Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan

Predictors of hospital readmission rate in geriatric patients

BACKGROUND

Hospital readmissions among older adults are associated with progressive functional worsening, increased institutionalization and mortality.

AIM

Identify the main predictors of readmission in older adults.

METHODS

We examined readmission predictors in 777 hospitalized subjects (mean age 84.40 ± 6.77 years) assessed with Comprehensive Geriatric Assessment (CGA), clinical, anthropometric and biochemical evaluations. Comorbidity burden was estimated by Charlson Comorbidity Index (CCI). Median follow-up was 365 days.

RESULTS

358 patients (46.1%) had a second admission within 365 days of discharge. Estimated probability of having a second admission was 0.119 (95%C.I. 0.095-0.141), 0.158 (95%C.I. 0.131-0.183), and 0.496 (95%C.I. 0.458-0.532) at 21, 30 and 356 days, respectively. Main predictors of readmission at 1 year were length of stay (LOS) > 14 days (p < 0.001), albumin level < 30 g/l (p 0.018), values of glomerular filtration rate (eGFR) < 40 ml/min (p < 0.001), systolic blood pressure < 115 mmHg (p < 0.001), CCI ≥ 6 (p < 0.001), and cardiovascular diagnoses. When the joint effects of selected prognostic variables were accounted for, LOS > 14 days, worse renal function, systolic blood pressure < 115 mmHg, higher comorbidity burden remained independently associated with higher readmission risk.

DISCUSSION

Selected predictors are associated with higher readmission risk, and the relationship evolves with time.

CONCLUSIONS

This study highlights the importance of performing an accurate CGA, since defined domains and variables contained in the CGA (i.e., LOS, lower albumin and systolic blood pressure, poor renal function, and greater comorbidity burden), when combined altogether, may offer a valid tool to identify the most fragile patients with clinical and functional impairment enhancing their risk of unplanned early and late readmission.

Relationship between Anemia and Readmission among Older Patients in Rural Community Hospitals: A Retrospective Cohort Study

Readmission rates among older adults are a growing concern, and the association of readmission with anemia and the potential benefits of a systematic assessment and intervention remain unclear. This study investigated the association between anemia and readmission within 28 and 90 days in an older population. Data from 1280 patients admitted to the Department of General Medicine of Unnan City Hospital between April 2020 and December 2021 were retrospectively analyzed. Variables such as anemia status, Charlson comorbidity index (CCI) score, Functional Independence Measure (FIM) score, and dependent status were evaluated. Multivariate logistic regression was used to determine the associations between 28-day and 90-day readmissions. The average age was 84.9 years, and the prevalence of anemia was 36.4%. The readmission rates within 28 and 90 days were 10.4% and 19.1%, respectively. Anemia was significantly associated with readmission in both periods (28-day adjusted odds ratio, 2.28; 90-day adjusted odds ratio, 1.65). CCI score, FIM score, and dependent status were also identified as significant factors. Anemia is significantly associated with short- and medium-term readmissions in older patients. Addressing anemia, along with other identified factors, may help reduce readmission rates.

Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach

BACKGROUND

Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit.

METHODS

Our goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach.

RESULTS

The two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature.

CONCLUSIONS

The proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.

Depressive symptoms and malnutrition are associated with other geriatric syndromes and increase risk for 30-Day readmission in hospitalized older adults: a prospective cohort study

BACKGROUND

Readmission in older adults is typically complex with multiple contributing factors. We aim to examine how two prevalent and potentially modifiable geriatric conditions - depressive symptoms and malnutrition - relate to other geriatric syndromes and 30-day readmission in hospitalized older adults.

METHODS

Consecutive admissions of patients ≥ 65 years to a general medical department were recruited over 16 months. Patients were screened for depression, malnutrition, delirium, cognitive impairment, and frailty at admission. Medical records were reviewed for poor oral intake and functional decline during hospitalization. Unplanned readmission within 30-days of discharge was tracked through the hospital's electronic health records and follow-up telephone interviews. We use directed acyclic graphs (DAGs) to depict the relationship of depressive symptoms and malnutrition with geriatric syndromes that constitute covariates of interest and 30-day readmission outcome. Multiple logistic regression was performed for the independent associations of depressive symptoms and malnutrition with 30-day readmission, adjusting for variables based on DAG-identified minimal adjustment set.

RESULTS

We recruited 1619 consecutive admissions, with mean age 76.4 (7.9) years and 51.3% females. 30-day readmission occurred in 331 (22.0%) of 1,507 patients with follow-up data. Depressive symptoms, malnutrition, higher comorbidity burden, hospitalization in the one-year preceding index admission, frailty, delirium, as well as functional decline and poor oral intake during the index admission, were more commonly observed among patients who were readmitted within 30 days of discharge (P < 0.05). Patients with active depressive symptoms were significantly more likely to be frail (OR = 1.62, 95% CI 1.22-2.16), had poor oral intake (OR = 1.35, 95% CI 1.02-1.79) and functional decline during admission (OR = 1.58, 95% CI 1.11-2.23). Malnutrition at admission was significantly associated with frailty (OR = 1.53, 95% CI 1.07-2.19), delirium (OR = 2.33, 95% CI 1.60-3.39) cognitive impairment (OR = 1.88, 95% CI 1.39-2.54) and poor oral intake during hospitalization (OR = 2.70, 95% CI 2.01-3.64). In minimal adjustment set identified by DAG, depressive symptoms (OR = 1.38, 95% CI 1.02-1.86) remained significantly associated with 30-day readmission. The association of malnutrition with 30-day readmission was no longer statistically significant after adjusting for age, ethnicity and depressive symptoms in the minimal adjustment set (OR = 1.40, 95% CI 0.99-1.98).

CONCLUSION

The observed causal associations support screening and targeted interventions for depressive symptoms and malnutrition during admission and in the post-acute period.

Older persons with multiple chronic conditions' experiences of unplanned readmission: An integrative review

BACKGROUND

As persons, 60 years of age and older live longer, they are more likely to develop one or more chronic conditions. Rising numbers of older persons with multiple chronic conditions (MCCs) will increase the need for home healthcare services and hospital services and unplanned readmissions will increase globally.

AIM

The aim of this integrative review was to explore the experiences of older persons with MCCs' unplanned readmission from home to hospital within 30 days of discharge using an integrative review.

METHOD

Whittemore and Knafl's method was followed to address the research aim. Four databases (Ovid MEDLINE, Scopus, CINAHL and Embase) were searched between 2005 and 2020, suitability for inclusion was assessed, and data were extracted and analysed using content analysis.

RESULTS

Thirteen articles (10 qualitative, one quantitative, and two mixed methods) were included in this review. Three themes emerged from the data that reflected older persons with MCCs' unplanned readmission experiences. These themes included (a) feelings of security, support and relief; (b) undesirable challenges at home (struggling to manage care and balancing support needs); and (c) unpleasant feelings and emotions (feelings of fear and mistrust, feelings of disappointment and loss, feelings of anxiousness and pressure).

CONCLUSION

Research about unplanned readmission to the hospital does not provide sufficient detail or understanding about older persons with MCCs' experiences or their psychosocial experiences. Addressing research gaps related to the psychosocial processes and factors associated with unplanned readmission is needed to expand the current understanding of the process and concept of unplanned readmission.

Safety of Elderly Fallers: Identifying Associated Risk Factors for 30-Day Unplanned Readmissions Using a Clinical Data Warehouse

BACKGROUND

Hospital readmissions are a major problem in the older people as they are frequent, costly, and life-threatening. Falls among older adults are the leading cause of injury, deaths, and emergency department visits for trauma.

OBJECTIVE

The main objective was to determine risk factors associated with a 30-day readmission after index hospital admission for fall-related injuries.

METHODS

A retrospective nested case-control study was conducted. Data from elderly patients initially hospitalized for fall-related injuries in 2019, in 11 of the Greater Paris University Hospitals and discharged home, were retrieved from the clinical data warehouse. Cases were admission of elderly patients who subsequently experienced a readmission within 30 days after discharge from the index admission. Controls were admission of elderly patients who were not readmitted to hospital.

