Association between daily step counts and healthy life years: a national cross-sectional study in Japan

BACKGROUND

Despite accumulating evidence concerning the association between daily step counts and mortality or disease risks, it is unclear whether daily step counts are associated with healthy life years.

METHODS

We used the combined dataset of the Comprehensive Survey of Living Conditions and the National Health and Nutrition Survey conducted for a randomly sampled general population in Japan, 2019. Daily step counts were measured for 4957 adult participants. The associations of daily step counts with activity limitations in daily living and self-assessed health were evaluated using a multivariable logistic regression model. The bootstrap method was employed to mitigate uncertainties in estimating the threshold of daily step counts.

RESULTS

The median age was 60 (44-71) years, and 2592 (52.3%) were female. The median daily step counts were 5650 (3332-8452). The adjusted OR of activity limitations in daily living for the adjacent daily step counts was 0.27 (95% CI 0.26 to 0.27) for all ages and 0.25 (95% CI 0.25 to 0.26) for older adults at the lowest, with the thresholds of significant association at 9000 step counts. The OR of self-assessed unhealthy status was 0.45 (95% CI 0.44 to 0.46) for all ages and 0.42 (95% CI 0.41 to 0.43) for older adults at the lowest, with the thresholds at 11 000 step counts.

CONCLUSION

Daily step counts were significantly associated with activity limitations in daily living and self-assessed health as determinants of healthy life years, up to 9000 and 11 000 step counts, respectively. These results suggest a target of daily step counts to prolong healthy life years within health initiatives.

'If you build it, they will come…to the wrong door: evaluating patient and caregiver-initiated ethics consultations via a patient portal'

OBJECTIVES

Memorial Sloan Kettering Cancer Center (MSK) sought to empower patients and caregivers to be more proactive in requesting ethics consultations.

METHODS

Functionality was developed on MSK's electronic patient portal that allowed patients and/or caregivers to request ethics consultations. The Ethics Consultation Service (ECS) responded to all requests, which were documented and analysed.

RESULTS

Of the 74 requests made through the portal, only one fell under the purview of the ECS. The others were primarily requests for assistance with coordinating clinical care, hospital resources or frustrations with the hospital or clinical team.

DISCUSSION

To better empower patients and caregivers to engage Ethics, healthcare organisations and ECSs must first provide them with accessible, understandable and iterative educational resources.

CONCLUSION

After 19.5 months, the 'Request Ethics Consultation' functionality on the patient portal was suspended. Developing resources on the role of Ethics for our patients and caregivers remains a priority.

Editing Physicians' Responses Using GPT-4 for Academic Research

The integration of Artificial Intelligence (AI) into digital healthcare, particularly in the anonymisation and processing of health information, holds considerable potential.

OBJECTIVES

To develop a methodology using Generative Pre-trained Transformer (GPT) models to preserve the essence of medical advice in doctors' responses, while editing them for use in scientific studies.

METHODS

German and English responses from EXABO, a rare respiratory disease platform, were processed using iterative refinement and other prompt engineering techniques, with a focus on removing identifiable and irrelevant content.

RESULTS

Of 40 responses tested, 31 were accurately modified according to the developed guidelines. Challenges included misclassification and incomplete removal, with incremental prompting proving more accurate than combined prompting.

CONCLUSION

GPT-4 models show promise in medical response editing, but face challenges in accuracy and consistency. Precision in prompt engineering is essential in medical contexts to minimise bias and retain relevant information.

ML-based risk assessment tool to rule out empiric use of ESBL-targeted therapy in endemic areas

BACKGROUND

Antimicrobial stewardship focuses on identifying patients who require ESBL-targeted therapy. Rule-in tools have been extensively researched in areas of low endemicity; however, such tools are inadequate for areas with high rates of ESBL, as almost all patients will be selected.

AIM

To develop a machine learning-based rule-out tool suitable for areas with high levels of resistance.

METHODS

We used gradient boosted decision trees to train and validate a risk prediction model on data from 17,913 (45% ESBL) patients with Escherichia coli and Klebsiella pneumoniae in urine cultures. We evaluated the predictive power of different sets of variables, using Shapley values to evaluate variable contributions.

FINDINGS

Our model successfully identified patients with low risk of ESBL resistance in ESBL-endemic areas (AUC-ROC 0.72). When used to select the 30% of patients with the lowest predicted risk, the model yielded a negative predictive value ≥ 0.74. We also demonstrated that a model with seven input features can perform nearly as well as our full model. This simplified model is freely accessible as a web application.

CONCLUSIONS

Our study demonstrates that a risk calculator for antibiotic resistance can be a viable rule-out strategy to reduce ESBL-targeted therapy usage in ESBL-endemic areas. Robust performance of a model with only limited features makes the clinical use of such a tool feasible. In an era with growing rates of ESBL where some experts have called for empirical use of carbapenems as first-line therapy for all patients in high-ESBL-prevalence areas, our tool provides an important alternative.

Definitions of digital biomarkers: a systematic mapping of the biomedical literature

BACKGROUND

Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research.

METHODS

We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses.

RESULTS

We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions.

CONCLUSIONS

The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.

Implications of conscious AI in primary healthcare

The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of AI identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients', healthcare workers' and policy-makers' attitudes towards consciousness of AI systems in primary healthcare settings.

Codesign of health technology interventions to support best-practice perioperative care and surgical waitlist management

OBJECTIVES

This project aimed to determine where health technology can support best-practice perioperative care for patients waiting for surgery.

METHODS

An exploratory codesign process used personas and journey mapping in three interprofessional workshops to identify key challenges in perioperative care across four health districts in Sydney, Australia. Through participatory methodology, the research inquiry directly involved perioperative clinicians. In three facilitated workshops, clinician and patient participants codesigned potential digital interventions to support perioperative pathways. Workshop output was coded and thematically analysed, using design principles.

