Investigating the Benefit-Risk Profile of Drugs: From Spontaneous Reporting Systems to Real-World Data for Pharmacovigilance.

Rapid and detailed post-marketing surveillance of drugs and vaccine is required to enable assessment of their real-world safety and effectiveness. Spontaneous reporting from healthcare professionals and citizens is recognized as the basic method in the passive post-marketing surveillance of drugs and vaccines, allowing the identification of rare adverse drug reactions (ADRs) and adverse events following immunization (AEFIs). According to the current law, online platforms for ADRs and AEFI reporting and related databases are available in every country and at the global level. Recently, the use of electronic health records and the establishment of networks of databases as different sources of real-world data is emerging allowing high-quality, large-scale evaluations and providing real-world evidence on questions of clinical and regulatory interests. Here, we summarize the adverse event pharmacovigilance reporting systems in place at the global, European and in some European countries, and provide examples from recent literature of how the analysis of pharmacovigilance reports can provide evidence for unexpected and novel adverse drug reactions. Furthermore, we discuss the role of real-world data to generate real-world evidence in pharmacovigilance and regulatory activities.

Using precision periodontal health care chart (PPHCC) to improve periodontal health.

OBJECTIVES: Developing a Precision Periodontal Health Care Chart (PPHCC) in the electronic dental record (EDR) system and evaluating its clinical usability and effects on clinical outcomes. MATERIALS AND METHODS: A survey with ten questions based on the System Usability Scale (SUS) and six questions about assessing clinical impact was used to evaluate the satisfaction of periodontitis patients and care providers with PPHCC before and after non-surgical periodontal therapy (NSPT). The clinical outcomes, including probing depth (PD), interdental clinical attachment loss (CAL), and bleeding on probing (BOP), in patients who used PPHCC (PC) were compared to those in patients without using PPHCC (control). The associations between risk assessments included in PPHCC and clinical outcomes of NSPT were also analyzed. RESULTS: The mean scores of SUS questions at the initial periodontal examination were 74.26 ± 18.89 (n = 37) for patients and 88.31 ± 14.14 (n = 37) for care providers. The mean scores of SUS questions at re-evaluation were 74.84 ± 17.78 (n = 16) for patients and 89.63 ± 13.48 (n = 20) for care providers. The changes in the percentages of teeth with interdental CAL 1-2 mm (p = 0.019) and CAL 3-4 mm (p = 0.026) at the re-evaluation visit were significantly different between the PC and control groups, but the other parameters were not. CONCLUSIONS: Both patients and care providers were satisfied with using PPHCC in the clinic. However, the short-term clinical outcomes in the PC group were similar to those in the control group. CLINICAL RELEVANCE: PPHCC, as a tool for delivering clinical and educational information, can motivate patients to control periodontitis and assist clinicians in making a personalized treatment plan.

Electronic Health Record Nudges and Health Care Quality and Outcomes in Primary Care: A Systematic Review.

Importance: Nudges have been increasingly studied as a tool for facilitating behavior change and may represent a novel way to modify the electronic health record (EHR) to encourage evidence-based care. Objective: To evaluate the association between EHR nudges and health care outcomes in primary care settings and describe implementation facilitators and barriers. Evidence Review: On June 9, 2023, an electronic search was performed in PubMed, Embase, PsycINFO, CINAHL, and Web of Science for all articles about clinician-facing EHR nudges. After reviewing titles, abstracts, and full texts, the present review was restricted to articles that used a randomized clinical trial (RCT) design, focused on primary care settings, and evaluated the association between EHR nudges and health care quality and patient outcome measures. Two reviewers abstracted the following elements: country, targeted clinician types, medical conditions studied, length of evaluation period, study design, sample size, intervention conditions, nudge mechanisms, implementation facilitators and barriers encountered, and major findings. The findings were qualitatively reported by type of health care quality and patient outcome and type of primary care condition targeted. The Risk of Bias 2.0 tool was adapted to evaluate the studies based on RCT design (cluster, parallel, crossover). Studies were scored from 0 to 5 points, with higher scores indicating lower risk of bias. Findings: Fifty-four studies met the inclusion criteria. Overall, most studies (79.6%) were assessed to have a moderate risk of bias. Most or all descriptive (eg, documentation patterns) (30 of 38) or patient-centeredness measures (4 of 4) had positive associations with EHR nudges. As for other measures of health care quality and patient outcomes, few had positive associations between EHR nudges and patient safety (4 of 12), effectiveness (19 of 48), efficiency (0 of 4), patient-reported outcomes (0 of 3), patient adherence (1 of 2), or clinical outcome measures (1 of 7). Conclusions and Relevance: This systematic review found low- and moderate-quality evidence that suggested that EHR nudges were associated with improved descriptive measures (eg, documentation patterns). Meanwhile, it was unclear whether EHR nudges were associated with improvements in other areas of health care quality, such as effectiveness and patient safety outcomes. Future research is needed using longer evaluation periods, a broader range of primary care conditions, and in deimplementation contexts.

Prevalence and general characteristics of dementia: a nationwide population-based study of electronic health records in Türkiye.

Background/aim: Türkiye is a country with an increasing life expectancy and an older adult population in parallel with the rest of the world. Several national small-scale studies were performed regarding the prevalence and characteristics of dementia in Türkiye, and the results of these studies differ from each other. We aimed to determine the prevalence of dementia in Türkiye to present the demographic characteristics, the frequency of use of health services, and the management of dementia. Materials and methods: Patients aged 65 years and over with a diagnosis of any type of dementia between January 1, 2019, and December 31, 2020, were retrospectively screened from the electronic health records of the Ministry of Health using ICD-10 codes. Results: In 2019, the total number of dementia cases identified in individuals aged 65 years and older was 247,727, of whom 150,529 (60.8%) were women. In 2020, the total number of dementia cases identified in this age group was 233,949, with 142,878 (61.1%) of these cases being women. The rate of patients admitted to the emergency department was 72.3% and 66.2% of all dementia patients in 2019 and 2020, respectively. In terms of the use of outpatient clinics, most patients with dementia were admitted to neurology (71.0% in 2019 and 62.4% in 2020). The geriatric medicine outpatient clinic was the least used by patients with dementia both in 2019 and 2020. Conclusion: The prevalence of patients living with dementia in Türkiye is lower than the global average. This suggests that most dementia cases are overlooked, highlighting the need to raise dementia awareness both in the community and among primary health care providers who frequently encounter older individuals. The study is significant in that it is the first to show the nationwide frequency of dementia in Türkiye.

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. OBJECTIVE: This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. METHODS: We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. RESULTS: Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records. CONCLUSIONS: Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents.

Extracting Critical Information from Unstructured Clinicians' Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study.

BACKGROUND: The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or "hidden" in unstructured text fields and not readily available for clinicians to act upon. OBJECTIVE: We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. METHODS: We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians' notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, "mild dementia" and "advanced Alzheimer disease"). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. RESULTS: We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an F1-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and F1-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. CONCLUSIONS: Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems.

Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.

OBJECTIVES: This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The algorithm addressed common challenges when applying ML to electronic medical record (EMR) data: (1) imbalanced outcome distribution; (2) heterogeneity across multisite EMR data and (3) evolving virological suppression thresholds. DESIGN: Observational study using a national EMR database. SETTING: Conducted in two regions in Tanzania, using data from the National HIV Care database. PARTICIPANTS: The study included over 6 million HIV care visit records from 295 961 PLHIV in two regions in Tanzania's National HIV Care database from January 2015 to May 2023. RESULTS: Our ML model effectively identified PLHIV at increased risk of adverse outcomes. Key predictors included past disengagement from care, antiretroviral therapy (ART) status (which tracks a patient's engagement with ART across visits), age and time on ART. The downsampling approach we implemented effectively managed imbalanced data to reduce prediction bias. Site-specific algorithms performed better compared with a universal approach, highlighting the importance of tailoring ML models to local contexts. A sensitivity analysis confirmed the model's robustness to changes in viral load suppression thresholds. CONCLUSIONS: ML models leveraging large-scale databases of patient data offer significant potential to identify PLHIV for interventions to enhance engagement in HIV care in resource-limited settings. Tailoring algorithms to local contexts and flexibility towards evolving clinical guidelines are essential for maximising their impact.

User-Centered Design and Usability of Voxe as a Pediatric Electronic Patient-Reported Outcome Measure Platform: Mixed Methods Evaluation Study.

BACKGROUND: Electronic patient-reported outcome measures (ePROMs) are standardized digital instruments integrated into clinical care to collect subjective data regarding patients' health-related quality of life, functional status, and symptoms. In documenting patient-reported progress, ePROMs can guide treatment decisions and encourage measurement-based care practices. Voxe is a pediatric and user-centered ePROM platform for patients with chronic health conditions. OBJECTIVE: We aimed to describe the user-centered design approach involving feedback from end users and usability testing of Voxe's platform features to support implementation in a pediatric health care setting. METHODS: Purposive sampling was used to recruit patients aged 8-17 years from 2 chronic illness populations in 2 pediatric hospitals in Canada. Patients' health care team members were also purposively recruited. One-on-one iterative testing sessions were conducted digitally by research team members with participants to obtain feedback on the appearance and functionalities of the Voxe platform prototype. Patients and health care providers (HCPs) completed Voxe-related task-based activities. International Organization for Standardization key performance indicators were tracked during HCP task-based activities. HCPs also completed the System Usability Scale. To test platform usability, the think-aloud technique was used by participants during the completion of structured tasks. After completing all task-based activities, patient participants selected 5 words from the Microsoft Desirability Toolkit to describe their overall impression and experience with the Voxe platform. Qualitative data about likes, dislikes, and ease of use were collected through semistructured interviews. Feedback testing sessions were conducted with patients and HCPs until Voxe was acceptable to participating end users, with no further refinements identified. Quantitative and qualitative data analysis were completed using descriptive statistics and content analysis. RESULTS: A total of 49 patients and 38 HCPs were recruited. Patients were positive about Voxe's child-centered design characteristics and notification settings. HCPs rated Voxe as user-friendly and efficient, with the time to complete tasks decreasing over time. HCPs were satisfied with the Voxe platform functionalities and identified the value of Voxe's system notifications, summarized display of ePROM results, and its capacity to integrate with electronic medical records. Patients' and HCPs' high satisfaction rates with the Voxe prototype highlight the importance of being responsive to user suggestions from the inception of eHealth platform developments to ensure their efficient and effective design. CONCLUSIONS: This paper describes the user-centered creation and usability testing of Voxe as an ePROM platform for implementation into clinical care for pediatric patients with chronic health conditions. As a patient-facing platform that can be integrated into electronic medical records, Voxe aligns with measurement-based care practices to foster quality patient-centered approaches to care. End users' positive feedback and evaluation of the platform's user-friendliness and efficiency suggest that Voxe represents a valuable and promising solution to systematically integrate patient-related outcome (PRO) data into complex and dynamic clinical health care settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-053119.

The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.