RESULTS

Among 670 eligible index admissions, 127 (18.9%) were followed by readmission within 30 days after discharge. After multivariate analysis, men sex (odds ratio [OR] = 2.29, 95% confidence interval [CI] = 1.45-3.61), abnormal concentration of C-reactive protein, and anemia (OR = 2.22, 95% CI = 1.28-3.85; OR = 1.85, 95% CI = 1.11-3.11, respectively) were associated with a higher risk of readmission. Oppositely, having a traumatic injury at index admission decreased this risk (OR = 0.47, 95% CI = 0.28-0.81).

CONCLUSIONS

Reducing early unplanned readmission is crucial, especially in elderly patients susceptible to falls. Our results indicate that the probability of unplanned readmission is higher for patients with specific characteristics that should be taken into consideration in interventions designed to reduce this burden.

Miller N, L. Pecina J, D. Decker M, I. Wi C, Garrison GM. Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

Background

While socioeconomic status has been linked to hospital readmissions for several conditions, reliable measures of individual socioeconomic status are often not available. HOUSES, a new measure of individual socioeconomic status based upon objective public data about one's housing unit, is inversely associated with overall hospitalization rate but it has not been studied with respect to readmissions.

Purpose

To determine if patients in the lowest HOUSES quartile are more likely to be readmitted within 30 days (short-term) and 180 days (long-term).

Methods

A retrospective cohort study of 11 993 patients having 21 633 admissions was conducted using generalized linear mixed-effects models.

Results

HOUSES quartile did not show any significant association with early readmission. However, when compared to the lowest HOUSES quartile, the second quartile (OR = 0.90, 95%CI 0.83-0.98) and the third quartile (OR = 0.91, 95%CI 0.83-0.99) were associated with lower odds of late readmission while the highest quartile (OR = 0.91, 95%CI 0.82-1.01) was not statistically different.

Conclusion

HOUSES was associated with late readmission, but not early readmission. This may be because early readmissions are influenced by medical conditions and hospital care while late readmissions are influenced by ambulatory care and home-based factors. Since HOUSES relies on public county assessor data, it is generally available and may be used to focus interventions on those at highest risk for late readmission.

Factors associated with early 14-day unplanned hospital readmission: a matched case-control study

Background/Purpose

Early unplanned hospital readmissions are burdensome health care events and indicate low care quality. Identifying at-risk patients enables timely intervention. This study identified predictors for 14-day unplanned readmission.

Methods

We conducted a retrospective, matched, case–control study between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Adult patients aged ≥ 20 years and readmitted for the same or related diagnosis within 14 days of discharge after initial admission (index admission) were included as cases. Cases were 1:1 matched for the disease-related group at index admission, age, and discharge date to controls. Variables were extracted from the hospital’s electronic health records.

Results

In total, 300 cases and 300 controls were analyzed. Six factors were independently associated with unplanned readmission within 14 days: previous admissions within 6 months (OR = 3.09; 95 % CI = 1.79–5.34, p < 0.001), number of diagnoses in the past year (OR = 1.07; 95 % CI = 1.01–1.13, p = 0.019), Malnutrition Universal Screening Tool score (OR = 1.46; 95 % CI = 1.04–2.05, p = 0.03), systolic blood pressure (OR = 0.98; 95 % CI = 0.97–0.99, p = 0.01) and ear temperature within 24 h before discharge (OR = 2.49; 95 % CI = 1.34–4.64, p = 0.004), and discharge with a nasogastric tube (OR = 0.13; 95 % CI = 0.03–0.60, p = 0.009).

Conclusions

Factors presented at admission (frequent prior hospitalizations, multimorbidity, and malnutrition) along with factors presented at discharge (clinical instability and the absence of a nasogastric tube) were associated with increased risk of early 14-day unplanned readmission.

Impact of COPD exacerbations leading to hospitalization on general and disease-specific quality of life

RATIONALE

Acute exacerbations negatively impact quality of life in patients with chronic obstructive pulmonary disease (COPD), but the impact of hospitalized exacerbations on quality of life is not clear. We hypothesized that patients with hospitalized exacerbations would benefit from hospitalization and experience improvement in general and disease-specific quality of life (as measured by the St. George's respiratory questionnaire (SGRQ) and the medical outcomes study 36-item short form health survey (SF-36)) compared to those without exacerbations, or with non-hospitalized acute exacerbations.

METHODS

1219 COPD patients enrolled in either the simvastatin for the prevention of exacerbations in moderate-to severe COPD Trial (STATCOPE) or azithromycin for prevention of exacerbations of COPD trial (MACRO) were analyzed. Demographic information, spirometry, and symptom scores were noted at baseline. Exacerbation events and changes in quality of life scores were assessed over a mean of 538 days of follow-up.

RESULTS

Of patients studied, 25.6% were hospitalized, 44.0% had at least one outpatient exacerbation, and 30.4% had no exacerbation. Baseline SGRQ and SF-36 scores were severely impaired in all groups studied. Over time, SF-36 scores did not change significantly between groups. SGRQ symptom domain scores improved in other groups but did not improve in those hospitalized for a COPD exacerbation.

CONCLUSIONS

At baseline, patients hospitalized for acute exacerbations of COPD had more impaired quality of life scores. Over time, SGRQ symptom domain scores improved in other groups but did not in those who were hospitalized. Other measurements of quality of life were not improved by hospitalization for COPD.

Incidence, reasons, and risk factors for 30-day readmission after lumbar spine surgery for degenerative spinal disease

This study investigated risk factors for 30-day readmission of discharged patients who had undergone lumbar spinal surgery. This retrospective, case–control study reviewed 3,933 patients discharged after elective spinal surgery for lumbar degenerative diseases from 2005 to 2012 at a university hospital. Of these patients, 102 were re-hospitalized within 30 days of discharge. Patient medical records were reviewed. The incidence of readmission within 30 days was 2.6%, and uncontrolled pain was the most common reason for readmission. In the univariate analysis, age, mental illness, the number of medical comorbidities, previous spinal surgery, fusion surgery, number of fusion levels, estimated blood loss, operation time, intensive care unit (ICU) admission, length of hospital stays, and total medical expenses were associated with a higher risk of readmission within 30 days. Multiple logistic regression analysis revealed that previous spinal surgery, operation time, ICU admission, length of hospital stays, and total medical expenses were independent risk factors for 30-day readmission. Independent risk factors for readmission were longer operation time, a previous spinal surgery, ICU admission, longer hospital stays, and higher medical expenses. Further studies controlling these risk factors could contribute to reducing readmission and thus improving the quality of care.

Patients' Perspectives on Reasons for Unplanned Readmissions

Massachusetts has one of the highest rates of 30-day readmissions in the country. To identify patient-reported factors that may contribute to readmissions, we conducted semi-structured interviews with patients with unplanned readmissions within 30 days of inpatient discharge from the medicine services at an urban medical center between June and August 2016. Interviews with patients and/or proxies were conducted in English, Spanish, Mandarin, or Cantonese, then translated to English if necessary, transcribed verbatim, and deidentified. A team of four coders conducted the thematic analysis. Most patients did not identify factors associated with readmission beyond their underlying illness; however, a mismatch between the patient's clinical care needs and services available at postacute facilities, as well as poor communication between providers, facilities, and patients/proxies, were identified as contributing factors to readmissions. Non-English speaking patients and their families reported confusion with written discharge instructions, even if an interpreter provided verbal instructions. Patients will benefit from future interventions that aim to improve transfers to postacute care facilities, develop written materials in languages prevalent in the local population, and improve communication among providers, facilities, and patients and their families.

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission

Background

Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge.

Methods

Using a cohort of surgical patients at a tertiary care academic medical center, surgical, demographic, lab, medication, care team, and current procedural terminology data were extracted from the electronic health record. The primary outcome was whether there existed a future hospital readmission originating from the emergency department within 30 days of surgery. Secondarily, the time interval from surgery to the prediction was analyzed at 0, 12, 24, 36, 48, and 60 h. Different machine learning models for predicting the primary outcome were evaluated with respect to the area under the receiver-operator characteristic curve metric using different permutations of the available features.

