Readmissions After Colon Cancer Surgery: Does It Matter Where Patients Are Readmitted?

Hospital Quality Mediates Impact of Care Fragmentation Following Elective Colectomy

BACKGROUND

Readmission at a non-index hospital, or care fragmentation (CF), has been previously linked to greater morbidity and resource utilization. However, a contemporary evaluation of the impact of CF on readmission outcomes following elective colectomy is lacking. We additionally sought to evaluate the role of hospital quality in mediating the effect of CF.

METHODS

All records for adults undergoing elective colectomy were tabulated from the 2016 to 2020 Nationwide Readmissions Database. Patients readmitted non-electively within 30 days to a non-index center comprised the CF cohort (others: Non-CF). Hierarchical mixed-effects models were constructed to ascertain risk-adjusted rates of major adverse events (MAEs, a composite of in-hospital mortality and any complication) attributable to center-level effects. Hospitals with risk-adjusted MAE rates ≥50th percentile were considered Low-Quality Hospitals (LQHs) (others: High-Quality Hospitals [HQHs]).

RESULTS

Of 68,185 patients readmitted non-electively within 30 days, 8968 (13.2%) were categorized as CF. On average, CF was older, of greater comorbidity burden, and more often underwent colectomy for cancer, relative to Non-CF. Following risk adjustment, CF remained independently associated with greater likelihood of MAE (adjusted odds ratio [AOR] 1.16, 95% Confidence Interval [CI] 1.05-1.27) and per-patient expenditures (β+$2,280, CI +$1080-3490). Further, readmission to non-index LQH was linked with significantly increased odds of MAE, following initial care at HQH (AOR 1.43, CI 1.03-1.99) and LQH (AOR 1.72, CI 1.30-2.28; Reference: Non-CF).

CONCLUSIONS

Care fragmentation was associated with greater morbidity and resource utilization at readmission following elective colectomy. Further, rehospitalization at non-index LQH conferred significantly inferior outcomes. Novel efforts are needed to improve continuity of care.

Designing a Future eHealth Service for Posthospitalization Self-management Support in Long-term Illness: Qualitative Interview Study

BACKGROUND

For patients with noncommunicable diseases (NCDs; eg, heart failure [HF] and colorectal cancer [CRC]), eHealth interventions could meet their posthospital discharge needs and strengthen their ability to self-manage. However, inconclusive evidence exists regarding how to design eHealth services to meet the complex needs of patients. To foster patient acceptability and ensure the successful development and implementation of eHealth solutions, it is beneficial to include different stakeholders (ie, patients and health care professionals) in the design and development phase of such services. The involvement of different stakeholders could contribute to ensuring feasible, acceptable, and usable solutions and that eHealth services are developed in response to users' supportive care needs when transitioning to home after hospitalization. This study is the first step of a larger complex intervention study aimed at meeting the postdischarge needs of 2 NCD populations.

OBJECTIVE

This study aimed to explore the perspectives of patients with HF and CRC and health care professionals on patient self-management needs following hospital discharge and investigate how a future nurse-assisted eHealth service could be best designed to foster patient acceptability, support self-management, and smooth the transition from hospital to home.

METHODS

A qualitative, explorative, and descriptive approach was used. We conducted 38 semistructured interviews with 10 patients with HF, 9 patients surgically treated for CRC with curative intent, 6 registered nurses recruited as nurse navigators of a planned eHealth service, and 13 general practitioners experienced in HF and CRC treatment and follow-up care. Patients were recruited conveniently from HF and CRC outpatient clinics, and the nurses were recruited from the cardiology and gastro-surgical departments at a university hospital in the southwest of Norway. The general practitioners were recruited from primary care in surrounding municipalities. Semistructured interview guides were used for data collection, and the data were analyzed using thematic analysis.

RESULTS

In total, 3 main themes were derived from the data analysis: expecting information, reassurance, and guidance when using eHealth for HF and CRC self-management; expecting eHealth to be comprehensible, supportive, and knowledge promoting; and recognizing both the advantages and disadvantages of eHealth for HF and CRC self-management. The data generated from this interview study depicted the diverse needs for self-management support of patients with CRC and HF after hospital discharge. In addition, valuable suggestions were identified regarding the design and content of the eHealth service. However, participants described both possible advantages and disadvantages of a remote eHealth service.

CONCLUSIONS

This study is the first step in the development of an eHealth service for posthospitalization self-management support for long-term illnesses. It concerns patients' supportive care needs and user requirements of an eHealth service. The findings of this study may add value to the planning and development of eHealth interventions for patients with NCDs.

Patient Outcomes Following Interhospital Care Fragmentation: A Systematic Review

INTRODUCTION

Interhospital fragmentation of care occurs when patients are admitted to different, disconnected hospitals. It has been hypothesized that this type of care fragmentation decreases the quality of care received and increases hospital costs and healthcare utilization. This systematic review aims to synthesize the existing literature exploring the association between interhospital fragmentation of care and patient outcomes.

METHODS

MEDLINE, the Cochrane Library, EMBASE, and the Science Citation Index were systematically searched for studies published up to April 30, 2018 reporting the association between interhospital fragmentation of care and patient outcomes. We included peer-reviewed observational studies conducted in adults that reported measures of association between interhospital care fragmentation and one or more of the following patient outcomes: mortality, hospital length of stay, cost, and subsequent hospital readmission.

RESULTS

Seventy-nine full texts were reviewed and 22 met inclusion criteria. Nearly all studies defined fragmentation of care as a readmission to a different hospital than the patient was previously discharged from. The strongest association reported was that between a fragmented readmission and in-hospital or short-term mortality (adjusted odds ratio range 0.95-3.62). Over half of the studies reporting length-of-stay showed longer length of stay in fragmented readmissions. All three studies that investigated healthcare utilization suggested an association between fragmented care and odds of subsequent readmission. The study populations and exposures were too heterogenous to perform a meta-analysis.

DISCUSSION

Our review suggests that fragmented hospital readmissions contribute to increased mortality, longer length-of-stay, and increased risk of readmission to the hospital.

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.

Impact of Location of Readmission on Outcomes After Major Cancer Surgery

BACKGROUND

The burden of readmissions after major cancer surgery is high. Prior work suggests that one-third of readmitted patients are readmitted to a different hospital than where the surgery was performed. The impact of this location of readmission needs to be more thoroughly understood.

METHODS

This retrospective cohort study was performed on Surveillance, Epidemiology, and End Results (SEER)-Medicare patients with bladder, esophagus, lung, or pancreas cancer diagnosed from 2001 to 2007 who underwent extirpative surgery and were readmitted within 90 days. Readmission location was classified as 'index' if readmission was at the hospital where surgery was performed, or 'different' if readmission was elsewhere. Outcomes including complications, reoperations, in-hospital mortality, 90-day mortality, and 90-day total costs were compared based on the location of readmission using a propensity score inverse probability treatment weight analysis.

RESULTS

Overall, 7903 (28 %) patients were readmitted within 90 days of index hospitalization. Thirty-three percent were readmitted to a different hospital (bladder 30 %, esophagus 34 %, lung 34 %, pancreas 34 %). Ninety-day mortality and total costs of care were not significantly different between the readmission location groups (all p > 0.05); however, substantial differences in the types of patients, and timing of and reasons for readmission were observed between the two groups.

CONCLUSIONS

Patients readmitted to different hospitals after major cancer surgery are a different group of patients than those readmitted to the index hospital. Accounting for this, we did not find significant differences in short-term clinical outcomes or costs of care based on readmission location; however, differences in long-term outcomes were observed that should be further explored in future studies.