Electronic Healthcare Record Research Articles

Data Verifiable Personalized Access Control Electronic Healthcare Record Sharing Based on Blockchain in IoT Environment

Data Verifiable Personalized Access Control Electronic Healthcare Record Sharing Based on Blockchain in IoT Environment

Real world data analysis of frontotemporal dementia: Implications for future clinical research

Frontotemporal dementia (FTD) is a progressive decline of cognitive abilities associated with other neuropsychiatric comorbidities. A real-world data (RWD) analysis of a large electronic healthcare records (EHR) database identified the comorbidities of FTD. Deidentified EHRs in the TriNetX Network database from >155,000,000 individuals in the United States established an FTD Cohort (ICD-10 Code G31.0) of adult patients who visited a healthcare provider in 2022. The non-FTD cohort were age-matched individuals who had not received a diagnosis of ICD-10 Code G31.0, and who had visited a healthcare provider in 2022. The median age of both cohorts was 73 years. A comparative analysis was performed between the FTD and non-FTD cohorts. There were 6660 individuals (aged ≥18) with FTD and 11,810,060 individuals (aged ≥63) without a diagnosis of FTD, with healthcare visits in 2022. There were 25 ICD-10 Codes for disorders that were present in >10% of FTD patients, with a Relative Risk (RR) of ≥2.0 compared the non-FTD cohort. Multiple neuropsychiatric disorders had RRs ≥ 2.0, with minimal evidence for significant involvement of other organ systems. These data document that FTD, as known previously, is associated with multiple neuropsychiatric comorbidities. There was minimal evidence of comorbid involvement of other organ systems. These data provide a baseline of general FTD symptoms for the rapidly evolving analysis of genetic subvariants of FTD. These data also provide insights into the clinical management of FTD, as well as recommendations for specific endpoints in clinical trials.

The Diagnostic Odyssey in Children and Adolescents With X-linked Hypophosphatemia: Population-Based, Case-Control Study.

X-linked hypophosphatemia (XLH) is a rare genetic disorder causing renal phosphate wasting, which predicates musculoskeletal manifestations such as rickets. Diagnosis is often delayed. To explore the recording of clinical features, and the diagnostic odyssey of children and adolescents with XLH in primary care electronic healthcare records (EHRs) in the United Kingdom. Using the Optimum Patient Care Research Database, individuals aged 20 years or younger after January 1, 2000, at date of recorded XLH diagnosis were identified using Systematized Nomenclature of Medicine Clinical Terms (SNOMED)/Read codes and age-matched to 100 controls. Recording of XLH-related clinical features was summarized then compared between cases and controls using chi-squared or Fisher's exact test. In total, 261 XLH cases were identified; 99 met the inclusion criteria. Of these, 84/99 had at least 1 XLH-related clinical feature recorded in their primary care EHR. Clinical codes for rickets, genu varum, and low phosphate were recorded prior to XLH diagnosis in under 20% of cases (median of 1, 1, and 3 years prior, respectively). Rickets, genu varum, low phosphate, nephrocalcinosis, and growth delay were significantly more likely to be recorded in cases. This characterization of the EHR phenotypes of children and adolescents with XLH may inform future case-finding approaches to expedite diagnosis in primary care.

Delirium is Under-Detected on Routine Screening with the CAM: a Sub-Study from World Delirium Awareness Day

