Self-reported penicillin allergies are highly prevalent in hospitalised patients and are associated with poor health and health service outcomes. Critically ill patients have historically been ...underrepresented in prospective delabelling studies in part due to concerns around clinical stability and reliability of penicillin skin testing. Allergy assessment tools exist to identify low-risk penicillin allergy phenotypes and facilitate direct oral challenge delabelling. PEN-FAST is a clinical decision rule that has been validated to predict true penicillin allergy in a cohort of non-critically ill patients. There is however limited evidence regarding the feasibility, safety and efficacy of direct oral challenges and the use of delabelling clinical decisions rules in the intensive care setting.
Critically ill patients in the intensive care unit (ICU) with low-risk penicillin allergy phenotypes (PEN-FAST score < 3) will be randomised 1:1 to direct oral penicillin challenge (single dose 250 mg oral amoxicillin or implicated penicillin) or routine care, followed by a 2-h observation period. Patients will receive a second oral challenge/observation prior to hospital discharge (with subsequent observation for 2 h). An assessment for antibiotic-associated adverse events will also be undertaken at 24 h and 5 days post each challenge/observation and again at 90 days post-randomisation. The primary outcome measures are feasibility (proportion of eligible patients recruited and protocol compliance) and safety (proportion of patients who experience an antibiotic-associated immune-mediated adverse event or serious adverse event).
We will report the feasibility and safety of point-of-care penicillin direct oral challenge in this first randomised controlled trial of low-risk penicillin allergy in critically ill hospitalised patients. Upon completion of the project, important findings will inform the design of planned large prospective multi-centre clinical trials in Australian and international ICUs, further examining safety and efficacy and exploring antimicrobial prescribing-related outcomes following penicillin oral challenge.
Australian New Zealand Clinical Trials Registry Registration Number: ACTRN12621000051842 Date registered: 20/01/2021 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379735&isReview=true.
Centralized drug repositories can reduce adverse events and inappropriate prescriptions by enabling access to dispensed medication data at the point of care; however, how they achieve this goal is ...largely unknown.
This study aims to understand the perceived clinical value; the barriers to and enablers of adoption; and the clinician groups for which a provincial, centralized drug repository may provide the most benefit.
A mixed methods approach, including a web-based survey and semistructured interviews, was used. Participants were clinicians (eg, nurses, physicians, and pharmacists) in Ontario who were eligible to use the digital health drug repository (DHDR), irrespective of actual use. Survey data were ranked on a 7-point adjectival scale and analyzed using descriptive statistics, and interviews were analyzed using qualitative descriptions.
Of the 161 survey respondents, only 40 (24.8%) actively used the DHDR. Perceptions of the utility of the DHDR were neutral (mean scores ranged from 4.11 to 4.76). Of the 75.2% (121/161) who did not use the DHDR, 97.5% (118/121) rated access to medication information (eg, dose, strength, and frequency) as important. Reasons for not using the DHDR included the cumbersome access process and the perception that available data were incomplete or inaccurate. Of the 33 interviews completed, 26 (79%) were active DHDR users. The DHDR was a satisfactory source of secondary information; however, the absence of medication instructions and prescribed medications (which were not dispensed) limited its ability to provide a comprehensive profile to meaningfully support clinical decision-making.
Digital drug repositories must be adjusted to align with the clinician's needs to provide value. Ensuring integration with point-of-care systems, comprehensive clinical data, and streamlined onboarding processes would optimize clinically meaningful use. The electronic provision of accessible drug information to providers across health care settings has the potential to improve efficiency and reduce medication errors.
Background
Elderly patients in senior communities faced high barriers to care during the COVID‐19 pandemic, including increased vulnerability to COVID‐19, long quarantines for clinic visits, and ...difficulties with telemedicine adoption.
Objective
To pilot a new model of dermatologic care to overcome barriers for senior living communities during the COVID‐19 pandemic and assess patient satisfaction.
