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Objective: To describe the use of privacy preserving linkage methods operationally in Australia, and to present insights and key learnings from their implementation., Methods: Privacy preserving record linkage (PPRL) utilising Bloom filters provides a unique practical mechanism that allows linkage to occur without the release of personally identifiable information (PII), while still ensuring high accuracy., Results: The methodology has received wide uptake within Australia, with four state linkage units with privacy preserving capability. It has enabled access to general practice and private pathology data amongst other, both much sought after datasets previous inaccessible for linkage., Conclusion: The Australian experience suggests privacy preserving linkage is a practical solution for improving data access for policy, planning and population health research. It is hoped interest in this methodology internationally continues to grow., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge in healthcare, ranging from mild discomfort to severe complications, and can impact patient safety and treatment outcomes., Methods: We explore the influence of domain shift between a set of dummy clinical notes and a real-world hospital corpus of Japanese clinical notes of breast cancer treatment when extracting ADEs from free text. We annotated a subset of the hospital dataset and used it to fine-tune a Named Entity Recognition (NER) model, initially trained with the set of dummy documents. We used increasing amounts of the annotated data and evaluated the impact on the model's performance. Additionally, we examined the extracted information to identify combinations of drugs that are likely to cause ADEs., Results: We show that domain adaptation can significantly improve model performance in the new domain, as by feeding a small subset of 100 documents for the fine-tuning process we saw a 40% improvement in model performance. However, we also noticed diminishing returns when fine-tuning the model with a larger dataset. For instance, by feeding eight times more data, we only saw further 18% improvement in extraction performance., Conclusion: While variations in writing style and vocabulary in clinical corpora can significantly impact the quality of NER results. We show that domain adaptation can be of great aid in mitigating these discrepancies and achieving better performance. Yet, while providing in-domain data to a model helps, there are diminishing returns when fine-tuning with large amounts of data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: Accurate diagnoses and personalized treatments in medicine rely on identifying causality. However, existing causal discovery algorithms often yield inconsistent results due to distinct learning mechanisms. To address this challenge, we introduce MINDMerge, a multi-causal investigation and discovery framework designed to synthesize causal graphs from various algorithms., Methods: MINDMerge integrates five causal models to reconcile inconsistencies arising from different algorithms. Employing credibility weighting and a novel cycle-breaking mechanism in causal networks, we initially developed and tested MINDMerge using three synthetic networks. Subsequently, we validated its effectiveness in discovering risk factors and predicting acute kidney injury (AKI) using two electronic medical records (EMR) datasets, eICU Collaborative Research Database and MIMIC-III Database. Causal reasoning was employed to analyze the relationships between risk factors and AKI. The identified causal risk factors of AKI were used in building a prediction model, and the prediction model was evaluated using the area under the receiver operating characteristics curve (AUC) and recall., Results: Synthetic data experiments demonstrated that our model outperformed significantly in capturing ground-truth network structure compared to other causal models. Application of MINDMerge on real-world data revealed direct connections of pulmonary disease, hypertension, diabetes, x-ray assessment, and BUN with AKI. With the identified variables, AKI risk can be inferred at the individual level based on established BNs and prior information. Compared against existing benchmark models, MINDMerge maintained a higher AUC for AKI prediction in both internal (AUC: 0.832) and external network validations (AUC: 0.861)., Conclusion: MINDMerge can identify causal risk factors of AKI, serving as a valuable diagnostic tool for clinical decision-making and facilitating effective intervention., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Delays in discharging patients from Acute Medical Units hamper patient flows throughout the hospital. The decision to discharge a patient is mainly based on the patients' physiological condition, but may vary between physicians. An objective decision-support system based on patients' physiological data may help minimizing unnecessary delays in discharge. The aim of this proof-of-concept study is to assess the feasibility of predicting whether patients in an Acute Medical Unit are physiologically fit-for-discharge using machine learning with commonly available hospital data. Furthermore, this study investigated how long before actual time of discharge from the Acute Medical Unit we could predict discharge fitness. Also, the predictive importance of features extracted from these data was assessed., Methods: Electronic Medical Records of patients who participated in a Randomized Controlled Trial conducted in an Acute Medical Unit were used retrospectively (N = 199). Only commonly available hospital data were used. Logistic Regression and Random Forest models were applied to predict every hour whether patients were physiologically fit-for-discharge. Nested 5-fold cross-validation with 5 repeats was used to optimize the model hyperparameters and to estimate the predictive performances., Results: Physiological discharge fitness was predictable with reasonable performance for Logistic Regression (mean AUROC: 0.67) and Random Forest (mean AUROC: 0.69). For an intuitively chosen classification threshold of 0.8, mean specificity was 93.3 % and sensitivity 14.1 %. Models could predict physiological discharge fitness more than 24 h earlier than actual time of discharge for most patients who were correctly predicted to be fit-for-discharge. Patient characteristics, vital signs and laboratory results were shown to be important predictors., Conclusion: This proof-of-concept study showed that it is feasible to predict with machine learning whether patients in an Acute Medical Unit are physiologically fit-for-discharge using commonly available hospital data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background: Stroke management requires a coordinated strategy, adhering to clinical pathways (CP) and value-based healthcare (VBHC) principles from onset to rehabilitation. However, the discrepancies between these pathways and actual patient experiences highlight the need for ongoing monitoring and addressing interoperability issues across multiple institutions in stroke care. To address this, the Fast Healthcare Interoperability Resource (FHIR) Implementation Guide (IG) standardizes the information exchange among these systems, considering a specific context of use., Objective: Develop an FHIR IG for stroke care rooted in established stroke CP and VBHC principles., Method: We represented the stroke patient journey by considering the core stroke CP, the International Consortium for Health Outcomes Measurement (ICHOM) dataset for stroke, and a Brazilian case study using the Business Process Model and Notation (BPMN). Next, we developed a data dictionary that aligns variables with existing FHIR resources and adapts profiling from the Brazilian National Health Data Network (BNHDN)., Results: Our BPMN model encompassed three critical phases that represent the entire patient journey from symptom onset to rehabilitation. The stroke data dictionary included 81 variables, which were expressed as questionnaires, profiles, and extensions. The FHIR IG comprised nine pages: Home, Stroke-CP, Data Dictionary, FHIR, ICHOM, Artifacts, Examples, Downloads, and Security. We developed 96 artifacts, including 7 questionnaires, 27 profiles with corresponding example instances, 3 extensions, 18 value sets, and 14 code systems pertinent to ICHOM outcome measures., Conclusion: The FHIR IG for stroke in this study represents a significant advancement in healthcare interoperability, streamlining the tracking of patient outcomes for quality enhancement, facilitating informed treatment choices, and enabling the development of dashboards to promote collaborative excellence in patient care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Objective: The objective of our study is to investigate the impacts of telemedicine technology and its specific tools on physicians' overall satisfaction, quality of care, and percentage of patient visits in ambulatory care settings after the COVID-19 lockdowns., Materials and Methods: Data for our analysis was sourced from the 2021 annual National Electronic Health Records Survey (NEHRS), which included 1,875 complete questionnaire responses from physicians in the 2021 NEHRS. We used regression models to test the effects of telemedicine on physicians' overall satisfaction, quality of care, and percentage of patients' visits., Results: We report that telemedicine technology has significant positive effects on physicians' satisfaction with telemedicine and quality of care evaluation, both at an aggregate level and at the disaggregate levels of individual telemedicine features, and partially significant effects on patients' telemedicine visits., Discussion: Telemedicine features that contributed significantly to physician satisfaction and quality of care evaluation were telephone, videoconferencing, standalone telemedicine platform, and telemedicine platform integrated with EHR, while only telephone and integrated telemedicine platform contributed significantly to patients' telemedicine visits., Conclusion: For telemedicine research and practice, this study confirms that telemedicine improves physician satisfaction and quality of care perceptions and will therefore be preferred by physicians. However, telemedicine has a mixed impact on percentage of patient visits, which suggests that providers may have to work harder to regularize telemedicine acceptance among patients in the post-COVID era., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Aims: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy., Methods: Employing the Logic Learning Machine (LLM), an advanced artificial intelligence system, we scrutinized electronic health records of 1.5 million patients treated in 271 diabetes clinics affiliated with the Italian Association of Medical Diabetologists from 2005 to 2019. Inclusion criteria comprised patients on metformin monotherapy with two consecutive mean HbA1c levels exceeding 7.0%. The cohort was divided into "inertia-NO" (20,067 patients with prompt intensification) and "inertia-YES" (13,029 patients without timely intensification)., Results: The LLM model demonstrated robust discriminatory ability among the two groups (ROC-AUC = 0.81, accuracy = 0.71, precision = 0.80, recall = 0.71, F1 score = 0.75). The main novelty of our results is indeed the identification of two main distinct subtypes of therapeutic inertia. The first exhibited a gradual but steady HbA1c increase, while the second featured a moderate, non-uniform rise with substantial fluctuations., Conclusions: Our analysis sheds light on the significant impact of HbA1c levels over time on therapeutic inertia in patients with T2D, emphasizing the importance of early intervention in the presence of specific HbA1c patterns., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Electronic health records are a valuable asset for research, but their use is challenging due to inconsistencies of records, heterogeneous formats and the distribution over multiple, non-integrated information systems. Hence, specialized health data engineering and data science expertise are required to enable research. To facilitate secondary use of clinical routine data collected in our intensive care wards, we developed a scalable approach, consisting of cohort generation, variable filtering and data extraction steps., Objective: With this report we share our workflow of data request, cohort identification and data extraction. We present an algorithm for automatic data extraction from our critical care information system (CCIS) that can be adapted to other object-oriented data bases., Methods: We introduced a data request process with functionalities for automated identification of patient cohorts and a specialized hierarchical data structure that supports filtering relevant variables from the CCIS and further systems for the specified cohorts. The data extraction algorithm takes patient pseudonyms and variable lists as inputs. Algorithms are implemented in Python, leveraging the PySpark framework running on our data lake infrastructure., Results: Our data request process is in operational use since June 2022. Since then we have served 121 projects with 148 service requests in total. We discuss the hierarchical structure and the frequently used data items of our CCIS in detail and present an application example, including cohort selection, data extraction and data transformation into an analyses-ready format., Conclusions: Using clinical routine data for secondary research is challenging and requires an interdisciplinary team. We developed a scalable approach that automates steps for cohort identification, data extraction and common data pre-processing steps. Additionally, we facilitate data harmonization, integration and consult on typical data analysis scenarios, machine learning algorithms and visualizations in dashboards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Digital healthcare aims to deliver on the quadruple aim: enhance patient experiences, improve population health, reduce costs and improve provider experiences. Despite large investments, it is unclear how advancing digital health enables these healthcare aims., Objective: Our objectives were to: 1) measure the correlation between digital capability and health system outcomes mapped to the quadruple aim, and 2) measure the longitudinal impact of electronic medical record implementations upon health system outcomes., Materials and Methods: We undertook two studies: 1) Digital health correlational study investigating the association among healthcare system capability and healthcare aims, and 2) Digital hospital longitudinal study investigating outcomes pre and post electronic medical record implementation., Results: Digital health capability was associated with lower staff turnover. Digitising healthcare services was associated with decreased medication errors, decreased nosocomial infections, increased hospital activity, and a transient increase in staff leave., Discussion: These results suggest positive impacts on the population health and healthcare costs aim, minimal impacts on the provider experience aim and no observed impacts to the patient experience aim., Conclusion: These findings should provide confidence to healthcare decision-makers investing in digital health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Patient engagement when providing patient access to health data results from an interaction between the available tools and individual capabilities. The recent digital advancements of the healthcare field have altered the manifestation and importance of patient engagement. However, a comprehensive assessment of what factors contribute to patient engagement remain absent. In this review article, we synthesised the most frequently discussed factors that can foster patient engagement with their health data., Methods: A scoping review was conducted in MEDLINE, Embase, and Google Scholar. Relevant data were synthesized within 7 layers using a thematic analysis: (1) social and demographic factors, (2) patient ability factors, (3) patient motivation factors, (4) factors related to healthcare professionals' attitudes and skills, (5) health system factors, (6) technological factors, and (7) policy factors., Results: We identified 5801 academic and 200 Gy literature records, and included 292 (4.83%) in this review. Overall, 44 factors that can affect patient engagement with their health data were extracted. We extracted 6 social and demographic factors, 