Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel.

Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings.

Factors influencing presentation delay among cancer patients: a cross-sectional study in Malaysia.

Background: Cancer represents a significant global public health challenge, with escalating incidence rates straining healthcare systems. Malaysia, like many nations, has witnessed a rise in cancer cases, particularly among the younger population. This study aligns with Malaysia's National Strategic Plan for Cancer Control Programme 2021-2025, emphasizing primary prevention and early detection to address cancer's impact.

OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study.

Background: Electronic health records (EHRs) in unstructured formats are valuable sources of information for research in both the clinical and biomedical domains. However, before such records can be used for research purposes, sensitive health information (SHI) must be removed in several cases to protect patient privacy. Rule-based and machine learning-based methods have been shown to be effective in deidentification.

The Role of Formative Evaluation in Promoting Digitally-based Health Equity and Reducing Bias for Resilient Health Systems: The Case of Patient Portals.

Objectives: Patient portals are increasingly implemented to improve patient involvement and engagement. We here seek to provide an overview of ways to mitigate existing concerns that these technologies increase inequity and bias and do not reach those who could benefit most from them.

Methods: Based on the current literature, we review the limitations of existing evaluations of patient portals in relation to addressing health equity, literacy and bias; outline challenges evaluators face when conducting such evaluations; and suggest methodological approaches that may address existing shortcomings.

Identifying and addressing digital health risks associated with emergency pandemic response: Problem identification, scoping review, and directions toward evidence-based evaluation.

Background And Objective: The COVID-19 pandemic has accelerated digital health applications in multifaceted disease management dimensions. This study aims (1) to identify risk issues relating to the rapid development and redeployment of COVID-19 related e-health systems, in primary care, and in the health ecosystems interacting with it and (2) to suggest evidence-based evaluation directions under emergency response.

Method: After initial brainstorming of digital health risks posed in this pandemic, a scoping review method was adopted to collect evidence across databases of PubMed, CINAHL, and EMBASE.

"Japan LIVE Dashboard" for COVID-19: A Scalable Solution to Monitor Real-Time and Regional-Level Epidemic Case Data.

Under pandemic conditions, it is important to communicate local infection risks to better enable the general population to adjust their behaviors accordingly. In Japan, our team operates a popular non-government and not-for-profit dashboard project - "Japan LIVE Dashboard" - which allows the public to easily grasp the evolution of the pandemic on the internet. We presented the Dashboard design concept with a generic framework integrating socio-technical theories, disease epidemiology and related contexts, and evidence-based approaches.

Managing Pandemic Responses with Health Informatics - Challenges for Assessing Digital Health Technologies.

Objectives: To highlight the role of technology assessment in the management of the COVID-19 pandemic.

Method: An overview of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems.

Results: Evaluation of digital health technologies for COVID-19 should be based on their technical maturity as well as the scale of implementation.

A Cost Projection of Scheduled Physician Home-Visit Services in Japan: 2014 to 2064.

This study predicts the volume and spending on scheduled physician home-visit (SPHV) services over five decades. This model-based evaluation study considered the following scenarios in Japan: (1) change in services-delivery; (2) technology-assisted services; (3) a combination of (1) and (2). The model predicted that the volume and spending on SPHV will increase as the population and working-age population decline.

Medication-rights detection using incident reports: A natural language processing and deep neural network approach.

Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using natural language processing and deep neural networks to automate medication-incident identification using free-text incident reports. We assessed the performance of deep neural network models in classifying the Advanced Incident Reporting System reports and compared the models' performance with that of other common classification methods (including logistic regression, support vector machines and the decision-tree method).

Exploring Hidden In-Hospital Fall Clusters from Incident Reports Using Text Analytics.

Retrospective analysing of fall incident reports can uncover hidden information, identify potential risk factors, and improve healthcare quality. This study explores potential fall incident clusters using word embeddings and hierarchical clustering. Fall incident reports from 7 local hospitals in Hong Kong were catalogued into 5 potential clusters with significantly different fall severity, gender, reporting department, and keywords.

Classification Scheme for Incident Reports of Medication Errors.

