing uncertainty in the economic evaluation of investment projects. It identifies sensitive factors that have a significant impact on the economic benefits indicators of investment projects from multiple uncertain factors, analyzes and calculates their impact and sensitivity on the economic benefits indicators of the project, and then judges the project’s ability to withstand risks. If a small change in a certain parameter can lead to a significant change in economic performance indicators, it is called a sensitive factor, on the contrary, it is called a non-sensitive factor [37].

In this experiment, due to the decision variable x_j of various indicators being 0 or 1, random number functions and rounding functions can be used to randomly generate 0 or 1 under different renovation indicators. Due to the 27 renovation indicators, there are a total of 702 permutations and combinations. To ensure that the exhaustive method could list all the possible permutations and combinations, a total of 20,000 x_j were generated. And then, the x_i permutation and combination with the highest improvement score could be selected based on the constraints of cost in different cost ranges and recorded its corresponding costs. Finally, the sensitivity was analyzed by the relationship between the budgets and changes in the total improvement score (Figure 4).

3. Results

3.1. FDM Results

Eight experts were invited to participate in the design variables scoring questionnaire [27], including two scholars from the architecture department of the university, two architects, two hospital managers, and two district government officials. Details are shown in

Table 6. Statistical Table of Expert Information.

Expert No.	Age	Company	Position	Education
Scholar 1	40–50	School of Architecture, Nanjing University of Technology	Professor	Doctor
Scholar 2	20–30	School of Architecture, South China University of Technology	Doctoral students	Master
Architect 1	40–50	Jiangsu Provincial Institute of Architectural Design and Research	Designer	Master
Architect 2	30–40	Jiangsu Provincial Institute of Architectural Design and Research	Designer	Master
Hospital Manager 1	40–50	Dean of Nanjing Xiaoxing Hospital	Dean	Doctor
Hospital Manager 2	30–40	Nanjing Dongshan Hospital Infrastructure Department	Director	Undergraduate
Government Official 1	30–40	Nanjing Qinhuai District Health Commission	Director	Undergraduate
Government Official 2	30–40	Nanjing Pukou District Health Commission	Director	Master

. These eight experts participated in community public hospital renovation projects from different perspectives, so they knew and made some contributions to the research field of this project. Eight experts referred to the description of the current situation of DS community public hospitals and rated 56 initial design variables with the most conservative, optimistic, and expert values, with a score range of 1–10 points.

The results of the scoring questionnaire are shown in

Table 7. Result analysis of expert scoring table applying FDM.

Category of Environmental Design Variables	No.	Most Conservative Value		Most Optimistic Value		The Geometric Mean of Expert Value	The Geometric Mean of the Most Conservative Value and the Most Optimistic Value		Gi
		CL	CU	OL	OU	EM	CM	OM
Layout	A1	3	8	8	10	7.96	5.26	9.23	7.25 *
	A2	3	7	8	9	7.20	4.69	8.74	6.72 *
	A3	1	6	1	10	5.65	3.56	6.82	5.19
	A4	1	8	3	10	6.68	4.31	8.04	6.18 *
	A5	2	7	8	10	6.81	3.90	8.85	6.38 *
Air	B1	2	7	6	9	6.79	4.34	8.43	6.39 *
	B2	2	7	6	9	6.40	3.76	8.19	5.96 *
	B3	1	5	7	9	6.36	3.45	8.19	5.82
Water	C1	1	6	6	9	6.40	3.59	7.93	5.76
	C2	2	7	8	10	7.83	5.05	9.11	7.08 *
	C3	1	4	1	9	4.38	2.33	5.48	3.91
	C4	1	7	7	9	6.88	4.09	8.17	6.13 *
Noise	D1	1	6	4	9	5.71	3.22	7.27	5.25
	D2	1	5	1	9	4.83	2.81	5.90	4.36
Daylighting and lighting	E1	2	7	6	10	6.85	4.48	8.11	6.30 *
	E2	1	5	6	10	6.05	3.56	7.59	5.58
Contact	F1	1	5	1	9	4.64	2.71	5.90	4.03
	F2	1	7	7	9	6.33	4.01	8.33	6.17 *
	F3	2	8	4	10	5.86	3.83	7.48	5.66
	F4	2	7	6	9	6.51	4.01	8.01	6.01 *
	F5	1	7	7	9	6.87	4.11	8.47	6.29 *
Nursing process	G1	2	7	7	9	6.96	4.38	8.47	6.43 *
The reduction of pain, stress, and depression; sleep promotion; the shortening of hospital stay	H1	1	7	7	9	6.37	3.92	8.19	6.06 *
	H2	2	7	6	9	6.28	3.9	8.05	5.98 *
	H3	2	7	7	9	6.90	4.37	8.34	6.36 *
	H4	1	7	6	9	5.76	3.50	7.65	5.58
	H5	3	6	6	9	5.71	3.83	7.42	5.63
	H6	1	4	3	9	4.47	2.41	6.45	4.43
	H7	1	6	4	9	5.03	2.87	7.05	4.96
	H8	2	6	6	9	6.09	3.83	7.42	5.63
The shortening of medical procedures	J1	2	7	7	10	7.29	4.34	8.69	6.52 *
	J2	2	7	7	10	7.43	4.76	8.69	6.73 *
Wayfinding system	K1	1	7	6	9	5.97	3.58	7.92	5.75
	K2	1	4	1	9	4.4.	2.50	5.41	3.96
	K3	1	6	3	10	5.44	2.98	7.05	5.02
	K4	1	7	7	10	6.95	4.22	8.44	6.33 *
Privacy protection	L1	1	6	4	10	5.34	2.77	6.72	4.75
	L2	1	6	4	9	5.19	3.11	6.78	4.95
	L3	1	6	4	9	5.16	3.22	6.73	4.96
Social support	M1	1	6	3	9	5.71	3.34	6.07	4.71
	M2	1	5	4	9	5.63	3.04	7.15	5.10
	M3	1	5	3	9	5.17	2.99	6.56	4.78
	M4	1	6	4	10	6.05	3.69	7.61	5.65
Employee function configuration	N1	1	6	1	9	4.83	3.33	6.10	4.72
	N2	1	5	3	9	4.86	2.78	6.69	4.74
	N3	1	6	3	9	5.94	3.71	7.14	5.43
	N4	2	6	6	10	5.82	4.10	7.72	5.91
	N5	2	7	6	10	6.45	4.38	7.77	6.08 *
Employee streamline optimization	O1	2	7	4	9	6.37	3.90	7.77	5.84
	O2	3	6	7	10	6.90	4.54	8.32	6.43 *
Priority transformation department	P1	2	7	7	10	7.02	4.15	8.84	6.50 *
	P2	1	7	5	10	6.95	4.28	8.46	6.37 *
	P3	3	7	7	10	7.14	4.70	8.44	6.57 *
	P4	3	7	7	10	6.85	4.37	8.32	6.35 *
	P5	2	8	5	10	6.61	4.11	7.91	6.01 *
	P6	1	6	5	9	5.81	3.40	7.63	5.52