6 patient ability factors, 12 patient motivation factors, 7 factors related to healthcare professionals' attitudes and skills, 4 health system factors, 6 technological factors, and 3 policy factors., Conclusions: Improving patient engagement with their health data enables the development of patient-centered healthcare, though it can also exacerbate existing inequities. While expanding patient access to health data is an important step towards fostering shared decision-making in healthcare and subsequently empowering patients, it is important to ensure that these developments reach all sectors of the community., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Psychiatric medication can have adverse effects such as weight gain, which is a metabolic risk factor for the development of cardiovascular disease and diabetes. This study aimed to assess whether an IT-supported task shift from physicians to pharmacists could improve clinical guideline compliance in assessing metabolic risk factors for psychiatric patients., Method: An IT tool was designed and implemented in the electronic health record to enable pharmacists to efficiently screen patients for metabolic risk factors. The tool provided a risk score for each patient based on criteria from the cross-regional guideline. All admitted patients with a score were assessed by the pharmacists, who referred and discussed the patients with a physician when deemed relevant. We measured guideline compliance during baseline (manual screening) and intervention (automated screening) after implementing the IT tool and pharmacist assessment. After the intervention period, we conducted follow-up interviews with all participating pharmacists., Results: Guideline compliance increased significantly from 26 % (baseline) to 63 % (intervention) (Fisher's exact test p < .001, N = 98). The task shift from physicians to pharmacists was also significant (Fisher's exact test, p < .001, N = 40). Interviews revealed that the pharmacists found the task shift meaningful and received positive feedback from the physicians. The facilitators of the task shift included interprofessional collaboration, physician shortage, provider empowerment, and the manageable nature of the task. The barriers included a need for further competence development and lack of pharmacist authorization. The IT tool was considered useful and suggestions for improvements emerged., Conclusion: The IT-supported task shift from physician to pharmacist significantly improved guideline compliance in the assessment of metabolic risk factors in psychiatric patients. The findings support increasing the pharmacist's role in psychiatric care to improve patient outcomes., Competing Interests: Declaration of competing interest Christine Flagstad Bech is affiliated with the organization where the pharmacists taking part in the study are employed. The remaining authors have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: The literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND)., Methods: This qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care., Results: Interview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation., Conclusions: The HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals' involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Objective: Unintended duplicate prescriptions of anticoagulants increase the risk of serious adverse events. Clinical Decision Support Systems (CDSSs) can help prevent such medication errors; however, sophisticated algorithms are needed to avoid alert fatigue. This article describes the steps taken in our hospital to develop a CDSS to prevent anticoagulant duplication (AD)., Methods: The project was composed of three phases. In phase I, the status quo was established. In phase II, a clinical pharmacist developed an algorithm to detect ADs using daily data exports. In phase III, the algorithm was integrated into the hospital's electronic health record system. Alerts were reviewed by clinical pharmacists before being sent to the prescribing physician. We conducted a retrospective analysis of all three phases to assess the impact of the interventions on the occurrence and duration of ADs. Phase III was analyzed in more detail regarding the acceptance rate, sensitivity, and specificity of the alerts., Results: We identified 91 ADs in 1581 patients receiving two or more anticoagulants during phase I, 70 ADs in 1692 patients in phase II, and 57 ADs in 1575 patients in phase III. Mean durations of ADs were 1.8, 1.4, and 1.1 calendar days during phases I, II, and III, respectively. In comparison to the baseline in phase I, the relative risk reduction of AD in patients treated with at least two different anticoagulants during phase III was 42% (RR: 0.58, CI: 0.42-0.81). A total of 429 alerts were generated during phase III, many of which were self-limiting, and 186 alerts were sent to the respective prescribing physician. The acceptance rate was high at 97%. We calculated a sensitivity of 87.4% and a specificity of 87.9%., Conclusion: The stepwise development of a CDSS for the detection of AD markedly reduced the frequency and duration of medication errors in our hospital, thereby improving patient safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers., Methods: We analyzed electronic medical records of 200,859 and 50,920 hospitalized patients within 24 h after being diagnosed with ARDS from the Philips eICU Institute (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset, respectively. Patients were sorted into three groups, including rapid death, long stay, and recovery, based on their condition or outcome between 24 and 72 h after ARDS diagnosis. To improve prediction performance and generalizability, a "pretrain-finetune" approach was applied, where we pretrained models on the eICU-CRD dataset and performed model finetuning using only a part (35%) of the MIMIC-IV dataset, and then tested the finetuned models on the remaining data from the MIMIC-IV dataset. Well-known machine-learning algorithms, including logistic regression, random forest, extreme gradient boosting, and multilayer perceptron neural networks, were employed to predict ARDS outcomes. Prediction performance was evaluated using the area under the receiver-operating characteristic curve (AUC)., Results: Results show that, in general, multilayer perceptron neural networks outperformed the other models. The use of pretrain-finetune yielded improved performance in predicting ARDS outcomes achieving a micro-AUC of 0.870 for the MIMIC-IV dataset, an improvement of 0.046 over the pretrain model., Conclusions: The proposed pretrain-finetune approach can effectively improve model generalizability from one to another dataset in ARDS prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

ObjectiveTo comparatively evaluate the effectiveness of three different methods involving end-users for detecting usability problems in an EHR: user testing, semi-structured interviews and surveys.Materials and methodsData were collected at two major urban dental schools from faculty, residents and dental students to assess the usability of a dental EHR for developing a treatment plan. These included user testing (N=32), semi-structured interviews (N=36), and surveys (N=35).ResultsThe three methods together identified a total of 187 usability violations: 54% via user testing, 28% via the semi-structured interview and 18% from the survey method, with modest overlap. These usability problems were classified into 24 problem themes in 3 broad categories. User testing covered the broadest range of themes (83%), followed by the interview (63%) and survey (29%) methods.DiscussionMultiple evaluation methods provide a comprehensive approach to identifying EHR usability challenges and specific problems. The three methods were found to be complementary, and thus each can provide unique insights for software enhancement. Interview and survey methods were found not to be sufficient by themselves, but when used in conjunction with the user testing method, they provided a comprehensive evaluation of the EHR.ConclusionWe recommend using a multi-method approach when testing the usability of health information technology because it provides a more comprehensive picture of usability challenges.

