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GNU Health, an open-source clinical information system, offers a comprehensive solution for managing health records, hospital information, and laboratory data. Despite its robust functionality and cost-effective nature, GNU Health remains underutilized in the European healthcare context. This paper explores the potential benefits of implementing GNU Health in European healthcare systems, emphasizing its capacity for customization, integration, and scalability. We also examine the barriers to its widespread adoption, including regulatory challenges, interoperability issues, and resistance to change from established proprietary systems. Through one case study and expert interviews, we provide insights into why these obstacles can hardly be overcome. [ABSTRACT FROM AUTHOR]

Usability is understood as a critical component to the success of electronic health records and other related healthcare technologies. Usability testing methods routinely employ scripts that help researchers understand how a particular tool works under real world conditions. This scoping review sought to better understand the guiding frameworks, principles, and methodologies employed when generating usability testing scripts to better understand how script generation occurs. Three main themes emerged through qualitative analysis: researchers sought to observe the baseline functionality being tested, the most representative tasks, or the most complex tasks. This scoping review highlights a lack of consistent processes in usability test script generation. There is a need to create standardized usability testing scripts for usability testing. [ABSTRACT FROM AUTHOR]

This paper describes the development and characteristics of a multi-disciplinary spina bifida clinic in Qatar considering the recently revised and globally available Guidelines for the Care of People with Spina Bifida (GCPSB).A retrospective chart review was performed on individuals in Sidra’s multidisciplinary spina bifida clinic database from January 2019 to June 2020. Their electronic health records were reviewed for demographics, as well as neurosurgical, urologic, rehabilitation, and orthopedic interventions.There were 127 patients in the database; 117 met inclusion criteria for diagnoses of myelomeningocele, meningocele, sacral agenesis/caudal regression, and/or spinal lipoma. Generally, Qatar is following GCPSB recommendations for multidisciplinary care. Consanguineous relationships, difficulties with access to urological and rehabilitation supplies and equipment, school access, and variable timing of neurosurgical closure were areas that demonstrated differences from GCPSB recommendations due to barriers in implementation.The GCPSB recommendations are applicable in an international setting such as Qatar. Despite a few barriers in implementing some of the recommendations, this new multi-disciplinary spina bifida clinic demonstrates alignment with many of the GCPSB guidelines. [ABSTRACT FROM AUTHOR]

The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, thus requiring the entire database to be decrypted for each access request, which is time-consuming. On the other hand, the use of EHR for medical research (e.g., development of precision medicine and diagnostics techniques) and optimisation of practices in healthcare organisations require the EHR to be analysed. To achieve that, they should be easily accessible without compromising the patient's privacy. In this paper, we propose an efficient technique called E-Tenon that not only securely keeps all EHR publicly accessible but also provides the desired security features. To the best of our knowledge, this is the first work in which an Open Database is used for protecting EHR. The proposed E-Tenon empowers patients to securely share their EHR under their own multi-level, fine-grained access policies. Analyses show that our system outperforms existing solutions in terms of computational complexity. [ABSTRACT FROM AUTHOR]

The global implementation of Electronic Health Records has significantly enhanced the quality of medical care and the overall delivery of public health services. The incorporation of Evidence-Based Medicine offers numerous benefits and enhances the efficacy of decision-making in areas such as prevention, prognosis, diagnosis, and therapeutic approaches. The objective of this paper is to propose an architectural design of an Evidence-Based Medicine information system based on the Electronic Health Record, taking into account the existing and future level of interoperability of health information systems in Greece. A study of the suggested evidence-based medicine architectures found in the existing literature was conducted. Moreover, the interoperability architecture of health information systems in Greece was analyzed. The architecture design reviewed by specialized personnel and their recommendations were incorporated into the final design of the proposed architecture. The proposed integrated architecture of an Evidence-Based Medicine system based on the Electronic Health Record integrates and utilizes citizens’ health data while leveraging the existing knowledge available in the literature. Taking into consideration the recently established National Interoperability Framework, which aligns with the European Interoperability Framework, the proposed realistic architectural approach contributes to improving the quality of healthcare provided through the ability to make safe, timely and accurate decisions by physicians. [ABSTRACT FROM AUTHOR]

BACKGROUND: Tools, such as the STarTBack Screening Tool (SBT), have been developed to identify risks of progressing to chronic disability in low back pain (LBP) patients in the primary care population. However, less is known about predictors of change in function after treatment in the specialty care population. OBJECTIVE: We pursued a retrospective observational cohort study involving LBP patients seen in a multidisciplinary specialty clinic to assess which features can predict change in function at follow-up. METHODS: The SBT was administered at initial visit, and a variety of patient characteristics were available in the chart including the presence of chronic overlapping pain conditions (COPCs). Patient Reported Outcomes Measurement Information System-10 (PROMIS-10) global physical health (PH) and global mental health (MH) were measured at baseline and at pragmatic time points during follow-up. Linear regression was used to estimate adjusted associations between available features and changes in PROMIS scores. RESULTS: 241 patients were followed for a mean of 17.0 ± 7.5 months. Mean baseline pain was 6.7 (SD 2.1), PROMIS-10 global MH score was 44.8 (SD 9.3), and PH score was 39.4 (SD 8.6). 29.7% were low-risk on the SBT, 41.8% were medium-risk, and 28.5% were high-risk. Mean change in MH and PH scores from baseline to the follow-up questionnaire were 0.86 (SD 8.11) and 2.39 (SD 7.52), respectively. Compared to low-risk patients, high-risk patients had a mean 4.35 points greater improvement in their MH score (p = 0.004) and a mean 3.54 points greater improvement in PH score (p = 0.006). Fewer COPCs also predicted greater improvement in MH and PH. CONCLUSIONS: SBT and the presence of COPC, which can be assessed at initial presentation to a specialty clinic, can predict change in PROMIS following treatment. Effort is needed to identify other factors that can help predict change in function after treatment in the specialty care setting. [ABSTRACT FROM AUTHOR]