RESULTS

Codesign workshops, involving 51 participants, were conducted October to November 2022. Participants designed seven patient personas, with consumer representatives confirming acceptability and diversity. Interprofessional team members and consumers mapped key clinical moments, feelings and barriers for each persona during a hypothetical perioperative journey. Six key themes were identified: 'preventative care', 'personalised care', 'integrated communication', 'shared decision-making', 'care transitions' and 'partnership'. Twenty potential solutions were proposed, with top priorities a digital dashboard and virtual care coordination.

DISCUSSION

Our findings emphasise the importance of interprofessional collaboration, patient and family engagement and supporting health technology infrastructure. Through user-based codesign, participants identified potential opportunities where health technology could improve system efficiencies and enhance care quality for patients waiting for surgical procedures. The codesign approach embedded users in the development of locally-driven, contextually oriented policies to address current perioperative service challenges, such as prolonged waiting times and care fragmentation.

CONCLUSION

Health technology innovation provides opportunities to improve perioperative care and integrate clinical information. Future research will prototype priority solutions for further implementation and evaluation.

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

The Ability of Primary Care Practices to Measure and Report on Care Quality

BACKGROUND

Creating useful clinical quality measure (CQM) reports in a busy primary care practice is known to depend on the capability of the electronic health record (EHR). Two other domains may also contribute: supportive leadership to prioritize the work and commit the necessary resources, and individuals with the necessary health information technology (IT) skills to do so. Here we describe the results of an assessment of the above 3 domains and their associations with successful CQM reporting during an initiative to improve smaller primary care practices' cardiovascular disease CQMs.

METHODS

The study took place within an AHRQ EvidenceNOW initiative of external support for smaller practices across Washington, Oregon and Idaho. Practice facilitators who provided this support completed an assessment of the 3 domains previously described for each of their assigned practices. Practices submitted 3 CQMs to the study team: appropriate aspirin prescribing, use of statins when indicated, blood pressure control, and tobacco screening/cessation.

RESULTS

Practices with advanced EHR reporting capability were more likely to report 2 or more CQMs. Only one-third of practices were "advanced" in this domain, and this domain had the highest proportion of practices (39.1%) assessed as "basic." The presence of advanced leadership or advanced skills did not appreciably increase the proportion of practices that reported 2 or more CQMs.

CONCLUSIONS

Our findings support previous reports of limited EHR reporting capabilities within smaller practices but extend these findings by demonstrating that practices with advanced capabilities in this domain are more likely to produce CQM reports.

Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM

Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.

Liver abnormalities following SARS-CoV-2 infection in children 1 to 10 years of age

OBJECTIVE

Beginning in October 2021 in the USA and elsewhere, cases of severe paediatric hepatitis of unknown aetiology were identified in young children. While the adenovirus and adenovirus-associated virus have emerged as leading aetiological suspects, we attempted to investigate a potential role for SARS-CoV-2 in the development of subsequent liver abnormalities.

DESIGN

We conducted a study using retrospective cohorts of deidentified, aggregated data from the electronic health records of over 100 million patients contributed by US healthcare organisations.

RESULTS

Compared with propensity score matched children with other respiratory infections, children aged 1-10 years with COVID-19 had a higher risk of elevated transaminases (HR (95% CI) 2.16 (1.74 to 2.69)) or total bilirubin (HR (95% CI) 3.02 (1.91 to 4.78)), or new diagnoses of liver diseases (HR (95% CI) 1.67 (1.21 to 2.30)) from 1 to 6 months after infection. Patients with pre-existing liver abnormalities, liver abnormalities surrounding acute infection, younger age (1-4 years) or illness requiring hospitalisation all had similarly elevated risk. Children who developed liver abnormalities following COVID-19 had more pre-existing conditions than those who developed abnormalities following other infections.

CONCLUSION

These results indicate that SARS-CoV-2 may prime the patient for subsequent development of liver infections or non-infectious liver diseases. While rare (~1 in 1000), SARS-CoV-2 is a risk for subsequent abnormalities in liver function or the diagnosis of diseases of the liver.

Characteristics of Japanese physician influencers on Twitter during the COVID-19 pandemic and fact-checking their tweets on COVID-19-related drugs

BACKGROUND

During the coronavirus disease-2019 (COVID-19) pandemic, segments of the public relied on social media platforms such as Twitter for medical information shared by medical personnel. Although physicians are likely to disseminate more accurate information on Twitter than non-medical individuals, it cannot be taken for granted. As such, tweets written by physicians in Japan should also be scrutinized for accuracy.

PURPOSE

The purpose of this study was to create a profile of the most popular physician influencers on Twitter in Japan, and to do a fact-check of their tweets regarding COVID-19-related drugs.

DESIGN

This is a retrospective observational study.

METHODS

We purchased Twitter data for Japan for the initial 9 months of the COVID-19 pandemic (from January 2020 to September 2020), and extracted tweets with keywords related to COVID-19 at a sampling rate of 3%. The most popular physicians were identified and selected consecutively by searching for the top 1000 accounts using Twitter's search function. These top accounts were considered influencers and their tweets and retweets concerning COVID-19-related drugs were fact-checked against scientific literature.

RESULTS

We identified 21 physician influencers with real names: most were male in their 40s and 50s working at private medical facilities. The contents of their tweets were mainly sourced from scientific publications that were current at that time. The fact-check revealed that only one of 50 tweets was not correct while the others had no identifiable inaccuracies.

CONCLUSIONS

Except for one tweet, tweets written and retweeted by Japanese physician influencers concerning the COVID-19-related drugs contained predominantly accurate information.

Development and validation of multicentre study on novel Artificial Intelligence-based Cardiovascular Risk Score (AICVD)

OBJECTIVE

Cardiovascular diseases (CVD) are one of the most prevalent diseases in India amounting for nearly 30% of total deaths. A dearth of research on CVD risk scores in Indian population, limited performance of conventional risk scores and inability to reproduce the initial accuracies in randomised clinical trials has led to this study on large-scale patient data. The objective is to develop an Artificial Intelligence-based Risk Score (AICVD) to predict CVD event (eg, acute myocardial infarction/acute coronary syndrome) in the next 10 years and compare the model with the Framingham Heart Risk Score (FHRS) and QRisk3.

