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Background: Health care providers worldwide are rapidly adopting electronic medical record (EMR) systems, replacing paper record-keeping systems. Despite numerous benefits to EMRs, the environmental emissions associated with medical record-keeping are unknown. Given the need for urgent climate action, understanding the carbon footprint of EMRs will assist in decarbonizing their adoption and use., Objective: We aimed to estimate and compare the environmental emissions associated with paper medical record-keeping and its replacement EMR system at a high-volume eye care facility in southern India., Methods: We conducted the life cycle assessment methodology per the ISO (International Organization for Standardization) 14040 standard, with primary data supplied by the eye care facility. Data on the paper record-keeping system include the production, use, and disposal of paper and writing utensils in 2016. The EMR system was adopted at this location in 2018. Data on the EMR system include the allocated production and disposal of capital equipment (such as computers and routers); the production, use, and disposal of consumable goods like paper and writing utensils; and the electricity required to run the EMR system. We excluded built infrastructure and cooling loads (eg. buildings and ventilation) from both systems. We used sensitivity analyses to model the effects of practice variation and data uncertainty and Monte Carlo assessments to statistically compare the 2 systems, with and without renewable electricity sources., Results: This location's EMR system was found to emit substantially more greenhouse gases (GHGs) than their paper medical record system (195,000 kg carbon dioxide equivalents [CO 2 e] per year or 0.361 kg CO 2 e per patient visit compared with 20,800 kg CO 2 e per year or 0.037 kg CO 2 e per patient). However, sensitivity analyses show that the effect of electricity sources is a major factor in determining which record-keeping system emits fewer GHGs. If the study hospital sourced all electricity from renewable sources such as solar or wind power rather than the Indian electric grid, their EMR emissions would drop to 24,900 kg CO 2 e (0.046 kg CO 2 e per patient), a level comparable to the paper record-keeping system. Energy-efficient EMR equipment (such as computers and monitors) is the next largest factor impacting emissions, followed by equipment life spans. Multimedia Appendix 1 includes other emissions impact categories., Conclusions: The climate-changing emissions associated with an EMR system are heavily dependent on the sources of electricity. With a decarbonized electricity source, the EMR system's GHG emissions are on par with paper medical record-keeping, and decarbonized grids would likely have a much broader benefit to society. Though we found that the EMR system produced more emissions than a paper record-keeping system, this study does not account for potential expanded environmental gains from EMRs, including expanding access to care while reducing patient travel and operational efficiencies that can reduce unnecessary or redundant care., (©Cordelia Kwon, Lernik Essayei, Michael Spencer, Tom Etheridge, Rengaraj Venkatesh, Natrajan Vengadesan, Cassandra L Thiel. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.02.2024.)

We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing. Additionally, we demonstrate how to learn model parameters from reference data of known outbreaks. We demonstrate model performance using semi-synthetic experiments., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: To evaluate the effectiveness, tolerability, and safety of topical amitriptyline as a potential route of administration for the management of burning mouth syndrome. Background: Burning mouth syndrome is a complex, idiopathic, and debilitating orofacial pain disorder that impairs quality of life, with a prevalence of up to 18% in menopausal women. Available drugs to alleviate its burning sensation have inconsistent and limited efficacy. Given its physicochemical properties, excellent tolerability, and ability to target peripheral pathways, topical amitriptyline seems a promising mechanistically specific analgesic drug for burning mouth syndrome. Methods: In this retrospective cross‐sectional real‐world evidence study, patients with burning mouth syndrome who were prescribed topical amitriptyline for 8 weeks were identified. Eligibility criteria stemmed from ICHD‐3, ICOP, and consensus definitions. The primary outcome measure was mean daily pain intensity (on a 0–10 scale); secondary outcomes included adverse events and patient global impression of improvement. Data are given as the mean ± SD. Results: A total of 15 patients fulfilling the eligibility criteria were included and analyzed. Mean daily pain was 6.7 ± 2.1 at baseline and 3.7 ± 2.3 after treatment, with a mean reduction of 3.1 ± 2.8 (p = 0.002). Half of the patients experienced a decrease in pain by at least 50% (p = 0.008). Several mild adverse events were reported, such as somnolence or dry mouth. Conclusions: Topical amitriptyline may be a safe and potent route of administration in the treatment of burning mouth syndrome, a hypothesis to be tested in further controlled trials. [ABSTRACT FROM AUTHOR]

