How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

Efficient and Effective Diabetes Care in the Era of Digitalization and Hypercompetitive Research Culture: A Focused Review in the Western Pacific Region with Malaysia as a Case Study

There are approximately 220 million (about 12% regional prevalence) adults living with diabetes mellitus (DM) with its related complications, and morbidity knowingly or unconsciously in the Western Pacific Region (WP). The estimated healthcare cost in the WP and Malaysia was 240 billion USD and 1.0 billion USD in 2021 and 2017, respectively, with unmeasurable suffering and loss of health quality and economic productivity. This urgently calls for nothing less than concerted and preventive efforts from all stakeholders to invest in transforming healthcare professionals and reforming the healthcare system that prioritizes primary medical care setting, empowering allied health professionals, improvising health organization for the healthcare providers, improving health facilities and non-medical support for the people with DM. This article alludes to challenges in optimal diabetes care and proposes evidence-based initiatives over a 5-year period in a detailed roadmap to bring about dynamic and efficient healthcare services that are effective in managing people with DM using Malaysia as a case study for reference of other countries with similar backgrounds and issues. This includes a scanning on the landscape of clinical research in DM, dimensions and spectrum of research misconducts, possible common biases along the whole research process, key preventive strategies, implementation and limitations toward high-quality research. Lastly, digital medicine and how artificial intelligence could contribute to diabetes care and open science practices in research are also discussed.

Artificial intelligence tool development: what clinicians need to know?

Digital medicine and smart healthcare will not be realised without the cognizant participation of clinicians. Artificial intelligence (AI) today primarily involves computers or machines designed to simulate aspects of human intelligence using mathematically designed neural networks, although early AI systems relied on a variety of non-neural network techniques. With the increased complexity of the neural layers, deep machine learning (ML) can self-learn and augment many human tasks that require decision-making on the basis of multiple sources of data. Clinicians are important stakeholders in the use of AI and ML tools. The review questions are as follows: What is the typical process of AI tool development in the full cycle? What are the important concepts and technical aspects of each step? This review synthesises a targeted literature review and reports and summarises online structured materials to present a succinct explanation of the whole development process of AI tools. The development of AI tools in healthcare involves a series of cyclical processes: (1) identifying clinical problems suitable for AI solutions, (2) forming project teams or collaborating with experts, (3) organising and curating relevant data, (4) establishing robust physical and virtual infrastructure, and computer systems' architecture that support subsequent stages, (5) exploring AI neural networks on open access platforms before making a new decision, (6) validating AI/ML models, (7) registration, (8) clinical deployment and continuous performance monitoring and (9) improving the AI ecosystem ensures its adaptability to evolving clinical needs. A sound understanding of this would help clinicians appreciate the development of AI tools and engage in codesigning, evaluating and monitoring the tools. This would facilitate broader use and closer regulation of AI/ML tools in healthcare settings.

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.

Headroom Analysis for Early Economic Evaluation: A Systematic Review

OBJECTIVES

The headroom analysis is an early economic evaluation that quantifies the highest price at which an intervention may still be cost effective. Currently, there is no comprehensive review on how it is applied. This study investigated the application of the headroom analysis, specifically (1) how the headroom analysis is framed (2) the analytical approach and sources of evidence used, and (3) how expert judgement is used and reported.

METHODS

A systematic search was conducted in PubMed, Embase, Web of Science, EconLit, and Google Scholar on 28 April 2022. Studies were eligible if they reported an application of the headroom analysis. Data were presented in tabular form and summarised descriptively.

RESULTS

We identified 42 relevant papers. The headroom analysis was applied to medicines (29%), diagnostic or screening tests (29%), procedures, programmes and systems (21%), medical devices (19%), and a combined test and device (2%). All studies used model-based analyses, with 40% using simple models and 60% using more comprehensive models. Thirty-three percent of the studies assumed perfect effectiveness of the health technology, while 67% adopted realistic assumptions. Ten percent of the studies calculated an effectiveness-seeking headroom instead of a cost-seeking headroom. Expert judgement was used in 71% of the studies; 23 studies (55%) used expert opinion, 6 studies (14%) used expert elicitation, and 1 study (2%) used both.

CONCLUSIONS

Because the application of the headroom analysis varies considerably, we recommend its appropriate use and clear reporting of analytical approaches, level of evidence available, and the use of expert judgement.