METHODS

Our study included 31 599 participants aged 18-91 years from 2009 to 2018 in six Apollo Hospitals in India. A multistep risk factors selection process using Spearman correlation coefficient and propensity score matching yielded 21 risk factors. A deep learning hazards model was built on risk factors to predict event occurrence (classification) and time to event (hazards model) using multilayered neural network. Further, the model was validated with independent retrospective cohorts of participants from India and the Netherlands and compared with FHRS and QRisk3.

RESULTS

The deep learning hazards model had a good performance (area under the curve (AUC) 0.853). Validation and comparative results showed AUCs between 0.84 and 0.92 with better positive likelihood ratio (AICVD -6.16 to FHRS -2.24 and QRisk3 -1.16) and accuracy (AICVD -80.15% to FHRS 59.71% and QRisk3 51.57%). In the Netherlands cohort, AICVD also outperformed the Framingham Heart Risk Model (AUC -0.737 vs 0.707).

CONCLUSIONS

This study concludes that the novel AI-based CVD Risk Score has a higher predictive performance for cardiac events than conventional risk scores in Indian population.

TRIAL REGISTRATION NUMBER

CTRI/2019/07/020471.

Enhancing pharmaceutical decision support system: evaluating antithrombotic-focused algorithms for addressing drug-related problems

OBJECTIVES

To evaluate the efficacy of integrating antithrombotic-focused pharmaceutical algorithms (PAs) into a pharmaceutical decision support system (PDSS) for detecting drug-related problems (DRPs) and facilitating pharmaceutical interventions.

METHODS

A set of 26 PAs (12.4%) out of a total of 210 were created to model patient situations involving antithrombotics, and their contributions were compared with the entire PDSS system.The observational prospective study was conducted between November 2019 and June 2023 in two health facilities with 1700 beds. Pharmacists, who followed a DRP resolution strategy to support human supervision, analysed alerts generated by these encoded PAs. They registered their interventions and the acceptance by physicians.

RESULTS

From 3290 alerts analysed targeting antithrombotics, the pharmacists issued 1170 interventions of which 676 (57.8%) were accepted by physicians. With the 184 other PAs, from 9484 alerts the pharmacists issued 3341 interventions of which 1785 were accepted (53.4%).Results indicate that the detection of DRPs related to antithrombotics usage represents a high proportion of those detected by the PDSS, highlighting the importance of incorporating tailored PA elements at the modelling stage.

CONCLUSIONS

The system evolves alongside the physiological changes associated to the patient situations, adapts the alerts and complements the current care. Therefore, we recommend that all PDSS should integrate specific algorithms targeting DRPs associated with antithrombotics to enhance pharmaceutical interventions and improve patient safety.

Fostering Digital Health in Universities: An Experience of the First Junior Scientific Committee of the Brazilian Congress of Health Informatics

OBJECTIVES

Digital health (DH) is a revolution driven by digital technologies to improve health. Despite the importance of DH, curricular updates in healthcare university programs are scarce, and DH remains undervalued. Therefore, this report describes the first Junior Scientific Committee (JSC) focusing on DH at a nationwide congress, with the aim of affirming its importance for promoting DH in universities.

METHODS

The scientific committee of the Brazilian Congress of Health Informatics (CBIS) extended invitations to students engaged in health-related fields, who were tasked with organizing a warm-up event and a 4-hour session at CBIS. Additionally, they were encouraged to take an active role in a workshop alongside distinguished experts to map out the current state of DH in Brazil.

RESULTS

The warm-up event focused on the topic "Artificial intelligence in healthcare: is a new concept of health about to arise?" and featured remote discussions by three professionals from diverse disciplines. At CBIS, the JSC's inaugural presentation concentrated on delineating the present state of DH education in Brazil, while the second presentation offered strategies to advance DH, incorporating viewpoints from within and beyond the academic sphere. During the workshop, participants deliberated on the most crucial competencies for future professionals in the DH domain.

CONCLUSIONS

Forming a JSC proved to be a valuable tool to foster DH, particularly due to the valuable interactions it facilitated between esteemed professionals and students. It also supports the cultivation of leadership skills in DH, a field that has not yet received the recognition it deserves.

Call for the responsible artificial intelligence in the healthcare

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI's capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review

OBJECTIVES

The emergence of artificial intelligence (AI) is catching the interest of hospital pharmacists. A massive collection of health data is now available to train AI models and hold the promise of disrupting codes and practices. The objective of this systematic review was to examine the state of the art of machine learning or deep learning models that detect inappropriate hospital medication orders.

METHODS

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE and Embase databases were searched from inception to May 2023. Studies were included if they reported and described an AI model intended for use by clinical pharmacists in hospitals. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

RESULTS

13 articles were selected after review: 12 studies were judged to have high risk of bias; 11 studies were published between 2020 and 2023; 8 were conducted in North America and Asia; 6 analysed orders and detected inappropriate prescriptions according to patient profiles and medication orders; and 7 detected specific inappropriate prescriptions, such as detecting antibiotic resistance, dosage abnormality in prescriptions, high alert drugs errors from prescriptions or predicting the risk of adverse drug events. Various AI models were used, mainly supervised learning techniques. The training datasets used were very heterogeneous; the length of study varied from 2 weeks to 7 years and the number of prescription orders analysed went from 31 to 5 804 192.

CONCLUSIONS

This systematic review points out that, to date, few original research studies report AI tools based on machine or deep learning in the field of hospital clinical pharmacy. However, these original articles, while preliminary, highlighted the potential value of integrating AI into clinical hospital pharmacy practice.

Does Clinical Decision Support Increase Appropriate Medication Prescribing for Cardiovascular Risk Reduction?

PURPOSE

To assess the impact of a clinical decision support (CDS) system's recommendations on prescribing patterns targeting cardiovascular disease (CVD) when the recommendations are prioritized in order from greatest to least benefit toward overall CVD risk reduction.