Background Acute otitis media (AOM) accounts for roughly 25% of antibiotics prescribed to children annually. Despite national guidelines that recommend short (5–7 days) durations of antibiotics for children 2 years and older with AOM, most receive long (10 day) courses. This study aims to evaluate antibiotic durations prescribed for children aged 2–17 years with uncomplicated AOM across two pediatric academic health systems, and to assess the variability in prescribed durations between and within each system. Methods Electronic medical record data from 135 care locations at two health systems were retrospectively analyzed. Outpatient encounters for children aged 2–17 years with a diagnosis of AOM from 2019 to 2022 were included. The primary outcome was the percent of 5-day prescriptions. Secondary outcomes included the proportion of 7-day prescriptions, 10-day prescriptions, prescriptions for nonfirst-line antibiotics, cases associated with treatment failure, AOM recurrence, and adverse drug events. Results Among 73 198 AOM encounters for children 2 years and older, 61 612 (84%) encounters resulted in an antibiotic prescription. Most prescriptions were for 10 days (45 689; 75%), 20% were for 7 days (12 060), and only 5% were for 5 days (3144). Treatment failure, AOM recurrence, adverse drug events, hospitalizations, and office, emergency department or urgent-care visits for AOM within 30 days after the index visit were rare. Conclusions Despite national guidelines that recommend shorter durations for children with uncomplicated AOM, 75% of our cohort received 10-day durations. Shortening durations of therapy for AOM could reduce antibiotic exposure and should be a priority of pediatric antibiotic stewardship programs. [ABSTRACT FROM AUTHOR]

Comprehensive health records (CHRs) that encompass patients' medical and dental information is here and now. CHRs enable health care providers to deliver personalized, precision medicine/dentistry effectively and efficiently. The objective of this report is to provide an overview of strengths, weaknesses, and opportunities associated with a comprehensive health care record system that integrates medical and dentalrecords. Strengths and benefits of CHRs (elevated patient safety, collaborative care, holistic patient care, enhanced research capabilities) are explained, and practical challenges associated with operating multiple record systems (specifically in medical and dental imaging), data migration and integration are explored. [ABSTRACT FROM AUTHOR]

Health information systems (HISs) are essential information systems used by organisations and individuals for various purposes. Past research has studied different types of HIS, such as rostering systems, Electronic Medical Records (EMRs), and Personal Health Records (PHRs). Although several past confirmatory studies have quantitatively examined EMR uptake by health professionals, there is a lack of exploratory and qualitative studies that uncover various drivers of healthcare professionals' uptake of EMRs. Applying an exploratory and qualitative approach, this study introduces various antecedents of healthcare professionals' uptake of EMRs. This study conducted 78 semi-structured, open-ended interviews with 15 groups of healthcare professional users of EMRs in two large Australian hospitals. Data analysis of qualitative data resulted in proposing a framework comprising 23 factors impacting healthcare professionals' uptake of EMRs, which are categorised into ten main categories: perceived benefits of EMR, perceived difficulties, hardware/software compatibility, job performance uncertainty, ease of operation, perceived risk, assistance society, user confidence, organisational support, and technological support. Our findings have important implications for various practitioner groups, such as healthcare policymakers, hospital executives, hospital middle and line managers, hospitals' IT departments, and healthcare professionals using EMRs. Implications of the findings for researchers and practitioners are provided herein in detail. [ABSTRACT FROM AUTHOR]

Background/Aims: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, 103.4 million cases and 1.1 million deaths have occurred nationally as of November 2023. Despite the benefit of mitigating measures, the pandemic's effect on participant safety is rarely documented. Methods: This study assessed noncompliance occurring from July 2019 to August 2021 that were stratified by the date of noncompliance (before or after restrictions). Events were described by size, site, noncompliance type, primary category, subcategory, and cause. In addition, noncompliance associated with COVID-19 was analyzed to determine characteristics. Results: In total, 323 noncompliance events occurred across 21,146 participants at risk in 35 protocols. The overall rate of noncompliance increased from 0.008 events per participant to 0.022 events per participant after the COVID-19 restrictions (p < 0.001). For onsite protocols, the median within protocol change in rates was 0.001 (interquartile range = 0.141) after the onset of COVID-19 restrictions (p = 0.54). For large-sized protocols (n ≥ 100), the median within protocol change in rates was also 0.001 (interquartile range = 0.017) after COVID-19 restrictions (p = 0.15). For events related to COVID-19 restrictions, 160/162 (99%) were minor deviations, 161/162 (99%) were procedural noncompliance, and 124/162 (77%) were an incomplete study visit. Conclusion: These noncompliance events have implications for clinical trial methodology because nonadherence to trial design can lead to participant safety concerns and loss of trial data validity. Protocols should be written to better facilitate the capture of all safety and efficacy data. This recommendation should be considered when changes occur to the protocol environment that are outside of the study team's control. [ABSTRACT FROM AUTHOR]