BACKGROUND: In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. OBJECTIVE: This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. METHODS: Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. RESULTS: In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. CONCLUSION: The proposed healthcare framework's efficiency is proved by the experimental evaluation. [ABSTRACT FROM AUTHOR]

Background: Nutrition has an impact on development and linear growth. However, a few studies examine the connections between children’s age standardized height and their nutritional status and diet quality. Objectives: This study aimed to find a relationship between dietary consumption and height for age among school students under a web-based Thai school lunch program. Methods: Anthropometric data and nutrient consumption were obtained from 24-hour records. Nutrient consumption was calculated using Inmucal. The parent and custodian’s data were from the electronic health records. Results: Children’s heights were not correlated with parents’ heights (P<0.720). Moreover, children with low height-forage Z-score (HAZ) had significantly lower intakes of minerals (iron, magnesium, and selenium) and vitamins (B6, B12, C, and E). On the other hand, magnesium, selenium, vitamin B12, and vitamin E intakes of the children with normal HAZ were higher than their custodians. Conclusion: The Thai school lunch program effectively maintains the normal HAZ of children. Nutritional education in the community is recommended. [ABSTRACT FROM AUTHOR]

Electronic health data concerning implantable medical devices (IMD) opens opportunities for dynamic real-world monitoring to assess associated risks related to implanted materials. Due to population ageing and expanding demands, total hip, knee, and shoulder arthroplasties are increasing. Automating the collection and analysis of orthopedic device features could benefit physicians and public health policies enabling early issue detection, IMD monitoring and patient safety assessment. A machine learning tool using natural language processing (NLP) was developed for the automated extraction of operation information from medical reports in orthopedics. A corpus of 959 orthopaedic operative reports from 5 centres was manually annotated using the Prodigy software® with a strong inter-annotator agreement of 0.80. Data to extract concerned key clinical and procedure information (n= 9) selected by a multidisciplinary group based on the French health authority checklist. Performances parameters of the NLP model estimated an overall strong precision and recall of respectively 97.0 and 96.0 with a F1-score 96.3. Systematic monitoring of orthopedic devices could be ensured by an automated tool, leveraging clinical data warehouses. Traceability of medical devices with implantation modalities will allow detection of implant factors leading to complications. The evidence from real-world data could provide concrete and dynamic insights to surgeons and infectious disease specialists concerning implant follow-up, guiding therapeutic decision-making, and informing public health policymakers. The tool will be applied on clinical data warehouses to automate information extraction and presentation, providing feedback on mandatory information completion and contents of operative reports to support improvements, and thereafter implant research projects. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the leading cause of death globally. Timely health services are fundamental to the appropriate prevention, identification, care and rehabilitation of these diseases. This study aimed to explore the potential of using electronic health records as a data source to help identify health system - related delays in care processes of cardiac patients. This retrospective registry study is based on a sample of electronic health records of 200 cardiac patients admitted to one out of twenty wellbeing services counties in Finland during the years 2021– 2022. A total of 426 health system -related delays were identified. All expressions were found in unstructured format and most of these (58.7%) were generated by nurses. These results show that the electronic health records contained a variety of information on health system -related patient care delays, and that most delays were associated with difficulties in finding a bed for the patient in a post-acute care facility (49.8%), but also in-hospital process delays were common (27.7%). These findings show great potential for exploring electronic health record data with natural language processing methods in the future for the development of tools to better identify and monitor different types of delays in care processes. Such tools may support leadership to respond to organisational procedures in need of improvement. [ABSTRACT FROM AUTHOR]

Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning model using structured and unstructured electronic health record (EHR) data to predict when children in EDs might need intervention from child protective services. We used a case-control study design, analyzing data from a pediatric ED. Clinical notes were processed with natural language processing (NLP) techniques to identify suspected cases and matched in a 1:9 ratio to ensure dataset balance. The features from these notes were combined with structured EHR data to construct a model using the XGBoost algorithm. The model achieved a precision of 0.95, recall of 0.88, and F1-score of 0.92, with improvements seen from integrating NLP-derived data. Key indicators for abuse included hospital admissions, extended ED stays, and specific clinical orders. The model's accuracy and the utility of NLP suggest the potential for EDs to better identify at-risk children. Future work should validate the model further and explore additional features while considering ethical implications to aid healthcare providers in safeguarding children. [ABSTRACT FROM AUTHOR]

Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applications, e.g. Pharmacovigilance (PV) signal detection upon RealWorld Data. The objective of this study is to demonstrate the use of CDL for potential PV signal validation using Electronic Health Records as input data source. [ABSTRACT FROM AUTHOR]

Annotated language resources derived from clinical routine documentation form an intriguing asset for secondary use case scenarios. In this investigation, we report on how such a resource can be leveraged to identify additional term candidates for a chosen set of ICD-10 codes. We conducted a loglikelihood analysis, considering the co-occurrence of approximately 1.9 million deidentified ICD-10 codes alongside corresponding brief textual entries from problem lists in German. This analysis aimed to identify potential candidates with statistical significance set at p < 0.01, which were used as seed terms to harvest additional candidates by interfacing to a large language model in a second step. The proposed approach can identify additional term candidates at suitable performance values: hypernyms MAP@5=0.801, synonyms MAP@5 = 0.723 and hyponyms MAP@5 = 0.507. The re-use of existing annotated clinical datasets, in combination with large language models, presents an interesting strategy to bridge the lexical gap in standardized clinical terminologies and real-world jargon. [ABSTRACT FROM AUTHOR]

Automatic deidentification of Electronic Health Records (EHR) is a crucial step in secondary usage for biomedical research. This study introduces evaluation of an intricate hybrid deidentification strategy to enhance patient privacy in secondary usage of EHR. Specifically, this study focuses on assessing automatic deidentification using OpenDeID pipeline across diverse corpora for safeguarding sensitive information within EHR datasets by incorporating diverse corpora. Three distinct corpora were utilized: the OpenDeID v2 corpus containing pathology reports from Australian hospitals, the 2014 i2b2/UTHealth deidentification corpus with clinical narratives from the USA, and the 2016 CEGS N-GRID identification corpus comprising psychiatric notes. The OpenDeID pipeline employs a hybrid approach based on deep learning and contextual rules. Pre-processing steps involved harmonizing and addressing encoding and format issues. Precision, Recall, Fmeasure metrics were used to assess the performance. The evaluation metrics demonstrated the superior performance of the Discharge Summary BioBERT model. Trained on three corpora with a total of 4,038 reports, the best performing model exhibited robust deidentification capabilities when applied to EHR. It achieved impressive micro-averaged F1-scores of 0.9248 and 0.9692 for strict and relaxed settings, respectively. These results offer valuable insights into the model's efficacy and its potential role in safeguarding patient privacy in secondary usage of EHR. [ABSTRACT FROM AUTHOR]

Background: Urothelial Bladder Cancer (UBC) is a common cancer with a high risk of recurrence, which is influenced by the TNM classification, grading, age, and other factors. Recent studies demonstrate reliable and accurate recurrence prediction using Machine Learning (ML) algorithms and even outperform traditional approaches. However, most ML algorithms cannot process categorical input features, which must first be encoded into numerical values. Choosing the appropriate encoding strategy has a significant impact on the prediction quality. Objective: We investigate the impact of encoding strategies for ordinal features in the prediction quality of ML algorithms. Method: We compare three different encoding strategies namely one-hot, ordinal, and entity embedding in predicting the 2-year recurrence in UBC patients using an artificial neural network. We use ordered categorical and numerical data of UBC patients provided by the Cancer Registry Rhineland-Palatinate. Results: We show superior prediction quality using entity embedding encoding with 84.6% precision, an overall accuracy of 73.8%, and 68.9% AUC on testing data over 100 epochs after 30 runs compared to one-hot and ordinal encoding. Conclusion: We confirm the superiority of entity embedding encoding as it could provide a more detailed and accurate representation of ordinal features in numerical scales. This can lead to enhanced generalizability, resulting in significantly improved prediction quality. [ABSTRACT FROM AUTHOR]

This paper introduces a mobile framework designed to enhance citizen access to and sharing of health data, aiming to empower individuals with greater control over their personal health information. Accessing and sharing health-related data is essential in everyday scenarios, from routine doctor visits or viewing your health on your own to emergencies where swift access can save lives. It addresses the challenges posed by the fragmented nature of healthcare services and the barriers of language differences in patient records. The framework utilizes the EU eHealth Digital Service Infrastructure (eHDSI) OpenNCP for translating patient summaries and the FHIR Smart Health Links Protocol for secure sharing. A pilot study with 40 participants was conducted to assess the usability and effectiveness of the app, revealing a strong demand among citizens for such innovative health services. [ABSTRACT FROM AUTHOR]

Visit-to-visit blood pressure variability (BPV) is associated with cardiovascular disease (CVD) and its mortality, independent of mean blood pressure (BP). However, in real world clinical practice this phenomenon is under-appreciated by clinicians. Serial BPV measured at clinical visits are frequently considered random fluctuations. This scoping review aims to review methodologies for estimating BPV, including metrics, frequency of BP measurements, BPV observation and follow-up durations. The review also compares studies that used electronic health record (EHR) data and those that used non-EHR data to assess BPV. We found little or no consensus on metrics used for BPV estimation in either study using EHR or non-EHR data. The non-EHR studies followed a stricter protocol for BP measurement than the EHR-based studies. Both groups of studies used comparable methodologies to estimate BPV. [ABSTRACT FROM AUTHOR]

While the importance of Electronic Health Records (EHR) interoperability is widely recognised in the healthcare digitalisation context, its optimal governance structure remains controversial, requiring further research. Through the rapid literature review of 32 articles retrieved from PubMed and EBSCO, 47 distinct factors under ten categories were established. The three most cited factors in the reviewed 32 articles were “Robust inter-institutional connections, trust, and the technologies to ensure security”, “Legal adaptations to the evolving digitalisation needs”, and “Standardisation of terminologies and codes, and harmonised data structure”. This review contributes preliminary results for the ongoing research to optimise EHR interoperability governance. [ABSTRACT FROM AUTHOR]

Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensive analysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patientdriven donations of health data for clinical research. Leveraging the groundwork, laid by the national Electronic Health Record implementation in Austria (called ELGA), Smart FOX aims to harness structured datasets from ELGA for research purposes through an opt-in approach. With funding secured from the Austrian Research Promotion Agency, the project embarks on innovative solutions encompassing governance frameworks, community engagement, and technical infrastructure. The Smart FOX consortium, comprising key stakeholders across various healthcare-associated domains, will evaluate these efforts through demonstrators focusing on clinical registries, patient-generated data, and recruitment services. The project targets to accompany the development of future data donation infrastructure while ultimately advancing clinical research efficiency and bolstering Austria's preparedness for the European Health Data Space. This paper presents the first systematic evaluation of the technical concept and proposal for the federated system architecture of the Austrian Health Data Donation Space, which is the socio-technical goal of Smart FOX. [ABSTRACT FROM AUTHOR]

Background: Preclinical evidence suggests calcineurin inhibitors (CNIs) combat α-synuclein-induced neuronal dysfunction and motor impairments. However, whether CNIs prevent or treat Parkinson's disease (PD) in humans has never been investigated. Objective: We seek to ascertain if prescription of CNIs is linked to a decreased prevalence of PD in a varied patient population and to glimpse into the mechanism(s) and target site through which CNIs might decrease PD prevalence. Methods: We analyzed electronic health records (EHRs) from patients prescribed the brain penetrant CNI tacrolimus (TAC), the peripherally restricted CNI cyclosporine (CySp), or the non-CNI sirolimus (SIR). For comparison, EHRs from a diverse population from the same network served as a general population-like control. After propensity-score matching, prevalence, odds, and hazards of PD diagnoses among these cohorts were compared. Results: Patients prescribed CNIs have decreased odds of PD diagnosis compared to the general population-like control, while patients prescribed SIR do not. Notably, patients prescribed TAC have a decreased prevalence of PD compared to patients prescribed SIR or CySp. Conclusions: Our results suggest CNIs, especially those acting within the brain, may prevent PD. The reduced prevalence of PD in patients prescribed TAC, compared to patients prescribed SIR, suggests that mechanisms of calcineurin inhibition— other than immunosuppression, which is common to both drugs— are driving the reduction. Therefore, CNIs may provide a promising therapeutic approach for PD. [ABSTRACT FROM AUTHOR]

Burnout and workforce shortages are having a negative impact on nurses globally, particularly after the COVID-19 pandemic. Within the United States, excessive documentation burden (DocBurden) has been linked to nurse burnout. The experience of a system or system-imposed process inhibiting patient care is a core focus area of nursing informatics research. The American Medical Informatics Association (AMIA) 25x5 Task Force to Reduce DocBurden was created in 2022 to decrease U.S. health professionals' excessive DocBurden to 25% of current state within five years through impactful solutions across health systems that decrease non-value-added documentation, and leverage public/private partnerships and advocacy. This case study will describe the work of the 25x5 Task Force that is relevant to nursing practice. Specifically, we will describe three projects: A) Toolkit for Reducing Excessive DocBurden, B) Development of Pulse Survey for Health Professionals Perceived DocBurden, and C) HIT Roadmap to Promote Interoperability. [ABSTRACT FROM AUTHOR]

Despite widespread adoption and maturity, paper persistence endures in many Electronic Health Record (EHR) systems, particularly for complex workflows involving multiple steps from different stakeholders separated in time. In our health system, Latent Tuberculosis Infection (LTBI) testing was one such workflow where a Tuberculin Skin Test (TST) must be administered and then correctly read 48-72 hours later and documented. This paper discusses a low-resource workflow analysis and clinical decision support approach to replace a paper workflow and garner the benefits of the EHR for clearer documentation and retrieval of LTBI results. Our approach resulted in a significant increase in completed TST documentation, 57% (24/42) to 95% (18/19), P < 0.003. Human-centered design practices such as work system analysis and formative usability testing are feasible with limited resources and improve the likelihood of success of electronic workflows by designing solutions that fit existing clinical workflows and automating processes wherever possible. [ABSTRACT FROM AUTHOR]

The complex nature of verbal patient-nurse communication holds valuable insights for nursing research, but traditional documentation methods often miss these crucial details. This study explores the emerging role of speech processing technology in nursing research, emphasizing patient-nurse verbal communication. We conducted case studies across various healthcare settings, revealing a substantial gap in electronic health records for capturing vital patient-nurse encounters. Our research demonstrates that speech processing technology can effectively bridge this gap, enhancing documentation accuracy and enriching data for quality care assessment and risk prediction. The technology's application in home healthcare, outpatient settings, and specialized areas like dementia care illustrates its versatility. It offers the potential for real-time decision support, improved communication training, and enhanced telehealth practices. This paper provides insights into the promises and challenges of integrating speech processing into nursing practice, paving the way for future patient care and healthcare data management advancements. [ABSTRACT FROM AUTHOR]

Visit-to-visit (VVV) blood pressure variability (BPV) is associated with cardiovascular disease. However, in practice, BPV at sequential clinic visits is often regarded as mere random fluctuations and frequently under-appreciated by the clinicians. Therefore, this meta-analysis aims to compare the effect size of VVV BPV on cardiovascular outcome, by comparing studies that have used the electronic health record (EHR) and non-EHR data. The pooled hazard ratio for VVV BPV is comparable between studies using EHR and non-EHR data. Studies using EHR reported a pooled hazard ratio (HR) for VVV systolic BPV of 1.22 (95% CI: 1.07-1.38), while non-EHR studies had a HR of 1.16 (95% CI: 1.10-1.22). The pooled HR for VVV diastolic BPV in EHR studies was 1.09 (95% CI: 0.86-1.39), whereas non-EHR studies showed a HR of 1.10 (95% CI: 1.04-1.17). EHR data is a reliable source for assessing BPV, which in turn can predict the CVD outcomes. [ABSTRACT FROM AUTHOR]

EHR Interoperability is crucial to obtain a set of benefits. This can be achieved by using data standards, like ontologies. The Portuguese Nursing Ontology (NursingOntos) is a reference model describing a set of nursing concepts and their relationships, to represent nursing knowledge in the Electronic Health Records (EHR). The purpose of this work was to define a set of correspondences between Nursing Ontology concepts of NursingOntos and other terminologies, which have the same or similar meaning. In this project, we are using the ISO/TR12300:2016 standard on the principles of mapping between terminological systems. Regarding the domain of "airway clearance", we can say that Portuguese Nursing Ontology has a good level of mapping with other terminologies. In conclusion, we can say that Portuguese Nursing Ontology can be used in EHR with the purpose of a global digitalization of health. [ABSTRACT FROM AUTHOR]

This study delves into the impact of Information Technology (IT) on nursing practice in Japan, focusing on patient safety within the 2021-2022 Japanese Medical Accident Report Data. The research aims to understand how IT factors contribute to nursing-related medical incidents in a healthcare landscape rapidly integrating IT. The study identifies IT-related incidents through a retrospective analysis of medical incident reports, primarily in nursing, by analyzing categorized data and free-text descriptions for IT-related keywords. The findings indicate significant IT-related issues, with 'Other EHR Related' problems (36%) and 'EHR Reporting' errors (25%) being the most prevalent. These incidents often involve challenges in patient identification and medication management. The study suggests improvements like enhanced verification processes and automated systems to mitigate these risks. Conclusively, it underscores the dual nature of IT in nursing: while it holds the potential to enhance patient care, it also introduces challenges that necessitate specialized informatics expertise to ensure its beneficial integration into nursing practices. [ABSTRACT FROM AUTHOR]

The full potential for electronic health record systems in facilitating a positive transformation in care, with improvements in quality and safety, has yet to be realised. There remains a need to reconceptualise the structure, content and use of the nursing component of electronic health record systems. The aim of this study was to engage and involve a diverse group of stakeholders, including nurses and electronic health record system developers, in exploring together both issues and possible new approaches to documentation that better fit with practice, and that facilitate the optimal use of recorded data. Three focus groups were held in the UK and USA, using a semi-structured interview guide, and a common reflexive approach to analysis. The findings were synthesised into themes that were further developed into a set of development principles that might be used to inform a novel electronic health record system specification to support nursing practice. [ABSTRACT FROM AUTHOR]

This study aimed to validate and refine an information model on pain management in a Brazilian hospital, considering the institutional culture, using an expert consensus approach. The first stage took place through a computerized questionnaire and Content Validity Index calculation. Pain management attributes were considered validated with 75% consensus among 19 experts. The second stage validated and refined the information model by three experts via an online meeting. Results showed that out of 11 evaluated attributes, five were validated. In the second stage, the inclusion of new attributes was suggested to address institutional culture. The final information model resulted from 23 sets of revised attributes: 12 validated, seven suggested and four not validated. The resulting Brazilian model has the potential to support the implementation of interventions and propose improvements to the institution's electronic system, which can be reused in other institutions. [ABSTRACT FROM AUTHOR]

BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS: The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS: The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS: The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information. [ABSTRACT FROM AUTHOR]

BACKGROUND: Return-to-work (RTW) programs are provided as rehabilitation for people who have taken sick leave from work because of mental health problems. However, methods to present this information to workplaces objectively remain limited. OBJECTIVE: This study aimed to conduct an exploratory investigation of the relationship between duration of sick leave and time variation of words used in RTW programs for depression from textual data collected from electronic medical records as a new evaluation indicator. METHODS: The study subjects were those who had taken sick leave because of major depressive or adjustment disorder and had participated in an RTW program. The study data comprised demographic characteristics and texts. Textual data were collected from electronic medical records and classified based on the SOAP note. Thereafter, the textual data were quantified into category scores based on a standard text analysis dictionary. A generalized linear mixed model was used for the statistical analysis, with the score for each category (emotional, social, cognitive, perceptual, biological, motivational, relativity, and informal) as the dependent variable and the duration of sick leave, time, and interaction between the duration of sick leave and time as the independent variables. The level of statistical significance was set at 0.05. RESULTS: In total, 42 participants were included in the analysis. The results revealed a significant interaction between the social (p = 0.001) and emotional (p = 0.002) categories. CONCLUSION: The findings suggest a relationship between word changes in electronic medical records and the duration of sick leave. [ABSTRACT FROM AUTHOR]

Background: Approximately 40% of all recorded deaths in Austria are due to behavioral risks. These risks could be avoided with appropriate measures. Objectives: Extension of the concept of EHR and EMR to an electronic prevention record, focusing on primary and secondary prevention. Methods: The concept of a structured prevention pathway, based on the principles of P4 Medicine, was developed for a multidisciplinary prevention network. An IT infrastructure based on HL7 FHIR and the OHDSI OMOP common data model was designed. Results: An IT solution supporting a structured and modular prevention pathway was conceptualized. It contained a personalized management of prevention, risk assessment, diagnostic and preventive measures supported by a modular, interoperable IT infrastructure including a health app, prevention record webservice, decision support modules and a smart prevention registry, separating primary and secondary use of data. Conclusion: A concept was created on how an electronic health prevention record based on HL7 FHIR and the OMOP common data model can be implemented. [ABSTRACT FROM AUTHOR]

While adherence to clinical guidelines improves the quality and consistency of care, personalized healthcare also requires a deep understanding of individual disease models and treatment plans. The structured preparation of medical routine data in a certain clinical context, e.g. a treatment pathway outlined in a medical guideline, is currently a challenging task. Medical data is often stored in diverse formats and systems, and the relevant clinical knowledge defining the context is not available in machine-readable formats. We present an approach to extract information from medical free text documentation by using structured clinical knowledge to guide information extraction into a structured and encoded format, overcoming the known challenges for natural language processing algorithms. Preliminary results have been encouraging, as one of our methods managed to extract 100% of all data-points with 85% accuracy in details. These advancements show the potential of our approach to effectively use unstructured clinical data to elevate the quality of patient care and reduce the workload of medical personnel. [ABSTRACT FROM AUTHOR]