Note: The * data is the selected data greater than the measured value of 5.96.

. Cronbach’s alpha was applied to measure the reliability of the questionnaire. Due to the internal consistency of all variables exceeding 0.80, the questionnaire was acceptable. The experts considered that C and O were equally important, while E was more important. Therefore the most conservative mean (C_M), the most optimistic mean (O_M), and the geometric mean (E_M) values were weighted at 20%, 20%, and 60%. The measurement value M was calculated by Formulas (8) and (9). The measurement value $\overline{M}$ of G_i was determined as 5.96 as the screening condition. After the operation of FDM, 26 important design variables were selected, and 30 design variables were excluded in the first stage (

Table 8. Design variables screened according to the score statistics table.

Category of Healthcare Outcomes	Healthcare Outcomes	Category of Environment Design Variables	Environment Design Variables
Patient safety	The reduction of hospital infection	Layout	A1. Reducing the intersection of flow lines between healthy people and unhealthy groups; A2. Vaccination space; A4. Fever clinic design according to the standard of three zones and two channels; A5. The reasonable arrangement of nurse stations.
		Air	B1. The optimization of the air filtration and circulation system; B2. The installation of barriers.
		Water	C2. The sewage treatment for key departments; C4. The optimization of water facilities.
	The reduction of medical accidents	Daylighting and lighting	E1. The site selection and the orientation of buildings.
		Contact	F2. The optimization of contact surface materials; F4. The optimization of toilet design; F5. The proper place of handrails.
		Nursing process	G1. The nursing process adapted to the urgency of the disease.
Other patient outcomes	The reduction of pain, stress, and depression; sleep promotion; the shortening of hospital stay		H1. The window area; H2. Providing natural or stimulated-natural window vision; H3. Outdoor landscape area.
	The shortening of medical procedures		J1. The optimization of the outpatient process and plan; J2. The optimization of emergency processes and plans.
	Wayfinding system		K4. The reasonable layout of the information desk.
Staff outcomes	The reduction of employee injuries and stress	Employee function configuration	N5. The optimization of the configuration in the care unit.
		Employee streamline optimization	O2. The shortening of nursing moving distance
Priority transformation department			P1. Outpatient area; P2. Emergency department; P3. Medical technology; P4. Inpatient department; P5. Maternal and child healthcare.

