Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness

In anticipation of potential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and clinicians in managing subsequent infectious diseases, and how legislators and policymakers could support these efforts, to bring learning healthcare system (LHS) from guidelines to real-world implementation. This report chronicles the trajectory of the COVID-19 pandemic, emphasizing the diverse data sets generated throughout its course. We propose strategies for harnessing this data via AI and predictive modelling to enhance the functioning of LHS. The challenges faced by patients and healthcare systems around the world during this unprecedented crisis could have been mitigated with an informed and timely adoption of the three pillars of the LHS: Knowledge, Data and Practice. By harnessing AI and predictive analytics, we can develop tools that not only detect potential pandemic-prone diseases early on but also assist in patient management, provide decision support, offer treatment recommendations, deliver patient outcome triage, predict post-recovery long-term disease impacts, monitor viral mutations and variant emergence, and assess vaccine and treatment efficacy in real-time. A patient-centric approach remains paramount, ensuring patients are both informed and actively involved in disease mitigation strategies.

NHS national data opt-outs: trends and potential consequences for health data research

BACKGROUND

The English NHS data opt-out allows people to prevent use of their health data for purposes other than direct care. In 2021, the number of opt-outs increased in response to government-led proposals to create a centralised pseudonymised primary care record database.

AIM

To describe the potential impact of NHS national data opt-outs in 2021 on health data research.

DESIGN & SETTING

We conducted a descriptive analysis of opt-outs using publicly available data and the potential consequences on research are discussed.

METHOD

Trends in opt-outs in England were described by age, sex, and region. Using a hypothetical study, we explored statistical and epidemiological implications of opt-outs.

RESULTS

During the lead up to a key government-led deadline for registering opt-outs (from 31 May 2021-30 June 2021), 1 339 862 national data opt-outs were recorded; increasing the percentage of opt-outs in England from 2.77% to 4.97% of the population. Among females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared with 76% in males (from 2.51% to 4.41%). Across age groups, the highest relative increase was among people aged 40-49 years, which rose from 2.89% to 6.04%. Considerable geographical variation was not clearly related to deprivation. Key research consequences of opt-outs include reductions in sample size and unpredictable distortion of observed measures of the frequency of health events or associations between these events.

CONCLUSION

Opt-out rates varied by age, sex, and place. The impact of this and variation by other characteristics on research is not quantifiable. Potential effects of opt-outs on research and consequences for health policies based on this research must be considered when creating future opt-out solutions.

Impact of fatigue on work productivity and health-related job loss

BACKGROUND

Fatigue is commonly reported in population surveys and has been identified in patients with health conditions as a key co-morbidity which makes remaining in work challenging. Such patients, however, rarely have access to programmes to help them manage their fatigue.

AIMS

To quantify the relationship between fatigue, work impairment and health-related job loss.

METHODS

We use data from the Health and Employment After Fifty study, a longitudinal study of people aged 50-64 years when recruited through general practices in England in 2013-14. During follow-up, fatigue was measured using the Fatigue Assessment Scale, work impairment was assessed using the Work Productivity and Activity Impairment scale, and changes in employment status were recorded.

RESULTS

A total of 2743 participants were eligible for the current analysis; 23% satisfied criteria for being fatigued. People who were fatigued were less likely to have a partner, university degree, be physically active and were more likely to be obese. Their job was more likely to involve shifts, be perceived as insecure, have reported difficulties coping with job demands, and be unsatisfying. After adjustment for socio-economic, lifestyle and work-related factors, they were almost twice as likely to report both work impairment (relative risk 1.8; 95% confidence interval [CI] 1.6, 2.1) and future health-related job loss, although the latter effect was only in those with other morbidities (incidence rate ratio 1.96; 95% CI 1.03-3.72).

CONCLUSIONS

Providing evidence-based support for workers with health conditions who experience fatigue may have an important impact at a population level in terms of extending working lives.

Rasch analysis of the modified Fatigue Severity Scale in neuromuscular disorders and comparison between sex, age and diagnoses

INTRODUCTION/AIMS

Fatigue is a common and debilitating symptom encountered in the neuromuscular clinic. The 7-item Fatigue Severity Scale (FSS-7) is a Rasch-modified assessment validated in inflammatory neuropathies but not across a typical neuromuscular patient population. The aim of this study was to validate this measure in neuromuscular disorders and to compare between patient sex, age and diagnoses.

METHODS

The modified FSS-7 was mailed to patients recruited from a specialist neuromuscular clinic at the Walton Centre. Responses were subjected to Rasch analysis and descriptive statistics were performed on the Rasch converted data.

RESULTS

The mFSS-7 met the Rasch model expectations with an overall Chi-square probability of 0.4918, a strict unidimensional scale free from differential item functioning (DIF) that satisfied the model with substantial test-retest reliability using Lin's concordance correlation coefficient 0.71 (95% CI 0.63-0.77). A 15.7% ceiling effect was observed in this patient cohort. Post hoc analysis did not show any significant difference in fatigue between sex, age or neuromuscular diagnoses.

DISCUSSION

The self-completed Rasch mFSS-7 showed acceptable test-retest reliability across patients with varied disorders under follow-up in a specialist neuromuscular clinic. The ceiling effect constrains its use for those with the most severe fatigue. Future considerations could include assessment of the benefits of clinical interventions, particularly multidisciplinary team input or dedicated fatigue clinics.

Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation

Objective

To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.

Method

We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes.

Results

The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people.

Conclusions

Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.

Embracing the (r)evolution of social media and digital scholarship in pediatric nephrology education

Free Open-Access Medical Education (FOAMed) has transformed medical education in the past decade by complementing and substituting for traditional medical education when needed. The attractiveness of FOAMed resources is due to their inexpensive nature, wide availability, and user ability to access on demand across a variety of devices, making it easy to create, share, and participate. The subject of nephrology is complex, fascinating, and challenging. Traditional didactic lectures can be passive and ineffective in uncovering these difficult concepts and may need frequent revisions. Active teaching methods like flipped classrooms have shown some benefits, and these benefits can only be multifold with current social media tools. Social media will inspire the involvement of students and allow them to create and share educational content in a "trendy way," encouraging the participation of their peers and thus building an educational environment more conducive to them while promoting revision and retainment. FOAMed also promotes asynchronous learning, spaced learning, microlearning, and multimodal presentation with a meaningful variation. This article discusses the evolution of digital education, social media platforms, tools for creating and developing FOAMed resources, and digital scholarship.

Efficacy of a Telemonitoring System as a Complementary Strategy in the Treatment of Patients With Heart Failure: Randomized Clinical Trial

Episodes of decompensation are the main cause of hospital admissions in patients with heart failure. For this reason, the use of mobile apps emerges as an excellent strategy to improve coverage, real-time monitoring, and timeliness of care. ControlVit is an electronic application for early detection of complications studied within the context of a tertiary university hospital. Patients were randomized to the use of ControlVit versus placebo, during a 6-month follow-up. The primary outcome was the difference in numbers of readmissions and deaths for heart failure between both groups. One hundred forty patients were included (intervention = 71, placebo = 69), with an average age of 66 years old; 71% were men. The main etiology of heart failure was ischemic (60%), whereas the main comorbidities were arterial hypertension (44%), dyslipidemia (42%), hypothyroidism (38%), chronic kidney disease (38%), and diabetes mellitus (27%). The primary outcome occurred more frequently in the control group: readmission due to decompensation for heart failure (control group n = 14 vs intervention group n = 3; P = .0081), and death (control group n = 11 vs intervention group n = 3; P = .024). In heart failure patients, ControlVit is a useful and supplementary tool, which reduces hospital admissions due to episodes of decompensation.

Individual-Level Digital Determinants of Health and Technology Acceptance of Patient Portals: Cross-Sectional Assessment

BACKGROUND

Digital determinants of health (DDoH), including access to technological tools and digital health literacy, function independently as barriers to health. Assessment for DDoH is not routine within most health care systems, although addressing DDoH could help mitigate differential health outcomes and the digital divide.

OBJECTIVE

This study aims to assess the role of individual-level DDoH factors on patient enrollment in and use of the patient portal.

METHODS

We developed a multimodal, cross-sectional survey and deployed it to 11,424 individuals based on their preferred mode and language documented within the electronic medical record. Based on the Technology Acceptance Model, enrollment in and intent to use the patient portal were the outcomes of interest. Perceived usefulness and ease of use were assessed to determine construct validity, and exploratory investigations included individual-level DDoH, including internet and device access, availability of technological support, medical complexity, individual relationship with the health care system, and digital health literacy. Counts (n) and proportions (%) were used to describe response categories, and adjusted and unadjusted odds ratios are reported.

RESULTS

This study included 1850 respondents (11,424 invited, 16.2% response rate), who were mostly female (1048/1850, 56.6%) and White (1240/1850, 67%), with an average age of 63 years. In the validation of the Technology Acceptance Model, measures of perceived ease of use (ie, using the patient portal will require a lot of mental effort; the patient portal will be very easy to use) and perceived usefulness (ie, the usefulness of the patient portal to send and receive messages with providers, schedule appointments, and refill medications) were positively associated with both enrollment in and intent to use the patient portal. Within adjusted models, perceived ease of use and perceived usefulness constructs, in addition to constructs of digital health literacy, knowing what health resources are available on the internet (adjusted odds ratio [aOR] 3.5, 95% CI 1.8-6.6), portal ease of use (aOR 2.8, 95% CI 1.6-5), and portal usefulness (aOR 2.4, 95% CI 1.4-4.2) were significantly associated with patient portal enrollment. Other factors associated with patient portal enrollment and intent to use included being comfortable reading and speaking English, reported use of the internet to surf the web or to send or receive emails, home internet access, and access to technology devices (computer, tablet, smartphone, etc).

