Hospital strategy uptake and reductions in unplanned readmission rates for patients with heart failure: a prospective study

Discharge medication delivery location and postpartum blood pressure control in patients with hypertensive disorders of pregnancy

OBJECTIVE

This study examined whether use of bedside medication delivery (Meds to Beds, M2B) or on-campus pharmacy at discharge was associated with improved postpartum blood pressure (BP) control compared to outside pharmacy use in patients with hypertensive disorders of pregnancy (HDP).

STUDY DESIGN

This was a secondary analysis of 357 patients with HDP enrolled in STAMPP-HTN (Systematic Treatment and Management of Postpartum Hypertension Program) who were discharged from delivery admission with antihypertensives between October 2018 and June 2020. Patients were grouped by discharge medication location: M2B/on-campus pharmacy (on-site) versus outside pharmacy (off-site).

MAIN OUTCOME MEASURES

The primary outcome was BP values at the immediate postpartum visit. Secondary outcomes included six-week visit BP values, attendance at both visits, and readmission within six weeks.

RESULTS

Median BP values were no different based on pharmacy location at immediate postpartum visit for both systolic ((135 [IQR 127, 139] on-site vs 137 [127, 145] off-site, p = 0.22) and diastolic (81 [74, 91] vs 83 [76, 92], p = 0.45) values. Similar findings were noted at six weeks. Patients who used an off-site pharmacy had higher attendance rates at the immediate postpartum visit but this difference was attenuated after adjusting for group differences (OR 0.67 [95 % CI 0.37-1.20], p = 0.18). Readmission rates were also not different between groups (12.2 % on-site vs 15.8 % off-site pharmacy, p = 0.43).

CONCLUSION

In the context of a preexisting multicomponent HDP quality improvement program, on-campus pharmacy and bedside medication delivery use was not associated with additional improvement in postpartum BP control, follow-up rates, or readmission rates.

Medication Management Through Collaborative Practice for Children With Medical Complexity: A Prospective Case Series

OBJECTIVE

Care coordination for children and youth with special health care needs and medical complexity (CYSHCN-CMC), especially medication management, is difficult for providers, parents/caregivers, and -patients. This report describes the creation of a clinical pharmacotherapy practice in a pediatric long-term care facility (pLTCF), application of standard operating procedures to guide comprehensive medication management (CMM), and establishment of a collaborative practice agreement (CPA) to guide drug therapy.

METHODS

In a prospective case series, 102 patients characterized as CYSHCN-CMC were included in this pLTCF quality improvement project during a 9-month period.

RESULTS

Pharmacists identified, prevented, or resolved 1355 drug therapy problems (DTP) with an average of 13 interventions per patient. The patients averaged 9.5 complex chronic medical conditions with a -median length of stay of 2815 days (7.7 years). The most common medications discontinued due to pharmacist assessment and recommendation included diphenhydramine, albuterol, sodium phosphate enema, ipratropium, and metoclopramide. The average number of medications per patient was reduced from 23 to 20. A pharmacoeconomic analysis of 244 of the interventions revealed a monthly direct cost savings of $44,304 ($434 per patient per month) and monthly cost avoidance of $48,835 ($479 per patient per month). Twenty-eight ED visits/admissions and 61 clinic and urgent care visits were avoided. Hospital -readmissions were reduced by 44%. Pharmacist recommendations had a 98% acceptance rate.

CONCLUSIONS

Use of a CPA to conduct CMM in CYSHCN-CMC decreased medication burden, resolved, and prevented adverse events, reduced health care-related costs, reduced hospital readmissions and was well-accepted and implemented collaboratively with pLTCF providers.

Early and short-term intensive management after discharge for patients hospitalized with acute heart failure: a randomized study (ECAD-HF)

AIMS

Hospitalization for acute heart failure (HF) is followed by a vulnerable time with increased risk of readmission or death, thus requiring particular attention after discharge. In this study, we examined the impact of intensive, early follow-up among patients at high readmission risk at discharge after treatment for acute HF.

METHODS AND RESULTS

Hospitalized acute HF patients were included with at least one of the following: previous acute HF < 6 months, systolic blood pressure ≤ 110 mmHg, creatininaemia ≥ 180 µmol/L, or B-type natriuretic peptide ≥ 350 pg/mL or N-terminal pro B-type natriuretic peptide ≥ 2200 pg/mL. Patients were randomized to either optimized care and education with serial consultations with HF specialist and dietician during the first 2-3 weeks, or to standard post-discharge care according to guidelines. The primary endpoint was all-cause death or first unplanned hospitalization during 6-month follow-up. Among 482 randomized patients (median age 77 and median left ventricular ejection fraction 35%), 224 were hospitalized or died. In the intensive group, loop diuretics (46%), beta-blockers (49%), angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (39%) and mineralocorticoid receptor antagonists (47%) were titrated. No difference was observed between groups for the primary endpoint (hazard ratio 0.97; 95% confidence interval 0.74-1.26), nor for mortality at 6 or 12 months or unplanned HF rehospitalization. Additionally, no difference between groups according to age, previous HF and left ventricular ejection fraction was found.

