DESIGN FOR CHANGE PROPOSAL
Re-hospitalization of older adults has become prevalent in most of the health facilities in Canada, posing financial challenges for the health system as well as the older adults. Besides the rise in healthcare expenses that emanate from this problem, readmissions lead to other health-related challenges. The health issues that are related to readmissions include functional disintegration and increased mortality cases. Concerning the cost of readmission, the Canadian health system spends more than $1.8 billion annually due to readmissions. This has reached an alarming level, and prompt measures need to be taken to elevate the problem. Apparently, in the past decade, the Canadian population has increased by roughly 16%, but the number of Canadian hospital beds has not increased in significant percentages. Therefore, with constant hospital readmissions, the quality of care reduces significantly. In fact, one of the indicators of an institution’s inability to offer quality care is a high rate of readmissions. With the desire to enhance the satisfaction of adult patients, health facilities should understand that patient’s comfort correlates directly with lower readmission rates (Philp, Mills, Long, Thanvi & Ghosh, 2013). Therefore, to improve patient outcomes, it will be imperative to adopt a change model in the health facility that will ensure reduced older adults’ readmissions and offer more capacity within the existing resources.
Overview of the Change Model
First, the integration of services will take quite a long time between five and ten years, but some immediate targeted changes can be implemented on the system and processes of care within the health facilities. These changes will not be dependent on a sustained and consistent political leadership as is the case for the huge system changes. Therefore, they will have a more immediate effect on the quality of care extended to the older people, preventing their constant re-hospitalization and length of stay in the health facilities (Kripalani, Theobald, Anctil & Vasilevskis, 2014). Older people require a balance in both their health care and social care, and therefore a model that takes into account improvements in the communication modalities and coordination of services will be essential (Futoma, Morris & Lucas, 2015). Additionally, the change model should take into account the need to incorporate access to robust, reliable and up to date patient data. Therefore, information systems adopted by the health facilities have to be current with the health records and social needs of the patients. Another factor that the change model will have to incorporate is the input of specialist geriatricians who will offer regular, timely and focused case reviews on the status of the older persons. Readmission of the elderly people especially within the next 30 days in the ” Predictive Modeling” that features modalities can be minimized through discharge planning, discharge nurses, patient education and follow up activities (Futoma, Morris & Lucas, 2015).
The predictive modeling approach is essential since it allows nurses to offer care in a patient-centric manner. With the adoption of the modalities presented in predictive modeling, health care providers will have access to information that may influence readmissions. They include age, sex, whether patients have primary caregivers at home, the number of acute admissions that the patient has had over the past six months and the current clinical conditions of the patient. Additionally, the systems factors will be explored which include hospital effects such as the hospital’s length of stay variance and the size of the hospital. They can cause early discharge of patients several other readmissions (Shrader, 2014). Lastly, predictive modeling will factor in some of the community effects facing the client such as the rural residence of the older person, as well as the income and socio-demographics of the neighborhood where the patient lives.
The database to ensure that readmissions are tracked...
References
Futoma, J., Morris, J., & Lucas, J. (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics, 56, 229-238. http://dx.doi.org/10.1016/j.jbi.2015.05.016
Kripalani, S., Theobald, C., Anctil, B., & Vasilevskis, E. (2014). Reducing Hospital Readmission Rates: Current Strategies and Future Directions. Annual Review of Medicine, 65(1), 471-485. http://dx.doi.org/10.1146/annurev-med-022613-090415
Philp, I., Mills, K., Long, J., Thanvi, B., & Ghosh, K. (2013). Reducing hospital bed use by frail older people: results from a systematic review of the literature. International Journal of Integrated Care, 13(4). http://dx.doi.org/10.5334/ijic.1148
Shrader, R. (2014). Predictive analytics drive down hospital readmissions. Healthcare IT News. Retrieved 14 November 2017, from http://www.healthcareitnews.com /predictive-analytics-drive-down-hospital-readmissions
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