Application of Plan-Do-Check-Act cycle based on software, hardware, environment, liveware model in preventing infection after endoscopic mucosal resection

Abstract

BACKGROUND

Endoscopic mucosal resection (EMR) is widely used for superficial gastrointestinal tumors but carries a notable risk of surgical site infection (SSI), which impairs recovery and increases morbidity. Existing infection control strategies often lack systematic integration of human, environmental, and procedural factors. The software, hardware, environment, liveware (SHEL) model offers a comprehensive framework to identify such multidimensional risks, while the Plan-Do-Check-Act (PDCA) cycle enables continuous quality improvement. We hypothesized that integrating the SHEL model into a PDCA-based nursing management protocol would significantly reduce post-EMR SSI rates and enhance patient outcomes compared to conventional care.

AIM

To investigate the effect of a SHEL-based PDCA cycle in preventing EMR infection.

METHODS

This study was conducted in Shanxi Bethune Hospital with 140 EMR patients, randomly assigned to control (routine perioperative nursing) or observation (SHEL model-integrated PDCA cycle nursing) groups (n = 70 each). Outcomes included postoperative incisional infection rate, recovery, operating room care quality, and patient satisfaction. Data were analyzed using χ² tests and t-tests.

RESULTS

The incidence of postoperative SSI in the observation group was significantly lower than that in the control group (5.71% vs 18.57%, P = 0.020). Postoperative recovery indicators, including first flatus time (7.34 ± 1.37 hours vs 9.89 ± 1.37 hours, P < 0.001), first defecation time (12.59 ± 2.42 hours vs 17.21 ± 2.44 hours, P < 0.001), first ambulation time (10.01 ± 1.27 hours vs 12.81 ± 1.51 hours, P < 0.001), and hospital stay (7.10 ± 1.66 days vs 12.98 ± 1.80 days, P < 0.001), were all significantly shorter in the observation group. Operating room care quality scores and overall patient satisfaction (84.29% vs 65.71%, P = 0.011) were also significantly higher in the intervention group.

CONCLUSION

The SHEL-based PDCA cycle significantly reduces infection rates, accelerates recovery, and improves nursing quality and patient satisfaction after EMR.

Key Words: Postoperative infection; Endoscopic mucosal resection; Plan-Do-Check-Act cycle; Software, hardware, environment, liveware model; Surgical site infection

Core Tip: This study validated the value of a collaborative intervention based on the Software, hardware, environment, liveware model of an integrated Plan-Do-Check-Act cycle in preventing infections after endoscopic mucosal resection. The findings emphasize the interplay of systematic risk identification and dynamic quality improvement in reducing surgical site infections and accelerating postoperative recovery. The comprehensive model effectively reduced infection rates and facilitated patient recovery, providing a practical tool for standardized perioperative management. This study fills a gap in systemic infection control in endoscopic surgery and highlights the need for risk analysis and iterative optimization in care management.

INTRODUCTION

According to the 2020 Global Cancer Epidemiology Database, approximately 19.3 million new cancer cases are diagnosed globally each year. Colorectal cancer and gastric cancer rank third and fifth in global cancer incidence, respectively, and are both leading causes of cancer death[1]. With the development of endoscopic technology, endoscopic submucosal resection (EMR), as a minimally invasive treatment, has been widely used in the treatment of early gastrointestinal tumors, precancerous lesions, and polyps[2-4]. Compared with traditional open surgery, EMR has advantages such as less trauma, faster recovery, and shorter hospital stay, which can significantly improve the rate of complete resection and avoid complications and mortality risks associated with open surgery. However, surgical site infection (SSI) after EMR, as one of the most common complications, remains a significant challenge in clinical practice[5,6]. SSI not only delays wound healing and exacerbates patient pain, but may also prolong hospital stay, increase medical costs, and even lead to systemic infection, seriously affecting the patient's recovery process and quality of life[7-9]. Currently, routine perioperative infection control measures mostly rely on traditional models such as basic nursing care systems, aseptic operation standards, and environmental cleaning. While these have some effect, they still have limitations in terms of systemic risk identification, continuous quality improvement, and multi-dimensional collaborative management. To prevent postoperative infections after EMR more systematically and accurately, this study introduces a management framework combining the SHEL model and the Plan-Do-Check-Act (PDCA) cycle. The SHEL model comprehensively identifies risk points in the medical process from four dimensions: Software (S), hardware (H), environment (E), and human factors (L), providing a structured analysis tool for infection control[10]. The PDCA cycle, through the continuous improvement mechanism of “PDCA” achieves dynamic optimization and quality improvement of the nursing process[11-13]. The combination of the two can identify infection risks from multiple dimensions and implement and iterate measures through the cyclical mechanism, which is expected to build a more efficient and sustainable infection prevention system. Therefore, this study aims to explore the application effect of the PDCA cycle based on the SHEL model in preventing postoperative infection after EMR. By optimizing the perioperative nursing process and establishing a systematic and structured infection control model, we can reduce the incidence of postoperative infection, accelerate patient recovery, improve nursing quality and patient satisfaction, and provide a management solution that can be used as a reference for clinical practice.

