Evaluation Of Classification Methods For The Prediction Of Hospital Length

The issue of longer wait time can results in a major implication to the hospital overall workflow, community reach, quality, patient perception, profitability, efficiency, and can lead to failure in meeting regulatory guidelines and standards affecting the organization operations.

These patients’ outcomes were greatly impacted by the initial response of the ED providers. For example, if sepsis goes unrecognized in a patient within the hour they potentially may be so behind in their fluid status and antibiotic intervention that the delay would mean the difference between life or

One of the primary issues the Healthgrades report identified was the impact and direct costs involved with patient complications and mortality. Healthgrades focuses on making the hospitals accountable and transparent into their clinical performance thereby providing care that is of higher quality, save lives and decrease costs (Healthgrades, 2013). Everyone who works in an acute care setting hears the term “increased length of stay” and according to Healthgrades complications account for 31% to 68% of the total variation in direct hospital costs. In fact, increased length of stay has the biggest impact on direct cost to the hospital

In this paper I discuss how holding patients in the Emergency Department (ED) has a negative effect on patients. To many patients in the ED , medication errors and patients lingering in the ED instead of being in the Intensive Care Unit (ICU) are the main cause of mortality and morbidity. For this assignment, I gathered information to figure out if the increased number of patients in the ED, medication errors, and the length of time ICU patients are held in the ED at Ohio Valley Medical Center (OVMC) is an actual issue that is effecting our patients. After doing a complete assessment and gathering the needed information, a plan will be put together to cut back

The length of stay of a hospitalized patient is an excellent proxy measure for a hospital’s ability to quickly and effectively diagnose and treat patients in a coordinated fashion. Historically, patients were hospitalized until all of their complaints were diagnosed, treated, and symptoms resolved (Kalra, Fisher & Axelrod, 2010, p. 930). However, hospitals are now focused on providing treatment to stabilize conditions, reduce the time spent in the hospital, and provide complete care on an outpatient basis (Kalra, et al, 2010, p.930). This change in the healthcare delivery model was inevitable, since hospitals are now paid the same amount of money for procedures regardless of the number of days spent in the hospital. For example, “Medicare

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The mean number of hospitalization stay in the intervention and control groups was 31.26±16.89 days and 41.82±23.07 days, respectively. According to the independent t-test’s results, there was statistically a significant between-groups difference in terms of the length of hospital stay (p=0.022) (Table

patient time and can also improve the care the doctor is able to provide to the patient.

desires and choices of the patient. Good quality of care will not initiate harm to their patient

Better quality patient management would include proper patient history including demographics, current medications and adverse events, discharge summaries and clinical measurements which will allow for more efficient and focused

Patient state at arrival: this shows if the patient planned the admission or it was a case of emergency. According to Healthcare Analysis and Forecasting, the financial risk associated with emergency admissions is up to 3-times higher than due to chance variation alone. There are considerable implications to the longer-term bed requirements of hospitals, to health care costs, commissioning and financial risk. We should also consider if they are coming in with one or more conditions.

Some illnesses require longer hospital stay than others. For example, a woman who gives birth to a baby using a caesarean section, is required to stay in the hospital for 2 days or more for close monitoring. This may not be the case for a person who just had a heart transplant. He might be required to stay in the hospital longer than 2 days after the surgery, as opposed to the pregnant mother who had a baby. Both scenarios are completely different, and this can make up for the disparity in the average length of stay amongst all 3 countries. Unless being treated for the same illness, it is difficult to make comparisons among all 3 countries to determine the average length of stay after a curative care has been given.

Length of stay in the Emergency Department (ED) is one important aspect of healthcare that can affect patient satisfaction as well as the number of patients that can be seen by a physician in a day. Length of stay (LOS) is measured as the time a person spends at the ED between arrival and departure (1). A longer LOS will not only affect patient satisfaction negatively by creating unnecessary frustrations, but can also cause ED overcrowding, leading to poor patient care (2).

The dynamics of hospital performance are often associated with effective patients’ care and length of stay. Hospitals are predisposed to pursue excellence and achieve and sustain improvement with respect to length of stay, over time. In this case study, the hospital governance is seeking to reduce the length of stay in the inpatient setting at four of the system 's hospitals. Each hospital management team has commenced strategies to reduce the length of stay over the previous year. The dynamics of hospital performance excellence and the entire process effectiveness and efficiency in patient care, reflect the instituting, sustaining of a principles and values of quality (Silow-Carrol, Alteras, & Meyer, 2007). Generally, we will conduct an analysis of the raw data provided to identify the hospital that is most likely achieving the lowest length of stay. We will present some descriptive statistical analysis of the data on reducing the length of stay in the inpatient setting. Furthermore, we will investigate other possible set of data elements that would be helpful for a researcher to collect. Moreover, we will explore the statistical mean, mode, standard deviation, variance and critical values at the 95% confidence interval in the analyses and presentation of results.

There are growing researches in data mining as a part of education. This new developing field, called Educational Data Mining, concerns with creating techniques that find information from data originate from educational situations. The data can be collected structure verifiable and operational data dwell in the databases of educational establishments. The understudy data can be close to home or scholastic. Additionally it can be gathered from e-learning frameworks which have a vast measure of data utilized by mostly organizations. Educational data mining utilized numerous strategies, for example, decision trees, neural systems, k-nearest Neighbor, Naive Bayes, help vector machines and numerous