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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
The Intersection of Health and Social Services: How to Leverage Community Partnerships to Deliver Whole-Person Care
Taylor Justice, MBA, President of Unite Us
Case Study: Encouraging Patients to Schedule Annual Physicals
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.
Competing Strategies

Pharmacist postdischarge follow-up program. Between December 2014 and June 2015, we conducted a quality improvement project in partnership with our medical hospitalist services to screen high-risk patients at risk of readmission. Upon admission, high-risk patients were identified as meeting 1 or more of the following 3 criteria: (1) receiving 10 or more chronic medications, (2) having a diagnosis of pneumonia or congestive heart failure, and (3) receiving anticoagulants, as well as having low scores on an organizational medication literacy and adherence algorithm. These criteria were selected based on their association with hospital readmissions and expert opinion from our medical staff. Pharmacy staff were trained to use the algorithm when performing admission medication reconciliation.

Predischarge medication reconciliation was performed as part of usual care, primarily by physicians and allied health professionals, for an average of 90% of patients during the study period.

High-risk patients were selected for postdischarge follow-up by a transition of care (TOC) pharmacist. The TOC pharmacist compared the prior-to-admission medication list of each eligible patient with the after-visit summary discharge medication list. The TOC pharmacist contacted the discharging physician prior to and/or after patient contact to discuss and resolve any DRPs identified and to ensure that the physician’s intent was carried out during the telephone follow-up call. 

The objectives of each call were to ensure the patient had an accurate medication list, had obtained new medications initiated during hospitalization, understood how to take new and existing medications, and was taking them correctly. The TOC pharmacist conducted phone calls within 72 hours of discharge using a standardized procedure called “G.O. P.A.T.I.E.N.T.” to ensure that these objectives were met (eAppendix [available at]). Pharmacists were trained on the standardized procedure, which included how to manage barriers to adherence, such as access to medications, literacy, and cultural beliefs. DRPs attributable to the patient and physician were captured during the call. Patient DRPs included nonadherence, misunderstanding medication directions for use, and taking extraneous medications. Prescriber DRPs included incorrect medications, doses, frequencies, or duration; doses not adjusted for organ dysfunction, drug–drug or drug–disease interactions; extraneous, duplicate, or omitted medications; and/or incomplete or inconsistent medications prescribed versus intent of medication use based on the information documented in the electronic health record. The TOC pharmacist contacted the treating physician after the patient interview to resolve DRPs and recontacted the patient as needed. The hospital discharge medication list was updated to ensure that errors were not propagated in future patient encounters and hospitalizations. Two call attempts were made, and if the TOC pharmacist was unable to contact the patient or the patient refused to return the call, then the patient was considered lost to follow-up.   

Usual Care. During the study period, we monitored a control group of high-risk patients who received usual care and therefore did not receive pharmacist postdischarge follow-up.

Clinical Probability Estimates

Our base-case model incorporated a range of probability estimates governing the relative effectiveness of the intervention versus usual care (Table 1). First, because the pharmacist intervention can only be effective in patients who are successfully contacted, we accounted for the probability of reaching patients by telephone within the 72-hour postdischarge period. Next, among the subgroup of patients successfully contacted, we assessed the relative effectiveness of the intervention versus control, as measured by 30-day readmissions. Because the base-case data were derived from our local experience, we conducted sensitivity analyses to simulate alternative results and environments, as described in the Sensitivity Analyses section below. Finally, because the relative budget impact of the pharmacist program will depend on the number of high-risk patients eligible to benefit from the intervention, we modeled varying numbers of high-risk patients across a range of hospital sizes.

Cost Estimates

We performed direct cost accounting to compare the pharmacist postdischarge program versus usual care. Because the pharmacist intervention is principally designed to drive value of care—meaning to improve outcomes while reducing costs—we employed a population health–based managed care perspective in which the health system is financially responsible for the index hospitalization as well as any readmissions. Specifically, we measured the direct outlay by the hospital for the total care of the patient. We included up-front costs of the program pharmacists, comprising salary and benefits for 1 pharmacist, 1 pharmacy resident, and 3 technicians hired by the hospital for the pilot program. To project cost-effectiveness in hospitals of different size and patient burden, we also modeled a broad range of team sizes, salaries, and benefit plans. Finally, we included downstream readmission costs based on direct cost accounting for resources consumed in the care of the readmitted patient, including staff time and supplies. All estimates used 2015 US$. Because our study cohort was followed within a 1-year period, discounting was not performed.

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