Germany\'s Digital Healthcare Act allows doctors to prescribe digital health applications (DiGAs) for reimbursement. DiGAs must demonstrate safety, data security, and a \"positive impact on care\" to be listed in the official directory. Previously, data for permanently listed DiGAs was analyzed. The work presented here evaluates additional data fields for the currently listed DiGAs (both provisionally and permanently included) and aims to assess the completeness, details and consistency of the information. The data for this analysis was scraped from the directory and evaluated to identify potential shortcomings in the information provided.

UNASSIGNED: In-hospital hyperglycemia poses significant risks for patients with diabetes mellitus undergoing coronary artery bypass graft (CABG) surgery. Electronic glycemic management systems (eGMSs) like InsulinAPP offer promise in standardizing and improving glycemic control (GC) in these settings. This study evaluated the efficacy of the InsulinAPP protocol in optimizing GC and reducing adverse outcomes post-CABG.
UNASSIGNED: This prospective, randomized, open-label study was conducted with 100 adult type 2 diabetes mellitus (T2DM) patients post-CABG surgery, who were randomized into two groups: conventional care (gCONV) and eGMS protocol (gAPP). The gAPP used InsulinAPP for insulin therapy management, whereas the gCONV received standard clinical care. The primary outcome was a composite of hospital-acquired infections, renal function deterioration, and symptomatic atrial arrhythmia. Secondary outcomes included GC, hypoglycemia incidence, hospital stay length, and costs.
UNASSIGNED: The gAPP achieved lower mean glucose levels (167.2 ± 42.5 mg/dL vs 188.7 ± 54.4 mg/dL; P = .040) and fewer patients-day with BG above 180 mg/dL (51.3% vs 74.8%, P = .011). The gAPP received an insulin regimen that included more prandial bolus and correction insulin (either bolus-correction or basal-bolus regimens) than the gCONV (90.3% vs 16.7%). The primary composite outcome occurred in 16% of gAPP patients compared with 58% in gCONV (P < .010). Hypoglycemia incidence was lower in the gAPP (4% vs 16%, P = .046). The gAPP protocol also resulted in shorter hospital stays and reduced costs.
UNASSIGNED: The InsulinAPP protocol effectively optimizes GC and reduces adverse outcomes in T2DM patients\' post-CABG surgery, offering a cost-effective solution for inpatient diabetes management.

OBJECTIVE: Patient-clinician communication and shared decision-making face challenges in the perioperative period. Chatbots have emerged as valuable support tools in perioperative care. A simultaneous and complete comparison of overall benefits and harm of chatbot application is conducted.
METHODS: MEDLINE, EMBASE and the Cochrane Library were systematically searched for studies published before May 2023 on the benefits and harm of chatbots used in the perioperative period. The major outcomes assessed were patient satisfaction and knowledge acquisition. Untransformed proportion (PR) with a 95% CI was used for the analysis of continuous data. Risk of bias was assessed using the Cochrane Risk of Bias assessment tool version 2 and the Methodological Index for Non-Randomised Studies.
RESULTS: Eight trials comprising 1073 adults from four countries were included. Most interventions (n = 5, 62.5%) targeted perioperative care in orthopaedics. Most interventions use rule-based chatbots (n = 7, 87.5%). This meta-analysis found that the majority of the participants were satisfied with the use of chatbots (mean proportion=0.73; 95% CI: 0.62 to 0.85), and agreed that they gained knowledge in their perioperative period (mean proportion=0.80; 95% CI: 0.74 to 0.87).
CONCLUSIONS: This review demonstrates that perioperative chatbots are well received by the majority of patients with no reports of harm to-date. Chatbots may be considered as an aid in perioperative communication between patients and clinicians and shared decision-making. These findings may be used to guide the healthcare providers, policymakers and researchers for enhancing perioperative care.

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

BACKGROUND: Adopting advanced digital technologies as diagnostic support tools in healthcare is an unquestionable trend accelerated by the COVID-19 pandemic. However, their accuracy in suggesting diagnoses remains controversial and needs to be explored. We aimed to evaluate and compare the diagnostic accuracy of two free accessible internet search tools: Google and ChatGPT 3.5.
METHODS: To assess the effectiveness of both medical platforms, we conducted evaluations using a sample of 60 clinical cases related to urological pathologies. We organized the urological cases into two distinct categories for our analysis: (i) prevalent conditions, which were compiled using the most common symptoms, as outlined by EAU and UpToDate guidelines, and (ii) unusual disorders, identified through case reports published in the \'Urology Case Reports\' journal from 2022 to 2023. The outcomes were meticulously classified into three categories to determine the accuracy of each platform: \"correct diagnosis\", \"likely differential diagnosis\", and \"incorrect diagnosis\". A group of experts evaluated the responses blindly and randomly.
RESULTS: For commonly encountered urological conditions, Google\'s accuracy was 53.3%, with an additional 23.3% of its results falling within a plausible range of differential diagnoses, and the remaining outcomes were incorrect. ChatGPT 3.5 outperformed Google with an accuracy of 86.6%, provided a likely differential diagnosis in 13.3% of cases, and made no unsuitable diagnosis. In evaluating unusual disorders, Google failed to deliver any correct diagnoses but proposed a likely differential diagnosis in 20% of cases. ChatGPT 3.5 identified the proper diagnosis in 16.6% of rare cases and offered a reasonable differential diagnosis in half of the cases.
CONCLUSIONS: ChatGPT 3.5 demonstrated higher diagnostic accuracy than Google in both contexts. The platform showed satisfactory accuracy when diagnosing common cases, yet its performance in identifying rare conditions remains limited.

