https://orcid.org/0000-0003-3593-165X
Abstract
Purpose
Artificial intelligence (AI) is increasingly influencing various medical fields, including anesthesiology. The Introduction of artificial intelligent patient-controlled analgesia (Ai-PCA) has been seen as a significant advancement in pain management. However, the adoption and practical application of Ai-PCA by medical staff, particularly in anesthesia and thoracic surgery, have not been extensively studied. This study aimed to investigate the knowledge, attitudes and practices (KAP) among anesthesia and thoracic surgery medical staff toward artificial intelligent patient-controlled analgesia (Ai-PCA).
Participants and Methods
This web-based cross-sectional study was conducted between November 1, 2023 and November 15, 2023 at Jiangsu Cancer Hospital. A self-designed questionnaire was developed to collect demographic information of anesthesia and thoracic surgery medical staff, and to assess their knowledge, attitudes and practices toward Ai-PCA.
Results
A total of 519 valid questionnaires were collected. Among the participants, 278 (53.56%) were female, 497 (95.76%) were employed in the field of anesthesiology, and 188 (36.22%) had participated in Ai-PCA training. The mean knowledge, attitude, and practice scores were 7.8±1.75 (possible range: 0–10), 37.43±4.16 (possible range: 9–45), and 28.38±9.27 (possible range: 9–45), respectively.
Conclusion
The findings revealed that anesthesia and thoracic surgery medical staff have sufficient knowledge, active attitudes, but poor practices toward the Ai-PCA. Comprehensive training programs are needed to improve anesthesia and thoracic surgery medical staff’s practices in this area.
Introduction
The capabilities of artificial intelligence (AI) algorithms extend to machine reasoning and decision-making. In the field of anesthesiology, these AI applications have made significant strides in various areas, including the monitoring of anesthesia depth, anesthesia control, risk prediction, and logistics management.Citation1,Citation2 Within this landscape of AI innovations, artificial intelligent patient-controlled analgesia (Ai-PCA) emerges as an advanced paradigm for pain management.
The utilization of Ai-PCA in postoperative pain management is an undeniable trend.Citation3 In contrast to conventional Patient-Controlled Analgesia (PCA) methods, Ai-PCA presents a range of noteworthy advantages. It allows for precise adjustments in drug infusion, thereby reducing medication wastage. Moreover, Ai-PCA enables the systematic documentation of pain progression and medication utilization, which is pivotal for generating essential healthcare data.Citation3,Citation4 Furthermore, Ai-PCA engenders a heightened sense of control and pain relief for patients, ultimately enhancing overall patient satisfaction.Citation5
Despite these acknowledged potential and benefits associated with Ai-PCA, its practical application in clinical settings remains incomplete. Several studies have reported that although medical workers have expressed interest in the potential benefits of AI-driven models for enhancing their clinical practice, they also always exhibited a certain degree of reluctance in incorporating this tool into their clinical routines.Citation6–8 However, the perception, awareness, and clinical utilization of Ai-PCA among anesthesia and thoracic medical workers, a group that encounters Ai-PCA most frequently in clinical practice, remains largely unknown.
The Knowledge, Attitude, and Practices (KAP) survey serves as a diagnostic research tool within the domain of health literacy, shedding light on a group’s understanding, beliefs, and actions related to a specific subject.Citation9–11 Research on the KAP among anesthesia and thoracic surgery medical staff toward Ai-PCA is essential due to their interactions with this technology in clinical settings. This study is pivotal for identifying gaps in knowledge, as well as levels of resistance or acceptance among these professionals, who play a critical role in the implementation and optimization of Ai-PCA. By gaining insights into their perspectives, it is possible to develop targeted educational and training programs that increase their familiarity and comfort with Ai-PCA. Such initiatives ultimately aim to enhance patient outcomes in pain management and increase operational efficiency in clinical practices.Citation3 Therefore, this study aimed to assess the knowledge, attitudes and practices among anesthesia and thoracic surgery medical staff toward Ai-PCA.
