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From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives.

ABSTRACT: Pain relief is a major concern for patients who have undergone surgery, and it is an eternal pursuit for anesthesiologists. However, postoperative pain management is far from satisfactory, though the past decades have witnessed great progress in the development of novel analgesics and analgesic techniques. A Cochrane systematic review showed that patient-controlled analgesia (PCA) achieved better pain relief and greater patient satisfaction than traditional "on-demand" parenteral analgesia, suggesting that it might be the manner of analgesia implementation that matters for effective postoperative pain management. A wireless intelligent PCA (Wi-PCA) system that incorporated remote monitoring, an intelligent alarm, intelligent analysis and assessment of the PCA equipment, as well as automatically recording and reserving key information functions under a wireless environment was introduced in our department in 2018. The practice showed that the Wi-PCA system significantly reduced the incidence of moderate to severe postoperative pain and relevant adverse effects, shortened hospital stays, and improved patient satisfaction with postoperative pain relief. Nevertheless, for both traditional and Wi-PCA, analgesics are only administered when pain occurs, leaving behind a realm of possibilities for better postoperative pain management. With the rapid development of machinery and deep learning algorithms, artificial intelligence (AI) is changing the mode of clinical decision making. Integrating the big data collected by state-of-the-art monitoring sensors, the Internet of Things and AI algorithms, an AI-assisted PCA (Ai-PCA) may be a promising future direction for postoperative pain management.

SUBMITTER: Wang R

PROVIDER: S-EPMC7326064 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives.

Wang Rui R Wang Shaoshuang S Duan Na N Wang Qiang Q

Frontiers in medicine 20200522

Pain relief is a major concern for patients who have undergone surgery, and it is an eternal pursuit for anesthesiologists. However, postoperative pain management is far from satisfactory, though the past decades have witnessed great progress in the development of novel analgesics and analgesic techniques. A Cochrane systematic review showed that patient-controlled analgesia (PCA) achieved better pain relief and greater patient satisfaction than traditional "on-demand" parenteral analgesia, sugg ...[more]

PMID: 32671076

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Project description:BACKGROUND:Patients are increasingly seeking Web-based symptom checkers to obtain diagnoses. However, little is known about the characteristics of the patients who use these resources, their rationale for use, and whether they find them accurate and useful. OBJECTIVE:The study aimed to examine patients' experiences using an artificial intelligence (AI)-assisted online symptom checker. METHODS:An online survey was administered between March 2, 2018, through March 15, 2018, to US users of the Isabel Symptom Checker within 6 months of their use. User characteristics, experiences of symptom checker use, experiences discussing results with physicians, and prior personal history of experiencing a diagnostic error were collected. RESULTS:A total of 329 usable responses was obtained. The mean respondent age was 48.0 (SD 16.7) years; most were women (230/304, 75.7%) and white (271/304, 89.1%). Patients most commonly used the symptom checker to better understand the causes of their symptoms (232/304, 76.3%), followed by for deciding whether to seek care (101/304, 33.2%) or where (eg, primary or urgent care: 63/304, 20.7%), obtaining medical advice without going to a doctor (48/304, 15.8%), and understanding their diagnoses better (39/304, 12.8%). Most patients reported receiving useful information for their health problems (274/304, 90.1%), with half reporting positive health effects (154/302, 51.0%). Most patients perceived it to be useful as a diagnostic tool (253/301, 84.1%), as a tool providing insights leading them closer to correct diagnoses (231/303, 76.2%), and reported they would use it again (278/304, 91.4%). Patients who discussed findings with their physicians (103/213, 48.4%) more often felt physicians were interested (42/103, 40.8%) than not interested in learning about the tool's results (24/103, 23.3%) and more often felt physicians were open (62/103, 60.2%) than not open (21/103, 20.4%) to discussing the results. Compared with patients who had not previously experienced diagnostic errors (missed or delayed diagnoses: 123/304, 40.5%), patients who had previously experienced diagnostic errors (181/304, 59.5%) were more likely to use the symptom checker to determine where they should seek care (15/123, 12.2% vs 48/181, 26.5%; P=.002), but they less often felt that physicians were interested in discussing the tool's results (20/34, 59% vs 22/69, 32%; P=.04). CONCLUSIONS:Despite ongoing concerns about symptom checker accuracy, a large patient-user group perceived an AI-assisted symptom checker as useful for diagnosis. Formal validation studies evaluating symptom checker accuracy and effectiveness in real-world practice could provide additional useful information about their benefit.

