Incorporating a Pharmacist Into the Discharge Process: A Unit-Based Transitions of Care Pilot

Abstract

Objective: To evaluate the impact of a multifaceted, pharmacy-driven, unit-based transitions of care (TOC) program on all-cause 30-day readmission rates and to assess readmission rates in predefined subgroup patient populations.

Methods: This prospective study included adult patients who were discharged from the pilot unit from January 5 to January 30, 2015. Patients who expired during hospitalization, left the hospital against medical advice, or transferred to another unit or nonaffiliated hospital were excluded. Possible pharmacist interventions included daily medication profile review, delivery of discharge medications to the bedside, counseling, and communication of a discharge medication list to follow-up providers. Patients had a 30-day follow-up period from the date of discharge to assess for readmission.

Results: A total of 131 patients were screened and 94 patients were included. The primary outcome evaluating 30-day readmission rates occurred in 12.8% of patients in the pilot group versus 18.8% of patients in the historical control group (p = .26). None of the patients who received all possible pharmacist interventions were readmitted. Secondary outcomes assessing readmission rates in predefined subgroup populations as well as length of stay were comparable between the 2 groups. All identified medication discrepancies were resolved prior to discharge.

Conclusion: Readmission rates during the pilot were numerically lower but not statistically significant when compared with historical data. Enhancement of the pharmacy-driven TOC services through allocation of additional resources is in progress. Further investigation is warranted to determine the impact of a TOC pharmacist after the service is sustained.

BACKGROUND

The Affordable Care Act established the Hospital Readmissions Reduction Program, which allows the Centers for Medicare and Medicaid Services (CMS) to reduce payments to hospitals that have excess readmissions. Traditionally, CMS measured readmission rates for pneumonia, acute myocardial infarction (AMI), and heart failure. Chronic obstructive pulmonary disease (COPD) and total hip arthroplasty (THA)/total knee arthroplasty (TKA) were new readmission measures added in 2015.1

There are multiple risk factors for hospital readmission, including medication errors and adverse drug events (ADEs) that occur during transitions of care (TOC).2,3 Studies have identified select high-risk medications that are associated with a greater risk of readmission.4–7 Risk factors for ADEs during TOC include advanced age, polypharmacy, low health literacy, and cognitive impairment.8 Medication reconciliation allows for resolution of medication discrepancies across the continuum of care.9,10

An interdisciplinary approach is needed to reduce medication-related adverse events and avoidable readmissions. Pharmacists can contribute relevant pharmacotherapy recommendations during medication reconciliation at discharge; such contributions have been shown to reduce the average length of stay.11 In addition, pharmacists can assist with The Joint Commission National Patient Safety Goal of ensuring communication of accurate medication information through patient and caregiver counseling. 12 Studies have demonstrated a reduced rate of hospital readmissions when pharmacists are included as part of TOC programs.8,13–15

This was a unit-based pilot study conducted on a general adult medicine unit at a tertiary care academic medical center, which is within a large health care system. During the study period, approximately 200 patient discharges occurred per month from the pilot unit and 70% to 80% of these patients were cared for by hospitalist physicians. Currently, medication reconciliation at admission is completed for all patients at the institution. However, pharmacist-led medication review and counseling at discharge is only available for select high-risk patients such as solid organ transplant and bone marrow transplant recipients.

The purpose of this study was to evaluate the impact of a multifaceted, pharmacy-driven, unitbased TOC program on all-cause 30-day readmission rates and to assess readmission rates in predefined subgroup patient populations.

METHODS

Study Design and Patient Population

This was a prospective pilot study conducted from January 5 to January 30, 2015. Patients had a 30-day follow-up period from the date of discharge to assess for readmission. All hospitalist patients 18 years of age or older who were discharged from the pilot unit during the study period were included. Patients who expired during hospitalization, left the hospital against medical advice, or transferred to another service or nonaffiliated hospital were excluded. This study was approved by the institutional review board.