).

$$M_i=\frac{C^i+3E^i+O^i}{5}$$

(8)

$$\overline{M}=\frac{M_1+M_2+\cdots +M_{56}}{56}$$

(9)

3.2. I-S Model Results

The importance-satisfaction questionnaire was formulated in this stage. 75 doctors and nurses from DS Community Public Hospital and 75 visitors familiar with the hospital (including physical examination, maternal and child healthcare, drug dispensing, visits, inpatients, and chronic disease consultants) were invited to participate in the questionnaire survey. Meanwhile, the questionnaire analysis was conducted through the I-S model to find out that the potential environmental transformation factors of DS Community Public Hospital were the priority transformation goals [27]. The questionnaire was distributed in the hospital for a week in October 2022. The 120 valid questionnaires were recovered, including 60 for medical staff and 60 for visitors, with a recovery rate of 80%. The male-female ratio of medical staff is 1:4, and the male-female ratio of patients is 1:2. In terms of age, the average age of medical staff is about 30 years old, and the average age of patients is about 45 years old. SPSS 26.0 was applied to evaluate the reliability of the questionnaire. The results showed that the importance of evaluation design variables and the satisfaction of Cronbach’s Alpha coefficients were higher than 0.8, indicating that the reliability of importance and satisfaction was acceptable. Therefore the questionnaire passed the reliability test.

In this study, importance (I)was the horizontal axis, and satisfaction (S) was the vertical axis. In addition, the importance and average satisfaction of 26 design variables were taken as the starting point to draw a matrix for the ratings of doctors, nurses, and visitors (Figure 5 and Figure 6). After then, it was found that the design variables in each quadrant of medical care and patients were quite different, so statistics and analysis were carried out. The results are listed in

Table 9. Comparison of I-S results between medical staff and patients.

Index	Environmental Design Variables Valued by Staff	Environmental Design Variables Valued by Patients and Accompany
The average value of importance	4.34	4.28
The average value of satisfaction	3.43	3.38
Quadrant I (excellent)	A2 *. The vaccination space; F5 *. The proper place of handrails; G1. The nursing process adapted to the urgency of the disease; J2 *. The optimization of emergency processes and plans; K4 *. The reasonable layout of the information desk; P1. Outpatient service; P2. Emergency; P3. Medical technology; P4. Inpatient department; P5 *. Maternal and child healthcare.	A2. COVID-19 Vaccination; C2 *. The sewage treatment for key departments; E1 *. The site selection and the orientation of buildings; G1. The nursing process adapted to the urgency of the disease; P1. Outpatient service; P2. Emergency; P3. Medical technology; P4. Inpatient department.
Quadrant II (surplus)	A5. The arrangement of nurse stations; E1 *. The site selection and the orientation of buildings; H1. The window area; H2 *. Providing natural or stimulated-natural window vision; F2 *. Contact surface materials; N5. The configuration in the care unit.	A5. The arrangement of nurse stations; H1. The window area; K4 *. The reasonable layout of the information desk; N5. The configuration in the care unit; O2 *. The shortening of nursing moving distance.
Quadrant III (without trouble)	B2. The installation of barriers; B4 *. The optimization of toilet design; H3 *. Outdoor landscape area; O2 *. The shortening of nursing moving distance.	B2. The installation of barriers; C4 *. The optimization of water facilities; F2 *. Contact surface materials; F5 *. The proper place of handrails; H2 *. Providing natural or stimulated-natural window vision; J1 *. The optimization of the outpatient process and plan.
Quadrant IV (to be improved)	A1. Reducing the intersection of flow lines between healthy and unhealthy groups; A4. The fever clinic is designed according to the standard of three zones and two channels; B1. The optimization of the air filtration and circulation system; C2 *. The sewage treatment for key departments; C4 *. The optimization of water facilities; J1 *. The optimization of the outpatient process and plan.	A1. Reducing the intersection of flow lines between healthy and unhealthy groups; A4. The fever clinic is designed according to the standard of three zones and two channels; B1. The optimization of the air filtration and circulation system; F4 *. The optimization of toilet design; H3 *. Outdoor landscape area; J2 *. The optimization of emergency processes and plans; P5. Maternal and child healthcare.

Note: The * represents the difference indicator.

.

As shown in Figure 7 and Figure 8, according to the average score, the importance and satisfaction of medical staff with the current situation are higher than the visiting population. This phenomenon indicates that doctors are more adaptable to the hospital environment compared to patients. If a visitor is a patient, the deviation will be higher due to his/her medical condition. However, the average value shows that the satisfaction of the survey objects to the current environment is lower than the importance, indicating the necessity of environmental transformation.

The quality improvement coefficient was calculated for the two survey populations based on the ten design variables to be improved in the fourth quadrant. First, the emergency transformation needs of the medical and nursing population for the project were arranged in the following order: The optimization of the air filtration and circulation system (−0.356); The optimization of the outpatient process and plan (−0.302); Fever clinic design according to the standard of three zones and two channels (−0.301); Reducing the intersection of flow lines between healthy and unhealthy groups (−0.297); The sewage treatment for key departments (−0.275); and The optimization of water facilities (−0.238). Then, the priority of visitors was as follows: Outdoor landscape area (−0.446); Fever clinic design according to the standard of three zones and two channels (−0.416); Reducing the intersection of flow lines between healthy and unhealthy groups (−0.367); Maternal and child healthcare (−0.306); The optimization of the air filtration and circulation system (−0.284); The optimization of toilet design (−0.273); and The optimization of emergency process and plan (−0.229).