Background Delirium is an acute change in mental status that affects more than 2.6 million hospitalised adults annually. Patients who experience delirium suffer extended hospital stays and increased mortality and morbidity. Delirium screening is fundamental in driving prevention and early detection. This project aimed to examine delirium assessment, recognition for patients over 18, barriers to implementing delirium prevention treatment, and report qualitative findings for delirium care. Method In mid-March 2023, 11 Clinical Nurse Leaders in the Southeast Region of the United States participated in a study to increase awareness of delirium called World Delirium Awareness Day. Data was collected using an online survey to assess unit census, the number of CAM assessments, and positive CAM screening at 8:00 A.M (+/- 4 hrs) and 8:00 PM (+/- 4 hrs). Patients were defined as having delirium if their electronic healthcare record documentation reflected at least one occurrence of a positive CAM score. The survey comprised 39 “select all that apply” questions and three open-ended questions. Results Results show 418 (64%) CAM assessments were completed. Greater compliance was found during the morning assessment than the evening shift. Differences in nursing practice and opinions were also identified between General and High Acuity Units. Among patients receiving assessments, delirium recognition was 2% (8:00 A.M.) and 3% (8:00 P.M.). There was a significant difference in the use of multi-professional daily goals between the two unit types as a non-pharmacologic intervention for delirium (General: 0% vs. High Acuity: 100%, p=.003). Barriers identified: shortage of personnel, interprofessional communication gaps, and difficult-to-assess patients. Recommendations for delirium care include staff education and implementing an EHR tool to remind nurses to assess and document delirium. Conclusion Findings from this study reveal the need for staff education, support, and the enhancement of resources to promote delirium prevention.

Implementing American Academy of Neurology Quality Measures in Antigua Using Quality Improvement Methodology.

The American Academy of Neurology has developed quality measures related to various neurologic disorders. A gap exists in the implementation of these measures in the different health care systems. To date, there has been no electronic health care record nor implementation of quality measures in Antigua. Therefore, we aimed to increase the percent of patients who have epilepsy quality measures documented using standardized common data elements in the outpatient neurology clinic at Sir Lester Bird Medical Center from 0% to 80% per week by June 1, 2022 and sustain for 6 months. We used the Institute for Health care Improvement Model for Improvement methodology. A data use agreement was implemented. Data were displayed using statistical process control charts and the American Society for Quality criteria to determine statistical significance and centerline shifts. Current and future state process maps were developed to determine areas of opportunity for interventions. Interventions were developed following a "Plan-Do-Study-Act cycle." One intervention was the creation of a RedCap survey and database to be used by health care providers during clinical patient encounters. Because of multiple interventions, we achieved a 100% utilization of the survey for clinical care. Quality improvement (QI) methodology can be used for implementation of quality measures in various settings to improve patient care outcomes without use of significant resources. Implementation of quality measures can increase efficiency in clinical delivery. Similar QI methodology could be implemented in other resource-limited countries of the Caribbean and globally.

Real-world analyses of mortality risk after androgen deprivation therapy initiation in Black vs. White patients with prostate cancer.

84 Background: 2 studies found significantly longer overall survival in Black vs. White patients with metastatic castration-resistant prostate cancer (PCa): a 2019 meta-analysis of 9 phase III trials and a 2020 registry study. Our real-world data study compared all-cause mortality risk for Black vs. White PCa patients. Compared to the prior studies, our study encompasses a broader scope and is not exclusive to men with castration-resistant PCa. Methods: Data were collected from the Decision Resources Group Real World Evidence repository, which links medical and prescription claims, and US Electronic Healthcare Records. The analysis set included PCa patients who received ≥1 androgen deprivation therapy (ADT) injection between 1991-2020. Cox regression was used to compare all-cause mortality rates between White and Black patients. Multivariable regression model accounted for the following variables: baseline metastasis, BMI, oncology vs. urology setting, antagonist vs. agonist, personal major adverse cardiovascular event (MACE) history, tobacco history, baseline PSA (>4 vs. ≤4 ng/mL), race (White vs. Black), statin use, increasing age per year, ethnicity (non-Hispanic vs. Hispanic), increasing ADT exposure per year, diabetes, hypertension, and family MACE history. Results: 44,439 patients were included for the all-patient analyses. 34,762 patients were included in the Black vs White analyses: 5,817 and 28,945 were Black and White, respectively. Overall, mortality risk was 2.6% and 17% at 1 and 4 years after ADT initiation, respectively. Mortality risk after ADT initiation was 1.6% and 2.6% at 1 year and 11.7% and 18.1% at 4 years for Black and White patients, respectively. Both unadjusted (HR=1.66, 95% CI 1.53-1.80, p<0.05) and adjusted (HR=1.24, 95% CI 1.01-1.52, p<0.05) mortality risks were higher for White vs. Black patients. Conclusions: All-cause mortality incidence was higher in White vs. Black patients. Adding to the body of evidence, our research also reveals that Black race is associated with a protective effect on survival for all-cause mortality in men undergoing ADT. Potential hypotheses for higher mortality in White vs. Black patients include survival bias to MACE (i.e., Black patients may have died from PCa before having MACE), survival bias to be diagnosed with PCa (i.e., Black patients may have higher rate of CV death and kidney failure prior to PCa diagnosis), and protective effects of higher BMI in Black patients against cancer cachexia. The large size (~45,000 patients from a database with >300 million patients), long follow-up (10 years for some patients), recent clinical experience (99% from 2010-2020), and diversity of the dataset give weight to the results being an accurate representation of current clinical experience. Future studies should confirm our findings that White PCa patients have higher mortality risk and investigate the above hypotheses.