This study aimed to develop a classification scheme for retrieving information from incident reports of medication errors. This 15-category classification scheme captures minimal medication-incident related information from incident reports and thus serves as an information model for automatic information retrieval solution. The automatic solution uses recent advances in artificial intelligence methods to learn from incident report resources and is promising to the prevention of adverse drug events and promotion of safety in medical care.

Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications.

Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance.

Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems.

Results: There is a rich history and tradition of evaluating AI in healthcare.

Long-term Results of Mini Asymmetric Radial Keratotomy and Corneal Cross-linking for the Treatment of Keratoconus.

Purpose: To investigate the long-term results (at least 5 years of follow-up) of the mini asymmetric radial keratotomy (MARK) and corneal cross-linking (CXL) combined intervention, also known as the 'Rome protocol,' for patients with progressive stage I and II keratoconus and contact lens intolerance.

Methods: This was a retrospective observational case series. Fifteen eyes of 12 patients were evaluated, with a mean follow-up of 6.

A Framework of Rebalancing Imbalanced Healthcare Data for Rare Events' Classification: A Case of Look-Alike Sound-Alike Mix-Up Incident Detection.

Identifying rare but significant healthcare events in massive unstructured datasets has become a common task in healthcare data analytics. However, imbalanced class distribution in many practical datasets greatly hampers the detection of rare events, as most classification methods implicitly assume an equal occurrence of classes and are designed to maximize the overall classification accuracy. In this study, we develop a framework for learning healthcare data with imbalanced distribution via incorporating different rebalancing strategies.

The Safe and Effective Use of Shared Data Underpinned by Stakeholder Engagement and Evaluation Practice.

Objectives:  The paper draws attention to: i) key considerations involving the confidentiality, privacy, and security of shared data; and ii) the requirements needed to build collaborative arrangements encompassing all stakeholders with the goal of ensuring safe, secure, and quality use of shared data.

Method:  A narrative review of existing research and policy approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Care and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems.

Results:  The technological ability to merge, link, re-use, and exchange data has outpaced the establishment of policies, procedures, and processes to monitor the ethics and legality of shared use of data.

Temporal prediction of in-hospital falls using tensor factorisation.

In-hospital fall incidence is a critical indicator of healthcare outcome. Predictive models for fall incidents could facilitate optimal resource planning and allocation for healthcare providers. In this paper, we proposed a tensor factorisation-based framework to capture the latent features for fall incidents prediction over time.

Uncovering Hidden Topics in Hong Kong Clinical Research Through Hospital Authority Convention Publications.

Uncovering clinical research trends allows us to understand the direction of healthcare services and is essential for longer-term healthcare planning. The Hospital Authority Convention is a mainstream annual healthcare conference that gathers up-to-date Hong Kong medical research. We propose to use state-of-the-art medical document mining and topic modelling methods to uncover latent themes and structures in the publications.

Context Sensitive Health Informatics: Delivering 21st Century Healthcare - Building a Quality-and-Efficiency Driven System.

Health informatics applications will be a cornerstone in the next generation quality-and-efficiency health care system. Health care is delivered from many different specialties, to many different patients with complex diseases and comorbidity. A one size fits all approach is not adequate to reach the Triple Aim of improving the patient experience of care, improving the health of populations, and reducing the per capita cost of health care.

Economic Evaluation of Individual School Closure Strategies: The Hong Kong 2009 H1N1 Pandemic.

Background: School closures as a means of containing the spread of disease have received considerable attention from the public health community. Although they have been implemented during previous pandemics, the epidemiological and economic effects of the closure of individual schools remain unclear.

Methodology: This study used data from the 2009 H1N1 pandemic in Hong Kong to develop a simulation model of an influenza pandemic with a localised population structure to provide scientific justifications for and economic evaluations of individual-level school closure strategies.

Statistical classification of drug incidents due to look-alike sound-alike mix-ups.

It has been recognised that medication names that look or sound similar are a cause of medication errors. This study builds statistical classifiers for identifying medication incidents due to look-alike sound-alike mix-ups. A total of 227 patient safety incident advisories related to medication were obtained from the Canadian Patient Safety Institute's Global Patient Safety Alerts system.

Statistical text classifier to detect specific type of medical incidents.

WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner.