Electronic Medical Data Research Articles

TINJAUAN DATA SOSIAL UNTUK ENTRY DATA IDENTITAS PASIEN DI RSI JAKARTA SUKAPURA

Social data is used to complete the RME when officers identify new patients into the medical record database. social data helps fill in electronic medical record data. The purpose of this study is to review filling in social data to complete the identity of patients at the Islamic Hospital of Jakarta Sukapura. The research method used descriptive method with quantitative analysis of 81 samples of new outpatient identities. The results of the study There are already standard operating procedures for outpatient registration of new patients, the average completeness of social data for new patients is 62 samples (76%) and 19 samples are incomplete (24%), complete average RME data is complete 65 samples (80%) and incomplete 16 samples (20%). the filling of social data and RME is not in accordance with the minimum service standards set by PERMENKES, which is 100% filled. social data does not include all patient identity data in the RME. Identity data in RME is more complete than social data, in social data there are 21 items while in RME there are 25 items. Conclusion: social data has not been able to fully assist officers in completing electronic medical record patient identity data.

Deploying the Red Cross Red Crescent Health Information System (RCHIS) into a Type-One Fixed EMT: How the Use of a Custom Built Application for Electronic Medical Records and Data Reporting Improves Patient Care and Mandatory Reporting.

Introduction:RCHIS is an Electronic Medical Record (EMR) and Health Information System (HIS) that has been purpose-built for use by Red Cross Red Crescent (RCRC) Emergency Response Units (ERUs), which are the equivalent of Type 1 (fixed and mobile) and Type 2 facilities in the Emergency Medical Teams (EMT) classification.Method:A three day in-person super user training was held with 13 participants: 9 first aid volunteers, 2 nurses and 2 medical doctors. Seven of the delegates had experience using an EMR. These super users served as trainers for staff at the pilot.The pilot occurred with the Portuguese Red Cross (PRC) for the Peregrinação de Fátima, where 200,000 people were in attendance. The PRC was part of a wider coordination cell with the civil defense authority, who required live reporting from the three Type 1 fixed clinics PRC had set up.Results:In total there were 77 user accounts, of which, 243 patients were consulted with and either discharged or referred throughout the four day pilot.The delegates shared feedback directly and through a survey. 88% stated that RCHIS was ‘very easy’ to use with the majority of delegates requiring less than 20 minutes of training to be using the application and inputting patient data related to their user role. Additionally, after their training 95% of delegates stated they had sufficient training to use RCHIS to its full extent. Informal feedback from delegates was hugely positive, indicating that it was improving patient care and also continuity of care when a patient returned the next day.The civil defense authorities were able to utilize the real-time reporting to assist in their operational response. The application was well received by the wider civil defense authority.Conclusion:The first RCHIS pilot was very successful from both a technical and organizational perspective.

Clinical profile and demographic distribution of pigment dispersion syndrome: An electronic medical record-driven big data analytics from an eye care network in India

Objectives: The purpose of this study was to describe the demographics and clinical profile of pigment dispersion syndrome (PDS) in patients presenting to a multi-tier ophthalmology hospital network in India. Material and Methods: This cross-sectional hospital-based study included 2,961,706 new patients presenting between August 2010 and September 2021. Patients with a clinical diagnosis of PDS in at least one eye were included as cases. The data were collected using an electronic medical record system. Results: Overall, 403 (0.014%) patients were diagnosed with PDS. Three fourth of the patients were male (75.43%) and 91.81% had bilateral affliction. The most common age group at presentation was during the fourth decade of life with 100 (24.81%) patients. In the 773 eyes, 443 (57.31%) eyes had mild or no visual impairment (<20/70) and blindness (>20/400) in 81 (10.48%) eyes. Krukenberg spindle was seen in 445 (57.57%) eyes and iris transillumination defects in 33 (4.27%) eyes. About a third of the eyes, 241 (31.18%) eyes had an intraocular pressure (IOP) >21 mm of Hg at presentation. Three hundred and twenty-eight (42.43%) eyes were on more than one anti-glaucoma medication. YAG peripheral iridotomy was documented in 100 (12.94%) eyes. In the 46 (5.95%) eyes that required a glaucoma related surgical intervention, combined surgery was performed in 30 (3.88%) eyes and trabeculectomy in 16 (2.07%) eyes. Conclusion: PDS is more common in males presenting during the fourth decade of life and is predominantly bilateral. A third of the eyes have raised IOP and a tenth of them are affected with blindness at presentation.