Drug Repurposing in Crohn's Disease Using Danish Real-World Data.

Drug repurposing, utilizing electronic healthcare records (EHRs), offers a promising alternative by repurposing existing drugs for new therapeutic indications, especially for patients lacking effective therapies. Intestinal fibrosis, a severe complication of Crohn's disease (CD), poses significant challenges, increasing morbidity and mortality without available pharmacological treatments. This article focuses on identifying medications associated with an elevated or reduced risk of fibrosis in CD patients through a population-wide real-world data and artificial intelligence (AI) approach. Patients aged 65 or older with a diagnosis of CD from 1996 to 2019 in the Danish EHRs were followed for up to 24 years. The primary outcome was the need of specific surgical procedures, namely proctocolectomy with ileostomy and ileocecal resection as proxies of intestinal fibrosis. The study explored drugs linked to an increased or reduced risk of the study outcome through machine-learning driven survival analysis. Among the 9179 CD patients, 1029 (11.2%) underwent surgery, primarily men (58.5%), with a mean age of 76 years, 10 drugs were linked to an elevated risk of surgery for proctocolectomy with ileostomy and ileocecal resection. In contrast, 10 drugs were associated with a reduced risk of undergoing surgery for these conditions. This study focuses on repurposing existing drugs to prevent surgery related to intestinal fibrosis in CD patients, using Danish EHRs and advanced statistical methods. The findings offer valuable insights into potential treatments for this condition, addressing a critical unmet medical need. Further research and clinical trials are warranted to validate the effectiveness of these repurposed drugs in preventing surgery related to intestinal fibrosis in CD patients.

Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an opportunity to analyse fall risk factor trajectories through Natural Language Processing techniques, specifically dynamic topic modelling (DTM). This study aims to uncover fall-related topics for new fallers and track their evolving trends leading up to falls. This case-cohort study utilised primary care EHR data covering information on older adults between 2016 and 2019. Cases were individuals who fell in 2019 but had no falls in the preceding three years (2016-18). The control group was randomly sampled individuals, with similar size to the cases group, who did not endure falls during the whole study follow-up period. We applied DTM on the clinical notes collected between 2016 and 2018. We compared the trend lines of the case and control groups using the slopes, which indicate direction and steepness of the change over time. A total of 2,384 fallers (cases) and an equal number of controls were included. We identified 25 topics that showed significant differences in trends between the case and control groups. Topics such as medications, renal care, family caregivers, hospital admission/discharge and referral/streamlining diagnostic pathways exhibited a consistent increase in steepness over time within the cases group before the occurrence of falls. Early recognition of health conditions demanding care is crucial for applying proactive and comprehensive multifactorial assessments that address underlying causes, ultimately reducing falls and fall-related injuries.

Patterns of multimorbidity in primary care electronic health records: A systematic review.

