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

Physician and patient perspectives on inpatients’ understanding of their care and mixed messages during hospitalization

Patient understanding, improved communication between providers and patients, and patient engagement have been linked to improved patient health outcomes. However, factors such as the complexity and ever-changing nature of the hospital environment as well as complex patient conditions limit the above communication, in turn decreasing patient understanding and engagement. We wish to elucidate clinician views on patients’ understanding of their care during hospitalization, as well as patient and clinician perspectives on possible solutions for improving patients’ understanding.C To, MSPH (Section of General Internal Medicine, Boston Medical Center), Jeffrey Schnipper, MD (General Medicine, Brigham and Women’s Hospital), Jenica Cimino, BA (Division of Hospital Medicine, University of California San Francisco), Elizabeth Bambury, BS (aptihealth, Kansas City), James D. Harrison, PhD (Division of Hospital Medicine, University of California), Mariam K. Atkinson, PhD (Department of Health Policy and Management, Harvard TH Chan School of Public Health)Includes bibliographical reference

Effects of a Multimodal Transitional Care Intervention in Patients at High Risk of Readmission: The TARGET-READ Randomized Clinical Trial.

IMPORTANCE Hospital readmissions are frequent, costly, and sometimes preventable. Although these issues have been well publicized and incentives to reduce them introduced, the best interventions for reducing readmissions remain unclear. OBJECTIVES To evaluate the effects of a multimodal transitional care intervention targeting patients at high risk of hospital readmission on the composite outcome of 30-day unplanned readmission or death. DESIGN, SETTING, AND PARTICIPANTS A single-blinded, multicenter randomized clinical trial was conducted from April 2018 to January 2020, with a 30-day follow-up in 4 medium-to-large-sized teaching hospitals in Switzerland. Participants were consecutive patients discharged from general internal medicine wards and at higher risk of unplanned readmission based on their simplified HOSPITAL score (≥4 points). Data were analyzed between April and September 2022. INTERVENTIONS The intervention group underwent systematic medication reconciliation, a 15-minute patient education session with teach-back, a planned first follow-up visit with their primary care physician, and postdischarge follow-up telephone calls from the study team at 3 and 14 days. The control group received usual care from their hospitalist, plus a 1-page standard study information sheet. MAIN OUTCOMES AND MEASURES Thirty-day postdischarge unplanned readmission or death. RESULTS A total of 1386 patients were included with a mean (SD) age of 72 (14) years; 712 (51%) were male. The composite outcome of 30-day unplanned readmission or death was 21% (95% CI, 18% to 24%) in the intervention group and 19% (95% CI, 17% to 22%) in the control group. The intention-to-treat analysis risk difference was 1.7% (95% CI, -2.5% to 5.9%; P = .44). There was no evidence of any intervention effects on time to unplanned readmission or death, postdischarge health care use, patient satisfaction with the quality of their care transition, or readmission costs. CONCLUSIONS AND RELEVANCE In this randomized clinical trial, use of a standardized multimodal care transition intervention targeting higher-risk patients did not significantly decrease the risks of 30-day postdischarge unplanned readmission or death; it demonstrated the difficulties in preventing hospital readmissions, even when multimodal interventions specifically target higher-risk patients. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03496896

Effectiveness of Transition Care Intervention Targeted to High-Risk Patients to Reduce Readmissions: Study Protocol for the TARGET-READ Multicenter Randomized-Controlled Trial.

Hospital readmissions within 30 days represent a burden for the patients and the entire health care system. Improving the care around hospital discharge period could decrease the risk of avoidable readmissions. We describe the methods of a trial that aims to evaluate the effect of a structured multimodal transitional care intervention targeted to higher-risk medical patients on 30-day unplanned readmissions and death. The TARGET-READ study is an investigator-initiated, pragmatic single-blinded randomized multicenter controlled trial with two parallel groups. We include all adult patients at risk of hospital readmission based on a simplified HOSPITAL score of ≥4 who are discharged home or nursing home after a hospital stay of one day or more in the department of medicine of the four participating hospitals. The patients randomized to the intervention group will receive a pre-discharge intervention by a study nurse with patient education, medication reconciliation, and follow-up appointment with their referring physician. They will receive short follow-up phone calls at 3 and 14 days after discharge to ensure medication adherence and follow-up by the ambulatory care physician. A blind study nurse will collect outcomes at 1 month by phone call interview. The control group will receive usual care. The TARGET-READ study aims to increase the knowledge about the efficacy of a bundled intervention aimed at reducing 30-day hospital readmission or death in higher-risk medical patients

