The main aim of this paper is to identify and specify factors that influence business analytics. A factor in this context refers to any significant characteristic that defines the environment in ...which business analytics and business in general are conducted. Factors and their understanding are essential for the quality of final business analytics solutions, given their complexity and interconnectedness. Factors play an extremely important role in analytic thinking and business analysts’ skills and knowledge. These factors determine effective approaches and procedures for business analytics, and, in some cases, they also aid in the decision to delay a business analytics solution given a situation. This paper has used the case study method, a qualitative research method, due to the need to carry out investigation within the actual business (company) environment, in order to be able to fully understand and verify factors affecting analytics from the viewpoint of all stakeholders. This study provides a set of 15 factors from business, company, and market environments, including their importance in business analytics.
Millions of people have limited access to specialty care. The problem is exacerbated by ineffective specialty visits due to incomplete prereferral workup, leading to delays in diagnosis and ...treatment. Existing processes to guide prereferral diagnostic workup are labor-intensive (ie, building a consensus guideline between primary care doctors and specialists) and require the availability of the specialists (ie, electronic consultation).
Using pediatric endocrinology as an example, we develop a recommender algorithm to anticipate patients' initial workup needs at the time of specialty referral and compare it to a reference benchmark using the most common workup orders. We also evaluate the clinical appropriateness of the algorithm recommendations.
Electronic health record data were extracted from 3424 pediatric patients with new outpatient endocrinology referrals at an academic institution from 2015 to 2020. Using item co-occurrence statistics, we predicted the initial workup orders that would be entered by specialists and assessed the recommender's performance in a holdout data set based on what the specialists actually ordered. We surveyed endocrinologists to assess the clinical appropriateness of the predicted orders and to understand the initial workup process.
Specialists (n=12) indicated that <50% of new patient referrals arrive with complete initial workup for common referral reasons. The algorithm achieved an area under the receiver operating characteristic curve of 0.95 (95% CI 0.95-0.96). Compared to a reference benchmark using the most common orders, precision and recall improved from 37% to 48% (P<.001) and from 27% to 39% (P<.001) for the top 4 recommendations, respectively. The top 4 recommendations generated for common referral conditions (abnormal thyroid studies, obesity, amenorrhea) were considered clinically appropriate the majority of the time by specialists surveyed and practice guidelines reviewed.
An item association-based recommender algorithm can predict appropriate specialists' workup orders with high discriminatory accuracy. This could support future clinical decision support tools to increase effectiveness and access to specialty referrals. Our study demonstrates important first steps toward a data-driven paradigm for outpatient specialty consultation with a tier of automated recommendations that proactively enable initial workup that would otherwise be delayed by awaiting an in-person visit.
This paper describes the results of an investigation of the interparticle interactions and reactivities in the assembly of gold nanoparticles mediated by cyanine dyes. The combination of the ...positively charged indolenine cyanine dyes and the negatively charged gold nanoparticles is shown to form a J-aggregate bridged assembly of nanoparticles, in addition to hydrophobic interparticle and electrostatic dye−particle interactions. Such interparticle interactions and reactivities are studied by probing the absorption of J-aggregates and fluorescence from the dyes and the surface plasmon resonance absorption from the nanoparticles. The J-aggregation of the dyes adsorbed on the nanoparticles is shown to play an important role in the assembly of nanoparticles. The spectral evolution of the J-band of the dyes and the surface plasmon resonance band of the nanoparticles was found to be sensitive to the nature of the charge and the structure of the dyes. The fluorescence quenching for the dyes was shown to be quantitatively related to the surface coverage of the dyes on the nanocrystal surfaces. These findings have provided important information for assessing a two-step process involving a rapid adsorption of the dyes on the nanoparticles and a subsequent assembly of the nanoparticles involving a combination of interparticle J-aggregation and hydrophobic interactions of the adsorbed dyes. The results are discussed in terms of the structural effects of the dyes, and the interparticle molecular interactions and reactivities, which provide important physical and chemical insights into the design of dye−nanoparticle structured functional nanomaterials.