Methods
Study Design and Participants
This cross-sectional survey was conducted between November 1, 2023 and November 15, 2023 at Jiangsu Cancer Hospital among anesthesia and thoracic surgery medical staff. The study was ethically approved by the Ethics Committee of Jiangsu Cancer Hospital (Approval number 043) and informed consent was obtained from the study participants. The inclusion criteria were defined as follows: 1) Clinical anesthesia and thoracic surgery medical staff, including anesthesiologists, surgeons, and nurses; 2) Willing participation in the study and the signing of informed consent. Retired and rehired anesthesia or thoracic surgery medical personnel were excluded from this study, as the experience of retired or rehired personnel might not accurately represent current operational and clinical conditions.
Questionnaire
The Questionnaire was developed with guidance from the “Intelligent Patient-Controlled Pain Management Expert Consensus”,Citation12 and relevant literatures.Citation3,Citation13,Citation14 The initial draft underwent revisions based on feedback from two senior experts specializing in anesthesia and thoracic surgery both holding the title of Chief Physician. Subsequently, a preliminary trial was conducted on a limited scale (n=48), resulting in a Cronbach’s alpha coefficient value of 0.875, indicating good internal consistency.
The final questionnaire was in Chinese and comprised four dimensions: demographic information, knowledge, attitudes, and practices. The demographic information section included 10 items, while the knowledge, attitudes, and practices dimensions had 12, 9, and 9 items, respectively. Knowledge items were scored 1 point for a correct answer and 0 points for incorrect responses. Questions K3 and K4 were designed as trap questions (the questionnaires that choose the same option at the same time will be considered as logically incorrect and will be eliminated) and did not contribute to the total score calculation. Thus the possible score range of knowledge is 0–10. Attitude items were assessed using a five-point Likert scale, with responses ranging from very positive (5 points) to very negative (1 point), resulting in a potential score range of 9–45. Except for question A3 and A6, which featured a reverse scale for negative statements (a-e:1–5), all other attitude questions followed a positive scale (a-e:5–1). The practice items were also scored using a five-point Likert scale, with options ranging from always (5 points) to never (1 point), and this yielded a potential score range of 9–45 (a-e:5–1).
To effectively manage the process of distributing, collecting, and maintaining the quality control of the questionnaires, face-to-face training to four research assistants was conducted. Data collection for this study was facilitated through an online questionnaire hosted on Sojump (http://www.sojump.com). In order to prevent duplicate responses, an IP restriction was implemented, ensuring that each survey could only be completed once per unique IP address. The questionnaire was disseminated using QR codes, patient groups, social networks, and paper-based forms.
Statistical Analysis
STATA 17.0 (Stata Corporation, College Station, TX, USA) was used for statistical analysis. The continuous variables were expressed as mean ± SD, and the categorical variables was expressed as n (%). The continuous variables conformed to a normal distribution were tested by the t-test or ANOVA. In multivariate analysis, 70% of the total score was used as the cut-off value. Pearson correlation was used to analyze the correlation between knowledge, attitudes, and practices. In this analysis, P<0.05 was considered statistically significant.
Results
A total of 519 participants enrolled in this study, among them, 278 (53.56%) were females, with mean age of 37.82±10.17 years and mean work experience of 13.80±10.64 years. 386 (74.37%) were married, 239 (46.05%) had education of college and below, 497 (95.76%) were working in anesthesiology department, 439 (84.59%) were doctors, 184 (35.45%) had a junior occupational title, and 188 (36.22%) had participated in Ai-PCA training ().
Table 1 Demographic Characteristics and KAP Scores
The mean knowledge, attitude, and practice scores were 7.8±1.75 (possible range: 0–10), 37.43±4.16 (possible range: 9–45), and 28.38±9.27 (possible range: 9–45), respectively. Higher knowledge scores were observed in male participants (P=0.042), participants from department of anesthesiology (P=0.001), doctors (P=0.003), senior medical workers (P=0.014), and those have participated in Ai-PCA training (P<0.001). Higher attitude scores were observed in male participants (P=0.018), married (P=0.029), department of anesthesiology (P=0.043), doctors (P=0.008), associate medical workers (P=0.019) and those have participated in Ai-PCA training (P<0.001). Higher practice scores were observed in married participants (P=0.002), non-doctor (P=0.001), and those have participated in Ai-PCA training (P<0.001) ().