MATERIALS AND METHODS

Baseline information

Total of 140 patients who underwent endoscopic mucosal resection (EMR) in Shanxi Bethune Hospital were selected and were randomly assigned to either a control group or an observation group using a computer-generated random number table, with 70 cases in each group; among them, the control group adopted conventional nursing management, while the observation group implemented the PDCA cycle-based nursing management grounded in the SHEL model, to record and compare the incidence of postoperative incision infection, postoperative recovery, quality of operating room care and patients’ satisfaction were recorded and compared between the two groups. The comparison of baseline data between the two groups of patients revealed no statistically significant differences (P > 0.05), suggesting that the two groups were highly comparable. The detailed data are as follows: In the control group, there were 37 male and 33 female participants; in the observation group, the breakdown was 35 males and 35 females. With regard to age, the control group had a mean age of 46.93 ± 11.14 years, while the observation group had a mean age of 46.64 ± 10. 55 years. In terms of disease type, there were 37 cases of early gastrointestinal tumors and 33 cases of submucosal lesions of the gastrointestinal tract in the control group, and 34 cases of early gastrointestinal tumors and 36 cases of submucosal lesions of the gastrointestinal tract in the observation group. Prior to initiating the research, each enrolled patient received a comprehensive explanation regarding the study’s objectives, methodologies, possible hazards, and anticipated advantages. All participants then voluntarily appended their signatures to an informed consent document. The study protocol has passed the strict review of the Medical Ethics Committee of our hospital and was formally approved.

Inclusion and exclusion standards

Inclusion standards: (1) Patients who receive EMR; (2) Age within the range of 22 years to 75 years; (3) No contraindications to EMR; (4) No psychiatric disease and cognitive dysfunction; and (5) Know all the contents of the study and sign a written informed consent.

Exclusion standards: (1) People with immunodeficiency or susceptibility to infection risk factors; (2) People with malignant tumors, hematological diseases, and hepatic and renal insufficiency; (3) People with coagulation dysfunction or people who had taken antiplatelet drugs, aspirin and other drugs 1 week before the operation; (4) People with incomplete case data and missing key information; and (5) People who did not cooperate with the study.

Methods

Control group: The implementation of perioperative routine nursing management, as follows: (1) Implement basic nursing systems, such as preoperative inform the patient of the surgical process, precautions, during the operation with the doctor routine operation, postoperative monitoring of patients’ vital signs and wound dressing, dietary guidance, and other basic care; (2) To strengthen the knowledge of health care personnel training, and regularly carry out the aseptic operation norms; (3) To optimize the hardware conditions of the operating room, routine maintenance of optimize the hardware conditions of the operating room, routinely maintain the surgical equipment and environmental cleanliness, sterilize the surgical instruments according to the standard, and clean the surgical room daily; and (4) Closely observe the incision condition of the patients after the operation, and deal with the signs of infection such as redness, swelling and pain in a timely manner if they appear.