The 2014-2015 Ebola outbreak is the largest and most widespread to date. In order to estimate ongoing transmission in the affected countries, we estimated the weekly average number of secondary cases ...caused by one individual infected with Ebola throughout the infectious period for each affected West African country using a stochastic hidden Markov model fitted to case data from the World Health Organization. If the average number of infections caused by one Ebola infection is less than 1.0, the epidemic is subcritical and cannot sustain itself. The epidemics in Liberia and Sierra Leone have approached subcriticality at some point during the epidemic; the epidemic in Guinea is ongoing with no evidence that it is subcritical. Response efforts to control the epidemic should continue in order to eliminate Ebola cases in West Africa.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The understanding of the detailed molecular interactions between (GSH) glutathione molecules in the assembly of metal nanoparticles is important for the exploitation of the biological reactivity. We ...report herein results of an investigation of the assembly of gold nanoparticles mediated by glutathione and the disassembly under controlled conditions. The interparticle interactions and reactivities were characterized by monitoring the evolution of the surface plasmon resonance band using the spectrophotometric method and the hydrodynamic sizes of the nanoparticle assemblies using the dynamic light scattering technique. The interparticle reactivity of glutathiones adsorbed on gold nanoparticles depends on the particle sizes and the ionic strength of the solution. Larger-sized particles were found to exhibit a higher degree of interparticle assembly than smaller-sized particles. The assembly−disassembly reversibility is shown to be highly dependent on pH and additives in the solution. The interactions of the negatively charged citrates surrounding the GSH monolayer on the particle surface were believed to produce more effective interparticle spatial and electrostatic isolation than the case of OH− groups surrounding the GSH monolayer. The results have provided new insights into the hydrogen-bonding character of the interparticle molecular interaction of glutathiones bound on gold nanoparticles. The fact that the interparticle hydrogen-bonding interactions in the assembly and disassembly processes can be finely tuned by pH and chemical means has implications to the exploitation of the glutathione−nanoparticle system in biological detection and biosensors.
This paper reports the findings of an investigation of the reactivity and assembly of gold nanoparticles mediated by homocysteine (Hcys), a thiol-containing amino acid found in plasma. The aim is to ...gain insight into the interparticle interaction and reactivity, which has potential application for the detection of thiol-containing amino acids. By monitoring the evolution of the surface plasmon resonance absorption and the dynamic light scattering of gold nanoparticles in the presence of Hcys, the assembly was shown to be dependent on the nature and concentration of the electrolytes, reflecting an effective screening of the diffuse layer around the initial citrate-capped nanoparticles that decreases the barrier to the Hcys adsorption onto the surface, and around the subsequent Hcys-capped nanoparticles that facilitate the zwitterion-type electrostatic interactions between amino acid groups of Hcys bound to different nanoparticles. A key element of the finding is that the interparticle zwitterion interaction of the Hcys−Au system is much stronger than the expectation for a simple Hcys or Au solution, a new phenomenon originating from the unique nanoscale interparticle interaction. The strength and reversibility of the interparticle zwitterion-type electrostatic interactions between amino acid groups are evidenced by the slow disassembly upon increasing pH at ambient temperatures and its acceleration at elevated temperature. These findings provide new insight into the precise control of interfacial interactions and reactivities between amino acids anchored to nanoparticles and have broad implications in the development of colorimetric nanoprobes for amino acids.
•Telemedicine within Emergency Departments has been pervasive throughout COVID-19.•More orders placed for patients with chest pain who received telemedicine.•Telemedicine had minimal impacts on ...timeliness of care provided.•Telemedicine within the Emergency Department has many uses outside of COVID-19.
The outbreak of the COVID-19 pandemic has led to the rapid adoption of novel telemedicine programs within the emergency department (ED) to minimize provider exposure and conserve personal protective equipment (PPE). In this study, we sought to assess how the adoption of telemedicine in the ED impacted clinical order patterns for patients with chest pain. We hypothesize that clinicians would rely more on imaging and laboratory workup for patients receiving telemedicine due to limitation in physical exams.
A single-center, retrospective, propensity score matched study was designed for patients presenting with chest pain at an ED. The study period was defined between April 1st, 2020 and September 30th, 2020. The frequency of the most frequent lab, imaging, and medication orders were compared. In addition, poisson regression analysis was performed to compare the overall number of orders between the two groups.
455 patients with chest pain who received telemedicine were matched to 455 similar patients without telemedicine with standardized mean difference < 0.1 for all matched covariates. The proportion of frequent lab, imaging, and medication orders were similar between the two groups. However, telemedicine patients received more orders overall (RR, 1.19, 95% CI, 1.11, 1.28, p-value < 0.001) as well as more imaging, lab, and nursing orders. The number of medication orders between the two groups remained similar.
Frequent labs, imaging, and medications were ordered in similar proportions between the two cohorts. However, telemedicine patients had more orders placed overall. This study is an important objective assessment of the impact that telemedicine has upon clinical practice patterns and can guide future telemedicine implementation after the COVID-19 pandemic.