CONCLUSIONS

In high-risk HF, intensive follow-up early post-discharge did not improve outcomes. This vulnerable post-discharge time requires further studies to clarify useful transitional care services.

Nurse-led ambulatory care supported by non-invasive haemodynamic assessment after acute heart failure decompensation

Heart failure (HF) is characterized by frequent decompensation and an unpredictable trajectory. To prevent early hospital readmission, coordinated discharge planning and individual therapeutic approach are recommended.

Aims

We aimed to assess the effect of 1 month of ambulatory care, led by nurses and supported by non‐invasive haemodynamic assessment, on the functional status, well‐being, and haemodynamic status of patients post‐acute HF decompensation.

Methods and results

This study had a multicentre, prospective, and observational design and included patients with at least one hospitalization due to acute HF decompensation within 6 months prior to enrolment. The 1 month ambulatory care included three visits led by a nurse when the haemodynamic state of each patient was assessed non‐invasively by impedance cardiography, including thoracic fluid content assessment. The pharmacotherapy was modified basing on haemodynamic assessment. Sixty eight of 73 recruited patients (median age = 67 years; median left ventricular ejection fraction = 30%) finished 1 month follow‐up. A significant improvement was observed in both the patients' functional status as defined by New York Heart Association class (P = 0.013) and sense of well‐being as evaluated by a visual analogue score (P = 0.002). The detailed patients' assessment on subsequent visits resulted in changes of pharmacotherapy in a significant percentage of patients (Visit 2 = 39% and Visit 3 = 44%).

Conclusions

The proposed model of nurse‐led ambulatory care for patients after acute HF decompensation, with consequent assessment of the haemodynamic profile, resulted in: (i) improvement in the functional status, (ii) improvement in the well‐being, and (iii) high rate of pharmacotherapy modifications.

Involvement of Acute Care Physical Therapists in Care Transitions for Older Adults Following Acute Hospitalization: A Cross-sectional National Survey

BACKGROUND AND PURPOSE

Recent evidence has suggested physical therapist involvement in care transitions after hospitalization is associated with reduced rates of hospital readmissions. However, little is known about how physical therapists participate in care transitions for older adults, the content of care communications, and the facilitators and barriers of implementing evidence-based care transition strategies into practice. Thus, the purpose of this article is to evaluate participation in care transition activities known to influence readmission risk among older adults, and understand perceptions of and barriers to participation in these activities.

METHODS

We developed a survey questionnaire to quantify hospital-based physical therapist participation in care transitions and validated it using cognitive interviewing. It was introduced to a cross-sectional national sample of physical therapists who participate in the Academy of Acute Care Physical Therapy electronic discussion board using a SurveyMonkey tool.

RESULTS AND DISCUSSION

More than 90% of respondents agreed they routinely recommended a discharge location and provided recommendations for durable medical equipment for patients at the time of hospital discharge. Respondents did not routinely initiate communication with therapists in other care settings, or follow up with patients to determine whether recommendations were followed. A majority of respondents agreed their facilities would not consider many key care transition activities to count as productive time.This survey provides a novel insight into how hospital-based physical therapists participate in care transitions. Communications between rehabilitation providers across care settings are infrequent, even though those communications are recommended to help reduce readmissions. However, administrative barriers were elucidated in this study that may help explain lack of therapist involvement.

CONCLUSIONS

Physical therapists' communications across health care setting about older adults discharging from acute care hospitalization are infrequent, but may represent a meaningful intervention target for future studies. Future research is needed to evaluate best practices for hospital-based physical therapists during care transitions.

Remote Supervision to Decrease Hospitalization Rate (RESULT) study in patients with implanted cardioverter-defibrillator

Aims

The number of patients with heart failure (HF) and implantable cardiac electronic devices has been growing steadily. Remote monitoring care (RC) of cardiac implantable electronic devices can facilitate patient-healthcare clinical interactions and prompt preventive activities to improve HF outcomes. However, studies that have investigated the efficacy of remote monitoring have shown mixed findings, with better results for the system including daily verification of transmission. The purpose of the RESULT study was to analyse the impact of remote monitoring on clinical outcomes in HF patients with implantable cardioverter-defibrillator [ICD/cardiac resynchronization therapy-defibrillator (CRT-D)] in real-life conditions.

Methods and results

The RESULT is a prospective, single-centre, randomized trial. Patients with HF and de novo ICD or CRT-D implantation were randomized to undergo RC vs. in-office follow-ups (SC, standard care). The primary endpoint was a composite of all-cause death and hospitalization due to cardiovascular reasons within 12 months after randomization. We randomly assigned 600 eligible patients (299 in RC vs. 301 in SC). Baseline clinical and echocardiographic characteristics were well-balanced and similar in both arms. The incidence of the primary endpoint differed significantly between RC and SC and involved 39.5% and 48.5% of patients, respectively, (P = 0.048) within the 12-month follow-up. The rate of all-cause mortality was similar between the studied groups (6% vs. 6%, P = 0.9), whereas hospitalization rate due to cardiovascular reasons was higher in SC (37.1% vs. 45.5%, P = 0.045).

Conclusion

Remote monitoring of HF patients with implanted ICD or CRT-D significantly reduced the primary endpoint rate, mostly as a result of a lower hospitalization rate in the RC arm (ClinicalTrials.gov Identifier: NCT02409225).

