Características clínicas de reanimações cardiopulmonares intra-hospitalares registradas em prontuário: estudo transversal / Clinical characteristics of in-hospital cardiopulmonary resuscitations recorded in medical records: a cross-sectional study / Características clínicas de las reanimaciones cardiopulmonares intrahospitalarias registradas en las historias clínicas: estudio transversal

Objetivo: identificar características clínicas das paradas cardiopulmonares e reanimações cardiopulmonares ocorridas em ambiente intra-hospitalar. Método: estudo quantitativo, prospectivo e observacional, a partir de informações de prontuários de pacientes submetidos a manobras de reanimação devido à parada cardiopulmonar entre janeiro e dezembro de 2021. Utilizou-se um instrumento baseado nas variáveis do modelo de registro Utstein. Resultados: em 12 meses foram registradas 37 paradas cardiopulmonares. A maioria ocorreu na unidade de terapia intensiva respiratória, com causa clínica mais prevalente hipóxia. 65% dos pacientes foram intubados no atendimento e 57% apresentaram ritmo atividade elétrica sem pulso. A duração da reanimação variou entre menos de cinco a mais de 20 minutos. Como desfecho imediato, 57% sobreviveram. Conclusão: dentre os registros analisados, a maior ocorrência de paradas cardiopulmonares foi na unidade de terapia intensiva respiratória, relacionada à Covid-19. Foram encontrados registros incompletos e ausência de padronização nas condutas.

Objective: identify the clinical characteristics of cardiopulmonary arrests and cardiopulmonary resuscitations in the in-hospital environment. Method: this is a quantitative, prospective and observational study based on information from the medical records of patients who underwent resuscitation maneuvers due to cardiopulmonary arrest between January and December 2021. An instrument based on the variables of the Utstein registration protocol was used. Results: thirty-seven cardiopulmonary arrests were recorded in 12 months. The majority occurred in a respiratory intensive care unit, with hypoxia being the most prevalent clinical cause. Sixty-five percent of the patients were intubated and 57% had pulseless electrical activity. The duration of resuscitation ranged from less than five to more than 20 min. As for the immediate outcome, 57% survived. Conclusion: among the records analyzed, the highest occurrence of cardiopulmonary arrests was in respiratory intensive care units, and they were related to Covid-19. Moreover, incomplete records and a lack of standardization in cardiopulmonary resuscitation procedures were found.

Objetivo: Identificar las características clínicas de paros cardiopulmonares y reanimaciones cardiopulmonares que ocurren en un ambiente hospitalario. Método: estudio cuantitativo, prospectivo y observacional, realizado a partir de información presente en historias clínicas de pacientes sometidos a maniobras de reanimación por paro cardiorrespiratorio entre enero y diciembre de 2021. Se utilizó un instrumento basado en las variables del modelo de registro Utstein. Resultados: en 12 meses se registraron 37 paros cardiopulmonares. La mayoría ocurrió en la unidad de cuidados intensivos respiratorios, la causa clínica más prevalente fue la hipoxia. El 65% de los pacientes fue intubado durante la atención y el 57% presentaba un ritmo de actividad eléctrica sin pulso. La duración de la reanimación varió entre menos de cinco y más de 20 minutos. Como resultado inmediato, el 57% sobrevivió. Conclusión: entre los registros analizados, la mayor cantidad de paros cardiopulmonares se dio en la unidad de cuidados intensivos respiratorios, relacionada con Covid-19. Se encontraron registros incompletos y falta de estandarización en el procedimiento.

Outcome evaluation of technical strategies on reduction of patient waiting time in the outpatient department at Kilimanjaro Christian Medical Centre-Northern Tanzania.