The potential reasons for the differences:

• Some visitors did not experience all the services of the community public hospital, so they were not familiar with the internal management system, such as the sewage treatment system;
• This survey was conducted during COVID-19 epidemic prevention and control. Therefore, nearly half of the medical staff needed to go out to collect nucleic acids. As a result, many services could not be carried out. This situation would affect the views of doctors, nurses, and patients.
• Medical staff easily ignored the inconvenience, such as the noise of children and the horn in the hall, because of the excessive familiarity with the hospital environment.
• Medical staff focused more on the perfection of primary functions, and visitors paid more attention to the visiting experience.
• Due to the different medical conditions of the visitor, their perception of the environment can also be affected.

For each indicator of quadrant IV, the questions collected in the questionnaire are summarized as follows (

Table 10. Statistical table of opinions on environmental design variables to be improved in community public hospitals.

Category of Healthcare Outcomes	Design Variables	Description	Pictures
Patient safety	A1. Reducing the intersection of flow lines between healthy and unhealthy groups.	A1(1). Healthy and unhealthy groups share the same entrance and exit (a); A1(2). Healthy and unhealthy groups share the same elevator with medical staff.
	A4. The design of the fever clinic is according to the standard of three zones and two channels.	A4(1). Many people did not know there was no fever clinic until they arrived at the hospital (b); A4(2). A specially-assigned person at the gate is responsible for informing that the community public hospital has no fever clinic and sets up a vertical board for the hospital; A4(3). The patient strongly wishes hospitals to set up a fever clinic; A4(4). Medical staff believe that it is an indispensable function for hospitals to develop better in the future.
	B1. The optimization of the air filtration and circulation system.	B1(1). The power of the current air conditioning system is too weak to meet the heating requirements of the outpatient hall (c).
	C2. The sewage treatment for key departments.	C2(1). The current sewage in the internal dispensary, treatment room, and inpatient department toilet is not pretreated.	-
	C4. The optimization of water facilities.	C4(1). Some treatment rooms are not equipped with pools (d); C4(2). The water switch is not inductive (e).
	F4. The optimization of toilet design.	F4(1). The door of the department toilet is damaged (f); F4(2). The bathroom in the ward occupies the place with the best lighting (g); F4(3). No toilet for children.
Other patient outcomes	H3. Outdoor landscape area.	H3(1). At present, there is no outdoor landscape garden in the community public hospital, and the ground space is used for parking (h). H3(2). Inpatients are calling for outdoor gardens.
	J1. The optimization of the outpatient process and plan.	J1(1). The registration and medicine-taking queues in the outpatient hall are easy to cross (i).
	J2. The optimization of emergency processes and plans.	J2(1). Small area; J2(2). No distinction between emergency and first-aid streamline (j); J2(3). Narrow surgical queuing area.
Priority transformation department	P5. Maternal and child healthcare.	P5(1). Too small area; P5(2). Mixed healthcare for women and children (k); P5(3). Too noisy corridor area (l).

):

Based on the I-S model, in the three aspects of outcomes, patient safety has the most content, followed by the other results of patients, and medical staff-related content disappeared, indicating that DS community public hospital pay more attention to patients’ healthcare outcomes.

3.3. ZOIP Results

According to the results and analysis of the critical design variables in the fourth quadrant, a centralized discussion was held, and multiple teams were invited to participate in the formulation of transformation strategies [27]. The team consisted of 15 core members, including 3 governmental officials, 3 medical staff representatives, 3 patient representatives, 3 architects, and 3 construction contractors. A centralized meeting was held on 25 October 2022. In this meeting, 10 transformation design variables were further developed into 27 transformation strategies to evaluate the suitability score of the strategies. The strategy suitability score aims to evaluate the suitability of the environmental transformation strategy or the applied technology in community public hospitals, especially in the design, construction, and subsequent maintenance. The evaluation method was the Likert scale of 1–9. The higher the score, the higher the suitability of environmental transformation. After the meeting, the team decided to expand the old building and adjust its functions of the old building to meet future development needs.

In order to ensure the convergence of the data, this study normalized the priority improvement coefficients of users (average value of medical staff and visitors) and the suitability scores of experts, and controlled the scores between 0 and 1 [38]. The final strategy score, as shown in

Table 11. Update strategy and cost estimation.