Optimising standards of care of heart failure in general practice the OSCAR-HF pilot study protocol

Background: Heart failure (HF) imposes a burden for patients and health economics. General practitioners (GPs) are confronted with the broadest range of HF management. Although guidelines exist, they are not fully implemented in the Belgian health care system. Methods: We will conduct a non-randomised, non-controlled prospective observational trial (six months follow-up) to implement a multifaceted intervention in Belgian general practice to support GPs in the implementation of evidence-based HF guidelines. The multifaceted intervention consists of an audit and feedback method to detect previously unrecognised patients with HF and to increase awareness for proactive HF management, an NT-proBNP point-of-care test to improve detection and adequate diagnosis of patients with HF and a specialist HF nurse to assist GPs in the education of patients, optimisation of treatment and follow-up after hospitalisation. All patients aged 40 years and older with a confirmed diagnosis of HF by their GP based on the clinical audit are eligible for participation. The main objective of this pilot study is to evaluate the feasibility of this multifaceted intervention and the evolution of predefined quality indicators. We will measure the impact on HF diagnosis, medication optimisation, multidisciplinary follow-up and patients' quality of life after six months. Additionally, the experiences of GPs and investigators will be studied. Conclusions: Heart failure is an important health problem in which GPs play a key role. Therefore, we will evaluate the feasibility of a multifaceted intervention to optimise diagnosis as well as implement the guideline recommended therapies in patients with HF in general practice.

What is the impact on the readmission ratio of taking into account readmissions to other hospitals? A cross-sectional study

OBJECTIVES

Readmissions are used widespread as an indicator of the quality of care within hospitals. Including readmissions to other hospitals might have consequences for hospitals. The aim of our study is to determine the impact of taking into account readmissions to other hospitals on the readmission ratio.

DESIGN AND SETTING

We performed a cross-sectional study and used administrative data from 77 Dutch hospitals (2 333 173 admissions) in 2015 and 2016 (97% of all hospitals). We performed logistic regression analyses to calculate 30-day readmission ratios for each hospital (the number of observed admissions divided by the number of expected readmissions based on the case mix of the hospital, multiplied by 100). We then compared two models: one with readmissions only to the same hospital, and another with readmissions to any hospital in the Netherlands. The models were calculated on the hospital level for all in-patients and, in more detail, on the level of medical specialties.

MAIN OUTCOME MEASURES

Percentage of readmissions to another hospital, readmission ratios same hospital and any hospital and C-statistic of each model in order to determine the discriminative ability.

RESULTS

The overall percentage of readmissions was 10.3%, of which 91.1% were to the same hospital and 8.9% to another hospital. Patients who went to another hospital were younger, more often men and had fewer comorbidities. The readmission ratios for any hospital versus the same hospital were strongly correlated (r=0.91). There were differences between the medical specialties in percentage of readmissions to another hospital and C-statistic.

CONCLUSIONS

The overall impact of taking into account readmissions to other hospitals seems to be limited in the Netherlands. However, it does have consequences for some hospitals. It would be interesting to explore what causes this difference for some hospitals and if it is related to the quality of care.

Trends in Pediatric Hospitalizations and Readmissions: 2010-2016

BACKGROUND

Health reform and policy initiatives over the last 2 decades have led to significant changes in pediatric clinical practice. However, little is known about recent trends in pediatric hospitalizations and readmissions at a national level.

METHODS

Data from the 2010-2016 Healthcare Cost and Utilization Project Nationwide Readmissions Database and National Inpatient Sample were analyzed to characterize patient-level and hospital-level trends in annual pediatric (ages 1-17 years) admissions and 30-day readmissions. Poisson regression was used to evaluate trends in pediatric readmissions over time.

RESULTS

From 2010 to 2016, the total number of index admissions decreased by 21.3%, but the percentage of admissions for children with complex chronic conditions increased by 5.7%. Unadjusted pediatric 30-day readmission rates increased over time from 6.26% in 2010 to 7.02% in 2016 with a corresponding increase in numbers of admissions for patients with complex chronic conditions. When stratified by complex or chronic conditions, readmission rates declined or remained stable across patient subgroups. Mean risk-adjusted hospital readmission rates increased over time overall (6.46% in 2010 to 7.14% in 2016) and in most hospital subgroups but decreased over time in metropolitan teaching hospitals.

CONCLUSIONS

Pediatric admissions declined from 2010 to 2016 as 30-day readmission rates increased. The increase in readmission rates was associated with greater numbers of admissions for children with chronic conditions. Hospitals serving pediatric patients need to account for the rising complexity of pediatric admissions and develop strategies for reducing readmissions in this high-risk population.

The effect of assigning dedicated general practitioners to nursing homes

OBJECTIVE

To determine whether assigning a dedicated general practitioner (GP) to a nursing home reduces hospitalizations and readmissions.

DATA SOURCES/STUDY SETTING

Secondary data on hospitalizations and deaths by month for the universe of nursing home residents in Denmark from January 2011 through February 2014.