Project description:ImportanceThe use of artificial intelligence (AI) is expanding throughout the field of medicine. In dermatology, researchers are evaluating the potential for direct-to-patient and clinician decision-support AI tools to classify skin lesions. Although AI is poised to change how patients engage in health care, patient perspectives remain poorly understood.ObjectiveTo explore how patients conceptualize AI and perceive the use of AI for skin cancer screening.Design, setting, and participantsA qualitative study using a grounded theory approach to semistructured interview analysis was conducted in general dermatology clinics at the Brigham and Women's Hospital and melanoma clinics at the Dana-Farber Cancer Institute. Forty-eight patients were enrolled. Each interview was independently coded by 2 researchers with interrater reliability measurement; reconciled codes were used to assess code frequency. The study was conducted from May 6 to July 8, 2019.Main outcomes and measuresArtificial intelligence concept, perceived benefits and risks of AI, strengths and weaknesses of AI, AI implementation, response to conflict between human and AI clinical decision-making, and recommendation for or against AI.ResultsOf 48 patients enrolled, 26 participants (54%) were women; mean (SD) age was 53.3 (21.7) years. Sixteen patients (33%) had a history of melanoma, 16 patients (33%) had a history of nonmelanoma skin cancer only, and 16 patients (33%) had no history of skin cancer. Twenty-four patients were interviewed about a direct-to-patient AI tool and 24 patients were interviewed about a clinician decision-support AI tool. Interrater reliability ratings for the 2 coding teams were κ = 0.94 and κ = 0.89. Patients primarily conceptualized AI in terms of cognition. Increased diagnostic speed (29 participants [60%]) and health care access (29 [60%]) were the most commonly perceived benefits of AI for skin cancer screening; increased patient anxiety was the most commonly perceived risk (19 [40%]). Patients perceived both more accurate diagnosis (33 [69%]) and less accurate diagnosis (41 [85%]) to be the greatest strength and weakness of AI, respectively. The dominant theme that emerged was the importance of symbiosis between humans and AI (45 [94%]). Seeking biopsy was the most common response to conflict between human and AI clinical decision-making (32 [67%]). Overall, 36 patients (75%) would recommend AI to family members and friends.Conclusions and relevanceIn this qualitative study, patients appeared to be receptive to the use of AI for skin cancer screening if implemented in a manner that preserves the integrity of the human physician-patient relationship.

