Electronic Healthcare Data Research Articles

A discovery and verification approach to pharmacovigilance using electronic healthcare data.

Pharmacovigilance is vital for drug safety. The process typically involves two key steps: initial signal generation from spontaneous reporting systems (SRSs) and subsequent expert review to assess the signals' (potential) causality and decide on the appropriate action. We propose a novel discovery and verification approach to pharmacovigilance based on electronic healthcare data. We enhance the signal detection phase by introducing an ensemble of methods which generated signals are combined using Borda count ranking; a method designed to emphasize consensus. Ensemble methods tend to perform better when data is noisy and leverage the strengths of individual classifiers, while trying to mitigate some of their limitations. Additionally, we offer the committee of medical experts with the option to perform an in-depth investigation of selected signals through tailored pharmacoepidemiological studies to evaluate their plausibility or spuriousness. To illustrate our approach, we utilize data from the German Pharmacoepidemiological Research Database, focusing on drug reactions to the direct oral anticoagulant rivaroxaban. In this example, the ensemble method is built upon the Bayesian confidence propagation neural network, longitudinal Gamma Poisson shrinker, penalized regression and random forests. We also conduct a pharmacoepidemiological verification study in the form of a nested active comparator case-control study, involving patients diagnosed with atrial fibrillation who initiated anticoagulant treatment between 2011 and 2017. The case study reveals our ability to detect known adverse drug reactions and discover new signals. Importantly, the ensemble method is computationally efficient. Hasty false conclusions can be avoided by a verification study, which is, however, time-consuming to carry out. We provide an online tool for easy application: https://borda.bips.eu.

Considerations for practical use of tree-based scan statistics for signal detection using electronic healthcare data: a case study with insulin glargine

ABSTRACT Background Hypothesis-free signal detection (HFSD) methods such as tree-based scan statistics (TBSS) applied to longitudinal electronic healthcare data (EHD) are increasingly used in safety monitoring. However, challenges may arise in interpreting HFSD results alongside results from disproportionality analysis of spontaneous reporting. Research design and methods Using the anti-diabetes drug insulin glargine (Lantus®) we apply two different tree-based scan designs using TreeScan™ software on retrospective EHD and compare the results to one another as well as to results from a disproportionality analysis using SRD. Results The self-controlled tree temporal scan method produced the larger number of alerts relative to propensity-score matched approach; however, far fewer alerts were observed when analyses were limited to EHD in inpatient/emergency room settings only. Very few reference adverse events were observed using TBSS methods on EHD relative to disproportionality methods in SRD. Conclusion Differences in detected alerts between TBSS methods and between TBSS and disproportionality analysis of SRD are likely attributable to differences in data, comparator, and study design. Our results suggest that HFDS methods like TBSS applied to EHD may complement more traditional approaches such as disproportionality analysis of SRD to provide a more complete picture of product safety in the post-approval setting.

US Real-World Effectiveness Study of Nirmatrelvir/Ritonavir in Preventing Hospitalization of High-Risk COVID-19 Patients