BACKGROUND: The Tanzania healthcare system is beset by prolonged waiting time in its hospitals particularly in the outpatient departments (OPD). Previous studies conducted at Kilimanjaro Christian Medical Centre (KCMC) revealed that patients typically waited an average of six hours before receiving the services at the OPD making KCMC have the longest waiting time of all the Zonal and National Referral Hospitals. KCMC implemented various interventions from 2016 to 2021 to reduce the waiting time. This study evaluates the outcome of the interventions on waiting time at the OPD. METHODS: This is an analytical cross-sectional mixed method using an explanatory sequential design. The study enrolled 412 patients who completed a structured questionnaire and in-depth interviews (IDI) were conducted among 24 participants (i.e., 12 healthcare providers and 12 patients) from 3rd to 14th July, 2023. Also, a documentary review was conducted to review benchmarks with regards to waiting time. Quantitative data analysis included descriptive statistics, bivariable and multivariable. All statistical tests were conducted at 5% significance level. Thematic analysis was used to analyse qualitative data. RESULTS: The findings suggest that post-intervention of technical strategies, the overall median OPD waiting time significantly decreased to 3 h 30 min IQR (2.51-4.08), marking a 45% reduction from the previous six-hour wait. Substantial improvements were observed in the waiting time for registration (9 min), payment (10 min), triage (14 min for insured patients), and pharmacy (4 min). Among the implemented strategies, electronic medical records emerged as a significant predictor to reduced waiting time (AOR = 2.08, 95% CI, 1.10-3.94, p-value = 0.025). IDI findings suggested a positive shift in patients' perceptions of OPD waiting time. Problems identified that still need addressing include, ineffective implementation of block appointment and extension of clinic days was linked to issues of ownership, organizational culture, insufficient training, and ineffective follow-up. The shared use of central modern diagnostic equipment between inpatient and outpatient services at the radiology department resulted in delays. CONCLUSION: The established technical strategies have been effective in reducing waiting time, although further action is needed to attain the global standard of 30 min to 2 h OPD waiting time.

Improve the efficiency and accuracy of ophthalmologists' clinical decision-making based on AI technology.

BACKGROUND: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. METHODS: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. RESULTS: The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. CONCLUSION: The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example.

BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV. OBJECTIVE: A given HIV clinic's electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure. METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware. RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate. CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic's EHR without the resource-intensive elucidation of an informative prior.

A Cross-Sectional Study on Whether Comprehensively Gathering Information From Medical Records Is Useful for the Collection of Operational Characteristics.

This study tests whether comprehensively gathering information from medical records is useful for developing clinical decision support systems using Bayes' theorem. Using a single-center cross-sectional study, we retrospectively extracted medical records of 270 patients aged ≥16 years who visited the emergency room at the Tokyo Metropolitan Tama Medical Center with a chief complaint of experiencing headaches. The medical records of cases were analyzed in this study. We manually extracted diagnoses, unique keywords, and annotated keywords, classifying them as either positive or negative. Cross tables were created, and the proportion of combinations for which the likelihood ratios could be calculated was evaluated. Probability functions for the appearance of new unique keywords were modeled, and theoretical values were calculated. We extracted 623 unique keywords, 26 diagnoses, and 6,904 annotated keywords. Likelihood ratios could be calculated only for 276 combinations (1.70%), of which 24 (0.15%) exhibited significant differences. The power function+constant was the best fit for new unique keywords. The increase in the number of combinations after increasing the number of cases indicated that while it is theoretically possible to comprehensively gather information from medical records in this way, doing so presents difficulties related to human costs. It also does not necessarily solve the fundamental issues with medical informatics or with developing clinical decision support systems. Therefore, we recommend using methods other than comprehensive information gathering with Bayes' theorem as the classifier to develop such systems.

Agreement between Clinical Frailty Scale-scores based on information from patient interviews and Clinical Frailty Scale-scores based on information from medical records - a cross sectional study.

INTRODUCTION: Frailty is an age-related condition with increased risk for adverse health outcomes. Assessing frailty according to the Clinical Frailty Scale (CFS) based on data from medical records is useful for previously unassessed patients, but the validity of such scores in exclusively geriatric populations and in patients with dementia is relatively unknown. METHODS: Patients admitted for the first time to one of two geriatric wards at Örebro University hospital between January 1st - December 31st, 2021, were included in this study if they had been appointed a CFS-score by anamnestic interview (CFSI) at admission. CFS scores based on medical records (CFSR) were appointed by a single medical student, who was blinded to the CFSI score. Score-agreement was evaluated with quadratic weighted Cohen's kappa (κ). RESULTS: In total, 145 patients between the age of 55-101 were included in the study. The CFSR and CFSI scores agreed perfectly in 102 cases (0.7, 95% CI 0.65-0.77). There was no significant difference regarding age, sex, comorbidity, or number of patients diagnosed with dementia between the patients with complete agreement and the patients whose scores did not agree. Agreement between the scores was substantial, κ = 0.66, 95% CI 0.53-0.80. CONCLUSIONS: CFS scores based on information from medical records can be generated with substantial agreement to CFS scores based on in-person anamnestic interviews. A dementia diagnosis does not influence the agreement between the scores. Therefore, these scores are a useful tool for assessing frailty in geriatric patients who previously lack a frailty assessment, both in clinical practice and future research. The results support previous findings, but larger studies are warranted.

