BMJ Health and Care Informatics

Background The use of digital technology in healthcare promises to improve quality of care and reduce costs over time. This promise will be difficult to attain without interoperability: facilitating seamless health information exchange between the deployed digital health information systems (HIS). Objective To determine the maturity readiness of the interoperability capacity of Kenya’s HIS. Methods We used the HIS Interoperability Maturity Toolkit, developed by MEASURE Evaluation and the Health Data Collaborative’s Digital Health and Interoperability Working Group. The assessment was undertaken by eHealth stakeholder representatives primarily from the Ministry of Health’s Digital Health Technical Working Group. The toolkit focused on three major domains: leadership and governance, human resources and technology. Results Most domains are at the lowest two levels of maturity: nascent or emerging. At the nascent level, HIS activities happen by chance or represent isolated, ad hoc efforts. An emerging maturity level characterises a system with defined HIS processes and structures. However, such processes are not systematically documented and lack ongoing monitoring mechanisms. Conclusion None of the domains had a maturity level greater than level 2 (emerging). The subdomains of governance structures for HIS, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for operations and maintenance of hardware attained higher maturity levels. These findings are similar to those from interoperability maturity assessments done in Ghana and Uganda.

Job Nyangena

Rohini Rajgopal

Elizabeth Adhiambo Ombech

Enock Oloo

Humphrey Luchetu

Sam Wambugu

Onesmus Kamau

Charles Nzioka

Samson Gwer

Moses Ndiritu Ndirangu

BMJ Health & Care Informatics

Maturity assessment of Kenya’s health information system interoperability readiness

Background Innovation spread is a key policy objective for health systems world-wide, but adoption success varies enormously. We have developed a set of short generic user-reported measures to help understand how and why healthcare innovations spread. This work builds on the literature and on practical experience in developing and using patient-reported outcome measures. Measures The Innovation Readiness Score measures user perceptions of how much they are open to and up-to-date with new ideas, and whether their organisations are receptive to and capable of innovation. It is based on Rogers’ classification of innovativeness (innovator, early adopter, early majority, etc). The Digital Confidence Score rates users’ digital literacy and confidence to use digital products, with dimensions of familiarity, social pressure, support and digital self-efficacy. The Innovation Adoption Score rates the adoption process in terms of coherence and reflective thought before, during and after implementation. It is based on Normalisation Process Theory. The User Satisfaction measure assesses a digital product in terms of usefulness, ease of use, support and satisfaction. The Behaviour Change measure covers user perceptions of their capability, opportunity and motivation to change behaviour, based on the COM-B model. These measures have been mapped onto Greenhalgh’s NASSS Framework (non-adoption, abandonment and challenges to scale-up, spread and sustainability of health and care technologies). Conclusion These tools measure different aspects of digital health innovations and may help predict the success of innovation dissemination, diffusion and spread programmes.

Tim Benson

Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change

Background The prevalence of chronic coronary heart diseases (CHDs) increases with age in the elderly, which represents one of the top-ranked causes of death and disease burden. Methods This study aimed to investigate the management efficiency of telemedicine based on the remote multiparameter monitoring in elderly patients with CHD. A total of 1248 elderly patients diagnosed with CHD were enrolled. The subjects were randomly divided into two groups, wearable equipment-based telemedical management (WTM) group and traditional follow-up management (TFM) group. Face-to-face clinical interview at least once every 2 months was required in TFM group to collect the medical records. Patients in WTM group were provided with wearable equipment to complete remote monitoring, real-time alerts and health intervention via virtual consultations and remote medication recommendations. Results The mean age of patients in WTM group and TFM group was 71.1 (68.0–82.0) years and 71.0 (68.0–81.0) years, respectively‏. After a 12-month management, patients in WTM group presented a lower occurrence of hospitalisation (HR 0.59, 95% CI=0.47 to 0.73, p&lt;0.0001) and major adverse cardiac events (HR 0.60, 95% CI=0.44 to 0.82, p=0.0012) compared with patients in TFM group. Conclusion The multiparameter telemedical management could help with the out-of-hospital management and reduce the incidence of rehospitalisation in elderly patients with CHD.