CONCLUSIONS

Assessing for and addressing individual-level DDoH, including digital health literacy, access to digital tools and technologies, and support of the relational aspects between patients, social support systems, and health care providers, could help mitigate disparities in health. By focusing efforts to assess for and address individual-level DDoH, an opportunity exists to improve digitally driven health care delivery outcomes like access and structural outcomes like bias built within algorithms created with incomplete representation across communities.

Predictive Factors of Physicians' Satisfaction and Quality of Work Under Teleconsultation Conditions: Structural Equation Analysis

BACKGROUND

The COVID-19 pandemic contributed to an increase in teleconsultation adoption in the Polish primary health care system. It is expected that in the long run, teleconsultations will successfully replace a significant part of face-to-face visits. Therefore, a significant challenge facing primary health care facilities (PHCs) is the acceptance of teleconsultations by their users, especially physicians.

OBJECTIVE

This study aimed to explore physicians' acceptance of teleconsultations during the COVID-19 pandemic in Poland.

METHODS

A representative survey was conducted among 361 physicians of PHCs across Poland in 2021. For the purposes of the study, we developed a modified Technology Acceptance Model (TAM) model. Based on the modified TAM, we analyzed the impact of perceived usefulness (PU), perceived ease of use (PEU), and intention to use teleconsultation (INT) on physicians' satisfaction (SAT) and quality of work (Q). The psychometric properties of the research instrument were examined using exploratory factor analysis. Finally, structural equation modeling was used for data analysis.

RESULTS

The results indicated a generally high level of PU (mean 3.85-4.36, SD 0.87-1.18), PEU (mean 3.81-4.60, SD 0.60-1.42), INT (mean 3.87-4.22, SD 0.89-1.12), and SAT (mean 3.55-4.13, SD 0.88-1.16); the lowest rated dimension in TAM was Q (mean 3.28-3.73, SD 1.06-1.26). The most important independent variable was PU. The influence of PU on INT (estimate=0.63, critical ratio [CR]=15.84, P<.001) and of PU on SAT (estimate=0.44, CR= 9.53, P<.001) was strong. INT was also a key factor influencing SAT (estimate=0.4, CR=8.57, P<.001). A weaker relationship was noted in the effect of PEU on INT (estimate=0.17, CR=4.31, P<.001). In turn, Q was positively influenced by INT (estimate=0.179, CR=3.64, P<.001), PU (estimate=0.246, CR=4.79, P<.001), PEU (estimate=0.18, CR=4.93, P<.001), and SAT (estimate=0.357, CR=6.97, P<.001). All paths between the constructs (PU, PEU, INT, SAT, and Q) were statistically significant, which highlights the multifaceted nature of the adoption of teleconsultations among physicians.

CONCLUSIONS

Our findings provide strong empirical support for the hypothesized relationships in TAM. The findings suggest that the PU and PEU of teleconsultation have a significant impact on the intention of physicians to adopt teleconsultation. This results in an improvement in the satisfaction of Polish physicians with the use of teleconsultation and an increase in Q. The study contributes to both theory and practice by identifying important prognostic factors affecting physicians' acceptance of teleconsultation systems.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

BACKGROUND

Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.

METHODS

This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.

RESULTS

Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.

CONCLUSION

In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

Prevalence of Cardiovascular Risk Factors in Rural Kenya-2023: Vihiga II

BACKGROUND

The aim of this study was to estimate the prevalence of cardiovascular disease (CVD) risk factors among adults in Vihiga County, Western Kenya.

METHODS

A cross-sectional population-based study was completed. Participants (n = 575) were selected at the household level using a systematic random sample, and sociodemographic, anthropometrical, clinical, and biochemical data were collected.

RESULTS

The sample consisted of 71.8% women and 28.2% men. The prevalence of high blood pressure (BP) was 56.4%. A greater proportion of women (62.8%) had low levels of high-density lipoprotein (HDL) compared with men (44.7%) (odds ratio [OR], 2.99; 95% CI, 1.59-5.64). High triglyceride levels were more prevalent in men (16.4%) compared with women (15.4%) (OR, 0.37; 95% CI, 0.19-0.71). Based on average waist to hip ratio, abdominal obesity was significantly higher in women (55.4%) compared with men (27.6%) (OR, 2.62; 95% CI, 1.52-4.49). Low levels of physical inactivity were prevalent in women (36.3%) at significantly higher proportions compared with men (22.7%) (OR, 1.73; 95% CI, 1.02-2.92). Women used tobacco at significantly lower proportions (0.5%) compared with men (27.2%) (OR, 0.01; 95% CI, 0.00-0.06). A higher proportion of women (47.9%) rated their general health as poor compared with men (37.1%), a statistically significant difference (OR, 2.0; 95% CI, 1.23-3.28).

CONCLUSION

The prevalence of CVD risk factors, such as hypertension, obesity, dyslipidemia, low physical activity, and tobacco use indicates the urgent need for comprehensive health promotion programs to improve public health in rural areas of Kenya.

Exploring the impact of primary care utilization and health information exchange upon treatment patterns and clinical outcomes of glioblastoma patients

PURPOSE

There is limited literature describing care coordination for patients with glioblastoma (GBM). We aimed to investigate the impact of primary care and electronic health information exchange (HIE) between neurosurgeons, oncologists, and primary care providers (PCP) on GBM treatment patterns, postoperative outcomes, and survival.

METHODS

We identified adult GBM patients undergoing primary resection at our institution (2007-2020). HIE was defined as shared electronic medical information between PCPs, oncologists, and neurosurgeons. Multivariate logistic regression analyses were used to determine the effect of PCPs and HIE upon initiation and completion of adjuvant therapy. Kaplan-Meier and multivariate Cox regression models were used to evaluate overall survival (OS).

RESULTS

Among 374 patients (mean age ± SD: 57.7 ± 13.5, 39.0% female), 81.0% had a PCP and 62.4% had electronic HIE. In multivariate analyses, having a PCP was associated with initiation (OR: 7.9, P < 0.001) and completion (OR: 4.4, P < 0.001) of 6 weeks of concomitant chemoradiation, as well as initiation (OR: 4.0, P < 0.001) and completion (OR: 3.0, P = 0.007) of 6 cycles of maintenance temozolomide thereafter. Having a PCP (median OS [95%CI]: 14.6[13.1-16.1] vs. 10.8[8.2-13.3] months, P = 0.005) and HIE (15.40[12.82-17.98] vs. 13.80[12.51-15.09] months, P = 0.029) were associated with improved OS relative to counterparts in Kaplan-Meier analysis and in multivariate Cox regression analysis (hazard ratio [HR] = 0.7, [95% CI] 0.5-1.0, P = 0.048). In multivariate analyses, chemoradiation (HR = 0.34, [95% CI] 0.2-0.7, P = 0.002) and maintenance temozolomide (HR = 0.5, 95%CI 0.3-0.8, P = 0.002) were associated with improved OS relative to counterparts.

CONCLUSION

Effective care coordination between neurosurgeons, oncologists, and PCPs may offer a modifiable avenue to improve GBM outcomes.

Future Possible Changes in Medically Underserved Areas in Japan: A Geographic Information System-Based Simulation Study

BACKGROUND

A decrease in populations could affect healthcare access and systems, particularly in medically underserved areas (MUAs) where depopulation is becoming more prevalent. This study aimed to simulate the future population and land areas of MUAs in Japan.

METHODS

This study covered 380,948 1 km meshes, 87,942 clinics, and 8354 hospitals throughout Japan as of 2020. The areas outside a 4 km radius of medical institutions were considered as MUAs, based on the measure of areas in the current Japanese Medical Care Act. Based on the population estimate for a 1 km mesh, the population of mesh numbers of MUAs was predicted for every 10 years from 2020 to 2050 using geographic information system analysis. If the population within a 4 km radius from a medical institution fell below 1000, the institution was operationally assumed to be closed.

RESULTS

The number of MUAs was predicted to decrease from 964,310 (0.77% of the total Japanese population) in 2020 to 763,410 (0.75%) by 2050. By 2050, 48,105 meshes (13% of the total meshes in Japan) were predicted to be new MUAs, indicating a 31% increase in MUAs from 2020 to 2050. By 2050, 1601 medical institutions were tentatively estimated to be in close proximity.

CONCLUSIONS

In Japan, the population of MUAs will decrease, while the land area of MUAs will increase. Such changes may reform rural healthcare policy and systems.

Artificial Intelligence in Medicine: A Caution About Good Intentions and Where It May Lead

Implementing Artificial Intelligence in medicine is revolutionizing how medicine is practiced. It has much promise in bringing about improved clinical outcomes and efficiency while decreasing costs. There are also concerns and unintended consequences that are being realized and significant efforts to consider ethical principles in the implementation of Artificial Intelligence in medicine. One potential consequence may be the loss of what has been described as the soul of medicine: the physician-patient relationship. This relationship is especially precious in the context of what the US Surgeon General Vivek H. Murthy MD has called an "Epidemic of Loneliness and Isolation." This commentary describes considerations and potential steps to protect this vital relationship while implementing Artificial Intelligence approaches to improving patient care. If not vigilant, Artificial Intelligence may unintentionally erode the physician-patient relationship resulting in physician/patient isolation.

Methods, Indicators, and End-User Involvement in the Evaluation of Digital Health Interventions for the Public: Scoping Review

BACKGROUND

Digital health interventions (DHIs) have the potential to enable public end users, such as citizens and patients, to manage and improve their health. Although the number of available DHIs is increasing, examples of successfully established DHIs in public health systems are limited. To counteract the nonuse of DHIs, they should be comprehensively evaluated while integrating end users. Unfortunately, there is a wide variability and heterogeneity according to the approaches of evaluation, which creates a methodological challenge.