STUDY DESIGN

In 2012, Denmark initiated a program in seven nursing homes that volunteered to participate. We used a difference-in-differences model to estimate the effect of assigning a dedicated GP to a nursing home on the likelihood that a nursing home resident will be hospitalized, will experience a preventable hospitalization, and will be readmitted. The unit of observation is a resident-month.

DATA COLLECTION/EXTRACTION METHODS

Data were extracted from the Danish public administrative register dataset.

PRINCIPAL FINDINGS

We found that assigning a GP to a nursing home was associated with a 0.55 [95 percent CI, 0.08 to 1.02] percentage point reduction in the monthly probability of a preventable hospitalization, which was a 26 percent reduction from the preintervention level of 2.13 percentage points. The associated reduction in the monthly probability of a readmission was 0.68 [95 percent CI, -0.01 to 1.37] percentage points, which was a 25 percent reduction from the baseline level of 2.68 percentage points. Survey results indicated that the likely mechanism for the effect was more efficient and consistent communication between GPs and nursing home personnel.

CONCLUSIONS

Assigning a dedicated physician in a nursing home can reduce medical spending and improve patients' health.

To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database

BACKGROUND

It is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmissions on the hospital level after adjusting for case-mix factors.

METHODS

We performed multilevel logistic regression analyses with a random intercept for the factor 'hospital' to estimate the variance on the hospital level after adjustment for case-mix variables. We used administrative data from 53 Dutch hospitals from 2010 to 2012 (58% of all Dutch hospitals; 2,577,053 admissions). We calculated models for the top ten diagnosis groups with the highest number of readmissions after an index admission for a surgical procedure. We calculated intraclass correlation coefficients (ICC) per diagnosis group in order to explore the variation in readmissions between hospitals. Furthermore, we determined C-statistics for the models with and without a random effect on the hospital level to determine the discriminative ability.

RESULTS

The ICCs on the hospital level ranged from 0.48 to 2.70% per diagnosis group. The C-statistics of the models with a random effect on the hospital level ranged from 0.58 to 0.65 for the different diagnosis groups. The C-statistics of the models that included the hospital level were higher compared to the models without this level.

CONCLUSIONS

For some diagnosis groups, a small part of the explained variation in readmissions was found on the hospital level, after adjusting for case-mix variables. However, the C-statistics of the prediction models are moderate, so the discriminative ability is limited. Readmission indicators might be useful for identifying areas for improving quality within hospitals on the level of diagnosis or specialty.

Effective mental health interventions to reduce hospital readmission rates: a systematic review

BACKGROUND

Hospitals in the United States are financially penalized for having a higher than expected thirty-day readmission ratio among patients initially hospitalized for heart failure, acute myocardial infarction (AMI), pneumonia, chronic obstructive pulmonary disease (COPD), coronary artery bypass graft (CABG) surgery, or hip and knee replacement. Patients hospitalized for these conditions that have comorbid mental health diagnoses or symptoms are at high risk for readmission.

METHODS

We conducted a systematic review to determine if interventions, that are specifically designed to assess or treat mental health symptoms, can effectively reduce risk of readmission following hospitalization for physical health conditions. We searched on PubMed and Google Scholar for peer-reviewed articles published between January 2010 and June 2018 that examined the impact of mental-health interventions on readmissions for physical conditions.

RESULTS

After screening 81 full text articles, we found eleven intervention studies, one meta-analysis, and one cross-sectional study that met our inclusion criteria. Only three of the intervention studies found significant differences in readmission rates between intervention and comparison groups. Each of these interventions targeted patients after discharge from the hospital. One of the interventions was a physical health telemonitoring and individual psychotherapy intervention for patients that were initially admitted for heart failure. The second intervention was individual and group psychotherapy sessions for patients who were initially admitted for AMI. The third intervention was a nurse-driven depression care management protocol for home care patients with depressive symptoms who were initially admitted for any physical health condition. The cross-sectional study showed that communities with a stronger, social-based public mental health infrastructure had significantly lower physical health readmission rates.

CONCLUSIONS

The literature identified in this review, appears to provide support for the use of mental health interventions after discharge as a mechanism for reducing physical health condition readmissions. Future research is needed to determine if these interventions can specifically reduce thirty-day readmissions for the six conditions linked to financial penalties.

Association Between Community Social Capital and Hospital Readmission Rates

Hospital readmissions remain frequent, and are partly attributable to patients' social needs. The authors sought to examine whether local community levels of social capital are associated with hospital readmission rates. Social capital refers to the connections among members of a society that foster norms of reciprocity and trust, which may influence the availability of support for postdischarge recovery after hospitalization. Associations between hospital-wide, risk-stratified readmission rates for hospitals in the United States (n = 4298) and levels of social capital in the hospitals' service areas were examined. Social capital was measured by an index of participation in associational activities and civic affairs. A multivariate linear regression model was used to adjust for hospital and community factors such as hospital financial performance, race, income, and availability of heath care services. Results showed that higher social capital was significantly associated with lower readmission rates (P < .01), a finding that held across income-stratified analyses as well as sensitivity analyses that included hospital performance on process quality measures and hospital community engagement activities. A hospital is unlikely to be able to influence prevailing levels of social capital in its region, but in areas of low social capital, it may be possible for public or philanthropic sectors to buttress the types of institutions that address nonmedical causes of readmission.