Audit logs in electronic health record (EHR) systems capture interactions of providers with clinical data. We determine if machine learning (ML) models trained using audit logs in conjunction with ...clinical data ("observational supervision") outperform ML models trained using clinical data alone in clinical outcome prediction tasks, and whether they are more robust to temporal distribution shifts in the data.
Using clinical and audit log data from Stanford Healthcare, we trained and evaluated various ML models including logistic regression, support vector machine (SVM) classifiers, neural networks, random forests, and gradient boosted machines (GBMs) on clinical EHR data, with and without audit logs for two clinical outcome prediction tasks: major adverse kidney events within 120 days of ICU admission (MAKE-120) in acute kidney injury (AKI) patients and 30-day readmission in acute stroke patients. We further tested the best performing models using patient data acquired during different time-intervals to evaluate the impact of temporal distribution shifts on model performance.
Performance generally improved for all models when trained with clinical EHR data and audit log data compared with those trained with only clinical EHR data, with GBMs tending to have the overall best performance. GBMs trained with clinical EHR data and audit logs outperformed GBMs trained without audit logs in both clinical outcome prediction tasks: AUROC 0.88 (95% CI: 0.85-0.91) vs. 0.79 (95% CI: 0.77-0.81), respectively, for MAKE-120 prediction in AKI patients, and AUROC 0.74 (95% CI: 0.71-0.77) vs. 0.63 (95% CI: 0.62-0.64), respectively, for 30-day readmission prediction in acute stroke patients. The performance of GBM models trained using audit log and clinical data degraded less in later time-intervals than models trained using only clinical data.
Observational supervision with audit logs improved the performance of ML models trained to predict important clinical outcomes in patients with AKI and acute stroke, and improved robustness to temporal distribution shifts.
Abstract
Introduction
Narcolepsy is a rare, chronic sleep disorder whose symptoms detrimentally impact quality of life. Narcolepsy has a complex phenotype associated with multiple comorbid ...conditions. This is the first study to use aggregate electronic health record (EHR) data to characterize the demographics and comorbidities of patients with narcolepsy.
Methods
An EHR-based search identified first-time Mayo Clinic patients between 2000–2020. Included patients had ≥1 narcolepsy-specific ICD-9/10 code and ≥1 disease-supportive statement in the clinical notes (identified using a natural language processing algorithm). A control cohort was propensity-matched for birth year, age at first institutional encounter, sex, race, ethnicity, number of diagnosis codes, and mortality. Common comorbidities were compared and ranked between cohorts by odds ratio (OR); P values were adjusted and calculated based on Bonferroni correction.
Results
In the EHR database (N=6,389,186 patients), 2057 patients with narcolepsy were identified (median age at first presentation to Mayo clinic, 32 y range, 17–48; 59.6% female; 92.6% white; 89.2% non-Hispanic) and propensity-matched with a control cohort of 2057 patients (median age at first presentation to Mayo clinic, 35 y range, 12–52; 58.9% female; 94.6% white; 84.5% non-Hispanic). Among the top comorbidities that occurred more frequently in the narcolepsy cohort compared to the control cohort (OR 95% CI; P< 0.001) were sleep disorders (restless leg syndrome, 3.94 3.09–5.02; obstructive sleep apnea, 3.27 2.83–3.79; insomnia, 1.84 1.57–2.17); mood disorders (depression, 2.11 1.86-2.40; dysthymia, 1.86 1.54–2.25; anxiety, 1.67 1.46–1.89); and pain disorders (chronic pain syndrome, 2.20 1.76–2.76; migraine, 1.96 1.66–2.31; fibromyalgia, 1.90 1.61–2.25; carpal tunnel syndrome, 1.80 1.46–2.22; myalgia, 1.69 1.45–1.97). Other comorbidities statistically significantly associated with narcolepsy were (OR range, 1.33–1.95) irritable bowel syndrome (P< 0.001), asthma (P< 0.001), cervical spondylosis (P< 0.01), syncope (P< 0.01), and hypothyroidism (P< 0.05).
Conclusion
This propensity-matched cohort study affirmed prior studies of increased psychiatric and sleep disorders in patients with narcolepsy. Narcolepsy patients were twice as likely to experience chronic pain syndrome compared to the matched control group. Understanding common narcolepsy comorbidities may help optimize treatment efficacy and increase understanding of the medical/psychiatric challenges of narcolepsy patients.
Support (if any)
Avadel Pharmaceuticals
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