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The American Journal of Accountable Care June 2018
Amazing Grace: A Free Clinic's Transformation to the Patient-Centered Medical Home Model
Jason Alexander, BS, PCMH CCE; Jordon Schagrin, MHCI, PCMH CCE; Scott Langdon, BA; Meghan Hufstader Gabriel, PhD; Kendall Cortelyou-Ward, PhD; Kourtney Nieves, PhD; Lauren Thawley, MSHSA; and Vincent Pereira, MHA, PCMH CCE
Lessons Learned in Implementing Behavioral Screening and Intervention
Richard L. Brown, MD, MPH
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Taylor Justice, MBA, President of Unite Us
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Nicholas Ma
Effects of an Integrated Medication Therapy Management Program in a Pioneer ACO
William R. Doucette, PhD; Yiran Zhang, PhD, BSPharm; Jane F. Pendergast, PhD; and John Witt, BS
Are Medical Offices Ready for Value-Based Reimbursement? Staff Perceptions of a Workplace Climate for Value and Efficiency
Rodney K. McCurdy, PhD, and William E. Encinosa, PhD
Utilizing Community Resources, New Payment Models, Technology to Deliver Accountable Care
Laura Joszt, MA
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Cost-Effectiveness of Pharmacist Postdischarge Follow-Up to Prevent Medication-Related Admissions
Brennan Spiegel, MD, MSHS; Rita Shane, PharmD; Katherine Palmer, PharmD; and Duong Donna Luong, PharmD

Cost-Effectiveness of Pharmacist Postdischarge Follow-Up to Prevent Medication-Related Admissions

Brennan Spiegel, MD, MSHS; Rita Shane, PharmD; Katherine Palmer, PharmD; and Duong Donna Luong, PharmD
A multivariable sensitivity analysis conducted on a pharmacy follow-up program of high-risk patients demonstrated cost savings to hospitals in 98.3% of head-to-head trials across 1000 hypothetical hospitals.
Sensitivity Analyses

Because our base-case cost and probabilities will not apply to all hospitals, we conducted 1-way sensitivity analyses for all estimates. We also conducted a Monte Carlo simulation, assuming that all variables followed a triangular distribution, which is widely accepted and standard practice,21 with base-case, minimum, and maximum values listed in Table 1 and the Figure. We also used results from the Monte Carlo simulation to generate a cost-effectiveness scatterplot and tested willingness-to-pay thresholds. One thousand trials were simulated and a 95% confidence ellipse was graphed around all trials in the scatterplot. We present both the confidence ellipse and 95% CIs around the base-case result. Finally, we created an ROI lookup table that accounts for the size of the hospital and staffing resources to implement the program.


The Cedars-Sinai Institutional Review Board approved this study (IRB Pro00038038).


Base-Case Results

Table 2 lists the characteristics of the study participants in the pharmacist program (n = 185) and usual care (n = 51). The usual care group represented patients who had no pharmacist postdischarge follow-up due to limited resources; they served as a control group. There were no significant differences in patients enrolled between the 2 groups. The average age across groups was 70.4 years, 58% were male, and participants used 13.2 medications on average. Of the 185 patients in the pharmacist quality improvement program, 90% could be successfully contacted within 72 hours of discharge; of this group, 86.4% were found to have 1 or more medication discrepancies. The mean and median numbers of discrepancies were 2.8 and 2.0, respectively, which includes both physician-related (ie, errors of medication reconciliation) and patient-related (ie, due to patient misunderstanding) DRPs. The most commonly found DRPs were patient-related nonadherence (39.0%), prescriber-related omission of order (15.3%), wrong dose or frequency (12.5%), and duplicate therapy (10.9%). The overall 30-day readmission rates for the program and usual care were 16.2% and 21.6%, respectively (relative risk reduction, 0.248; 95% CI, –39.4 to 59.5).

The average costs per patient for the program and for usual care were $3433 and $4015, respectively (difference, $582 per patient; Monte Carlo 95% CI, $528-$635). In multivariable sensitivity analysis across 1000 hypothetical hospitals of varying size and staffing, and assuming a willingness-to-pay threshold of only $10,000 per readmission prevented, the intervention remained cost-saving in 98.3% of head-to-head trials. In a projected 1-year analysis, and assuming the pharmacy team could successfully contact and manage 150 high-risk patients per month, the net annual savings to the hospital was $1,047,600.

Base-Case Sensitivity Analyses

Because the base-case assumptions of the model may not be reproducible across hospitals, we performed sensitivity analysis to test the model using other probability and cost estimates. One-way sensitivity analysis revealed that the pharmacy program would remain cost-saving so long as the following are true: (1) at least 34 patients are contacted by a pharmacist per month, (2) at least 21% of patients can be contacted by the pharmacist and/or do not refuse to speak, (3) the average direct hospital cost of a readmission is at least $3660, and (4) the intervention reduces readmissions by at least 9% relative to usual care.

The model was highly sensitive to the number of patients served by the pharmacy team and the size and cost of the team (Table 3). For example, if 200 patients are served per month at an annual staffing cost of $300,000 in salary and benefits, then the cost savings to the hospital is $1,696,800; if 300 are served at a team cost of $250,000, then $2,242,800 is saved. Table 3 provides an ROI lookup table to assist hospitals of different sizes and staffing levels in projecting the potential cost savings of implementing the pharmacist program. 

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