Results

Surgical hospital admissions (N = 34,532) from April 2013 to December 2016 were included in the analysis. Surgical and demographic features led to moderate discrimination for prediction after discharge (area under the curve: 0.74 to 0.76), whereas medication, consulting team, and current procedural terminology features did not improve the discrimination. Lab features improved discrimination, with gradient-boosted trees attaining the best performance (area under the curve: 0.866, SD 0.006). This performance was sustained during temporal validation with 2017 to 2018 data (area under the curve: 0.85 to 0.88). Lastly, the discrimination of the predictions calculated 36 h after surgery (area under the curve: 0.88 to 0.89) nearly matched those from time of discharge.

Conclusions

A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data.

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Predictors of hospital readmission among older adults with cancer

OBJECTIVE

Older adults with cancer are at higher risk for costly and potentially dangerous hospital readmissions. Identifying risk factors for readmission in this population is important for future prevention of readmission.

MATERIALS AND METHODS

Hospital discharges among patients ≥ 65 years with solid tumors on non-surgical services from 2006-2011 were reviewed in this matched case-control study. We abstracted patient/cancer characteristics; functional status; fall risk; chemotherapy line; comorbidities; laboratory values; discharge parameters; and miscellaneous information (Do Not Resuscitate Order, pain scores) from medical records. Conditional logistic regression was used for univariate and multivariable analysis.

RESULTS

This analysis included 184 case-patients readmitted within 30 days after discharge from the index admission and 184 sex- and age-matched control-patients discharged from index admission within three months of the cases with no readmission. Cases and controls had no differences in terms of primary cancer type, treatment, and index admission reason. Cases were more likely to have abnormal hemoglobin, albumin, sodium, and SGOT on discharge. Compared to those with ≤1 abnormal laboratory test, patients with 2 or more abnormal test results were 3 times more likely to be readmitted within 30 days.

CONCLUSION

This study demonstrated that older adults with cancer who had at least 2 abnormal laboratory results (hemoglobin, albumin, sodium, and SGOT) at discharge were 3 times more likely to be readmitted within 30 days compared to those with ≤1 abnormal results. These laboratory values may be predictive of the risk of readmission, and should be monitored before discharge to potentially prevent readmission.

Postoperative Serum Albumin Decrease Independently Predicts Delirium in the Elderly Subjects after Total Joint Arthroplasty

Background:

Postoperative delirium (POD), a neurobehavioral syndrome induced by dysfunction of neural activity, is a common and serious complication. This current study aimed to investigate independent predictors for POD in elderly subjects after total joint arthroplasty (TJA).

Methods:

Eligible elderly patients (≥65 years) who underwent elective unilateral primary hip or knee arthroplasty under epidural anesthesia from October 2016 to January 2019 were consecutively enrolled. POD was diagnosed following the guidance of the 5th edition of Diagnostic and Statistical Manual of Mental Disorders, (DSM V, 2013). The relative change in serum Alb (ΔAlb) was defined as the absolute value of (preoperative Alb value– nadir value within postoperative day 2)/preoperative Alb ×100%. The predictive value of ΔAlb for POD was evaluated by receiver operating characteristic (ROC) curve analysis. Univariate and multivariate logistic regression analyses were used for evaluating risk factors for POD.

Results:

A total of 328 patients were enrolled in the analysis, of which 68 (20.7%, 68/328) patients developed POD within postoperative 7 days. ΔAlb was an effective predictor for POD with an area under the curve (AUC) of 0.821, a sensitivity of 76.15% and a specificity of 70.59%, respectively (P<0.001). Univariate and multivariate logistic regression analyses indicated that ΔAlb was the only independent risk factor for POD (OR: 2.43, 95%CI: 1.17–4.86, P=0.015).

Conclusions:

ΔAlb was an independent risk factor for POD in elderly subjects after TJA.

Incidence and Reason for Readmission and Unscheduled Health Care Contact After Distal Radius Fracture

Background: Understanding risk factors for readmission may help decrease the rate of these costly events. The purpose of this study is to define the incidence of 30-day readmission and unscheduled health care contact (UHC) after distal radius fracture (DRF). In addition, we aim to define risk factors for readmission and UHC. Methods: A retrospective review of patients who sustained a DRF at our trauma center was performed. We recorded baseline demographics, fracture characteristics, and treatment. Any UHC or readmission (including emergency department [ED] visits) was documented. Reasons for readmission and UHC were stratified by cause. We utilized a case-control design comparing patients readmitted within 30 days after DRF versus those who were not, as well as patients with and without UHC. Results: About 353 patients were identified. The 30-day incidence of readmission after DRF was 7% with 2% of patients readmitted for reasons related to their fracture. Twenty percent of patients had UHC within 30 days, most frequently due to pain. Patients with anxiety or depression and those with open fractures were more likely to be readmitted. Patients with UHC were younger, more likely to have depression or anxiety, and more likely to have undergone operative treatment. Conclusions: For patients sustaining DRF, we report a 30-day readmission rate of 7% with 20% of patients having UHC. Patients with depression or anxiety were more likely to be both readmitted and have UHC. Identifying risk factors for readmission during initial presentation may help reduce readmissions. Improving pain relief strategies early may aid in decreasing the burden of UHC.

Depression is independently associated with increased length of stay and readmissions in multimorbid inpatients

BACKGROUND

Little is known about the impact of depression across a broad range of multimorbid patients hospitalized for reasons other than depression. The objective of the study was to investigate in a large sample of multimorbid inpatients whether ancillary depression is associated with increased length of stay (LOS) and readmissions, two important clinical outcomes with implications for healthcare utilization and costs.

METHODS

We retrospectively analyzed a cohort of 253,009 multimorbid inpatients aged ≥18 at an academic medical center, 8/2009-8/2017.

PRIMARY OUTCOME

LOS.

SECONDARY OUTCOMES

LOS related to different main diagnoses, readmissions within 1, 3, 6, 12, and 24-months after discharge.

RESULTS

Multivariable linear regression showed 24% longer LOS in patients with ancillary depression (1.24; 95% confidence interval [CI]: 1.22, 1.25). Females stayed 22% longer (1.22; 95% CI: 1.20, 1.25), males 24% (1.24; 95% CI: 1.22, 1.27). We identified 16 main diagnosis clusters in which ancillary depression was associated with significant LOS increases, with associations being strongest for "Failure and rejection of transplanted organs and tissues", "Other noninfective gastroenteritis and colitis", and "Other soft tissue disorders, not elsewhere classified". Multivariable logistic and Poisson regression showed independent associations of ancillary depression with increased readmission odds and frequencies at 1, 3, 6, 12, and 24 months.

CONCLUSIONS

Ancillary depression was independently associated with increased LOS and more readmissions across a broad range of multimorbid inpatients.

A Case-Control Study: The Impact of Unintentional Discrepancies and Pharmacist Discharge Prescription Review on 30-Day Hospital Readmission

Introduction: Medication discrepancies on hospital discharge are common and occur despite the use of technology to generate electronically created discharge (e-discharge) prescriptions, justifying pharmacist involvement. No published studies have focused on medication discrepancies as a risk factor for readmission. The aim was to explore the relationship between medication discrepancies on discharge and readmission rates, and how both are affected by pharmacist intervention. Objectives: The primary objective was to establish the relationship between medication discrepancies on the e-discharge prescription and hospital readmissions within 30 days of discharge. Secondary objectives were to determine the 30-day readmission rate with and without pharmacist involvement, and risk factors for 30-day readmission. Methods: This was a matched case-control study where cases and controls consisted of patients readmitted and not readmitted to hospital within 30 days of discharge from the general medicine service, respectively. Case patients were defined as patients who had been readmitted to the hospital within 30 days of discharge from the general medicine unit. Control patients were defined as patients who had not been readmitted to the hospital within 30 days of discharge. Chi-square statistics was used to analyze the association between the presence of medication discrepancy at discharge and 30-day readmission. Multivariate logistic regression was used to further analyze the associations to determine which risk factors best relate to 30-day readmission. Results: Between January 1, 2017 and December 31, 2017, a total of 401 e-discharge prescriptions were reviewed, and 194 cases were readmitted within 30 days of discharge. Similar proportions of patients were readmitted compared with not readmitted regardless of whether discrepancies were identified on the e-discharge prescriptions, and there was no relationship identified between medication discrepancies and readmission within 30 days (odds ratio [OR] = 1.04; P = .854). The readmission rate with and without pharmacist involvement was similar between the case group (50%) and control group (48.0%). The proportion of discharge prescriptions with discrepancies was 48.8% in the group that had pharmacist involvement and 47.0% in the group that had no pharmacist involvement. Additionally, a LACE score of 12 or greater was identified as a statistically significant risk factor for readmission (OR = 2.13; P < .001). Conclusions: Pharmacist review of the e-discharge prescription did not affect the readmission rate. A LACE score of 12 or greater was associated with a higher risk of readmission. Future studies are needed to identify patient groups at high risk of readmission and to determine pharmacist interventions that could reduce readmission rates.