A total of 5 subgroup patient populations were defined: (1) readmission risk per the Predixion assessment tool16 (a standardized tool utilized within the health care system), (2) polypharmacy (>9 discharge medications), (3) presence of any of the high-risk medications on the discharge list (anticoagulant/antiplatelet, opioid, antihyperglycemic, antibiotic, or per pharmacist's discretion), (4) any CMS readmission measure disease state as a discharge diagnosis, and (5) full versus partial intervention.

Pharmacist Interventions

Pharmacist services were provided Monday through Friday from 7:30 a.m. to 3:30 p.m. Four main interventions were attempted for all patients, if applicable. These included (1) daily medication profile review and creation of a best possible medication discharge list (BPMDL; the BPMDL was compared with the actual discharge list to identify and resolve any medication discrepancies), (2) delivery of discharge medications to the bedside prior to discharge, (3) patient or caregiver counseling with a focus on high-risk medications, select disease states, and/or at the pharmacist's discretion, and (4) communication of the discharge medication list to follow-up providers. Partial intervention was defined as any patient who could have reasonably received all interventions but did not. Patients were only eligible for the discharge medication to bedside program if they were discharged to home. Additional interventions, such as dose adjustments, antibiotic selection and duration, and formulation conversion, were made during daily rounds.

Data Collection and Outcomes

Pilot data were collected prospectively via electronic chart review and patient/caregiver interview. Historical data for patients discharged from the pilot unit in 2014 were retrieved by the Dickson Advanced Analytics Group applying the same inclusion and exclusion criteria. The primary outcome of the study was the difference in all-cause 30-day readmission rates between the hospitalist patients discharged in January 2015 and hospitalist patients discharged in January 2014. January 2014 was selected as the comparator month to account for seasonal influence (ie, readmissions related to influenza) and acuity of illness (measured by case mix index), which was most similar in January 2014 and January 2015 compared to other months in 2014. An unplanned rehospitalization to any affiliated hospital was captured as a readmission.

Secondary outcomes included the difference in all-cause 30-day readmission rates between all patients discharged from the pilot unit in January 2015 compared to January 2014, readmission rates observed in predefined subgroup patient populations during the pilot study, change in patient average length of stay (LOS) in January 2015 compared to January 2014, and number and type of medication discrepancies identified and resolved during discharge medication review.

Statistical Analysis

Descriptive statistics were calculated in the form of means and standard deviations for continuous variables and frequencies and percentages for categorical variables. The primary and secondary outcomes were analyzed using either a chi-square test or Fisher's exact test to compare the readmission rates within 30 days, before and after implementation of the pilot TOC program. A subgroup analysis was conducted on the January 2015 cohort comparing patients readmitted to patients not readmitted on readmission risk scores, polypharmacy, high-risk medications at discharge, discharge diagnosis, and extent of intervention. For the subgroup analysis, chi-square test or Fishers exact test was used. LOS was analyzed using the Wilcoxon rank-sum test. SAS Enterprise Guide (version 6.1; SAS Institute Inc., Cary, NC) was used for all analyses. A 2-tailed p-value of less than .05 was considered statistically significant.

RESULTS

During the study period, 131 patients were screened and 94 patients were enrolled (Figure 1). Baseline characteristics for patients included in the pilot study group are summarized in Table 1. Twelve of 94 (12.8%) patients discharged from the pilot unit in January 2015 were readmitted within 30 days, compared to 18 of 96 (18.8%) patients readmitted during the historical control period of January 2014.

Figure 1.

Patient population. A patient could have received multiple interventions. BPMDL = best possible medication discharge list.

Table 1.

Baseline characteristics of study population (N = 94)

All patients received at least one intervention. Reasons for partial intervention are described in Table 1. Primary and secondary outcomes are shown in Table 2 and Table 3.

Table 2.

Readmission data comparison: Pilot study vs historical control periods

Table 3.