Clinical Profile and Demographic Distribution of Acanthamoeba Keratitis: An Electronic Medical Record-Driven Data Analytics from an Eye Care Network in India

ABSTRACT Objective To describe the clinical profile of patients presented with Acanthamoeba keratitis (AK) to a multi-tier ophthalmology hospital network in India. Methods This cross-sectional hospital-based study included 1945339 new patients registering between September 2016 and May 2022. Patients with clinically confirmed diagnosis of AK in one or both eyes were included in the study. All the relevant data were documented using an electronic medical record (EMR) system. Results A total of 245 (0.013%) patients were diagnosed with AK and majority were male (62.86%) with unilateral (99.59%) affliction. The most common age group was during the fourth decade of life, 65 (26.53%) patients and predominantly were adults (95.51%). The prevalence of the infection was higher in patients from a lower socioeconomic status (43.27%) from rural geography (52.24%) and in agriculture-related work (28.16%). The most common inciting factor was injury with vegetative matter (8.98%), dust (7.76%) and contact lens wear (4.49%). The majority of the eyes had blindness (20/400 to 20/1200) in 116 (47.15%) eyes with a presenting visual acuity (logMAR) of 2.14 ± 1.04. Among the surgical interventions, therapeutic keratoplasty was performed in 41 (16.67%) eyes, penetrating keratoplasty in 22 (8.94%) eyes, and evisceration in 2 (0.81%) eyes. Conclusion AK more commonly affects males presenting during the fourth decade of life from lower socio-economic status and is predominantly unilateral. A fourth of the affected eyes underwent keratoplasty and the majority had significant visual impairment at presentation.

Cholinergic urticaria: search for predictors of course severity and response to therapy

BACKGROUND: Cholinergic urticaria has affected 0.0211.2% of the population. Young adults are the most frequently affected age group, and the long-term duration of the disease is typical. Urticarial elements can often be generalized, and systemic reactions are often present in patients with cholinergic urticaria, significantly reducing their quality of life. At present, no clear predictors of anaphylaxis and a severe disease course have been identified.
 AIM: To evaluate the clinical, laboratory, and pathogenetic features of patients with cholinergic urticaria and identify correlations between them, disease prognosis/severity, and effectiveness of therapy.
 MATERIALS AND METHODS: A retrospective-prospective analysis of data from the Moscow EMIAS electronic medical data system recorded between 2017 and 2021 was performed, of which 613 patients were presumed to have cholinergic urticaria by outpatient specialists. During the interview, 86 people qualified for and accepted the screening. Between January 2022 and December 2022, patients were invited to outpatient appointments/hospitalization and underwent a comprehensive evaluation. In 38 patients with a relevant trigger-associated history, the diagnosis of cholinergic urticaria was verified by provocation testing.
 RESULTS: Women predominated in the study (68.4%). The mean patient age was 26.5 years, the age at disease onset was 18 years, and the disease duration was 57.5 months. Allergic diseases were diagnosed in 50% of the patients. Anaphylaxis was recorded in 26% of the cases, and angioedema was significantly more frequently observed in these patients. Patients who tested positive with autologous serum were characterized by older age, longer disease duration, and lower efficacy with standard doses of antihistamines. In an analysis of the Dermatology Life Quality Index validated questionnaire, nearly 80% of the patients reported a moderate, strong, or extremely strong effect on their quality of life.
 CONCLUSIONS: The results showed a significant effect of cholinergic urticaria on the quality of life. This analysis reveals important correlations and patterns an association between early age at disease onset and longer disease duration, presence of angioedema and anaphylaxis, a positive test with autologous serum and duration of urticaria symptoms, and response to antihistamine therapy. Patients with a history of anaphylaxis must be identified as a separate high-risk group to increase the awareness of medical staff and educate such patients on the use of epinephrine.

