Patient engagement with digital disease management and readmission rates: The case of congestive heart failure

Congestive heart failure (CHF) is one of the most common diagnoses in the elderly United States Medicare (⩾ age 65) population. This patient population has a particularly high readmission rate, with one estimate of the 6-month readmission rate topping 40%. The rapid rise of mobile health (mHealth) presents a promising new pathway for reducing hospital readmissions of CHF, and, more generally, the management of chronic conditions. Using a randomized research design and a multivariate regression model, we evaluated the effectiveness of a hybrid mHealth model—the integration of remote patient monitoring with an applied health technology and digital disease management platform—on 45-day hospital readmissions for patients diagnosed with CHF. We find a 78% decrease in the likelihood of CHF hospital readmission for patients who were assigned to the digital disease management platform as compared to patients assigned to control.

Transition of Care for Discharged Patients

Practice Problem: The 30-day readmission rate for patients discharged from the hospital and returned to their primary care in a clinical office setting (21%) was higher than the national average readmission rate (17%). The high readmission rate suggested patients were receiving transitional care that was fragmented and non-standardized. Therefore, the implementation of a collaborative transition of care practice was vital to reduce avoidable readmissions. PICOT: The PICOT question that guided this project was, “In adult patients with chronic conditions, what is the effect of a transition of care practice, versus a non-standardized practice, on reducing 30-day readmissions, within a 30-day period?” Evidence: Evidence suggests that implementing a multidisciplinary Transition of Care practice for patients who are discharged from the hospital to home decreases the 30-day readmission rate. Intervention: Using a multidisciplinary approach, the registered nurse implemented a Transition of Care practice, consisting of 10 evidence-based interventions, applied to help the patient transition from hospital to home. Outcome: The results of this project revealed a decrease in the 30-day readmission rate from 23% to 15%. Also, seven of the 10 interventions were successfully implemented at a rate of higher than 85%. Conclusion: The reduction in the percent of 30-day readmissions was statistically and clinically significant between the pre-transition of care and the post-transition of care participants. In addition, the transition of care interventions were successfully implemented to standardize an evidence-based practice for patients transitioning from the hospital to their home.

Incidence and Risk Factors for 30-Day Readmission after Inpatient Chemotherapy Among Acute Lymphoblastic Leukemia Patients

Background: chemotherapy for acute lymphoblastic leukemia (ALL) patients is complex and intense, resulting in their high readmission rate. Modifiable risk factors may exist to reduce their readmission. Objectives: To identify the incidence, causes, and risk factors of readmission following inpatient chemotherapy among ALL patients. Design: A retrospective cohort using the 2016 National Readmission Database. Subjects: ALL children and adults receiving inpatient chemotherapy. Measurements: We applied three different definitions of 30-day readmission: (1) nonelective readmission based on readmission type reported by hospitals, (2) unplanned readmission defined by the algorithm developed by the Center for Medicare and Medicaid Services, and (3) unintentional readmission, combining of (1) and (2). We used a weighted survey procedure to calculate incidence and unweighted, multivariable Poisson regression with robust variance estimates for risk factors analysis, including patient-, hospital- and admission-related characteristics. Results: Percentage for nonelective, unplanned, and unintentional readmission were 33.3%, 22.4%, and 18.5%, respectively. Top three causes for unplanned readmissions were neutropenia/agranulocytosis (27.8%), septicemia (15.3%) and pancytopenia (11.5%). Risk ratios for unintentional readmission were 1.21 (1.08-1.36) for nonelective vs elective admission, 1.19 (1.06-1.33) for public vs private insurance enrollees, 0.96 (0.95-0.98) for each day of hospital stay, 0.77 (0.62-0.95) for large, teaching, and 0.87 (0.70-1.08) for small teaching vs non-teaching hospitals. Conclusions: Possible strategies to reduce readmission among ALL patients could be shortening the gap in quality of care among teaching vs non-teaching hospitals, understanding the difference of privately vs publicly insured patients, and avoiding aggressive discharge after chemotherapy. Disclosures No relevant conflicts of interest to declare.

