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Simulation Of Patient Discharge Process And Its Improvement

583 words - 3 pages

SIMULATION OF PATIENT DISCHARGE PROCESS AND ITS IMPROVEMENT
This article is about a case study by Khurma, Salamati, and Pasek discussing about the results of a simulation based recommendations conducted with a regional hospital and concerned with inpatient discharge process in the Canadian healthcare system. The main objective was to reduce the alternative level of care or length of stay. This recommendations were obtained with various simulations and data gathered from a process of patient treatments in hospitals.

While conducting the study focusing on lengthy patient episodes it was found that there are four types of system obstacles that prevented the timely discharge. This obstacles were patient care issues, financial issues, administrative issues and deficiencies in coordination between hospital and community personnel. Because of this nonmedical reasons were displayed it ...view middle of the document...

Because of this patient flow a regression analysis performed on the patient data and it indicated that the most persistent category of patients contributing to this alternative level of care were the ones requiring placement in long term. It was determined the top ranked medical units sending most patients with a sample of data collection. Then a model was structured in a way similar to the sequence of events of discharge planning to run simulations a determine recommendations. The complex process was simplified since the data collection process was really extensive. Control charts were created to make a better analysis and possible causes for the problem. The goal to be achieved by this statistical tests is to get outputs that are similar to the actual lengths of stay of patients from the different units.

As the model was created and run for a sample of patients equal in volume to the chosen sample size of 152. Determined that the Long term Care facilities and complex continuing care facilities cause more persistent alternative level of care cases and longer alternative level of care days. This might lead to the conclusion that the hospital cannot do anything about it, as it was suspected from the data gathering. The data leads Khurma, Salamati, and Pasek that they can either focus into minimizing that delay, or quantifying that delay accurately to help with the predictions for patient discharge, by these the can help the admission process. Then in the case of alternative level of care patients going to long term care was used to see whether in fact there is something that the hospital can do to minimize length of stay. The simulation model created was compared with another that proposed referral to social work and this time for involvement of social work to be strictly set to a maximum of 3 and 2 days. Finally this recommended improvement was evident with a median decrease of about 5 days in length of stay of patients who wait alternative level of care days.

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