Holiday Discharges Are Associated with Higher 30-Day General Internal Medicine Hospital Readmissions at an Academic Medical Center

OBJECTIVE

Academic medical centers face unique challenges in educating physician trainees in effective discharge practices to prevent readmissions. Meanwhile, residents must handle high workloads coupled with frequent rotations to different services. This study aimed to determine whether daily service census, service turnover, time of discharge, and day of discharge increase the risk of 30-day readmission.

METHODS

All of the discharges from two academic general internal medicine teaching services between October 1, 2013 and September 30, 2014 were included in this observational data analysis. Variables were fit to a 30-day, all-cause readmission outcome using multiple logistic regression with inverse probability of treatment weighting and multiple imputations with chained equations. The following potential confounding variables were included in the model: health system utilization, demographics, laboratory values, and comorbidities.

RESULTS

Among 1935 total discharges, 258 patients (13.3%) were readmitted within 30 days of the index discharge. Turnover, service census, weekend discharge, and time of discharge were not significantly associated with the risk of readmission. Patients discharged during holiday periods had higher odds of readmission (odds ratio 2.56, 95% confidence interval 2.01-3.25), whereas patients discharged on an intern switch day had lower odds of readmission (odds ratio 0.33, 95% confidence interval 0.27-0.41).

CONCLUSIONS

Patients who are discharged during holiday periods are at a higher risk of readmission after adjusting for potential confounders. These results also suggest that discharge on an intern switch day had a protective effect on readmission. Further work is needed to examine whether these findings can be replicated, and, if confirmed, to determine to what extent these associations are causal.

The Association of Medication Knowledge and Adherence Scores With Hospital Readmission

Introduction: Readmission scoring systems are used to predict 30-day hospital readmission. These prediction tools do not considerlack of patient medication knowledge or adherence which can worsen disease outcomes or increase risk of readmissions. Objective: To determine if medication knowledge and adherence, as assessed by validated questionnaires, are associated with an increased rate of 30-day readmission. Methods: Adult medical inpatients were randomly selected for a prospective, single center study that was conducted from January to August 2017. Patients were asked the 4-question Morisky Green Levine Scale (MGLS) and the 4-question Medication Knowledge Score (MKS). Validated readmission score; MKS; and MGLS, as well as baseline information and readmission status within 30 days after the index admission were recorded. Mean or median scores were compared for patients readmitted within 30 days with those not readmitted using descriptive and univariate inferential statistics. Results: Data from 119 patients showed a mean age of 63 years (SD = 16). There was no difference in baseline information: age, sex, or number of scheduled home medications between those readmitted within 30 days and those not readmitted. Patients readmitted within 30 days had a statistically higher readmission score compared to patients not readmitted (66.4 vs 57.1, P = .017). There was no difference in median MKS or mean MGLS between patients readmitted within 30 days and those not readmitted (MKS: 4.0 vs 3.0, P = .753; MGLS: 1 vs 1.3, P = .162). Conclusions: In this prospective study, neither the MKS nor the MGLS scores were associated with 30-day hospital readmission.

Effect of post-hospital discharge telephonic intervention on hospital readmissions in a privately insured population in Australia

Objective The aim of the present study was to evaluate the effect of telephone support after hospital discharge to reduce early hospital readmission among members of the disease management program My Health Guardian (MHG) offered by the Hospitals Contribution Fund of Australia (HCF). Methods A quasi-experimental retrospective design compared 28-day readmissions of patients with chronic disease between two groups: (1) a treatment group, consisting of MHG program members who participated in a hospital discharge (HODI) call; and (2) a comparison group of non-participating MHG members. Study groups were matched for age, gender, length of stay, index admission diagnoses and prior MHG program exposure. Adjusted incidence rate ratios (IRR) and odds ratios (OR) were estimated using zero-inflated negative binomial and logistic regression models respectively. Results The treatment group exhibited a 29% lower incidence of 28-day readmissions than the comparison group (adjusted IRR 0.71; 95% confidence interval (CI) 0.59-0.86). The odds of treatment group members being readmitted at least once within 28 days of discharge were 25% lower than the odds for comparison members (adjusted OR 0.75; 95% CI 0.63-0.89). Reduction in readmission incidence was estimated to avoid A$713730 in cost. Conclusions The HODI program post-discharge telephonic support to patients recently discharged from a hospital effectively reduced the incidence and odds of hospital 28-day readmission in a diseased population. What is known about the topic? High readmission rates are a recognised problem in Australia and contribute to the over 600000 potentially preventable hospitalisations per year. What does this paper add? The present study is the first study of a scalable intervention delivered to an Australian population with a wide variety of conditions for the purpose of reducing readmissions. The intervention reduced 28-day readmission incidence by 29%. What are the implications for practitioners? The significant and sizable effect of the intervention support the delivery of telephonic support after hospital discharge as a scalable approach to reduce readmissions.

Racial and Ethnic Differences in 30-Day Hospital Readmissions Among US Adults With Diabetes

IMPORTANCE

Differences in readmission rates among racial and ethnic minorities have been reported, but data among people with diabetes are lacking despite the high burden of diabetes and its complications in these populations.

OBJECTIVES

To examine racial/ethnic differences in all-cause readmission among US adults with diabetes and categorize patient- and system-level factors associated with these differences.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study includes 272 758 adult patients with diabetes, discharged alive from the hospital between January 1, 2009, and December 31, 2014, and stratified by race/ethnicity. An administrative claims data set of commercially insured and Medicare Advantage beneficiaries across the United States was used. Data analysis took place between October 2016 and February 2019.

MAIN OUTCOMES AND MEASURES

Unplanned all-cause readmission within 30 days of discharge and individual-, clinical-, economic-, index hospitalization-, and hospital-level risk factors for readmission.

RESULTS

A total of 467 324 index hospitalizations among 272 758 adults with diabetes (mean [SD] age, 67.7 [12.7]; 143 498 [52.6%] women) were examined. The rates of 30-day all-cause readmission were 10.2% (33 683 of 329 264) among white individuals, 12.2% (11 014 of 89 989) among black individuals, 10.9% (4151 of 38 137) among Hispanic individuals, and 9.9% (980 of 9934) among Asian individuals (P < .001). After adjustment for all factors, only black patients had a higher risk of readmission compared with white patients (odds ratio, 1.05; 95% CI, 1.02-1.08). This increased readmission risk among black patients was sequentially attenuated, but not entirely explained, by other demographic factors, comorbidities, income, reason for index hospitalization, or place of hospitalization. Compared with white patients, both black and Hispanic patients had the highest observed-to-expected (OE) readmission rate ratio when their income was low (annual household income <$40 000 among black patients: OE ratio, 1.11; 95% CI, 1.09-1.14; among Hispanic patients: OE ratio, 1.11; 95% CI, 1.07-1.16) and when they were hospitalized in nonprofit hospitals (black patients: OE ratio, 1.10; 95% CI, 1.08-1.12; among Hispanic patients: OE ratio, 1.08; 95% CI, 1.05-1.12), academic hospitals (black patients: OE ratio, 1.16; 95% CI, 1.13-1.20; Hispanic patients: OE ratio, 1.12; 95% CI, 1.06-1.19), or large hospitals (ie, with ≥400 beds; black patients: OE ratio, 1.11; 95% CI, 1.09-1.14; Hispanic patients: OE ratio, 1.09; 95% CI, 1.04-1.14).

CONCLUSIONS AND RELEVANCE

In this study, black patients with diabetes had a significantly higher risk of readmission than members of other racial/ethnic groups. This increased risk was most pronounced among lower-income patients hospitalized in nonprofit, academic, or large hospitals. These findings reinforce the importance of identifying and addressing the many reasons for persistent racial/ethnic differences in health care quality and outcomes.