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records. [ABSTRACT FROM AUTHOR]

Background: Medical records are a valuable source for understanding patient health conditions. Doctors often use these records to assess health without solely depending on time-consuming and complex examinations. However, these records may not always be directly relevant to a patient's current health issue. For instance, information about common colds may not be relevant to a more specific health condition. While experienced doctors can effectively navigate through unnecessary details in medical records, this excess information presents a challenge for machine learning models in predicting diseases electronically. To address this, we have developed 'al-BERT', a new disease prediction model that leverages the BERT framework. This model is designed to identify crucial information from medical records and use it to predict diseases. 'al-BERT' operates on the principle that the structure of sentences in diagnostic records is similar to regular linguistic patterns. However, just as stuttering in speech can introduce 'noise' or irrelevant information, similar issues can arise in written records, complicating model training. To overcome this, 'al-BERT' incorporates a semi-supervised layer that filters out irrelevant data from patient visitation records. This process aims to refine the data, resulting in more reliable indicators for disease correlations and enhancing the model's predictive accuracy and utility in medical diagnostics. Method: To discern noise diseases within patient records, especially those resembling influenza-like illnesses, our approach employs a customized semi-supervised learning algorithm equipped with a focused attention mechanism. This mechanism is specifically calibrated to enhance the model's sensitivity to chronic conditions while concurrently distilling salient features from patient records, thereby augmenting the predictive accuracy and utility of the model in clinical settings. We evaluate the performance of al-BERT using real-world health insurance data provided by Taiwan's National Health Insurance. Result: In our study, we evaluated our model against two others: one based on BERT that uses complete disease records, and another variant that includes extra filtering techniques. Our findings show that models incorporating filtering mechanisms typically perform better than those using the entire, unfiltered dataset. Our approach resulted in improved outcomes across several key measures: AUC-ROC (an indicator of a model's ability to distinguish between classes), precision (the accuracy of positive predictions), recall (the model's ability to find all relevant cases), and overall accuracy. Most notably, our model showed a 15% improvement in recall compared to the current best-performing method in the field of disease prediction. Conclusion: The conducted ablation study affirms the advantages of our attention mechanism and underscores the crucial role of the selection module within al-BERT. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a progressive neurodegenerative condition. Chinese medicine therapies have demonstrated effectiveness for PD in controlled settings. However, the utilization of Chinese medicine therapies for PD in real-world clinical practice and the characteristics of patients seeking these therapies have not been thoroughly summarized. Method: The study retrospectively analyzed initial patient encounters (PEs) with a first-listed diagnosis of PD, based on electronic medical records from Guangdong Provincial Hospital of Chinese Medicine between July 2018 and July 2023. Results: A total of 3,206 PEs, each corresponding to an individual patient, were eligible for analyses. Approximately 60% of patients made initial visits to the Chinesemedicine hospital after receiving a PD diagnosis, around 4.59 years after the onset of motor symptoms. Over 75% of the patients visited the Internal Medicine Outpatient Clinic at their initial visits, while a mere 13.85% visited PD Chronic Care Clinic. Rest tremor (61.98%) and bradykinesia (52.34%) are the most commonly reported motor symptoms, followed by rigidity (40.70%). The most commonly recorded non-motor symptoms included constipation (31.88%) and sleep disturbance (25.27%). Integration of Chinese medicine and conventional medicine therapies was the most common treatment method (39.15%), followed by single use of Chinese herbal medicine (27.14%). The most frequently prescribed herbs for PD included Glycyrrhiza uralensis Fisch. (gan cao), Astragalus mongholicus Bunge (huang qi), Atractylodes macrocephala Koidz. (bai zhu), Angelica sinensis (Oliv.) Diels (dang gui), Rehmannia glutinosa (Gaertn.) DC. (di huang), Paeonia lactiflora Pall. (bai shao), Bupleurum chinense DC. (chai hu), Citrus aurantium L. (zhi qiao/zhi shi/chen pi), Panax ginseng C. A. Mey. (ren shen), and Poria cocos (Schw.) Wolf (fu ling). These herbs contribute to formulation of Bu zhong yi qi tang (BZYQT). Conclusion: Patients typically initiated Chinese medical care after the establishment of PD diagnosis, 4.59 years post-onset of motor symptoms. The prevalent utilization of CHM decoctions and patented Chinese herbal medicine products, underscores its potential in addressing both motor and non-motor symptoms. Despite available evidence, rigorous clinical trials are needed to validate and optimize the integration of CHM, particularly BZYQT, into therapeutic strategies for PD. [ABSTRACT FROM AUTHOR]