Observation group: Implement PDCA cycle nursing management based on SHEL model, as follows. First, planning stage (P): Set up a management team consisting of the treatment team leader, the physician in charge, ward nurses and specialized nurses in the operating room, and analyze the potential causes of postoperative infection after endoscopic mucous membrane resection with the SHEL model: (1) Software factors (S): The existing infection control system is imperfect, and training and assessment are not rigorous enough; (2) Hardware factors (H): The layout of surgical instruments is unreasonable, and the maintenance of disinfection equipment is insufficient; (3) Environmental factors (E): The temperature and humidity of the operating room are not well controlled, and the air cleanliness does not meet the standards; and (4) Human factors (liveware): Medical staff have weak awareness of aseptic operation, and the preoperative assessment of patients’ nutritional status and underlying diseases is incomplete. Based on the above reasons, we have developed targeted improvement plans and clarified the responsibilities of each link. Second, implementation phase (D): Based on the potential causes of infection during EMR, take appropriate measures to prevent postoperative infection, including: (1) Improving the infection control system and improving intraoperative operating procedures, such as the classification and placement of surgical instruments, changing gloves and rinsing consumables before abdominal suturing; (2) Conducting specialized training on hand hygiene and aseptic techniques, using lectures, practical demonstrations, and emergency drills to ensure that all personnel master these skills; (3) Optimizing the layout of the operating room and standardizing personnel and material access channels; (4) Regularly maintaining the laminar flow system and disinfection equipment, having professional institutions test dust particles and static pressure differentials annually, replacing high-efficiency filters annually, and cleaning return air vents daily; (5) Dynamically monitor the temperature, humidity and pressure difference in the operating room to ensure the efficient operation of the laminar flow system; (6) Wet cleaning is carried out before and after the operation, the operating table and equipment are cleaned with disinfectant, and medical waste is classified and stored in a sealed manner; (7) Sterile items are managed by a dedicated person, and the packaging, labeling, sterilization effect and registration are checked daily; (8) The patient’s nutritional status and blood glucose level are comprehensively assessed before the operation to ensure that the plasma albumin is ≥ 35 g/L and the blood glucose is stable, and psychological guidance for the patient is strengthened; (9) The medical staff’s awareness of aseptic operation is strengthened, the uniforms are standardized, the movement of people in the operating room is reduced, and the operation time is shortened; and (10) The incision is closely observed after the operation, and the redness and swelling of the incision and exudation are monitored every 0.5 hour to 1 hour, and abnormal situations are dealt with in a timely manner. Third, checking stage (C): Regular checking by the quality control team: Monthly assessment of infection prevention and control theoretical knowledge and operational skills of medical and nursing staff, with the results linked to performance; daily supervision of the implementation of aseptic operation and other intraoperative operational standards, air cleanliness and other environmental monitoring indicators, as well as the patient’s blood glucose, nutritional status and other postoperative indicators. Fourth, acting stage (A): Monthly meetings are held to summarize the deficiencies in the management process, analyze the reasons and designate corrective measures such as adjusting the training methods and increasing the frequency of equipment maintenance; effective measures are incorporated into the routine system, which continues to enter into the next PDCA cycle, realizing the dynamic improvement of the quality of care.

Observation indexes

Incidence of postoperative SSI: Compare the incidence of SSI after EMR in the two groups, and the judgment of infection is strictly based on the hospital infection standard.

Postoperative recovery effect of the two groups of patients: Record and compare the postoperative hospitalization time, the time of the first exhaustion, the time of the first time to get out of bed, and the time of the first defecation of the two groups of patients, so as to assess the postoperative recovery.

Scoring of operating room nursing care quality: Scoring in 4 dimensions, namely, environment management, nursing training, nursing care safety, sterilization and isolation, each dimension is rated on a scale of 0 to 100, with higher scores indicating superior nursing care quality in that particular dimension.

Comparison of nursing satisfaction: Patients were evaluated using the hospital’s own nursing satisfaction questionnaire, which included nursing attitude, nursing skills and other aspects, and was categorized into four grades: Very satisfied, satisfied, neutral, and dissatisfied. The formula for calculating total satisfaction is: (number of very satisfied cases + number of satisfied cases)/total number of cases × 100%.

Statistical analysis

In this research, data normalization and analysis were performed using SPSS 25.0 software. Quantitative data were presented as mean ± SD, with intergroup comparisons conducted via independent samples t-test; categorical data were expressed as n (%), and intergroup comparisons were analyzed using the χ² test. For all statistical analyses, P < 0.05 was considered indicative of statistically significant differences.

RESULTS

Incidence of postoperative incision infection in patients

As shown in Table 1, a significant difference was observed in the incidence of postoperative incision infection between the two groups (χ² = 5.423, P = 0.020). In the observation group, 4 out of 70 patients (5.71%) developed infection, whereas in the control group, 13 out of 70 patients (18.57%) were infected. The incidence of postoperative incision infection was significantly lower in the observation group compared to the control group, indicating that PDCA cycle nursing management based on the SHEL model can effectively reduce the risk of incision infection following EMR.

Table 1 Incidence of postoperative incision infection in two groups of patients, n (%).

Groups	n	Incidence of infection
Observation group	70	4 (5.71)
Control group	70	13 (18.57)
χ2	-	5.423
P value	-	0.020

Postoperative recovery effects of patients in the two groups

As shown in Table 2, patients in the observation group exhibited significantly shorter postoperative recovery times compared to the control group (all P < 0.001). Specifically, first flatus time (7.34 ± 1.37 hours vs 9.89 ± 1.37 hours), first defecation time (12.59 ± 2.42 hours vs 17.21 ± 2.44 hours), first ambulation time (10.01 ± 1.27 hours vs 12.81 ± 1.51 hours), and length of hospital stay (7.10 ± 1.66 days vs 12.98 ± 1.80 days) were all reduced in the intervention group. These results demonstrate that the SHEL model-based PDCA cycle nursing management effectively accelerates postoperative recovery and shortens the recovery cycle.

