Amanda H. Salanitro

Risk Prediction Models for Hospital Readmission: A Systematic Review

CONTEXT Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison. OBJECTIVE To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. DATA SOURCES AND STUDY SELECTION The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in both derivation and validation cohorts. DATA EXTRACTION Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. DATA SYNTHESIS Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health. CONCLUSIONS Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.

Effect of Patient- and Medication-Related Factors on Inpatient Medication Reconciliation Errors

ABSTRACTBackgroundLittle research has examined the incidence, clinical relevance, and predictors of medication reconciliation errors at hospital admission and discharge.ObjectiveTo identify patient- and medication-related factors that contribute to pre-admission medication list (PAML) errors and admission order errors, and to test whether such errors persist in the discharge medication list.Design, ParticipantsWe conducted a cross-sectional analysis of 423 adults with acute coronary syndromes or acute decompensated heart failure admitted to two academic hospitals who received pharmacist-assisted medication reconciliation during the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL–CVD) Study.Main MeasuresPharmacists assessed the number of total and clinically relevant errors in the PAML and admission and discharge medication orders. We used negative binomial regression and report incidence rate ratios (IRR) of predictors of reconciliation errors.Key ResultsOn admission, 174 of 413 patients (42%) had ≥1 PAML error, and 73 (18%) had ≥1 clinically relevant PAML error. At discharge, 158 of 405 patients (39%) had ≥1 discharge medication error, and 126 (31%) had ≥1 clinically relevant discharge medication error. Clinically relevant PAML errors were associated with older age (IRR = 1.46; 95% CI, 1.00– 2.12) and number of pre-admission medications (IRR = 1.17; 95% CI, 1.10–1.25), and were less likely when a recent medication list was present in the electronic medical record (EMR) (IRR = 0.54; 95% CI, 0.30–0.96). Clinically relevant admission order errors were also associated with older age and number of pre-admission medications. Clinically relevant discharge medication errors were more likely for every PAML error (IRR = 1.31; 95% CI, 1.19–1.45) and number of medications changed prior to discharge (IRR = 1.06; 95% CI, 1.01–1.11).ConclusionsMedication reconciliation errors are common at hospital admission and discharge. Errors in preadmission medication histories are associated with older age and number of medications and lead to more discharge reconciliation errors. A recent medication list in the EMR is protective against medication reconciliation errors.

Estimating and Reporting on the Quality of Inpatient Stroke Care by Veterans Health Administration Medical Centers

Background— Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error caused by small numbers of eligible patients per facility. We applied multilevel modeling and empirical Bayes (EB) estimation in addressing this issue in performance reporting of stroke care quality in the Medical Centers. Methods and Results— We studied a retrospective cohort of 3812 veterans admitted to 106 Medical Centers with ischemic stroke during fiscal year 2007. The median number of study patients per facility was 34 (range, 12–105). Inpatient stroke care quality was measured with 13 evidence-based QIs. Eligible patients could either pass or fail each indicator. Multilevel modeling of a patients pass/fail on individual QIs was used to produce facility-level EB-estimated QI pass rates and confidence intervals. The EB estimation reduced interfacility variation in QI rates. Small facilities and those with exceptionally high or low rates were most affected. We recommended 8 of the 13 QIs for performance reporting: dysphagia screening, National Institutes of Health Stroke Scale documentation, early ambulation, fall risk assessment, pressure ulcer risk assessment, Functional Independence Measure documentation, lipid management, and deep vein thrombosis prophylaxis. These QIs displayed sufficient variation across facilities, had room for improvement, and identified sites with performance that was significantly above or below the population average. The remaining 5 QIs were not recommended because of too few eligible patients or high pass rates with little variation. Conclusions— Considerations of statistical uncertainty should inform the choice of QIs and their application to performance reporting.

Rationale and design of the Multicenter Medication Reconciliation Quality Improvement Study (MARQUIS)

BackgroundUnresolved medication discrepancies during hospitalization can contribute to adverse drug events, resulting in patient harm. Discrepancies can be reduced by performing medication reconciliation; however, effective implementation of medication reconciliation has proven to be challenging. The goals of the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS) are to operationalize best practices for inpatient medication reconciliation, test their effect on potentially harmful unintentional medication discrepancies, and understand barriers and facilitators of successful implementation.MethodsSix U.S. hospitals are participating in this quality improvement mentored implementation study. Each hospital has collected baseline data on the primary outcome: the number of potentially harmful unintentional medication discrepancies per patient, as determined by a trained on-site pharmacist taking a “gold standard” medication history. With the guidance of their mentors, each site has also begun to implement one or more of 11 best practices to improve medication reconciliation. To understand the effect of the implemented interventions on hospital staff and culture, we are performing mixed methods program evaluation including surveys, interviews, and focus groups of front line staff and hospital leaders.DiscussionAt baseline the number of unintentional medication discrepancies in admission and discharge orders per patient varies by site from 2.35 to 4.67 (mean=3.35). Most discrepancies are due to history errors (mean 2.12 per patient) as opposed to reconciliation errors (mean 1.23 per patient). Potentially harmful medication discrepancies averages 0.45 per patient and varies by site from 0.13 to 0.82 per patient. We discuss several barriers to implementation encountered thus far. In the end, we anticipate that MARQUIS tools and lessons learned have the potential to decrease medication discrepancies and improve patient outcomes.Trial registrationClinicaltrials.gov identifier NCT01337063

Regardless of age: Incorporating principles from geriatric medicine to improve care transitions for patients with complex needs

ABSTRACTWith its focus on holistic approaches to patient care, caregiver support, and delivery system redesign, geriatrics has advanced our understanding of optimal care during transitions. This article provides a framework for incorporating geriatrics principles into care transition activities by discussing the following elements: (1) identifying factors that make transitions more complex, (2) engaging care “receivers” and tailoring home care to meet patient needs, (3) building “recovery plans” into transitional care, (4) predicting and avoiding preventable readmissions, and (5) adopting a palliative approach, when appropriate, that optimizes patient and family goals of care. The article concludes with a discussion of practical aspects of designing, implementing, and evaluating care transitions programs for those with complex care needs, as well as implications for public policy.

