The Benefits of Enterprise Data Warehouse Implementation in Cloud vs. On-premises

When we think about running enterprise applications on-premises, enterprises do two things for their servers, databases, and storage. Enterprises provision for peaks and put a lot of infrastructures to handle peak demand, although a lot of this capacity is not used at normal times. The other thing is a few instances that each application needs to have, typically between five and six. Multiplying this number by many times due to various applications causes a lot of costs and creates capacity that is not used. For such reasons, the enterprise applications in the cloud seem reasonable. In the cloud, two things are possible again. Instead of overprovisioning for peaks, enterprises can scale the capacity on on-demand and spin up instances on demand. This means a certain amount of cost-saving by running at a normal level instead of overprovisioning. In this paper, various factors will be considered, and the benefits for enterprise data warehouse implementation in the cloud vs. on-premises will be stated. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Prevalence of potentially harmful multidrug interactions on medication lists of elderly ambulatory patients

Background It has been hypothesized that polypharmacy may increase the frequency of multidrug interactions (MDIs) where one drug interacts with two or more other drugs, amplifying the risk of associated adverse drug events (ADEs). The main objective of this study was to determine the prevalence of MDIs in medication lists of elderly ambulatory patients and to identify the medications most commonly involved in MDIs that amplify the risk of ADEs. Methods Medication lists stored in the electronic health record (EHR) of 6,545 outpatients ≥60 years old were extracted from the enterprise data warehouse. Network analysis identified patients with three or more interacting medications from their medication lists. Potentially harmful interactions were identified from the enterprise drug-drug interaction alerting system. MDIs were considered to amplify the risk if interactions could increase the probability of ADEs. Results MDIs were identified in 1.3 % of the medication lists, the majority of which involved three interacting drugs (75.6 %) while the remainder involved four (15.6 %) or five or more (8.9 %) interacting drugs. The average number of medications on the lists was 3.1 ± 2.3 in patients with no drug interactions and 8.6 ± 3.4 in patients with MDIs. The prevalence of MDIs on medication lists was greater than 10 % in patients prescribed bupropion, tramadol, trazodone, cyclobenzaprine, fluoxetine, ondansetron, or quetiapine and greater than 20 % in patients prescribed amiodarone or methotrexate. All MDIs were potentially risk-amplifying due to pharmacodynamic interactions, where three or more medications were associated with the same ADE, or pharmacokinetic, where two or more drugs reduced the metabolism of a third drug. The most common drugs involved in MDIs were psychotropic, comprising 35.1 % of all drugs involved. The most common serious potential ADEs associated with the interactions were serotonin syndrome, seizures, prolonged QT interval and bleeding. Conclusions An identifiable number of medications, the majority of which are psychotropic, may be involved in MDIs in elderly ambulatory patients which may amplify the risk of serious ADEs. To mitigate the risk, providers will need to pay special attention to the overlapping drug-drug interactions which result in MDIs.

Efficient Transfer of data from RDBMS to HDFS and conversion to JSON format

Apache Sqoop is mainly used to efficiently transfer large volumes of data between Apache Hadoop and relational databases. It helps to certain tasks, such as ETL (Extract transform load) processing, from an enterprise data warehouse to Hadoop, for efficient execution at a much less cost. Here first we import the table which presents in MYSQL Database with the help of command-line interface application called Sqoop and there is a chance of addition of new rows and updating new rows then we have to execute the query again. So, with the help of our project there is no need of executing queries again for that we are using Sqoop job, which consists of total commands for import and next after import we retrieve the data from hive using Java JDBC and we convert the data to JSON Format, which consists of data in an organized way and easy to access manner by using GSON Library. Keywords: Sqoop, Json, Gson, Maven and JDBC

Understanding Facilitators and Barriers in the Hospital Discharge Processes of Newly Prescribed Insulin: A Mixed-Methods Study