METHODS

Secondary analysis of trial data from September 20, 2018, to March 15, 2020, where 70 community health center clinics were cluster-randomized to the CDS intervention (42 clinics; 8 organizations) or control group (28 clinics; 7 organizations). Included patients were medication-naïve and aged 40 to 75 years with ≥1 uncontrolled cardiovascular disease risk factor, with known diabetes or cardiovascular disease, or ≥10% 10-year reversible CVD risk.

RESULTS

Among eligible encounters with 29,771 patients, the probability of prescribing a medication targeting hypertension was greater at intervention clinic encounters when CDS was used (34.9% [95% CI, 31.5 to 38.3]) versus dismissed (29.6% [95% CI, 26.7 to 32.6]; P < .001), but not when compared with control clinic encounters (34.9% [95% CI, 31.1 to 38.7]; P = .998). Prescribing for dyslipidemia was significantly higher at intervention encounters where the CDS system was used (11.3% [95% CI, 9.3 to 13.3]) compared with dismissed (7.7% [95% CI, 6.1 to 9.3]; P = .003) and to control encounters (8.7% [95% CI, 7.0 to 10.4]; P = .044); smoking cessation medication showed a similar pattern. Except for dyslipidemia, prescribing rates increased according to their prioritization.

CONCLUSIONS

Use of this CDS system was associated with significantly higher prescribing targeting most cardiovascular risk factors. These results highlight how displaying prioritized actions to reduce reversible CVD risk could improve risk management.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records

OBJECTIVES

Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence.

METHODS

The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains.

RESULTS

Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied.

CONCLUSIONS

Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.

Evaluating the Landscape of Personal Health Records in Korea: Results of the National Health Informatization Survey

OBJECTIVES

This study examined the adoption and utilization of personal health records (PHR) across Korean medical institutions using data from the 2020 National Health and Medical Informatization Survey.

METHODS

Spearheaded by the Ministry of Health and Welfare and prominent academic societies, this study surveyed PHR utilization in 574 medical institutions.

RESULTS

Among these institutions, 84.9% (487 hospitals) maintained medical portals. However, just 14.1% (81 hospitals) had web-based or mobile PHRs, with 66.7% (28 of 42) of tertiary care hospitals adopting them. Tertiary hospitals led in PHR services: 87.8% offered certification issuance, 51.2% provided educational information, 63.4% supported online payment, and 95.1% managed appointment reservations. In contrast, general and smaller hospitals had lower rates. Online medical information viewing was prominent in tertiary hospitals (64.3%). Most patients accessed test results via PHRs, but other data types were less frequent, and only a few allowed downloads. Despite the widespread access to medical data through PHRs, integration with wearables and biometric data transfers to electronic medical records remained low, with limited plans for expansion in the coming three years.

CONCLUSIONS

Approximately two-thirds of the surveyed medical institutions provided PHRs, but hospitals and clinics in charge of community care had very limited PHR implementation. Government-led leadership is required to invigorate the use of PHRs in medical institutions.

Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards

INTRODUCTION

Amid clinicians' challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study compares the proficiency of ChatGPT3 against human evaluation in scoring abstracts to determine its potential as a tool for evidence synthesis.

METHODS

We compared ChatGPT's scoring of implant dentistry abstracts with human evaluators using the Consolidated Standards of Reporting Trials for Abstracts reporting standards checklist, yielding an overall compliance score (OCS). Bland-Altman analysis assessed agreement between human and AI-generated OCS percentages. Additional error analysis included mean difference of OCS subscores, Welch's t-test and Pearson's correlation coefficient.

RESULTS

Bland-Altman analysis showed a mean difference of 4.92% (95% CI 0.62%, 0.37%) in OCS between human evaluation and ChatGPT. Error analysis displayed small mean differences in most domains, with the highest in 'conclusion' (0.764 (95% CI 0.186, 0.280)) and the lowest in 'blinding' (0.034 (95% CI 0.818, 0.895)). The strongest correlations between were in 'harms' (r=0.32, p<0.001) and 'trial registration' (r=0.34, p=0.002), whereas the weakest were in 'intervention' (r=0.02, p<0.001) and 'objective' (r=0.06, p<0.001).

CONCLUSION

LLMs like ChatGPT can help automate appraisal of medical literature, aiding in the identification of accurately reported research. Possible applications of ChatGPT include integration within medical databases for abstract evaluation. Current limitations include the token limit, restricting its usage to abstracts. As AI technology advances, future versions like GPT4 could offer more reliable, comprehensive evaluations, enhancing the identification of high-quality research and potentially improving patient outcomes.

Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort

OBJECTIVE

To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort.

METHODS

We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from 2016 to 2019 (MV non-COVID-19, n=3905). We collected a second validation cohort of hospitalised patients with COVID-19 in 2020 (COVID+, n=5672). We trained an LSTM model using 132 structured features on the MV non-COVID-19 training cohort and validated on the MV non-COVID-19 validation and COVID-19 cohorts.

RESULTS

Applying LSTM (model score 0.9) on the MV non-COVID-19 validation cohort had a sensitivity of 86% and specificity of 57%. The model identified the risk of ARDS 10 hours before ARDS and 9.4 days before death. The sensitivity (70%) and specificity (84%) of the model on the COVID-19 cohort are lower than MV non-COVID-19 cohort. For the COVID-19 + cohort and MV COVID-19 + patients, the model identified the risk of in-hospital mortality 2.4 days and 1.54 days before death, respectively.

DISCUSSION

Our LSTM algorithm accurately and timely identified the risk of ARDS or death in MV non-COVID-19 and COVID+ patients. By alerting the risk of ARDS or death, we can improve the implementation of evidence-based ARDS management and facilitate goals-of-care discussions in high-risk patients.

CONCLUSION

Using the LSTM algorithm in hospitalised patients identifies the risk of ARDS or death.

Embracing digital and technological solutions in nursing: A scoping review and conceptual framework

INTRODUCTION

Digital and technological solutions (DTS) are emerging as promising avenues to improve the quality and efficiency of healthcare. This scoping review aimed to map the literature on embracing DTS in nursing, from primary to tertiary care settings.

