Addressing the needs of a patient, one must bear in mind that the services required by the latter should be provided to them in a timely manner. Therefore, a range of treatment outcomes and often even the recovery hinge on the precision of determining the patient’s length of stay in the hospital. As a recent study explains, “Predicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible” (Hachesu, Ahmadi, Alizadeh, and Sadoughi 21). In other words, the time that the patient will spend in the hospital setting must be distributed carefully so that the corresponding treatment could facilitated to them properly.
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However, in some cases, determining the time of the patient’s stay in the hospital may be quite tricky due to the factors such as the healthcare opportunities that the hospital can provide, the severity of the patient’s disease or disorder and the skills of the corresponding healthcare expert. At this point, the fact that the key factors affecting the length of the patient’s stay are split into two large categories should be brought up. Particularly, internal (patient-induced) and external (enhanced by the hospital setting) factors exist (Schneider et al. 156). In other words, the factors such as the patient’s age and health issues can be defined as the internal ones, whereas the hospital’s capacity and the physician’s or the surgeon’s workload are the external factors (Schneider et al 153). Seeing that the latter can be controlled, it is reasonable to assume that a tool for managing these factors should be introduced into the hospital setting.
Therefore, the process of identifying the length of the patient’s stay is complicated, and its results can be deemed as fairly approximate. Because of the lack of precision in the current methods of identification of the patient’s stay length and the negative outcomes that the given inconsistency triggers, the need to address the problem in question arises. Addressing the problem of locating the measurement tools for identifying the length of the patient’s stay is obvious, as the prolonged stay in the hospital setting affects the patient’s health just as negatively as an early discharge.
Research Design and Data Collection Plan
Analytics Goal and Business Insight
To create the model that will allow for a comparatively precise identification of the number of days that the patient must stay in the hospital setting, one will have to conduct a thorough and detailed study. The research design can be characterized as mixed since both qualitative and quantitative data will have to be gathered and analyzed to determine the key constituents of the model in question.
The data regarding the average length of the patients’ stay, as well as the factors that affect it and the measures that the healthcare staff undertakes in order to reduce the length thereof, will be collected by considering both the patients’ personal records and the therapists’ professional ones. Thus, the key information, such as the age and health status of the people, who need the hospital’s services, and the essential external factors, including the proficiency of the hospital’s staff, will be made available to the researchers.
Length of Stay and Other Measurements: Analysis
The quantitative part of the analysis will be carried out with the help of a standard set of statistical tools, including the location of the mean, the standard deviation in the length of the patients’ stay at the hospital, confidence limits of a measurement and the sample bias identification. Traditionally, the factors such as “age, presenting complaint, and physician diagnosis” (Mak, Grant, McKenzie, and McCabe 2) affect the determination of the patients’ stay in the hospital and the duration of the stay. Seeing that the personal factors affect the subject matter just as much as the external ones do, it is reasonable to assume that a complex approach including the consideration of the factors such as age, health state and diagnosis, on the one hand, and the proficiency of the therapist or surgeon, the quality of care and the expertise of the nurses, on the other hand, should be assessed so that the length of the patient’s stay could be determined precisely.
Another possible approach to the process of determining the patient’s stay duration, the use of tracking tools can be viewed as a possibility. Helping trace the progress of the patient at the first stages of their therapy, the specified tool will provide the data that will help make predictions concerning the observable future. One must admit, though, that the specified tool is typically considered less efficient than the one described above, as it only implies the influence of external and internal factors, yet does not presuppose their analysis (Davis and Lacour 131).
The location of the average forecasting error rate (AFET) should also be viewed as an essential step towards improving the quality of healthcare services in the hospital setting (Dua, Sahni, and Goyal 226). Despite the fact that the tool in question cannot be viewed as a method that stands on its own, it can be combined with one of the approaches listed above to make them more sustainable, therefore, allowing for the acquisition of trustworthy outcomes.
Individual and Total Forecasts: Difference
Naturally, the process of forecasting the number of days that an individual patient is going to stay in the hospital setting is going to be quite different from making a prediction concerning an entire group of patients. First and most obvious, the precision rates are going to be much lower in the latter case, as it means rounding down the data retrieved in the course of analyzing patients’ records significantly. Thus, the average number of days that a group of patients is going to stay in the hospital setting is going to be more approximate than the forecast made for a single patient (Vincent 382).
Herein the key problem with making the forecasts of the specified type lies; no matter how careful the calculations might be, there will always be some degree of approximation of the forecast results. It would be wrong to assume that the forecast outcomes made for one patient are going to be completely true; there is a certain amount of guesswork in the location of the length of the patient’s stay in the hospital. Nevertheless, when it comes to the forecasts made based on a set of unique characteristics of a patient and the ones that are designed for an entire group of people, the latter are bound to be less precise.
Davis, Nadinia A. and Melissa Lacour. Health Information Technology. St. Louis, Missouri: Elsevier Health Sciences.
Dua, Sumeet, Sartaj Sahni, and Dev P. Goyal. Information Intelligence, Systems, Technology and Management. Berlin: Springer Science & Business Media, 2011. Print.
Hachesu, Peyman Rezaei, Maryam Ahmadi, Somayyeh Alizadeh, and Farahnaz Sadoughi. “Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients.” Healthcare Informatics Research 19.2 (2014): 121–129. Print.
Mak, Fregory, William D. Grant, James C. McKenzie, and John B. McCabe. “Physicians’ Ability to Predict Hospital Length of Stay for Patients Admitted to the Hospital from the Emergency Department.” Hindawi Publishing Corporation Emergency Medicine International 1.1 (2012): 1–4. Print.
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Vincent, Charles. Patient Safety. New York City, New York: John Wiley & Sons, 2011. Print.