Commentary: Clinical profile, demographic distribution, and outcomes of ocular siderosis: Electronic medical record-driven big data analytics from an eye care network in India.

Commentary: Clinical profile, demographic distribution, and outcomes of ocular siderosis: Electronic medical record-driven big data analytics from an eye care network in India.

Australian emergency department care for older adults diagnosed with low back pain of lumbar spine origin: a retrospective analysis of electronic medical record system data (2016–2019)

BackgroundIn Australian emergency departments, 30% of all back pain presentations are for older adults. Relatively little is known about the care that this population receives during an emergency department stay, including admission to hospital. The aim of this study is to describe emergency department management of older adults diagnosed with a lumbar spine condition and to determine predictors of healthcare use in this population.MethodsA retrospective analysis of electronic medical record data of adults aged ≥ 65 years with a lumbar spine discharge diagnosis. Demographic, clinical care (date and time of presentation and discharge, length of stay in the emergency department, mode of arrival, triage category, re-presentations to the emergency department (within 48 h), discharge mode, the administration of pain-relieving medicines, lumbar imaging, and laboratory tests) and costs data were extracted from the electronic medical record system. Descriptive analyses and multilevel mixed-effects logistic regression models were performed.ResultsOver the period January 2016 to December 2019 there were 4,093 presentations to emergency departments by older adults with a lumbar spine discharge diagnosis (82.0% were non-specific low back pain). Most were female (58.3%), 39.9% had some form of lumbar imaging, and 34.1% were admitted to hospital. The most administered pain medicines were opioid analgesics (67.1%), followed by paracetamol (63.9%) and NSAIDs (33.0%). Predictors of healthcare use and hospital inpatient admission were receiving a laboratory test and receiving any opioid. For the financial period 2019-20, the mean (SD) total cost of care per presentation was $5,629 ($11,982).ConclusionIn the emergency department, more than two thirds of older adults with a lumbar spine condition received opioid analgesics. They often received imaging and laboratory tests, had high costs and were admitted to hospital. Alternative pathways of care are needed to support older adults with low back pain, to receive guideline-concordant emergency department care and have good health outcomes.

Clinical profile, demographic distribution, and outcomes of ocular siderosis: Electronic medical record-driven big data analytics from an eye care network in India.

To describe the demographics, clinical profile, and outcomes of ocular siderosis in patients presenting to a multi-tier ophthalmology hospital network in India. This cross-sectional and hospital-based study included 3,082,727 new patients who presented between August 2010 and December 2021. Patients with a clinical diagnosis of ocular siderosis in at least one eye were included. Overall, 58 eyes of 57 patients (0.002%) were diagnosed with ocular siderosis. The majority were men (96.49%) and had unilateral (98.25%) affliction. The most common age group at presentation was during the third decade of life with 24 patients (42.11%). A clear history of ocular trauma was documented in 47 patients (81.03%). Major clinical signs included corneal pigment deposition in nearly half of the eyes (27/58 eyes, 46.55%), corneal scar (20/58 eyes, 34.48%), cataract (22/58 eyes, 37.93%) and retinal detachment (11/58 eyes, 18.96%). The intraocular foreign body (IOFB) was anatomically localized in a majority of the eyes (i.e., 45/58 eyes, 77.59%). The most common location of the IOFB was in the posterior segment (22/58 eyes, 37.93%). The eyes that underwent a vitreoretinal surgery with removal of IOFB had a slightly better BCVA (1.0 ± 1.01) when compared to eyes with non-removal of IOFB (1.58 ± 1.00). Ocular siderosis is a rare sight-threatening entity, with half of the affected eyes exhibiting severe visual impairment. Majority of the eyes in ocular siderosis will have a detectable IOFB. Surgical removal of IOFB may lead to a better visual gain when compared to non-removal.

Associations in Cigarette Smoking and Health Conditions by Race/Ethnicity Among a Diverse Sample of Patients Receiving Treatment in a Federally Qualified Health Care Setting in Chicago.