Prospective validation of a mobile health application for blood pressure management in patients with hypertensive disorders of pregnancy: study protocol for a randomized controlled trial.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) pose significant risks to both maternal and fetal health, contributing to global morbidity and mortality. Management of HDP is complex, particularly because of concerns regarding potential negative effects on utero-placental circulation and limited therapeutic options due to fetal safety. Our study investigates whether blood pressure monitoring through a mobile health (mHealth) application can aid in addressing the challenges of blood pressure management in pregnant individuals with HDP. Additionally, we aim to assess whether this intervention can improve short-term maternal and fetal outcomes and potentially mitigate long-term cardiovascular consequences. METHODS: This prospective, randomized, single-center trial will include 580 pregnant participants who meet the HDP criteria or who have a heightened risk of pregnancy-related hypertension due to factors such as multiple pregnancies, obesity, diabetes, or a history of HDP in prior pregnancies leading to preterm birth. Participants will be randomized to either the mHealth intervention group or the standard care group. The primary endpoint is the difference in systolic blood pressure from enrollment to 1 month after childbirth. The secondary endpoints include various blood pressure parameters, obstetric outcomes, body mass index trajectory, step counts, mood assessment, and drug adherence. CONCLUSIONS: This study emphasizes the potential of mHealth interventions, such as the Heart4U application, to improve blood pressure management in pregnant individuals with HDP. By leveraging technology to enhance engagement, communication, and monitoring, this study aims to positively impact maternal, fetal, and postpartum outcomes associated with HDP. This innovative approach demonstrates the potential of personalized technology-driven solutions for managing complex health conditions. TRIAL REGISTRATION: ClinicalTrials.gov NCT05995106. Registered on 16 August 2023.

Trajectories of BMI before and after diagnosis of type 2 diabetes in a real-world population.

AIMS/HYPOTHESIS: Few studies have examined the clinical characteristics associated with changes in weight before and after diagnosis of type 2 diabetes. Using a large real-world cohort, we derived trajectories of BMI before and after diabetes diagnosis, and examined the clinical characteristics associated with these trajectories, including assessing the impact of pre-diagnosis weight change on post-diagnosis weight change. METHODS: We performed an observational cohort study using electronic medical records from individuals in the Scottish Care Information Diabetes Collaboration database. Two trajectories were calculated, based on observed BMI measurements between 3 years and 6 months before diagnosis and between 1 and 5 years after diagnosis. In the post-diagnosis trajectory, each BMI measurement was time-dependently adjusted for the effects of diabetes medications and HbA1c change. RESULTS: A total of 2736 individuals were included in the study. There was a pattern of pre-diagnosis weight gain, with 1944 individuals (71%) gaining weight overall, and 875 (32%) gaining more than 0.5 kg/m2 per year. This was followed by a pattern of weight loss after diagnosis, with 1722 individuals (63%) losing weight. Younger age and greater social deprivation were associated with increased weight gain before diagnosis. Pre-diagnosis weight change was unrelated to post-diagnosis weight change, but post-diagnosis weight loss was associated with older age, female sex, higher BMI, higher HbA1c and weight gain during the peri-diagnosis period. When considering the peri-diagnostic period (defined as from 6 months before to 12 months after diagnosis), we identified 986 (36%) individuals who had a high HbA1c at diagnosis but who lost weight rapidly and were most aggressively treated at 1 year; this subgroup had the best glycaemic control at 5 years. CONCLUSIONS/INTERPRETATION: Average weight increases before diagnosis and decreases after diagnosis; however, there were significant differences across the population in terms of weight changes. Younger individuals gained weight pre-diagnosis, but, in older individuals, type 2 diabetes is less associated with weight gain, consistent with other drivers for diabetes aetiology in older adults. We have identified a substantial group of individuals who have a rapid deterioration in glycaemic control, together with weight loss, around the time of diagnosis, and who subsequently stabilise, suggesting that a high HbA1c at diagnosis is not inevitably associated with a poor outcome and may be driven by reversible glucose toxicity.

