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Time Management of Tuzla University Clinical Center Health Managers.

ABSTRACT: Time management is an important aspect of successful leadership and involves mastering your own work and time instead of allowing them to master you. Time management is a daily process that is applied in order to rationally use time, work schedules, lists of tasks, delegation of responsibilities and everything else that helps to efficiently use time. This research was prospective, descriptive, analytical and controlled. There was 100 medical worker involved, who have managing jobs of different levels and 100 medical workers who do not have managing jobs, who represented the control group. The questionnaire on "time stealers" according to Sewert. Demographic data was gathered according to the questionnaire on self construction. Based on conducted research, it was established that there were differences in frequencies of certain "time stealers" in managers of different levels of University Clinical Center Tuzla; the "time stealers" are more frequent among these with shorter length of service; These differences are significantly more frequent (p<0.05) for some questions e.g. being distracted by phone calls, meetings that are held just for the sake of having a meetings, insufficient, one-way or bad communication with superiors, and have negative effects on management of business hours.

SUBMITTER: Sinanovic S

PROVIDER: S-EPMC6195399 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

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Time Management of Tuzla University Clinical Center Health Managers.

Sinanovic Selma S Loga Slobodan S Muftic Mirsad M Jahic Mirza M Glinac Alma A Smajovic Munib M

Materia socio-medica 20181001 3

<h4>Introduction</h4>Time management is an important aspect of successful leadership and involves mastering your own work and time instead of allowing them to master you. Time management is a daily process that is applied in order to rationally use time, work schedules, lists of tasks, delegation of responsibilities and everything else that helps to efficiently use time.<h4>Material and methods</h4>This research was prospective, descriptive, analytical and controlled. There was 100 medical worke ...[more]

PMID: 30515053

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Project description:We reviewed the electronic medical records (EMR) of patients hospitalized during the peak of the pandemic, March 1st through March 31st, to document the type and frequency of neurological problems seen in patients with COVID-19 at presentation to the emergency room. Secondary aims were to determine: 1) the frequency of neurological complaints during the hospital stay; 2) whether the presence of any neurological complaint at presentation or any of the individual types of neurological complaints at admission predicted three separate outcomes: death, length of hospital stay, or the need for intubation; and 3) if the presence of any neurological complaint or any of the individual types of neurological complaints developed during hospital stay predicted the previous three outcomes.SettingThe Louisiana Health Sciences Center - New Orleans Institutional Review Board and the University Medical Center Clinical Research Review Committee approved the study protocol.Data acquisitionWe reviewed the electronic medical records (EMR) of patients hospitalized during March (March 1st through March 31st) 2020 at the University Medical Center New Orleans (UMCNO), who tested positive for SARS-CoV-2 during the same hospitalization. The EMR team generated a list of 257 patients admitted for COVID-19. We excluded seven patients because of a negative COVID-19 test result or incomplete medical record documentation. Three neurology residents (DC, MS, DB) reviewed the EMR in detail to capture the relevant medical history, clinical course, and laboratory test results and abstracted data into an electronic data collection spreadsheet.We recorded the presentation or development of the following neurological complaints: headache, syncope, altered mental status, seizure, status epilepticus, and ischemic or hemorrhagic stroke.Statistical analysisWe used "R" (statistics software) and Microsoft Excel to generate summary tables. To analyze hospital length of stay or death, we fitted a competing risks proportional hazards model for time to discharge or death using the crr() function in R version 4.0.0. The competing risks model allowed the analysis of hospital stay, taking into account that the censoring of cases due to death was not random. To predict the likelihood of intubation, we used the glm() function in R to fit a logistic regression model. For each model, we determined baseline demographic variables predictive of the outcomes and generated adjusted models. For variables with less than five cases per cell, we reported the p-values for Fisher's Exact Test.The analyses and results are published in:Chachkhiani, David et al. "Neurological complications in a predominantly African American population of COVID-19 predict worse outcomes during hospitalization." Clinical Neurology and Neurosurgery (in press).These data will be useful for researchers trying to build larger datasets regarding COVID19 neurological complications for metanalysis or to answer other questions requiring larger sample sizes.

Project description:BACKGROUND/AIMS:In our 2009 article, we showed that Bayesian methods had established a foothold in developing therapies in our institutional oncology trials. In this article, we will document what has happened since that time. In addition, we will describe barriers to implementing Bayesian clinical trials, as well as our experience overcoming them. METHODS:We reviewed MD Anderson Cancer Center clinical trials submitted to the institutional protocol office for scientific and ethical review between January 2009 and December 2013, the same length time period as the previous article. We tabulated Bayesian methods implemented for design or analyses for each trial and then compared these to our previous findings. RESULTS:Overall, we identified 1020 trials and found that 283 (28%) had Bayesian components so we designated them as Bayesian trials. Among MD Anderson-only and multicenter trials, 56% and 14%, respectively, were Bayesian, higher rates than our previous study. Bayesian trials were more common in phase I/II trials (34%) than in phase III/IV (6%) trials. Among Bayesian trials, the most commonly used features were for toxicity monitoring (65%), efficacy monitoring (36%), and dose finding (22%). The majority (86%) of Bayesian trials used non-informative priors. A total of 75 (27%) trials applied Bayesian methods for trial design and primary endpoint analysis. Among this latter group, the most commonly used methods were the Bayesian logistic regression model (N?=?22), the continual reassessment method (N?=?20), and adaptive randomization (N?=?16). Median institutional review board approval time from protocol submission was the same 1.4?months for Bayesian and non-Bayesian trials. Since the previous publication, the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial was the first large-scale decision trial combining multiple treatments in a single trial. Since then, two regimens in breast cancer therapy have been identified and published from the cooperative Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY 2), enhancing cooperation among investigators and drug developers across the nation, as well as advancing information needed for personalized medicine. Many software programs and Shiny applications for Bayesian trial design and calculations are available from our website which has had more than 21,000 downloads worldwide since 2004. CONCLUSION:Bayesian trials have the increased flexibility in trial design needed for personalized medicine, resulting in more cooperation among researchers working to fight against cancer. Some disadvantages of Bayesian trials remain, but new methods and software are available to improve their function and incorporation into cancer clinical research.

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Time Management of Tuzla University Clinical Center Health Managers.

Publications

Time Management of Tuzla University Clinical Center Health Managers.

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