Jeffrey L. Schnipper

Hospital-Based Medication Reconciliation Practices: A Systematic Review

BACKGROUND Medication discrepancies at care transitions are common and lead to patient harm. Medication reconciliation is a strategy to reduce this risk. OBJECTIVES To summarize available evidence on medication reconciliation interventions in the hospital setting and to identify the most effective practices. DATA SOURCES MEDLINE (1966 through February 2012) and a manual search of article bibliographies. STUDY SELECTION Twenty-six controlled studies. DATA EXTRACTION Data were extracted on study design, setting, participants, inclusion/exclusion criteria, intervention components, timing, comparison group, outcome measures, and results. DATA SYNTHESIS Studies were grouped by type of medication reconciliation intervention-pharmacist related, information technology (IT), or other-and were assigned quality ratings using US Preventive Services Task Force criteria. RESULTS Fifteen of 26 studies reported pharmacist-related interventions, 6 evaluated IT interventions, and 5 studied other interventions. Six studies were classified as good quality. The comparison group for all the studies was usual care; no studies compared different types of interventions. Studies consistently demonstrated a reduction in medication discrepancies (17 of 17 studies), potential adverse drug events (5 of 6 studies), and adverse drug events (2 of 2 studies) but showed an inconsistent reduction in postdischarge health care utilization (improvement in 2 of 8 studies). Key aspects of successful interventions included intensive pharmacy staff involvement and targeting the intervention to a high-risk patient population. CONCLUSIONS Rigorously designed studies comparing different inpatient medication reconciliation practices and their effects on clinical outcomes are scarce. Available evidence supports medication reconciliation interventions that heavily use pharmacy staff and focus on patients at high risk for adverse events. Higher-quality studies are needed to determine the most effective approaches to inpatient medication reconciliation.

Classifying and Predicting Errors of Inpatient Medication Reconciliation

BackgroundFailure to reconcile medications across transitions in care is an important source of potential harm to patients. Little is known about the predictors of unintentional medication discrepancies and how, when, and where they occur.ObjectiveTo determine the reasons, timing, and predictors of potentially harmful medication discrepancies.DesignProspective observational study.PatientsAdmitted general medical patients.MeasurementsStudy pharmacists took gold-standard medication histories and compared them with medical teams’ medication histories, admission and discharge orders. Blinded teams of physicians adjudicated all unexplained discrepancies using a modification of an existing typology. The main outcome was the number of potentially harmful unintentional medication discrepancies per patient (potential adverse drug events or PADEs).ResultsAmong 180 patients, 2066 medication discrepancies were identified, and 257 (12%) were unintentional and had potential for harm (1.4 per patient). Of these, 186 (72%) were due to errors taking the preadmission medication history, while 68 (26%) were due to errors reconciling the medication history with discharge orders. Most PADEs occurred at discharge (75%). In multivariable analyses, low patient understanding of preadmission medications, number of medication changes from preadmission to discharge, and medication history taken by an intern were associated with PADEs.ConclusionsUnintentional medication discrepancies are common and more often due to errors taking an accurate medication history than errors reconciling this history with patient orders. Focusing on accurate medication histories, on potential medication errors at discharge, and on identifying high-risk patients for more intensive interventions may improve medication safety during and after hospitalization.

Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients: Derivation and Validation of a Prediction Model

IMPORTANCE Because effective interventions to reduce hospital readmissions are often expensive to implement, a score to predict potentially avoidable readmissions may help target the patients most likely to benefit. OBJECTIVE To derive and internally validate a prediction model for potentially avoidable 30-day hospital readmissions in medical patients using administrative and clinical data readily available prior to discharge. DESIGN Retrospective cohort study. SETTING Academic medical center in Boston, Massachusetts. PARTICIPANTS All patient discharges from any medical services between July 1, 2009, and June 30, 2010. MAIN OUTCOME MEASURES Potentially avoidable 30-day readmissions to 3 hospitals of the Partners HealthCare network were identified using a validated computerized algorithm based on administrative data (SQLape). A simple score was developed using multivariable logistic regression, with two-thirds of the sample randomly selected as the derivation cohort and one-third as the validation cohort. RESULTS Among 10 731 eligible discharges, 2398 discharges (22.3%) were followed by a 30-day readmission, of which 879 (8.5% of all discharges) were identified as potentially avoidable. The prediction score identified 7 independent factors, referred to as the HOSPITAL score: h emoglobin at discharge, discharge from an o ncology service, s odium level at discharge, p rocedure during the index admission, i ndex t ype of admission, number of a dmissions during the last 12 months, and l ength of stay. In the validation set, 26.7% of the patients were classified as high risk, with an estimated potentially avoidable readmission risk of 18.0% (observed, 18.2%). The HOSPITAL score had fair discriminatory power (C statistic, 0.71) and had good calibration. CONCLUSIONS AND RELEVANCE This simple prediction model identifies before discharge the risk of potentially avoidable 30-day readmission in medical patients. This score has potential to easily identify patients who may need more intensive transitional care interventions.