METHODS

The Joanna Briggs Institute guidance for scoping reviews was used. The authors searched MEDLINE, Embase, CINAHL, Scopus, Web of Science, Cochrane Library, and PROSPERO databases for published articles and relevant peer-reviewed research protocols. Screening and data abstraction were conducted by two reviewers independently, with a third reviewer resolving discrepancies. Frequency and thematic analyses were conducted.

RESULTS

The study highlights the crucial role nurses play in introducing, implementing, and using DTS. The summarized literature emphasizes that cultivating positive attitudes, possessing sufficient knowledge, competencies, self-efficacy, and displaying appropriate behaviors toward such technologies are vital in ensuring their effective incorporation into nursing practice.

DISCUSSION AND CONCLUSIONS

The findings of this scoping review provide a foundation for future research on DTS adoption in nursing and support the development of evidence-based strategies to improve nursing practice through DTS implementation. Therefore, the article proposes the Digital and Technological Framework (Digitech-F) for healthcare professionals as a comprehensive conceptual framework that addresses skills, knowledge, attitude, and competence to ensure the effective adoption of DTS in nursing.

Frequency of paediatric patients administered extemporaneous preparations at a Swedish university hospital: a registry-based study comparing two study-years, 10 years apart

BACKGROUND

Lack of child-friendly dosage forms and strengths often leads to manipulation of medicines at hospital units or by caregivers in the home setting. One alternative to manipulating dosage forms is the use of extemporaneous preparations. In Sweden, these are produced according to good manufacturing practice by a few extemporaneous pharmacies.

OBJECTIVES

To compare frequencies of patients administered extemporaneous preparations in two separate years, 10 years apart.

METHODS

This registry-based study describes and compares the frequency of extemporaneous oral preparations administered to paediatric patients in 2009 and 2019 at a Swedish university hospital.The study included 117 023 oral administrations (to 4905 patients) and 128 638 oral administrations (to 4718 patients) from 2009 and 2019, respectively.

RESULTS

The frequency of inpatients administered one or more extemporaneous preparations increased from 22% in 2009 to 40% in 2019 (p<0.0001). The increase was observed in all age groups. The use of some active pharmaceutical ingredients increased (eg, captopril, clonidine, hydrocortisone, melatonin and propranolol), and some active pharmaceutical ingredients decreased between the study years (eg, midazolam and sildenafil).

CONCLUSIONS

The introduction of new authorised products has decreased the need for manipulation or extemporaneous preparations in some therapeutic groups. There remains, however, a pronounced lack of commercially available child-friendly dosage forms and suitable strengths enabling safe administration of medicines to children, indicated by the large percentage of patients receiving at least one extemporaneous preparation.

Methods Used in the Development of Common Data Models for Health Data: Scoping Review

BACKGROUND

Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems.

OBJECTIVE

This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level.

METHODS

This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development.

RESULTS

We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process.

CONCLUSIONS

The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.

Descriptive forest: experiments on a novel tree-structure-generalization method for describing cardiovascular diseases

BACKGROUND

A decision tree is a crucial tool for describing the factors related to cardiovascular disease (CVD) risk and for predicting and explaining it for patients. Notably, the decision tree must be simplified because patients may have different primary topics or factors related to the CVD risk. Many decision trees can describe the data collected from multiple environmental heart disease risk datasets or a forest, where each tree describes the CVD risk for each primary topic.

METHODS

We demonstrate the presence of trees, or a forest, using an integrated CVD dataset obtained from multiple datasets. Moreover, we apply a novel method to an association-rule tree to discover each primary topic hidden within a dataset. To generalize the tree structure for descriptive tasks, each primary topic is a boundary node acting as a root node of a C4.5 tree with the least prodigality for the tree structure (PTS). All trees are assigned to a descriptive forest describing the CVD risks in a dataset. A descriptive forest is used to describe each CVD patient's primary risk topics and related factors. We describe eight primary topics in a descriptive forest acquired from 918 records of a heart failure-prediction dataset with 11 features obtained from five datasets. We apply the proposed method to 253,680 records with 22 features from imbalanced classes of a heart disease health-indicators dataset.

RESULTS

The usability of the descriptive forest is demonstrated by a comparative study (on qualitative and quantitative tasks of the CVD-risk explanation) with a C4.5 tree generated from the same dataset but with the least PTS. The qualitative descriptive task confirms that compared to a single C4.5 tree, the descriptive forest is more flexible and can better describe the CVD risk, whereas the quantitative descriptive task confirms that it achieved higher coverage (recall) and correctness (accuracy and precision) and provided more detailed explanations. Additionally, for these tasks, the descriptive forest still outperforms the C4.5 tree. To reduce the problem of imbalanced classes, the ratio of classes in each subdataset generating each tree is investigated.

CONCLUSION

The results provide confidence for using the descriptive forest.

Influence of Practice Characteristics on the Adoption of Electronic Dental Records in Jeddah, Saudi Arabia

OBJECTIVES

The adoption of electronic dental records (EDRs) is less extensively studied than electronic medical records (EMRs) in Saudi Arabia. Therefore, a multivariate analysis was conducted to calculate the adoption of EDRs and determine the practice characteristics that influence adoption.

METHODS

An online survey was conducted with 220 dental practices in Jeddah from August to December 2021. The questionnaire contained 10 items that measured the adoption of EDRs and identified the region, district, practice characteristics, and practice size. A regression analysis was used to ascertain the relationships between EDR adoption and the predictor variables.

RESULTS

About 93% of the dental practices, we surveyed in Jeddah had adopted EDRs. Public dental practices and large practices were associated with higher rates of adoption (respectively, 97.0%, p = 0.016; 97.8%, p = 0.009). The logistic regression model showed statistically significant results regarding practice characteristics, practice size, and the acceptance of insurance patients. EDR adoption was 89% less likely for private dental practices, 99% less likely for smaller dental practices (≥2 dentists), and 98% less likely in dental practices that did not treat patients with insurance.

CONCLUSIONS

Our study sample showed a high rate of EDR adoption. Among the participants, public practices, large practices, and practices that treat patients with insurance were the most positively inclined toward EDR adoption.

