Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

BackgroundEvidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.Challenges with the three components of EBPClinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.AI assistance for the three components of EBPAI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.ConclusionThis review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare

‘I was young, I wanted to return to sport, and re-ruptured my ACL’ – young active female patients’ voices on the experience of sustaining an ACL re-rupture, a qualitative study

publishedVersio

Generalized Joint Hypermobility and Specific Knee Laxity: Aspects of influence on the Anterior Cruciate Ligament Injured Knee

Injury to the anterior cruciate ligament (ACL) is one of the most serious sports-related injuries, with significant short- and long-term morbidity. Generalized joint hypermobility (GJH) and specific knee laxity are factors that have been associated with an increased risk of ACL injury and inferior postoperative outcome, but the state of the evidence is unclear and the available information is limited. This thesis consists of five studies with the overall aim of investigating how two main concepts, GJH and specific knee laxity, affect the outcome after ACL reconstruction and how the two concepts affect each other. Study I is a systematic review aiming to investigate the influence of GJH on ACL injury risk and postoperative outcome. Study I comprised 21 studies. While the data synthesis demonstrated GJH as a risk factor for ACL injury in males, the results were conflicting in females. Moreover, there was limited evidence indicating that GJH is associated with increased postoperative knee laxity and inferior patient-reported outcome after ACL reconstruction. Study II is a register-based cohort study comprising 142 patients undergoing ACL reconstruction. The outcome variables were assessed one year after ACL reconstruction and were analyzed using two methods: (1) dichotomization based on the presence of GJH and (2) linear regression to investigate continuous associations with the Beighton score. Interestingly, and contrary to the hypothesis, the analysis revealed that the KOOS sports and recreation subscale was associated with the continuous Beighton score. Functional performance, evaluated with hop and strength tests, was acceptable, regardless of the presence of GJH. Study III is an international multicenter cohort study investigating the correlation between the Beighton score and rotatory knee laxity in 96 ACL-injured patients. Rotatory knee laxity was evaluated using with the pivot-shift test, using two devices to quantify laxity. No correlations between GJH and quantitative rotatory knee laxity were observed in the ACL-injured knee. However, in the contralateral healthy knee, a weak yet significant correlation was observed. Study IV is a retrospective register-based cohort study comprising 8,502 patients undergoing ACL reconstruction. The patients were divided into four subgroups based on the degree of hyperextension of the contralateral healthy knee. The degree of contralateral hyperextension was analyzed in relation to anterior tibial translation (ATT), using the KT-1000 arthrometer, and in relation to the frequency of concomitant intra-articular injuries in the ACL-injured knee. The ATT was examined six months postoperatively. The study identified an association between contralateral knee hyperextension and greater ATT in the ACL injured knee. Interestingly, there was an inverse relationship between the degree of contralateral hyperextension and the frequency of meniscal injuries. Study V is a retrospective cohort study, based on two previous randomized, controlled cohorts, comprising 147 patients undergoing ACL reconstruction. The study analyzed the influence of increased knee laxity assessed two years postoperatively on clinical outcome variables 16 years postoperatively. This study determined that increased ATT, measured with the Lachman test and the anterior drawer test, was associated with inferior patient-reported outcome 16 years postoperatively. Moreover, increased rotatory knee laxity, measured with the pivot-shift test, was associated with inferior patient-reported outcome and a lower level of physical activity after 16 years. Taken together, this thesis provides an overview of all the currently available studies on the subject of the influence of GJH on ACL injury risk and postoperative outcome. It further demonstrates that acceptable short-term functional results could be found in patients with GJH after ACL reconstruction and that patients with increased hypermobility may have short-term subjectively perceived advantages. Moreover, the thesis provides the first correlation analysis between quantitative pivot shift and GJH, finding no association in the ACL-injured knee but a weak correlation in the contralateral healthy knee. Knee hyperextension, a part of GJH, is demonstrated to be associated with increased anterior knee laxity. As identified by Study V, increased anterior and rotatory knee laxity is associated with inferior long-term patient-reported outcome and a lower level of activity after 16 years, results that elucidate the importance of reducing postoperative knee laxity. Considering the accumulated evidence from the current thesis, reduction of postoperative knee laxity is probably particularly important in the susceptible group of individuals with GJH

Young age and high BMI are predictors of early revision surgery after primary anterior cruciate ligament reconstruction: a cohort study from the Swedish and Norwegian knee ligament registries based on 30,747 patients