Multimorbidity, the coexistence of multiple chronic conditions in an individual, is a complex phenomenon that is highly prevalent in primary care settings, particularly in older individuals. This systematic review summarises the current evidence on multimorbidity patterns identified in primary care electronic health record (EHR) data. Three databases were searched from inception to April 2022 to identify studies that derived original multimorbidity patterns from primary care EHR data. The quality of the included studies was assessed using a modified version of the Newcastle-Ottawa Quality Assessment Scale. Sixteen studies were included in this systematic review, none of which was of low quality. Most studies were conducted in Spain, and only one study was conducted outside of Europe. The prevalence of multimorbidity (i.e. two or more conditions) ranged from 14.0% to 93.9%. The most common stratification variable in disease clustering models was sex, followed by age and calendar year. Despite significant heterogeneity in clustering methods and disease classification tools, consistent patterns of multimorbidity emerged. Mental health and cardiovascular patterns were identified in all studies, often in combination with diseases of other organ systems (e.g. neurological, endocrine). These findings emphasise the frequent coexistence of physical and mental health conditions in primary care, and provide useful information for the development of targeted preventive and management strategies. Future research should explore mechanisms underlying multimorbidity patterns, prioritise methodological harmonisation to facilitate the comparability of findings, and promote the use of EHR data globally to enhance our understanding of multimorbidity in more diverse populations.

Unscheduled Emergency Department Revisits Within 48 Hours of Discharge.

This study aimed to analyze early revisits (within 48 hours of discharge) in an Emergency Department. Among the 178,295 visits, 11,686 were revisits, resulting in a rate of 6.55% (95%CI 6.43-6.67). A total of 1,410 revisits required hospitalization, and 252 were due to preventable errors (17.87%). These errors were mainly related to an inadequate therapeutic plan at discharge (47.22%), an incomplete diagnostic process (29.37%), and misdiagnoses (13.10%). These findings represent a technology-enabled clinical audit tool. Electronic Healthcare Records have the potential to: provide quality metrics of hospital performance, help to keep revisit rates updated (assessment through a real-time dashboard), and improve clinical management (by transparency initiatives about errors, and a supportive learning environment regarding lessons learned).

Reducing Time to Electroencephalography in Pediatric Convulsive Status Epilepticus: A Quality Improvement Initiative

BackgroundPediatric convulsive status epilepticus (CSE) is a neurological emergency utilizing electroencephalography (EEG) to guide therapeutic interventions. Guidelines recommend EEG initiation within one hour of seizure onset, but logistic and structural barriers often lead to significant delays. We aimed to reduce the time to EEG in pediatric CSE. MethodsFrom 2017 to 2022, we implemented process improvements, including EEG order sets with priority-based timing guidance, technologist workflow changes, a satisfaction survey, and feedback from key stakeholder groups, over five plan-do-study-act (PDSA) cycles. Seizure start time, time of EEG order, and time to EEG initiation were extracted. Time to interpretable EEG was determined from manual review of the EEG tracing. ResultsTime from EEG order to interpretable EEG decreased by nearly 50%, from a median of 90 minutes to 48 minutes. There were clinically and statistically significant improvements in time from EEG order to EEG initiation, time from EEG order to interpretable EEG, and EEG start to interpretable EEG. Ongoing provider education and guidance enabled improvements, whereas a new electronic health care record negatively impacted electronic ordering. EEG technologists reported that they understood the importance of emergent EEG for clinical care and did not find that the new workflow caused excessive disruption. ConclusionsTimely access to EEG for pediatric patients with CSE can be improved through clinical processes that use existing devices and that maintain the benefits of full-montage EEG recordings. Similar process improvement efforts may be generalizable to other institutions to increase adherence to guidelines and provide improved care.

Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 24% of variants. One hundred and fifty-six clinically actionable variant reinterpretations were made. A total of 739 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot-spots for pathogenic genetic variation such as PEX6-associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet-Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA-approved therapeutics. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.

Iraqi Population Trusts in Electronic Healthcare Records: A Cross-sectional Study.