To examine the association of cigarette use and smoking-related health conditions by race/ethnicity among diverse and low-income patients at a federally qualified health center (FQHC). Demographics, smoking status, health conditions, death, and health service use were extracted from electronic medical data for patients seen between September 1, 2018, and August 31, 2020 (n=51,670). Smoking categories included everyday/heavy smoker, someday/light smoker, former smoker, or never smoker. Current and former smoking rates were 20.1% and 15.2%, respectively. Males, Black, White, non-partnered, older, and Medicaid/Medicare patients were more likely to smoke. Compared with never smokers, former and heavy smokers had higher odds for all health conditions except respiratory failure, and light smokers had higher odds of asthma, chronic obstructive pulmonary disease, emphysema, and peripheral vascular disease. All smoking categories had more emergency department visits and hospitalizations than never smokers. The associations between smoking status and health conditions differed by race/ethnicity. White patients who smoked had a greater increase in odds of stroke and other cardiovascular diseases compared with Hispanic and Black patients. Black patients who smoked had a greater increase in odds of emphysema and respiratory failure compared with Hispanic patients. Black and Hispanic patients who smoked had a greater increase in emergency care use compared with White patients. Smoking was associated with disease burden and emergency care and differed by race/ethnicity. Resources to document smoking status and offer cessation services should be increased in FQHCs to promote health equity for lower income populations.

The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network

How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical use in predicting the risk of concurrent medical use, most existing methods pay less attention to the temporal property of medical-use trajectory and the potential correlation between the different trajectories of medical use, resulting in limited concurrent multi-trajectory applications. To address the problem, we proposed a multi-stage neural network-based application mode of multi-dimensional time series data for feature learning of high-dimensional electronic medical data in adverse event prediction. We designed a synthetic factor for the multiple -trajectories of medical use with the combination of a Long Short Term Memory–Deep Auto Encoder neural network and bisecting k-means clustering method. Then, we used a deep neural network to produce two kinds of feature vectors for risk prediction and risk-related factor analysis, respectively. We conducted extensive experiments on a real-world dataset. The results showed that our proposed method increased the accuracy by 5%~10%, and reduced the false rate by 3%~5% in the risk prediction of concurrent medical use. Our proposed method contributes not only to clinical research, where it helps clinicians make effective decisions and establish appropriate therapy programs, but also to the application optimization of multi-dimensional time series data for big data analysis.

Effect of gabapentin on length of stay, opioid use, and pain scores in posterior spinal fusion for adolescent idiopathic scoliosis: a retrospective review across a multi-hospital system

BackgroundGabapentin has been adopted in Enhanced Recovery After Surgery protocols as a means to reduce opioid consumption while maintaining adequate post-operative analgesia. The purpose of our study was to review and compare changes in length of stay, opioid use, and patient reported pain scores after the addition of gabapentin into five, distinct pain protocols for posterior spinal fusion in adolescent idiopathic scoliosis.MethodsA retrospective review was completed using a database of electronic medical data from a single pediatric orthopedic healthcare system that was queried for patients with adolescent idiopathic scoliosis who underwent first-time posterior spinal fusion. Perioperative data including demographics, hospital length of stay, surgical details, opioid use, patient reported pain scores, and non-opioid analgesic use were collected.ResultsFrom December 2012 to February 2019, 682 hospitalizations for posterior spinal fusion in adolescent idiopathic scoliosis were identified with complete inpatient data; 49% were administered gabapentin. For the gabapentin cohort, the system saw no statistically significant effect on length of stay or pain averaged over POD#0–3. Opioid use was statistically lower averaged over POD#0–3. Individual sites saw variation on length of stay and opioid use compared to the system.ConclusionIn conclusion, system-wide data showed gabapentin containing protocols reduced opioid use while maintaining clinically equivalent analgesia. However, variations of individual site results make it difficult to conclude the degree to which gabapentin were responsible for this effect.

Using ICD-9 diagnostic codes for external validation of topic models derived from primary care electronic medical record clinical text data.