The Hospital Readmissions Reduction Program-learning from failure of a healthcare policy

Heart failure is the leading cause of readmissions in patients aged ≥65 years with high associated societal and economic costs. The utilization metric of 30-day risk standardized readmission rates (RSRRs) has therefore become a target to reduce healthcare costs. In this review, we discuss in detail the implementation, effectiveness, and unintended consequences of the Hospital Readmissions Reduction Program (HRRP)-the major healthcare policy approach in the U.S. to reduce readmissions by financially penalizing hospitals with higher than average 30-day RSRRs. The HRRP was enacted by the Patient Protection and Affordable Care Act of 2010 (popularly known as 'Obamacare'). The public reporting of RSRRs began in June 2009 and the HRRP readmission penalties went into effect starting fiscal year 2013. The policy had limited success in achieving its primary objective of reducing readmissions as the achieved reduction in heart failure readmissions was much smaller (∼9%) than anticipated (∼25%) with some of the reduction in RSRRs attributable to the artifact of administrative upcoding post-HRRP rather than an actual decline in readmissions. From the time of passage of this law, there have been significant concerns regarding gaming of the system such as increase in observation stays, delaying readmissions beyond discharge day 30, and inappropriate triage strategies in emergency departments in order to achieve lower readmission rates to avoid penalties. A series of independent reports have now suggested that implementation of the HRRP was associated with an increase in 30-day, 90-day, and 1-year risk-adjusted heart failure mortality in the U.S. with reversal in decade long trend of declining heart failure mortality. We review the evidence behind effect of the HRRP on readmissions and mortality outcomes as well as discuss various lessons to be learned from the design, implementation, and consequences of this policy.

The Impact of the Hospital Readmissions Reduction Program across Insurance Types in California

OBJECTIVE

Examine 30-day readmission rates for indicator conditions before and after adoption of the Hospital Readmissions Reduction Program (HRRP).

DATA

California hospital discharge data, 2005 to 2014.

STUDY DESIGN

Estimated difference between pre-HRRP trends and post-HRRP rates of hospital readmissions after hospitalization for indicator conditions targeted by the HRRP (heart attack, heart failure, and pneumonia) by payer among insured adults.

PRINCIPAL FINDINGS

Post-HRRP, reductions occurred for the three conditions among Fee-for-Service (FFS) Medicare. Readmissions decreased for heart attack and heart failure in Medicare Managed Care (MC). No reductions were observed in the younger commercially insured.

CONCLUSIONS

Post-HRRP, greater than expected reductions occurred in rehospitalizations for patients with Medicare FFS and Medicare MC. HRRP incentives may be influencing system-wide changes influencing care outside of traditional Medicare.

Early Identification of Patients With Acute Decompensated Heart Failure

BACKGROUND

Interventions to reduce readmissions after acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) with the use of data derived from the electronic health record.

METHODS AND RESULTS

We included 37,229 hospitalizations of adult patients at a single hospital during 2013-2015. We developed 4 algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of 1 of 3 clinical characteristics, 2) logistic regression of 31 structured data elements, 3) machine learning with unstructured data, and 4) machine learning with the use of both structured and unstructured data. In data validation, algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96, and both machine learning algorithms had AUCs of 0.99. Based on a brief survey of 3 providers who perform chart review for ADHF, we estimated that providers spent 8.6 minutes per chart review; using this this parameter, we estimated that providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening were done with algorithms 1, 2, 3, and 4, respectively.

CONCLUSIONS

Machine learning algorithms with unstructured notes had the best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions.

Effects of an Intervention to Reduce Hospitalizations From Nursing Homes: A Randomized Implementation Trial of the INTERACT Program

IMPORTANCE

Medicare payment initiatives are spurring efforts to reduce potentially avoidable hospitalizations.

OBJECTIVE

To determine whether training and support for implementation of a nursing home (NH) quality improvement program (Interventions to Reduce Acute Care Transfers [INTERACT]) reduced hospital admissions and emergency department (ED) visits.

DESIGN, SETTING, AND PARTICIPANTS

This analysis compared changes in hospitalization and ED visit rates between the preintervention and postintervention periods for NHs randomly assigned to receive training and implementation support on INTERACT to changes in control NHs. The analysis focused on 85 NHs (36 717 NH residents) that reported no use of INTERACT during the preintervention period.

INTERVENTIONS

The study team provided training and support for implementing INTERACT, which included tools that help NH staff identify and evaluate acute changes in NH resident condition and document communication between physicians; care paths to avoid hospitalization when safe and feasible; and advance care planning and quality improvement tools.

MAIN OUTCOMES AND MEASURES

All-cause hospitalizations, hospitalizations considered potentially avoidable, 30-day hospital readmissions, and ED visits without admission. All-cause hospitalization rates were calculated for all resident-days, high-risk days (0-30 days after NH admission), and lower-risk days (≥31 days after NH admission).