The three knowledge items with the highest correctness rates were as follows: “Clinical practice with Ai-PCA should follow the principles of full participation and comprehensive quality management, spanning from preoperative patient education to formal use”. (K12) with 95.57%, “In response to Ai-PCA-related adverse reactions, prompt evaluation and intervention should be carried out”. (K11) with 94.03%, and “During the use of Ai-PCA, close attention should be paid to patient signs, monitoring the operation of Ai-PCA, and analyzing the patient’s pain relief needs”. (K9) with 93.83%. On the contrary, the three items with the lowest correctness rates were “Ai-PCA does not design dosing plans and configure pain relief drugs on its own; it does not automate these processes”. (K6) with 10.21%, “For Ai-PCA ward rounds, ensuring once a week is sufficient”. (K8) with 49.33%, and “Ai-PCA is very safe, and there are no pain relief-related adverse reactions”. (K10) with 70.91% ().
Table 2 Distribution of Knowledge
Most of the participants acknowledged that receiving Ai-PCA training are crucial (95.76% strongly agree or agree) (A1) and confirmed the necessity of discussing problems encountered in Ai-PCA clinical practice with colleagues and the need for solutions (97.11% strongly agree or agree) (A2). However, 35.45% of them thought that learning Ai-PCA expert consensus and updating relevant knowledge is not very important (A3). Moreover, 28.13% of them believe that adverse events such as drowsiness and nausea and vomiting should not receive excessive attention in Ai-PCA clinical practice (A6). Nevertheless, 96.92% believed to varying degrees that all healthcare professionals are an important part of this chain (A8) ().
Table 3 Distribution of Attitudes
Participants exhibited diverse practices and corresponding frequencies. Specifically, 33.91% of participants occasionally proactively sought Ai-PCA relevant knowledge (P1). Additionally, 28.71% frequently provided in-hospital education to patients (P2), while 24.86% never adjusted the number of visits in a timely manner based on Ai-PCA usage (P3). Furthermore, 29.87% frequently initiated surveys on patients’ analgesic needs (P4), and a substantial 65.32% displayed a high frequency of concern for suspected analgesia-related adverse reactions in patients (P6). Moreover, 35.07% frequently inquired about patients’ satisfaction with analgesia (P8). A majority of 52.02% either always or often summarized the Ai-PCA analgesic experience and applied it to their subsequent analgesic practices (P9) ().
Table 4 Distribution of Practices
Correlation analysis showed that significant positive correlations were found between knowledge and attitude (r=0.318, P<0.001), as well as between attitude and practice (r=0.185, P<0.001) ().
Table 5 Pearson’s Analysis
Multivariate logistic regression showed that attitudes (OR=1.086, 95% CI: 1.037–1.137, P<0.001), non-physician occupations (OR=1.938, 95% CI: 1.16–3.238, P=0.012), and never participated in Ai-PCA training (OR=0.353, 95% CI: 0.24–0.519, P<0.001) were independently associated with proactive practice ().
Table 6 Multivariate Analyses of Practices
Discussion
The findings revealed that anesthesia and thoracic surgery medical staff have sufficient knowledge, active attitudes, but poor practices toward the Ai-PCA.
These findings indicate a certain level of preparedness and willingness among the staff to embrace Ai-PCA as a valuable tool in clinical practice. However, to further enhance clinical practice in this context, it is imperative to focus on bridging the gap between knowledge and practice.Citation15,Citation16 This can be achieved through tailored training programs and continuous education to actively encourage and empower medical staff to translate their knowledge and positive attitudes into more proactive and effective practices.Citation17,Citation18 Additionally, fostering interdisciplinary collaboration and communication among staff members can be instrumental in promoting a seamless integration of Ai-PCA into clinical settings, thereby optimizing patient care and outcomes.Citation19
The influence of various demographic factors, including gender, department, title, job type, participation in Ai-PCA training, and marital status, was evident in the differences in knowledge, attitude, and practice scores among participants. These findings underscore the importance of tailoring strategies to address these variations and foster a more consistent and proactive adoption of Ai-PCA in clinical practice. Multivariate logistic regression further revealed that attitude, non-physician occupations, and participation in Ai-PCA training independently associated with practices. It’s also noteworthy that the correlation between knowledge and practice was found to be non-significant, suggesting that possessing knowledge alone may not necessarily translate into active practice. As such, interventions should focus on enhancing not only knowledge but also attitude alignment and the active engagement of medical staff.Citation20,Citation21 This can be achieved through targeted training programs, interdisciplinary collaboration, and structured initiatives that account for the diverse backgrounds and roles of healthcare professionals, ultimately promoting the optimal integration of Ai-PCA and improving patient care outcomes.Citation22