Background: The Bulgaria Diabetes Register (BDR) contains more than 380 millions of pseudonymized outpatient records with proprietary data structures and format. Objectives: This paper presents the application results and experience acquired during the process of mapping such observational health data to OMOP CDM with the objective of publishing it in the European Health Data and Evidence Network (EHDEN) Portal. Methods: The data mapping follows the activities of the well-structured Extract-Transform-Load process. Unlike other publications, we focus on the need for preprocessing the data structures of raw data, cleaning data and procedures for assuring quality of data. Results: This paper provides quantitative and statistical measures for the records in the CDM database as published in the EHDEN Portal. Conclusion: The mapping of data from the BDR to OMOP CDM provides the EHDEN community with opportunities for including these data in large-scale project for evidence generation by applying standard analytical tools. [ABSTRACT FROM AUTHOR]

When pediatric anesthesia emergencies occur, situations can deteriorate rapidly. At our hospital, the Society for Pediatric Anesthesia's (SPA) emergency algorithms are used as cognitive aids during crises, and nurses are tasked with accessing the algorithms. Operating room nurses' typical workflow includes continuous display of the of the electronic health record (EHR) intraoperative navigator, which can delay navigating to the virtual desktop window and the algorithms' icon. Thus, we implemented a button in the intraoperative navigator's toolbar to access the algorithms with one click. We conducted an observational study of the time required to access and display overhead an algorithm using the new button and old method. We surveyed participants on usability. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is a recent developing technology in the field of smart healthcare. But it is difficult to transfer the patient's health record as a centralized network. So, "blockchain technology" has excellent consideration due to its unique qualities such as decentralized network, openness, irreversible data, and cryptography functions. Blockchain technology depends on cryptography hash techniques for safe transmission. For increased security, it transforms the variable size inputs into a constant length hash result. Current cryptographic hash algorithms with digital signatures are only able to access keys up to a size of 256 bytes and have concerns with single node accessibility. It just uses the bits that serve as the key to access the data. This paper proposes the "Revised Elliptic Curve Cryptography Multi-Signature Scheme" (RECC-MSS) for multinode availability to find the nearest path for secure communications with the medical image as keys. Here, the input image key can be converted into an array of data that can be extended up to 512 bytes of size. The performance of the proposed algorithm is analyzed with other cryptography hash functions like Secure Hashing Algorithms (SHAs) such as "SHA224", "SHA256", "SHA384", "SHA512", "SHA3-224", "SHA3-256", "SHA3-384", "SHA3-512", and "Message Digest5" (MD5) by "One-way ANOVA" test in terms of "accuracy", "throughput" and "time complexity". The proposed scheme with ECC achieved the throughput of 17.07 kilobytes per 200 nano seconds, 93.25% of accuracy, 1.5 nanoseconds latency of signature generation, 1.48 nanoseconds latency of signature verification, 1.5 nanoseconds of time complexity with 128 bytes of hash signature. The RECC-MSS achieved the significance of 0.001 for accuracy and 0.002 for time complexity which are less than 0.05. From the statistical analysis, the proposed algorithm has significantly high accuracy, high throughput and less time complexity than other cryptography hash algorithms. [ABSTRACT FROM AUTHOR]

Background: Past research suggests associations between heavy alcohol use and later life dementia. However, little is known about whether opioid use disorder (OUD) and dementia share this association, especially among age groups younger than 65 years old. Objective: Examine the association between OUD and Alzheimer's disease (AD) and dementia. Methods: Electronic health records between 2000 and 2021 for patients age 12 or older were identified in the Cerner Real-World database™. Patients with a prior diagnosis of dementia were excluded. Patients were followed for 1-10 years (grouped by one, three, five, and ten-year follow-up periods) in a matched retrospective cohort study. Cox proportional hazards regressions were used to estimate adjusted hazard ratios (aHRs) of incident AD/dementia stratified by age and follow-up group. Results: A sample of 627,810 individuals with OUD were compared to 646,340 without OUD. Individuals with OUD exhibited 88% higher risk for developing AD/dementia compared to those without OUD (aHR = 1.88, 95% CI 1.74, 2.03) within 1 year follow-up and 211% (aHR = 3.11, 95% CI 2.63, 3.69) within 10 years follow-up. When stratifying by age, younger patients (age 12-44) had a greater disparity in odds of AD/dementia between OUD and non-OUD groups compared with patients older than 65 years. Conclusions: Additional research is needed to understand why an association exists between OUD and AD/dementia, especially among younger populations. The results suggest that cognitive functioning screening programs for younger people diagnosed with OUD may be useful for targeting early identification and intervention for AD/dementia in particularly high risk and marginalized populations. [ABSTRACT FROM AUTHOR]

BACKGROUND: The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. The National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases. OBJECTIVE: To evaluate if NEWS can be an indicator to measure COVID-19 patient status in-hospital. METHODS: We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models' performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score. RESULTS: Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient's age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score – 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage. CONCLUSION: Our model demonstrated incremental value for COVID-19 patient's status evaluation. [ABSTRACT FROM AUTHOR]