BackgroundPoor usability is one of the major barriers for optimally using electronic health records (EHRs). Dentists are increasingly adopting EHRs, and are using structured data entry interfaces to enter data such that the data can be easily retrieved and exchanged. Until recently, dentists have lacked a standardized terminology to consistently represent oral health diagnoses.ObjectivesIn this study we evaluated the usability of a widely used EHR interface that allow the entry of diagnostic terms, using multi-faceted methods to identify problems and work with the vendor to correct them using an iterative design method.MethodsFieldwork was undertaken at two clinical sites, and dental providers as subjects participated in user testing (n=32), interviews (n=36) and observations (n=24).ResultsUser testing revealed that only 22-41% of users were able to successfully complete a simple task of entering one diagnosis, while no user was able to complete a more complex task. We identified and characterized 24 high-level usability problems reducing efficiency and causing user errors. Interface-related problems included unexpected approaches for displaying diagnosis, lack of visibility, and inconsistent use of UI widgets. Terminology related issues included missing and mis-categorized concepts. Work domain issues involved both absent and superfluous functions. In collaboration with the vendor, each usability problem was prioritized and a timeline set to resolve the concerns.DiscussionMixed methods evaluations identified a number of critical usability issues relating to the user interface, underlying terminology of the work domain. The usability challenges were found to prevent most users from successfully completing the tasks. Our further work we will determine if changes to the interface, terminology and work domain do result in improved usability.

Introduction: Despite the improvements made in recent decades, the OECD regards hospital-acquired pressure injuries (HAPI) as high priority areas for actions to ensure patient safety. This study was aimed at investigating the degree of utilization of two types of electronic patient record systems for wound care on lowering HAPI rates. Furthermore, the effect of user satisfaction with the systems and perceived alignment with clinical processes should be studied., Material and Methods: A regression analysis of post-stratified data from German hospitals obtained from the Hospital Quality Reports (observed/expected HAPI ratio) and the IT Report Healthcare was performed. The sample comprised 319 hospitals reporting on digital wound record systems and 199 hospitals on digital nursing record systems for system utilization and the subset of hospitals using a digital system for user satisfaction and process alignment., Results: The study revealed a significant effect of hospital ownership for both types of systems and a significant interaction of ownership and system utilization for digital wound record systems: Only the for-profit hospitals benefited from a higher degree of system utilization with a lower HAPI ratio. In contrast, non-profit hospitals yielded a reversed pattern, with increasing HAPI rates matching an increased system utilization. User satisfaction (significant) and the perceived alignment of the clinical process (trend) of the digital nursing record system were related with lower HAPI ratios., Discussion: These findings point to a differential effect of system utilization on HAPI ratios depending on hospital ownership, and they demonstrate that those users who are satisfied with the system can act as catalysts for better care. The explained variance was small but comparable to other studies. Furthermore, it shows that explaining quality care is a complex undertaking. Sheer utilization has no effect while a differential perspective on the facilitators and barriers might help to explain the patient outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: Electronic health records (EHR) are of great value for clinical research. However, EHR consists primarily of unstructured text which must be analysed by a human and coded into a database before data analysis- a time-consuming and costly process limiting research efficiency. Natural language processing (NLP) can facilitate data retrieval from unstructured text. During AssistMED project, we developed a practical, NLP tool that automatically provides comprehensive clinical characteristics of patients from EHR, that is tailored to clinical researchers needs., Material and Methods: AssistMED retrieves patient characteristics regarding clinical conditions, medications with dosage, and echocardiographic parameters with clinically oriented data structure and provides researcher-friendly database output. We validate the algorithm performance against manual data retrieval and provide critical quantitative and qualitative analysis., Results: AssistMED analysed the presence of 56 clinical conditions, medications from 16 drug groups with dosage and 15 numeric echocardiographic parameters in a sample of 400 patients hospitalized in the cardiology unit. No statistically significant differences between algorithm and human retrieval were noted. Qualitative analysis revealed that disagreements with manual annotation were primarily accounted to random algorithm errors, erroneous human annotation and lack of advanced context awareness of our tool., Conclusions: Current NLP approaches are feasible to acquire accurate and detailed patient characteristics tailored to clinical researchers' needs from EHR. We present an in-depth description of an algorithm development and validation process, discuss obstacles and pinpoint potential solutions, including opportunities arising with recent advancements in the field of NLP, such as large language models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Implementation of an Electronic Patient Record (EPR) in a key milestone in the digital strategy of modern healthcare organisations. The implementation of EPR systems can be viewed as challenging and complex., Objective: The aim of the study was to investigate user perspectives and experiences of the implementation of an Electronic Medical Record in a major academic teaching hospital, with simultaneous 'go-live' across the whole hospital taking place., Methods: Focus groups and individual in-depth interviews were conducted with stakeholders and users (n = 105), approximately nine months post-EPR implementation. The study explored EPR users' perceptions using an extended theoretical framework of the DeLone and McLean Information Systems Success Model (2003), which measured information systems, system quality, information quality, service quality, use/perceived usefulness & user satisfaction and net benefits., Results: Staff engagement and satisfaction was high and the EPR is accepted as the new standard way of completing care. There was agreement that the EPR affords transparency, and greater accountability. There was some concern expressed regarding impact of the EPR on interprofessional and patient/provider interactions and communication. Physicians reported the inputting of social history through free text as an issue of concern and time consuming. The Big Bang approach with mandatory conversion was key to the successful adoption of EPR. There was consensus across professional and administrative respondents that there was no appetite to return to paper-based records., Conclusion: The successful roll out of the EPR reflects the digital readiness of healthcare providers and organisations. The potential for unintended consequences on work process requires continual monitoring. A key future benefit of the EPR will be the capacity to