Design Variables	Strategies	User Data		Expert Data		Total Improvement Score	Cost Estimation
		Improvement Coefficient	Normalized Improvement Coefficient	Evaluation Value	Normalized Suitability Score		Unit: ¥10,0000
A1. Reducing the intersection of flow lines between healthy and unhealthy groups	A1-1. Adjust the position of the health care area and the medical function area to prevent the streamline from crossing, and add a special entrance.	0.332	0.795	8	1	0.898	15.7
A4. The design of the fever clinic according to the standard of three zones and two channels	A4-1. Arrange the fever clinic on the first floor (100 m2).	0.359	1.000	6	0.5	0.750	2.5
	B4-2. Add a special entrance and exit for the fever clinic	0.359	1.000	4	0	0.500	3
B1. The optimization of the air filtration and circulation system	B1-1. Replacement of high-power air conditioning system in the outpatient hall (1000 m2).	0.32	0.705	8	1	0.852	2
	B1-2. Provide a separate air compressor for dentistry (80 m2).	0.32	0.705	5	0.25	0.477	0.2
C2. The sewage treatment for key departments	C2-1. Add an oil separator for oily wastewater treatment	0.256	0.220	8	1	0.610	1
	C2-2. Add primary sewage pretreatment and secondary discharge standard measures for some outpatient clinics and wards.	0.256	0.220	7	0.75	0.485	1
C4. The optimization of water facilities	C4-1. Pipeline laying and additional construction for some consulting rooms without hand basins.	0.256	0.220	8	1	0.610	3
	C4-2. Replace the water switch with an inductive type.	0.256	0.220	4	0	0.110	2
	D4-3. Add a water dispenser in the vaccination area.	0.256	0.220	5	0.25	0.235	0.2
F4. The optimization of toilet design	F4-1. Replace damaged parts of some public toilets.	0.273	0.348	7	0.75	0.549	1
	F4-2 Additional toilets in the healthcare area for children (20 m2).	0.273	0.348	7	0.75	0.549	0.8
	F4-3. Restrooms in 90 wards of the inpatient department are reconstructed from the window to the entrance.	0.273	0.348	8	1	0.674	16.2
H3. Outdoor landscape area	H3-1. Add a healing garden near the entrance of the inpatient department (300 m2).	0.3	0.553	8	1	0.777	6
	H3-2. Add physical exercise equipment and playground (50 m2).	0.3	0.553	6	0.5	0.527	1
	H3-3. Add a landscape sketch in the medical area (100 m2) for road guidance.	0.3	0.553	6	0.5	0.527	0.2
	H3-4. Reduce the ground parking space and expand the underground parking area (1600 m2).	0.3	0.553	8	1	0.777	48
J1. The optimization of the outpatient process and plan	J1-1. Adjust the position of registration and medicine-taking windows in the outpatient hall to solve the problem of cross-queuing.	0.243	0.121	8	1	0.561	0.1
	J1-2. Add a window for quick drug delivery, and shorten the process from registration-consultation-drug delivery to registration-drug delivery for routine dispensing of chronic and habitual diseases (30 m2).	0.243	0.121	6	0.5	0.311	0.15
J2. The optimization of emergency processes and plans	J2-1. Expand the emergency area (40 m2).	0.227	0.000	8	1	0.500	0.8
	J2-2. Enlarge the area of the duty room (30 m2).	0.227	0.000	7	0.75	0.375	0.45
	J2-3. Expand the emergency waiting area(20 m2).	0.227	0.000	8	1	0.500	0.6
	J2-4. Distinguish between emergency and first-aid areas.	0.227	0.000	5	0.25	0.125	0.2
	J2-5. Add emergency pharmacy (15 m2).	0.227	0.000	7	0.75	0.375	0.75
P5. Maternal and child healthcare	P5-1. Expansion (400 m2) and refurbishment (300 m2) of healthcare area for women.	0.253	0.197	8	1	0.598	30.1
	P5-2. Expansion(200 m2) and refurbishment (200 m2) of healthcare area for children	0.253	0.197	8	1	0.598	18.92
	P5-3. Additional activity room(60 m2) in the healthcare area for children.	0.253	0.197	7	0.75	0.473	2.76

Note: The user improvement coefficient is the average value of the improvement coefficient of staff and visitors.

, is the average of user improvement scores and expert fitness scores so that the opinions of users and experts are fair in the decision-making process. The table shows the total improvement score calculation and cost estimation of 27 transformation measures. The highest three design variables were: adjusting the position of the health care area and the medical function area to prevent the streamline from crossing, adding a special entrance, replacing of high-power air conditioning system in the outpatient hall, and adding a healing garden near the entrance of the inpatient department and reduce the ground parking space and expand the underground parking area, as shown in Table 11. All these transformation measures required a 15.863 million yuan fund.

Based on the analysis of renovation strategies by ZOIP model, patient safety still represents the most renovation strategies, followed by the shortening of medical procedures, indicating that these two parts have more renovation contents for the DS community public hospital.

3.4. Budget Sensitivity Analysis

Due to the Chinese national conditions, the government funded the environmental transformation of most community public hospitals. However, the government budget is usually limited, so it is impossible to complete all the strategies required for the transformation in many cases. Therefore, this study continued the sensitivity analysis through ZOIP model calculation to verify the practicability of ODM and the benefits of budget allocation [39]. Furthermore, this study explored the benefits of hospital transformation under different budget plans, providing a reference for future government budget planning [27].