TransFAIR study: a European multicentre experimental comparison of EHR2EDC technology to the usual manual method for eCRF data collection

PURPOSE

Regulatory authorities including the Food and Drug Administration and the European Medicines Agency are encouraging to conduct clinical trials using routinely collected data. The aim of the TransFAIR experimental comparison was to evaluate, within real-life conditions, the ability of the Electronic Health Records to Electronic Data Capture (EHR2EDC) module to accurately transfer from EHRs to EDC systems patients' data of clinical studies in various therapeutic areas.

METHODS

A prospective study including six clinical trials from three different sponsors running in three hospitals across Europe has been conducted. The same data from the six studies were collected using both traditional manual data entry and the EHR2EDC module. The outcome variable was the percentage of data accurately transferred using the EHR2EDC technology. This percentage was calculated considering all collected data and the data in four domains: demographics (DM), vital signs (VS), laboratories (LB) and concomitant medications (CM).

RESULTS

Overall, 6143 data points (39.6% of the data in the scope of the TransFAIR study and 16.9% when considering all data) were accurately transferred using the platform. LB data represented 65.4% of the data transferred; VS data, 30.8%; DM data, 0.7% and CM data, 3.1%.

CONCLUSIONS

The objective of accurately transferring at least 15% of the manually entered trial datapoints using the EHR2EDC module was achieved. Collaboration and codesign by hospitals, industry, technology company, supported by the Institute of Innovation through Health Data was a success factor in accomplishing these results. Further work should focus on the harmonisation of data standards and improved interoperability to extend the scope of transferable EHR data.

Performance and risks of ChatGPT used in drug information: an exploratory real-world analysis

OBJECTIVES

To investigate the performance and risk associated with the usage of Chat Generative Pre-trained Transformer (ChatGPT) to answer drug-related questions.

METHODS

A sample of 50 drug-related questions were consecutively collected and entered in the artificial intelligence software application ChatGPT. Answers were documented and rated in a standardised consensus process by six senior hospital pharmacists in the domains content (correct, incomplete, false), patient management (possible, insufficient, not possible) and risk (no risk, low risk, high risk). As reference, answers were researched in adherence to the German guideline of drug information and stratified in four categories according to the sources used. In addition, the reproducibility of ChatGPT's answers was analysed by entering three questions at different timepoints repeatedly (day 1, day 2, week 2, week 3).

RESULTS

Overall, only 13 of 50 answers provided correct content and had enough information to initiate management with no risk of patient harm. The majority of answers were either false (38%, n=19) or had partly correct content (36%, n=18) and no references were provided. A high risk of patient harm was likely in 26% (n=13) of the cases and risk was judged low for 28% (n=14) of the cases. In all high-risk cases, actions could have been initiated based on the provided information. The answers of ChatGPT varied over time when entered repeatedly and only three out of 12 answers were identical, showing no reproducibility to low reproducibility.

CONCLUSION

In a real-world sample of 50 drug-related questions, ChatGPT answered the majority of questions wrong or partly wrong. The use of artificial intelligence applications in drug information is not possible as long as barriers like wrong content, missing references and reproducibility remain.

Insights into the FAIRness of the German Network University Medicine: A Survey

The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.

Proposing a Novel Hybrid Short-Term Exchange Program in Biomedical and Health Informatics Education

International student exchange is a valuable opportunity for Biomedical and Health Informatics students to gain new perspectives and experiences. In the past, such exchanges have been made possible through international partnerships between universities. Unfortunately, numerous obstacles such as housing, financial concerns, and environmental implications related to travel, have made it difficult to continue international exchange. Experiences with hybrid and online education during covid-19 paved the way for a new approach that allows for short international exchange with a hybrid online-offline supervision model. This will be initiated with an exploration project between two international universities , each related to their respective institute's research focus.

Bioengineering and medical informatics education in MD programs: perspectives from three Italian experiences

BACKGROUND

Given the impact of bioengineering and medical informatics technologies in health care, the design and implementation of education programs able to combine medical curricula with a proper teaching on engineering and informatics is now of paramount importance. In Italy, this goal has to fit in with the existing higher education system, which is structured into Bachelor programs and Master programs. Medicine and Surgery programs, instead, are designed as a six-year single-cycle Degree Program in Medicine and Surgery which comprises both class attendance and hospital internship and training. This program allows students to become Medical Doctors (MD). The different organization of this University program makes it not easy to introduce further contents, namely hard science courses, in the educational program. Notwithstanding this, we present here some recent innovative programs aimed at widening MD curriculum by including biomedical engineering and informatics subjects. In particular, we will introduce three of them. Two are joint-degree programs, the first between Humanitas University and Politecnico di Milano (MEDTEC School), and the second between University of Calabria and University Magna Graecia of Catanzaro (Medicina e Chirurgia TD). The Third one is a Professional Master coupled with an MD degree, based on a joint program among Pavia University, Pisa University, the Institute of Advanced studies in Pavia and the Scuola Superiore S. Anna in Pisa (MEET).

CONTRIBUTION

The paper provides a description of the fundamental design principles of the three above mentioned programs, and explores some aspects of the teaching modules, highlighting their positive aspects. In particular, we show how the three different programs allow students to enrich their knowledge by studying engineering subjects and innovative methods and technologies, as well as their applications to patient care.

CONCLUSIONS

The MEDTEC program is the first degree program at Italian and international scale which integrates medical and engineering subjects. In the following years, other programs were issued in Italy, defining similar education programs to couple a degree in medicine education with bioengineering and medical informatics, among which Medicina e Chirurgia TD and MEET. We believe the experiences described here in this paper represent the possibility of bridging the gap between medical and technological competencies.

Digitalisation of the drug prescribing process in Swiss hospitals - results of a survey

BACKGROUND

The state of digitalisation in the healthcare sector in Switzerland is lagging, even as the national electronic health record (EHR) is being gradually implemented. Little is known about the implementation of electronic prescribing systems, their auxiliary features or drug datasets in Swiss hospitals.The aim of this study was to understand which electronic systems are implemented to support doctors in Swiss hospitals during the medication prescribing process.