Effect of an Electronic Medication Reconciliation Application and Process Redesign on Potential Adverse Drug Events A Cluster-Randomized Trial

BACKGROUND Medication reconciliation at transitions in care is a national patient safety goal, but its effects on important patient outcomes require further evaluation. We sought to measure the impact of an information technology-based medication reconciliation intervention on medication discrepancies with potential for harm (potential adverse drug events [PADEs]). METHODS We performed a controlled trial, randomized by medical team, on general medical inpatient units at 2 academic hospitals from May to June 2006. We enrolled 322 patients admitted to 14 medical teams, for whom a medication history could be obtained before discharge. The intervention was a computerized medication reconciliation tool and process redesign involving physicians, nurses, and pharmacists. The main outcome was unintentional discrepancies between preadmission medications and admission or discharge medications that had potential for harm (PADEs). RESULTS Among 160 control patients, there were 230 PADEs (1.44 per patient), while among 162 intervention patients there were 170 PADEs (1.05 per patient) (adjusted relative risk [ARR], 0.72; 95% confidence interval [CI], 0.52-0.99). A significant benefit was found at hospital 1 (ARR, 0.60; 95% CI, 0.38-0.97) but not at hospital 2 (ARR, 0.87; 95% CI, 0.57-1.32) (P = .32 for test of effect modification). Hospitals differed in the extent of integration of the medication reconciliation tool into computerized provider order entry applications at discharge. CONCLUSIONS A computerized medication reconciliation tool and process redesign were associated with a decrease in unintentional medication discrepancies with potential for patient harm. Software integration issues are likely important for successful implementation of computerized medication reconciliation tools.

Effect of a Pharmacist Intervention on Clinically Important Medication Errors After Hospital Discharge: A Randomized Trial

BACKGROUND Clinically important medication errors are common after hospital discharge. They include preventable or ameliorable adverse drug events (ADEs), as well as medication discrepancies or nonadherence with high potential for future harm (potential ADEs). OBJECTIVE To determine the effect of a tailored intervention on the occurrence of clinically important medication errors after hospital discharge. DESIGN Randomized, controlled trial with concealed allocation and blinded outcome assessors. (ClinicalTrials.gov registration number: NCT00632021) SETTING Two tertiary care academic hospitals. PATIENTS Adults hospitalized with acute coronary syndromes or acute decompensated heart failure. INTERVENTION Pharmacist-assisted medication reconciliation, inpatient pharmacist counseling, low-literacy adherence aids, and individualized telephone follow-up after discharge. MEASUREMENTS The primary outcome was the number of clinically important medication errors per patient during the first 30 days after hospital discharge. Secondary outcomes included preventable or ameliorable ADEs, as well as potential ADEs. RESULTS Among 851 participants, 432 (50.8%) had 1 or more clinically important medication errors; 22.9% of such errors were judged to be serious and 1.8% life-threatening. Adverse drug events occurred in 258 patients (30.3%) and potential ADEs in 253 patients (29.7%). The intervention did not significantly alter the per-patient number of clinically important medication errors (unadjusted incidence rate ratio, 0.92 [95% CI, 0.77 to 1.10]) or ADEs (unadjusted incidence rate ratio, 1.09 [CI, 0.86 to 1.39]). Patients in the intervention group tended to have fewer potential ADEs (unadjusted incidence rate ratio, 0.80 [CI, 0.61 to 1.04]). LIMITATION The characteristics of the study hospitals and participants may limit generalizability. CONCLUSION Clinically important medication errors were present among one half of patients after hospital discharge and were not significantly reduced by a health-literacy-sensitive, pharmacist-delivered intervention. PRIMARY FUNDING SOURCE National Heart, Lung, and Blood Institute.