BACKGROUND: Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations and concerns about older people in electronic health records (EHR), including free-text progress notes. These insights are yet to be unleashed. OBJECTIVE: This study explored the risk factors for malnutrition in structured and unstructured electronic health data. METHODS: Data of weight loss and malnutrition were extracted from the de-identified EHR records of a large aged care organization in Australia. A literature review was conducted to identify causative factors for malnutrition. Natural language processing (NLP) techniques were applied to progress notes to extract these causative factors. The NLP performance was evaluated by the parameters of sensitivity, specificity and F1-Score. RESULTS: The NLP methods were highly accurate in extracting the key data, values for 46 causative variables, from the free-text client progress notes. Thirty three percent (1,469 out of 4,405) of the clients were malnourished. The structured, tabulated data only recorded 48% of these malnourished clients, far less than that (82%) identified from the progress notes, suggesting the importance of using NLP technology to uncover the information from nursing notes to fully understand the health status of the vulnerable older people in residential aged care. CONCLUSION: This study identified 33% of older people suffered from malnutrition, lower than those reported in the similar setting in previous studies. Our study demonstrates that NLP technology is important for uncovering the key information about health risks for older people in residential aged care. Future research can apply NLP to predict other health risks for older people in this setting. [ABSTRACT FROM AUTHOR]

Background: Multiple algorithms with variable performance have been developed to identify dementia using combinations of billing codes and medication data that are widely available from electronic health records (EHR). If the characteristics of misclassified patients are clearly identified, modifying existing algorithms to improve performance may be possible. Objective: To examine the performance of a code-based algorithm to identify dementia cases in the population-based Mayo Clinic Study of Aging (MCSA) where dementia diagnosis (i.e., reference standard) is actively assessed through routine follow-up and describe the characteristics of persons incorrectly categorized. Methods: There were 5,316 participants (age at baseline (mean (SD)): 73.3 (9.68) years; 50.7% male) without dementia at baseline and available EHR data. ICD-9/10 codes and prescription medications for dementia were extracted between baseline and one year after an MCSA dementia diagnosis or last follow-up. Fisher's exact or Kruskal-Wallis tests were used to compare characteristics between groups. Results: Algorithm sensitivity and specificity were 0.70 (95% CI: 0.67, 0.74) and 0.95 (95% CI: 0.95, 0.96). False positives (i.e., participants falsely diagnosed with dementia by the algorithm) were older, with higher Charlson comorbidity index, more likely to have mild cognitive impairment (MCI), and longer follow-up (versus true negatives). False negatives (versus true positives) were older, more likely to have MCI, or have more functional limitations. Conclusions: We observed a moderate-high performance of the code-based diagnosis method against the population-based MCSA reference standard dementia diagnosis. Older participants and those with MCI at baseline were more likely to be misclassified. [ABSTRACT FROM AUTHOR]

PURPOSE: The purpose of this project was to establish a pathway for electronic medical record (EMR) customization, utilizing quality improvement methodology, to both identify and address adverse social determinants of health (SDOH) among a diverse spina bifida (SB) population. METHODS: Starting in September 2020, the four fundamental steps were to (1) facilitate an advisory committee to safeguard the standard clinical protocols, (2) characterize barriers to implementation, (3) evaluate workflow to sustain data entry capture, and (4) manage the technology platform for seamless integration. The SB clinic was the first clinic within the enterprise to rollout the use of an adverse SDOH mitigation activity. A Spanish-speaking interpreter was scheduled for all clinics, as many families were limited in English proficiency. RESULTS: The customization of the EMR to support an efficient workflow to address SDOH was feasible in a large and diverse urban medical center. Of the 758 patients served in the clinic, a myelomeningocele diagnosis was present in 86% of individuals. While 52% of participants were female, ethnically 52% of individuals served were Latino. Many of these individuals disclosed being recent immigrants to the United States. Often immigration and asylum related issues were at the forefront of the SDOH issues addressed. CONCLUSION: Given the occurrence of adverse SDOH among individuals with SB, many of whom are new Latin-American immigrants, meaningful clinical efforts are needed to both identify and address the causes of the observed disparities. EMR customization is feasible and can identify and, through social prescriptions, address SDOH to support the provision of safe, high quality, and equitable care for vulnerable and medically complex populations at home and potentially abroad. [ABSTRACT FROM AUTHOR]

Background: Evidence suggests patients prescribed calcineurin inhibitors (CNIs) have a reduced prevalence of dementia, including Alzheimer's disease (AD); however, this result has never been replicated in a large cohort and the involved mechanism(s) and site of action (central versus periphery) remain unclear. Objective: We aim to determine if prescription of CNIs is associated with reduced prevalence of dementia, including AD, in a large, diverse patient population. Furthermore, we aim to gain insight into the mechanism(s) and site of action for CNIs to reduce dementia prevalence. Methods: Electronic health records (EHRs) from patients prescribed tacrolimus, cyclosporine, or sirolimus were analyzed to compare prevalence, odds, and hazard ratios related to dementia diagnoses among cohorts. EHRs from a random, heterogeneous population from the same network were obtained to generate a general population-like control. Results: All drugs examined reduced dementia prevalence compared to the general population-like control. There were no differences in dementia diagnoses upon comparing tacrolimus and sirolimus; however, patients prescribed tacrolimus had a reduced dementia prevalence relative to cyclosporine. Conclusion: Converging mechanisms of action between tacrolimus and sirolimus likely explain the similar dementia prevalence between the cohorts. Calcineurin inhibition within the brain has a greater probability of reducing dementia relative to peripherally-restricted calcineurin inhibition. Overall, immunosuppressants provide a promising therapeutic avenue for dementia, with emphasis on the brain-penetrant CNI tacrolimus. [ABSTRACT FROM AUTHOR]