Tingting Lu

Ruihua Cao

Yujia Wang

Xiaoxuan Kong

Huiquan Wang

Guanghua Sun

Shan Gao

Yabin Wang

Yuan Yuan

Xiaoying Shen

Li Fan

Jun Ren

Feng Cao

Wearable equipment-based telemedical management via multiparameter monitoring on cardiovascular outcomes in elderly patients with chronic coronary heart disease: an open-labelled, randomised, controlled trial

基于可穿戴设备的多参数监测在老年慢性冠心病患者心血管结局中的远程医疗管理：一项开放标签、随机、对照试验

Objective To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals. Methods The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5 million. We conducted a participatory action research study with multidisciplinary healthcare professionals, managers, data analysts, consumer representatives and industry professionals (n=16) across three phases: (1) defining the problem, (2) current workflow and co-designing a future workflow and (3) developing proof-of-concept AI-based workflows. Co-designed workflows were deductively mapped to a validated feasibility framework to inform future clinical piloting. Qualitative data underwent inductive thematic analysis. Results Between 2020 and 2022 (n=16 health services), 175 282 PREMs inpatient surveys received 23 982 open-ended responses (mean response rate, 13.7%). Existing PREMs workflows were problematic due to overwhelming data volume, analytical limitations, poor integration with health service workflows and inequitable resource distribution. Three potential semiautomated, AI-based (unsupervised machine learning) workflows were developed to address the identified problems: (1) no code (simple reports, no analytics), (2) low code (PowerBI dashboard, descriptive analytics) and (3) high code (Power BI dashboard, descriptive analytics, clinical unit-level interactive reporting). Discussion The manual analysis of free-text PREMs data is laborious and difficult at scale. Automating analysis with AI could sharpen the focus on consumer input and accelerate quality improvement cycles in hospitals. Future research should investigate how AI-based workflows impact healthcare quality and safety. Conclusion AI-based clinical informatics workflows to routinely analyse free-text PREMs data were co-designed with multidisciplinary end-users and are ready for clinical piloting.

Oliver J Canfell

Wilkin Chan

Jason D Pole

Teyl Engstrom

Tim Saul

Jacqueline Daly

Clair Sullivan

Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals

床边之后的人工智能：共同设计基于AI的临床信息学工作流程，以常规分析医院中的患者报告体验指标

Introduction In Asian countries, stigma against psychiatric disorders and shortage of manpower are the two major challenges that hinder people from receiving treatments. Chatbots can surely help people surpass the stigmatising and manpower shortage challenges. Since a comprehensive review in the Asian context is lacking, this systematic review will evaluate the effects of chatbot interventions on the mental well-being of the general population in Asia. Methods and analysis Four electronic databases (PubMed, CINAHL, PsycINFO and MEDLINE) will be searched until December 2024. Randomised controlled trials with English/Chinese full text available will be included. Random-effect models will be used for meta-analyses. The risk of bias (RoB) and certainty of evidence across studies will be assessed using the Cochrane RoB2 and Grading of Recommendation Assessment, Development and Evaluation tools, respectively. Ethics and dissemination This study will not require ethical approval. The findings will be disseminated through peer-reviewed publications. Funding School Research Grant of the Tung Wah College (2023-04-52-SRG230401) PROSPERO registration number CRD42024546316.