• Step 1: With the budget of 8 million yuan as the benchmark and ± 12.5% as the range, gradually set the change value X of the transformation budget (up to 75%: ¥14 million, down to −75%: ¥2 million).
• Step 2: ZOIP optimization is carried out under different budget constraints to obtain the highest improvement score (S) in each cost range as well as its corresponding cost and update strategy combination (as shown in

  Table 12. Optimal update policy combination selection.

  Budget (¥10,000)	10–20	20–30	30–40	40–50	50–60	60–70	70–80	80–90	90–100	100–110	110–120	120–130	130–140
  A1-1	0	0	1	0	1	1	1	1	1	1	1	1	1
  A4-1	0	1	1	1	1	1	1	1	1	1	1	1	1
  A4-2	1	0	0	1	1	1	1	1	1	1	1	1	1
  B1-1	1	1	1	1	1	1	1	1	1	1	1	1	1
  B1-2	1	1	1	0	1	1	1	1	1	1	1	1	1
  C2-1	1	1	1	1	1	1	1	1	1	1	1	1	1
  C2-2	1	1	1	1	1	1	1	1	1	0	1	1	1
  C4-1	1	1	1	1	1	1	1	1	1	1	1	1	1
  C4-2	1	1	1	1	0	1	1	1	1	1	1	1	1
  C4-3	0	0	1	1	1	1	1	1	1	1	1	1	1
  F4-1	1	0	1	1	1	1	1	1	1	1	1	1	1
  F4-2	1	1	1	1	1	1	1	1	1	1	1	1	1
  F4-3	0	0	0	0	1	0	1	1	1	1	1	1	1
  H3-1	0	1	1	1	1	1	1	1	1	1	1	1	1
  H3-2	1	1	1	1	1	1	1	1	1	1	1	1	1
  H3-3	1	1	1	1	1	1	1	1	1	1	1	1	1
  H3-4	0	0	0	0	0	0	0	0	0	0	0	1	1
  J1-1	0	0	0	1	1	1	1	1	1	1	1	1	1
  J1-2	1	1	0	1	1	1	1	1	1	1	1	1	1
  J2-1	1	0	1	1	1	1	1	1	1	1	1	1	1
  J2-2	1	1	1	1	1	1	1	1	1	1	1	1	1
  J2-3	1	1	1	1	1	1	1	1	1	1	1	1	1
  J2-4	0	1	1	0	1	1	1	1	1	1	1	1	1
  J2-5	0	1	1	1	1	1	0	1	1	1	1	1	1
  P5-1	0	0	0	0	0	0	0	0	1	1	1	0	1
  P5-2	0	0	0	1	0	1	1	1	0	1	1	1	0
  P5-3	1	0	0	1	1	1	1	1	1	1	1	1	1
  Highest improvement score	7.95	7.96	9.83	10.77	12.24	12.27	12.57	12.95	12.95	13.45	13.55	13.72	13.72
  Cost (¥10,000)	19.96	21.85	39.4	48.23	59.61	64.33	79.78	80.53	91.71	108.63	110.63	128.53	139.71

  Note: 1 indicates that the policy is selected.

  ).
• Step 3: Sensitivity analysis can be obtained by plotting the change values of Y and X, as shown in

  Table 13. Sensitivity analysis of budget and score changes.

  Budget (B, ¥10,000)	X = (B-Benchmark Budget)/Benchmark Budget * 100%	Total Improvement Coefficient Score	Y = (S-Basic Score)/Basic Score * 100%
  140	75%	13.72	9.15%
  130	62.5%	13.72	9.15%
  120	50%	13.55	7.8%
  110	37.5%	13.44	6.92%
  100	25%	12.95	3.02%
  90	12.5%	12.95	3.02%
  80 *	0.0%	12.57	0.00%
  70	−12.5%	12.27	−2.39%
  60	−25%	12.24	−2.63%
  50	−37.5%	10.77	−14.32%
  40	−50%	9.83	−21.8%
  30	−62.5%	7.96	−36.67%
  20	−75%	7.95	−36.75%

  Note: * is the benchmark budget.

  and Figure 9.

According to the Figure 9, the crease slope tends to zero when the budget is in the range of 2–3, 6–7, and 13–14 million yuan, indicating that the increasing investment amount in this range will not affect its improvement score. When the budget amount is in the range of 3–6 and 7–13 million yuan, the broken line shows an upward trend, indicating that the more investment amount in this range, the higher the total improvement score. However, the score slope increase is larger when the budget is between 3 million and 6 million yuan, showing better efficiency. Although the improvement score also increases when the budget exceeds 6 million yuan, the budget income representing the governmental investment decreases due to the decline of its slope, which will also cause a certain waste of budget allocation. On the contrary, it also demonstrates that the improvement score will not be significantly reduced, and efficiency can still be guaranteed even if the government reduces part of the budget.