Organizational transformation in health care: an activity theoretical analysis

PURPOSE

Older patients are at high risk of hospital readmission, which has led to an increasing number of screening and intervention programs. Knowledge on implementing screening tools for preventing readmissions in emergency department (ED), where the primary focus is often the present-day flow of patients, is scant. The purpose of this paper is to explore whether a new screening tool for predicting readmissions and functional decline in medical patients>65 years of age could be implemented and its influence on cross-continuum collaborations between the primary and secondary sectors.

DESIGN/METHODOLOGY/APPROACH

The study took place in an ED in Denmark, in collaboration with the surrounding municipalities. An evaluation workshop with nurses and leaders from the ED and the surrounding municipalities took place with the aim of investigating the organizational changes that occurred in daily practice after the implementation of the screening tool. The workshop was designed and analyzed using cultural historical activity theory (CHAT).

FINDINGS

The results showed that it was possible to develop collaboration between the two sectors during the test period. However, the screening tool created different transformations for the municipality employees and in the ED. The contradictions indicated that the screening tool did not mediate a general and sustained transformation in the cross-continuum collaboration.

RESEARCH LIMITATIONS/IMPLICATIONS

Screening tools are not objective, neutral or "acontexual" artifacts and must always be adapted to the local context and sectors. CHAT offers a perspective to understand the collective object when working with organizational transformations and implementation.

PRACTICAL IMPLICATIONS

The study have shown that screening tools are not objective, neutral or "acontexual" artifacts and must always be adapted to the local context. This is called adaption process. This adaption requires time and resources that should be taken into consideration from the beginning of introduction of new screens.

ORIGINALITY/VALUE

This paper contributes with knowledge about CHAT which offers a way to understand the leading collective object when working with organizational transformations and implementation. CHAT focuses not only on the structural changes but also on the cultural aspects of organizational changes, which is important if we want to reach a sustained change and implement the new screening tool in different sectors.

90-day Readmission in Elective Primary Lumbar Spine Surgery in the Inpatient Setting: A Nationwide Readmissions Database Sample Analysis

STUDY DESIGN

Secondary analysis of a large administrative database.

OBJECTIVE

The objectives of this study are to: 1) identify the incidence and cause of 90-day readmissions following primary elective lumbar spine surgery, 2) offer insight into potential risk factors that contribute to these readmissions, and 3) quantify the cost associated with these readmissions.

SUMMARY OF BACKGROUND DATA

As bundled-payment models for the reimbursement of surgical services become more popular in spine, the focus is shifting toward long-term patient outcomes in the context of 90-day episodes of care. With limited data available on national 90-day readmission statistics available, we hope to provide evidence that will aid in the development of more cost-effective perioperative care models.

METHODS

Using ICD-9 coding, we identified all patients 18 years of age and older in the 2014 Nationwide Readmissions Database (NRD) who underwent an elective, inpatient, primary lumbar spine surgery. Using multivariate logistic regression, we identified independent predictors of 90-day readmission while controlling for a multitude of confounding variables and completed a comparative cost analysis.

RESULTS

We identified 169,788 patients who underwent a primary lumbar spine procedure. In total 4268 (2.5%) were readmitted within 90 days. There was no difference in comorbidity burden between cohorts (readmitted vs. not readmitted) as quantified by the Elixhauser Comorbidity index. Independent predictors of increased odds of 90-day readmission were: anemia, uncomplicated diabetes and diabetes with chronic complications, surgical wound disruption and acute myocardial infarction at the time of the index admission, self-pay status, and an anterior surgical approach. Implant complications were identified as the primary related cause of readmission. These readmissions were associated with a significant cost increase.

CONCLUSION

There are clearly identifiable risk factors that increase the odds of hospital readmission within 90 days of primary lumbar spine surgery. An overall 90-day readmission rate of 2.5%, while relatively low, carries significantly increased cost to both the patient and hospital.

LEVEL OF EVIDENCE

3.

Impact of Pharmacist-Driven Heart Failure in-Home Counseling on 30-Day Readmission Rates

PURPOSE/OBJECTIVE

This study examines the impact of a pharmacist-driven intervention specific to heart failure patients with the goal of reducing readmission rates and improving quality of life in this population.

FINDINGS/CONCLUSIONS

A total of 21 patients were included in the study. Twelve patients were female and 9 were male. The mean age was 76 years with a range of 65-93 years. Two of the 21 patients were readmitted within 30 days. One of the 2 readmitted patients died soon after admission was in the final stages of his or her disease and passed away soon after; it is unlikely for a home visit to have altered their path. This study did not meet the goal sample size due to some unforeseen limitations. However, the limited data that were obtained suggest a strong basis for further research.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

During a patient's transition in care from hospital to home, he or she is most vulnerable for complications and readmission. Intervention during this time will not only improve patient care and quality of life but also contribute to a notable cost savings for each prevented readmission. Pharmacist intervention, as part of the health care team, during this tenuous time has shown to make a valuable impact.

Family Medicine Patients Have Shorter Length of Stay When Cared for on a Family Medicine Inpatient Service

Introduction: Hospitalists have been shown to have shorter lengths of stays than physicians with concurrent outpatient practices. However, hospitalists at academic medical centers may be less aware of local resources that can support the hospital to home transition for local primary care patients. We hypothesized that local family medicine patients admitted to a family medicine inpatient service have shorter length of stay than those admitted to general hospitalist services which also care for tertiary patients at an academic medical center. Methods: A retrospective cohort study was conducted at an academic medical center with a department of family medicine providing primary care to over 80 000 local patients. A total of 3100 consecutive family medicine patients admitted to either the family medicine inpatient service or a general medicine inpatient service over 3 years were studied. The primary outcome was length of stay, which was adjusted using multivariate linear regression for demographics, prior utilization, diagnosis, and disease severity. Results: Adjusted length of stay was 33% longer (95% CI 24%-44%) for local family medicine patients admitted to general medicine inpatient services as compared with the family medicine inpatient service. Readmission rates within 30 days were not different (19% vs 16%, P = .14). Conclusions: Local primary care patients were safely discharged from the hospital sooner on the family medicine inpatient service than on general medicine inpatient services. This is likely because the family physicians staffing their inpatient service are more familiar with outpatient resources that can be effectively marshaled to help local patients with the transition from hospital to home.

A systematic literature review and narrative synthesis on the risks of medical discharge letters for patients' safety

Background

The medical discharge letter is an important communication tool between hospitals and other healthcare providers. Despite its high status, it often does not meet the desired requirements in everyday clinical practice. Occurring risks create barriers for patients and doctors. This present review summarizes risks of the medical discharge letter.

Methods

The research question was answered with a systematic literature research and results were summarized narratively. A literature search in the databases PubMed and Cochrane Library for Studies between January 2008 and May 2018 was performed. Two authors reviewed the full texts of potentially relevant studies to determine eligibility for inclusion. Literature on possible risks associated with the medical discharge letter was discussed.

Results

In total, 29 studies were included in this review. The major identified risk factors are the delayed sending of the discharge letter to doctors for further treatments, unintelligible (not patient-centered) medical discharge letters, low quality of the discharge letter, and lack of information as well as absence of training in writing medical discharge letters during medical education.

Conclusions

Multiple risks factors are associated with the medical discharge letter. There is a need for further research to improve the quality of the medical discharge letter to minimize risks and increase patients’ safety.

Self-Identified Social Determinants of Health during Transitions of Care in the Medically Underserved: a Narrative Review

BACKGROUND

Medically underserved or low socioeconomic status (SES) patients face significant vulnerability and a high risk of adverse events following hospital discharge. The environmental, social, and economic factors, otherwise known as social determinants, that compound this risk have been ineffectually described in this population. As the underserved comprise 30% of patients discharged from the hospital, improving transitional care and preventing readmission in this group has profound quality of care and financial implications.

METHOD

EMBASE and MEDLINE searches were conducted to examine specific barriers to care transitions in underserved patients following an episode of acute care. Articles were reviewed for barriers and categorized within the context of five general themes.