Background Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year. Methods Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal–external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups. Results The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal–external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, −0.87; 95% CI: −0.96 to −0.78). Clinical utility on external validation was improved after recalibration. Conclusion The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems. [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 In-hospital delirium is associated with adverse outcomes and is underdiagnosed, limiting research and clinical follow-up. Objective To compare the incidence of in-hospital delirium determined by chart-based review of electronic medical records (D-CBR) with delirium discharge diagnoses (D-DD). Furthermore, to identify differences in symptoms, treatments and delirium triggers between D-CBR and D-DD. Method The community-based cohort included 2,115 participants in the Hordaland Health Study born between 1925 and 1927. Between 2018 and 2022, we retrospectively reviewed hospital electronic medical records from baseline (1997–99) until death prior to 2023. D-DD and D-CBR were validated using The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for delirium. Results Of the 2,115 participants, 638 had in-hospital delirium. The incidence rate (IR) of D-CBR was 29.8 [95% confidence interval 28, 32] per 1,000 person-years, whereas the IR by D-DD was 3.4 [2.8, 4.2]. The IR ratio was 9.14 (P  < 0.001). Patients who received pharmacological treatment for delirium (n  = 121, odds ratio (OR) 3.4, [2.1, 5.4], P  < 0.001), who were affected by acute memory impairment (n  = 149, OR 2.8, [1.8, 4.5], P  < 0.001), or change in perception (n  = 137, OR 2.9, [1.8, 4.6] P  < 0.001) had higher odds for D-DD. In contrast, post-operative cases (OR 0.2, [0.1, 0.4], P  < 0.001) had lower odds for D-DD. Conclusion Underdiagnosis of in-hospital delirium was a major issue in our study, especially in less severe delirium cases. Our findings emphasise the need for integrating systematic delirium diagnostics and documentation into hospital admission and discharge routines. [ABSTRACT FROM AUTHOR]

Background: In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice. Objective: The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals' digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations. Methods: A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus. Results: Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents. Conclusions: A total of 17 recommendations related to key aspects of health care professionals' digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice. [ABSTRACT FROM AUTHOR]

A growing number of medical records are changed by the electronic folders that can be shared and transmitted in real-time in recent years as a result of the speedy improvement of data mechanisms and network methodologies. Yet, security breaches and privacy issues could affect medical data sent over open communication channels. Due to its distinctive qualities including immutability, blockchain technology, anonymity, decentralization, and, verifiability has drawn more notice in various fields. With a variety of multimedia techniques, healthcare organizations all over the globe are evolving into more user-centered, harmonized, and effective models. The management of enormous amounts of information, like records and photographs of all the individual improve the person's labor requirements and the security hazards. Recent advances in the healthcare industry have led to the creation of enormous numbers of electronic health records (EHRs). The owner of the data can manage the EHR data and share thanks with certain individuals in the EHR system. It is challenging for information to guarantee protection and detection procedures due to the enormous amount of information in the medical care models. Hence, in order to secure the EHR, the development of artificial intelligence techniques like blockchain and a new privacy preservation model is recommended for this work. Particularly, the Merkle tree is a key component of blockchain technology. In this proposed model, the Merkle tree is applied that helps for effective and safe authentication of huge data frameworks with the purpose of verifiability of the posted patient data. This model consists of two main phases, such as data sanitization and data restoration. Here, the sanitization operation is based on the creation of the optimal key using a new iteration-based firefly reptile search algorithm (IFRSA). The creation of the optimal key is established by functioning a multi-objective function which includes the factors like Euclidean distance, hiding ratio, and, data preservation ratio among encrypted data using the original key and the key with a variation. Finally, the electronic health care model becomes more private, dependable, and helpful through our suggested system. The comparison shows the supremacy of the developed approach. [ABSTRACT FROM AUTHOR]