Accuracy of ICD-9 coding for Clostridium difficile infections: a retrospective cohort

Clostridium difficile (C. diff) is a major nosocomial problem. Epidemiological surveillance of the disease can be accomplished by microbiological or administrative data. Microbiological tracking is problematic since it does not always translate into clinical disease, and it is not always available. Tracking by administrative data is attractive, but ICD-9 code accuracy for C. diff is unknown. By using a large administrative database of hospitalized patients with C. diff (by ICD-9 code or cytotoxic assay), this study found that the sensitivity, specificity, positive, and negative predictive values of ICD-9 coding were 71%, 99%, 87%, and 96% respectively (using micro data as the gold standard). When only using symptomatic patients the sensitivity increased to 82% and when only using symptomatic patients whose test results were available at discharge, the sensitivity increased to 88%. C. diff ICD-9 codes closely approximate true C. diff infection, especially in symptomatic patients whose test results are available at the time of discharge, and can therefore be used as a reasonable alternative to microbiological data for tracking purposes.

Causes and patterns of readmissions in patients with common comorbidities: retrospective cohort study

Objective To evaluate the primary diagnoses and patterns of 30 day readmissions and potentially avoidable readmissions in medical patients with each of the most common comorbidities. Design Retrospective cohort study. Setting Academic tertiary medical centre in Boston, 2009-10. Participants 10 731 consecutive adult discharges from a medical department. Main outcome measures Primary readmission diagnoses of readmissions within 30 days of discharge and potentially avoidable 30 day readmissions to the index hospital or two other hospitals in its network. Results Among 10 731 discharges, 2398 (22.3%) were followed by a 30 day readmission, of which 858 (8.0%) were identified as potentially avoidable. Overall, infection, neoplasm, heart failure, gastrointestinal disorder, and liver disorder were the most frequent primary diagnoses of potentially avoidable readmissions. Almost all of the top five diagnoses of potentially avoidable readmissions for each comorbidity were possible direct or indirect complications of that comorbidity. In patients with a comorbidity of heart failure, diabetes, ischemic heart disease, atrial fibrillation, or chronic kidney disease, the most common diagnosis of potentially avoidable readmission was acute heart failure. Patients with neoplasm, heart failure, and chronic kidney disease had a higher risk of potentially avoidable readmissions than did those without those comorbidities. Conclusions The five most common primary diagnoses of potentially avoidable readmissions were usually possible complications of an underlying comorbidity. Post-discharge care should focus attention not just on the primary index admission diagnosis but also on the comorbidities patients have.

Effects of a subcutaneous insulin protocol, clinical education, and computerized order set on the quality of inpatient management of hyperglycemia: Results of a clinical trial†‡

BACKGROUND Inpatient hyperglycemia is associated with poor patient outcomes. It is unknown how best to implement glycemic management strategies in the non-intensive care unit (ICU) setting. OBJECTIVE To determine the effects of a multifaceted quality improvement intervention on the management of medical inpatients with diabetes mellitus or hyperglycemia. DESIGN Before-after trial. SETTING Geographically localized general medical service staffed by physicians assistants (PAs) and hospitalists. PATIENTS Consecutively enrolled patients with type 2 diabetes or inpatient hyperglycemia. INTERVENTION A detailed subcutaneous insulin protocol, an admission order set built into the hospitals computerized order entry system, and case-based educational workshops and lectures to nurses, physicians, and PAs. MEASUREMENTS Mean percent of glucose readings per patient between 60 and 180 mg/dL; percent patient-days with hypoglycemia; insulin use patterns; and hospital length of stay. RESULTS The mean percent of readings per patient between 60 and 180 mg/dL was 59% prior to the intervention and 65% afterward (adjusted effect size 9.7%; 95% confidence interval [CI], 0.6%-18.8%). The percent of patient days with any hypoglycemia was 5.5% preintervention and 6.1% afterward (adjusted odds ratio 1.1; 95% CI, 0.6-2.1). Use of scheduled nutritional insulin increased from 40% to 75% (odds ratio 4.5; 95% CI, 2.0-9.9) and adjusted length of stay decreased by 25% (95% CI, 9%-44%). Daily insulin adjustment did not improve, nor did glucose control beyond hospital day 3. CONCLUSIONS This multifaceted intervention, which was easy to implement and required minimal resources, was associated with improvements in both insulin ordering practices and glycemic control among non-ICU medical patients.