RESULTS

We found that of 85 NHs, those that received implementation training and support exhibited statistically nonsignificant reductions in hospitalization rates compared with control NHs (net difference, -0.13 per 1000 resident-days; P = .25), hospitalizations during the first 30 days after NH admission (net difference, -0.37 per 1000 resident-days; P = .48), hospitalizations during periods more than 30 days after NH admission (net difference, -0.09 per 1000 resident-days; P = .39), 30-day readmission rates (net change in rate among hospital discharges, -0.01; P = .36), and ED visits without admission (net difference, 0.02 per 1000 resident-days; P = .83). Intervention NHs exhibited a reduction in potentially avoidable hospitalizations overall (net difference, -0.18 per 1000 resident-days, P = .01); however, this effect was not robust to a Bonferroni correction for multiple comparisons.

CONCLUSIONS AND RELEVANCE

Training and support for INTERACT implementation as carried out in this study had no effect on hospitalization or ED visit rates in the overall population of residents in participating NHs. The results have several important implications for implementing quality improvement initiatives in NHs.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT02177058.

Outcomes after Rehospitalization at the Same Hospital or a Different Hospital Following Critical Illness

RATIONALE

Intensive care unit (ICU) patients who receive mechanical ventilation are at high risk for early rehospitalization. Given the medical complexity of these patients, a lack of continuity of care may adversely affect their outcomes during rehospitalization.

OBJECTIVES

To determine whether outcomes differ for patients who are rehospitalized at a different hospital versus the hospital of their index ICU stay.

METHODS

We conducted a retrospective cohort study of mechanically ventilated ICU patients rehospitalized within 30 days in New York State hospitals between 2008 and 2013.

MEASUREMENTS AND MAIN RESULTS

We measured frequency of rehospitalization at a different hospital, mortality, length of stay, and costs during rehospitalization. Of 26,947 mechanically ventilated ICU patients rehospitalized within 30 days of discharge, 8,443 (31.3%) were rehospitalized at a different hospital than that of the index ICU stay. For patients at a different hospital, 13.7% died during rehospitalization versus 11.1% who died at the index hospital (adjusted rate ratio [aRR], 1.11; 95% confidence interval [CI], 1.03-1.20; P = 0.009). Patients who died at a different hospital had shorter length of stay (aRR, 0.80; 95% CI, 0.70-0.92; P = 0.001) and decreased costs (adjusted mean difference, -$9,632.73; 95% CI, -$16,387.60 to -$2,877.88; P = 0.005), whereas survivors of rehospitalization at a different hospital had a modest increase in length of stay (aRR, 1.06; 95% CI, 1.01-1.11; P = 0.009) and increased costs of care (adjusted mean difference, $1,665.34; 95% CI, $602.12-$2,728.56; P = 0.002).

CONCLUSIONS

Almost one-third of mechanically ventilated critically ill patients were rehospitalized at a different hospital than that of the index ICU stay. This care discontinuity was associated with increased mortality.

Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge

IMPORTANCE

The Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.

OBJECTIVE

To examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014.

EXPOSURE

Thirty-day risk-adjusted readmission rate (RARR).

MAIN OUTCOMES AND MEASURES

Thirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition.

RESULTS

In 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality.

CONCLUSIONS AND RELEVANCE

Among Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.

Trends in 30-Day Readmission Rates for Patients Hospitalized With Heart Failure: Findings From the Get With The Guidelines-Heart Failure Registry

BACKGROUND

Reducing hospital readmissions for patients with heart failure is a national priority, and quality improvement campaigns are targeting reductions of ≥20%. However, there are limited data on whether such targets have been met.

METHODS AND RESULTS

We analyzed data from the American Heart Association's Get With The Guidelines-Heart Failure registry linked to Medicare claims between 2009 and 2012 to describe trends and relative reduction of rates of 30-day all-cause readmission among patients with heart failure. A total of 21,264 patients with heart failure were included from 70 US sites from January 2009 to October 2012. Overall hospital-level, risk-adjusted, 30-day all-cause readmission rates declined slightly, from 20.0% (SD, 1.3%) in 2009 to 19.0% (SD, 1.2%) in 2012 (P=0.001). Only 1 in 70 (1.4%) hospitals achieved the 20% relative reduction in 30-day risk-adjusted readmission rates. A multivariable linear regression model was used to determine hospital-level factors associated with relative improvements in 30-day risk-adjusted readmissions between 2009 and 2012. Teaching hospitals had higher relative readmission rates as compared with their peers, and hospitals that used postdischarge heart failure disease management programs had lower relative readmission rates.

CONCLUSIONS

Although there has been slight improvement in 30-day all-cause readmission rates during the past 4 years in patients with heart failure, few hospitals have seen large success.

