The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. These results reflect the overall impact observed following the entire course of the COVID-19 pandemic and are not specific to a particular wave. The analysis revealed that older participants experienced a more pronounced decline in productivity, with a mean decrease of 35% compared to younger participants. Female participants, on average, had a 28% decrease in productivity compared to their male counterparts. Moreover, individuals with lower socioeconomic status exhibited a substantial decline in productivity, experiencing an average decrease of 40% compared to those with higher socioeconomic status. Similarly, participants who slept for fewer hours per night had a significant decline in productivity, with an average decrease of 33% compared to those who had sufficient sleep. The machine learning analysis identified age, specialty, COVID-19 complications, socioeconomic status, and sleeping time as crucial predictors of productivity score. The study highlights the significant impact of post-COVID-19 on the productivity of medical staff and doctors in Iraq. The findings can aid healthcare organizations in devising strategies to mitigate the negative consequences of COVID-19 on medical staff and doctors' productivity.
Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s
... Show Moreمنذ بداية نشوء المستشفيات كمنظمات تعنى بصحة الناس، عُرف الصراع (Conflict) بين جماعتي الإدارة والأطباء. وقد تركز محوره في معظم الأحيان حول سلطة أي منهما هي الأقوى في اتخاذ قرارات المستشفى.. هذا إذا ما علمنا أن كل من الطرفين المتعارضين يستمد قوته ويعزز مكانته من خلال سلطته في اتخاذ القرارات. إن جهود كلا الجماعتين الطبية والإدارية تصب في منهل واحد ألا وهو خدمة المريض والارتقاء بالمستوى الصحي ل
... Show MoreThere is limited data and evidence about the effects of COVID-19 on Maternal health, especially when new information is emerging daily, through pregnancy, child birth and post natal period, women are vulnerable to have the infection, this article, aimed to show the suitable measures that should be applied for women at reproductive age who are suspected /confirmed with COVID -19 infection,
During pregnancy it is advisable to continue the antenatal care schedule, although reducing face to face visit is recommended (unless the pregnant condition required that ),and prioritize ANC at health facilities for high-risk pregnancy and during second half of pregnancy with adequate infection prevention control measures.
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... Show MoreHeart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms,
... Show MoreBackground: COVID-19 is a disease that started in Wuhan/China in late 2019 and continued through 2020 worldwide. Scientists worldwide continue to research to find vaccines, treatments, and medication for this disease. Studies also conenue to find the pathogenicity and epidemiology mechanisms. Materials and Methods: In this work, we analyzed cases obtained from Alshifaa center in Baghdad/Iraq for 23/2/2020-31/5/2020 with total instances of 797, positive cases of 393, and death cases of 30. Results: Results showed that the highest infection cases were among people aged between 41-45. Also, it was found that males' number of cases was more than females. In contrast, death cases were significantly higher in males than females. It was not
... Show MoreThe study aims to determine the responsibility of nursing staff for medical errors that accompany the surgery at the hospital , which is one of the important issues that the importance of determining the responsibility of landlords nursing medical errors in the hospital was the rationale in choosing the subject. Since study included cases of errors nursing staff of (22) case. While the checklist distributed to a sample of nursing Angel Group in Sadr Teaching Hospital 's (100) nurs According to the problem, the study in determining the responsibility of nursing staff for medical errors that are not important for the hospitals. Has shown results of the study agreement members of the study sample that the clarity of the power posses
... Show MoreObjective: To assess role of obesity in Covid-19 patients on antibodies production, diabetes development, and treatment of this disease. Methodology: This observational study included 200 Covid-19 patients in privet centers from January 1, 2021 to January 1, 2022. All patients had fasting blood sugars and anti-Covid-19 antibodies. Anthropometric parameters were measured in all participants. Results: The patients were divided into two groups according to body weight; normal body weight (50) and excess body weight (150). There was a significant difference between them regarding age. Diabetes mellitus developed in 20% of normal weight patients while 80% of excess weight patients had diabetes (p=0.0001). Antibodies production (IgM and
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