OBJECTIVE

This scoping review aims to provide an overview of the current established processes for evaluating DHIs, including methods, indicators, and end-user involvement. The review is not limited to a specific medical field or type of DHI but offers a holistic overview.

METHODS

This scoping review was conducted following the JBI methodology for scoping reviews based on the framework by Arksey & O'Malley and complies with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Three scientific databases (PubMed, Scopus, and Science Direct) were searched in April 2023. English and German studies between 2008 and 2023 were considered when evaluating DHIs that explicitly address public end users. The process of study selection was carried out by several researchers to avoid reviewer bias.

RESULTS

The search strategy identified 9618 publications, of which 160 were included. Among these included articles, 200 evaluations were derived and analyzed. The results showed that there is neither a consensus on the methods to evaluate DHIs nor a commonly agreed definition or usage of the evaluated indicators, which results in a broad variety of evaluation practices. This aligns with observations of the existing literature. It was found that there is a lack of references to existing frameworks for the evaluation of DHIs. The majority of the included studies referred to user-centered approaches and involved end users in the evaluation process. As assistance for people developing and evaluating DHIs and as a basis for thinking about appropriate ways to evaluate DHIs, a results matrix was created where the findings were combined per DHI cluster. Additionally, general recommendations for the evaluators of DHIs were formulated.

CONCLUSIONS

The findings of this scoping review offer a holistic overview of the variety and heterogeneity according to the approaches of evaluation of DHIs for public end users. Evaluators of these DHIs should be encouraged to reference established frameworks or measurements for justification. This would ease the transferability of the results among similar evaluation studies within the digital health sector, thereby enhancing the coherence and comparability of research in this area.

A Profile of Influenza Vaccine Coverage for 2019-2020: Database Study of the English Primary Care Sentinel Cohort

BACKGROUND

Innovation in seasonal influenza vaccine development has resulted in a wider range of formulations becoming available. Understanding vaccine coverage across populations including the timing of administration is important when evaluating vaccine benefits and risks.

OBJECTIVE

This study aims to report the representativeness, uptake of influenza vaccines, different formulations of influenza vaccines, and timing of administration within the English Primary Care Sentinel Cohort (PCSC).

METHODS

We used the PCSC of the Oxford-Royal College of General Practitioners Research and Surveillance Centre. We included patients of all ages registered with PCSC member general practices, reporting influenza vaccine coverage between September 1, 2019, and January 29, 2020. We identified influenza vaccination recipients and characterized them by age, clinical risk groups, and vaccine type. We reported the date of influenza vaccination within the PCSC by International Standard Organization (ISO) week. The representativeness of the PCSC population was compared with population data provided by the Office for National Statistics. PCSC influenza vaccine coverage was compared with published UK Health Security Agency's national data. We used paired t tests to compare populations, reported with 95% CI.

RESULTS

The PCSC comprised 7,010,627 people from 693 general practices. The study population included a greater proportion of people aged 18-49 years (2,982,390/7,010,627, 42.5%; 95% CI 42.5%-42.6%) compared with the Office for National Statistics 2019 midyear population estimates (23,219,730/56,286,961, 41.3%; 95% CI 4.12%-41.3%; P<.001). People who are more deprived were underrepresented and those in the least deprived quintile were overrepresented. Within the study population, 24.7% (1,731,062/7,010,627; 95% CI 24.7%-24.7%) of people of all ages received an influenza vaccine compared with 24.2% (14,468,665/59,764,928; 95% CI 24.2%-24.2%; P<.001) in national data. The highest coverage was in people aged ≥65 years (913,695/1,264,700, 72.3%; 95% CI 72.2%-72.3%). The proportion of people in risk groups who received an influenza vaccine was also higher; for example, 69.8% (284,280/407,228; 95% CI 69.7%-70%) of people with diabetes in the PCSC received an influenza vaccine compared with 61.2% (983,727/1,607,996; 95% CI 61.1%-61.3%; P<.001) in national data. In the PCSC, vaccine type and brand information were available for 71.8% (358,365/498,923; 95% CI 71.7%-72%) of people aged 16-64 years and 81.9% (748,312/913,695; 95% CI 81.8%-82%) of people aged ≥65 years, compared with 23.6% (696,880/2,900,000) and 17.8% (1,385,888/7,700,000), respectively, of the same age groups in national data. Vaccination commenced during ISO week 35, continued until ISO week 3, and peaked during ISO week 41. The in-week peak in vaccination administration was on Saturdays.

CONCLUSIONS

The PCSC's sociodemographic profile was similar to the national population and captured more data about risk groups, vaccine brands, and batches. This may reflect higher data quality. Its capabilities included reporting precise dates of administration. The PCSC is suitable for undertaking studies of influenza vaccine coverage.

Human-Centered Design and Benefit Realization Management in Digital Health Care Solution Development: Protocol for a Systematic Review

BACKGROUND

Earlier research shows that a significant number of resources are wasted on software projects delivering less than the planned benefits. It has, however, been evidenced that adopting a human-centered design approach when designing health devices can be beneficial. This understanding from earlier research has raised our interest in investigating how human-centered design might contribute to realizing the potential benefits of health care software projects. To our current knowledge, this intersection of human-centered design and benefit realization management has not yet comprehensively and consistently been researched within the context of digital health care solutions. Therefore, there is a need for evidence synthesis using systematic reviews to address this potential research gap.

OBJECTIVE

The objective of this study is to examine if human-centered design helps benefit realization management processes in the development of digital health care solutions and thereby enables better benefit realization. We explore the evidence of assumed or confirmed benefits of using human-centered design in the health care domain and whether better results have been reported when the benefit realization management process is followed.

METHODS

This protocol was developed following the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. The proposed review would use a planned and systematic approach to identify, evaluate, and synthesize relevant and recent studies (reported in English) to see if there is evidence that using human-centered design and benefit realization management has a positive effect on realizing set benefits in those projects. We will commence a systematic literature search using human-centered design, benefit realization management, and health care-related search terms within 5 repositories (ACM Digital Library, PubMed Central, Scopus, PubMed, and Web of Science). After removing duplicate results, a preliminary scan for titles and abstracts will be done by at least 2 reviewers. Any incongruities regarding whether to include articles for full-text review will be resolved by a third reviewer based on the predefined criteria.

RESULTS

Initial queries of 2086 records have been executed and papers are being prescreened for inclusion. The search was initiated in December 2023 and the results are expected in 2024. We anticipate finding evidence of the use of human-centered design in the development of digital health care solutions. However, we expect evidence of benefitting from both human-centered design and benefit realization management in this context to be scarce.

CONCLUSIONS

This protocol will guide the review of existing literature on the use of human-centered design and benefit realization management when developing digital health care solutions. The review will specifically focus on finding evidence of confirmed benefits derived from the use of human-centered design and benefit realization management. There may be an opportunity to gain a broader understanding of the tools or approaches that provide evidence of increased benefit realization within the health care domain.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56125.

Ethnic inequalities in age-related patterns of multiple long-term conditions in England: Analysis of primary care and nationally representative survey data

Little is known about the patterning of multiple long-term conditions (MLTCs) by age, ethnicity and across conceptualisations of MLTCs (e.g. MLTCs with/without mental health conditions [MHCs]). We examined ethnic inequalities in age-related patterns of MLTCs, and combinations of physical and MHCs using the English GP Patient Survey and Clinical Practice Research Datalink. We described the association between MLTCs and age using multilevel regression models adjusting for sex and area-level deprivation with patients nested within GP practices. Similar analyses were repeated for MLTCs that include MHCs. We observed ethnic inequalities from middle-age onwards such as older Pakistani, Indian, Black Caribbean and Other ethnic people had increased risk of MLTCs compared to white British people, even after adjusting for area-level deprivation. Compared to white British people, Gypsy and Irish Travellers had higher levels of MLTCs across the age groups, and Chinese people had lower levels. Pakistani and Bangladeshi people aged 50-74 years were more likely than white people to report MLTCs that included MHCs. We find clear evidence of ethnic inequalities in MLTCs. The lower prevalence of MLTCs that include MHCs among some minoritised ethnic groups may be an underestimation due to underdiagnosis and/or inadequate primary care and requires further scrutiny.

Researching big IT in the UK National Health Service: A systematic review of theory-based studies

OBJECTIVE

To identify and discuss theory-based studies of large-scale health information technology programs in the UK National Health Service.

MATERIALS AND METHODS

Using the PRISMA systematic review framework, we searched Scopus, PubMed and CINAHL databases from inception to March 2022 for theory-based studies of large-scale health IT implementations. We undertook detailed full-text analyses of papers meeting our inclusion criteria.

RESULTS

Forty-six studies were included after assessment for eligibility, of which twenty-five applied theories from the information systems arena (socio-technical approaches, normalization process theory, user acceptance theories, diffusion of innovation), twelve from sociology (structuration theory, actor-network theory, institutional theory), while nine adopted other theories. Most investigated England's National Program for IT (2002-2011), exploring various technologies among which electronic records predominated. Research themes were categorized into user factors, program factors, process outcomes, clinical impact, technology, and organizational factors. Most research was qualitative, often using a case study strategy with a longitudinal or cross-sectional approach. Data were typically collected through interviews, observation, and document analysis; sampling was generally purposive; and most studies used thematic or related analyses. Theories were generally applied in a superficial or fragmentary manner; and articles frequently lacked detail on how theoretical constructs and relationships aided organization, analysis, and interpretation of data.

CONCLUSION

Theory-based studies of large NHS IT programs are relatively uncommon. As large healthcare programs evolve over a long timeframe in complex and dynamic environments, wider adoption of theory-based methods could strengthen the explanatory and predictive utility of research findings across multiple evaluation studies. Our review has confirmed earlier suggestions for theory selection, and we suggest there is scope for more explicit use of such theoretical constructs to strengthen the conceptual foundations of health informatics research. Additionally, the challenges of large national health informatics programs afford wide-ranging opportunities to test, refine, and adapt sociological and information systems theories.