This study investigated the perceptions of Iraqi patients regarding Electronic Healthcare Records (EHRs) in terms of trust and propensity to share and exchange medical and personal information and data within the healthcare ecosystem. During the period of April to June 2022, a researcher-assisted questionnaire was disseminated to adult Iraqi patients attending public or private healthcare facilities in a subset of Iraqi governorates. Data collection was followed by descriptive and inferential analyses. In total, 552 respondents filled out the questionnaire. The findings revealed that 71.6% of respondents were conversant with EHRs and trusted them as data collection and storage systems. In addition, 10% of respondents did not want their EHRs to be shared between healthcare professionals and institutions. However, only 3.6% of participants were willing to share all of their personal information with healthcare professionals. Female respondents were considerably more willing to share their full names with healthcare professionals than male respondents, despite the society's reputation for conservatism. The findings of this study highlighted the necessity of tailoring initiatives to enhance patients' trust in EHRs and their interactions with healthcare professionals other than medical physicians.

UK Electronic Healthcare Records for Research: A Scientometric Analysis of Respiratory, Cardiovascular, and COVID-19 Publications.

Routinely collected electronic healthcare records (EHRs) document many details of a person's health, including demographics, preventive services, symptoms, tests, disease diagnoses and prescriptions. Although not collected for research purposes, these data provide a wealth of information which can be incorporated into epidemiological investigations, and records can be analysed to understand a range of important health questions. We aimed to understand the use of routinely collected health data in epidemiological studies relating to three of the most common chronic respiratory conditions, namely: asthma, chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD). We also characterised studies using EHR data to investigate respiratory diseases more generally, relative to cardiovascular disease and COVID-19, to understand trends in the use of these data. We conducted a search of the Scopus database, to identify original research articles (irrespective of date) which used data from one of the following most frequently used UK EHR databases: Clinical Practice Research Datalink (including General Practice Research Database (CPRD's predecessor)), The Health Improvement Network and QResearch, defined through the presence of keywords. These databases were selected as they had been previously included in the works of Vezyridis and Timmons. A total of 716 manuscripts were included in the analysis of the three chronic respiratory conditions. The majority investigated either asthma or COPD, whilst only 28 manuscripts investigated ILD. The number of publications has increased for respiratory conditions over the past 10 years (888% increase from 2000 to 2022) but not as much as for cardiovascular diseases (1105%). These data have been used to investigate comorbidities, off-target effects of medication, as well as assessing disease incidence and prevalence. Most papers published across all three domains were in journals with an impact factor less than 10.

Secure Electronics Medical Infrastructure for Healthcare 4.0: A Voice Identity Management-Based Approach

Securing Electronic Medical Records for Healthcare 4.0 is one of the prime requirements in the medical care service domains. Healthcare 4.0 refers to the evolution process of healthcare systems that leverage digital technologies, connectivity, and data-driven insights to provide more personalized and efficient care. With the increasing adoption of electronic healthcare records and the growing importance of data in the healthcare sector, ensuring the privacy of patient data becomes paramount. To ensuring the security for healthcare 4.0 in the current work a voice authentication-based system is devised. This enables the protection of electronic healthcare records (EHRs) in the association of healthcare 4.0. More precisely here the voice authentication is used as an additional layer of security for accessing and managing EHRs. The Voice authentication can also be categorized as the voice biometric. This is a technology that uses unique voice characteristics and patterns to verify the identity of individuals. The result establishes the innovative smart security arrangement for the Healthcare 4.0 using the voice authentication-based identity management infrastructure; and a modified version of asymmetric cryptosystem has been used inside the blockchain. The simulation outcome indicates better performance in the form of block generation time, block number, ensure the transaction, and minimize the latency of the transaction.

Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study

IntroductionHeart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However,...