Background/Objectives: Unsupervised topic models are often used to facilitate improved understanding of large unstructured clinical text datasets. In this study we investigated how ICD-9 diagnostic codes, collected alongside clinical text data, could be used to establish concurrent-, convergent- and discriminant-validity of learned topic models. Design/Setting: Retrospective open cohort design. Data were collected from primary care clinics located in Toronto, Canada between 01/01/2017 through 12/31/2020. Methods: We fit a non-negative matrix factorization topic model, with K = 50 latent topics/themes, to our input document term matrix (DTM). We estimated the magnitude of association between each Boolean-valued ICD-9 diagnostic code and each continuous latent topical vector. We identified ICD-9 diagnostic codes most strongly associated with each latent topical vector; and qualitatively interpreted how these codes could be used for external validation of the learned topic model. Results: The DTM consisted of 382,666 documents and 2210 words/tokens. We correlated concurrently assigned ICD-9 diagnostic codes with learned topical vectors, and observed semantic agreement for a subset of latent constructs (e.g. conditions of the breast, disorders of the female genital tract, respiratory disease, viral infection, eye/ear/nose/throat conditions, conditions of the urinary system, and dermatological conditions, etc.). Conclusions: When fitting topic models to clinical text corpora, researchers can leverage contemporaneously collected electronic medical record data to investigate the external validity of fitted latent variable models.

Retracted: Diagnosis and Treatment Rules of Chronic Kidney Disease and Nursing Intervention Models of Related Mental Diseases Using Electronic Medical Records and Data Mining.

[This retracts the article DOI: 10.1155/2021/5187837.].

Quantum Digital Signatures (QDS): A novel approach to safeguarding electronic health records

Electronic Health Records (EHRs) form the backbone of modern healthcare, ensuring seamless access to a patient’s medical history and fostering effective patient-doctor relationships. Their digital nature, however, exposes them to myriad security threats, including unauthorized access, tampering, and fraudulent alterations. Traditional cryptographic techniques, while effective in the current digital landscape, might not stand robust against future threats, especially those posed by quantum computers. This paper introduces Quantum Digital Signatures (QDS) as a cutting-edge approach to address this burgeoning concern. QDS leverages the principles of quantum mechanics to provide an additional layer of security, making it computationally infeasible for adversaries to forge or alter signed messages, including sensitive EHR data. Unlike conventional digital signatures that depend on the presumed difficulty of certain mathematical problems, the security attributes of QDS stem from the fundamental laws of quantum physics, ensuring a higher level of integrity and authenticity. We present a comprehensive overview of the QDS protocol and its integration into current EHR systems. Our comparative analysis with existing cryptographic techniques illuminates the advantages of QDS, especially in preventing undetected alterations and ensuring non-repudiation. Practical deployment challenges, including quantum device imperfections and potential integration bottlenecks, are also addressed. The adoption of Quantum Digital Signatures in safeguarding EHRs ushers in a new era of medical data security. While the large-scale implementation of such quantum technologies is still in its infancy, proactive research and early adoption are pivotal in preparing healthcare systems for future challenges. Through this research, we advocate for the broader integration of quantum cryptographic techniques, such as QDS, to uphold the sanctity and trustworthiness of electronic medical data in the face of evolving digital threats.

619: THE IMPACT OF NEIGHBORHOOD FACTORS ON PATIENT DISEASE SEVERITY ON PRESENTATION TO THE PICU

Introduction: Health disparities yield worse health outcomes for critically ill patients. Neighborhoods capture social and physical attributes experienced by residents. Increasingly, neighborhood-level geographic variations in health disparities have been reported. Individuals are commonly clustered into neighborhoods based on socioeconomic status. The impact of sociodemographic factors on outcomes for PICU patients is understudied. Few studies delve into the specific neighborhood characteristics affecting disease severity on PICU admission. To address this gap, we conducted a retrospective study exploring the association between geography and disease severity on PICU presentation, with sociodemographic neighborhood data and clinical data as additional predictors. We anticipate patients with higher disease severity will be clustered in low opportunity neighborhoods. Methods: Electronic medical record patient data were collected for non-elective admissions to the PICU from Jan 2013 to Dec 2019. The peak Pediatric Logistic Organ Dysfunction-2 (PELOD-2) score for the first 72 hours of admission was calculated for each encounter. Addresses were geocoded into census tracts. The Childhood Opportunity Index (COI), Social Vulnerability Index (SVI), and Neighborhood Disorder Index (NDI) were matched to each census tract. We report descriptive analyses, Spearman correlation coefficients for continuous variables, and Kruskal-Wallis test results for categorical variables. Results: There were 8497 unique patient encounters for 5874 distinct patients. Data were skewed right with a PELOD-2 score mean of 3.2 and median of 3.0. There was no correlation between the SVI (p=0.129), COI (p=0.957) or NDI (p=0.363) and peak PELOD-2 score. There was a correlation between age (rho 0.102, p< 0.001) and number of comorbidities (rho 0.373, p< 0.001) and peak PELOD-2 scores. There was a difference between median PELOD-2 scores for varying races and insurance types (p< 0.001). Conclusions: There was no association between neighborhood resources and peak PELOD-2 scores; however, there was a difference in mean PELOD-2 scores across varying races and insurance types. This indicates a need for more analyses to evaluate the association between neighborhood indices and peak PELOD-2 scores when accounting for other sociodemographic variables.