reach a broader understanding and analysis of variation in processes and outcomes within healthcare organisations. It is clear that skills in data analytics will be needed to mine data successfully., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: Theoretically, the added value of electronic health records (EHRs) is extensive. Reusable data capture in EHRs could lead to major improvements in quality measurement, scientific research, and decision support. To achieve these goals, structured and standardized recording of healthcare data is a prerequisite. However, time spent on EHRs by physicians is already high. This study evaluated the effect of implementing an EHR embedded care pathway with structured data recording on the EHR burden of physicians., Materials and Methods: Before and six months after implementation, consultations were recorded and analyzed with video-analytic software. Main outcome measures were time spent on specific tasks within the EHR, total consultation duration, and usability indicators such as required mouse clicks and keystrokes. Additionally, a validated questionnaire was completed twice to evaluate changes in physician perception of EHR system factors and documentation process factors., Results: Total EHR time in initial oncology consultations was significantly reduced by 3.7 min, a 27 % decrease. In contrast, although a decrease of 13 % in consultation duration was observed, no significant effect on EHR time was found in follow-up consultations. Additionally, perceptions of physicians regarding the EHR and documentation improved significantly., Discussion: Our results have shown that it is possible to achieve structured data capture while simultaneously reducing the EHR burden, which is a decisive factor in end-user acceptance of documentation systems. Proper alignment of structured documentation with workflows is critical for success., Conclusion: Implementing an EHR embedded care pathway with structured documentation led to decreased EHR burden., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: Electronic health records contain an enormous amount of valuable information recorded in free text. Information extraction is the strategy to transform free text into structured data, but some of its components require annotated data to tune, which has become a bottleneck. Large language models achieve good performances on various downstream NLP tasks without parameter tuning, becoming a possible way to extract information in a zero-shot manner., Methods: In this study, we aim to explore whether the most popular large language model, ChatGPT, can extract information from the radiological reports. We first design the prompt template for the interested information in the CT reports. Then, we generate the prompts by combining the prompt template with the CT reports as the inputs of ChatGPT to obtain the responses. A post-processing module is developed to transform the responses into structured extraction results. Besides, we add prior medical knowledge to the prompt template to reduce wrong extraction results. We also explore the consistency of the extraction results., Results: We conducted the experiments with 847 real CT reports. The experimental results indicate that ChatGPT can achieve competitive performances for some extraction tasks like tumor location, tumor long and short diameters compared with the baseline information extraction system. By adding some prior medical knowledge to the prompt template, extraction tasks about tumor spiculations and lobulations obtain significant improvements but tasks about tumor density and lymph node status do not achieve better performances., Conclusion: ChatGPT can achieve competitive information extraction for radiological reports in a zero-shot manner. Adding prior medical knowledge as instructions can further improve performances for some extraction tasks but may lead to worse performances for some complex extraction tasks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Spiritual care has been associated with better health outcomes. Despite increasing evidence of the benefits of spiritual care for older patients coping with illness and aggressive treatment, the role of spirituality is not well understood and implemented. Nurses, as frontline holistic healthcare providers, are in a position to address patients' spiritual needs and support them in finding meaning in life. This study aimed to identify spiritual care by analyzing nursing data and to compare the psychological and physical comfort between older chronically ill patients who received spiritual care versus those who did not receive spiritual care., Material and Methods: A propensity score matched cohort utilizing nursing care plan data was used to construct balanced groups based on patient characteristics at admission. 45 older patients (≥65 years) with chronic illnesses received spiritual care with measured psychological or physical comfort and 90 matched controls. To ensure the robustness of our results, two sensitivity analyses were performed. Group comparisons were performed to assess the average treatment effect of spiritual care on psychological and physical comfort outcomes., Results: The mean psychological comfort was 4.3 (SD = 0.5) for spiritual care receivers and 3.9 (SD = 0.9) for non-receivers. Regression analysis showed that spiritual care was associated with better psychological comfort (estimate = 0.479, std. error = 0.225, p = 0.041). While its effect on physical comfort was not statistically significant (estimate = -0.265, std. error = 0.234, p = 0.261). This study provides suggestive evidence of the positive impact of nurses' spiritual care in improving psychological comfort for older patients with chronic illnesses., Conclusion: Using interoperable nursing data, our findings suggest that spiritual care improves psychological comfort in older patients facing illness. This finding suggests that nurses may integrate spiritual care into their usual care to support patients experiencing distress., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Introduction: Electronic messages are growing as an important form of patient-provider communication, particularly in the primary care setting. However, adoption of healthcare technology has been under-utilized by underserved patient populations. The purpose of this study was to describe how adoption and utilization of electronic messaging occurred within a large primary care urban-based patient population., Methods: In this retrospective study, the frequency of electronic messages initiated by adult outpatient primary care patients was observed. Patients were classified as either non-portal adopters, non-message utilizers, low message utilizers, and high message utilizers. Logistic regression modeling was used to compare factors associated with message utilization rates to determine disparities in access., Results: Among a sample of 27,453 ethnically diverse adult patients from the Houston, Texas Metropolitan area, 33,497 unique messages were sent (1.22 messages/patient). Message burden was predominantly derived by a small number of high utilizers (individuals who sent 3 or more messages), who comprised 15.7 % of the study population (n = 4302) but accounted for 77 % of the message volume (n = 25,776). These high utilizers were typically older, White, English speaking, from middle to upper income zip codes, had higher number of comorbidities, and a higher number of clinical visits., Conclusions: Most inbox messages were generated by a small number of patients. While it was reassuring to see older and sicker individuals utilizing electronic messaging, patients from minority and/or lower income background