RESULTS

This review yielded 17 peer-reviewed articles. Common factors affecting care transitions were cost of medications, access to care, housing instability, and transportation. When categorized within themes, social fragility and access failures, as well as therapeutic misalignment, disease behavior, and issues with accountability were noted.

DISCUSSION

Providers and health systems caring for medically underserved patients may benefit through dedicating increased resources and broadening collaboration with community partners in order to expand health care access and enhance coordination of social services within this population. Future studies are needed to identify potential interventions targeting underserved patients to improve their post-hospital care.

Predictive models for hospital readmission risk: A systematic review of methods

OBJECTIVES

Hospital readmission risk prediction facilitates the identification of patients potentially at high risk so that resources can be used more efficiently in terms of cost-benefit. In this context, several models for readmission risk prediction have been proposed in recent years. The goal of this review is to give an overview of prediction models for hospital readmission, describe the data analysis methods and algorithms used for building the models, and synthesize their results.

METHODS

Studies that reported the predictive performance of a model for hospital readmission risk were included. We defined the scope of the review and accordingly built a search query to select the candidate papers. This query string was used as input for the chosen search engines, namely PubMed and Google Scholar. For each study, we recorded the population, feature selection method, classification algorithm, sample size, readmission threshold, readmission rate and predictive performance of the model.

RESULTS

We identified 77 studies that met the inclusion criteria, out of 265 citations. In 68% of the studies (n = 52) logistic regression or other regression techniques were utilized as the main method. Ten (13%) studies used survival analysis for model construction, while 14 (18%) used machine learning techniques for classification, of which decision tree-based methods and SVM were the most utilized algorithms. Among these, only four studies reported the use of any class imbalance addressing technique, of which resampling is the most frequent (75%). The performance of the models varied significantly among studies, with Area Under the ROC Curve (AUC) values in the ranges between 0.54 and 0.92.

CONCLUSION

Logistic regression and survival analysis have been traditionally the most widely used techniques for model building. Nevertheless, machine learning techniques are becoming increasingly popular in recent years. Recent comparative studies suggest that machine learning techniques can improve prediction ability over traditional statistical approaches. Regardless, the lack of an appropriate benchmark dataset of hospital readmissions makes a comparison of models' performance across different studies difficult.

A Storytelling Approach: Insights from the Shambaa

Narrative medicine explores the stories that patients tell; this paper, conversely, looks at some of the stories that patients are told. The paper starts by examining the 'story' told by the Shambaa people of Tanzania to explain the bubonic plague and contrasts this with the stories told by Ghanaian communities to explain lymphatic filariasis. By harnessing insights from memory studies, these stories' memorability is claimed to be due to their use mnemonic devices woven into stories. The paper suggests that stories can be unpatronising, informative, and appropriate vehicles for communicating medical information to all age groups across all cultures.

Impact of Health Literacy on Time Spent Seeking Hand Care

BACKGROUND

Patients with limited health literacy may have less knowledge and fewer resources for efficient access and navigation of the health care system. We tested the null hypothesis that there is no correlation between health literacy and total time spent seeking hand surgery care.

METHODS

New patients visiting a hand surgery clinic at a suburban academic medical center were asked to complete a questionnaire to determine demographics, total time spent seeking hand surgery care, and outcomes. A total of 112 patients were included in this study.

RESULTS

We found health literacy levels did not correlate with total time seeking hand surgery care or from booking an appointment to being evaluated in clinic.

CONCLUSIONS

In this suburban academic medical center, patients with low health literacy do not spend more time seeking hand surgery care and do have longer delays between seeking and receiving care. The finding that-at least in this setting-health literacy does not impact patient time seeking hand care suggests that resources to improve health disparities can be focused elsewhere in the care continuum.

Causes and correlates of 30 day and 180 day readmission following discharge from a Medicine for the Elderly Rehabilitation unit

Background

Recently hospitalized patients experience a period of generalized risk of adverse health events. This study examined reasons for, and predictors of, readmission to acute care facilities within 30 and 180 days of discharge from an inpatient rehabilitation unit for older people.

Methods

Routinely collected, linked clinical data on admissions to a single inpatient rehabilitation facility over a 13-year period were analysed. Data were available regarding demographics, comorbid disease, admission and discharge Barthel scores, length of hospital stay, and number of medications on discharge. Discharge diagnoses for the index admission and readmissions were available from hospital episode statistics. Univariate and multivariate Cox regression analyses were performed to identify baseline factors that predicted 30 and 180-day readmission.

Results

A total of 3984 patients were included in the analysis. The cohort had a mean age of 84.1 years (SD 7.4), and 39.7% were male. Overall, 5.6% (n = 222) and 23.2% (n = 926) of the patients were readmitted within 30 days and 180 days of discharge respectively. For patients readmitted to hospital, 26.6% and 21.1% of patients were readmitted with the same condition as their initial admission at 30 days and 180 respectively. For patients readmitted within 30 days, 13.5% (n = 30) were readmitted with the same condition with the most common diagnoses associated with readmission being chest infection, falls/immobility and stroke. For patients readmitted within 180 days, 12.4% (n = 115) of patients were readmitted with the same condition as the index condition with the most common diagnoses associated with readmission being falls/immobility, cancer and chest infections. In multivariable Cox regression analyses, older age, male sex, length of stay and heart failure predicted 30 or 180-day readmission. In addition, discharge from hospital to patients own home predicted 30-day readmission, whereas diagnoses of cancer, previous myocardial infarction or chronic obstructive pulmonary disease predicted 180-day readmission.

Conclusion

Most readmissions of older people after discharge from inpatient rehabilitation occurred for different reasons to the original hospital admission. Patterns of predictors for early and late readmission differed, suggesting the need for different mitigation strategies.

The Electronic Pharmaceutical Record: A new method for medication reconciliation

RATIONALE, AIM, AND OBJECTIVE

There are several ways to establish an accurate medication list in the hospital admission medication reconciliation (MedRec). The challenge for MedRec lies in the availability, reliability, and completeness of the data used. In France, the Electronic Pharmaceutical Record (ePR) was developed to register each medication taken by ambulatory patients, primarily to make dispensation in community pharmacies safe. We evaluated the suitability of this tool in the MedRec when patients were admitted to the hospital.

METHOD

We conducted a 6-month pilot study of 249 MedRec files from a hospital diabetology department. The analysis was supplemented by the ePR for any patient for whom this information was recorded. The study evaluated the ePR as a new MedRec tool, as well as the clinical impact (CI) of the new data collected.

RESULTS

The ePR was contributory for 28% of the patients. Discrepancies were associated with polypharmacy, most of which had a CI = 1. Medication omission was the most frequently found discrepancy (72%), but self-medication (8%) and lack of medication adherence (9%) were also observed.

CONCLUSION

This tool provided added value for reconciliation, as it quickly identifies regular medications, adherence, and self-medication behaviour. The ePR is essential for conducting a thorough MedRec.

Readmissions Among Sepsis Survivors: Risk Factors and Prevention

Hospital readmissions are common and result in increased mortality and cost while reducing quality of life. Readmission rates have been subjected to increasing scrutiny in recent years as part of a larger effort to improve the quality and value of healthcare in the United States. Emerging evidence suggests that sepsis survivors are at high risk for hospital readmission and experience readmission rates comparable to survivors of congestive heart failure, acute myocardial infarction, pneumonia, and chronic obstructive pulmonary disease, diseases whose readmission rates determine reimbursement penalties from the federal government. In this article, we review the unique challenges that sepsis survivors face as well as the patient-level and hospital-level risk factors that are known to be associated with hospital readmission after sepsis survival. Additionally, we identify the causes and outcomes of readmissions in this population before concluding with a discussion of readmission prevention strategies and future directions.

Incidence of delirium in the Canadian emergency department and its consequences on hospital length of stay: a prospective observational multicentre cohort study

OBJECTIVE

We aim to determine the incidence of delirium and describe its impacts on hospital length of stay (LOS) among non-delirious community-dwelling older adults with an 8-hour exposure to the emergency department (ED) environment.

DESIGN

This is a prospective observational multicentre cohort study (March-July 2015). Patients were assessed two times per day during their entire ED stay and up to 24 hours on hospital ward.

SETTING

The study took place in four Canadian EDs.