Using Routinely Collected Electronic Healthcare Record Data to Investigate Fibrotic Multimorbidity in England [Response to Letter

Using Routinely Collected Electronic Healthcare Record Data to Investigate Fibrotic Multimorbidity in England [Response to Letter

Trends and Futuristic Applications of Big Data and Electronic Health Record Data in Empowering Constructive Clinical Decision Support Systems

Modern healthcare benefits significantly from big data, electronic healthcare record data technologies, and artificial intelligence, which provide robust tools for the collection and analysis of vast and diverse datasets originating from various sources, such as clinical care, administration, and research. This advancement enables the creation of information technology infrastructures that facilitate the realization of the "Learning Healthcare System Cycle," wherein healthcare practice and research seamlessly intertwine in a synergistic manner. This review focuses on illustrating how the integration of extensive data collections, empowered by big data, can enhance clinical decision-making and advance biomedical research. Most importantly, electronic health records offer several benefits, including heightened accessibility to patient information, enhanced interdisciplinary communication, improved continuity of care, legible documentation, minimized duplication, and increased efficiency. The incorporation of computerized physician order entry within electronic medical records contributes to patient safety by mitigating medication errors and offering clinical guidance through prompts and alerts during electronic order entry. Furthermore, when evidence-based clinical decision support is integrated with electronic health records, it serves as a valuable tool for guiding healthcare providers and clinicians in aligning their clinical practices with meaningful use standards and compliance with quality metrics. This review also outlines the contemporary application of clinical decision support systems in the field of medicine. Its types, their existing practical applications with documented effectiveness, prevalent challenges, and potential adverse consequences. The review concludes by offering evidence-based guidelines aimed at mitigating risks associated with support systems design, implementation, evaluation, and ongoing maintenance.

S3EF-HBCAs: Secure and Sustainable Software Engineering Framework for Healthcare Blockchain Applications.

Blockchain applications in healthcare have grown rapidly. They include record-keeping, clinical trials, medical supply chains, patient monitoring, etc., where blockchain characteristics are needed to improve safety, privacy, and security. Blockchain technology is one of the most significant disruptive technologies today. However, Porru et al.1 reported that it lacks processes, tools, and techniques. Therefore, this paper provides a systematic framework for a secure and sustainable software engineering framework for healthcare blockchain applications (S3EF-HBCA). S3EF-HBCA is a significant contribution that includes requirements engineering for healthcare, business process modeling for healthcare, domain modeling for healthcare, a reference architecture for healthcare, and validation by a case study on electronic healthcare record management system (EHR), and simulation with business process modeling notation (BPMN) tools. The simulation shows it has taken 10.45 min to process 100 instances of real-time data and service requests. The overall result shows encouragement regarding process, tools, standards, and testing.

Axiomatizing SNOMED CT Disorders: Should There Be Room for Interpretation?

SNOMED CT is a large concept-based terminology designed according to epistemic, semantic and pragmatic principles relevant to clinicians. Its goal is structured clinical reporting in electronic healthcare records (EHRs). The Basic Formal Ontology (BFO) is an ontology designed on the basis of types claimed to exist in reality based on a domain-independent ontological theory. Its goal is faithful representation of reality within that theory. The Ontology for General Medical Science (OGMS) extends the BFO by providing definitions for types relevant within the clinical domain. Combining SNOMED CT with the ontological rigor of BFO and OGMS might improve clinical reporting by, f.i., preventing data entry mistakes and inconsistencies, and make EHRs more comparable. To that end, we are developing a logical framework capable of exploiting what SNOMED CT offers terminologically and realism-based ontologies such as the BFO and the OGMS ontologically by means of bridging axioms compatible with the BFO, and expressed in the same CLIF-dialect as used in its axiomatization in first order logic. In this paper, we report on our attempts to detect in the combinations of binary relations that are used in the definition of SNOMED CT's definitions of disorder concepts patterns which might at least partially automate the construction of such axioms. Our findings suggest that this partial automation is indeed possible, but to a smaller extent than we had hoped for. We compare our approach with a recent proposal that seeks to bring SNOMED CT and BFO closer together by reinterpreting SNOMED CT disorders as clinical occurrents. The proposal has its merit in providing a realist underpinning for that part of SNOMED CT's concept model in terms of the BFO, but is not discriminatory enough for an automatic translation into OGMS. Key problem is the lack of face validity of SNOMED CT disorder terms as compared to the formal definitions they are given and this in absence of textual definitions.