Residuals in space: Potential pitfalls and applications from single-institution survival analysis.

In practice, survival analyses appear in pharmaceutical testing, procedural recovery environments, and registry-based epidemiological studies, each reasonably assuming a known patient population. Less commonly discussed is the additional complexity introduced by non-registry and spatially-referenced data with time-dependent covariates in observational settings. In this short report we discuss residual diagnostics and interpretation from an extended Cox proportional hazard model intended to assess the effects of wildfire evacuation on risk of a secondary cardiovascular events for patients of a specific healthcare system on the California's central coast. We describe how traditional residuals obscure important spatial patterns indicative of true geographical variation, and their impacts on model parameter estimates. We briefly discuss alternative approaches to dealing with spatial correlation in the context of Bayesian hierarchical models. Our findings/experience suggest that careful attention is needed in observational healthcare data and survival analysis contexts, but also highlights potential applications for detecting observed hospital service areas.

Epidemiology of Injuries Among National Basketball Association Players: 2013-2014 Through 2018-2019.

BACKGROUND: Understanding the epidemiology of injuries to athletes is essential to informing injury prevention efforts. HYPOTHESIS: The incidence and impact of basketball-related injuries among National Basketball Association (NBA) players from 2013-2014 through 2018-2019 is relatively stable over time. STUDY DESIGN: Descriptive epidemiology study. LEVEL OF EVIDENCE: Level 3. METHODS: Injuries from 2013-2014 through 2018-2019 were analyzed using the NBA Injury and Illness Database from an electronic medical record system. Descriptive statistics were calculated for injuries by season, game-loss, and onset. Incidence rates were estimated using Poisson models and linear trend tests. RESULTS: Between 552 and 606 players participated in ≥1 game per season during the study. Annual injury incidence ranged from 1550 to 1892, with 33.6% to 38.5% resulting in a missed NBA game. Game-loss injury rates ranged from 5.6 to 7.0 injuries per 10,000 player-minutes from 2014-2015 through 2018-2019 (P = 0.19); the rate was lower in 2013-2014 (5.0 injuries per 10,000 player-minutes), partly due to increased preseason injury rates and transition of reporting processes. The 6-year game-loss injury rate in preseason and regular season games was 6.9 (95% CI 6.0, 8.0) and 6.2 (95% CI 6.0, 6.5) injuries per 10,000 player-minutes; the rate in playoff games was lower (P < 0.01) at 2.8 (95% CI 2.2, 3.6). Most (73%) game-loss injuries had acute onset; 44.4% to 52.5% of these involved contact with another player. CONCLUSION: From 2013-2014 through 2018-2019, over one-third of injuries resulted in missed NBA games, with highest rates of game-loss injuries in preseason games and lowest rates in playoff games. Most game-loss injuries had acute onset, and half of those involved contact with another player. CLINICAL RELEVANCE: These findings - through reliable data reporting by team medical staff in an audited system - can guide evidence-based injury reduction strategies and inform player health priorities.

Prescriptions of opioid-containing drugs in patients with chronic cough.

BACKGROUND: Chronic cough (CC) affects about 10% of adults, but opioid use in CC is not well understood. OBJECTIVES: To determine the use of opioid-containing cough suppressant (OCCS) prescriptions in patients with CC using electronic health records. DESIGN: Retrospective cohort study. METHODS: Through retrospective analysis of Midwestern U.S. electronic health records, diagnoses, prescriptions, and natural language processing identified CC - at least three medical encounters with cough, with 56-120 days between first and last encounter - and a 'non-chronic cohort'. Student's t-test, Pearson's chi-square, and zero-inflated Poisson models were used. RESULTS: About 20% of 23,210 patients with CC were prescribed OCCS; odds of an OCCS prescription were twice as great in CC. In CC, OCCS drugs were ordered in 38% with Medicaid insurance and 15% with commercial insurance. CONCLUSION: Findings identify an important role for opioids in CC, and opportunity to learn more about the drugs' effectiveness.

Development of Recommendations for the Digital Sharing of Notes With Adolescents in Mental Health Care: Delphi Study.