utilized electronic messaging much less. This may propagate systematic bias and decrease the level of care for traditionally underserved patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Seeking and receiving care requires disclosure of personal information which is recorded as health data in electronic health records. Thereafter, restricting the flow of information is dependent on data protection, information security, ethical conduct, and law. Privacy concerns may arise as patients' options concerning privacy have been balanced to cater both the privacy of patients and the needs of healthcare, as well as secondary use of data., Methods: This study examined privacy concerns among the users of a national patient portal in a representative sample of Finnish adults aged 20 to 99 years old (n = 3,731). We used logistic regression analysis with population weights to seek answers to which factors are associated with privacy concerns. The cross-sectional survey data was collected in 2020., Results: Every third patient portal user had privacy concerns. Those who were 50 to 59 years old (p = 0.030) had privacy concerns more often than 20 to 49-year-olds. Those who had financial difficulties (p = 0.003) also had privacy concerns more often while those, who had good digital skills (p=<0.026), did not need guidance on telehealth service use (p=<0.001) and found telehealth service use to be beneficial (p = 0.008), had privacy concerns less often., Conclusion: The usefulness of telehealth seems to play an important role in privacy concerns. Another important factor is the skills required to use telehealth services. We encourage providing guidance to those who lack the necessary skills for telehealth service use. We also encourage putting effort not only into data protection and information security measures of telehealth services, but also into providing transparent and comprehensible privacy information for the service users as privacy concerns are common., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Aim: To identify the key requirements and challenges to interoperability between patient portals and electronic health records (EHRs)., Introduction: Patient portals provide patients with access to their health information directly from EHRs within hospitals, primary care centres and general practices (GPs). Patient portals offer many benefits to patients including improved communication with healthcare providers and care coordination. However, many challenges exist with the integration and automatic and secure sharing of information between EHRs and patient portals. It is critical that countries learn from international experiences to successfully develop interoperable national patient portals., Methods: A scoping review methodology was undertaken. A search strategy using index terms and keywords was applied across four key databases, an additional grey literature search was also run. The identified studies were screened by two reviewers to determine eligibility against defined inclusion criteria. Data were abstracted from the eligible studies and reviewed to identify the key requirements and challenges to interoperability of patient portals with EHRs., Results: After screening 3,462 studies, 34 were included across 11 countries. Of the 29 unique patient portals studied, few offered patients access to their entire healthcare record across multiple sites and a number of different functionalities were available. Key interoperability requirements and challenges identified were: Data Sharing Incentives & Supports; Heterogenous Organisations & Information Systems; Data Storage & Management; Available Information & Functionalities; Data Formats & Standards; Identification of Individuals; User Access, Control & Consent; and Security & Privacy., Conclusion: Seamless exchange of health information across patient portals and EHRs required organisational and individual factors, as well as technical considerations. Interorganisational collaboration and engagement of key stakeholders to determine standards and guidelines for consent and sharing of information, as well as technical standards and security measures were recommended., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: A commercial federated network called TriNetX has connected electronic health record (EHR) data from academic medical centers (AMCs) with biopharmaceutical sponsors in a privacy-preserving manner to promote sponsor-initiated clinical trials. Little is known about how AMCs have implemented TriNetX to support clinical trials., Findings: At our AMC over a six-year period, TriNetX integrated into existing institutional workflows enabled 402 requests for sponsor-initiated clinical trials, 14 % (n = 56) of which local investigators expressed interest in conducting. Although clinical trials administrators indicated TriNetX yielded unique study opportunities, measurement of impact of institutional participation in the network was challenging due to lack of a common trial identifier shared across TriNetX, sponsor, and our institution., Conclusion: To the best of our knowledge, this study is among the first to describe integration of a federated network of EHR data into institutional workflows for sponsor-initiated clinical trials. This case report may inform efforts at other institutions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Clinical decision support (CDS) alerts and reminders aim to influence clinical decisions, yet they are often designed without considering human decision-making behaviour. While this behaviour is comprehensively described by behavioural economics (BE), the sheer volume of BE literature poses a challenge to designers when identifying behavioural effects with utility to alert and reminder designs. This study tackles this challenge by focusing on the MINDSPACE framework for behaviour change, which collates nine behavioural effects that profoundly influence human decision-making behaviour: Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitment, and Ego., Method: A systematic review searching MEDLINE, Embase, PsycINFO, and CINAHL Plus to explore (i) the usage of MINDSPACE effects in alert and reminder designs and (ii) the efficacy of those alerts and reminders in influencing clinical decisions. The search queries comprised ten Boolean searches, with nine focusing on the MINDSPACE effects and one focusing on the term mindspace., Results: 50 studies were selected from 1791 peer-reviewed journal articles in English from 1970 to 2022. Except for ego, eight of nine MINDSPACE effects were utilised to design alerts and reminders, with defaults and norms utilised the most in alerts and reminders, respectively. Overall, alerts and reminders informed by MINDSPACE effects showed an average 71% success rate in influencing clinical decisions (alerts 73%, reminders 69%). Most studies utilised a single effect in their design, with higher efficacy for alerts (64%) than reminders (41%). Others utilised multiple effects, showing higher efficacy for reminders (28%) than alerts (9%)., Conclusion: This review presents sufficient evidence demonstrating the MINDSPACE framework's merits for designing CDS alerts and reminders with human decision-making considerations. The framework can adequately address challenges in identifying behavioural effects pertinent to the effective design of CDS alerts and reminders. The review also identified opportunities for future research into other relevant effects (e.g., framing)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: Identifying patient safety events using electronic health records (EHRs) and automated machine learning-based detection methods can help improve the efficiency