PurposeWe describe nirmatrelvir/ritonavir (NMV/r) effectiveness in preventing hospitalization among COVID-19 patients at high risk of severe disease. MethodsAn ongoing US population-based observational cohort study with retrospective and prospective collection of national electronic healthcare data collected from the US Optum® deidentified COVID-19 Electronic Health Record dataset during December 22, 2021−July 20, 2022. Participants were ≥12 years old; had a positive SARS-CoV-2 test, COVID-19 diagnosis, or NMV/r prescription; and were at high risk of severe COVID-19 based on demographic/clinical characteristics. Potential confounders between groups were balanced using propensity score matching. Immortal time bias was addressed. Hospitalization rates within 30 days from COVID-19 diagnosis were evaluated. Sensitivity analyses included 15-day hospitalization, chart review to investigate incidental hospitalization effects, and exclusion of patients identified as having COVID-19 based on NMV/r prescription alone. Outcomes were also evaluated by race, age, and COVID-19 vaccine status. FindingsOverall, 12,440 and 234,123 patients were included in the NMV/r and non-NMV/r groups, respectively. After propensity score matching, baseline characteristics were well balanced across groups (NMV/r, n = 12,439; non-NMV/r, n = 36,490). Incidence of hospitalization (95% CI) within 30 days was 0.90% (0.74%−1.08%) for the NMV/r group and 5.91% (5.67%−6.16%) for the non-NMV/r group, with relative risk (95% CI) of 0.15 (0.13−0.18; 85% risk reduction). NMV/r was comparably effective in Black patients (relative risk, 0.19 [0.10−0.34]; 81% risk reduction). Sensitivity analyses supported the main outcomes. ImplicationsReal-world NMV/r effectiveness against hospitalization during Omicron predominance among COVID-19 patients at high risk of severe disease supports demonstrated clinical trial efficacy. Black patients underutilized NMV/r despite high effectiveness.

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives.

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

The effect of SGLT2 inhibition on prostate cancer: Mendelian randomization and observational analysis using electronic healthcare and cohort data

We evaluated the effect of sodium-glucose cotransporter 2 (SGLT2) inhibition on prostate cancer by evidence triangulation. Using Mendelian randomization, we found that genetically proxied SGLT2 inhibition reduced the risk of overall (odds ratio= 0.56, 95% confidence interval [CI]= 0.38 to 0.82; 79,148 prostate cancer cases and 61,106 controls), advanced, and early-onset prostate cancer. Using electronic healthcare data (nSGLT2i= 24,155; nDPP4i= 24,155), we found that the use of SGLT2 inhibitors was associated with a 23% reduced risk of prostate cancer (hazard ratio= 0.77, 95% CI= 0.61 to 0.99) in men with diabetes. Using data from two prospective cohorts (n4C= 57,779; nUK_Biobank= 165,430), we found little evidence to support the association of HbA1c with prostate cancer, implying a non-glycemic effect of SGLT2 inhibition on prostate cancer. In summary, this study provides multiple layers of evidence to support the beneficial effect of SGLT2 inhibition on reducing prostate cancer risk. Future trials are warranted to investigate whether SGLT2 inhibitors can be recommended for prostate cancer prevention.

Validation of a Major and Clinically Relevant Nonmajor Bleeding Phenotyping Algorithm on Electronic Health Records.

Bleeding is an important health outcome of interest in epidemiological studies. We aimed to develop and validate rule-based algorithms to identify (1) major bleeding and (2) all clinically relevant bleeding (CRB) (composite of major and all clinically relevant nonmajor bleeding) within real-world electronic healthcare data. We took a random sample (n = 1630) of inpatient admissions to Singapore public healthcare institutions in 2019 and 2020, stratifying by hospital and year. We included patients of all age groups, sex, and ethnicities. Presence of major bleeding and CRB were ascertained by two annotators through chart review. A total of 630 and 1000 records were used for algorithm development and validation, respectively. We formulated two algorithms: sensitivity- and positive predictive value (PPV)-optimized algorithms. A combination of hemoglobin test patterns and diagnosis codes were used in the final algorithms. During validation, diagnosis codes alone yielded low sensitivities for major bleeding (0.16) and CRB (0.24), although specificities and PPV were high (>0.97). For major bleeding, the sensitivity-optimized algorithm had much higher sensitivity and negative predictive values (NPVs) (sensitivity = 0.94, NPV = 1.00), however false positive rates were also relatively high (specificity = 0.90, PPV = 0.34). PPV-optimized algorithm had improved specificity and PPV (specificity = 0.96, PPV = 0.52), with little reduction in sensitivity and NPV (sensitivity = 0.88, NPV = 0.99). For CRB events, our algorithms had lower sensitivities (0.50-0.56). The use of diagnosis codes alone misses many genuine major bleeding events. We have developed major bleeding algorithms with high sensitivities, which can ascertain events within populations of interest.

Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design

BackgroundPregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions.MethodsEmploying a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically.ResultsFrom 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including ‘limited resources’, ‘low maternal health priority’, ‘lack of knowledge’, ‘ineffective systems’ and ‘new mum syndrome’. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities.ConclusionsCurrent health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.

Implementation of Outpatient Automated Stewardship Information System (OASIS©) Audit and Feedback in Two Healthcare Systems

Background: Combating antibiotic resistance, exacerbated by widespread unnecessary outpatient antibiotic prescriptions, necessitates innovative stewardship solutions. Audit and feedback reports are effective but often resource heavy. We introduced a free, open-source system, Outpatient Automated Stewardship Information System (OASIS©), for automating the creation and distribution of recurring audit and feedback reports to clinicians to improve antibiotic prescribing. Methods: We used mixed methods to evaluate implementation of OASIS© across 11 clinics at Denver Health and Hospital Authority (DHHA) and Children’s Hospital Colorado (CHCO) from July 2022 to August 2023. Both sites host their own Epic® electronic healthcare and enterprise data warehouse systems. R statistical software was utilized to retrieve and process the data needed to create individual[HCM1] clinician audit and feedback reports with peer comparison. Reports were provided for 1) antibiotics prescribed for respiratory diagnoses, 2) antibiotics prescribed for respiratory diagnoses where antibiotics are never indicated, 3) first-line antibiotic prescribing for acute otitis media (AOM), and 4) five-day duration of antibiotics for children two years and older with AOM. Feedback reports for each metric were emailed to clinicians for three consecutive months. The primary outcome was adaptations needed to implement OASIS©. Secondary outcomes included fidelity (measured by email readership), time to set up and maintain the program, and barriers and facilitators to implementation (assessed by four qualitative interviews with OASIS© stakeholders). Results: The most significant adaptations made pertained to the automation of OASIS© reports for organizations not using R for data retrieval and reporting, setting up OASIS© specific email addresses, and validating clinician fidelity via read receipts. Fidelity was higher at DHHA (91-100%) compared to CHCO (10-30%). When interviewed, data analysts expressed that time for initial setup ranged from 1-6 hours. After reporting was automated, the estimated monthly time to send reports was 10 minutes. Views on setup complexity were split, but all recognized the readability of the reports and OASIS©’s value for improving prescribing behaviors. The greatest barriers to implementation included obtaining analytic resources for initial setup and the need to download additional R packages. No interviewee had prior experience creating audit and feedback reports. Conclusions: Implementing OASIS© requires addressing system diversity and knowledge gaps in outpatient informatics and antibiotic stewardship. Despite these challenges, the tool proved efficient and beneficial for monitoring and reporting antimicrobial prescribing. This free tool could likely be effectively disseminated to other health systems given the limited time and resources required for adaptations, setup, and monitoring. [HCM1]I

Use of the Sentinel System to Examine Medical Product Use and Outcomes During Pregnancy.

While many pregnant individuals use prescription medications, evidence supporting product safety during pregnancy is often inadequate. Existing electronic healthcare data sources provide large, diverse samples of health plan members to allow for the study of medical product utilization during pregnancy, as well as pregnancy, maternal, and infant outcomes. The Sentinel System is a national medical product surveillance system that includes administrative claims and electronic health record databases from large national and regional health insurers. In addition to these data sources, Sentinel develops and maintains a sizeable selection of analytic tools to facilitate epidemiologic analyses in a way that protects patient privacy and health system autonomy. In this article, we provide an overview of Sentinel System infrastructure, including the Mother-Infant Linkage Table, parameterizable analytic tools, and algorithms to estimate gestational age and identify pregnancy outcomes. We also describe past and future Sentinel work that contributes to our understanding of the way medical products are used and the safety of these products during pregnancy.