Background: Collaborative documentation (CD) is a behavioral health practice involving shared writing of clinic visit notes by providers and consumers. Despite widespread dissemination of CD, research on its effectiveness or impact on person-centered care (PCC) has been limited. Principles of PCC planning, a recovery-based approach to service planning that operationalizes PCC, can inform the measurement of person-centeredness within clinical documentation. Objective: This study aims to use the clinical informatics approach of natural language processing (NLP) to examine the impact of CD on person-centeredness in clinic visit notes. Using a dictionary-based approach, this study conducts a textual analysis of clinic notes from a community mental health center before and after staff were trained in CD. Methods: This study used visit notes (n=1981) from 10 providers in a community mental health center 6 months before and after training in CD. LIWC-22 was used to assess all notes using the Linguistic Inquiry and Word Count (LIWC) dictionary, which categorizes over 5000 linguistic and psychological words. Twelve LIWC categories were selected and mapped onto PCC planning principles through the consensus of 3 domain experts. The LIWC-22 contextualizer was used to extract sentence fragments from notes corresponding to LIWC categories. Then, fixed-effects modeling was used to identify differences in notes before and after CD training while accounting for nesting within the provider. Results: Sentence fragments identified by the contextualizing process illustrated how visit notes demonstrated PCC. The fixed effects analysis found a significant positive shift toward person-centeredness; this was observed in 6 of the selected LIWC categories post CD. Specifically, there was a notable increase in words associated with achievement (ß=.774, P<.001), power (ß=.831, P<.001), money (ß=.204, P<.001), physical health (ß=.427, P=.03), while leisure words decreased (ß=-.166, P=.002). Conclusions: By using a dictionary-based approach, the study identified how CD might influence the integration of PCC principles within clinical notes. Although the results were mixed, the findings highlight the potential effectiveness of CD in enhancing person-centeredness in clinic notes. By leveraging NLP techniques, this research illuminated the value of narrative clinical notes in assessing the quality of care in behavioral health contexts. These findings underscore the promise of NLP for quality assurance in health care settings and emphasize the need for refining algorithms to more accurately measure PCC.

Mapping the evaluation of the electronic health system PEC e-SUS APS in Brazil: a scoping review protocol.

BACKGROUND: The Brazilian Ministry of Health has developed and provided the Citizen's Electronic Health Record (PEC e-SUS APS), a health information system freely available for utilization by all municipalities. Given the substantial financial investment being made to enhance the quality of health services in the country, it is crucial to understand how users evaluate this product. Consequently, this scoping review aims to map studies that have evaluated the PEC e-SUS APS. METHODS: This scoping review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) framework, as well as by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Checklist extension for scoping reviews (PRISMA-ScR). The research question was framed based on the "CoCoPop" mnemonic (Condition, Context, Population). The final question posed is, "How has the Citizen's Electronic Health Record (PEC e-SUS APS) been evaluated?" The search strategy will be executed across various databases (LILACS, PubMed/MEDLINE, Scopus, Web of Science, ACM Digital Library, and IEEE Digital Library), along with gray literature from ProQuest Dissertation and Theses Global and Google Scholar, with assistance from a professional healthcare librarian skilled in supporting systematic reviews. The database search will encompass the period from 2013 to 2024. Articles included will be selected by three independent reviewers in two stages, and the findings will undergo a descriptive analysis and synthesis following a "narrative review" approach. Independent reviewers will chart the data as outlined in the literature. DISCUSSION: The implementation process for the PEC e-SUS APS can be influenced by the varying characteristics of the over 5500 Brazilian municipalities. These factors and other challenges encountered by health professionals and managers may prove pivotal for a municipality's adoption of the PEC e-SUS APS system. With the literature mapping to be obtained from this review, vital insights into how users have evaluated the PEC will be obtained. SYSTEMATIC REVIEW REGISTRATION: The protocol has been registered prospectively at the Open Science Framework platform under the number 10.17605/OSF.IO/NPKRU.

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership.

BACKGROUND: Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils. METHODS: Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils' use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana's largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints. RESULTS: A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method. CONCLUSIONS: Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training.