Outcomes after observation stays among older adult Medicare beneficiaries in the USA: retrospective cohort study

Objective To characterize rates and trends over time of emergency department treatment-and-discharge stays, repeat observation stays, inpatient stays, any hospital revisit, and death within 30 days of discharge from observation stays.Design Retrospective cohort study.Setting 4750 hospitals in the USA.Participants Nationally representative sample of Medicare fee for service beneficiaries aged 65 or over discharged after 363 037 index observation stays, 2 540 000 index emergency department treatment-and-discharge stays, and 2 667 525 index inpatient stays from 2006-11.Main outcome measures Rates of emergency department treatment-and-discharge stays, observation stays, inpatient stays, any hospital revisit, and death within 30 days of discharge from index observation stays. Rates were compared with corresponding outcomes within 30 days of discharge from both index emergency department treatment-and-discharge stays and index inpatient stays.Results Among 363 037 index observation stays resulting in discharge from 2006-11, 30 day rates of emergency department treatment-and-discharge stays were 8.4%, repeat observation stays were 2.9%, inpatient stays were 11.2%, any hospital revisit was 20.1%, and death was 1.8%. Of all revisits, 49.7% were for inpatient stays. Revisit rates for emergency department treatment-and-discharge stays, repeat observation stays, and any hospital revisit increased from 2006-11 (P<0.001 for trend), while 30 day rates of inpatient stays (P=0.054 for trend) and 30 day mortality (P=0.091 for trend) were both unchanged. Averaged over the study period, 30 day rates of any hospital revisit were similar after discharge from index emergency department treatment-and-discharge stays (19.9%) and index observation stays (20.1%), as was 30 day mortality (1.8% for both). Rates of any hospital revisit (21.8%) and death (5.2%) were highest after discharge from index inpatient stays.Conclusions Hospital revisits are common after discharge from observation stays, frequently result in inpatient hospitalizations, and have increased over time among Medicare beneficiaries. As revisit rates are similar after emergency department and observation stays, strategies shown to enhance emergency department transitional care may be reasonable starting points to improve post-observation outcomes.

Hospital Readmission and Social Risk Factors Identified from Physician Notes

OBJECTIVE

To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions.

STUDY SETTING

A multihospital academic health system in southeastern Massachusetts.

STUDY DESIGN

An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics.

DATA COLLECTION/EXTRACTION METHODS

All-payer claims, EHR data, and physician notes extracted from a centralized clinical registry.

PRINCIPAL FINDINGS

All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk-adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01).

CONCLUSIONS

The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

Financial impact of ivabradine on reducing heart failure penalties under the Hospital Readmission Reduction Program

OBJECTIVE

The introduction of the Hospital Readmission Reduction Program (HRRP) has led to renewed interest in developing strategies to reduce 30 day readmissions among patients with heart failure (HF). In this study, a model was developed to investigate whether the addition of ivabradine to a standard-of-care (SoC) treatment regimen for patients with HF would reduce HRRP penalties incurred by a hypothetical hospital with excess 30 day readmissions.

RESEARCH DESIGN

A model using a Monte Carlo simulation framework was developed. Model inputs included national hospital characteristics, hospital-specific characteristics, and the ivabradine treatment effect as quantified by a post hoc analysis of the Systolic Heart failure treatment with the I_f inhibitor ivabradine Trial (SHIFT).

RESULTS

The model computed an 83% reduction in HF readmission penalty payments in a hypothetical hospital with a readmission rate of 22.95% (excess readmission ratio = 1.056 over the national average readmission rate of 21.73%), translating into net savings of $44,016. A sensitivity analysis indicated that the readmission penalty is affected by the specific characteristics of the hospital, including the readmission rate, size of the ivabradine-eligible population, and ivabradine utilization.

CONCLUSIONS

The results of this study indicate that the addition of ivabradine to an SoC treatment regimen for patients with HF may lead to a reduction in the penalties incurred by hospitals under the HRRP. This highlights the role ivabradine can play as part of a wider effort to optimize the care of patients with HF.

Vital Signs Are Still Vital: Instability on Discharge and the Risk of Post-Discharge Adverse Outcomes

BACKGROUND

Vital sign instability on discharge could be a clinically objective means of assessing readiness and safety for discharge; however, the association between vital sign instability on discharge and post-hospital outcomes is unclear.

OBJECTIVE

To assess the association between vital sign instability at hospital discharge and post-discharge adverse outcomes.

DESIGN

Multi-center observational cohort study using electronic health record data. Abnormalities in temperature, heart rate, blood pressure, respiratory rate, and oxygen saturation were assessed within 24 hours of discharge. We used logistic regression adjusted for predictors of 30-day death and readmission.

PARTICIPANTS

Adults (≥18 years) with a hospitalization to any medicine service in 2009-2010 at six hospitals (safety-net, community, teaching, and non-teaching) in north Texas.

MAIN MEASURES

Death or non-elective readmission within 30 days after discharge.

KEY RESULTS

Of 32,835 individuals, 18.7 % were discharged with one or more vital sign instabilities. Overall, 12.8 % of individuals with no instabilities on discharge died or were readmitted, compared to 16.9 % with one instability, 21.2 % with two instabilities, and 26.0 % with three or more instabilities (p < 0.001). The presence of any (≥1) instability was associated with higher risk-adjusted odds of either death or readmission (AOR 1.36, 95 % CI 1.26-1.48), and was more strongly associated with death (AOR 2.31, 95 % CI 1.91-2.79). Individuals with three or more instabilities had nearly fourfold increased odds of death (AOR 3.91, 95 % CI 1.69-9.06) and increased odds of 30-day readmission (AOR 1.36, 95 % 0.81-2.30) compared to individuals with no instabilities. Having two or more vital sign instabilities at discharge had a positive predictive value of 22 % and positive likelihood ratio of 1.8 for 30-day death or readmission.