Cystic fibrosis patient characteristics and healthcare resource utilization in Finland using linked registries

BackgroundKnowledge of prevalence and epidemiology of cystic fibrosis (CF) and healthcare resource use among Finnish people with CF is incomplete. MethodsWe conducted a population-based matched cohort study using retrospective real-world data from linked Finnish national registries. Electronic healthcare data and drug purchases of 102 people with CF were analyzed between January 2015 and December 2019 (follow-up). A 5-fold control population was matched by sex, age, and place of residence. Comorbidities and medication use that occurred at any time during follow-up were assessed; annual rates of hospital service use were adjusted for follow-up. ResultsThe prevalence of CF in Finland was 1.85 per 100,000. Median age at diagnosis was 1 year, with 60 % diagnosed at age <2 years and 80 % at age <10 years. Median age at death in people with CF was 31.4 years (n < 5); no controls died. The most common comorbidities included chronic sinusitis (39.2 %), pneumonia (38.2 %), diabetes (20.6 %), and nasal polyps (18.6 %). The most purchased medications were antibiotics (99.0 %) and pancreatic enzymes (84.3 %). The annualized rate of hospital visits was higher in people with CF vs controls (outpatient: mean [SD], 17.4 [14.5] vs 0.9 [3.3]; median, 13.6 vs 0.4, respectively; inpatient: mean [SD], 1.0 [1.66] vs 0.03 [0.14]; median, 0.34 vs 0, respectively). ConclusionsThe prevalence of CF in Finland is remarkably low, likely reflecting unique population characteristics and, in part, delayed diagnosis. Antibiotic use is frequent among Finnish people with CF. Inpatient hospital visits are >30 times higher in people with CF than matched controls.

High-fidelity synthetic patient data applications and privacy considerations

This paper explores the potential applications of high-fidelity synthetic patient data in the context of healthcare research, including challenges and benefits. The paper starts by defining synthetic data, types of synthetic data and approaches to generating synthetic data. It then discusses the potential applications of synthetic data in addition to as a privacy enhancing technology and current debates around whether synthetic data should be considered personal data and,therefore, should be subjected to privacy controls to minimise reidentification risks. This will be followed by a discussion of privacy preservation approaches and privacy metrics that can be applied in the context of synthetic data. The paper includes a case study based on synthetic electronic healthcare record data from the Clinical Practice Research Datalink on how privacy concerns due to reidentification have been addressed in order to make this data available for research purposes. The authors conclude that synthetic data, particularly high-fidelity synthetic patient data, has the potential to add value over and above real data for public health and that it is possible to address privacy concerns to make synthetic data available via a combination of privacy measures applied during the synthetic data generation process and post-generation reidentification risk assessments as part of data protection impact assessments.

Accuracy of the recording of pneumonia events in English electronic healthcare record data in patients with chronic obstructive pulmonary disease

BackgroundIn primary care, identifying pneumonia events in people with chronic obstructive pulmonary disease (COPD) may be challenging due to similarities in symptoms with COPD exacerbations and lack of diagnostic testing. This study explored the accuracy of pneumonia diagnosis coded in primary care by comparing diagnosis in primary care with diagnosis in hospital.MethodsA study population of people with COPD in England was created using the Clinical Practice Research Datalink Aurum database linked with Hospital Episode Statistics inpatient data. Pneumonia codes only, and pneumonia code with associated clinical and/or treatment codes (chest x-ray, symptoms, antibiotics, sputum and blood culture) were used to determine pneumonia events in primary care. Events that were followed by hospitalisation within 7 days were used to estimate the positive predictive value (PPV) of pneumonia coding in primary care, using primary diagnosis of pneumonia in secondary care as the gold standard. The PPV of primary care recording of hospitalised pneumonia was also calculated.ResultsTwo hundred seventy-four thousand one hundred fifty-six COPD patients were eligible for inclusion, of whom 7,560 had an eligible pneumonia event in primary care diagnosed between 2015–2019 which was not ‘hospital-acquired’ and was diagnosed and entered on the same day. Of the 2,094 events which were followed by hospitalisation within 7 days, 1,208 had a primary diagnosis of pneumonia in hospital, representing a PPV of pneumonia coding in primary care of 57.7% (95% CI 55.6%-59.8%). Another 284 (13.6%) were diagnosed as a COPD exacerbation and 114 (5.4%) were diagnosed as another respiratory disease. Use of additional pneumonia clinical and treatment codes had a modest effect on the PPV but substantially lowered the number of events. Of the 33,603 eligible pneumonia events identified in secondary care, only 11,445 were recorded in primary care within 42 days, representing a sensitivity of 34.1% (95% CI 33.6%-34.6%).ConclusionsUse of primary care pneumonia codes and associated clinical and treatment codes to determine pneumonia is not recommended due to significant levels of misdiagnosis and many hospitalised events failing to be recorded in primary care.