Characteristics of subgroup study populations

Patients who received all possible interventions were not readmitted. Of the 12 patients who were readmitted, the majority received 2 or more interventions: daily medication review and creation of BPMDL (n = 12), discharge medication delivery to bedside (n = 2), counseling (n = 4), and discharge medication list sent to follow-up providers (n = 8). Impaired cognitive status was more commonly identified as a reason for partial intervention among patients who were readmitted. Most of the readmitted patients were hospitalized due to chronic conditions.

The impact of other combinations of interventions on readmission was also assessed. Readmission rates based on number of partial interventions received are described in Table 4. All patients of the cohort received daily medication profile review and creation of BPMDL.

Table 4.

Readmission rates for patients receiving partial interventions

Twelve drug-related problems were identified when the BPDML and actual discharge medication list were compared. Four were intentional, and 8 were unintentional. Discrepancies were categorized as wrong drug (n = 1), wrong dose (n = 4), duplication (n = 4), omission (n = 4), and wrong frequency (n = 2); each drug-related problem could be described by more than one category. All discrepancies were resolved prior to discharge through contacting providers, communicating changes to outpatient/community pharmacies, and educating patients and caregivers about updated regimens.

DISCUSSION

Readmission rates during the pilot study were numerically lower but not statistically significant when compared to historical data from January 2014. Disparity in size of the study population and that of comparator group was one limitation recognized by Christy et al in a similar TOC pilot.17 By selecting January 2014 as the comparator month, as previously described, our study closely matched the study population to that of a historical control (94 vs 96). To account for other confounding factors (ie, other initiatives implemented on the pilot unit after January 2014 to lower readmission rates), additional comparison analyses using data from December 2014 and an average of data from January to December 2014 were performed. The readmission rates for hospitalist patients were 15.9% and 13.8% for each of these 2 additional comparator groups, respectively. Readmission rates in both comparisons were again higher than that of the pilot study.

One-third of patients in the study were 65 years of age or older; this patient population is prone to ADEs associated with high-risk medications. The definition of high-risk medication varies in the literature. Previous studies have suggested that ADEs associated with anticoagulants, insulin, antihypertensives, and opioids are associated with an increased risk of hospital readmission.4,5 In addition, data have shown that anticholinergic agents, benzodiazepines, anticonvulsants, and corticosteroids are of special concern in the elderly population.6,7 Our study detected a statistical correlation between antihyperglycemic agents and rehospitalization. In general, readmission rates for patients with diabetes are growing. It is reported that approximately 25% of hospitalizations in the United States may be attributed to diabetes.18 An education program that offers one-on-one patient counseling with certified diabetes educators is available at the hospital. Pharmacy collaboration with existing services may offer patients more comprehensive education and enhance discharge counseling.

In contrast to the results described by Still et al,19 our study did not find an association between readmission risk prediction scores and 30-day readmission rates. This may be due to our comparison of very high risk versus a composite of high, moderate, and low risk due to the small number of patients in the latter categories. However, it is also imperative to recognize that various readmission assessment tools use different risk factors and scoring systems. The screening tool for risk stratification described by Still et al19 covered 11 risk criteria, each worth 2 points. Risk categories included high, moderate, and low. In comparison, the tool used within our health care system encompasses a much more extensive list of criteria that evaluates 40 factors. These factors are anchored in 5 key areas including demographics, psychosocial, laboratory values/vital signs/medications, comorbidities, and utilization. Each patient is stratified into very high, high, moderate, and low risk.

There was no statistical significance in readmission rates based on polypharmacy, which was defined as greater than 9 medications including both maintenance and as-needed medications. Currently, there is a lack of universal definition of polypharmacy. Studies have proposed definitions that ranged from 5 to 16 medications.20,21 We selected a definition of greater than 9 medications as internal quality data suggested this was a key driver for readmission in patients discharged from our institution. Therefore, our definition may not be generalized to other institutions.