Methods
From 16 November 2020 to 9 July 2021, this quality improvement programme combined in‐residence full body imaging with real‐time outlier lesion identification and virtual teledermatology. Residents from the Sequoias Portola Valley Senior Living Retirement Community (Portola Valley, California) voluntarily enroled in the Stanford Skin Scan Programme. Non‐physician clinical staff with a recent negative COVID‐19 test travelled on‐site to obtain in‐residence full body photographs using a mobile app‐based system on an iPad called SkinIO that leverages deep learning to analyse patient images and suggest suspicious, outlier lesions for dermoscopic photos. A single dermatologist reviewed photographs with the patient and provided recommendations via a video visit. Objective measures included follow‐up course and number of skin cancers detected. Subjective findings were obtained through patient experience surveys.
Results
Twenty‐seven individuals participated, three skin cancers were identified, with 11 individuals scheduled for a follow up in‐person visit and four individuals starting home treatment. Overall, 88% of patients were satisfied with the Skin Scan programme, with 77% likely to recommend the programme to others. 92% of patients agreed that the Skin Scan photographs were representative of their skin. In the context of the COVID‐19 pandemic, 100% of patients felt the process was safer or comparable to an in‐person visit. Despite overall appreciation for the programme, 31% of patients reported that they would prefer to see dermatologist in‐person after the pandemic.
Conclusions
This programme offers a framework for how a hybrid skin scan programme may provide high utility for individuals with barriers to accessing in‐person clinics.
To understand and highlight the differences in clinical, demographic, and image quality characteristics between patient-taken (PAT) and clinic-taken (CLIN) photographs of skin conditions.
This ...retrospective study applied logistic regression to data from 2500 deidentified cases in Stanford Health Care’s eConsult system, from November 2015 to January 2021. Cases with undiagnosable or multiple conditions or cases with both patient and clinician image sources were excluded, leaving 628 PAT cases and 1719 CLIN cases. Demographic characteristic factors, such as age and sex were self-reported, whereas anatomic location, estimated skin type, clinical signs and symptoms, condition duration, and condition frequency were summarized from patient health records. Image quality variables such as blur, lighting issues and whether the image contained skin, hair, or nails were estimated through a deep learning model.
Factors that were positively associated with CLIN photographs, post-2020 were as follows: age 60 years or older, darker skin types (eFST V/VI), and presence of skin growths. By contrast, factors that were positively associated with PAT photographs include conditions appearing intermittently, cases with blurry photographs, photographs with substantial nonskin (or nail/hair) regions and cases with more than 3 photographs. Within the PAT cohort, older age was associated with blurry photographs.
There are various demographic, clinical, and image quality characteristic differences between PAT and CLIN photographs of skin concerns. The demographic characteristic differences present important considerations for improving digital literacy or access, whereas the image quality differences point to the need for improved patient education and better image capture workflows, particularly among elderly patients.
In the past 35 years, significant findings have been made in relation to angiogenesis, and how this usually normal physiological function is converted into an abnormal state in cancer. To search for ...agents that can inhibit angiogenesis, and thereby prevent a tumour from proliferation and spread that is ultimately fatal to the patient, various in‐vitro assays have been developed. In addition, older assays have been refined usually into high throughput screening formats, mainly by the biopharmaceutical industry in their attempts to develop novel therapeutic molecules and maintain a pipeline of lead candidates. The central aim is to extract more accurate data that would facilitate the birth of innovative mechanisms to defeat aberrant angiogenesis in‐vivo. At the same time, better in‐vivo models have been established, with the goal to mimic as close as possible the natural progression of various types of neoplasms in response to a good angiogenic response. More clinically relevant models are needed as anti‐angiogenesis drug discovery and drug development companies fast track their lead molecules from preclinical investigations to phase I clinical trials.
IMPORTANCE: Telemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical ...determination. OBJECTIVE: To determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients. DESIGN, SETTING, AND PARTICIPANTS: This quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone. INTERVENTIONS: During the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient’s assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos. MAIN OUTCOMES AND MEASURES: The main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support. RESULTS: Of the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%. CONCLUSIONS AND RELEVANCE: In this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.
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