Society of hospital medicine glycemic control task force summary: Practical recommendations for assessing the impact of glycemic control efforts

5 University of California San Diego, Division of Hospital Medicine, Department of Medicine, San Diego, California D ata collection, analysis, and presentation are key to the success of any hospital glycemic control initiative. Such efforts enable the management team to track improvements in processes and outcomes, make necessary changes to their quality improvement efforts, justify the provision of necessary time and resources, and share their results with others. Reliable metrics for assessing glycemic control and frequency of hypoglycemia are essential to accomplish these tasks and to assess whether interventions result in more benefit than harm. Hypoglycemia metrics must be especially convincing because fear of hypoglycemia remains a major source of clinical inertia, impeding efforts to improve glucose control. Currently, there are no official standards or guidelines for formulating metrics on the quality of inpatient glycemic control. This creates several problems. First, different metrics vary in their biases and in their responsiveness to change. Thus, use of a poor metric could lead to either a falsely positive or falsely negative impression that a quality improvement intervention is in fact improving glycemic control. Second, the proliferation of different measures and analytical plans in the research and quality improvement literature make it very difficult for hospitals to compare baseline performance, determine need for improvement, and understand which interventionsmay bemost effective. A related article in this supplement provides the rationale for improved inpatient glycemic control. That article argues that the current state of inpatient glycemic control, with the frequent occurrence of severe hyperglycemia and irrational insulin ordering, cannot be considered acceptable, especially given the large body of data (albeit largely observational) linking hyperglycemia to negative patient outcomes. However, regardless of whether one is an advocate or skeptic of tighter glucose control in the intensive care unit (ICU) and especially the non-ICU setting, there is no question that standardized, valid, and reliable metrics are needed to compare efforts to improve glycemic control, better understand whether such control actually improves patient care, and closely monitor patient safety. This article provides a summary of practical suggestions to assess glycemic control, insulin use patterns, and safety (hypoglycemia and severe hyperglycemia). In particular, we discuss the pros and cons of various measurement choices. We conclude with a tiered summary of recommendations for practical metrics No honoraria were paid to any authors for time and expertise spent on the writing of this article.

Implementation of a physician assistant/hospitalist service in an academic medical center: Impact on efficiency and patient outcomes

BACKGROUND Accreditation Council on Graduate Medical Education (ACGME) duty hour restrictions have led to the widespread implementation of non-house staff services in academic medical centers, yet little is known about the quality and efficiency of patient care on such services. OBJECTIVE To evaluate the quality and efficiency of patient care on a physician assistant/hospitalist service compared with that of traditional house staff services. DESIGN Retrospective cohort study. SETTING Inpatient general medicine service of a 747-bed academic medical center. PATIENTS A total of 5194 consecutive patients admitted to the general medical service from July 2005 to June 2006, including 992 patients on the physician assistant/hospitalist service and 4202 patients on a traditional house staff service. INTERVENTION A geographically localized service staffed with physician assistants and supervised by hospitalists. MEASUREMENTS Length of stay (LOS), cost of care, inpatient mortality, intensive care unit (ICU) transfers, readmissions, and patient satisfaction. RESULTS Patients admitted to the study service were younger, had lower comorbidity scores, and were more likely to be admitted at night. After adjustment for these and other factors, and for clustering by attending physician, total cost of care was marginally lower on the study service (adjusted costs 3.9% lower; 95% confidence interval [CI] -7.5% to -0.3%), but LOS was not significantly different (adjusted LOS 5.0% higher; 95% CI, -0.4% to +10%) as compared with house staff services. No difference was seen in inpatient mortality, ICU transfers, readmissions, or patient satisfaction. CONCLUSIONS For general medicine inpatients admitted to an academic medical center, a service staffed by hospitalists and physician assistants can provide a safe alternative to house staff services, with comparable efficiency.