PARTICIPANTS

338 included patients: (1) aged ≥65 years; (2) who had an ED stay ≥8 hours; (3) were admitted to hospital ward and (4) were independent/semi-independent.

MAIN OUTCOMES AND MEASURES

The primary outcomes of this study were incident delirium in the ED or within 24 hours of ward admission and ED and hospital LOS. Functional and cognitive status were assessed using validated Older Americans Resources and Services and the modified Telephone Interview for Cognitive Status tools. The Confusion Assessment Method was used to detect incident delirium. Univariate and multivariate analyses were conducted to evaluate outcomes.

RESULTS

Mean age was 76.8 (±8.1), 17.7% were aged >85 years old and 48.8% were men. The mean incidence of delirium was 12.1% (n=41). Median IQR ED LOS was 32.4 (24.5-47.9) hours and hospital LOS was 146.6 (75.2-267.8) hours. Adjusted mean hospital LOS was increased by 105.4 hours (4.4 days) (95% CI 25.1 to 162.0, P<0.001) for patients who developed an episode of delirium compared with non-delirious patient.

CONCLUSIONS

An incident delirium was observed in one of eight independent/semi-independent older adults after an 8-hour ED exposure. An episode of delirium increases hospital LOS by 4 days and therefore has important implications for patients and could contribute to ED overcrowding through a deleterious feedback loop.

The association of discharge decisions after deceased donor kidney transplantation with the risk of early readmission: Results from the deceased donor study

BACKGROUND

Kidney transplant (KT) recipients experience high rates of early (≤30 days) hospital readmission (EHR) after KT, and existing studies provide limited data on modifiable discharge factors that may mitigate EHR risk.

METHODS

We performed a retrospective cohort study of 468 adult deceased donor KT recipients transplanted between 4/2010 and 11/2013 at 5 United States transplant centers. We fit multivariable mixed effects models to assess the association of two potentially modifiable discharge factors with the probability of EHR after KT: (i) weekend discharge and (ii) days to first scheduled follow-up.

RESULTS

Among 468 KT recipients, 38% (n = 178) experienced EHR after KT. In fully adjusted analyses, compared to weekday discharges, KT recipients discharged on the weekend had a 29% lower risk of EHR (adjusted odds ratio [aOR] 0.71, 95% confidence interval [CI] 0.41-0.94). Compared to follow-up within 2 days of discharge, KT recipients with follow-up within 3 to 6 days had a 28% higher probability of EHR (aOR 1.28, 95% CI 1.13-1.45).

CONCLUSIONS

These findings suggest that clinical decisions related to the timing of discharge and follow-up modify EHR risk after KT, independent of traditional risk factors.

Iatrogenic risk factors associated with hospital readmission of elderly patients: A matched case-control study using a clinical data warehouse

WHAT IS KNOWN

Hospital readmission within 30 days of patient discharge has become a standard to judge the quality of hospitalizations. It is estimated that 14% of the elderly, people over 75 years old or those over 65 with comorbidities, are at risk of readmission, of which 23% are avoidable. It may be possible to identify elderly patients at risk of readmission and implement steps to reduce avoidable readmissions.

OBJECTIVE

The aim of this study was to identify iatrogenic risk factors for readmission. The secondary objective was to evaluate the rate of drug-related readmissions (DRRs) among all readmissions and compare it to the rate of readmissions for other reasons.

METHODS

We conducted a retrospective, matched, case-control study to identify non-demographic risk factors for avoidable readmission, specifically DRRs. The study included patients hospitalized between 1 September 2014 and 31 October 2015 in an 800-bed university hospital. We included patients aged 75 and over. Cases consisted of patients readmitted to the emergency department within 30 days of initial discharge. Controls did not return to the emergency department within 30 days. Cases and controls were matched on sex and age because they are known as readmissions risk factors. After comparison of the mean or percentage between cases and controls for each variable, we conducted a conditional logistic regression.

RESULTS

The risk factors identified were an emergency admission at the index hospitalization, returning home after discharge, a history of unplanned readmissions and prescription of nervous system drugs. Otherwise, 11.4% of the readmissions were DRRs, of which 30% were caused by an overdose of antihypertensive. The number of drugs at readmission was higher, and potentially inappropriate medications were more widely prescribed for DRRs than for readmissions for other reasons.

WHAT IS NEW AND CONCLUSION

In this matched case-control retrospective study, after controlling for gender and age, we identified the typical profile of elderly patients at risk of readmission. These patients had an unplanned admission at the index hospitalization and prescribed nervous system drugs at discharge from the index admission; they have a history of unplanned readmission within 30 days and return home after discharge.

Symptoms Reported by Frail Elderly Adults Independently Predict 30-Day Hospital Readmission or Emergency Department Care

OBJECTIVES

To assess the degree to which self-reported symptoms predict unplanned readmission or emergency department (ED) care within 30 days of high-risk, elderly adults enrolled in a posthospitalization care transition program (CTP).

DESIGN

Retrospective cohort study.

SETTING

Posthospitalization CTP at Mayo Clinic, Rochester, Minnesota, from January 1, 2013, through March 3, 2015.

PARTICIPANTS

Frail, elderly adults (N = 230; mean age 83.5 ± 8.3, 46.5% male).

MEASUREMENTS

Charlson Comorbidity Index (CCI) and self-reported symptoms, measured using the Edmonton Symptom Assessment System (ESAS), were ascertained upon CTP enrollment.

RESULTS

Mean CCI was 3.9 ± 2.3. Of 51 participants returning to the hospital within 30 days of discharge, 13 had ED visits, and 38 were readmitted. Age, sex, and CCI were not significantly different between returning and nonreturning participants, but returning participants were significantly more likely to report shortness of breath (P = .004), anxiety (P = .02), depression (P = .02), and drowsiness (P = .01). Overall ESAS score was also a significant predictor of hospital return (P = .01).

CONCLUSION

Four self-reported symptoms and overall ESAS score, but not CCI, ascertained after hospital discharge were strong predictors of hospital return within 30 days. Including symptoms in risk stratification of high-risk elderly adults may help target interventions and reduce readmissions.

Dramatic Reduction in 30-Day Readmissions Through High-Risk Screening and Two-Phase Interdisciplinary Care

OBJECTIVES

Thirty-day readmissions are common, serious, and costly. Most important, often they are preventable. The purpose of this quality improvement study was to evaluate an interdisciplinary, two-phase intervention to reduce 30-day readmissions among high-risk medical patients. One or two high-risk patients were selected each weekday by a hospitalist using literature-based, locally tested criteria that included common medical illnesses, active psychiatric illness, and recent or recurrent hospital admissions.

METHODS

Patients admitted to 1 of 5 medical hospitalist teams were selected to receive the intervention; patients admitted to the 4 remaining teams were used for comparison. The two-phase care coordination intervention consisted of a daily interdisciplinary team meeting for the selected high-risk patients and postdischarge interventions that included outpatient care coordination until the patients' first follow-up appointment. The care plan addressed medical/geriatric assessment, social stability, medication reconciliation, nutritional needs, care coordination including future appointments/testing, and community services. Eighty-five patients in the intervention group were compared with 84 patients from the comparison group using propensity score matching. Patient characteristics were similar at baseline.

RESULTS

The intervention group demonstrated a reduction in 30-day readmissions by 52% (11 vs 23, P = 0.019). Length of stay was reduced: 5.5 days compared with 7.2 days (P = 0.258).

CONCLUSIONS

This intervention produced a significant reduction in 30-day readmissions for high-risk patients and a trend for shorter lengths of stay compared with similarly matched patients. Future research trials are needed to verify these results.

90-day Readmission After Lumbar Spinal Fusion Surgery in New York State Between 2005 and 2014: A 10-year Analysis of a Statewide Cohort

MINI: We assessed 90-day readmission and evaluated risk factors associated with readmission after lumbar spinal fusion surgery in New York State. The overall 90-day readmission rate was 24.8%. Age, sex, race, insurance, procedure, number of operated spinal levels, health service area, and comorbidities are major risk factors for 90-day readmission.

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

The aim of this study was to assess 90-day readmission and evaluate risk factors associated with readmission after lumbar fusion in New York State.

