This thesis study evaluates access to care at an internal medicine unit with solely semi-private rooms at Baystate Medical Center (BMC). Patients are divided into two types: Type I patient consumes one bed; Type II patient occupies two beds or an entire semi-private room as a private space for clinical reasons, resulting in one empty but unavailable (blocked) bed per Type II patient. Because little data is available on blocked beds and Type II patients, unit-level hospital bed planning studies that consider blocked beds have been lacking. This thesis study bridges that gap by building a single-stream and a two-stream discrete micro-simulation model in Excel VBA to describe unit-level bed queue dynamics at hourly granularity in the next 48-hour time horizon, using historical arrival rates and census-dependent discharge rates, supplemented with qualitative results on complexity of patient-level discharge prediction. Results showed that while we increase additional semiprivate beds, there was notable difference between the traditional single-stream model and the two-stream model concerning improvement in bed queue size. Possible directions for future research include patient-level discharge prediction considering both clinical and nonclinical milestones, and strategic redesign of hospital unit(s) considering overflows and internal transfers.