The distribution of knowledge among medical staff regarding Ai-PCA reveals a combination of correct understanding and misconceptions. While there is a strong grasp of certain key aspects, such as Ai-PCA’s integration of IoT and AI for intelligent pain relief and the importance of close attention to patient signs during its use, there are notable deficiencies in understanding, such as the misconception that Ai-PCA does not design dosing plans and configure pain relief drugs on its own. These findings highlight a need to address specific knowledge gaps and dispel misconceptions through targeted educational initiatives and training programs.Citation23,Citation24 Furthermore, it is essential to emphasize the holistic approach to Ai-PCA implementation, covering aspects from preoperative patient education to formal use, as a means of improving clinical practice.Citation25 Initiatives should focus on enhancing staff knowledge, fostering a culture of continuous learning, and promoting comprehensive quality management, with the ultimate goal of optimizing patient care and ensuring the safe and effective use of Ai-PCA in clinical settings.Citation26
The distribution of attitudes among medical staff regarding Ai-PCA reveals a mix of positive and negative perceptions. While a substantial portion of the staff recognizes the importance of personnel receiving Ai-PCA training, is willing to collaborate with peers in problem-solving, and acknowledges the advantages of Ai-PCA in data feedback and patient satisfaction, there are noticeable deficiencies in attitude, such as the belief that learning Ai-PCA expert consensus and updating relevant knowledge is not very important and downplaying the significance of addressing adverse events. These findings underscore the need for initiatives to foster a more positive and informed attitude towards Ai-PCA in clinical practice. To improve clinical practice, it is essential to prioritize ongoing education and training, emphasizing the value of staying updated with expert consensus.Citation27,Citation28 Additionally, there should be a focus on raising awareness about the importance of addressing adverse events, as patient safety is paramount. Encouraging a culture of open communication and collaboration among healthcare workers can further enhance the collective approach to patient care and clinical application quality, aligning with the principle of full participation. Recognizing the vast potential of Ai-PCA in enhancing healthcare quality control and pain relief quality should be a driving force in promoting a more optimistic and proactive attitude toward its use.Citation29–31
The distribution of practices among medical staff in the context of Ai-PCA implementation reveals a varied landscape, with some areas exhibiting proactive behaviors while others demonstrate room for improvement. Initiatives to enhance clinical practice should target deficiencies and capitalize on existing strengths. To address these issues, a multifaceted approach is recommended. Firstly, the promotion of active knowledge acquisition through various channels, including continuous training, reading relevant literature, and fostering communication among healthcare workers, should be encouraged. Secondly, the frequency of in-house education for patients should be increased, focusing on dispelling misconceptions about medications and providing comprehensive preoperative education.Citation32 Thirdly, practices should be adjusted promptly based on Ai-PCA usage, with particular emphasis on inquiring about patient pain relief needs during rounds and actively responding to backend data analysis. Monitoring and reporting of suspected pain relief-related adverse reactions, coupled with case Discussions during morning meetings, can enhance patient safety. Additionally, the regular assessment of patient satisfaction and the systematic application of pain relief experiences to subsequent practices should be institutionalized.Citation33
This study had limitations. First, it is a single-center study conducted at Jiangsu Cancer Hospital, which might restrict the generalizability of the findings to a wider population of anesthesia and thoracic surgery medical staff. Moreover, the study relies on self-reported data gathered through a questionnaire, which is susceptible to response bias and may not always accurately represent the actual knowledge and behavior of the participants.
In conclusion, anesthesia and thoracic surgery medical staff have sufficient knowledge, active attitudes, and poor practices toward the Ai-PCA. Implementing comprehensive training programs can bridge the gap between knowledge and practice.
Abbreviations
AI, Artificial intelligence; Ai-PCA, artificial intelligent patient-controlled analgesia; KAP, knowledge, attitude, and practice.
Ethics Approval and Consent to Participate
This work has been carried out in accordance with the Declaration of Helsinki (2000) of the World Medical Association. This study was approved by Ethics Committee of Jiangsu Cancer Hospital[2023-043], and all participants provided written informed consent.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare that they have no competing interests for this work.
Data Sharing Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Additional information
Funding
References
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