METHODS

The survey was sent in spring 2021 to the chief pharmacists of the main Swiss hospitals. The survey focused on the introduction of the EHR, the clinical information system (CIS) and its prescribing module, as well as drug information data and clinical decision support systems (CDSS).

RESULTS

The response rate was 98% (58/59 hospitals). Almost half of the hospitals (47%) were connected to the national EHR, almost all hospitals (86%) used a CIS and a vast majority of the hospitals (84%) had implemented electronic prescribing systems in their CIS. 10 years ago, around 63% of hospitals used a CIS and 40% were equipped with an electronic prescribing system. Today, CDSS of any kind were implemented in 50% of the hospitals, predominantly for drug-drug interactions. Drug master data were maintained in most hospitals (76%) via an automated interface, but mostly supplemented manually. Clinical drug information data were maintained in 74% of hospitals. In 67% of hospitals, datasets were imported via an automated interface.

CONCLUSIONS

The digitalisation of the medical prescribing process in Swiss hospitals has progressed over the last decade. Drug prescriptions via electronic prescribing systems were introduced in most hospitals. However, this survey suggests that the current use of CDSS is far from exhausted, and that clinical drug information data could be maintained more efficiently. Optimising electronic support for healthcare professionals during the prescribing process still has considerable potential.

Public information on shortages in the EU/EEA: improvements made between 2018 and 2020

BACKGROUND

In July 2019, the Heads of Medicines Agencies/European Medicines Agency (HMA/EMA) Task Force on Availability of Authorised Medicines for Human and Veterinary Use (TFAAM) published good practice guidance which provides key principles for European Union (EU) regulatory authorities for communication on shortages and availability issues. The use of a shortage catalogue was a key recommendation.

OBJECTIVES

To assess how EU/European Economic Area (EEA) national competent authorities have implemented the recommendations of the good practice guidance.

METHODS

A survey was run in 2020 among EU/EEA national competent authorities to assess communication practices. The results were compared with those of a similar survey carried out 2 years earlier, before publication of the guidance. The survey covered human medicines only and was sent to 31 authorities: one per EU/EEA member state (and two to Germany's two medicines regulatory authorities).

RESULTS

In 2020, 81% of authorities (25/31) had a dedicated public shortage catalogue on their website. This was an increase from 74% (23/31) in 2018, when a similar survey was run. In future this is expected to increase to 87% with two more member states making plans to implement catalogues. Although many member states publish information on shortages there is still selection in terms of the details that are being published, and there is further scope to extend the information currently provided.

CONCLUSION

Since publication of the EMA/HMA good practice guide in 2019, transparency has increased across the EU/EEA, and public catalogues of shortages are now a routine tool used by many medicines agencies.Further opportunities to improve transparency on supply issues lie ahead with the EMA network strategy to 2025, the revised EU pharmaceutical legislation and the new legal mandate reinforcing the role of the EMA.

IT risk management for medical devices in hospital IT networks: a catalogue of measures and indicators

OBJECTIVES

Connecting medical devices to hospital IT networks can create threats that must be covered by IT risk management. In practice, implementing such risk management is not trivial because the IEC 80001-1, as the existing state-of-the-art, do not describe sufficiently concrete implementation measures or evaluation indicators. The aim of the present work was to develop and evaluate a catalogue of measures and indicators to help hospitals implement and evaluate risk management in accordance with IEC 80001-1.

METHODS

We conducted a Delphi study with 22 experts. In the first round, we performed interviews to identify implementation measures and evaluation indicators using qualitative content analysis. In the second round, a quantitative experts' survey confirmed the results of the first survey round and identified relationships between the measures and indicators. Based on these results, we then developed a catalogue containing the identified measures and indicators. Finally, we performed a case study to verify the practicability of this catalogue.

RESULTS

We developed and verified a catalogue of 49 measures and 18 indicators to help hospitals implement and evaluate risk management following IEC 80001-1. The case study confirmed the practicability of the catalogue.

DISCUSSION

Compared with IEC 80001-1, our catalogue goes into further detail to offer hospitals a stepwise implementation and evaluation approach. However, the catalogue must be tested in further case studies and evaluated in terms of generalisation.

CONCLUSIONS

The catalogue will enable hospitals to overcome recent difficulties in implementing and evaluating IT risk management for medical devices according to IEC 80001-1.

Healthcare Information Technology: A Systematic Mapping Study

OBJECTIVES

This paper presents a systematic mapping of studies related to information systems and technology in the field of healthcare, enabling a visual mapping of the different lines of knowledge that can provide an overview of the scientific literature in this field. This map can help to clarify critical aspects of healthcare informatics, such as the main types of information systems, the ways in which they integrate with each other, and the technological trends in this field.

METHODS

Systematic mapping refers to a process of classifying information in a given area of knowledge. It provides an overview of the state of the art in a particular discipline or area of knowledge, establishing a map that describes how knowledge is structured in that particular area. In this study, we proposed and carried out a specific implementation of the methodology for mapping. In total, 1,619 studies that combine knowledge related to information systems, computer science, and healthcare were selected and compiled from prestigious publications.

RESULTS

The results established a distribution of the available literature and identified papers related to certain research questions, thereby providing a map of knowledge that structures the different trends and main areas of research, making it possible to address the research questions and serving as a guide to deepen specific aspects of the field of study.

CONCLUSIONS

We project and propose future research for the trends that stand out because of their interest and the possibility of exploring these topics in greater depth.