Effect of fluoridated water on invasive NHS dental treatments for adults: the LOTUS retrospective cohort study and economic evaluation

Background

Most water fluoridation studies were conducted on children before the widespread introduction of fluoride toothpastes. There is a lack of evidence that can be applied to contemporary populations, particularly adolescents and adults.

Objective

To pragmatically assess the clinical and cost effectiveness of water fluoridation for preventing dental treatment and improving oral health in a contemporary population of adults, using a natural experiment design.

Design

Retrospective cohort study using routinely collected National Health Service dental claims (FP17) data.

Setting

National Health Service primary dental care: general dental practices, prisons, community dental services, domiciliary settings, urgent/out-of-hours and specialised referral-only services.

Participants

Dental patients aged 12 years and over living in England (n = 6,370,280).

Intervention and comparison

Individuals exposed to drinking water with a fluoride concentration ≥ 0.7 mg F/l between 2010 and 2020 were matched to non-exposed individuals on key characteristics using propensity scores.

Outcome measures

Primary: number of National Health Service invasive dental treatments (restorations/'fillings' and extractions) received per person between 2010 and 2020. Secondary: decayed, missing and filled teeth, missing teeth, inequalities, cost effectiveness and return on investment.

Data sources

National Health Service Business Services Authority dental claims data. Water quality monitoring data.

Primary outcome

Predicted mean number of invasive dental treatments was 3% lower in the optimally fluoridated group than in the sub/non-optimally fluoridated group (incidence rate ratio 0.969, 95% CI 0.967 to 0.971), a difference of -0.173 invasive dental treatments (95% CI -0.185 to -0.161). This magnitude of effect is smaller than what most stakeholders we engaged with (n = 50/54) considered meaningful.

Secondary outcomes

Mean decayed, missing and filled teeth were 2% lower in the optimally fluoridated group, with a difference of -0.212 decayed, missing and filled teeth (95% CI -0.229 to -0.194). There was no statistically significant difference in the mean number of missing teeth per person (0.006, 95% CI -0.008 to 0.021). There was no compelling evidence that water fluoridation reduced social inequalities in treatments received or missing teeth; however, decayed, missing and filled teeth data did not demonstrate a typical inequalities gradient. Optimal water fluoridation in England in 2010-20 was estimated to cost £10.30 per person (excluding original setup costs). Mean National Health Service treatment costs for fluoridated patients 2010-20 were 5.5% lower per person, by £22.26 (95% CI -£23.09 to -£21.43), and patients paid £7.64 less in National Health Service dental charges per person (2020 prices).

Limitations

Pragmatic, observational study with potential for non-differential errors of misclassification in fluoridation assignment and outcome measurement and residual and/or unmeasured confounding. Decayed, missing and filled teeth data have not been validated. Water fluoridation cost estimates are based on existing programmes between 2010 and 2020, and therefore do not include the potentially significant capital investment required for new programmes.

Conclusions

Receipt of optimal water fluoridation between 2010 and 2020 resulted in very small health effects, which may not be meaningful for individuals, and we could find no evidence of a reduction in social inequalities. Existing water fluoridation programmes in England produced a positive return on investment between 2010 and 2020 due to slightly lower National Health Service treatment costs. These relatively small savings should be evaluated against the projected costs and lifespan of any proposed capital investment in water fluoridation, including new programmes.

Future work

National Health Service dental data are a valuable resource for research. Further validation and measures to improve quality and completeness are warranted.

Trial registrations

This trial is registered as ISRCTN96479279, CAG: 20/CAG/0072, IRAS: 20/NE/0144.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: NIHR128533) and is published in full in Public Health Research; Vol. 12, No. 5. See the NIHR Funding and Awards website for further award information.

The mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey

AIMS

To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL.

DESIGN

A cross-sectional questionnaire study.

METHODS

Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used.

RESULTS

ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0-100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks.

CONCLUSION

ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL.

IMPACT

This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Digital Self-Management Platform for Adult Asthma: Randomized Attention-Placebo Controlled Trial

BACKGROUND

Asthma is one of the most common chronic conditions worldwide, with a substantial individual and health care burden. Digital apps hold promise as a highly accessible, low-cost method of enhancing self-management in asthma, which is critical to effective asthma control.

OBJECTIVE

We conducted a fully remote randomized controlled trial (RCT) to assess the efficacy of juli, a commercially available smartphone self-management platform for asthma.

METHODS

We conducted a pragmatic single-blind, RCT of juli for asthma management. Our study included participants aged 18 years and older who self-identified as having asthma and had an Asthma Control Test (ACT) score of 19 or lower (indicating uncontrolled asthma) at the beginning of the trial. Participants were randomized (1:1 ratio) to receive juli for 8 weeks or a limited attention-placebo control version of the app. The primary outcome measure was the difference in ACT scores after 8 weeks. Secondary outcomes included remission (ACT score greater than 19), minimal clinically important difference (an improvement of 3 or more points on the ACT), worsening of asthma, and health-related quality of life. The primary analysis included participants using the app for 8 weeks (per-protocol analysis), and the secondary analysis used a modified intention-to-treat (ITT) analysis.

RESULTS

We randomized 411 participants between May 2021 and April 2023: a total of 152 (37%) participants engaged with the app for 8 weeks and were included in the per-protocol analysis, and 262 (63.7%) participants completed the week-2 outcome assessment and were included in the modified ITT analysis. Total attrition between baseline and week 8 was 259 (63%) individuals. In the per-protocol analysis, the intervention group had a higher mean ACT score (17.93, SD 4.72) than the control group (16.24, SD 5.78) by week 8 (baseline adjusted coefficient 1.91, 95% CI 0.31-3.51; P=.02). Participants using juli had greater odds of achieving or exceeding the minimal clinically important difference at 8 weeks (adjusted odds ratio 2.38, 95% CI 1.20-4.70; P=.01). There were no between group differences in the other secondary outcomes at 8 weeks. The results from the modified ITT analyses were similar.

CONCLUSIONS

Users of juli had improved asthma symptom control over 8 weeks compared with users of a version of the app with limited functionality. These findings suggest that juli is an effective digital self-management platform that could augment existing care pathways for asthma. The retention of patients in RCTs and real-world use of digital health care apps is a major challenge.

TRIAL REGISTRATION

International Standard Randomised Controlled Trial Number (ISRCTN) registry ISRCTN87679686; https://www.isrctn.com/ISRCTN87679686.

How do opt-in versus opt-out settings nudge patients toward electronic health record adoption? An exploratory study of facilitators and barriers in Austria and France

BACKGROUND

Electronic health records (EHR) are becoming an integral part of the health system in many developed countries, though implementations and settings vary across countries. Some countries have adopted an opt-out policy, in which patients are enrolled in the EHR system following a default nudge, while others have applied an opt-in policy, where patients have to take action to opt into the system. While opt-in systems may exhibit lower levels of active user requests for access, this contrasts with opt-out systems where a notable percentage of users may passively retain access. Thus, our research endeavor aims to explore facilitators and barriers that contribute to explaining EHR usage (i.e., actively accessing the EHR system) in two countries with either an opt-in or opt-out setting, exemplified by France and Austria.

METHODS

A qualitative exploratory approach using a semi-structured interview guideline was undertaken in both countries: 1) In Austria, with four homogenously composed group discussions, and 2) in France, with 19 single patient interviews. The data were collected from October 2020 to January 2021.

RESULTS

Influencing factors were categorized into twelve subcategories. Patients have similar experiences in both countries with regard to all facilitating categories, for instance, the role of health providers, awareness of EHR and social norms. However, we highlighted important differences between the two systems regarding hurdles impeding EHR usage, namely, a lack of communication as well as transparency or information security about EHR.

CONCLUSION

Implementing additional safeguards to enhance privacy protection and supporting patients to improve their digital ability may help to diminish the perception of EHR-induced barriers and improve patients' health and commitment in the long term.

PRACTICAL IMPLICATIONS

Understanding the differences and similarities will help to develop practical implications to tackle the problem of low EHR usage rates in the long run. This problem is prevalent in countries with both types of EHR default settings.

Pre-exposure prophylaxis and telemedicine during coronavirus (COVID-19): a qualitative study of the experiences of health care professionals in Mexico

Background Telemedicine, which involves utilising technologies for remote health care delivery, proved useful to continue offering certain health services during the coronavirus disease 2019 (COVID-19) lockdown. However, the extent of its effectiveness in delivering pre-exposure prophylaxis services for HIV prevention remains underexplored from the viewpoint of health care providers. Therefore, this study aimed to assess the experiences of health care professionals in Mexico who utilised telemedicine for delivering pre-exposure prophylaxis services during the COVID-19 contingency. Methods A qualitative study was performed: 15 virtual interviews with health care professionals were conducted, transcribed and coded in ATLAS.ti. Results The results indicate that telemedicine effectively mitigated COVID-19 exposure, facilitated users' access to pre-exposure prophylaxis counselling, minimised waiting times and enhanced health care professionals' perceived control during sessions. While implementing remote services, certain organisational challenges, which were somewhat latent before the pandemic, became more apparent: colleagues recognised the necessity for more formal communication channels to disseminate information effectively. Additionally, there was a recognised need for electronic patient files to streamline data-sharing processes. An optimal approach would involve a blend of face-to-face and virtual services, contingent upon the availability of essential infrastructure, well-defined implementation protocols and comprehensive training programs. Conclusions Telemedicine streamlined certain processes, garnered positive acceptance from healthcare professionals and holds promise as a valuable post-pandemic tool for improving retention among pre-exposure prophylaxis users.