Ensuring comprehensive medical records is a crucial aspect of delivering effective healthcare in hospitals, with a particular focus on dental services. This research delves into the dynamics of the relationship between a dentist's behavior and the completeness of odontogram filling. The study focuses on a comprehensive analysis of performance expectations, effort expectations, social influence, and facilitating conditions. Through this examination, the goal is to positively influence dentist behavior, leading to improvements in the quality of odontography documentation. The findings aim to contribute valuable insights for enhancing dental practices. Adopting a qualitative research methodology with a scoping review approach, the investigation identifies various categories of dentist behavior in filling out electronic medical records. These categories are delineated based on the multifaceted aspects of performance expectations, effort expectations, social influence, and facilitating conditions. To augment the completeness of odontography filling, the research underscores the importance of considering the factors identified within these aspects. By addressing the nuanced interplay between these elements, interventions can be strategically designed to positively impact dentist behavior and, consequently, elevate the standard of odontography documentation. This research contributes valuable insights to the ongoing efforts aimed at refining the implementation of health services and ensuring meticulous medical record-keeping, particularly in the dental domain. [ABSTRACT FROM AUTHOR]

Real-world data sources can facilitate essential understanding of the epidemiological features of anaphylaxis. However, the accuracy of case-identifying definitions based on diagnosis codes for anaphylaxis in healthcare databases remains understudied. We conducted a cross-sectional study analyzing claims data from the largest multi-institutional healthcare system in Taiwan during 2017–2021. We included patients with incident anaphylaxis identified by either ICD-10-CM codes for anaphylaxis (Group 1) or ICD-10-CM codes for severe allergic or drug adverse events and additional modifier codes for acute allergy events (Group 2). We randomly selected 20% of the cases to determine the positive predictive value (PPV) of anaphylaxis case-identifying definitions in Groups 1 and 2 after review of electronic medical records by two physicians. From the original cohort (n = 2,176), we randomly selected 433 patients with either a diagnosis of anaphylaxis (Group 1), or a diagnosis of severe allergic and drug adverse events with additional modifier codes for acute allergy events (Group 2). In Group 1, we judged 135 / 170 patients as true anaphylaxis cases, giving a PPV of 79.4% (95% CI: 73.3–85.5). In Group 2, we judged 47 / 263 patients as true anaphylaxis cases, giving a PPV of 17.9% (95% CI: 13.3–22.5). In conclusion, acceptable PPVs were observed when anaphylaxis cases were identified by ICD-10-CM codes for anaphylaxis, but not by ICD-10-CM codes for severe allergic or drug adverse event with additional modifier codes for acute allergy events. Our multi-institutional findings could serve as a fundamental reference for further studies of anaphylaxis based on real-world healthcare databases. [ABSTRACT FROM AUTHOR]

