In this perspective article, we consider the use of predictive models in healthcare and associated challenges. We will argue that patients can play a valuable role in supporting the safe and practicable embedding of such tools and provide some examples.
Background: Prognostic models help aid medical decision-making. Various prognostic models are available via websites such as MDCalc, but these models typically predict one outcome, for example, stroke risk. Each model requires individual predictors, for example, age, lab results and comorbidities. There is no clinical tool available to predict multiple outcomes from a list of common medical predictors.
Objective: Identify a constrained set of outcome-agnostic predictors.
Methods: We proposed a novel technique aggregating the standardised mean difference across hundreds of outcomes to learn a constrained set of predictors that appear to be predictive of many outcomes. Model performance was evaluated using the constrained set of predictors across eight prediction tasks. We compared against existing models, models using only age/sex predictors and models without any predictor constraints.
Results: We identified 67 predictors in our constrained set, plus age/sex. Our predictors included illnesses in the following categories: cardiovascular, kidney/liver, mental health, gastrointestinal, infectious and oncologic. Models developed using the constrained set of predictors achieved comparable discrimination compared with models using hundreds or thousands of predictors for five of the eight prediction tasks and slightly lower discrimination for three of the eight tasks. The constrained predictor models performed as good or better than all existing clinical models.
Conclusions: It is possible to develop models for hundreds or thousands of outcomes that use the same small set of predictors. This makes it feasible to implement many prediction models via a single website form. Our set of predictors can also be used for future models and prognostic model research.
Aim: We aimed to identify enablers and barriers of using primary care routine data for healthcare research, to formulate recommendations for improving efficiency in knowledge discovery.
Background: Data recorded routinely in primary care can be used for estimating the impact of interventions provided within routine care for all people who are clinically eligible. Despite official promotion of 'efficient trial designs', anecdotally researchers in the Asthma UK Centre for Applied Research (AUKCAR) have encountered multiple barriers to accessing and using routine data.
Methods: Using studies within the AUKCAR portfolio as exemplars, we captured limitations, barriers, successes, and strengths through correspondence and discussions with the principal investigators and project managers of the case studies.
Results: We identified 14 studies (8 trials, 2 developmental studies and 4 observational studies). Investigators agreed that using routine primary care data potentially offered a convenient collection of data for effectiveness outcomes, health economic assessment and process evaluation in one data extraction. However, this advantage was overshadowed by time-consuming processes that were major barriers to conducting efficient research. Common themes were multiple layers of information governance approvals in addition to the ethics and local governance approvals required by all health service research; lack of standardisation so that local approvals required diverse paperwork and reached conflicting conclusions as to whether a study should be approved. Practical consequences included a trial that over-recruited by 20% in order to randomise 144 practices with all required permissions, and a 5-year delay in reporting a trial while retrospectively applied regulations were satisfied to allow data linkage.
Conclusions: Overcoming the substantial barriers of using routine primary care data will require a streamlined governance process, standardised understanding/application of regulations and adequate National Health Service IT (Information Technology) capability. Without policy-driven prioritisation of these changes, the potential of this valuable resource will not be leveraged.
Background: In Australia, with the recent introduction of electronic health records (EHRs) into hospitals, the use of hospital-based EHRs for research is a relatively new concept. The aim of this study was to explore the attitudes of older healthcare consumers on sharing their health data with an emerging EHR-based Research Data Platform within the National Centre for Healthy Ageing.
Methods: This was a qualitative study. Two workshops were conducted in March 2022 with consumer representatives across Peninsula Health, Victoria, Australia. The workshops comprised three parts: (1) an ice-breaker (2) an introduction to EHR-based research through the presentation of 'use case' scenarios and (3) focus group discussions. Qualitative data were analysed using reflexive thematic analysis.
Results: Consumer participants (n=16) were aged between 62 and 83 years and were of mixed gender. The overarching theme was related to trust in the use of EHR data for research; themes included: (1) benefits of sharing data, (2) uncertainty around data collection processes and (3) data sharing fears. The three themes within the overarching theme all reflect participants' levels of trust.
Conclusion: Our study identified fundamental issues related to trust in the use of EHR data for research, with both healthcare and broader societal factors contributing to consumer attitudes. Processes to support transparent and clear communication with consumers are essential to support the responsible use of EHR data for research.
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<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.
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.
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.
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.
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