PDF-6.4MB - Faculty of Industrial Engineering and Management

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Number of cases 120 100 80 60 40 20 Arrivals to queue September 2001 0 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time (Resolution 30 sec.) 04.09.2001 11.09.2001 18.09.2001 25.09.2001
Optimizing Patient Flow by Managing Its Variability C H A P T E R 4 FIGURE 4.1 Tracking Patient Census This graph represents typical hospital census for weekdays (each point represents a day). The peaks and valleys represent residuals from the mean census identified by the dashed line. decision to discharge a patient from the ED or maybe to transfer a patient when, under normal circumstances, the patient would be admitted.Thus, a hospital underutilizes its resources on one day, and the next day these resources are put under stress with resultant consequences for access to and quality of care. One may conclude that hospital capacity in its current form is not sufficient to guarantee quality care. Does the health care delivery system need additional resources? The typical answer is “yes.”Then, the next logical question is What additional resources are needed to guarantee quality care? For example,What kind of beds does a particular hospital need? Does it need more ICU beds? more maternity beds? more telemetry beds? If yes, how many? Surprisingly, not many hospitals, if any, can justify their answers to those questions. They cannot specifically demonstrate how many of which types of beds will guarantee quality of care. But consider an individual going to the bank under similar circumstances to borrow money. In response, the bank, asks two basic 97
Page 1 and 2: Service Engineering Class 5 Fluid/F
Page 3 and 4: Types of Queues • Perpetual Queue
Page 5 and 6: The “Fluid View” or Flow Models
Page 7 and 8: Contents • Scenario Analysis: Emp
Page 9 and 10: Shouldice Hospital: Flow Chart of P
Page 11 and 12: Daily Queues Israeli Call Center, N
Page 13: Number of cases 9000 8000 7000 6000
Page 17 and 18: Patients per Hour Average Patients
Page 27 and 28: Optimizing Patient Flow by Managing
Page 29 and 30: ✬ ✫ Service Times: The Human Fa
Page 31: Scales (Fig. 2.1 in Newell’s book
Page 34 and 35: 41 Arrivals Process, in 1976 Arriva
Page 38 and 39: Fluid Models and Empirical Models S
Page 43 and 44: This suggests that our empirical mo
Page 45 and 46: Predictable Queues Fluid Models and
Page 47 and 48: Time-Varying Queues with Abandonmen
Page 49 and 50: Fluid Model Replacing random proces
Page 51: Starting Empty and Approaching Stat
Page 54 and 55: 8 350 300 250 200 150 100 50 Lambda
Page 56 and 57: What if Pr{Retrial} increases to 0.
Page 58: Bottleneck Analysis Inventory Build

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