Table 2 Postoperative recovery outcomes in both groups, mean ± SD.

Groups	n	Time to first flatus (hour)	Time to first defecation (hour)	Time to first bed movement (hour)	Length of stay (day)
Observation group	70	7.34 ± 1.37	12.59 ± 2.42	10.01 ± 1.27	7.10 ± 1.66
Control group	70	9.89 ± 1.37	17.21 ± 2.44	12.81 ± 1.51	12.98 ± 1.80
t	-	10.984	11.222	11.896	20.121
P value	-	< 0.001	< 0.001	< 0.001	< 0.001

Quality scores of operating room care in the two groups

As shown in Table 3, the operating room care quality scores of the observation group were significantly higher than those of the control group across all four dimensions (all P < 0.001). Specifically, the scores for environmental management (93.66 ± 1.36 vs 87.71 ± 1.49), nursing training (92.59 ± 1.10 vs 86.44 ± 1.24), nursing safety (94.26 ± 0.94 vs 86.84 ± 1.10), and sterilization and isolation (95.17 ± 1.23 vs 88.39 ± 0.89) were all superior in the observation group. These results indicate that the SHEL model-based PDCA cycle nursing management can significantly enhance the quality of nursing care within the operating room setting.

Table 3 Quality of care scores in the operating room in both groups, mean ± SD.

Groups	n	Environmental management	Nursing training	Nursing safety	Sterilization and isolation
Observation group	70	93.66 ± 1.36	92.59 ± 1.10	94.26 ± 0.94	95.17 ± 1.23
Control group	70	87.71 ± 1.49	86.44 ± 1.24	86.84 ± 1.10	88.39 ± 0.89
t	-	24.680	31.038	42.844	37.455
P value	-	< 0.001	< 0.001	< 0.001	< 0.001

Satisfaction with nursing care

As shown in Table 4, nursing care satisfaction differed significantly between the observation and control groups (χ² = 6.438, P = 0.011). In the observation group, 38 (54.29%) patients were very satisfied and 21 (30.00%) were satisfied, resulting in an overall satisfaction rate of 84.29%. In contrast, the control group had 28 (40.00%) very satisfied and 18 (25.71%) satisfied patients, with an overall satisfaction rate of 65.71%. The proportion of patients who were very satisfied or satisfied was higher in the observation group, while the proportion with neutral or dissatisfied responses was lower. These findings suggest that the SHEL model-based PDCA cycle nursing management can effectively improve patient acceptance of nursing care.

Table 4 Comparison of patient care satisfaction between the two groups, n (%).

Groups	n	Very satisfied	Satisfied	Neutral	Dissatisfied	Total satisfaction
Observation group	70	38 (54.29)	21 (30.00)	7 (10.00)	4 (5.71)	59 (84.29)
Control group	70	28 (40.00)	18 (25.71)	14 (20.00)	10 (14.29)	46 (65.71)
χ2	-	-	-	-	-	6.438
P value	-	-	-	-	-	0.011

DISCUSSION

As a minimally invasive approach, EMR has found extensive application in addressing numerous illnesses such as superficial gastrointestinal tumors, precancerous lesions, and polyps, by virtue of its advantages of minimal trauma and rapid recovery[14-16]. However, SSIs, as a common complication after EMR and also one of the most common forms of hospital-acquired infections, remain an important challenge to be addressed in clinical practice[17]. The factors leading to SSIs are numerous and complex. Relevant studies have shown that hypothermia in patients during the perioperative period may inhibit the body’s immune function and reduce resistance, thus increasing the potential of infection[18]; the surgical suite, as a place of high incidence of infection, its air quality, particulate matter, and hand hygiene of healthcare personnel can be a potential route of infection. In addition, quality factors such as the environmental management of the operating room and personnel behavioral norms have a significant impact on the incidence of SSIs, and the standardized environmental management and personnel behavior can reduce the occurrence of infection to some degree[19-21]. Apart from the above factors, elevated body mass index, low albumin level, malnutrition, and prolonged operation time can also significantly increase the risk of postoperative infections; such infections not only hinder wound healing and exacerbate patient's pain, but also prolong the recovery period, increase the burden of medical care, and in severe cases, even lead to systemic infections and even death, and other serious consequences[7,8,22].