Patients, newly prescribed insulin, being discharged from the hospital are at high risk of adverse outcomes. An electronic enterprise data warehouse (EDW) algorithm was created and validated to identify these inpatients electronically. Qualitative interviews were also conducted to assess barriers in the discharge process. The EDW algorithm to identify inpatients (09/01/18-08/31/19), newly prescribed insulin at discharge, was created by identifying screening indicators (e.g., admission/discharge medication lists, discharge summary). Iterative adjustments to the algorithm were made after chart review and included review of medication reconciliation (med rec), admission/discharge orders, and insulin orders (types/delivery). The EDW list was compared to the list of patients who received insulin teaching from the Certified Diabetes Care and Education Specialist (CDCES), during the same period. Providers (N=8, 3 endocrine attending MDs, 2 fellow MDs, 3 resident MDs) were interviewed in key informant interviews (N=3) and focus groups (N=2); transcripts were independently coded by 2 coders, utilizing a constant comparative method to generate key themes. The EDW list (N=554) was audited by EHR review (n = 42, 8%); 83% (35/42) were correctly identified as newly discharged on insulin. Of the 7 incorrectly identified, 4 likely had incomplete med rec. The EDW algorithm was unable to correctly identify patients with inaccurate/incomplete med rec, patients transferring from outside hospitals or those without e-Rx at discharge (vouchers, call-in). The CDCES list (N=257) was audited (n=25, 10%), and of patients not meeting criteria (n=15), some had prior insulin prescribed (n=5), and most ended up not discharged on insulin after CDCES insulin teaching (n=9). Comparison of the EDW and CDCES lists had 177 patients (32% of EDW list) in common, with 377 on the EDW list with no CDCES consultation. An audit (n=21/377, 5%) of these EDW patients, who did not have CDCES or endocrinology consultation, revealed patients across service lines, with minimal formal documentation of insulin training/education. Key identified themes from interviews identified barriers including lack of availability of a CDCES after-hours and on weekends, low health literacy/numeracy, and lack of time during stay. In training MDs noted variability in discharge prescribing by supervising MDs and the need to assess “chart lore,” given cut and paste documentation in EHR. This study suggests that an EDW algorithm can be used to identify patients newly being discharged on insulin, for whom teaching by a CDCES is recommended. The data suggest the need for more targeted and increased CDCES capacity as only a portion of those eligible for insulin teaching were seen while others were seen but then not discharged on insulin. Additional resources for insulin teaching are needed and standardized training and documentation need to be developed.

CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis

Background We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. Methods We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March–2 May) and prospective (n = 2205, 3–14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). Results In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. Conclusions CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection.

COVID-19 Outpatient Screening: a Prediction Score for Adverse Events

ABSTRACTBackgroundWe sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak.MethodsSingle-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed ratio (E/O). Discrimination was assessed by C-statistics (AUC).ResultsIn the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate.ConclusionsCoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.

Abstract P413: Latent Class Trajectory Modeling Identifies Distinct Clinical Phenotypes in a Cohort of Patients With Elevated Troponin Levels

Introduction: Clinicians widely use cardiac troponins I and T, sensitive and specific biomarkers of myocardial damage, as diagnostic criteria for myocardial infarction (MI). A common approach, dichotomizing based on whether troponin levels meet thresholds for MI, may miss meaningful heterogeneity in degree and pattern of troponin elevation, with wider clinical implications. Hypothesis: Using latent class trajectory modeling (LCTM) with patients’ patterns of troponin elevations will yield trajectories that represent clinically and prognostically distinct groups of patients. Methods: We used Northwestern Medicine’s Enterprise Data Warehouse to identify patients with at least 3 troponin measurements of which at least one was elevated during their inpatient stay. We built a LCTM to capture troponin trajectories from 13,432 patients with 65,162 measurements since 2000, using Bayesian Information Criterion to select the final model (# of classes varied from 3-7). We then described the clinical and prognostic factors associated with trajectory class membership. Results: LCTM identified 3 troponin trajectories: stable low (77.3%), moderate rise (9.5%), and severe rise-fall (13.2%). The average posterior probability of assignment was >0.95 for all classes. The low group had the most comorbidities vs moderate/severe groups, including cancer (21.9% vs 13.9%/11.3%), heart failure (12.6% vs 8.9%/7.5%), and COPD (15.0% vs 11.8%/10.1%). The moderate/severe rising trajectories had a greater rate of clinically-diagnosed MI than the stable group (34.5%/44.1% vs 5.6%), with relatively more ST-elevation MI in the severe group (19.8% vs 5.3%) and Non-ST-elevation MI in the moderate group (29.3% vs 24.3%). One-year mortality was greatest in the low group vs moderate/severe groups (25.5% vs 21.4%/18.8%). Conclusions: In a cohort of hospitalized patients with elevated troponins, LCTM identified 3 distinct, clinically interpretable troponin trajectories which were associated with differing clinical phenotypes.

CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation

Summary Methods for quantifying the imbalance in CpG methylation between alleles genome-wide have been described but their algorithmic time complexity is quadratic and their practical use requires painstaking attention to infrastructure choice, implementation and execution. To solve this problem, we developed CloudASM, a scalable, ultra-efficient, turn-key, portable pipeline on Google Cloud Platform (GCP) that uses a novel pipeline manager and GCP’s serverless enterprise data warehouse. Availability and implementation CloudASM is freely available in the GitHub repository https://github.com/TyckoLab/CloudASM and a sample dataset and its results are also freely available at https://console.cloud.google.com/storage/browser/cloudasm. Contact [email protected]

CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation

SummaryMethods for quantifying the imbalance in CpG methylation between alleles genome-wide have been described but their algorithmic time complexity is quadratic and their practical use requires painstaking attention to infrastructure choice, implementation, and execution. To solve this problem, we developed CloudASM, a scalable, ultra-efficient, turn-key, portable pipeline on Google Cloud Computing (GCP) that uses a novel pipeline manager and GCP’s serverless enterprise data warehouse.Availability and ImplementationCloudASM is freely available in the GitHub repository https://github.com/TyckoLab/CloudASM and a sample dataset and its results are also freely available at https://console.cloud.google.com/storage/browser/[email protected] informationNone.