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.

New-onset Fibromyalgia After Total Knee Replacement in Patients With Osteoarthritis: A Propensity-score-matched Cohort Study in the United States.

BACKGROUND/AIM: The risk of new-onset fibromyalgia after total knee replacement (TKR) in osteoarthritis patients is not well-established. This study aimed to assess the risk of developing fibromyalgia post-TKR, considering potential variations across age and sex. PATIENTS AND METHODS: Utilizing a multicenter retrospective cohort design and data from the TriNetX research network, electronic health records of osteoarthritis patients who underwent TKR and the same number of matched controls were analyzed. Propensity-score matching was performed by matching critical confounders. Hazard ratios were evaluated to assess fibromyalgia risk in the TKR cohort compared to non-TKR controls. RESULTS: The hazard ratio of future fibromyalgia for the TKR cohort was 2.08 (95% confidence interval=1.74-2.49) for 1 year after the index date, 1.81 (95% confidence interval=1.62-2.02) for 3 years, and 1.69 (95% confidence interval=1.54-1.86) for 5 years compared with non-TKR controls. The significant association remained in sensitivity models and stratification analyses in different age and sex subgroups. CONCLUSION: Clinicians should be vigilant about the potential for fibromyalgia development post-TKR and consider tailored interventions; our findings emphasize the need for further research to elucidate underlying mechanisms and identify modifiable risk factors.

TREATMENT OF HIP AND KNEE PERIPROSTHETIC JOINT INFECTION REQUIRES EXTENSIVE ADMINISTRATIVE WORK.

INTRODUCTION: Treatment of periprosthetic joint infections (PJI) typically requires more resource utilization than primary total joint arthroplasty (TJA). This study quantifies the amount of time spent in the electronic medical record (EMR) for patients who have PJI requiring surgical intervention. METHODS: A retrospective analysis of EMR activity for 165 hip and knee PJI was performed to capture work during the preoperative and postoperative time periods. Independent sample t tests were conducted to compare total time based on procedure, age, insurance, health literacy, sex, race, and ethnicity. RESULTS: The EMR work performed by the orthopaedic team was 338.4 minutes (min) (SD [standard deviation] 130.3), with 119.4 minutes (SD 62.8) occurring preoperatively and 219.0 minutes (SD 112.9) postoperatively. Preoperatively, the surgeon's work accounted for 35.7 minutes (SD 25.4), mid-level providers 21.3 minutes (SD 15.9), nurses 38.6 minutes (SD 36.8), and office staff 32.7 minutes (SD 29.9). Infectious Disease (ID) colleagues independently performed 158.9 minutes (SD 108.5) of postoperative work. Overall, PJI of the knees required more postoperative work. Secondary analysis revealed that patients who have hip PJI and a BMI < 30 and patients < 65 years required more work when compared to the PJI of heavier and older individuals. There was no difference in total work based on insurance, health literacy, race, or ethnicity. CONCLUSION: Over 8 hours of administrative work is required for surgical management of PJI. Surgeons alone performed 451% more work for PJI during the preoperative period (7.9 versus 35.7min) compared to primary TJA. In efforts to provide best care for our sickest patients, much work is required perioperatively. This work is necessary to consider when assigning value and physician reimbursement.

Long-term cardiovascular outcomes in a population-based multicentric cohort of northern Portugal: Validation of the ESC/EAS prognostic risk classification.

BACKGROUND: Cardiovascular (CV) risk scores identify individuals at higher long-term risk of CV events that may benefit from more aggressive preventive interventions. OBJECTIVE: To assess the association of CV-risk categories and criteria with long-term CV events. METHODS: Observational cohort study between 2000-2019 on patients aged 40-80 years, followed by 14 primary care centers assisted by 1 hospital in Portugal. Follow-up began when electronic health records data allowed for CV-risk classification and dynamic reassessment per 2019 ESC/EAS Guidelines. Inclusion criteria required at least one appointment with a primary care physician within three years before follow-up initiation. We assessed the 10-year adjusted hazard-ratio of combined CV death and non-fatal Atherosclerotic Cardiovascular Disease (ASCVD) hospitalization, across SCORE risk categories and criteria, using Cox proportional hazards models adjusted for sex, age, competing comorbidities, and medication. RESULTS: The study included 161 681 observations from 87 035 unique patients. During the observation period, 71 787 patients were classified as low/moderate, 51 476 as high and 38 418 as very-high CV-risk categories. In the very-high group, prevalent comorbidities were hypertension (69%), hypercholesterolemia (69%) and type 2 diabetes (61%), and 13% were hospitalized for ASCVD. The adjusted 10-year hazard ratio of the composite of CV death or ASCVD hospitalization was 2.10 (95% CI: 1.91-2.32) for high-risk and 3.56 (95% CI: 3.21-3.96) for very-high-risk patients (low-risk as reference). CONCLUSION: Our study reinforces the prognostic relevance of CV-risk stratification for long-term prediction of CV death and ASCVD hospitalization in an unselected cohort, independently of sex, age, competing comorbidities and medication.