and quality of healthcare service provision., Objective: This study aimed to systematically review machine learning-based methods and techniques, as well as their results for patient safety event management using EHRs., Methods: We reviewed the studies that focused on machine learning techniques, including automatic prediction and detection of patient safety events and medical errors through EHR analysis to manage patient safety events. The data were collected by searching Scopus, PubMed (Medline), Web of Science, EMBASE, and IEEE Xplore databases., Results: After screening, 41 papers were reviewed. Support vector machine (SVM), random forest, conditional random field (CRF), and bidirectional long short-term memory with conditional random field (BiLSTM-CRF) algorithms were mostly applied to predict, identify, and classify patient safety events using EHRs; however, they had different performances. BiLSTM-CRF was employed in most of the studies to extract and identify concepts, e.g., adverse drug events (ADEs) and adverse drug reactions (ADRs), as well as relationships between drug and severity, drug and ADEs, drug and ADRs. Recurrent neural networks (RNN) and BiLSTM-CRF had the best results in detecting ADEs compared to other patient safety events. Linear classifiers and Naive Bayes (NB) had the highest performance for ADR detection. Logistic regression had the best results in detecting surgical site infections. According to the findings, the quality of articles has non-significantly improved in recent years, but they had low average scores., Conclusions: Machine learning can be useful in automatic detection and prediction of patient safety events. However, most of these algorithms have not yet been externally validated or prospectively tested. Therefore, further studies are required to improve the performance of these automated systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Preserving brain health is a critical priority in primary care, yet screening for these risk factors in face-to-face primary care visits is challenging to scale to large populations. We aimed to develop automated brain health risk scores calculated from data in the electronic health record (EHR) enabling population-wide brain health screening in advance of patient care visits., Methods: This retrospective cohort study included patients with visits to an outpatient neurology clinic at Massachusetts General Hospital, between January 2010 and March 2021. Survival analysis with an 11-year follow-up period was performed to predict the risk of intracranial hemorrhage, ischemic stroke, depression, death and composite outcome of dementia, Alzheimer's disease, and mild cognitive impairment. Variables included age, sex, vital signs, laboratory values, employment status and social covariates pertaining to marital, tobacco and alcohol status. Random sampling was performed to create a training (70%) set for hyperparameter tuning in internal 5-fold cross validation and an external hold-out testing (30%) set of patients, both stratified by age. Risk ratios for high and low risk groups were evaluated in the hold-out test set, using 1000 bootstrapping iterations to calculate 95% confidence intervals (CI)., Results: The cohort comprised 17,040 patients with an average age of 49 ± 15.6 years; majority were males (57 %), White (78 %) and non-Hispanic (80 %). The low and high groups average risk ratios [95 % CI] were: intracranial hemorrhage 0.46 [0.45-0.48] and 2.07 [1.95-2.20], ischemic stroke 0.57 [0.57-0.59] and 1.64 [1.52-1.69], depression 0.68 [0.39-0.74] and 1.29 [0.78-1.38], composite of dementia 0.27 [0.26-0.28] and 3.52 [3.18-3.81] and death 0.24 [0.24-0.24] and 3.96 [3.91-4.00]., Conclusions: Simple risk scores derived from routinely collected EHR accurately quantify the risk of developing common neurologic and psychiatric diseases. These scores can be computed automatically, prior to medical care visits, and may thus be useful for large-scale brain health screening., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: In Iran, the Integrated Electronic Health Record system, called SEPAS, has been established to store all patient encounters of individuals referring to healthcare facilities., Objective: We aimed to develop a model for cleaning SEPAS and applying its data in other databases., Methods: We used cancer data from SEPAS as the sample. We developed a guideline to identify codes for cancer-related diagnoses and services in the database. Furthermore, we searched the SEPAS database based on ICD-10 and the diagnosis description in English and Farsi in an Excel sheet. We added codes and descriptions of pharmaceuticals and procedures to the list. We applied the above database and linked it to the patient records to identify cancer patients. A dashboard was designed based on this information for every cancer patient., Results: We selected 5,841 diagnostic codes and phrases, 9,300 cancer pharmaceutics codes, and 452 codes from cancer-specific items related to the diagnostic procedures and treatment methods. Linkage of this list to the patient list generated a database of about 197,164 cancer patients for linkage in the registry database., Conclusions: Patient registries are one of the most important sources of information in healthcare systems. Data linkage between Electronic Health Record Systems (EHRs) and registries, despite its challenges, is profitable. EHRs can be used for case finding in any patient registry to reduce the time and cost of case finding., Competing Interests: Declaration of Competing Interest The authors declare that they have no known conflict of interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Clinical decision support (CDS) tools improve adherence to evidence-based practices but are dependent upon data quality in the electronic health record (EHR). Mental status is an integral component of many risk stratification scores, but it is not known whether EHR-measures of altered mental status are reliable. The Glasgow Coma Scale (GCS) is a measure of altered mentation that is widely adopted and entered in the EHR in structured format. We sought to determine the accuracy GCS < 15 as an EHR-measure of altered mentation compared to ED provider documentation., Methods: In patients presenting to an academic Emergency Department (ED) with pneumonia we abstracted GCS values entered by nurses during routine care and in a randomly selected subset manually reviewed provider documentation for evidence of altered mental status. We defined eConfusion as present if GCS < 15 at any point during the ED encounter. We then calculated the CURB-65 score and corresponding suggested disposition using each method. Performance of eConfusion and corresponding CURB-65 compared to manual versions was measured using agreement (Cohen's K), sensitivity, and specificity., Results: Among 300 randomly selected encounters, 47 (16 %) had eConfusion present and 46 (15 %) had evidence of altered mental status in provider documentation with Cohen's K 0.73. eConfusion had 78 % sensitivity and 96 % specificity for provider documented altered mental status. When input into CURB-65 to recommend inpatient disposition, eConfusion had 95 % sensitivity, and recommended discordant disposition for 3 %., Conclusions: There was modest agreement between eConfusion and provider documentation of altered mental status. eConfusion had good specificity but low sensitivity which resulted in under-estimation of the CURB-65 score and occasional inappropriate disposition recommendations compared to provider documentation. These data do not support the use of GCS as a measure for altered mentation for use in CDS tools in the