Incidence and Risk Factors for 30-Day Readmission after Inpatient Chemotherapy among Acute Lymphoblastic Leukemia Patients

Chemotherapy for acute lymphoblastic leukemia (ALL) patients is complex and intense, resulting in a high readmission rate. We aimed to identify the incidence, causes, and risk factors of readmission following inpatient chemotherapy among ALL patients, using 2016 National Readmission Database. We applied three different definitions of 30-day readmission: (1) nonelective readmission based on readmission type, (2) unplanned readmission defined by CMS, and (3) unintentional readmission, combining (1) and (2). We used unweighted multivariable Poisson regression with robust variance estimates for risk factors analysis, including patient-, hospital-, and admission-related characteristics. Percentage for nonelective, unplanned, and unintentional readmission were 33.3%, 22.4%, and 18.5%, respectively. The top three causes for unplanned readmissions were neutropenia/agranulocytosis (27.8%), septicemia (15.3%), and pancytopenia (11.5%). Risk ratios for unintentional readmission were 1.21 (1.08–1.36) for nonelective vs. elective admission, 1.19 (1.06–1.33) for public vs. private insurance enrollees, 0.96 (0.95–0.98) for each day of hospital stay, 0.77 (0.62–0.95) for large teaching and 0.87 (0.70–1.08) for small teaching vs. nonteaching hospitals. Possible strategies to reduce readmission among ALL patients could be shortening the gap in quality of care among teaching vs. non-teaching hospitals, understanding the difference between privately vs. publicly insured patients, and avoiding aggressive discharge after chemotherapy.

Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients

Introduction. The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (GBTM) was performed on the patient cohort (2006-2009) identified through national administrative data from all NHS English hospitals. Proc Traj software was used in SAS program to conduct GBTM, which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair. Based on the trends of readmission rates, patients were classified into low- and high-impact users. The high-impact group had a higher annual readmission rate throughout 5-year follow-up. Short-term high-impact users had initial high readmission rate followed by rapid decline, whereas chronic high-impact users continued to have high readmission rate. Results. Based on the trends in readmission rates, GBTM classified elective AAA repair (n=16,973) patients into 2 groups: low impact (82.0%) and high impact (18.0%). High-impact users were significantly associated with female sex (P=0.001) undergoing other vascular procedures (P=0.003), poor socioeconomic status index (P<0.001), older age (P<0.001), and higher comorbidity score (P<0.001). The AUC for c-statistics was 0.84. Patients with ruptured AAA repair (n=4144) had 3 groups: low impact (82.7%), short-term high impact (7.2%), and chronic high impact (10.1%). Chronic high impact users were significantly associated with renal failure (P<0.001), heart failure (P = 0.01), peripheral vascular disease (P<0.001), female sex (P = 0.02), open repair (P<0.001), and undergoing other related procedures (P=0.05). The AUC for c-statistics was 0.71. Conclusion. Patients with persistent high readmission rates exist among AAA population; however, their readmissions and mortality are not related to AAA repair. They may benefit from optimization of their medical management of comorbidities perioperatively and during their follow-up.

Common Sequences of Emergency Readmissions among High-Impact Users following AAA Repair

Introduction. The aim of the study was to examine common sequences of causes of readmissions among those patients with multiple hospital admissions, high-impact users, after abdominal aortic aneurysm (AAA) repair and to focus on strategies to reduce long-term readmission rate. Methods. The patient cohort (2006–2009) included patients from Hospital Episodes Statistics, the national administrative data of all NHS English hospitals, and followed up for 5 years. Group-based trajectory modelling and sequence analysis were performed on the data. Results. From a total of 16,973 elective AAA repair patients, 18% (n=3055) were high-impact users. The high-impact users among ruptured abdominal aortic aneurysm (rAAA) repair constituted 17.3% of the patient population (n=4144). There were 2 subtypes of high-impact users, short-term (7.2%) with initial high readmission rate following by rapid decline and chronic high-impact (10.1%) with persistently high readmission rate. Common causes of readmissions following elective AAA repair were respiratory tract infection (7.3%), aortic graft complications (6.0%), unspecified chest pain (5.8%), and gastrointestinal haemorrhage (4.8%). However, high-impact users included significantly increased number of patients with multiple readmissions and distinct sequences of readmissions mainly consisting of COPD (4.7%), respiratory tract infection (4.7%), and ischaemic heart disease (3.3%). Conclusion. A significant number of patients were high-impact users after AAA repair. They had a common and distinct sequence of causes of readmissions following AAA repair, mainly consisting of cardiopulmonary conditions and aortic graft complications. The common causes of long-term mortality were not related to AAA repair. The quality of care can be improved by identifying these patients early and focusing on prevention of cardiopulmonary diseases in the community.

