Potential Cost Avoidance of Pharmacy Students' Activities
Potential Cost Avoidance of Pharmacy Students' Activities
The P4 year at the Northeastern University School of Pharmacy included six 6-week APPEs, 4 of which were required: ambulatory care, community practice, general medicine, and health system pharmacy practice experiences. The new Web-based E-Value intervention system contained fields to document patient demographics, drug-therapy problem category, description, drugs and disease states involved, recommendations made, acceptance by the healthcare provider, economic impact, and preceptor involvement. For purposes of this study, an intervention was an actionable activity or recommendation that a student completed under the supervision of the preceptor. All student activities had the potential to be influenced by the preceptor; therefore, the cost avoidance impact reported in this study was attributed to the students and preceptors as a group.
During the initial group training in using the documentation system, students were instructed to document significant and meaningful interventions, resulting in an action or recommendation, rather than simply documenting all chart reviews, medication dispensing, etc. Students were also instructed in how to improve the consistency of their intervention documentation in PxDx. A handout summarizing documentation steps and definitions of terms was provided at the time of training and posted on the school's Blackboard site (Blackboard, Inc; Washington, DC) (Table 1). Additionally, the students as a group documented several interventions during the training to further improve consistency of documentation. During the APPE year, student documentation of interventions in the PxDx database was voluntary and depended on the type of practice experiences completed and preceptor requirements to avoid duplication of documentation efforts at sites that had their own intervention system. All preceptors were informed about the existence of PxDx and encouraged to require students to document their interventions and clinical services either in PxDx or in a site-specific database. Preceptors received the same training materials as the students and were instructed on how to review and sign off on student interventions. Preceptors had an option of approving the student intervention or asking for modifications of the intervention prior to approval.
To establish the potential cost avoidance associated with interventions, we conducted a literature search and reviewed data provided by Pharmacy OneSource for their Quantifi software (Pharmacy OneSource, Bellevue, WA). A search of PubMed between January 1990 to January 2013 was conducted with the terms "pharmacy student" and "cost avoidance" which yielded 6 articles, 2 of which were excluded because they focused on medication therapy management. References cited within the articles were also scanned to identify any other pertinent literature with a focus on publications within the last decade. The Quantifi algorithm provided a conservative estimation of cost avoidance for ADEs by accounting for the likelihood of a preventable ADE occurring in combination with an estimated healthcare inflation rate factor that adjusted for the average cost of ADEs reported in the literature.
Identified costs were adjusted for inflation to December 2011 US dollars using the consumer price index for medical care. This month was chosen because it was approximately the midpoint of the academic year in which interventions were reported. The costs were derived from similar intervention categories described in previously published articles. When multiple sources were identified for a specific category, inflated costs were averaged. Final costs used in the analyses are presented in Table 2. In cases when the published literature did not indicate the year the dollar amounts were calculated, the date of publication became a conservative strategy for estimating costs for this analysis. For purposes of this study, we included both immediate (eg, discontinuation of a medication) and long-term costs (eg, addition of a medication to bring care to the standard and avoid future morbidity and mortality) when calculating cost avoidance. To explore the uncertainty associated with the estimates used, we conducted sensitivity analyses using the following scenarios: (1) low and high dollar values reported in the literature for interventions where a range was available; (2) zero dollars of cost avoidance assigned to "provider education"; and (3) cost avoidance if zero ADEs were prevented. The latter 2 scenarios were chosen because these intervention categories were associated with the highest cost savings estimates.
All interventions entered into the E-Value PxDx system between May 1, 2011, and May 1, 2012, a full academic year, were analyzed. We used descriptive statistics to analyze common intervention categories, percent of interventions accepted by providers, and student perceived clinical impact. Cost avoidance was estimated by multiplying the number of interventions in a particular category by the cost avoidance per intervention. Intervention categorization and the potential avoidance of ADEs were contained in different fields of the database; therefore the interventions within some of the categories were divided into 2 groups based on whether they resulted in ADE prevention to avoid double-counting. This study was approved by the Northeastern University Institutional Review Board.
