In this scenario, the patient is an 80-year called Richard who is scheduled for a total hip arthroplasty surgery. As part of the hospital’s practices, Richard was informed of the expected length of stay in the hospital following the procedure. The length that the patient was informed of surprised him, prompting him to seek answers for the underlying reasons for such customs. The doctor then explains to Richard the logic behind the length of stay, listing reasons such as the need for patient observation, age, medical history, and potential understaffing.
Since every hospital strives to be more efficient in its patient flow, it is not beneficial for it to keep its patients for observation post-surgery for an extended period. Therefore, hospitals try to minimize the length of stay (LOS) for each patient, optimizing the care they provide (Khosravizadeh et al., 2016). It is, therefore, logical that the reasons behind determining the length of stay of patients such as Richard are assessed in a way that only prolongs the stay if necessary.
Some factors that affect the length of stay are the marital status, employment, and age of the patient (Khosravizadeh et al., 2016). If the patient’s condition requires third-party surveillance after the operation or treatment, they must have the accommodations required for such surveillance outside the hospital. Such as, if the patient is married, the spouse might be able to look after them once the patient is out of the hospital. However, if the patient does not have anyone who can take care of them at home, it might be necessary to stay in the hospital for longer.
Furthermore, some of the reasons might lie in the hospital structure itself. According to Baek et al. (2018), understaffing and the absence of senior staff during weekends can also delay discharge, causing an extended length of stay. If there is not enough sufficiently qualified staff, it might affect the length of stay of the patients, as they require clearance before being discharged. However, with efficient planning, these problems can be avoided, and the patients can be discharged after an optimal amount of time, saving resources.
Baek, H., Cho, M., Kim, S., Hwang, H., Song, M. & Yoo, S. (2018). Analysis of length of hospital stay using electronic health records: A statistical and data mining approach. PLoS One, 13(4).
Khosravizadeh, O., Vatankhah, S., Bastani, P., Kalhor, R., Alirezaei S. & Doosty, F. (2016). Factors affecting length of stay in teaching hospitals of a middle-income country. Electron Physician, 8(10), 3042–3047.