Primary care is an important part of the help-seeking pathway for young people experiencing early psychosis, but sex differences in clinical presentation in these settings are unexplored. We aimed to identify sex differences in clinical presentation to primary care services in the 1-year period prior to a first diagnosis of psychotic disorder. We identified first-onset cases of non-affective psychotic disorder over a 10-year period (2005–2015) using health administrative data linked with electronic medical records (EMRs) from primary care (n = 465). Detailed information on encounters in the year prior to first diagnosis was abstracted, including psychiatric symptoms, other relevant behaviours, and diagnoses recorded by the family physician (FP). We used modified Poisson regression models to examine sex differences in the signs, symptoms, and diagnoses recorded by the FP, adjusting for various clinical and sociodemographic factors. Positive symptoms (PR = 0.76, 95%CI: 0.58, 0.98) and substance use (PR = 0.54, 95%CI: 0.40, 0.72) were less prevalent in the medical records of women. Visits by women were more likely to be assigned a diagnosis of depression or anxiety (PR = 1.18, 95%CI: 1.00, 1.38), personality disorder (PR = 5.49, 95%CI: 1.22, 24.62), psychological distress (PR = 11.29, 95%CI: 1.23, 103.91), and other mental or behavioral disorders (PR = 3.49, 95%CI: 1.14, 10.66) and less likely to be assigned a diagnosis of addiction (PR = 0.33, 95%CI: 0.13, 0.87). We identified evidence of sex differences in the clinical presentation of early psychosis and recorded diagnoses in the primary care EMR. Further research is needed to better understand sex differences in clinical presentation in the primary care context, which can facilitate better understanding, detection, and intervention for first-episode psychotic disorders. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact of using an academic electronic medical record program on first-year nursing students' confidence and skill in E-documentation after their hospital clinical placement. Background: Registered nurses are the largest user group of health information technology systems such as patient electronic medical records (eMR). As such, nurse undergraduate programs need to reflect contemporary practices and respond to emerging trends including digital technology, however integration of eMR learning has not occurred in many countries. To address this gap, a fit-for-purpose academic eMR simulation program was developed by nursing academics and a university Learning Design Department member. Study Design and Methods: A quasi-experimental study design, with self-administered pre-test, post-test surveys, was used with a convenience sample of all first-year nursing students at one regional university in NSW Australia in 2019 and 2021. Results: A total of 105 students completed the surveys (9.7% pre, and 7.4% post-test survey). Only 23% of respondents received training during hospital clinical placement on eMR and electronic observation charts. There was a significant increase in participant confidence and knowledge in documenting in electronic adult observational charts and notes after using the academic eMR program and attending clinical placement. Three themes emerged from the qualitative data: preparation for practice; more exposure increases confidence; and we can't forget the patient. Conclusion: Students acknowledged the need for repeated practice using an academic eMR program in university learning environments to ensure they would be work-ready. The identified challenge was the communication barrier (computer on wheels) and the potential negative impact on person-centred care and therapeutic communication. Implications for research, policy and practice: Further research is required to determine whether repeated practice with electronic documentation is best placed within a curriculum to increase learner confidence. Simulations that incorporate workstations on wheels should be tested to determine best practice for therapeutic communication. What is already known about this topic? • Registered nurses are the largest user group of health information technology systems. • Nursing undergraduate program needs to reflect contemporary practices including digital technologies. • Integration of eMR education in undergraduate nursing programs has not occurred in many countries. What this paper adds: • Evaluation of a fit-for-purpose academic electronic medical record program integrated into an undergraduate nursing student's curriculum. • There was a significant increase in participant confidence and knowledge in documenting in electronic adult observational charts and notes after using the academic eMR program. • Digital technology education tailored for students of different age groups may be required. [ABSTRACT FROM AUTHOR]

Health information permeates healthcare delivery from point-of-care, across the continuum of care and throughout the healthcare system's policy, population health, research, planning and funding arenas. Health information managers (HIMs) expertly manage that information. This commentary theorises the health information management profession for the first time. Its purpose is to identify and contextualise, via a historiographical account, the societal and political drivers that have shaped contemporary Australian health information management and HIMs' scientific work. It seeks to build our knowledge of the socio-political influences on the profession's emergence and development, and the projected drivers of its future. Eight critical, socio-political drivers were identified and are addressed in temporaneous order. Scientific medicine has reflected the influences on medicine in the past century and a half of the medical record and other technologies, laboratory-based sciences, evidence-based medicine and evidence-based health. Standardisation has underpinned and guided the profession's practice. The hegemony of non-medical healthcare managers and resource- and performance-related accountabilities emerged in the 1960s, as did the efficiencies of bureaucratisation in healthcare and post-bureaucratic shifts to textualisation and technogovernance. Technologisation has driven constant change in health information management, as have the forces of the fast-paced risk society. Since the 1980s, the health consumer movement has propelled regulatory mechanisms that accord patients' access rights to their medical records and mandate information privacy protections. Finally, a nascent commodification of health information has emerged. These forces exert ongoing impacts on the profession. They will, we conclude, singularly and collectively continue to shape its discourses and direction. [ABSTRACT FROM AUTHOR]

The impact of the 2019 coronavirus disease (COVID-19) pandemic is still being revealed, and little is known about the effect of COVID-19-induced outpatient and inpatient losses on hospital operations in many counties. Hence, we aimed to explore whether hospitals adopted profit compensation activities after the 2020 first-wave outbreak of COVID-19 in China. A total of 2,616,589 hospitalization records from 2018, 2019, and 2020 were extracted from 36 tertiary hospitals in a western province in China; we applied a difference-in-differences event study design to estimate the dynamic effect of COVID-19 on hospitalized patients' total expenses before and after the last confirmed case. We found that average total expenses for each patient increased by 8.7% to 16.7% in the first 25 weeks after the city reopened and hospital admissions returned to normal. Our findings emphasize that the increase in total inpatient expenses was mainly covered by claiming expenses from health insurance and was largely driven by an increase in the expenses for laboratory tests and medical consumables. Our study documents that there were profit compensation activities in hospitals after the 2020 first-wave outbreak of COVID-19 in China, which was driven by the loss of hospitalization admissions during this wave outbreak. [ABSTRACT FROM AUTHOR]