After further comparing the slopes between 3 and 6 million yuan, the slopes between 3 and 4 million yuan are greater than those between 5 and 6 million yuan. Meanwhile, the slopes between 4 and 5 million yuan are relatively the lowest. In other words, the transformation benefit of community public hospitals is the best if the government budget is between 3 to 4 million yuan or 5 to 6 million yuan. The research results can also provide a more effective and flexible budget allocation mechanism for the government to reduce the waste of funds or apply the surplus budget for other more critical livelihood improvement projects.

4. Discussion

The initial framework of environmental renovation design variables for this study was derived from two papers by R. Ulirich. Therefore, the text further discussed the adjustments and conclusions made based on different national contexts compared to the research results of R. Ulirich.

4.1. The Comparison between Table 1 and Table 4

First, three classification categories of R. Ulirich for healthcare outcomes were used, but the priority transformation department classification category was added to clarify the focus of transformation to decision-makers at the beginning of planning.

According to the result comparison between the healthcare outcomes in Table 1 and Table 4, the final patient fall in patient safety does not appear in Table 4. This finding indicates that contemporary China pays less attention to patient falls. The reason for this is that both hospitalized and outpatient patients have a high proportion of family members accompanying or hiring caregivers, making them less likely to fall.

In other patient outcomes, Table 4 increases the importance of pathfinding systems to become an important category, as the importance of pathfinding is relatively high in China. This is a community public hospital in China that generally has multiple departments such as outpatient, emergency, medical technology, hospitalization, logistics, management, and preventive healthcare, usually with a large area. For example, the DS community public hospital with 200 beds this time has a total construction area of 18,000 square meters, so finding a way has become an important issue. Finally, there were no changes in employee outcomes.

4.2. The Comparison between Table 2 and Table 4

First, a comparison was made between the environmental design variables in Table 2 and Table 4. The environmental design variables in Table 2 that were present but not present in Table 4 were music, video games, internet access, visual stimulation on the ceiling, easy control of light and temperature, air quality in the operating room, and patient selection of art and decorative labels. Table 4 had the environmental design variables that Table 2 did not have: there was a part for COVID-19, reducing the flow line intersection between healthy people and non-healthy people, providing rehabilitation sites and equipment, optimizing outpatient and emergency plans and routes, and creating nursing processes that adapt to the urgency of the disease. In medical support, many design variables in Table 2 emphasize improving the quality of various spaces, while the design variables in Table 4 focus on increasing the communication, rest, and private spaces of the users.

4.3. The Comparison between Table 4 and Table 8

Table 4 shows the environmental design variables for community public hospital renovation, and Table 8 shows the environment and DS community public hospitals. The environmental design variables in Table 4 but not in Table 8 are noise, social support for patients, and logistics. Except for the information desk, in the employee support classification, except for the configuration in the nursing unit and the shortening of nursing moving distance, as well as the reservation of COVID-19 detection space, the corresponding HVAC design for rooms with different functions, the optimization of rainwater and sewage separation system, the setting of reclaimed water reuse system, install high-performance sound-absorbing materials, use a noiseless paging system, increase lighting and intensity in the work area, install alcohol hand sanitizer dispensers, appropriately increase single person wards, provide comprehensive rehabilitation facilities and equipment, install multimedia equipment, and increase communication space.

4.4. The Comparison between Table 8 and Table 9

After calculating through the I-S model, key transformation design variables that can significantly improve user satisfaction were selected from the design variables for the environmental renovation of DS community public hospitals in Table 8. According to Table 9, the priority renovation department is the maternal and child health department. Meanwhile, design variables decreased from 26 to 10. The environmental design variables in Table 8 but not in Table 9 were the categories of air, lighting, care processes, routing, and employee support. Besides those, vaccination space, reasonable layout of nurse stations, installation of barriers, optimization of water facilities, optimization of contact surface materials, appropriate placement of handrails, increasing window area, providing natural window views, or simulating nature were also considered.

4.5. Summary

To gain a clearer understanding of the results and changes, the detailed changes in the design variables in each step of the ODM model were statistically analyzed as follows (

Table 14. Statistic table for changes in design variables during the ODM experiment.

Category of Healthcare Outcomes	Healthcare Outcomes	Initial Design Variables	Design Variables Obtained from FDM	Design Variables Obtained from I-S	The Strategies Obtained by ZOIP
Patient safety	The reduction of hospital infection	A1, A2, A3, A4, A5 B1, B2, B3 C1, C2, C3, C4	A1, A2, A4, A5 B1, B2 C2, C4	A1, A4 B1 C2, C4	A1-1, A4-1, A4-2 B1-1, B1-2 C2-1, C2-2, C4-1, C4-2
	The reduction of medical accidents	D1, D2 E1, E2 F1, F2, F3, F4, F5 G1	E1 F2, F4, F5 G1	F4	F4-1, F4-2, F4-3
Other patient outcomes	The reduction of pain, stress, and depression; sleep promotion; the shortening of hospital stay	H1, H2, H3, H4, H5, H6, H7, H8	H1, H2, H3	H3	H3-1, H3-2, H3-3, H3-4
	The shortening of medical procedures	J1, J2	J1, J2	J1, J2	J1-1, J1-2, J2-1, J2-3, J2-4, J2-5
	Wayfinding system	K1, K2, K3, K4	K4	-	-
	Privacy protection	L1, L2, L3	-	-	-
	Social support	M1, M2, M3, M4	-	-	-
Staff outcomes	The reduction of employee injuries and stress	N1, N2, N3, N4, N5 O1, O2	N5, O2	-	-

