Method to determine allocation of clinical pharmacist resources

Hospital pharmacy departments often face challenges in allocating clinical pharmacist resources efficiently. Prior research has shown that subjective decisions may lead to suboptimal staffing. This gap motivated the need for an objective framework. Existing knowledge includes general staffing models, but no prior work had resolved how to integrate patient census and medication data into a decision-making tool. The medical center's need for a structured approach led to the development of a novel methodology. The lack of a standardized allocation system for clinical pharmacist specialists (CPS) created uncertainty in resource planning. This paper's contribution lies in introducing a data-driven method to guide CPS deployment. The approach aims to improve the precision of staffing decisions in hospital settings.

The study aimed to develop an objective methodology for allocating clinical pharmacist resources in a hospital setting. The specific problem addressed was the lack of a standardized system for deploying CPS staff. The motivation stemmed from the need to align staffing with patient care demands. The task force sought to evaluate the relative need for CPS expertise across hospital services. The goal was to identify metrics that best reflect the impact of CPS involvement. The methodology was designed to incorporate patient census and medication data. The approach was intended to support both existing and future staffing decisions. The study focused on creating a reproducible tool for pharmacy resource allocation.

The task force analyzed patient census and medication-use data over several years. Proprietary databases from Thomson Reuters were used to calculate pharmacy intensity scores. The study evaluated five metrics relevant to pharmacy coverage. These metrics included patient acuity, drug expenditures, and high-risk medication use. The methodology was applied to assess the need for CPS expertise in various services. The approach considered teaching involvement and service-specific data. The task force used the metrics to determine optimal CPS placement. The methodology was tested across multiple hospital units to validate its applicability.

The methodology identified oncology, bone marrow transplant, and intensive care units as high-priority areas. These services showed the highest pharmacy intensity scores. The tool confirmed the existing allocation of CPS staff in these units. The approach was used to guide budgeting for new CPS positions. The metrics included patient census and drug expenditures. The model incorporated the use of high-risk medications as a key factor. The tool was validated through its application in multiple hospital services. The results supported the use of the methodology for future staffing decisions.

The authors propose that the developed tool can guide the allocation of clinical pharmacist resources. The methodology provides an objective framework for CPS deployment. The tool incorporates patient census and medication data into staffing decisions. The findings suggest that oncology and intensive care units require higher CPS involvement. The approach was validated through its use in existing and new staffing scenarios. The study supports the use of data-driven metrics for resource allocation. The tool can be adapted for use in other hospital settings with similar models. The authors suggest that the methodology improves the precision of staffing decisions.