CONCLUSIONS

Vital sign instability on discharge is associated with increased risk-adjusted rates of 30-day mortality and readmission. These simple vital sign criteria could be used to assess safety for discharge, and to reduce 30-day mortality and readmissions.

Interventions for Reducing Hospital Readmission Rates: The Role of Hospice and Palliative Care

Background:

Despite evidence that enrollment with hospice services has the potential to reduce hospital readmission rates, previous research has not examined exactly how hospitals may promote the appropriate use of hospice and palliative care for their discharged patients. Therefore, we sought to explore the strategies used by hospitals to increase the use of hospice and palliative care for patients at risk of readmission.

Methods:

We conducted a secondary analysis of qualitative data from a study of hospitals that were participating in the State Action on Avoidable Readmissions (STAAR) initiative, a quality improvement collaborative. We used data attained from 46 in-depth interviews conducted during 10 hospital site visits using a standard discussion guide and protocol. We used a grounded theory approach using the constant comparative method to generate recurrent and unifying themes.

Results:

We found that a positive effect for hospitals participating in the STAAR initiative was enhanced engagement in efforts to promote greater use of hospice and palliative care as a possible method of reducing unplanned readmissions, the central goal of the STAAR initiative. Hospital staff described strategies to increase the use of hospice and palliative care that included (1) designing and implementing tracking systems to identify patients most at risk of being readmitted, (2) providing education about hospice and palliative care to family, internal and external clinical groups, and (3) establishing closer links to posthospital settings.

Conclusion:

National efforts to reduce rehospitalizations may result in improved integration of hospice and palliative care for patients who are at risk of readmission.

Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison

BACKGROUND

Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions.

OBJECTIVE

To develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models, LACE (includes Length of stay, Acute [nonelective] admission status, Charlson Comorbidity Index, and Emergency department visits in the past year), and HOSPITAL (includes Hemoglobin at discharge, discharge from Oncology service, Sodium level at discharge, Procedure during index hospitalization, Index hospitalization Type [nonelective], number of Admissions in the past year, and Length of stay).

DESIGN

Observational cohort study.

SUBJECTS

All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites.

MEASURES

Thirty-day nonelective readmissions were ascertained from 75 regional hospitals.

RESULTS

Among 32,922 admissions (validation = 16,430), 12.7% were readmitted. In addition to many first-day factors, we identified hospital-acquired Clostridium difficile infection (adjusted odds ratio [AOR]: 2.03, 95% confidence interval [CI]: 1.18-3.48), vital sign instability on discharge (AOR: 1.25, 95% CI: 1.15-1.36), hyponatremia on discharge (AOR: 1.34, 95% CI: 1.18-1.51), and length of stay (AOR: 1.06, 95% CI: 1.04-1.07) as significant predictors. The full-stay model had better discrimination than other models though the improvement was modest (C statistic 0.69 vs 0.64-0.67). It was also modestly better in identifying patients at highest risk for readmission (likelihood ratio +2.4 vs. 1.8-2.1) and in reclassifying individuals (net reclassification index 0.02-0.06).

CONCLUSIONS

Incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions. Given limited improvement in prediction despite incorporation of data on hospital complications, clinical instabilities, and trajectory, our findings suggest that many factors influencing readmissions remain unaccounted for. Further improvements in readmission models will likely require accounting for psychosocial and behavioral factors not currently captured by EHRs. Journal of Hospital Medicine 2016;11:473-480. © 2016 Society of Hospital Medicine.

Integrating new practices: a qualitative study of how hospital innovations become routine

Background

Hospital quality improvement efforts absorb substantial time and resources, but many innovations fail to integrate into organizational routines, undermining the potential to sustain the new practices. Despite a well-developed literature on the initial implementation of new practices, we have limited knowledge about the mechanisms by which integration occurs.

Methods

We conducted a qualitative study using a purposive sample of hospitals that participated in the State Action on Avoidable Rehospitalizations (STAAR) initiative, a collaborative to reduce hospital readmissions that encouraged members to adopt new practices. We selected hospitals where risk-standardized readmission rates (RSRR) had improved (n = 7) or deteriorated (n = 3) over the course of the first 2 years of the STAAR initiative (2010–2011 to 2011–2012) and interviewed a range of staff at each site (90 total). We recruited hospitals until reaching theoretical saturation. The constant comparative method was used to conduct coding and identification of key themes.

Results

When innovations were successfully integrated, participants consistently reported that a small number of key staff held the innovation in place for as long as a year while more permanent integrating mechanisms began to work. Depending on characteristics of the innovation, one of three categories of integrating mechanisms eventually took over the role of holding new practices in place. Innovations that proved intrinsically rewarding to the staff, by making their jobs easier or more gratifying, became integrated through shifts in attitudes and norms over time. Innovations for which the staff did not perceive benefits to themselves were integrated through revised performance standards if the innovation involved complex tasks and through automation if the innovation involved simple tasks.

Conclusions

Hospitals have an opportunity to promote the integration of new practices by planning for the extended effort required to hold a new practice in place while integration mechanisms take hold. By understanding how integrating mechanisms correspond to innovation characteristics, hospitals may be able to foster integrating mechanisms most likely to work for particular innovations.