):

It can be seen that patient safety has the most content in the three aspects of outcomes. Based on the ODM model, the overall design variables of DS community public hospital account for 53.6% of the initial value, and the healthcare outcomes of privacy protection and social support are filtered out. After I-S model calculation, there are only two classifications related to patients, of which patient safety is the most, and the healthcare part has disappeared. In the final renovation strategies, the content ranking is the same as the top two in the I-S model, i.e., the reduction of hospital infection has the highest content, followed by the shortening of medical procedures.

Through the above comparison, community public hospitals located in the center of major cities in China have some special characteristics. First, the community public hospitals in China contain a large and complex range of functions, requiring both healthcare and basic medical care. Based on this situation, it is necessary to separate healthy and non-healthy populations. In addition, the accompanying rate of domestic patients is very high. At present, the main transformation trends include the design variables such as emergency response to the emergence of COVID-19, blocking infection, and reducing the streamlined intersection between healthy and non-healthy people, and the shortening of medical procedures. Therefore, transformers should make changes to the literature results of R. Ulirich based on Chinese characteristics. The uniqueness of the DS community public hospital is the increasing demand for maternal and child health care, physical examinations, and comprehensive outpatient services for chronic diseases aroused by the significant increase in the number of people served and the proportion of elderly and maternal and child patients.

5. Conclusions

This study firstly focuses on small and medium-sized community public hospitals and has made theoretical contributions to the research on the environmental design of small and medium-sized hospitals. Secondly, design variables are created for the internal environmental renovation of small and medium-sized community public hospitals, and empirical research is conducted using ODM models in practice. Blind spots in investment are eliminated to support government and hospital decision-making by selecting key renovation objects and estimating renovation costs and benefits, extending the service life of buildings, and achieving the goal of sustainable development. The conclusions are as follows:

• The initial design variables for the environmental renovation of small and medium-sized community public hospitals created this time is suitable for community public hospitals located in resource-scarce areas in large city centers that require internal transformation.
• Through the FDM method in ODM, environmental design variables suitable for the hospital to be renovated can be selected from environmental renovation design variables through expert scoring. In this experiment, 26 design variables are ultimately selected.
• Through the I-S model in ODM, priority design variables for improvement can be obtained from the user perspective. In this experiment, there are similarities and differences in the treatment of important design variables to be improved between medical staff and patients. Medical staff value optimizing the air filtration and circulation system, designing fever clinics based on the “three zones and two channels” standard, optimizing outpatient process and layout, reducing the intersection of healthy and non-healthy populations, and optimizing sewage treatment and water facilities for key departments. Patients and their families valued seven design variables: optimizing the air filtration and circulation system, designing fever clinics based on the “three zones and two channels” standard, reducing the flow line intersection of healthy and non-healthy people, setting up outdoor landscape areas, optimizing bathroom design, maternal and child health care, and optimizing emergency procedures and layout.
• Through ZOIP in ODM, the total improvement score and cost estimation of specific renovation strategies can be calculated, and the benefits under different budgets can be evaluated. In this experiment, the three items with the highest improvement scores are: 1. Adjusting the position of the health and medical areas to prevent flow lines from crossing and adding a dedicated entrance and exit; 2. Replacing the high-power air conditioning system in the outpatient hall; 3. Adding a healing garden and reducing ground parking spaces near the entrance of the inpatient department, and expanding the underground parking area. In addition, the transformation efficiency of community public hospitals is the best, with government budgets ranging from 3 to 4 million yuan or 5 to 6 million yuan.

Through the development of environmental design variables and the application of ODM models, the final key transformation design variables and corresponding specific transformation strategies, as well as different cost inputs and benefit evaluations, were gradually derived. This study proves the applicability of the ODM model in refurbishing old community public hospitals in China. Although the budgets and conditions vary with projects in community public hospital refurbishment, this study has obtained relatively consistent improvement design variables and scores. Therefore, the results were adaptable.

There are some limitations in this study. Firstly, this study only conducts one case study for optimization. The results and the subsequent implementation and construction of this study still need to be validated. Secondly, the study is conducted during the COVID-19 pandemic, and the prevention and control policies might affect users’ attitudes to various degrees.

In future research, the researchers will still focus on small and medium-sized hospitals and study the key needs for indoor environmental renovation in different small and medium-sized specialized hospitals. In addition, this study has found significant differences in the evaluation of the medical environment between medical staff and patients. Through statistics, there are also significant differences in age composition and the male ratio between these two types of users. Therefore, in the future, researchers can further explore the preferences of users of different genders for environmental design at different age groups from the above aspects in the future.