Sequence Analysis of Long-Term Readmissions among High-Impact Users of Cerebrovascular Patients

Objective. Understanding the chronological order of the causes of readmissions may help us assess any repeated chain of events among high-impact users, those with high readmission rate. We aim to perform sequence analysis of administrative data to identify distinct sequences of emergency readmissions among the high-impact users. Methods. A retrospective cohort of all cerebrovascular patients identified through national administrative data and followed for 4 years. Results. Common discriminating subsequences in chronic high-impact users (n=2863) of ischaemic stroke (n=34208) were “urological conditions-chest infection,” “chest infection-urological conditions,” “injury-urological conditions,” “chest infection-ambulatory condition,” and “ambulatory condition-chest infection” (p<0.01). Among TIA patients (n=20549), common discriminating (p<0.01) subsequences among chronic high-impact users were “injury-urological conditions,” “urological conditions-chest infection,” “urological conditions-injury,” “ambulatory condition-urological conditions,” and “ambulatory condition-chest infection.” Among the chronic high-impact group of intracranial haemorrhage (n=2605) common discriminating subsequences (p<0.01) were “dementia-injury,” “chest infection-dementia,” “dementia-dementia-injury,” “dementia-urine infection,” and “injury-urine infection.” Conclusion. Although common causes of readmission are the same in different subgroups, the high-impact users had a higher proportion of patients with distinct common sequences of multiple readmissions as identified by the sequence analysis. Most of these causes are potentially preventable and can be avoided in the community.

Few modifiable factors predict readmission following radical cystectomy

Introduction: We sought to determine the patient and providerrelated factors associated with readmission after radical cystectomy (RC) for bladder cancer. In this era of healthcare reform, hospital performance measures, such as readmission, are beginning to affect provider reimbursement. Given its high readmission rate, RC could be a target for quality improvement.Methods: We reviewed bladder cancer patients who underwent RC in California’s State Inpatient Database (2005–2009) of the Healthcare Cost and Utilization Project. We examined patient- (e.g., race, discharge disposition) and provider-related factors (e.g., volume) and evaluated their association with 30-day readmission. Multivariable logistic regression was used to examine associations of interest.Results: Overall, 22.8% (n = 833) of the 3649 patients who underwent RC were readmitted within 30 days. Regarding disposition, 34.8%, 50.8%, and 12.2% were discharged home, home with home healthcare, and to a post-acute care facility (PACF), respectively. Within 30 days, 20.3%, 20.9%, and 42.3% discharged home, home with home healthcare, and to a PACF were readmitted, respectively. African Americans (odds ratio [OR] 1.64, 95% confidence interval [CI] 1.07–2.50), having ≥2 comorbidities (OR 1.42, 95% CI 1.06–1.91), receiving a neobladder (OR 1.45, 95% CI 1.09–1.93), and discharge to a PACF (OR 3.79, 95% CI 2.88–4.98) were independent factors associated with readmission. Hospital stays ≥15 days were associated with less readmission (OR 0.43, 95% CI 0.27–0.67, p = 0.0002). Procedure volume was not associated with complication, in-hospital mortality, or readmission.Conclusions: About one-fifth of patients undergoing RC are readmitted. Patients who are discharged to a PACF, African American, and who have more extensive comorbidities tend to experience more readmissions. Increased efforts with care coordination among these patients may help reduce readmissions.