Real-world evidence in multiple sclerosis (MS) is limited by the availability of data elements in individual real-world datasets. We introduce a novel, growing database which links administrative claims and medical records from an MS patient management system, allowing for the complete capture of patient profiles. Using the AOK PLUS sickness fund and the Multiple Sclerosis Documentation System MSDS3D from the Center of Clinical Neuroscience (ZKN) in Germany, a linked MS-specific database was developed (MSDS-AOK PLUS). Patients treated at ZKN and insured by AOK PLUS were recruited and asked for informed consent. For linkage, insurance IDs were mapped to registry IDs. After the deletion of insurance IDs, an anonymized dataset was provided to a university-affiliate, IPAM e.V., for further research applications. The dataset combines a complete record of patient diagnoses, treatment, healthcare resource use, and costs (AOK PLUS), with detailed clinical parameters including functional performance and patient-reported outcomes (MSDS3D). The dataset currently captures 500 patients; however, is actively expanding. To demonstrate its potential, we present a use case describing characteristics, treatment, resource use, and costs of a patient subsample. By linking administrative claims to clinical information in medical charts, the novel MSDS-AOK PLUS database can increase the quality and scope of real-world studies in MS. [ABSTRACT FROM AUTHOR]

Background: General practitioners are well positioned to contribute to the pharmacovigilance of medical cannabis via the general practice electronic medical record (EMR). The aim of this research is to interrogate de-identified patient data from the Patron primary care data repository for reports of medicinal cannabis to ascertain the feasibility of using EMRs to monitor medicinal cannabis prescribing in Australia. Methods: EMR rule-based digital phenotyping of 1 164 846 active patients from 109 practices was undertaken to investigate reports of medicinal cannabis use from September 2017 to September 2020. Results: Eighty patients with 170 prescriptions of medicinal cannabis were identified in the Patron repository. Reasons for prescription included anxiety, multiple sclerosis, cancer, nausea, and Crohn's disease. Nine patients showed symptoms of a possible adverse event, including depression, motor vehicle accident, gastrointestinal symptoms, and anxiety. Conclusions: The recording of medicinal cannabis effects in the patient EMR provides potential for medicinal cannabis monitoring in the community. This is especially feasible if monitoring were to be embedded into general practitioner workflow. General practitioners are well positioned to contribute to the pharmacovigilance of medical cannabis via the general practice electronic medical record (EMR). This research interrogated de-identified patient data from the Patron primary care data repository for reports of medicinal cannabis to ascertain the feasibility of the use of EMRs to monitor medicinal cannabis prescribing in Australia. This research found patient EMRs provide potential for medicinal cannabis monitoring in the community, especially if monitoring were to be embedded into general practitioner workflow. [ABSTRACT FROM AUTHOR]

Background: The SARS‐CoV‐19 pandemic and its associated lockdowns affected children's lifestyle dramatically. The effect of such changes on children's weight and obesity status is unknown. The aim of this study was to compare body weight and obesity rates in children from before the pandemic to 6 months after the major periods of lockdowns in Israel. Methods: We used data from medical records of pediatric emergency department visits, where weight is routinely measured, to compare weight and obesity prevalence in the fourth quartile of 2020 (n = 2468) as compared with the fourth quartiles of 2018–2019 (n = 5300). Weight was transformed to age‐ and sex‐specific standard‐deviation‐scores (SDS) for analysis. Results: Weight‐SDS increased by a mean of 0.07 during the first 6 months of the pandemic, yet this was only significant in preschoolers. Obesity rates also increased in this age group only, by 37%, from 8.1% to 11.1% (p = 0.01). Conclusions: Weight‐SDS and obesity prevalence increased during the SARS‐CoV‐19 pandemic, yet only in younger children. Additional studies from other populations are needed. [ABSTRACT FROM AUTHOR]

The role of medical records in the delivery of quality healthcare cannot be overemphasised. Thus, healthcare organisations such as hospitals across the globe rely heavily on accurate, well-organised and well-managed electronic medical records to deliver quality healthcare service. The records contain information related to the patient's medical history and other information that facilitates healthcare decisions, including easy access to the patient's health information. Therefore, easy access to a patient's medical history is an essential aspect of healthcare service delivery that must be regulated and monitored because of the sensitivity of the information. Hence, various countries of the world adopted blockchain technology in managing medical records in their hospitals to enhance security and transparency in generating, storing and sharing medical data. However, hospitals in African countries have not given much attention to blockchain technology in managing medical records. This is because current approaches adopted are mostly paper-based, leaving many hospitals with challenges of missing files, or records, lack of information sharing between healthcare providers, insecure records and inaccessibility of patients' health information for healthcare providers that are needed to make informed health decisions. Drawing from a case study conducted at teaching hospitals in Nigeria, this study used primary sources of data which was collected using questionnaire and secondary data (literature) to propose a scalable framework that supports the adoption of blockchain technology in addressing the challenges associated with paper-based medical records in the health sector. [ABSTRACT FROM AUTHOR]