Methods
The P4 year at the Northeastern University School of Pharmacy included six 6-week APPEs, 4 of which were required: ambulatory care, community practice, general medicine, and health system pharmacy practice experiences. The new Web-based E-Value intervention system contained fields to document patient demographics, drug-therapy problem category, description, drugs and disease states involved, recommendations made, acceptance by the healthcare provider, economic impact, and preceptor involvement. For purposes of this study, an intervention was an actionable activity or recommendation that a student completed under the supervision of the preceptor. All student activities had the potential to be influenced by the preceptor; therefore, the cost avoidance impact reported in this study was attributed to the students and preceptors as a group.
During the initial group training in using the documentation system, students were instructed to document significant and meaningful interventions, resulting in an action or recommendation, rather than simply documenting all chart reviews, medication dispensing, etc. Students were also instructed in how to improve the consistency of their intervention documentation in PxDx. A handout summarizing documentation steps and definitions of terms was provided at the time of training and posted on the school's Blackboard site (Blackboard, Inc; Washington, DC) (Table 1). Additionally, the students as a group documented several interventions during the training to further improve consistency of documentation. During the APPE year, student documentation of interventions in the PxDx database was voluntary and depended on the type of practice experiences completed and preceptor requirements to avoid duplication of documentation efforts at sites that had their own intervention system. All preceptors were informed about the existence of PxDx and encouraged to require students to document their interventions and clinical services either in PxDx or in a site-specific database. Preceptors received the same training materials as the students and were instructed on how to review and sign off on student interventions. Preceptors had an option of approving the student intervention or asking for modifications of the intervention prior to approval.
To establish the potential cost avoidance associated with interventions, we conducted a literature search and reviewed data provided by Pharmacy OneSource for their Quantifi software (Pharmacy OneSource, Bellevue, WA). A search of PubMed between January 1990 to January 2013 was conducted with the terms "pharmacy student" and "cost avoidance" which yielded 6 articles, 2 of which were excluded because they focused on medication therapy management. References cited within the articles were also scanned to identify any other pertinent literature with a focus on publications within the last decade. The Quantifi algorithm provided a conservative estimation of cost avoidance for ADEs by accounting for the likelihood of a preventable ADE occurring in combination with an estimated healthcare inflation rate factor that adjusted for the average cost of ADEs reported in the literature.
Identified costs were adjusted for inflation to December 2011 US dollars using the consumer price index for medical care. This month was chosen because it was approximately the midpoint of the academic year in which interventions were reported. The costs were derived from similar intervention categories described in previously published articles. When multiple sources were identified for a specific category, inflated costs were averaged. Final costs used in the analyses are presented in Table 2. In cases when the published literature did not indicate the year the dollar amounts were calculated, the date of publication became a conservative strategy for estimating costs for this analysis. For purposes of this study, we included both immediate (eg, discontinuation of a medication) and long-term costs (eg, addition of a medication to bring care to the standard and avoid future morbidity and mortality) when calculating cost avoidance. To explore the uncertainty associated with the estimates used, we conducted sensitivity analyses using the following scenarios: (1) low and high dollar values reported in the literature for interventions where a range was available; (2) zero dollars of cost avoidance assigned to "provider education"; and (3) cost avoidance if zero ADEs were prevented. The latter 2 scenarios were chosen because these intervention categories were associated with the highest cost savings estimates.
All interventions entered into the E-Value PxDx system between May 1, 2011, and May 1, 2012, a full academic year, were analyzed. We used descriptive statistics to analyze common intervention categories, percent of interventions accepted by providers, and student perceived clinical impact. Cost avoidance was estimated by multiplying the number of interventions in a particular category by the cost avoidance per intervention. Intervention categorization and the potential avoidance of ADEs were contained in different fields of the database; therefore the interventions within some of the categories were divided into 2 groups based on whether they resulted in ADE prevention to avoid double-counting. This study was approved by the Northeastern University Institutional Review Board.