Exploring the appropriateness of antibiotic prescribing for dental presentations in Australian general practice-A pilot study.

Dental pain is a common presentation in general medical practice for which patients are often prescribed antibiotics. The aim of this pilot study was to assess prescriptions by general medical practitioners (GPs) for dental presentations in Australia. Anonymised electronic medical data relating to antibiotic prescribing across 2 periods (3 August-30 November 2019 and 3 August-30 November 2020) were extracted from 8 general practices in Victoria that contributed data to the University of Melbourne's Data for Decisions programme. Extracted de-identified data included antibiotics prescribed for dental presentations, practice site number, visit date, patient age, sex, reason for prescription, reason for visit and the regimen of the antibiotic. Appropriateness of the prescribed antibiotic was assessed against the Australian national dental guidelines Therapeutic Guidelines Oral and Dental. From 13 641 recorded prescriptions, 178 were recorded for dental presentations, representing 1.3% of all antibiotic prescriptions. When assessed against the guidelines, 23.6% (n= 42) were considered appropriate. The top 3 most commonly prescribed antibiotics were amoxicillin (n =84, 47.2%), amoxicillin/clavulanic acid (n =52, 29.2%) and metronidazole (n =15, 8.4%). The reasons for inappropriate prescribing were as follows: third-line treatment being chosen, inappropriate spectrum, incorrect regimen or prescribing an antibiotic not recommended in the guidelines. These results indicate a need to better understand the potential patient drivers for antibiotic prescription for GPs and patients, and resources GPs need to manage dental presentations. These results will inform the codesign of targeted interventions to address any educational gaps and barriers to accessing dental treatment and thus improving antibiotic prescribing.

Improving Dementia Screening in India with Telemedicine and Artificial Intelligence: A Scalable Solution to Address Clinical Gaps in Care

AbstractBackgroundOver 4.4 million people live with Alzheimer’s and related dementia (ADRD) in India. There is significant clinical service gap of 90% and only 1 in 10 patients affected with ADRD in India receives screening, diagnosis, treatment, and care. Combining telemedicine with an “Expert system,” can help evaluate, screen, and triage patients with ADRD and reduce the time to diagnosis. We demonstrate early results from a feasibility study that uses state‐of‐the‐art Artificial Intelligence (AI), data analytics, and a HIPAA‐compliant Telehealth platform to screen patients over 65 years in India for ADRD.MethodWe integrated AI‐based platform and a standardized cognitive test into the telemedicine‐based primary‐care workflow. A primary care provider (PCP) screened every consenting patient aged 65 years and older on a telemedicine platform in India. The screening used an algorithm‐based intake tool, baseline Mini‐Mental Status Exam (MMSE), and clinical judgment of the PCP. We compared the final screening assessment generated by AI with the MMSE evaluation and clinical judgment of PCP for nine patients.ResultsNine patients (4 females, 5 males) in age group of 65‐90 years consented and enrolled in this initial study. Using the intake data as inputs, the AI model predicted MCI in 5 out of 9 patients (55%) and mild ADRD in 4 out of 9 patients (44%). The clinical severity from the tele‐encounter was rated as mild ADRD in 3 out of 9 patients (33%) and no ADRD/Mild Cognitive Impairment in 6 out of 9 patients (66%). Neuropsychological testing with the MMSE demonstrated scores from 24‐30 in 7 out of 9 patients (77%) and &lt;24 in 2 out of 9 patients (22%). The AI model accurately predicted the severity in 6 out of 9 patients (66.6%) of cases.ConclusionIt is feasible to combine AI and telemedicine to improve ADRD screening and clinical service gaps for healthcare delivery in India. AI severity models also demonstrated adequate predictability to triage patients who have cognitive impairment using digital intake data. The accuracy of the AI prediction models may be further improved with supplemental electronic medical data and translating the intake to a local language.