Codesigned standardised referral form: simplifying the complexity.

BACKGROUND: Referring providers are often critiqued for writing poor-quality referrals. This study characterised clinical referral guidelines and forms to understand which data consultant providers require. These data were then used to codesign an evidence-based, high-quality referral form. METHODS: This study used both observational and quality improvement approaches. Canadian referral guidelines were reviewed and summarised. Referral data fields from 150 randomly selected Ontario referral forms were categorised and counted. The referral guideline summary and referral data were then used by referring providers, consultant providers and administrators to codesign a referral form. RESULTS: Referral guidelines recommended 42 types of referral data be included in referrals. Referral data were categorised as patient demographics, provider demographics, reason for referral, clinical information and administrative information. The percentage of referral guidelines recommending inclusion of each type of referral data varied from 8% to 77%. Ontario referral forms requested 264 different types of referral data. Digital referral forms requested more referral data types than paper-based referral forms (55.0±10.6 vs 30.5±8.1; 95% CI p<0.01). A codesigned referral form was created across two sessions with 29 and 21 participants in each. DISCUSSION: Referral guidelines lack consistency and specificity, which makes writing high-quality referrals challenging. Digital referral forms tend to request more referral data than paper-based referrals, which creates administrative burdens for referring and consultant providers. We created the first codesigned referral form with referring providers, consultant providers and administrators. We recommend clinical adoption of this form to improve referral quality and minimise administrative burdens.

It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine.

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

Perspectives from neuro-oncology providers on patient access to electronic records: a survey study.

Aim: To evaluate the neuro-oncology providers' experience with patient online access to electronic records. Methods: Cross-sectional survey for physicians and advanced care providers within the field of neuro-oncology in the USA. Results: 65 providers completed the survey, from all major regions of the USA. 58% reported that at least once per month, patients contacted them outside of an office visit about provider notes or a laboratory or imaging finding accessed online. 54% of providers did not think that all laboratory results should be released automatically, and only 25% of providers thought that all radiology reads should be released immediately. 97% thought that some patients suffered substantial distress viewing test results prior to appointments. Qualitative responses aligned with the quantitative results. Conclusion: Most neuro-oncology providers are concerned about the immediate release of laboratory and imaging findings to patients without guidance.

Prior studies had investigated the perspectives of medical providers on patients having immediate access to medical records. However, almost none of them focus on neuro-oncology. In our study, we distributed a survey electronically to neuro-oncology providers across the USA to seek their perspectives. Our results show that most neuro-oncology providers found patients having immediate access to their records to be useful. However, they raised concerns about the immediate release of laboratory and imaging findings to patients without guidance. Our study also included free responses from the neuro-oncology providers that could help mitigate this concern.

The Telehealth chain: a framework for secure and transparent telemedicine transactions on the blockchain.

BACKGROUND: Blockchain technology provides a secure and decentralized platform for storing and transferring sensitive medical data, which can be utilized to enable remote medical consultations. AIM: A theoretical framework for creating a blockchain-based digital system created to facilitate telemedicine system. RESULTS: This paper proposes a theoretical framework based on Hyperledger fabric for creating a blockchain-based digital entity to facilitate telemedicine services. The proposed framework utilizes blockchain technology to provide a secure and reliable platform for medical practitioners to interact remotely with patient transactions. CONCLUSION: The blockchain will serve as a one-stop digital service to secure patient data, ensure privacy, and facilitate payments. The proposed framework leverages the existing Hyperledger fabric platform to build a secure blockchain-assisted telemedicine platform.