Progress with the Learning Health System 2.0: a rapid review of Learning Health Systems' responses to pandemics and climate change

BACKGROUND

Pandemics and climate change each challenge health systems through increasing numbers and new types of patients. To adapt to these challenges, leading health systems have embraced a Learning Health System (LHS) approach, aiming to increase the efficiency with which data is translated into actionable knowledge. This rapid review sought to determine how these health systems have used LHS frameworks to both address the challenges posed by the COVID-19 pandemic and climate change, and to prepare for future disturbances, and thus transition towards the LHS2.0.

METHODS

Three databases (Embase, Scopus, and PubMed) were searched for peer-reviewed literature published in English in the five years to March 2023. Publications were included if they described a real-world LHS's response to one or more of the following: the COVID-19 pandemic, future pandemics, current climate events, future climate change events. Data were extracted and thematically analyzed using the five dimensions of the Institute of Medicine/Zurynski-Braithwaite's LHS framework: Science and Informatics, Patient-Clinician Partnerships, Continuous Learning Culture, Incentives, and Structure and Governance.

RESULTS

The search yielded 182 unique publications, four of which reported on LHSs and climate change. Backward citation tracking yielded 13 additional pandemic-related publications. None of the climate change-related papers met the inclusion criteria. Thirty-two publications were included after full-text review. Most were case studies (n = 12, 38%), narrative descriptions (n = 9, 28%) or empirical studies (n = 9, 28%). Science and Informatics (n = 31, 97%), Continuous Learning Culture (n = 26, 81%), Structure and Governance (n = 23, 72%) were the most frequently discussed LHS dimensions. Incentives (n = 21, 66%) and Patient-Clinician Partnerships (n = 18, 56%) received less attention. Twenty-nine papers (91%) discussed benefits or opportunities created by pandemics to furthering the development of an LHS, compared to 22 papers (69%) that discussed challenges.

CONCLUSIONS

An LHS 2.0 approach appears well-suited to responding to the rapidly changing and uncertain conditions of a pandemic, and, by extension, to preparing health systems for the effects of climate change. LHSs that embrace a continuous learning culture can inform patient care, public policy, and public messaging, and those that wisely use IT systems for decision-making can more readily enact surveillance systems for future pandemics and climate change-related events.

TRIAL REGISTRATION

PROSPERO pre-registration: CRD42023408896.

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study

INTRODUCTION

For artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering marginalised communities to take an active role in constructing research agendas and outputs. Given the unmet needs of the LGBTQI+ (Lesbian, Gay, Bisexual, Transgender, Queer and Intersex) community in mental healthcare, there is a pressing need for participatory research to include a range of diverse queer perspectives on issues of data collection and use (in routine clinical care as well as for research) as well as AI design. Here we propose a protocol for a Delphi consensus process for the development of PARticipatory Queer AI Research for Mental Health (PARQAIR-MH) practices, aimed at informing digital health practices and policy.

METHODS AND ANALYSIS

The development of PARQAIR-MH is comprised of four stages. In stage 1, a review of recent literature and fact-finding consultation with stakeholder organisations will be conducted to define a terms-of-reference for stage 2, the Delphi process. Our Delphi process consists of three rounds, where the first two rounds will iterate and identify items to be included in the final Delphi survey for consensus ratings. Stage 3 consists of consensus meetings to review and aggregate the Delphi survey responses, leading to stage 4 where we will produce a reusable toolkit to facilitate participatory development of future bespoke LGBTQI+-adapted data collection, harmonisation, and use for data-driven AI applications specifically in mental healthcare settings.

ETHICS AND DISSEMINATION

PARQAIR-MH aims to deliver a toolkit that will help to ensure that the specific needs of LGBTQI+ communities are accounted for in mental health applications of data-driven technologies. The study is expected to run from June 2024 through January 2025, with the final outputs delivered in mid-2025. Participants in the Delphi process will be recruited by snowball and opportunistic sampling via professional networks and social media (but not by direct approach to healthcare service users, patients, specific clinical services, or via clinicians' caseloads). Participants will not be required to share personal narratives and experiences of healthcare or treatment for any condition. Before agreeing to participate, people will be given information about the issues considered to be in-scope for the Delphi (eg, developing best practices and methods for collecting and harmonising sensitive characteristics data; developing guidelines for data use/reuse) alongside specific risks of unintended harm from participating that can be reasonably anticipated. Outputs will be made available in open-access peer-reviewed publications, blogs, social media, and on a dedicated project website for future reuse.

The Impact of Digital Hospitals on Patient and Clinician Experience: Systematic Review and Qualitative Evidence Synthesis

BACKGROUND

The digital transformation of health care is advancing rapidly. A well-accepted framework for health care improvement is the Quadruple Aim: improved clinician experience, improved patient experience, improved population health, and reduced health care costs. Hospitals are attempting to improve care by using digital technologies, but the effectiveness of these technologies is often only measured against cost and quality indicators, and less is known about the clinician and patient experience.

OBJECTIVE

This study aims to conduct a systematic review and qualitative evidence synthesis to assess the clinician and patient experience of digital hospitals.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) guidelines were followed. The PubMed, Embase, Scopus, CINAHL, and PsycINFO databases were searched from January 2010 to June 2022. Studies that explored multidisciplinary clinician or adult inpatient experiences of digital hospitals (with a full electronic medical record) were included. Study quality was assessed using the Mixed Methods Appraisal Tool. Data synthesis was performed narratively for quantitative studies. Qualitative evidence synthesis was performed via (1) automated machine learning text analytics using Leximancer (Leximancer Pty Ltd) and (2) researcher-led inductive synthesis to generate themes.

RESULTS

A total of 61 studies (n=39, 64% quantitative; n=15, 25% qualitative; and n=7, 11% mixed methods) were included. Most studies (55/61, 90%) investigated clinician experiences, whereas few (10/61, 16%) investigated patient experiences. The study populations ranged from 8 to 3610 clinicians, 11 to 34,425 patients, and 5 to 2836 hospitals. Quantitative outcomes indicated that clinicians had a positive overall satisfaction (17/24, 71% of the studies) with digital hospitals, and most studies (11/19, 58%) reported a positive sentiment toward usability. Data accessibility was reported positively, whereas adaptation, clinician-patient interaction, and workload burnout were reported negatively. The effects of digital hospitals on patient safety and clinicians' ability to deliver patient care were mixed. The qualitative evidence synthesis of clinician experience studies (18/61, 30%) generated 7 themes: inefficient digital documentation, inconsistent data quality, disruptions to conventional health care relationships, acceptance, safety versus risk, reliance on hybrid (digital and paper) workflows, and patient data privacy. There was weak evidence of a positive association between digital hospitals and patient satisfaction scores.

CONCLUSIONS

Clinicians' experience of digital hospitals appears positive according to high-level indicators (eg, overall satisfaction and data accessibility), but the qualitative evidence synthesis revealed substantive tensions. There is insufficient evidence to draw a definitive conclusion on the patient experience within digital hospitals, but indications appear positive or agnostic. Future research must prioritize equitable investigation and definition of the digital clinician and patient experience to achieve the Quadruple Aim of health care.

The Acceptance and Use of Digital Technologies for Self-Reporting Medication Safety Events After Care Transitions to Home in Patients With Cancer: Survey Study

BACKGROUND

Actively engaging patients with cancer and their families in monitoring and reporting medication safety events during care transitions is indispensable for achieving optimal patient safety outcomes. However, existing patient self-reporting systems often cannot address patients' various experiences and concerns regarding medication safety over time. In addition, these systems are usually not designed for patients' just-in-time reporting. There is a significant knowledge gap in understanding the nature, scope, and causes of medication safety events after patients' transition back home because of a lack of patient engagement in self-monitoring and reporting of safety events. The challenges for patients with cancer in adopting digital technologies and engaging in self-reporting medication safety events during transitions of care have not been fully understood.

OBJECTIVE

We aim to assess oncology patients' perceptions of medication and communication safety during care transitions and their willingness to use digital technologies for self-reporting medication safety events and to identify factors associated with their technology acceptance.

METHODS

A cross-sectional survey study was conducted with adult patients with breast, prostate, lung, or colorectal cancer (N=204) who had experienced care transitions from hospitals or clinics to home in the past 1 year. Surveys were conducted via phone, the internet, or email between December 2021 and August 2022. Participants' perceptions of medication and communication safety and perceived usefulness, ease of use, attitude toward use, and intention to use a technology system to report their medication safety events from home were assessed as outcomes. Potential personal, clinical, and psychosocial factors were analyzed for their associations with participants' technology acceptance through bivariate correlation analyses and multiple logistic regressions.

RESULTS

Participants reported strong perceptions of medication and communication safety, positively correlated with medication self-management ability and patient activation. Although most participants perceived a medication safety self-reporting system as useful (158/204, 77.5%) and easy to use (157/204, 77%), had a positive attitude toward use (162/204, 79.4%), and were willing to use such a system (129/204, 63.2%), their technology acceptance was associated with their activation levels (odds ratio [OR] 1.83, 95% CI 1.12-2.98), their perceptions of communication safety (OR 1.64, 95% CI 1.08-2.47), and whether they could receive feedback after self-reporting (OR 3.27, 95% CI 1.37-7.78).

CONCLUSIONS

In general, oncology patients were willing to use digital technologies to report their medication events after care transitions back home because of their high concerns regarding medication safety. As informed and activated patients are more likely to have the knowledge and capability to initiate and engage in self-reporting, developing a patient-centered reporting system to empower patients and their families and facilitate safety health communications will help oncology patients in addressing their medication safety concerns, meeting their care needs, and holding promise to improve the quality of cancer care.

eHealth implementation in Europe: a scoping review on legal, ethical, financial, and technological aspects

Background

The evolution of eHealth development has shifted from standalone tools to comprehensive digital health environments, fostering data exchange among diverse stakeholders and systems. Nevertheless, existing research and implementation frameworks have primarily emphasized technological and organizational aspects of eHealth implementation, overlooking the intricate legal, ethical, and financial considerations. It is essential to discover what legal, ethical, financial, and technological challenges should be considered to ensure successful and sustainable implementation of eHealth.