Is there an association between depression, anxiety disorders and COVID-19 severity and mortality? A multicenter retrospective cohort study conducted in 50 hospitals in Germany

BackgroundThe aim of this retrospective cohort study was to investigate associations between depression and anxiety disorder and the risk of COVID-19 severity and mortality in patients treated in large hospitals in Germany. MethodsThis retrospective study was based on anonymized electronic medical data from 50 public healthcare service hospitals across Germany. Multivariable logistic regression models were used to study associations between depression, anxiety and mechanical ventilation and mortality due to COVID adjusted for age, sex, time of COVID-19 diagnosis, and pre-defined co-diagnoses. ResultsOf 28,311 patients diagnosed with COVID-19, 1970 (6.9%) had a diagnosis of depression and 369 (1.3%) had a diagnosis of anxiety disorder prior to contracting COVID-19. While multivariable logistic regression models did not indicate any association between depression diagnosis and the risk of mechanical ventilation, depression was associated with a decreased risk of mortality (OR: 0.71; 95% CI: 0.53–0.94). There was no association between anxiety disorders and risk of mortality, but there was a strong positive association between anxiety disorders and the risk of mechanical ventilation (OR: 2.04; 95% CI: 1.35–3.10). ConclusionIn the present study, depression and anxiety disorder diagnoses were not associated with increased COVID-19 mortality. Anxiety disorder was strongly associated with an increased risk of mechanical ventilation. Further studies are needed to clarify how depression and anxiety disorders may influence COVID-19 severity and mortality.

DEVELOPMENT OF THE PATIENT-IMPORTANT OUTCOMES NATIONAL DATA REPOSITORY (PIONEER) FOR HEREDITARY ANGIOEDEMA (HAE)

<h3>Introduction</h3> Here we describe the development and general characteristics of PIONEER-HAE, an enhanced database combining electronic medical data with insight from chart review, for the purpose of informing future studies. <h3>Methods</h3> Data source: Continuity of care documents (CCD), EMR (labs, procedures, treatment), and visit notes specific to patients in care by the Consortium of Independent Immunology Clinics (CIIC). Data extraction/collection process. [FIGURE] Unstructured data were extracted into electronic forms by clinically-trained scribes and included attack details, on demand treatment, symptoms onset and diagnosis, family history, and comorbidities. Form data were subjected to logical checks, random audit (1-2%), and tertiary data review after combining with CCD and EMR data. Inferred HAE diagnosis, based on treatment, ICD10 D84.1, and/or SNOMED 24743004, was confirmed or corrected by source physicians. <h3>Results</h3> As of Jun 2022, PIONEER-HAE contained data for 480 HAE-confirmed patients in care at 12 practices across 11 US states. Population characteristics: female 68% (327), mean (median) age 43 (42) years with 65% ≤50 years. Scribes reviewed visit notes for 265 patients and 1957 visits representing 1024 patient-years. For this subset, 83% (221/265) and 56% (148/265) patients had a visit in the last 36 or 12 months, respectively. HAE attacks were documented for 93% (246/265) patients at 69% (1358/1957) visits. Attack details included number/frequency (41%; 804/1358 visits), body location (74%; 1008/1358 visits), on-demand medication (72%, 971/1358 visits), and severity/resolution (90%; 1227/1358 visits). <h3>Conclusion</h3> PIONEER-HAE combines visit information with fielded data residing in EMR/CCD to enable better understanding of patient-important outcomes.

Security and Privacy for Healthcare Blockchains

Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This article focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.