Blockchain-enabled IoT access control model for sharing electronic healthcare data

Blockchain-enabled IoT access control model for sharing electronic healthcare data

Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.

This work describes the process by which the quality of electronic health care data for a public health study was determined. The objectives were to adapt, develop, and implement data quality assessments (DQAs) based on the National Institutes of Health Pragmatic Trials Collaboratory (NIHPTC) data quality framework within the three domains of completeness, accuracy, and consistency, for an investigation into oral health care disparities of a preventive care program. Electronic health record data for eligible children in a dental accountable care organization of 30 offices, in Oregon, were extracted iteratively from January 1, 2014, through March 31, 2022. Baseline eligibility criteria included: children ages 0-18 with a baseline examination, Oregon home address, and either Medicaid or commercial dental benefits at least once between 2014 and 2108. Using the NIHPTC framework as a guide, DQAs were conducted throughout data element identification, extraction, staging, profiling, review, and documentation. The data set included 91,487 subjects, 11 data tables comprising 75 data variables (columns), with a total of 6,861,525 data elements. Data completeness was 97.2%, the accuracy of EHR data elements in extracts was 100%, and consistency between offices was strong; 29 of 30 offices within 2 standard deviations of the mean (s = 94%). The NIHPTC framework proved to be a useful approach, to identify, document, and characterize the dataset. The concepts of completeness, accuracy, and consistency were adapted by the multidisciplinary research team and the overall quality of the data are demonstrated to be of high quality.

Implementing Indicators and Trajectories of Return to Work After Breast Cancer Diagnosis: A Mixed-Methods Study Using the French National Healthcare Insurance Database and Stakeholder Consultation

PurposeReturn to work (RTW) is important for quality of life after breast cancer but its analysis at the population-level remains limited in France. This study aimed to implement Electronic Healthcare Data (EHD)-based indicators and trajectories to measure RTW after breast cancer diagnosis, and to examine stakeholders’ perspectives regarding these indicators. MethodsWe followed a mixed-methods approach that consisted of (i) implementing RTW indicators and identifying clusters of trajectories using state sequence analysis with data from a representative sample of the French National Health Data System and (ii) exploring, through qualitative focus group and interviews, stakeholders’ perceptions on the interpretation, limitations, and utility of these indicators. ResultsWe extracted data from 317 women aged 25-55 years with a first diagnosis of early-stage breast cancer. The median number of sickness absence periods was 2 for a total of 434 days during the 3-year follow-up, and the median time to sustainable RTW was 240 days. Three clusters of RTW trajectories were identified: “early RTW” (49.5% of the population), “RTW after partial resumption” (37.5%) and “continuous compensation” (12.9%). Feedback from stakeholders highlighted the multi-factorial nature of RTW and underscored the added value of EHD for studying RTW, despite certain limitations. ConclusionsWe demonstrated the feasibility of calculating RTW indicators and identifying trajectories using the French National Health Data System. These indicators can serve as outcome measures in RTW promotion and provide a basis for designing targeted interventions for breast cancer survivors.