A TOC program described by Eisenhower et al15 also tracked LOS as one of the metrics. Notably, the pilot unit used in this study is a frequent discharge point and thus inherits LOS from other units. There is also an 8-bed subunit that supports patients waiting on charity placement who tend to have a longer hospital stay. In contrast, patients admitted directly to the unit tend to have a much shorter LOS. When patients were discharged less than 24 to 48 hours after admission to the unit, they tended to not receive all possible interventions. Total hospital LOS data in January 2015 was compared to that from January 2014. However, the polarized nature of LOS on the unit suggests that it may be more beneficial to assess the impact of pharmacist interventions using unit-specific LOS rather than total number of days of hospitalization.

Patients who received all possible interventions, except having a discharge medication list sent to follow-up providers and/or counseling, had zero percent readmission as well. This suggests that daily profile review and bedside delivery may be the most impactful interventions. However, the absolute numbers of patients in each subcategory were small and ranged widely. This may exaggerate the difference in percentage of readmissions rates.

Compared to the high number of medication discrepancies at hospital discharge described in other studies, a relatively small number of discrepancies were identified in our pilot.10,22 A proposed explanation for the finding is likely a result of daily interdisciplinary rounds at which the pharmacist made recommendations to the team when necessary. In addition to the 4 main interventions attempted for each patient, 68 additional interventions were documented to ensure a smooth TOC process.

There are a number of limitations to our study. Uncontrolled environmental factors had a notable impact on the pilot. The typical hospital-wide hospitalist census is approximately 180 patients per day. During the pilot, the census exceeded capacity and increased up to 260 patients per day. Due to the high census, there was a postponement of a standardized physician-rounding model in January 2015. The plan was to have 2 designated hospitalist physicians rounding on the unit, whereas in reality up to 7 or 8 hospitalist physicians participated in the care of patients during the pilot. The lack of a structured rounding model created challenges; for example, extra time was required to communicate recommendations to different providers. The short duration of the pilot and the fact that services were only offered during one shift were additional limitations. The study was conducted as a 1-month pilot, because this was most feasible as a prospective resident research project. The small sample size of patients within each subset of CMS target disease states, for example, decreased the ability to establish statistical significance and draw valid conclusions. Lack of other resources, such as pharmacy technicians to assist with medication delivery to bedside, potentially limited the number of patients who may have benefited from the service. Some patients also declined the bedside services due to differences in medication delivery times and proposed discharge time. Enhanced coordination of medication delivery with goal discharge time would expand the volume of patients who could receive such a service. Despite the fact that the same inclusion and exclusion criteria were applied while retrieving historical data, certain baseline data points were not retrievable (ie, number of home medications, CMS disease states per past medical history). Lastly, a power analysis was not performed to determine the minimum number of patients needed because the historical data were not readily available prior to implementation of the pilot.

In summary, our pilot study served as momentum to advance pharmacy TOC initiatives and supported the need for an interdisciplinary TOC approach within our institution. Although there was no statistical difference in outcomes measured, clinical benefits such as resolution of medication discrepancies and an absolute reduction in 30-day readmission rates by 6% were observed. Another promising finding was that patients who received all 4 interventions were not readmitted. The pilot therefore demonstrated the possibilities of incorporating a TOC pharmacist into the standard discharge process. Moreover, a LEAN Rapid Improvement Event (RIE) to develop a value stream mapping plan for the pilot unit occurred in February 2015, immediately after completion of the pilot. The RIE established that development of a standardized workflow for an interdisciplinary patient care team, including direct pharmacist involvement, was of highest priority. A full-time pharmacist has now been appointed to the unit to continue to assess expansion of pharmacy services and to participate in a pilot of the new standardized rounding model as an extension of the LEAN initiative.

Additional future opportunities include involving pharmacy technicians in the inpatient discharge TOC process, providing real-time notification when discharge orders are entered, and evaluating emergency department utilization post discharge. Furthermore, an ultimate goal will be collaboration with hospitalist physicians in a TOC clinic to provide continuity of care to patients.

CONCLUSION

Readmission rates during the pilot were numerically lower but not statistically significant when compared with historical data. Enhancement of the pharmacy-driven TOC services through allocation of additional resources is in progress. Further investigation would be warranted to determine the impact of a TOC pharmacist after the service enters a period of sustainability.