Background: Retinal nerve fiber layer (RNFL) thickness may reflect cerebral status. Objective: This study assessed the relationship between RNFL thickness and incident all-cause dementia in the European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) Eye Study. Methods: Glaucoma detection with variable corneal compensation (GDx-VCC) and Heidelberg Retinal Tomograph II (HRT II) derived global mean RNFL thickness from dementia-free participants at baseline within the EPIC-Norfolk Eye Study were analyzed. Incident dementia was identified through linkage to electronic medical records. Cox proportional hazard mixed-effects regression models adjusted for key confounders were used to examine the associations between RNFL thickness and incident dementia in four separate models. Results: 6,239 participants were included with 322 cases of incident dementia and mean age of 67.5-years old, with 49.7% women (median follow-up 13.2-years, interquartile range (11.7 to 14.6 years). Greater RNFL thickness (GDx-VCC) was not significantly associated with a lower risk of incident dementia in the full adjusted model [HR per quartile increase 0.95; 95% CI 0.82–1.10]. Similarly, RNFL thickness assessed with HRT II was also not associated with incident dementia in any model (full adjusted model; HR per quartile increase: 1.06; [95% CI 0.93–1.19]. Gender did not modify any associations under study. Conclusion: GDx-VCC and HRT II derived RNFL thickness are unlikely to be useful predictors of incident dementia. Higher resolution optical imaging technologies may clarify whether there are useful relationships between neuro-retinal morphology and brain measures. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is one of the most common hematological malignancies. The goal of this study was to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of multiple myeloma patients using EHR data from an academic medical center in New York City. The de-identified analytical dataset comprised 2,111 patients with MM who were stratified based on the length of survival into two groups. Demographic variables, cancer stage, income level, and genetic mutations were analyzed using descriptive statistics and logistic regression. Age, race, and cancer stage were all significant factors that affected the length of survival of multiple myeloma patients. In contrast, gender and income level were not significant factors based on the multivariate adjusted analysis. Older adults, African American patients, and patients who were diagnosed with stage III of multiple myeloma were the people most likely to exhibit short-term survival after the MM diagnosis. [ABSTRACT FROM AUTHOR]

This study aimed to develop and externally validate a prognostic prediction model for screening fetal growth restriction (FGR)/small for gestational age (SGA) using medical history. From a nationwide health insurance database (n=1,697,452), we retrospectively selected visits of 12-to-55-year-old females to healthcare providers. This study used machine learning (including deep learning) and 54 medical-history predictors. The best model was a deep-insight visible neural network (DI-VNN). It had area under the curve of receiver operating characteristics (AUROC) 0.742 (95% CI 0.734 to 0.750) and a sensitivity of 49.09% (95% CI 47.60% to 50.58% at with 95% specificity). Our model used medical history for screening FGR/SGA with moderate accuracy by DI-VNN. In future work, we will compare this model with those from systematically-reviewed, previous studies and evaluate if this model's usage impacts patient outcomes. [ABSTRACT FROM AUTHOR]

Hemodialysis (HD) is the main treatment for end-stage renal disease with high mortality and heavy economic burdens. Predicting the mortality risk in patients undergoing maintenance HD and identifying high-risk patients are critical to enable early intervention and improve quality of life. In this study, we proposed a two-stage protocol based on electronic health record (EHR) data to predict mortality risk of maintenance HD patients. First, we developed a multilayer perceptron (MLP) model to predict mortality risk. Second, an Active Contrastive Learning (ACL) method was proposed to select sample pairs and optimize the representation space to improve the prediction performance of the MLP model. Our ACL method outperforms other methods and has an average F1-score of 0.820 and an average area under the receiver operating characteristic curve of 0.853. This work is generalizable to analyses of cross-sectional EHR data, while this two-stage approach can be applied to other diseases as well. [ABSTRACT FROM AUTHOR]

Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. A machine learning based classification algorithm is trained to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train four classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99). [ABSTRACT FROM AUTHOR]

According to the World Stroke Organization, 12.2 million people worldwide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-based classifiers are rigid, resulting in inadequate performance. We report findings from a pilot study using NLP to improve relation detection for stroke assertion detection to support research studies and healthcare operations. [ABSTRACT FROM AUTHOR]

In the United States, more than 12% of the population will experience thyroid dysfunction. Patient symptoms often reported with thyroid dysfunction include fatigue and weight change. However, little is understood about the relationship between these symptoms documented in the outpatient setting and ordering patterns for thyroid testing among various patient groups by age and sex. We developed a natural language processing and deep learning pipeline to identify patient-reported outcomes of weight change and fatigue among patients with a thyroid stimulating hormone test. We built upon prior works by comparing 5 open-source, Bidirectional Encoder Representations from Transformers (BERT) to determine which models could accurately identify these symptoms from clinical texts. For both fatigue (f) and weight change (wc), Bio_ClinicalBERT achieved the highest F1-score (f: 0.900; wc: 0.906) compared BERT (f: 0.899; wc: 0.890), DistilBERT (f: 0.852; wc: 0.912), Biomedical RoBERTa (f: 0.864; wc: 0.904), and PubMedBERT (f: 0.882; wc: 0.892). [ABSTRACT FROM AUTHOR]

The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification. [ABSTRACT FROM AUTHOR]

Providing patient centered care is a crucial element of high quality care. It can be defined as a responsive way of caring for and empowering patients, embodying compassion, empathy, and responsiveness to the patient's needs. The aim of this study was to assess the potential of using EHRs as information source in the development of tools for assessing PCC. An annotation guide following the Person-centred Practice Framework proposed by McCance and McCormack was developed for the purpose of this study. Twenty patients' documents were manually annotated, resulting in 539 expressions. All dimensions of the framework were covered in the documents, with 61.3% of expressions describing the activity of engaging authentically with the patient. The results of this study indicate that electronic health records are one potential source of information in automated evaluation of patient centered care, however more information is still needed on how to interpret this information. [ABSTRACT FROM AUTHOR]