Purpose To analyse patient-related risk factors for 2-year ACL revision after primary reconstruction. The hypothesis was that younger athletes would have a higher incidence of an early ACL revision. Methods This prospective cohort study was based on data from the Norwegian and Swedish National Knee Ligament Registries and included patients who underwent primary ACL reconstruction from 2004 to 2014. The primary end-point was the 2-year incidence of ACL revision. The impact of activity at the time of injury, patient sex, age, height, weight, BMI, and tobacco usage on the incidence of early ACL revision were described by relative risks (RR) with 95% confidence intervals (CI). Results A total of 58,692 patients were evaluated for eligibility and 30,591 patients were included in the study. The mean incidence of ACL revision within 2 years was 2.82% (95% CI 2.64–3.02%). Young age (13–19) was associated with an increased risk of early ACL revision (males RR = 1.54 [95% CI 1.27–1.86] p < 0.001 and females RR = 1.58 [95% CI 1.28–1.96] p < 0.001). Females over 1 SD in weight ran an increased risk of early ACL revision (RR = 1.82, [95% CI 1.15–2.88] p = 0.0099). Individuals with a BMI of over 25 ran an increased risk of early ACL revision (males: RR = 1.78, [95% CI 1.38–2.30] p < 0.001 and females: RR = 1.84, [95% CI 1.29–2.63] p = 0.008). Conclusion Young age, a BMI over 25, and overweight females were risk factors for an early ACL revision. Level of evidence II

Generalised joint hypermobility increases ACL injury risk and is associated with inferior outcome after ACL reconstruction: a systematic review

ObjectivesTo investigate the association between generalised joint hypermobility (GJH) and ACL injury risk. Secondary aims involved evaluating associations between GJH and postoperative outcome (including graft-failure risk, knee laxity and patient-reported outcome). Furthermore, we aimed to compare the performance of different grafts in patients with GJH.MethodsDatabases MEDLINE/PubMed, EMBASE and the Cochrane Library were searched, including 2760 studies. Two reviewers independently screened studies for eligibility. A modified version of the MINORS score was applied for quality appraisal. Studies assessing GJH while reporting the risk of ACL injury and/or postoperative outcome were included.ResultsTwenty studies were included, using several different methods to determine GJH. There was consistent evidence showing that GJH is a risk factor for unilateral ACL injury in males, while in females, the results were conflicting. There was limited evidence associating GJH with increased knee laxity 5 years postoperatively. There was consistent evidence of inferior postoperative patient-reported outcome in patients with GJH. Moreover, there was limited yet consistent evidence indicating that patellar-tendon autografts are superior to hamstring-tendon autografts in patients with GJH in terms of knee laxity and patient-reported outcome. There was insufficient evidence to draw conclusions regarding the outcomes of bilateral ACL injury and graft failure.ConclusionsIn men, GJH was associated with an increased risk of unilateral ACL injury. Moreover, GJH was associated with greater postoperative knee laxity and inferior patient-reported outcome. Based on the available evidence, a patellar-tendon autograft appears to be superior to a hamstring-tendon autograft in patients with GJH. However, the included studies were heterogeneous and there is a need for consensus in the assessment of GJH within sports medicine

Generative Artificial Intelligence in Medicine: A Mixed Methods Survey of UK General Practitioners

Background:Since November 2022, with the debut of OpenAI’s ChatGPT, there has been growing interest in the use of generative artificial intelligence (AI), including in healthcare. However, there is only limited research into doctors’ adoption of these tools and their opinions about their application in clinical practice.Objective:This study aimed to explore the opinions of general practitioners (GPs) in the United Kingdom (UK) about the use of generative AI tools (ChatGPT/Bard/Bing AI) in primary care.Methods:Between February 2nd-24th 2024, using a convenience sample, we administered a web-based mixed methods survey of 1000 GPs in the UK to explore their experiences and opinions about the impact of generative AI on clinical practice. Participants were recruited from registered GPs currently working in the UK using the clinician marketing service Doctors.net.uk. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses and conducted a qualitative descriptive analysis of written responses (“comments”) to 2 open-ended questions embedded in the web-based questionnaire.Results:A total of 1006 GPs responded, with 53% being male and 54% aged 46 years and older. Most GPs (80%) expressed a need for more support and training in understanding these tools. GPs at least somewhat agreed AI would improve documentation (59%), patient information gathering (56%), treatment plans (41%), diagnostic accuracy (40%), and prognostic accuracy (38%). Additionally, 62% believed patients might rely more on AI, 55% felt it could increase inequities, and 54% saw potential for patient harm, but 47% believed it would enhance healthcare efficiency. GPs who used these tools were significantly more optimistic about the scope for generative AI in improving clinical tasks compared with those who did not report using them. Elaborating on the quantitative component of the survey, 31% (307/1006) left comments that were classified into 4 major themes in relation to generative AI in medicine: (1) lack of familiarity and understanding, (2) a role in clinical practice, (3) concerns, and (4) thoughts on future of healthcare.Conclusions:This study highlights UK GPs' perspectives on generative AI in clinical practice, emphasizing the need for more training. Many GPs reported a lack of knowledge and experience with this technology and a significant proportion used non-medical grade technology for clinical tasks, with the risks that this entails. Medical organizations must urgently invest in educating and guiding physicians on AI use and limitations