How Do Care Transitions Work?: Unraveling the Working Mechanisms of Care Transition Interventions

BACKGROUND: Failure of safe care transitions after hospital discharge results in unnecessary worsening of symptoms, extended period of illness or readmission to the hospital. OBJECTIVE: The objective of this study was to add to the understanding of the working of care transition interventions between hospital and home through unraveling the contextual elements and mechanisms that may have played a role in the success of these interventions, and by developing a conceptual model of how these components relate to each other. RESEARCH DESIGN: This was a qualitative study using in-person, semi-structured interviews, based on realist evaluation methods. SUBJECTS: A total of 26 researchers, designers, administrators, and/or practitioners of both current "leading" care transitions interventions and of less successful care transition intervention studies or practices. MEASURES: The contextual elements and working mechanisms of the different care transition intervention studies or practices. RESULTS: Three main contextual factors (internal environment, external environment, and patient population) and 7 working mechanisms (simplifiying, verifiying, connecting, translating, coaching, monitoring, and anticipating) were found to be relevant to the outcome of care transition interventions. Context, Intervention, Mechanism, and Outcome (CIMO) configurations revealed that, in response to these contextual factors, care transition interventions triggered one or several of the mechanisms, in turn generating outcomes, including a safer care transition. CONCLUSION: We developed a conceptual model which explains the working of care transition interventions within different contexts, and believe it can help support future successful implementation of care transition interventions

Implementation, evaluation, and recommendations for extension of AHRQ Common Formats to capture patient- and carepartner-generated safety data

Abstract Objectives The Common Formats, published by the Agency for Healthcare Research and Quality, represent a standard for safety event reporting used by Patient Safety Organizations (PSOs). We evaluated its ability to capture patient-reported safety events. Materials and methods We formally evaluated gaps between the Common Formats and a safety concern reporting system for use by patients and their carepartners (ie friends/families) at Brigham and Women’s Hospital. Results Overall, we found large gaps between Common Formats (versions 1.2, 2.0) and our patient/carepartner reporting system, with only 22–30% of the data elements matching. Discussion We recommend extensions to the Common Formats, including concepts that capture greater detail about the submitter and safety categories relevant to unsafe conditions and near misses that patients and carepartners routinely observe. Conclusion Extensions to the Common Formats could enable more complete safety data sets and greater understanding of safety from key stakeholder perspectives, especially patients, and carepartners. </jats:sec

Do Hospitalists or Physicians with Greater Inpatient HIV Experience Improve HIV Care in the Era of Highly Active Antiretroviral Therapy? Results from a Multicenter Trial of Academic Hospitalists

Background. Little is known about the effect of provider type and experience on outcomes, resource use, and processes of care of hospitalized patients with human immunodeficiency virus (HIV) infection. Hospitalists are caring for this population with increasing frequency. Methods. Data from a natural experiment in which patients were assigned to physicians on the basis of call cycle was used to study the effects of provider type—that is, hospitalist versus non hospitalist—and HIV-specific inpatient experience on resource use, outcomes, and selected measures of processes of care at 6 academic institutions. Administrative data, inpatient interviews, 30-day follow-up interviews, and the National Death Index were used to measure outcomes. Results. A total of 1207 patients were included in the analysis. There were few differences in resource use, outcomes, and processes of care by provider type and experience with HIV-infected inpatients. Patients who received hospitalist care demonstrated a trend toward increased length of hospital stay compared with patients who did not receive hospitalist care (6.0 days vs. 5.2 days; Pp .13). Inpatient providers with moderate experience with HIV-infected patients were more likely to coordinate care with outpatient providers (odds ratio, 2.40; Pp .05) than were those with the least experience with HIV-infected patients, but this pattern did not extend to providers with the highest level of experience. Conclusion. Provider type and attending physician experience with HIV-infected inpatients had minimal effect on the quality of care of HIV-infected inpatients. Approaches other than provider experience, such as the use of multidisciplinary inpatient teams, may be better targets for future studies of the outcomes, processes of care, and resource use of HIV-infected inpatients

Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial

Background and objective Clinical guidelines discourage antibiotic prescribing for many acute respiratory infections (ARIs), especially for non-antibiotic appropriate diagnoses. Electronic health record (EHR)-based clinical decision support has the potential to improve antibiotic prescribing for ARIs. Methods We randomly assigned 27 primary care clinics to receive an EHR-integrated, documentation based clinical decision support system for the care of patients with ARIs - the ARI Smart Form - or to offer usual care. The primary outcome was the antibiotic prescribing rate for ARIs in an intent-to-intervene analysis based on administrative diagnoses. Results During the intervention period, patients made 21 961 ARI visits to study clinics. Intervention clinicians used the ARI Smart Form in 6% of 11 954 ARI visits. The antibiotic prescribing rate in the intervention clinics was 39% versus 43% in the control clinics (odds ratio (OR), 0.8; 95% confidence interval (CI), 0.6_1.2, adjusted for clustering by clinic). For antibiotic appropriate ARI diagnoses, the antibiotic prescribing rate was 54% in the intervention clinics and 59% in the control clinics (OR, 0.8; 95% CI, 0.5_1.3). For non-antibiotic appropriate diagnoses, the antibiotic prescribing rate was 32% in the intervention clinics and 34% in the control clinics (OR, 0.9; 95% CI, 0.6_1.4). When the ARI Smart Form was used, based on diagnoses entered on the form, the antibiotic prescribing rate was 49% overall, 88% for antibiotic appropriate diagnoses and 27% for non-antibiotic appropriate diagnoses. In an as-used analysis, the ARI Smart Form was associated with a lower antibiotic prescribing rate for acute bronchitis (OR, 0.5; 95% CI, 0.3_0.8). Conclusions The ARI Smart Form neither reduced overall antibiotic prescribing nor significantly improved the appropriateness of antibiotic prescribing for ARIs, but it was not widely used. When used, the ARI Smart Form may improve diagnostic accuracy compared to administrative diagnoses and may reduce antibiotic prescribing for certain diagnoses

Design of MARQUIS2: study protocol for a mentored implementation study of an evidence-based toolkit to improve patient safety through medication reconciliation.

BackgroundThe first Multi-center Medication Reconciliation Quality Improvement Study (MARQUIS1) demonstrated that implementation of a medication reconciliation best practices toolkit decreased total unintentional medication discrepancies in five hospitals. We sought to implement the MARQUIS toolkit in more diverse hospitals, incorporating lessons learned from MARQUIS1.MethodsMARQUIS2 is a pragmatic, mentored implementation QI study which collected clinical and implementation outcomes. Sites implemented a revised toolkit, which included interventions from these domains: 1) best possible medication history (BPMH)-taking; 2) discharge medication reconciliation and patient/caregiver counseling; 3) identifying and defining clinician roles and responsibilities; 4) risk stratification; 5) health information technology improvements; 6) improved access to medication sources; 7) identification and correction of real-time discrepancies; and, 8) stakeholder engagement. Eight hospitalists mentored the sites via one site visit and monthly phone calls over the 18-month intervention period. Each site's local QI team assessed opportunities to improve, implemented at least one of the 17 toolkit components, and accessed a variety of resources (e.g. implementation manual, webinars, and workshops). Outcomes to be assessed will include unintentional medication discrepancies per patient.DiscussionA mentored multi-center medication reconciliation QI initiative using a best practices toolkit was successfully implemented across 18 medical centers. The 18 participating sites varied in size, teaching status, location, and electronic health record (EHR) platform. We introduce barriers to implementation and lessons learned from MARQUIS1, such as the importance of utilizing dedicated, trained medication history takers, simple EHR solutions, clarifying roles and responsibilities, and the input of patients and families when improving medication reconciliation

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

Background: Unresolved 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. Methods: Six 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. Discussion At 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 registration Clinicaltrials.gov identifier NCT0133706