ED., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Objectives: In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer., Methods: We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept and tested it on colorectal cancer data, which is actual real-world data., Results: We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction., Conclusions: In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Background: The implementation of electronic health records (EHRs) requires careful preparations but may still cause trouble. In this study we focus on one EHR - Epic.Purpose: We compare the experiences from implementing Epic in the UK and Denmark with the preparations for implementing it in Norway.Method: The study is based on document analysis (UK and Denmark) and interviews (Norway).Results: Epic had a troubled start in both the UK and Denmark with malfunctions in the interfaces to other clinical systems, disruptions in the continuity of care, and drops in performance. While the state of routine use has subsequently been reached in the UK, the transition process is still ongoing in Denmark. In Norway experiences from, especially, Denmark are heeded in planning the implementation of Epic, which is expected to deliver better care more efficiently. We discuss six pitfalls to achieving these benefits.Conclusion: Experiences from, especially, Denmark inform the Norwegian preparations, but these experiences point toward more challenges than solutions. The implementation of Epic in Norway is currently in a state of considerable uncertainty. [ABSTRACT FROM AUTHOR]

Background: Patient access to electronic health records (EHRs) is associated with several determinants and outcomes, which are interrelated. However, individual studies and the reviews summarizing them have only addressed particular aspects, such as policy, usability or health outcomes of adoption. Therefore, no comprehensive overview exists. Additionally, reviews used different theoretical frameworks, which makes results difficult to compare.Objective: We aimed to systematically review recent systematic reviews on determinants and outcomes of patient access to EHRs to create a comprehensive overview and inform policy-makers and EHR implementers about the available literature, and to identify knowledge gaps in the literature reviews.Methods: We searched MEDLINE, EMBASE and PsycINFO for systematic reviews on patient portals, personal health records, and patient access to records that addressed determinants and outcomes of adoption. We synthesized the results from these reviews into the Clinical Adoption Framework (CAF), by mapping quotes from the reviews to categories and dimensions of the CAF, starting with the most recent ones until saturation of the CAF had been reached. The risk of bias in the reviews was assessed using the AMSTAR2 checklist.Results: We included nineteen reviews from 8871 records that were retrieved until February 19th, 2018. The reviews had a median of 4 (IQR: 4-4) critical flaws according to the AMSTAR2 checklist. The reviews contained a total of 1054 quotes that were mapped to the CAF. All reviews reported on the dimension 'People' that can affect adoption (e.g. personal characteristics such as age) and the dimension 'HIS use' (health information system use). Most reviews reported the dimensions 'Organisation', 'Implementation', HIS 'System quality', and outcomes of HIS use. However, gaps in knowledge might exist on macro-level determinants and outcomes, such as healthcare standards, funding, and incentives, because few reviews addressed these aspects.Conclusions: No review covered all aspects of the CAF and there was a large variety in aspects that were addressed, but all dimensions of the CAF were addressed by at least two reviews. Although reviews had critical flaws according to the AMSTAR2 checklist, almost half of the reviews did use methods to assess bias in primary studies. Implementers can use the synthesized results from this study as a reference for implementation and development when taking quality restrictions into account. Researchers should address the risk of bias in primary studies in future reviews and use a framework such as CAF to make results more comparable and reusable. [ABSTRACT FROM AUTHOR]

Objective: Electronic Health Records (EHRs) interventions hold the promise for enabling better healthcare. However, the implementation of EHR systems has been scarce in developing countries. The objective of this study is to investigate the state of EHRs implementation in Morocco; and draw insights for potential improvements.Materials and Methods: University Medical Centers, known by locals in French as Centres Hospitalier Universitaires (CHU), are the largest and most advanced public healthcare centers in Morocco. A two-phase qualitative study was conducted in four out of the five CHUs. Phase One involved data collection through semi-structured interviews with 27 clinician champions, administrators, and medical directors. Phase Two included a brainstorming session during a health informatics conference held in Fes, Morocco. The data were analyzed using inductive analysis.Results: We identified five main categories of challenges due to silo strategies: (1) EHRs selection and weak bargaining power, (2) identical errors repeated across silos, (3) a lack of interoperability standards, (4) insufficient human and financial, and (5) missed cooperation and collaboration opportunities.Discussion: While identifying these silo challenges is an important milestone, proposing guidelines to address these challenges can bring Morocco and similar developing countries a step closer to improving healthcare through the use of health informatics and EHRs. Our recommendations for public healthcare organizations are threefold: (1) recognize the power of partnerships among all CHUs, (2) establish an e-health framework, and (3) seek national and international collaborations to drive and shape the eHealth agenda. Furthermore, we align our recommendations with the World Health Organization toolkit for an eHealth strategy to further benefit developing countries.Conclusion: This study identifies the challenges faced by the Moroccan EHRs implementation silo-ed strategy, and it proposes practical and fundamental guidelines to address these challenges and develop an interoperable and sustainable national eHealth system in Morocco and similar developing countries. [ABSTRACT FROM AUTHOR]

Objectives: To identify characteristics of office-based provider used as a usual source of care (USC) associated with secure electronic messaging (SM) use.Data Source: 2015 Medical Expenditure Panel Survey Household Component and the supplemental Medical Organizations Survey.Study Design: Cross-sectional analysis.Extraction Methods: Patients are linked to characteristics of their usual source of care provider.Main Findings: We found that 89 percent of patients whose USC had electronic health records were able to exchange secure messages with their provider. Patients whose USC reported being patient-centered medical homes (PCMHs) or that used other health information technology (HIT) were also more likely to have been able to exchange SM with their provider. Patients of independent group or solo practices were less likely to have been able to exchange SM relative to patients whose USC practice was hospital owned.Conclusions: Patients were more likely to have visited a USC that exchanged SMs if that practice also used other electronic health records functionalities. Study findings suggest that while patients' USC practices were likely to exchange secure messages, there is a disparity in SM use between physician-owned practices, and hospital-owned practices. [ABSTRACT FROM AUTHOR]