Objective

This review aims to provide insights into barriers and facilitators of legal, ethical, financial, and technological aspects for successful implementation of complex eHealth technologies, which impacts multiple levels and multiple stakeholders.

Methods

A scoping review was conducted by querying PubMed, Scopus, Web of Science, and ACM Digital Library (2018-2023) for studies describing the implementation process of eHealth technologies that facilitate data exchange. Studies solely reporting clinical outcomes or conducted outside Europe were excluded. Two independent reviewers selected the studies. A conceptual framework was constructed through axial and inductive coding, extracting data from literature on legal, ethical, financial, and technological aspects of eHealth implementation. This framework guided systematic extraction and interpretation.

Results

The search resulted in 7.308 studies that were screened for eligibility, of which 35 (0.48%) were included. Legal barriers revolve around data confidentiality and security, necessitating clear regulatory guidelines. Ethical barriers span consent, responsibility, liability, and validation complexities, necessitating robust frameworks. Financial barriers stem from inadequate funding, requiring (commercial) partnerships and business models. Technological issues include interoperability, integration, and malfunctioning, necessitating strategies for enhancing data reliability, improving accessibility, and aligning eHealth technology with existing systems for smoother integration.

Conclusions

This research highlights the multifaceted nature of eHealth implementation, encompassing legal, ethical, financial, and technological considerations. Collaborative stakeholder engagement is paramount for effective decision-making and aligns with the transition from standalone eHealth tools to integrated digital health environments. Identifying suitable stakeholders and recognizing their stakes and values enriches implementation strategies with expertise and guidance across all aspects. Future research should explore the timing of these considerations and practical solutions for regulatory compliance, funding, navigation of responsibility and liability, and business models for reimbursement strategies.

Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review

BACKGROUND

International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses.

OBJECTIVE

This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses.

METHODS

A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach.

RESULTS

Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level.

CONCLUSIONS

There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.

Impact of mental health comorbidity in children and young adults with inflammatory bowel disease: a UK population-based cohort study

OBJECTIVE

To evaluate the impact of mental health comorbidity in children and young adults with inflammatory bowel disease (IBD).

DESIGN

Retrospective observational study.

SETTING

Representative population, routinely collected primary care data from the UK Optimum Patient Care Research Database (2015-2019).

PARTICIPANTS

Patients with IBD aged 5-25 years with mental health conditions were compared with patients with IBD of the same age without mental health conditions.

PRIMARY AND SECONDARY OUTCOME MEASURES

Outcomes comprised quality-of-life indicators (low mood, self-harm, parasuicide, bowel symptoms, absence from school or work, unemployment, substance use and sleep disturbance), IBD interventions (medication, abdominal surgery, stoma formation and nutritional supplements) and healthcare utilisation (primary care interactions and hospital admissions).

RESULTS

Of 1943 individuals aged 5-25 years with IBD, 295 (15%) had a mental health comorbidity. Mental health comorbidity was associated with increased bowel symptoms (adjusted incident rate ratio (aIRR) 1.82; 95% CI 1.33 to 2.52), sleep disturbance (adjusted HR (aHR) 1.63; 95% CI 1.02 to 2.62), substance use (aHR 3.63; 95% CI 1.69 to 7.78), primary care interactions (aIRR 1.33; 95% CI 1.12 to 1.58) and hospital admissions (aIRR 1.87; 95%CI 1.29 to 2.75). In individuals ≥18 years old, mental health comorbidity was associated with increased time off work (aHR 1.55; 95% CI 1.21 to 1.99).

CONCLUSIONS

Mental health comorbidity in children and young adults with IBD is associated with poorer quality of life, higher healthcare utilisation and more time off work. It is imperative that affected young patients with IBD are monitored and receive early mental health support as part of their multidisciplinary care.

TRIAL REGISTRATION NUMBER

The study protocol was specified and registered a priori (ClinicalTrials.gov study identifier: NCT05206734).

Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity

Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond "White", "Black", "Asian", "Mixed" and "Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar

Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

Identification and optimization of pyridine carboxamide-based scaffold as a drug lead for Mycobacterium tuberculosis

New drugs with novel mechanisms of action are urgently needed to tackle the issue of drug-resistant tuberculosis. Here, we have performed phenotypic screening using the Pathogen Box library obtained from the Medicines for Malaria Venture against Mycobacterium tuberculosis in vitro. We have identified a pyridine carboxamide derivative, MMV687254, as a promising hit. This molecule is specifically active against M. tuberculosis and Mycobacterium bovis Bacillus Calmette-Guérin (M. bovis BCG) but inactive against Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumanii, Pseudomonas aeruginosa, and Escherichia coli pathogens. We demonstrate that MMV687254 inhibits M. tuberculosis growth in liquid cultures in a bacteriostatic manner. Surprisingly, MMV687254 was as active as isoniazid in macrophages and inhibited M. tuberculosis growth in a bactericidal manner. Mechanistic studies revealed that MMV687254 is a prodrug and that its anti-mycobacterial activity requires AmiC-dependent hydrolysis. We further demonstrate that MMV687254 inhibits M. tuberculosis growth in macrophages by inducing autophagy. In the present study, we have also carried out a detailed structure-activity relationship study and identified a promising novel lead candidate. The identified novel series of compounds also showed activity against drug-resistant M. bovis BCG and M. tuberculosis clinical strains. Finally, we demonstrate that in contrast to MMV687254, the lead molecule was able to inhibit M. tuberculosis growth in a chronic mouse model of infection. Taken together, we have identified a novel lead molecule with a dual mechanism of action that can be further optimized to design more potent anti-tubercular agents.

Mapping Theories, Models, and Frameworks to Evaluate Digital Health Interventions: Scoping Review

BACKGROUND

Digital health interventions (DHIs) are a central focus of health care transformation efforts, yet their uptake in practice continues to fall short of their potential. In order to achieve their desired outcomes and impact, DHIs need to reach their target population and need to be used. Many factors can rapidly intersect between this dynamic of users and interventions. The application of theories, models, and frameworks (TMFs) can facilitate the systematic understanding and explanation of the complex interactions between users, practices, technology, and health system factors that underpin research questions. There remains a gap in our understanding of how TMFs have been applied to guide the evaluation of DHIs with real-world health system operations.

OBJECTIVE

This study aims to map TMFs used in studies to guide the evaluation of DHIs. The objectives are to (1) describe the TMFs and the constructs they target, (2) identify how TMFs have been prospectively used (ie, their roles) in primary studies to evaluate DHIs, and (3) to reflect on the relevance and utility of our findings for knowledge users.

METHODS

This scoping review was conducted in partnership with knowledge users using an integrated knowledge translation approach. We included papers (eg, reports; empirical quantitative, qualitative, and mixed methods studies; conference proceedings; and dissertations) if primary insights resulting from the application of TMFs were presented. Any type of DHI was eligible. Papers published from 2000 and onward were mainly identified from the following databases: MEDLINE (Ovid), CINAHL Complete (EBSCOhost), PsycINFO (Ovid), EBM Reviews (Ovid), and Embase (Ovid).

RESULTS

A total of 156 studies published between 2000 and 2022 were included. A total of 68 distinct TMFs were identified across 85 individual studies. In more than half (85/156, 55%) of the included studies, 1 of following 6 prevailing TMFs were reported: Consolidated Framework for Implementation Research (n=39); the Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework (n=17); the Technology of Acceptance Model (n=16); the Unified Theory on Acceptance and Use of Technology (n=12); the Diffusion of Innovation Theory (n=10); and Normalization Process Theory (n=9). The most common intended roles of the 6 TMFs were to inform data collection (n=86), to inform data analysis (n=69), and to identify key constructs that may serve as barriers and facilitators (n=52).

CONCLUSIONS

As TMFs are most often reported to be applied to support data collection and analysis, researchers should consider more clearly synthesizing key insights as practical use cases to both increase the relevance and digestibility of their findings. There is also a need to adapt or develop guidelines for better reporting DHIs and the use of TMFs to guide evaluation. Hence, it would contribute to ensuring ongoing technology transformation efforts are evidence and theory informed rather than anecdotally driven.

Identifying and improving the "ground truth" of race in disparities research through improved EMR data reporting. A systematic review

BACKGROUND

Studies about racial disparities in healthcare are increasing in quantity; however, they are subject to vast differences in definition, classification, and utilization of race/ethnicity data. Improved standardization of this information can strengthen conclusions drawn from studies using such data. The objective of this study is to examine how data related to race/ethnicity are recorded in research through examining articles on race/ethnicity health disparities and examine problems and solutions in data reporting that may impact overall data quality.

METHODS

In this systematic review, Business Source Complete, Embase.com, IEEE Xplore, PubMed, Scopus and Web of Science Core Collection were searched for relevant articles published from 2000 to 2020. Search terms related to the concepts of electronic medical records, race/ethnicity, and data entry related to race/ethnicity were used. Exclusion criteria included articles not in the English language and those describing pediatric populations. Data were extracted from published articles. This review was organized and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement for systematic reviews.

FINDINGS

In this systematic review, 109 full text articles were reviewed. Weaknesses and possible solutions have been discussed in current literature, with the predominant problem and solution as follows: the electronic medical record (EMR) is vulnerable to inaccuracies and incompleteness in the methods that research staff collect this data; however, improved standardization of the collection and use of race data in patient care may help alleviate these inaccuracies.