Wilson Leung

Simon Ching Lam

Fowie Ng

Calvin Chi Kong Yip

Chi-Keung Chan

Effectiveness of chatbot-based interventions on mental well-being of the general population in Asia: protocol for a systematic review and meta-analysis of randomised controlled trials

基于聊天机器人的干预措施对亚洲普通人群心理健康的影响：一项系统综述和随机对照试验的荟萃分析方案

Objectives We aim to evaluate the accuracy of radiologists and radiology residents in the detection of paediatric appendicular fractures with and without the help of a commercially available fracture detection artificial intelligence (AI) solution in the hopes of showing potential clinical benefits in a general hospital setting. Methods This was a retrospective study involving three associate consultants (AC) and three senior residents (SR) in radiology, who acted as readers. One reader from each human group interpreted the radiographs with the aid of AI. Cases were categorised into concordant and discordant cases between each interpreting group. Discordant cases were further evaluated by three independent subspecialty radiology consultants to determine the final diagnosis. A total of 500 anonymised paediatric patient cases (aged 2–15 years) who presented to a tertiary general hospital with a Children’s emergency were retrospectively collected. Main outcome measures include the presence of fracture, accuracy of readers with and without AI, and total time taken to interpret the radiographs. Results The AI solution alone showed the highest accuracy (area under the receiver operating characteristic curve 0.97; AC: 95% CI −0.055 to 0.320, p=0; SR: 95% CI 0.244 to 0.598, p=0). The two readers aided with AI had higher area under curves compared with readers without AI support (AC: 95% CI −0.303 to 0.465, p=0; SR: 95% CI −0.154 to 0.331, p=0). These differences were statistically significant. Conclusion Our study demonstrates excellent results in the detection of paediatric appendicular fractures using a commercially available AI solution. There is potential for the AI solution to function autonomously.

Praveen M Yogendra

Adriel Guang Wei Goh

Sze Ying Yee

Freda Jawan

Kelvin Kay Nguan Koh

Timothy Shao Ern Tan

Tian Kai Woon

Phey Ming Yeap

Min On Tan

Accuracy of radiologists and radiology residents in detection of paediatric appendicular fractures with and without artificial intelligence

放射科医生和放射科住院医师在有无人工智能辅助下检测儿童四肢骨折的准确性

With the fast development of artificial intelligence (AI) and its applications in medicine, it is often said that the time for intelligent medicine is arriving, if not already have arrived. While there is no doubt that AI-centred intelligent medicine will transform current healthcare, it is necessary to test and re-test medical AI (MAI) products before they are implemented in the real world. From the perspective of ensuring safety, accuracy and efficiency, it is imperative that MAIs undergo stress tests in a systematic and comprehensive manner where stress tests subject MAIs to workloads and environments beyond tests carried out by their developers. In such stress tests, potential bottlenecks or failures of MAIs may be identified and fed back to developers to optimise the products. To avoid bias and ensure fairness, stress tests should be prepared and administered by an independent body.

Huai Chen

Yanmei Tie

Xinhua Cao

Geoffrey S. Young

Xiaoyin Xu

Strategies for creation of data reserve and stress testing of medical AI products

医疗人工智能产品数据储备与压力测试策略

Abbreviation	BMJ Health Care Inform
Journal Impact	4.27
Quartiles(Global)	MEDICAL INFORMATICS(Q2)

ISSN	2632-1009
h-index	43

Publication Information	Publisher: BMJ Publishing Group，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 84.62%，Self Citation Rate:2.40%， Gold OA Rate: 100.00%
Journal Fees	$Details
Average review cycle	18 weeks

BMJ Health and Care Informatics

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Maturity assessment of Kenya’s health information system interoperability readiness

Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change

Wearable equipment-based telemedical management via multiparameter monitoring on cardiovascular outcomes in elderly patients with chronic coronary heart disease: an open-labelled, randomised, controlled trial

Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals

Effectiveness of chatbot-based interventions on mental well-being of the general population in Asia: protocol for a systematic review and meta-analysis of randomised controlled trials

Wearable equipment-based telemedical management via multiparameter monitoring on cardiovascular outcomes in elderly patients with chronic coronary heart disease: an open-labelled, randomised, controlled trial

Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals

Effectiveness of chatbot-based interventions on mental well-being of the general population in Asia: protocol for a systematic review and meta-analysis of randomised controlled trials

Accuracy of radiologists and radiology residents in detection of paediatric appendicular fractures with and without artificial intelligence

Strategies for creation of data reserve and stress testing of medical AI products