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework.

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis.

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

In recent years, there has been an exponential increase in the generation and accessibility of electronic healthcare data, often referred to as “real-world data”. The landscape of data sources has significantly expanded to encompass traditional databases and newer sources such as the social media, wearables, and mobile devices. Advances in information technology, along with the growth in computational power and the evolution of analytical methods relying on bioinformatic tools and/or artificial intelligence techniques, have enhanced the potential for utilizing this data to generate real-world evidence and improve clinical practice. Indeed, these innovative analytical approaches enable the screening and analysis of large amounts of data to rapidly generate evidence. As such numerous practical uses of artificial intelligence in medicine have been successfully investigated for image processing, disease diagnosis and prediction, as well as the management of pharmacological treatments, thus highlighting the need to educate health professionals on these emerging approaches. This narrative review provides an overview of the foremost opportunities and challenges presented by artificial intelligence in pharmacology, and specifically concerning the drug post-marketing safety evaluation.

Expenditure and survival of adult patients with intestinal failure due to short bowel syndrome: real-world evidence from Southern Finland

Objectives Comprehensive follow-up data from the largest hospital district in Finland was used to assess hospital-based healthcare resource utilization (HCRU) and expenses, incidence and prevalence, survival, and effect of comorbidities/complications on survival of adult patients with intestinal failure due to short bowel syndrome (SBS-IF). Methods This study utilized electronic healthcare data covering all ≥18-year-old patients with SBS-IF at the Hospital District of Helsinki and Uusimaa in Finland between 2010 and 2019. Patients were followed from SBS-IF onset until the end of 2020 or death and compared to birth year and sex-matched control patients without SBS-IF. Results The study included 77 patients with SBS-IF (cases) and 363 controls. Cases had high HCRU; the cumulative expenses were about tenfold compared to the controls, at the end of the study (€123,000 vs. €14,000 per patient). The expenses were highest during the first year after SBS-IF onset (€53,000 per patient). Of the cases with a median age 62.5 years, 51.9% died during study time. The median survival was 4.4 years from SBS-IF onset and cases died 13.5 times more likely during the follow-up compared to controls. Mortality risk was lower in female cases (hazard ratio (HR) 0.46; 95% confidence intervals (CI) 0.24, 0.9) and higher with presence of comorbidities (Charlson comorbidity index HR 1.55; 95% CI 1.2, 2.0) and mesenteric infarction (HR 4.5; 95% CI 1.95, 10.36). The incidence of adult SBS-IF was 0.6 per 100,000 adults. Conclusion Our study demonstrates a high demand for healthcare support and elevated mortality in adult SBS-IF-patients. Our results suggest that the presence of comorbidities is a key driver for mortality.

Revocable and Traceable Undeniable Attribute-Based Encryption in Cloud-Enabled E-Health Systems.

The emerging cloud storage technology has significantly improved efficiency and productivity in the traditional electronic healthcare field. However, it has also brought about many security concerns. Ciphertext policy attribute-based encryption (CP-ABE) holds immense potential in achieving fine-grained access control, providing robust security for electronic healthcare data in the cloud. However, current CP-ABE schemes still face issues such as inflexible attribute revocation, relatively lower computational capabilities, and key management. To address these issues, this paper introduces a revocable and traceable undeniable ciphertext policy attribute-based encryption scheme (MA-RUABE). MA-RUABE not only enables fast and accurate data traceability, effectively preventing malicious user key leakage, but also includes a direct revocation feature, significantly enhancing computational efficiency. Furthermore, the introduction of a multi-permission mechanism resolves the issue of centralization of power caused by single-attribute permissions. Furthermore, a security analysis demonstrates that our system ensures resilience against chosen plaintext attacks. Experimental results demonstrate that MA-RUABE incurs lower computational overhead, effectively enhancing system performance and ensuring data-sharing security in cloud-based electronic healthcare systems.

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.