Using cognitive mapping to define key domains for successful attending rounds.

BACKGROUNDWard attending rounds are an integral part of internal medicine education. Being a good teacher is necessary, but not sufficient for successful rounds. Understanding perceptions of successful attending rounds (AR) may help define key areas of focus for enhancing learning, teaching and patient care.OBJECTIVEWe sought to expand the conceptual framework of 30 previously identified attributes contributing to successful AR by: 1) identifying the most important attributes, 2) grouping similar attributes, and 3) creating a cognitive map to define dimensions and domains contributing to successful rounds.DESIGNMulti-institutional, cross-sectional study design.PARTICIPANTSWe recruited residents and medical students from a university-based internal medicine residency program and a community-based family medicine residency program. Faculty attending a regional general medicine conference, affiliated with multiple institutions, also participated.MAIN MEASURESParticipants performed an unforced card-sorting exercise, grouping attributes based on perceived similarity, then rated the importance of attributes on a 5-point Likert scale. We translated our data into a cognitive map through multi-dimensional scaling and hierarchical cluster analysis.KEY RESULTSThirty-six faculty, 49 residents and 40 students participated. The highest rated attributes (mean rating) were “Teach by example (bedside manner)” (4.50), “Sharing of attending’s thought processes” (4.46), “Be approachable—not intimidating” (4.45), “Insist on respect for all team members” (4.43), “Conduct rounds in an organized, efficient & timely fashion” (4.39), and “State expectations for residents/students” (4.37). Attributes were plotted on a two-dimensional cognitive map, and adequate convergence was achieved. We identified five distinct domains of related attributes: 1) Learning Atmosphere, 2) Clinical Teaching, 3) Teaching Style, 4) Communicating Expectations, and 5) Team Management.CONCLUSIONSWe identified five domains of related attributes essential to the success of ward attending rounds.

Inflammatory biomarkers as predictors of hospitalization and death in community-dwelling older adults

Individuals with multimorbidity may be at increased risk of hospitalization and death. Comorbidity indexes do not capture severity of illness or healthcare utilization; however, inflammation biomarkers that are not disease-specific may predict hospitalization and death in older adults. We sought to predict hospitalization and mortality of older adults using inflammation biomarkers. From a prospective, observational study, 370 community-dwelling adults 65 years or older from central Alabama participated in an in-home assessment and provided fasting blood samples for inflammation biomarker testing in 2004. We calculated an inflammation summary score (range 0-4), one point each for low albumin, high C-reactive protein, low cholesterol, and high interleukin-6. Utilizing Cox proportional hazards models, inflammation summary scores were used to predicted time to hospitalization and death during a 4-year follow up period. The mean age was 73.7 (±5.9 yrs), and 53 (14%) participants had summary scores of 3 or 4. The rates of dying were significantly increased for participants with inflammation summary scores of 2, 3, or 4 (hazard ratio (HR) 2.22, 2.78, and 7.55, respectively; p<0.05). An inflammation summary score of 4 significantly predicted hospitalization (HR 5.92, p<0.05). Community-dwelling older adults with biomarkers positive for inflammation had increased rates of being hospitalized or dying during the follow up period. Assessment of the individual contribution of particular inflammation biomarkers in the prediction of health outcomes in older populations and the development of validated summary scores to predict morbidity and mortality are needed.

Symptom Burden Predicts Hospitalization Independent of Comorbidity in Community-Dwelling Older Adults

To determine whether cumulative symptom burden predicts hospitalization or emergency department (ED) visits in a cohort of older adults.

Blood Pressure Management in Patients With Diabetes

IN BRIEF Patients with diabetes who also have hypertension are at increased risk of morbidity and mortality from cardiovascular events. However, blood pressure control is frequently suboptimal in the primary care setting. Large clinical trials support the use of antihypertensive medications in these patients to reduce the risk of cardiovascular disease and death.

Implementation Research: Beyond the Traditional Randomized Controlled Trial

Implementation research is a new scientific discipline emerging from the recognition that the public does not derive sufficient or rapid benefit from advances in the health sciences (Berwick DM, JAMA 289:1969–1975, 2003; Lenfant C, N Engl J Med 349:868–874, 2003). One often-quoted estimate claims that it takes an average of 17 years for even well-established clinical knowledge to be fully adopted into routine practice (Kiefe CI, Sales A, J Gen Intern Med 21(Suppl 2):S67–S70, 2006). In this chapter, we will discuss particular barriers to evidence implementation, present tools for implementation research, and provide a framework for designing implementation research studies, emphasizing the randomized trial. The reader is advised that this chapter only provides a basic introduction to several concepts for which new approaches are rapidly emerging. Therefore, our goal is to stimulate interest and promote additional in-depth learning for those who wish to develop new implementation research projects or better understand this exciting field.