With the improvement of patients’ health awareness and requirements for medical care, as well as the increasing attention paid to bacterial infections in healthcare organizations, a variety of interventions have been put forward to prevent SSIs in recent years[23]. For example, wound protectants and antimicrobial sutures have been shown to have a preventive effect on SSIs associated with intra-abdominal infections; negative pressure wound therapy prevents SSIs and reduces postoperative wound complications[23]; preoperative oral antibiotic regimens used alongside intravenous antibiotic regimens significantly reduces the incidence of SSIs in intestinal surgeries[24]; and intra-operative interventions have been focused on three main areas: First, the use of soap and antiseptics for skin decontamination; second, the use of barrier means to block microbial invasion into the incision; and third, optimization of the patient's own body functions to promote optimal postoperative recovery. In addition, other interventions for SSI prevention are oriented toward the surgical environment, specifically covering operating room cleaning methods and operating room personnel flow management practices, etc. Reasonable cleaning methods and personnel flow management can effectively reduce the bacterial concentration in the operating room[25]. The SHEL model, as a comprehensive risk analysis framework, finds extensive application in hospital management. The model underscores that medical mistakes are linked not just to the competence and consciousness of physicians and patients, but also to the clinical setting, hospital administration, and various other elements; through the four dimensions of software (S), hardware (H), environment (E), and liveware (L), the SHEL model is able to identify the risk points in the healthcare process, provide a structured perspective for infection prevention and control, and thus improve the efficacy of the management of nosocomial infections[26,27]. The PDCA cycle, as a classic quality improvement tool, effectively improves the standardization and execution of nursing measures through a continuous optimization process of planning, executing, checking, and handling[11]. In gynecological surgery, the PDCA cycle has been shown to significantly enhance the standard of care and patient satisfaction, as well as to lower perioperative anxiety and depression[12]; for patients with sepsis, the PDCA model also improves the clinical staff's adherence to the sepsis bundle and the efficiency of treatment[28]. The combined application is more likely to elevate the standard of hospital management and care. For example, the integration of PDCA cycle management and risk factor-based nursing interventions can effectively lower the detection rate of pathogenic bacteria, the incidence of incision infections, and the frequency of irregular events in the surgical suite, and improve the rate of sterilization compliance[19]. Multidisciplinary team combined with SHEL model, on the other hand, significantly reduced intensive care unit multidrug-resistant organisms infections, and improved the sensory control ability of healthcare workers and the appropriate utilization of antibiotics[29].

In this study, the PDCA cycle integrated with the SHEL model was employed in the perioperative care of EMR patients, and significant results were achieved. The results showed that the incidence of postoperative incision infection in the observation group was lower than that in the control group, the core of which was the comprehensive identification of infection risk from the four dimensions of software, hardware, environment, and personnel through the SHEL model, and combined with the targeted improvement of the PDCA cycle mechanism, which effectively reduced the potential risk of infection; in the observation group, the time to first defecation, the time taken to get out of bed, and the length of hospital stay were all notably shorter than those in the control group. This outcome is directly linked to the effective control of infection. This result is directly connected to the successful management of infection, and the decrease in postoperative infection prevents the postponement of wound healing and the inhibition of gastrointestinal function by inflammation; at the same time, the comprehensive assessment and optimization of patients based on the SHEL model in the preoperative period, and the postoperative nursing care and early activity guidance through the PDCA cycle further shorter the recovery cycle; assessments of nursing care quality and satisfaction with nursing services indicated that the observation group obtained higher scores than the control group, which suggested that the PDCA cycle grounded in the SHEL model not only focuses on the upgrading of hardware facilities and the standardization of environmental management, but also emphasizes the improvement of the software system and the enhancement of the personnel’s competence; by constantly correcting the deficiencies in the nursing care process through the PDCA cycle, it not only improves the standardization and safety of nursing care, but also enhances the patients’ comfort and sense of trust, which in turn enhances the patient’s satisfaction[30]. This research demonstrates that the combined use of the SHEL model and the PDCA cycle can produce a synergistic effect and markedly enhance the standard of nursing care management. The SHEL model provides precise risk identification and improvement targets for the PDCA cycle, while the PDCA cycle provides an efficient implementation and continuous optimization mechanism for the risk control strategies of the SHEL model, thus realizing the control of the risk of postoperative infection after EMR in an all-round way.

CONCLUSION

In summary, the PDCA cycle based on the SHEL model can systematically reduce the risk of postoperative EMR infections, accelerate patients' recovery, and improve the quality of nursing care and patients' satisfaction, which has considerable clinical application value. However, this research is constrained by its single-center design and a relatively small sample size, and its long-term efficacy requires further validation. In the future, we can expand the sample size, carry out multicenter studies, and refine the infection risk factors of different populations by combining with big data analysis, so as to promote the development of this management model in the direction of more individualization and precision.