Medication recommendation is a crucial application of artificial intelligence in healthcare. Current methodologies mostly depend on patient-level longitudinal representation, which utilizes the entirety of historical electronic health records for making predictions. However, they tend to overlook a few key elements: (1) The need to analyze the impact of past medications on previous conditions. (2) Similarity in patient visits is more common than similarity in the complete medical histories of patients. (3) It is difficult to accurately represent patient-level longitudinal data due to the varying numbers of visits. To our knowledge, current models face difficulties in dealing with initial patient visits (i.e. in cold-start scenarios) which are common in clinical practice. This paper introduces DrugDoctor, an innovative drug recommendation model crafted to emulate the decision-making mechanics of human doctors. Unlike previous methods, DrugDoctor explores the visit-level relationship between prescriptions and diseases while considering the impact of past prescriptions on the patient's condition to provide more accurate recommendations. We design a plug-and-play block to effectively capture drug substructure-aware disease information and effectiveness-aware medication information, employing cross-attention and multi-head self-attention mechanisms. Furthermore, DrugDoctor adopts a fundamentally new visit-level training strategy, aligning more closely with the practices of doctors. Extensive experiments conducted on the MIMIC-III and MIMIC-IV datasets demonstrate that DrugDoctor outperforms 10 other state-of-the-art methods in terms of Jaccard, F1-score, and PRAUC. Moreover, DrugDoctor exhibits strong robustness in handling patients with varying numbers of visits and effectively tackles "cold-start" issues in medication combination recommendations.

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.

The impact of eHealth use on general practice workload in the pre-COVID-19 era: a systematic review.

BACKGROUND: In recent years, eHealth has received much attention as an opportunity to increase efficiency within healthcare organizations. Adoption of eHealth might consequently help to solve perceived health workforce challenges, including labor shortages and increasing workloads among primary care professionals, who serve as the first point of contact for healthcare in many countries. The purpose of this systematic review was to investigate the impact of general eHealth use and specific eHealth services use on general practice workload in the pre-COVID-19 era. METHODS: The databases of CINAHL, Cochrane, Embase, IEEE Xplore, Medline ALL, PsycINFO, Web of Science, and Google Scholar were searched, using combinations of keywords including 'eHealth', 'workload', and 'general practice'. Data extraction and quality assessment of the included studies were independently performed by at least two reviewers. Publications were included for the period 2010 - 2020, before the start of the COVID-19 pandemic. RESULTS: In total, 208 studies describing the impact of eHealth services use on general practice workload were identified. We found that two eHealth services were mainly investigated within this context, namely electronic health records and digital communication services, and that the largest share of the included studies used a qualitative study design. Overall, a small majority of the studies found that eHealth led to an increase in general practice workload. However, results differed between the various types of eHealth services, as a large share of the studies also reported a reduction or no change in workload. CONCLUSIONS: The impact of eHealth services use on general practice workload is ambiguous. While a small majority of the effects indicated that eHealth increased workload in general practice, a large share of the effects also showed that eHealth use reduced workload or had no impact. These results do not imply a definitive conclusion, which underscores the need for further explanatory research. Various factors, including the study setting, system design, and the phase of implementation, may influence this impact and should be taken into account when general practices adopt new eHealth services. STUDY REGISTRATION NUMBER: PROSPERO (International Prospective Register of Systematic Reviews) CRD42020199897; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=199897 .

Exploring the Knowledge and Use of Standardised Nursing Terminology Across Australia.

Standardised nursing terminologies (SNTs) support the visibility of nursing work and documentation, enabling data sharing and comparison. An online survey assessed the knowledge and use of SNTs and revealed barriers and enablers to their use by Australian nurses. Just over half of the respondents were familiar with SNTs before the survey, a quarter reported a reasonable understanding of SNTs, just under half reported previous use of a SNT, and less than 14% indicated a current use of a SNT in their workplace. Perceived benefits to SNTs identified by respondents included a reduction in variation and the ability to evaluate the effectiveness of nursing care by measuring outcomes. Both barriers and enablers to the use of SNTs included education and training, standardisation and contextualisation across Australia, and integration into any electronic medical record system. Nurses are poorly informed on what SNTs are and how they can be leveraged to support their work and documentation. There is a need for an Australia-wide strategic approach to ensure the future of nurses' work is visible, and SNTs are purposefully and correctly implemented across the country.