Large language models (LLMs) such as ChatGPT have potential applications in healthcare, including dentistry. Priming, the practice of providing LLMs with initial, relevant information, is an approach to improve their output quality. This study aimed to evaluate the performance of ChatGPT 3 and ChatGPT 4 on self-assessment questions for dentistry, through the Swiss Federal Licensing Examination in Dental Medicine (SFLEDM), and allergy and clinical immunology, through the European Examination in Allergy and Clinical Immunology (EEAACI). The second objective was to assess the impact of priming on ChatGPT\'s performance. The SFLEDM and EEAACI multiple-choice questions from the University of Bern\'s Institute for Medical Education platform were administered to both ChatGPT versions, with and without priming. Performance was analyzed based on correct responses. The statistical analysis included Wilcoxon rank sum tests (alpha=0.05). The average accuracy rates in the SFLEDM and EEAACI assessments were 63.3% and 79.3%, respectively. Both ChatGPT versions performed better on EEAACI than SFLEDM, with ChatGPT 4 outperforming ChatGPT 3 across all tests. ChatGPT 3\'s performance exhibited a significant improvement with priming for both EEAACI (p=0.017) and SFLEDM (p=0.024) assessments. For ChatGPT 4, the priming effect was significant only in the SFLEDM assessment (p=0.038). The performance disparity between SFLEDM and EEAACI assessments underscores ChatGPT\'s varying proficiency across different medical domains, likely tied to the nature and amount of training data available in each field. Priming can be a tool for enhancing output, especially in earlier LLMs. Advancements from ChatGPT 3 to 4 highlight the rapid developments in LLM technology. Yet, their use in critical fields such as healthcare must remain cautious owing to LLMs\' inherent limitations and risks.

BACKGROUND: Usability evaluation is difficult to reconcile with agile software development for eHealth systems, because traditional usability evaluation is often complex and cumbersome to implement. However, obtaining prospective users\' feedback during agile software development is crucial for improving the usability of eHealth systems, which is why there is an increasing need for agile eHealth usability evaluation.
OBJECTIVE: This study investigates whether agile usability evaluations are suitable to evaluate patient-centered eHealth systems being agile developed in health care and are applicable for prospective users, such as older persons suffering from age-related declines.
METHODS: A triangulation study was conducted combining iterative expert interviews with an exploratory case study.
RESULTS: The triangulation study revealed that the implementation of an agile eHealth usability evaluation with prospective users such as older persons proved to be possible.
CONCLUSIONS: Established eHealth usability evaluation methods must be further evolved to address age-related impairments of older persons.

BACKGROUND: The Bulgaria Diabetes Register (BDR) contains more than 380 millions of pseudonymized outpatient records with proprietary data structures and format.
OBJECTIVE: This paper presents the application results and experience acquired during the process of mapping such observational health data to OMOP CDM with the objective of publishing it in the European Health Data and Evidence Network (EHDEN) Portal.
METHODS: The data mapping follows the activities of the well-structured Extract-Transform-Load process. Unlike other publications, we focus on the need for preprocessing the data structures of raw data, cleaning data and procedures for assuring quality of data.
RESULTS: This paper provides quantitative and statistical measures for the records in the CDM database as published in the EHDEN Portal.
CONCLUSIONS: The mapping of data from the BDR to OMOP CDM provides the EHDEN community with opportunities for including these data in large-scale project for evidence generation by applying standard analytical tools.

OBJECTIVE: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF).
BACKGROUND: Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices.
METHODS: We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

BACKGROUND: The development of digital health tools that are clinically relevant requires a deep understanding of the unmet needs of stakeholders, such as clinicians and patients. One way to reveal unforeseen stakeholder needs is through qualitative research, including stakeholder interviews. However, conventional qualitative data analytical approaches are time-consuming and resource-intensive, rendering them untenable in many industry settings where digital tools are conceived of and developed. Thus, a more time-efficient process for identifying clinically relevant target needs for digital tool development is needed.
OBJECTIVE: The objective of this study was to address the need for an accessible, simple, and time-efficient alternative to conventional thematic analysis of qualitative research data through text analysis of semistructured interview transcripts. In addition, we sought to identify important themes across expert psychiatrist advisor interview transcripts to efficiently reveal areas for the development of digital tools that target unmet clinical needs.
METHODS: We conducted 10 (1-hour-long) semistructured interviews with US-based psychiatrists treating major depressive disorder. The interviews were conducted using an interview guide that comprised open-ended questions predesigned to (1) understand the clinicians\' experience of the care management process and (2) understand the clinicians\' perceptions of the patients\' experience of the care management process. We then implemented a hybrid analytical approach that combines computer-assisted text analyses with deductive analyses as an alternative to conventional qualitative thematic analysis to identify word combination frequencies, content categories, and broad themes characterizing unmet needs in the care management process.
RESULTS: Using this hybrid computer-assisted analytical approach, we were able to identify several key areas that are of interest to clinicians in the context of major depressive disorder and would be appropriate targets for digital tool development.
CONCLUSIONS: A hybrid approach to qualitative research combining computer-assisted techniques with deductive techniques provides a time-efficient approach to identifying unmet needs, targets, and relevant themes to inform digital tool development. This can increase the likelihood that useful and practical tools are built and implemented to ultimately improve health outcomes for patients.