Background: Few studies have developed automatic systems for identifying social distress, spiritual pain, and severe physical and phycological symptoms from text data in electronic medical records. Aim: To develop models to detect social distress, spiritual pain, and severe physical and psychological symptoms in terminally ill patients with cancer from unstructured text data contained in electronic medical records. Design: A retrospective study of 1,554,736 narrative clinical records was analyzed 1 month before patients died. Supervised machine learning models were trained to detect comprehensive symptoms, and the performance of the models was tested using the area under the receiver operating characteristic curve (AUROC) and precision recall curve (AUPRC). Setting/participants: A total of 808 patients was included in the study using records obtained from a university hospital in Japan between January 1, 2018 and December 31, 2019. As training data, we used medical records labeled for detecting social distress (n = 10,000) and spiritual pain (n = 10,000), and records that could be combined with the Support Team Assessment Schedule (based on date) for detecting severe physical/psychological symptoms (n = 5409). Results: Machine learning models for detecting social distress had AUROC and AUPRC values of 0.98 and 0.61, respectively; values for spiritual pain, were 0.90 and 0.58, respectively. The machine learning models accurately identified severe symptoms (pain, dyspnea, nausea, insomnia, and anxiety) with a high level of discrimination (AUROC > 0.8). Conclusion: The machine learning models could detect social distress, spiritual pain, and severe symptoms in terminally ill patients with cancer from text data contained in electronic medical records. [ABSTRACT FROM AUTHOR]

The SPARK cohort was established to facilitate recruitment in studies of large numbers of participants with autism spectrum disorder (ASD). Online registration requires participants to have received a lifetime professional diagnosis by health or school providers although diagnoses are not independently verified. This study was set to examine the validity of self- and caregiver-reported autism diagnoses. Electronic medical records (EMR) of 254 SPARK participants (77.6% male, age 10.7 years) were abstracted. Using two different methods, confirmation of ASD diagnosis in EMRs was obtained in 98.8% of cases. Core clinical features recorded in EMRs were typical of autism samples and showed very good agreement with SPARK cohort data, providing further evidence of the validity of clinical information in the SPARK database. [ABSTRACT FROM AUTHOR]

Background: To address the opioid epidemic, physicians are encouraged to identify means of reducing patient opioid exposure. Electronic medical records (EMRs) often include default order sets with automated orders for opioid medications, which may influence how much opioids physicians prescribe. We sought to evaluate the impact of de-selecting an automated order for oxycodone-acetaminophen from an EMR order set for postpartum vaginal deliveries on inpatient opioid exposure by comparing the proportion of patients who received an opioid after an uncomplicated vaginal delivery before and after the EMR change. As secondary outcomes, the impact on average total morphine milligram equivalents (MMEs) and discharge opioid prescriptions was investigated. Materials and Methods: A quality improvement study was conducted through retrospective chart review of uncomplicated vaginal deliveries for the four quarters before and after the EMR order set change occurred. The total proportion of patients who received an opioid in the postpartum period was then determined for the preexposure and postexposure groups. The total average MME consumed for patients who received an opioid in each group was determined and the total proportion of patients who received an opioid prescription at discharge was compared. Results: A total of 5826 records of uncomplicated vaginal deliveries met the criteria for analysis. In the preintervention group, 32.9% of patients received an opioid postpartum, compared to 12.5% of patients in the postintervention group, representing a decrease of 62.0% (p < 0.001). Of those who received opioids, the preintervention mean total opioid consumption was 28.4 MME (±27.6) compared to 33.6 MME (±46.4) postintervention, and there was no significant difference in median total opioid consumption: 22.5 MME (interquartile range [IQR]: 7.5–47.5) preintervention compared with 20.8 MME (IQR: 7.5–45.0) postintervention (p = 0.902). No significant difference was found with discharge opioid prescriptions between the two groups. Conclusion: Order sets within EMR systems appear to have a significant influence on physician prescribing behaviors and removing these automated orders for opioids should be considered. [ABSTRACT FROM AUTHOR]