INTERPRETATION

Conclusions drawn from large datasets concerning peoples of certain race/ethnic groups should be made cautiously, and a careful review of the methodology of each publication should be considered prior to implementation in patient care.

Linking migration and hospital data in England: linkage process and evaluation of bias

Introduction

Difficulties ascertaining migrant status in national data sources such as hospital records have limited large-scale evaluation of migrant healthcare needs in many countries, including England. Linkage of immigration data for migrants and refugees, with National Health Service (NHS) hospital care data enables research into the relationship between migration and health for a large cohort of international migrants.

Objectives

We aimed to describe the linkage process and compare linkage rates between migrant sub-groups to evaluate for potential bias for data on non-EU migrants and resettled refugees linked to Hospital Episode Statistics (HES) in England.

Methods

We used stepwise deterministic linkage to match records from migrants and refugees to a unique healthcare identifier indicating interaction with the NHS (linkage stage 1 to NHS Personal Demographic Services, PDS), and then to hospital records (linkage stage 2 to HES). We calculated linkage rates and compared linked and unlinked migrant characteristics for each linkage stage.

Results

Of the 1,799,307 unique migrant records, 1,134,007 (63%) linked to PDS and 451,689 (25%) linked to at least one hospital record between 01/01/2005 and 23/03/2020. Individuals on work, student, or working holiday visas were less likely to link to a hospital record than those on settlement and dependent visas and refugees. Migrants from the Middle East and North Africa and South Asia were four times more likely to link to at least one hospital record, compared to those from East Asia and the Pacific. Differences in age, sex, visa type, and region of origin between linked and unlinked samples were small to moderate.

Conclusion

This linked dataset represents a unique opportunity to explore healthcare use in migrants. However, lower linkage rates disproportionately affected individuals on shorter-term visas so future studies of these groups may be more biased as a result. Increasing the quality and completeness of identifiers recorded in administrative data could improve data linkage quality.

Improving our understanding of the social determinants of mental health: a data linkage study of mental health records and the 2011 UK census

OBJECTIVES

To address the lack of individual-level socioeconomic information in electronic healthcare records, we linked the 2011 census of England and Wales to patient records from a large mental healthcare provider. This paper describes the linkage process and methods for mitigating bias due to non-matching.

SETTING

South London and Maudsley NHS Foundation Trust (SLaM), a mental healthcare provider in Southeast London.

DESIGN

Clinical records from SLaM were supplied to the Office of National Statistics for linkage to the census through a deterministic matching algorithm. We examined clinical (International Classification of Disease-10 diagnosis, history of hospitalisation, frequency of service contact) and socio-demographic (age, gender, ethnicity, deprivation) information recorded in Clinical Record Interactive Search (CRIS) as predictors of linkage success with the 2011 census. To assess and adjust for potential biases caused by non-matching, we evaluated inverse probability weighting for mortality associations.

PARTICIPANTS

Individuals of all ages in contact with SLaM up until December 2019 (N=459 374).

OUTCOME MEASURES

Likelihood of mental health records' linkage to census.

RESULTS

220 864 (50.4%) records from CRIS linked to the 2011 census. Young adults (prevalence ratio (PR) 0.80, 95% CI 0.80 to 0.81), individuals living in more deprived areas (PR 0.78, 95% CI 0.78 to 0.79) and minority ethnic groups (eg, Black African, PR 0.67, 0.66 to 0.68) were less likely to match to census. After implementing inverse probability weighting, we observed little change in the strength of association between clinical/demographic characteristics and mortality (eg, presence of any psychiatric disorder: unweighted PR 2.66, 95% CI 2.52 to 2.80; weighted PR 2.70, 95% CI 2.56 to 2.84).

CONCLUSIONS

Lower response rates to the 2011 census among people with psychiatric disorders may have contributed to lower match rates, a potential concern as the census informs service planning and allocation of resources. Due to its size and unique characteristics, the linked data set will enable novel investigations into the relationship between socioeconomic factors and psychiatric disorders.

Value of Electronic Health Records Measured Using Financial and Clinical Outcomes: Quantitative Study

BACKGROUND

The Health Information Technology for Economic and Clinical Health Act of 2009 was legislated to reduce health care costs, improve quality, and increase patient safety. Providers and organizations were incentivized to exhibit meaningful use of certified electronic health record (EHR) systems in order to achieve this objective. EHR adoption is an expensive investment, given the resources and capital that are invested. Due to the cost of the investment, a return on the EHR adoption investment is expected.

OBJECTIVE

This study performed a value analysis of EHRs. The objective of this study was to investigate the relationship between EHR adoption levels and financial and clinical outcomes by combining both financial and clinical outcomes into one conceptual model.

METHODS

We examined the multivariate relationships between different levels of EHR adoption and financial and clinical outcomes, along with the time variant control variables, using moderation analysis with a longitudinal fixed effects model. Since it is unknown as to when hospitals begin experiencing improvements in financial outcomes, additional analysis was conducted using a 1- or 2-year lag for profit margin ratios.

RESULTS

A total of 5768 hospital-year observations were analyzed over the course of 4 years. According to the results of the moderation analysis, as the readmission rate increases by 1 unit, the effect of a 1-unit increase in EHR adoption level on the operating margin decreases by 5.38%. Hospitals with higher readmission payment adjustment factors have lower penalties.

CONCLUSIONS

This study fills the gap in the literature by evaluating individual relationships between EHR adoption levels and financial and clinical outcomes, in addition to evaluating the relationship between EHR adoption level and financial outcomes, with clinical outcomes as moderators. This study provided statistically significant evidence (P<.05), indicating that there is a relationship between EHR adoption level and operating margins when this relationship is moderated by readmission rates, meaning hospitals that have adopted EHRs could see a reduction in their readmission rates and an increase in operating margins. This finding could further be supported by evaluating more recent data to analyze whether hospitals increasing their level of EHR adoption would decrease readmission rates, resulting in an increase in operating margins. Hospitals would incur lower penalties as a result of improved readmission rates, which would contribute toward improved operating margins.

Characteristics and Range of Reviews About Technologies for Aging in Place: Scoping Review of Reviews

BACKGROUND

It is a contemporary and global challenge that the increasing number of older people requiring care will surpass the available caregivers. Solutions are needed to help older people maintain their health, prevent disability, and delay or avoid dependency on others. Technology can enable older people to age in place while maintaining their dignity and quality of life. Literature reviews on this topic have become important tools for researchers, practitioners, policy makers, and decision makers who need to navigate and access the extensive available evidence. Due to the large number and diversity of existing reviews, there is a need for a review of reviews that provides an overview of the range and characteristics of the evidence on technology for aging in place.

OBJECTIVE

This study aimed to explore the characteristics and the range of evidence on technologies for aging in place by conducting a scoping review of reviews and presenting an evidence map that researchers, policy makers, and practitioners may use to identify gaps and reviews of interest.

METHODS

The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Literature searches were conducted in Web of Science, PubMed, and Scopus using a search string that consisted of the terms "older people" and "technology for ageing in place," with alternate terms using Boolean operators and truncation, adapted to the rules for each database.

RESULTS

A total of 5447 studies were screened, with 344 studies included after full-text screening. The number of reviews on this topic has increased dramatically over time, and the literature is scattered across a variety of journals. Vocabularies and approaches used to describe technology, populations, and problems are highly heterogeneous. We have identified 3 principal ways that reviews have dealt with populations, 5 strategies that the reviews draw on to conceptualize technology, and 4 principal types of problems that they have dealt with. These may be understood as methods that can inform future reviews on this topic. The relationships among populations, technologies, and problems studied in the reviews are presented in an evidence map that includes pertinent gaps.

CONCLUSIONS

Redundancies and unexploited synergies between bodies of evidence on technology for aging in place are highly likely. These results can be used to decrease this risk if they are used to inform the design of future reviews on this topic. There is a need for an examination of the current state of the art in knowledge on technology for aging in place in low- and middle-income countries, especially in Africa.

Exploring the use of mobile translation applications for culturally and linguistically diverse patients during medical imaging examinations in Australia - a systematic review

Australian healthcare provides services to a vast culturally and linguistically diverse (CALD) population. Professional interpreters are the gold standard for medical interpretation during healthcare interactions with CALD patients with limited English proficiency (LEP). However, accessing interpretation services can be difficult and may not be appropriate when timely translation is needed. Mobile translation applications (MTAs) have been suggested as a way for healthcare workers (HCWs) to provide timely translation when engaging with CALD patients. This systematic review aimed to investigate the potential for MTAs to be used in Australian medical imaging (MI) departments to enhance communication and safety for CALD patients and HCWs. Enablers and limitations of MTAs were appraised for use in MI and important design considerations suggested. Results found that MTAs may enhance communication between CALD patients and MI professionals and uphold safety by more accurately performing procedure matching and healthcare assessments. MTAs also offer readily available translation during out of hours care, emergency scenarios and everyday care. However, reliability of free-input translation and patient confidentiality were flagged as important limitations of MTAs that need to be addressed should a safe MTA be designed for MI purposes. MTAs also need to be designed with consideration for CALD patients who have low literacy levels and mental impairment. Devices should be installed close to the point of care to enable MI professionals to easily retrieve and use the MTA. MTAs used in this way can potentially improve care of CALD patients in MI when professional interpreters are absent.

Hypertension Diagnosis and Management in Africa Using Mobile Phones: A Scoping Review

Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear.