Project description:BACKGROUND:This is an updated version of the original Cochrane review published in Issue 4, 2006. Patients may control postoperative pain by self administration of intravenous opioids using devices designed for this purpose (patient controlled analgesia or PCA). A 1992 meta-analysis by Ballantyne et al found a strong patient preference for PCA over non-patient controlled analgesia, but disclosed no differences in analgesic consumption or length of postoperative hospital stay. Although Ballantyne's meta-analysis found that PCA did have a small but statistically significant benefit upon pain intensity, a 2001 review by Walder et al did not find statistically significant differences in pain intensity or pain relief between PCA and groups treated with non-patient controlled analgesia. OBJECTIVES:To evaluate the efficacy and safety of patient controlled intravenous opioid analgesia (termed PCA in this review) versus non-patient controlled opioid analgesia of as-needed opioid analgesia for postoperative pain relief. SEARCH METHODS:We ran the search for the previous review in November 2004. For this update, we searched the Cochrane Central Register of Controlled Trials (CENTRAL 2014, Issue 12), MEDLINE (1966 to 28 January 2015), and EMBASE (1980 to 28 January 2015) for randomized controlled trials (RCTs) in any language, and reference lists of reviews and retrieved articles. SELECTION CRITERIA:We selected RCTs that assessed pain intensity as a primary or secondary outcome. These studies compared PCA without a continuous background infusion with non-patient controlled opioid analgesic regimens. We excluded studies that explicitly stated they involved patients with chronic pain. DATA COLLECTION AND ANALYSIS:Two review authors independently extracted data, which included demographic variables, type of surgery, interventions, efficacy, and adverse events. We graded each included study for methodological quality by assessing risk of bias and employed the GRADE approach to assess the overall quality of the evidence. We performed meta-analysis of outcomes that included pain intensity assessed by a 0 to 100 visual analog scale (VAS), opioid consumption, patient satisfaction, length of stay, and adverse events. MAIN RESULTS:Forty-nine studies with 1725 participants receiving PCA and 1687 participants assigned to a control group met the inclusion criteria. The original review included 55 studies with 2023 patients receiving PCA and 1838 patients assigned to a control group. There were fewer included studies in our updated review due to the revised exclusion criteria. For the primary outcome, participants receiving PCA had lower VAS pain intensity scores versus non-patient controlled analgesia over most time intervals, e.g., scores over 0 to 24 hours were nine points lower (95% confidence interval (CI) -13 to -5, moderate quality evidence) and over 0 to 48 hours were 10 points lower (95% CI -12 to -7, low quality evidence). Among the secondary outcomes, participants were more satisfied with PCA (81% versus 61%, P value = 0.002) and consumed higher amounts of opioids than controls (0 to 24 hours, 7 mg more of intravenous morphine equivalents, 95% CI 1 mg to 13 mg). Those receiving PCA had a higher incidence of pruritus (15% versus 8%, P value = 0.01) but had a similar incidence of other adverse events. There was no difference in the length of hospital stay. AUTHORS' CONCLUSIONS:Since the last version of this review, we have found new studies providing additional information. We reanalyzed the data but the results did not substantially alter any of our previously published conclusions. This review provides moderate to low quality evidence that PCA is an efficacious alternative to non-patient controlled systemic analgesia for postoperative pain control.

Project description:BackgroundThe emergence of artificial intelligence (AI) in medicine has prompted the development of numerous ethical guidelines, while the involvement of patients in the creation of these documents lags behind. As part of the European PROFID project we explore patient perspectives on the ethical implications of AI in care for patients at increased risk of sudden cardiac death (SCD).AimExplore perspectives of patients on the ethical use of AI, particularly in clinical decision-making regarding the implantation of an implantable cardioverter-defibrillator (ICD).MethodsSemi-structured, future scenario-based interviews were conducted among patients who had either an ICD and/or a heart condition with increased risk of SCD in Germany (n = 9) and the Netherlands (n = 15). We used the principles of the European Commission's Ethics Guidelines for Trustworthy AI to structure the interviews.ResultsSix themes arose from the interviews: the ability of AI to rectify human doctors' limitations; the objectivity of data; whether AI can serve as second opinion; AI explainability and patient trust; the importance of the 'human touch'; and the personalization of care. Overall, our results reveal a strong desire among patients for more personalized and patient-centered care in the context of ICD implantation. Participants in our study express significant concerns about the further loss of the 'human touch' in healthcare when AI is introduced in clinical settings. They believe that this aspect of care is currently inadequately recognized in clinical practice. Participants attribute to doctors the responsibility of evaluating AI recommendations for clinical relevance and aligning them with patients' individual contexts and values, in consultation with the patient.ConclusionThe 'human touch' patients exclusively ascribe to human medical practitioners extends beyond sympathy and kindness, and has clinical relevance in medical decision-making. Because this cannot be replaced by AI, we suggest that normative research into the 'right to a human doctor' is needed. Furthermore, policies on patient-centered AI integration in clinical practice should encompass the ethics of everyday practice rather than only principle-based ethics. We suggest that an empirical ethics approach grounded in ethnographic research is exceptionally well-suited to pave the way forward.

Dataset Information

From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives.

Publications

From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives.

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