Building a Natural Language Interface for FHIR Clinical Terminology Server.

While Fast Healthcare Interoperability Resources (FHIR) clinical terminology server enables quick and easy search and retrieval of coded medical data, it still has some drawbacks. When searching, any typographical errors, variations in word forms, or deviations in word sequence might lead to incorrect search outcomes. For retrieval, queries to the server must strictly follow the FHIR application programming interface format, which requires users to know the syntax and remember the attribute codes they wish to retrieve. To improve its functionalities, a natural language interface was built, that harnesses the capabilities of two preeminent large language models, along with other cutting-edge technologies such as speech-to-text conversion, vector semantic searching, and conversational artificial intelligence. Preliminary evaluation shows promising results in building a natural language interface for the FHIR clinical terminology system.

Adapting Large Language Models for Automated Summarisation of Electronic Medical Records in Clinical Coding.

Encapsulating a patient's clinical narrative into a condensed, informative summary is indispensable to clinical coding. The intricate nature of the clinical text makes the summarisation process challenging for clinical coders. Recent developments in large language models (LLMs) have shown promising performance in clinical text summarisation, particularly in radiology and echocardiographic reports, after adaptation to the clinical domain. To explore the summarisation potential of clinical domain adaptation of LLMs, a clinical text dataset, consisting of electronic medical records paired with "Brief Hospital Course" from the MIMIC-III database, was curated. Two open-source LLMs were then fine-tuned, one pre-trained on biomedical datasets and another on a general-content domain on the curated clinical dataset. The performance of the fine-tuned models against their base models were evaluated. The model pre-trained on biomedical data demonstrated superior performance after clinical domain adaptation. This finding highlights the potential benefits of adapting LLMs pre-trained on a related domain over a more generalised domain and suggests the possible role of clinically adapted LLMs as an assistive tool for clinical coders. Future work should explore adapting more advanced models to enhance model performance in higher-quality clinical datasets.

Current Challenges in Digital Representation of Variation in Cancer Care.

Advances in cancer treatment have improved patient outcomes and survival in recent decades. Increased complexity, duration, and individualisation of treatment protocols present an important challenge for care teams monitoring adherence to best-practice care. A rigid rules-based system for flagging outliers is not fit for purpose, as there are sound reasons for deviating from baseline protocols, such as the management of treatment side effects to a tolerable degree, however the methods for determining the bounds of appropriateness for variation are not well studied or understood. The development of digital representations to inform cancer care delivery in a timely and continuing manner is crucial. This scoping review seeks to identify gaps in current methods and propose a novel approach to digitally represent patient journeys in clinically meaningful visual and computational forms. These methods can be combined to produce real-time, clinically applicable tools such as group-level business-intelligence dashboards (are processes and resources adequate to ensure that patients are being treated according to best practice?) as well as individual-level decision support (what is the likely outcome for this patient if treatment is stopped early based on prior data?) and day to day clinical workflows (what has happened to this patient so far?).

Electronic Discharge Summaries: Perspectives on National Implementation from Clinicians in Hospital and Primary Care.

This paper describes clinicians' views on the structure and content of an electronic discharge summary (EDS). A sample EDS template was developed by building on existing Australian guidelines to illustrate some of the proposed elements required for a high-quality clinical document. Surveys were widely disseminated to gather feedback and perspectives of hospital and primary care clinicians. A pragmatic approach to this study was underpinned by a strong evidence base and informed by implementation science methods. Key themes were identified, including variability in workflow and clinical needs, digital maturity, and digital health literacy of the clinical workforce. Understanding different workflows and priorities between hospital and primary care clinicians was a significant barrier to implementing a high-quality EDS. The strong consensus for change from both hospital and primary care clinicians, however, signaled the workforce's readiness as a potential enabler of high-quality EDS documentation.