Many hospitals use hospitalists instead of primary care physicians to provide medical care to hospitalized patients. Hospitals have found that hospitalists are more efficient and cost effective than primary care physicians. However, after the implementation of electronic healthcare record systems (EHRs), hospitalists are facing the burden of having high workloads as they need to document patients’ records in the EHR. Also, hospitalists often experience the issue of workload variation among them, or time wasted activities resulting from patient assignment policies. Hospitalist productivity can be improved by having hospitalists work with scribes, as scribes help hospitalists with documenting patients’ records in the EHR. Also, improving patient assignment policies can help minimize the variation in hospitalist workloads. This dissertation consists of three papers that investigate hospitalist workload in order to improve hospitalist productivity. The first paper investigates the practice of having scribes work with hospitalists. This paper considers different approaches to quantify scribe’s benefits. Then, the financial impact of those approaches is analyzed in isolation or in different combinations to test if scribes working with hospitalists is cost efficient or not. The second paper develops stochastic models for three different staffing scenarios in order to help hospitals hire the optimum number of scribes to maximize the financial benefits of scribes and to ensure the reliability of the scribe program. The results of the stochastic models are analyzed and compared against each other to create an effective tool for hospital decision makers to use to determine the optimum number of scribes. The third paper studies the impact of different patient assignment policies on hospitalist workloads. This paper proposes new assignment policies that consider balancing hospitalist workloads based on patient acuity load instead of just balancing the number of patients each hospitalist has in order to reduce the variation in hospitalist workloads.

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