Biomedical informatics: characterization of the offer of massive open online courses

Introduction: Informatics applied to health sciences has brought cutting-edge solutions to healthcare problems. However, the number of health professionals trained in "Health Informatics" is low. Virtual education, such as massive online open courses, provide the opportunity for training in this field. Objective: To estimate the global offer of massive online open biomedical informatics courses and characterize their content. Methods: A search for massive online open courses was conducted throughout December 2021 on 25 platforms offering these courses. The search strategy included the terms "health informatics" and "biomedical informatics". The application areas of biomedical informatics, platform, institution, duration, time required per week, language, and subtitles available for each course were evaluated. Data were analyzed descriptively, reporting absolute and relative frequencies. Results; Our search strategy identified 1333 massive online open courses. Of these, only 79 were related to health informatics. Most of these courses (n = 44; 55.7%) were offered through Coursera. More than half (n = 55; 69.6%) were conducted by U.S. institutions in english (n = 76; 96.2%). Most courses focused on areas of translational bioinformatics (n = 27; 34.2%), followed by public health informatics (n = 23; 29.1%), and clinical research informatics (n = 13, 16.5%). Conclusions: We found a significant supply of massive online open courses on health informatics. These courses favor the training of more professionals worldwide, mostly addressing competencies to apply informatics in clinical practice, public health, and health research.

Extension of the Unified Theory of Acceptance and Use of Technology 2 model for predicting mHealth acceptance using diabetes as an example: a cross-sectional validation study

OBJECTIVES

Mobile health applications are instrumental in the self-management of chronic diseases like diabetes. Technology acceptance models such as Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) have proven essential for predicting the acceptance of information technology. However, earlier research has found that the constructs "perceived disease threat" and "trust" should be added to UTAUT2 in the mHealth acceptance context. This study aims to evaluate the extended UTAUT2 model for predicting mHealth acceptance, represented by behavioral intention, using mobile diabetes applications as an example.

METHODS

We extended UTAUT2 with the additional constructs "perceived disease threat" and "trust". We conducted a web-based survey in German-speaking countries focusing on patients with diabetes and their relatives who have been using mobile diabetes applications for at least 3 months. We analysed 413 completed questionnaires by structural equation modelling.

RESULTS

We could confirm that the newly added constructs "perceived disease threat" and "trust" indeed predict behavioural intention to use mobile diabetes applications. We could also confirm the UTAUT2 constructs "performance expectancy" and "habit" to predict behavioural intention to use mobile diabetes applications. The results show that the extended UTAUT2 model could explain 35.0% of the variance in behavioural intention.

DISCUSSION

Even if UTAUT2 is well established in the information technologies sector to predict technology acceptance, our results reveal that the original UTAUT2 should be extended by "perceived disease threat" and "trust" to better predict mHealth acceptance.

CONCLUSION

Despite the newly added constructs, UTAUT2 can only partially predict mHealth acceptance. Future research should investigate additional mHealth acceptance factors, including how patients perceive trust in mHealth applications.

Unsupervised Machine Learning to Identify Depressive Subtypes

OBJECTIVES

This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data.

METHODS

Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results.

RESULTS

Five subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17-1.43 and OR = 1.16; 95% CI, 1.05-1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78-0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity.

CONCLUSIONS

This study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.

The Podcast "Digitization of Medicine" as a Form of Science Communication

The aim of the podcast Digitization of Medicine is to interest a broader audience and, in particular, young women, in research and work in the field of medical informatics. This article presents the usage figures and discusses their significance for further research on the success of science communication. By 24/02/2022, a total of 24,351 downloads had been made. There were slightly more female than male listeners, and they tended to be younger. Despite the importance podcast are gaining for science communication, little is known about the respective user group and further research is needed. In this context, this paper aims to help make the effectiveness of podcasts comparable.

A Knowledge Graph Embedding Based Approach to Predict the Adverse Drug Reactions Using a Deep Neural Network

Recently Artificial Intelligence(AI) has not only been used to diagnose the disease but also to cure the disease. Researchers started using AI for drug discovery. Predicting the Adverse Drug Reactions(ADRs) caused by the drug in the manufacturing stage or in the clinical trial stage is a very important problem in drug discovery. ADRs have become a major concern resulting in injuries and also becoming fatal sometimes. Drug safety has gained much importance over the years propelling to the forefront investigation of predicting the ADRs. Although prior studies have queried diverse approaches to predict ADRs, very few were found to be effective. Also, the problem of having fewer reports makes the prediction of ADRs more difficult. To tackle this problem effectively, a novel method has been proposed in this paper. The proposed method is based on Knowledge Graph(KG) embedding. Using the KG embedding, we designed and trained a custom-made Deep Neural Network(DNN) called KGDNN(Knowledge Graph DNN) for predicting the ADRs. A KG has been constructed with 6 types of entities: drugs, ADRs, target proteins, indications, pathways, and genes. Using the Node2Vec algorithm, each node has been embedded into a feature space. Using those embeddings, the ADRs are classified by the KGDNN model. The proposed method has obtained an AUROC score of 0.917 and significantly outperformed the existing methods. Two case studies on drugs causing liver injury and COVID-19 recommended drugs have been performed to illustrate the model efficacy.

Policy and Strategy for Interoperability of Digital Health in Europe

The development of electronic services for healthcare presents challenges related to the effective cooperation of systems and stakeholders in a highly regulated environment. In order to facilitate healthcare for all at the point of need it is important to establish the necessary conditions to guide the development and implementation of digital health solutions that are interoperable by design. Interoperability in eHealth is challenging for various reasons, including the fact that different products and solutions in the market do not follow well-known standards and interoperability guidelines. The paper draws upon the global, European and national policies, strategies, and implementation initiatives to offer an integrated approach towards interoperability in healthcare ecosystems. The authors provide guidelines and recommendations to support interoperability at legal, organizational, semantic, and technical levels.

Medical Workflow Design and Planning Using Node-RED Data Fusion

The space of clinical planning requires a complex arrangement of information, often not capable of being captured in a singular dataset. As a result, data fusion techniques can be used to combine multiple data sources as a method of enriching data to mimic and compliment the nature of clinical planning. These techniques are capable of aiding healthcare providers to produce higher quality clinical plans and better progression monitoring techniques. Clinical planning and monitoring are important facets of healthcare which are essential to improving the prognosis and quality of life of patients with chronic and debilitating conditions such as COPD. To exemplify this concept, we utilize a Node-Red-based clinical planning and monitoring tool that combines data fusion techniques using the JDL Model for data fusion and a domain specific language which features a self-organizing abstract syntax tree.