Healthcare Optimization Discussion: Strategies and Applications

This discussion post addresses the optimization of healthcare systems, specifically focusing on strategies to improve patient care and resource allocation. The author suggests two primary optimization techniques: activity scheduling and the utilization of big data. Activity scheduling involves the structured planning and monitoring of patient diagnoses and treatments to ensure timely care, with a focus on efficient resource management such as optimizing surgery schedules. Big data applications are also recommended, emphasizing the integration of patient information from various sources to inform treatment plans and predict potential issues like hospital readmissions. The discussion highlights the importance of these strategies in improving patient outcomes and enhancing overall hospital efficiency. References include research papers that support the application of these strategies in healthcare settings.

Running head- DISCUSSION
Optimization in healthcare
Name of the Student
Name of the University
Author Note

1DISCUSSION
Optimization in healthcare system refers to use of collaborative steps that address
healthcare challenges (Cabrera et al., 2012). Two optimization strategies that can be
implemented by David are activity scheduling and use of big data.
Activity scheduling involves daily and regular planning and monitoring diagnosis,
control activities and treatment for individual patients. The primary objective of this optimization
technique is associated with treating all patients as soon as possible, while taking their medical
urgencies into account (Gartner & Kolisch, 2014). Certain aspects of this activity scheduling
may involve optimal surgery scheduling for reducing staff workload, waiting time and improving
resource efficiency. This will enable the staff to provide better vigilance and continuously assess
the physiological condition of the patients (Maenhout & Vanhoucke, 2013).
Using big data will also help in combining the ability to track relevant patient
information, from disparate sources, using advanced computer networks and will assist in
accessing a wide range of knowledge on medical treatments. Such techniques encompass
electronic health records, and evidence based practice (Raghupathi & Raghupathi, 2014). It will
help the healthcare professionals identify patients who are at an increased likelihood of acquiring
infections. Thus, they will be able to better prescribe medications and interventions. It will also
help to accurately predict the patients susceptible to hospital readmissions (Murdoch & Detsky,
2013). Thus, big data (patient trends, medical history and charts) can be applied to construct
customized treatment plans that will help in tracking infectious diseases.
To conclude, David should implement activity scheduling and big data techniques in the
hospital to ensure that all patients are being monitored adequately by the staff, thereby reducing
infection and readmission.

2DISCUSSION
References
Cabrera, E., Taboada, M., Iglesias, M. L., Epelde, F., & Luque, E. (2012). Simulation
optimization for healthcare emergency departments. Procedia Computer Science, 9,
1464-1473.
Gartner, D., & Kolisch, R. (2014). Scheduling the hospital-wide flow of elective
patients. European Journal of Operational Research, 233(3), 689-699.
Maenhout, B., & Vanhoucke, M. (2013). An integrated nurse staffing and scheduling analysis for
longer-term nursing staff allocation problems. Omega, 41(2), 485-499.
Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health
care. Jama, 309(13), 1351-1352.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), 3.