Experience of patients considering or using checkpoint inhibitors in cancer treatment: a systematic review of qualitative research

Increasing numbers of patients with cancer are considering or undergoing immunotherapy, however, little is known about patients' perspectives on this treatment. We undertook a systematic review for use by clinicians and researchers, consolidating published qualitative research studies on patient experience of checkpoint inhibitor therapy. A search of Medline, Embase, and PsycINFO was carried out for publications in English to 30 June 2022. Publications were selected if they reported a qualitative study of patient experience with checkpoint inhibitor therapy for cancer, either by patients or their families or carers. Quality was appraised using the Johanna Briggs Institute quality assessment tool for qualitative studies. A thematic synthesis was conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses standard was followed. We identified 17 eligible studies published between 2017 and 2022, 9 using mixed methods, and 8 solely using qualitative methods. Most studies reported on the experiences of patients with advanced stage melanoma and were using the earliest approved checkpoint inhibitors for cancer therapy. Studies met most formal quality criteria but varied in the extent of their qualitative explorations of data; some mixed methods studies had limited reporting of qualitative results. Through thematic synthesis, we categorized study findings into four domains: (1) treatment decision-making; (2) success with immunotherapy; (3) treatment-related adverse events (AEs); and (4) quality of life on immunotherapy. Our review identified several areas with potential for improving the care system. These include, for example: routinely linking patients to peers who have experienced this therapy; improving the capacity of patients and carers to identify and report AEs faster; and supporting patients and carers to live with changed circumstances after successful treatment. Most studies focused on patients who had successful treatment, effectively excluding those who do not respond or who discontinue due to serious side effects; future research targets are suggested.

Dissemination Strategies for mHealth Apps: Systematic Review

BACKGROUND

Among the millions of mobile apps in existence, thousands fall under the category of mobile health (mHealth). Although the utility of mHealth apps has been demonstrated for disease diagnosis, treatment data management, and health promotion strategies, to be effective they must reach and be used by their target audience. An appropriate marketing strategy can ensure that apps reach potential users and potentially convert them to actual users. Such a strategy requires definitions of target end users, communication channels, and advertising content, as well as a timeline for effectively reaching and motivating end users to adopt and maintain engagement with the mHealth app.

OBJECTIVE

The aim of this study was to identify strategies and elements that ensure that end users adopt and remain engaged with mHealth apps.

METHODS

A systematic search of the PubMed, PsycINFO, Scopus, and CINAHL databases was conducted for suitable studies published between January 1, 2018, and September 30, 2022. Two researchers independently screened studies for inclusion, extracted data, and assessed the risk of bias. The main outcome was dissemination strategies for mHealth apps.

RESULTS

Of the 648 papers retrieved from the selected databases, only 10 (1.5%) met the inclusion criteria. The marketing strategies used in these studies to inform potential users of the existence of mHealth apps and motivate download included both paid and unpaid strategies and used various channels, including social media, emails, printed posters, and face-to-face communication. Most of the studies reported a combination of marketing concepts used to advertise their mHealth apps. Advertising messages included instructions on where and how to download and install the apps. In most of the studies (6/10, 60%), instructions were oriented toward how to use the apps and maintain engagement with a health intervention. The most frequently used paid marketing platform was Facebook Ads Manager (2/10, 20%). Advertising performance was influenced by many factors, including but not limited to advertising content. In 1 (10%) of the 10 studies, animated graphics generated the greatest number of clicks compared with other image types. The metrics used to assess marketing strategy effectiveness were number of downloads; nonuse rate; dropout rate; adherence rate; duration of app use; and app usability over days, weeks, or months. Additional indicators such as cost per click, cost per install, and clickthrough rate were mainly used to assess the cost-effectiveness of paid marketing campaigns.

CONCLUSIONS

mHealth apps can be disseminated via paid and unpaid marketing strategies using various communication channels. The effects of these strategies are reflected in download numbers and user engagement with mHealth apps. Further research could provide guidance on a framework for disseminating mHealth apps and encouraging their routine use.

Assessing the Disruption Impact on Healthcare Delivery

Health emergency outbreaks such as the COVID-19 pandemic make it challenging for healthcare systems to ration medical resources and patient care. Such disastrous events have been increasing over the past years and are becoming inevitable, necessitating the need for healthcare to be well-prepared and resilient to unpredictable rises in demand. Quantitative and qualitative based decision support systems increase the effectiveness of planning, alleviating uncertainties associated with the crisis. This study aims to understand how the COVID-19 pandemic has affected the performance of healthcare systems in different areas and to address the associated disruption. A cross-sectional online survey was conducted in the Kingdom of Saudi Arabia and the United Arab Emirates among healthcare workers who worked during the pandemic. The pandemic-related disruption and its psychometric properties were assessed using Structural Equations Modeling (SEM) with 5 latent factors: Staff Mental Health, Communication Level, Planning and Readiness, Healthcare Supply Chain, and Telehealth. Responses from highly qualified participants with many years of experience in hospital settings were collected and analyzed. Results show that the model satisfactorily fits the data with a CLI of 0.91 and TLI of 0.88. The model indicates that enhancing supply chain management, planning, telehealth usage, and communication level across the healthcare system can mitigate the disruption. However, the lack of mental health management for healthcare workers can significantly disrupt the quality of delivered care. Staff mental health and healthcare supply chain, respectively, are the highest contributors to varying degrees of disruption in healthcare delivery. This study provides a direction for more research focusing on determinants of healthcare efficiency. It also provides decision-makers insights into the main factors leading to disruptions in healthcare systems, allowing them to shape their outbreak response and better prepare for future health emergencies.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives

BACKGROUND

Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

OBJECTIVES

This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process.

METHODS

We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories.

RESULTS

The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales.

CONCLUSION

This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Applications of natural language processing at emergency department triage: A narrative review

INTRODUCTION

Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data.

METHODS

All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies and high risk of bias, a metanalysis was not conducted. Instead, a narrative synthesis is provided.

RESULTS

In total, 3730 studies were screened, and 20 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 598 triage notes. The most common outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, triage score, critical illness, and mapping free-text chief complaints to structured fields. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. However, the majority of studies (80%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice.

CONCLUSION

Unstructured free-text triage notes have been used by NLP models to predict clinically relevant outcomes. However, the majority of studies have a high risk of bias, most research is retrospective, and there are few examples of implementation into clinical practice. Future work is needed to prospectively assess if applying NLP to data acquired at ED triage improves ED outcomes when compared to usual clinical practice.

Exploring challenges and recommendations for verbal autopsy implementation in low-/middle-income countries: a cross-sectional study of Iringa Region-Tanzania

BACKGROUND

Verbal autopsy (VA) plays a vital role in providing cause-of-death information in places where such information is not available. Many low-/middle-income countries (LMICs) including Tanzania are still struggling to yield quality and adequate cause-of-death data for Civil Registration and Vital Statistics (CRVS).

OBJECTIVE

To highlight challenges and recommendations for VA implementation to support LMICs yield quality and adequate mortality statistics for informed decisions on healthcare interventions.

DESIGN

Cross-sectional study.

STUDY SETTING

Iringa region in Tanzania.

PARTICIPANTS

41 people including 33 community health workers, 1 VA national coordinator, 5 national task force members, 1 VA regional coordinator and 1 member of the VA data management team.

RESULTS

The perceived challenges of key informants include a weak death notification system, lengthy VA questionnaire, poor data quality and inconsistent responses, lack of clarity in the inclusion criteria, poor commitment to roles and responsibilities, poor coordination, poor financial mechanism and no or delayed feedback to VA implementers. Based on these findings, we recommend the following strategies for effective adaptation and use of VAs: (1) reinforce or implement legislative procedures towards the legal requirement for death notification. (2) Engage key stakeholders in the overall implementation of VAs. (3) Build capacity for data collection, monitoring, processing and use of VA data. (4) Improve the VA questionnaire and quality control mechanism for optimal use in data collection. (5) Create sustainable financing mechanisms and institutionalisation of VA implementation. (6) Integrating VA Implementation in CRVS.

CONCLUSION

Effective VA implementation demands through planning, stakeholder engagement, upskilling of local experts and fair compensation for interviewers. Such coordinated endeavours will overcome systemic, technical and behavioural challenges hindering VA's successful implementation.

Challenges and strategies for conducting research in primary health care practice: an integrative review

BACKGROUND

Providing accessible and high-quality patient-centered healthcare remains a challenge in many countries, despite global efforts to strengthen primary health care (PHC). Research and knowledge management are integral to enhancing PHC, facilitating the implementation of successful strategies, and promoting the use of evidence-based practices. Practice-based research in primary care (PC-PBR) has emerged as a valuable approach, with its external validity to diverse PHC settings, making it an effective means of translating research findings into professional practice.

OBJECTIVE

To identify challenges and strategies for conducting practice-based research in primary health care services.

METHOD

An integrative literature review was conducted by searching the PubMed, Embase, Scopus, Web of Science, and Lilacs databases. The research question, guided by the PICo framework, directed the execution of study selection and data extraction. Data analysis followed the RAdAR method's three phases: pre-analysis, data analysis, and interpretation of results.

RESULTS

Out of 440 initially identified articles, 26 met the inclusion criteria. Most studies were conducted in high-income countries, primarily the United States. The challenges and strategies for PC-PBR were categorized into six themes: research planning, infrastructure, engagement of healthcare professionals, knowledge translation, the relationship between universities and health services, and international collaboration. Notable challenges included research planning complexities, lack of infrastructure, difficulties in engaging healthcare professionals, and barriers to knowledge translation. Strategies underscore the importance of adapting research agendas to local contexts, providing research training, fostering stakeholder engagement, and establishing practice-based research networks.

CONCLUSION

The challenges encountered in PC-PBR are consistent across various contexts, highlighting the need for systematic, long-term actions involving health managers, decision-makers, academics, diverse healthcare professionals, and patients. This approach is essential to transform primary care, especially in low- and middle-income countries, into an innovative, comprehensive, patient-centered, and accessible healthcare system. By addressing these challenges and implementing the strategies, PC-PBR can play a pivotal role in bridging the gap between research and practice, ultimately improving patient care and population health.