Do Combined Pharmacist and Prescriber Efforts on Medication Reconciliation Reduce Postdischarge Patient Emergency Department Visits and Hospital Readmissions?

We conducted a retrospective, observational, analytical cohort study using QuadraMed’s Computerized Patient Record and the EMITT (Electronic Medication Information Transfer Tool)¹⁵ to collect data from 2007 to 2011.

Setting

The study was conducted at a 417-bed tertiary care teaching hospital in Toronto, Ontario, Canada.

Med Rec Process and Description of Exposure (Intervention)

The targeted clinical areas had sustained interprofessional models of patient care in place from admission to discharge. They also were actively using an in-house EMITT to facilitate the documentation and tracking of Med Rec efforts throughout patient admission, transfer, and discharge.¹⁵ On admission, the pharmacist conducted a best possible medication history (BPMH). A BPMH provides the cornerstone for Med Rec. It differs from a routine medication history in that it involves (1) a systematic process for interviewing the patient (or family) and (2) a review of at least one other reliable source of information (eg, a provincial medication database, an inspection of medication vials, or contact with the community pharmacy) to obtain and verify patient medications (prescribed and nonprescribed). The pharmacist recorded the BPMH in the electronic patient record. The application supported admission and discharge Med Rec. On discharge, there were 2 options: (1) the prescriber alone would review and complete the discharge Med Rec and generate electronic prescriptions (Table 1, Silver level care) or (2) the pharmacist would collaborate with the prescriber to complete the discharge reconciliation and the prescriber would electronically generate prescriptions (Table 1, Gold level care). All clinical areas had a combined pharmacist and prescriber Med Rec model in place at admission, but the proportion of patients receiving discharge reconciliation completed by pharmacist and prescriber versus the prescriber-alone varied based on the individual clinician’s practices.

Patient Selection

All consecutive hospitalized patients admitted and discharged by the general internal medicine [GIM] service from March 2007 to December 2011 were included. The GIM service was chosen for the main analysis because they had been performing the intervention for the longest period of time and had the largest population of patients. Patients were identified via their hospital-specific medical record identification number and specific hospital-visit number. Patients were excluded if any of the following occurred: (1) the length of stay of their index admission was less than 24 hours; (2) they died during the visit; (3) they were transferred to a separate acute care inpatient facility; or (4) they left hospital against medical advice. Patient visits were excluded as index cases from the analysis if they were returning within 90 days of a previous discharge.

Outcomes

The primary study outcome was the occurrence of an inpatient readmission or ED visit within 30 days of discharge. In our secondary analyses, we examined the impact of the intervention on high-risk patient populations, such as those ≥65 years of age, with a length of stay, acuity of admission, Charlson comorbidity index, and emergency department visits in past 6 months (LACE) index score ≥10 (see supplementary Appendix 1 for LACE score description), on high-alert medications (1 or more of warfarin, insulin, digoxin, and opioids), and on ≥10 medications.

Data Collection

Identification of Exposure of Interest

We used the electronic database to capture all patients who received pharmacist and prescriber supported admission-to-discharge reconciliation. We explicitly defined increasing intensity of Med Rec care in categories of Bronze, Silver, and Gold care levels (Table 1). The exposed (intervention) group received an enhanced Med Rec bundle (patients receiving Gold level care). The control group was made of patients receiving a partial Med Rec Bundle (patients receiving Silver or Bronze level of care or below).

Determination of Hospital Visits

A search of administrative databases was used to determine if patients admitted to the targeted services had an ED visit or urgent inpatient admission to the study hospital within 30 days.

Statistical Analysis

A logistic regression for outcomes was performed. This yielded an adjusted odds ratio with a 95% confidence interval (CI) between the intervention and control groups. Statistical significance was determined with a 2-sided α level of 0.05. In the analysis, we used Statistical Analysis Software version 9.2.

In our multivariate logistic regression model, we adjusted for confounding factors that might influence the patients’ risk of readmission or the type of Med Rec they received upon discharge. By using administrative databases, patient level demographics, and the Charlson comorbidity index, the most responsible diagnosis and disease burden were collected. Medication-related factors collected included the number of medications on discharge and the presence of predefined high-alert medications. The number of medications on the medication discharge list was determined by using the electronic database. The final adjustment model included age, gender, the number of medications on discharge, and the LACE index score (supplementary Appendix 1). The LACE index score has been validated in Ontario, Canada, populations to quantify the risk of death or unplanned readmission within 30 days of discharge.²⁴