SUMMARY OF BACKGROUND DATA

Readmission is becoming an important metric for quality and efficiency of health care. Readmission and its predictors following spine surgery are overall poorly understood and limited evidence is available specifically in lumbar fusion.

METHODS

The New York Statewide Planning and Research Cooperative System (SPARCS) was utilized to capture patients undergoing lumbar fusion from 2005 to 2014. Temporal trend of 90-day readmission was assessed using Cochran-Armitage test. Logistic regression was used to examine predictors associated with 90-day readmission.

RESULTS

There were 86,869 patients included in this cohort study. The overall 90-day readmission rate was 24.8%. On a multivariable analysis model, age (odds ratio [OR] comparing ≥75 versus <35 years: 1.24, 95% confidence interval [CI]: 1.13-1.35), sex (OR female to male: 1.19, 95% CI: 1.15-1.23), race (OR African-American to white: 1.60, 95% CI: 1.52-1.69), insurance (OR Medicaid to Medicare: 1.42, 95% CI: 1.33-1.53), procedure (OR comparing thoracolumbar fusion, combined [International Classification of Disease, Ninth Revision, ICD-9: 81.04] to posterior lumbar interbody fusion/transforaminal lumbar spinal fusion [ICD-9: 81.08]: 2.10, 95% CI: 1.49-2.97), number of operated spinal levels (OR comparing four to eight vertebrae to two to three vertebrae: 2.39, 95% CI: 2.07-2.77), health service area ([HSA]; OR comparing Finger Lakes to New York-Pennsylvania border: 0.67, 95% CI: 0.61-0.73), and comorbidity, i.e., coronary artery disease (OR: 1.26, 95% CI: 1.19-1.33) were significantly associated with 90-day readmission. Directions of the odds ratios for these factors were consistent after stratification by procedure type.

CONCLUSION

Age, sex, race, insurance, procedure, number of operated spinal levels, HSA, and comorbidities are major risk factors for 90-day readmission. Our study allows risk calculation to determine high-risk patients before undergoing spinal fusion surgery to prevent early readmission, improve quality of care, and reduce health care expenditures.

LEVEL OF EVIDENCE

3.

Nucleated red blood cells, critical illness survivors and postdischarge outcomes: a cohort study

Background

Little is known about risk factors associated with out-of-hospital outcomes in survivors of critical illness. We hypothesized that the presence of nucleated red blood cells in patients who survived critical care would be associated with adverse outcomes following hospital discharge.

Methods

We performed a two-center observational cohort study of patients treated in medical and surgical intensive care units in Boston, Massachusetts. All data were obtained from the Research Patient Data Registry at Partners HealthCare. We studied 2878 patients, age ≥ 18 years, who received critical care between 2011 and 2015 and survived hospitalization. The exposure of interest was nucleated red blood cells occurring from 2 days prior to 7 days after critical care initiation. The primary outcome was mortality in the 90 days following hospital discharge. Secondary outcome was unplanned 30-day hospital readmission. Adjusted odds ratios were estimated by multivariable logistic regression models with inclusion of covariate terms thought to plausibly interact with both nucleated red blood cells and outcome. Adjustment included age, race (white versus nonwhite), gender, Deyo–Charlson Index, patient type (medical versus surgical), sepsis and acute organ failure.

Results

In patients who received critical care and survived hospitalization, the absolute risk of 90-day postdischarge mortality was 5.9%, 11.7%, 15.8% and 21.9% in patients with 0/μl, 1–100/μl, 101–200/μl and more than 200/μl nucleated red blood cells respectively. Nucleated red blood cells were a robust predictor of postdischarge mortality and remained so following multivariable adjustment. The fully adjusted odds of 90-day postdischarge mortality in patients with 1–100/μl, 101–200/μl and more than 200/μl nucleated red blood cells were 1.77 (95% CI, 1.23–2.54), 2.51 (95% CI, 1.36–4.62) and 3.72 (95% CI, 2.16–6.39) respectively, relative to patients without nucleated red blood cells. Further, the presence of nucleated red blood cells is a significant predictor of the odds of unplanned 30-day hospital readmission.

Conclusion

In critically ill patients who survive hospitalization, the presence of nucleated red blood cells is a robust predictor of postdischarge mortality and unplanned hospital readmission.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1724-z) contains supplementary material, which is available to authorized users.

The Relationship Between Index Hospitalizations, Sepsis, and Death or Transition to Hospice Care During 30-Day Hospital Readmissions

BACKGROUND

Hospital readmissions are common, expensive, and increasingly used as a metric for assessing quality of care. The relationship between index hospitalizations and specific outcomes among those readmitted remains largely unknown.

OBJECTIVES

Identify risk factors present during the index hospitalization associated with death or transition to hospice care during 30-day readmissions and examine the contribution of infection in readmissions resulting in death.

RESEARCH DESIGN

Retrospective cohort study.

SUBJECTS

A total of 17,716 30-day readmissions in an academic health system.

MEASURES

We used mixed-effects multivariable logistic regression models to identify risk factors associated with the primary outcome, in-hospital death, or transition to hospice during 30-day readmissions.

RESULTS

Of 17,716 30-day readmissions, 1144 readmissions resulted in death or transition to hospice care (6.5%). Risk factors identified included: age, burden, and type of comorbid conditions, recent hospitalizations, nonelective index admission type, outside hospital transfer, low discharge hemoglobin, low discharge sodium, high discharge red blood cell distribution width, and disposition to a setting other than home. Sepsis (OR=1.33; 95% CI, 1.02-1.72; P=0.03) and shock (OR=1.78; 95% CI, 1.22-2.58; P=0.002) during the index admission were associated with the primary outcome, and in-hospital mortality specifically. In patients who died, infection was the primary cause for readmission in 51.6% of readmissions after sepsis and 28.6% of readmissions after a nonsepsis hospitalization (P=0.009).

CONCLUSIONS

We identified factors, including sepsis and shock during the index hospitalization, associated with death or transition to hospice care during readmission. Infection was frequently implicated as the cause of a readmission that ended in death.

Patients With Limited Health Literacy Ask Fewer Questions During Office Visits With Hand Surgeons

BACKGROUND

In the midst of rapid expansion of medical knowledge and decision-support tools intended to benefit diverse patients, patients with limited health literacy (the ability to obtain, process, and understand information and services to make health decisions) will benefit from asking questions and engaging actively in their own care. But little is known regarding the relationship between health literacy and question-asking behavior during outpatient office visits.

QUESTIONS/PURPOSES

(1) Do patients with lower levels of health literacy ask fewer questions in general, and as stratified by types of questions? (2) What other patient characteristics are associated with the number of questions asked? (3) How often do surgeons prompt patients to ask questions during an office visit?

METHODS

We audio-recorded office visits of 84 patients visiting one of three orthopaedic hand surgeons for the first time. Patient questions were counted and coded using an adaptation of the Roter Interaction Analysis System in 11 categories: (1) therapeutic regimen; (2) medical condition; (3) lifestyle; (4) requests for services or medications; (5) psychosocial/feelings; (6) nonmedical/procedural; (7) asks for understanding; (8) asks for reassurance; (9) paraphrase/checks for understanding; (10) bid for repetition; and (11) personal remarks/social conversation. Directly after the visit, patients completed the Newest Vital Sign (NVS) health literacy test, a sociodemographic survey (including age, sex, race, work status, marital status, insurance status), and three Patient-Reported Outcomes Measurement Information System-based questionnaires: Upper-Extremity Function, Pain Interference, and Depression. The NVS scores were divided into limited (0-3) and adequate (4-6) health literacy as done by the tool's creators. We also assessed whether the surgeons prompted patients to ask questions during the encounter.

RESULTS

Patients with limited health literacy asked fewer questions than patients with adequate health literacy (5 ± 4 versus 9 ± 7; mean difference, -4; 95% CI, -7 to -1; p = 0.002). More specifically, patients with limited health literacy asked fewer questions regarding medical-care issues such as their therapeutic regimen (1 ± 2 versus 3 ± 4; mean difference, -2; 95% CI, -4 to -1]; p < 0.001) and condition (2 ± 2 versus 3 ± 3; mean difference, -1; 95% CI, -3 to 0; p = 0.022). Nonwhite patients asked fewer questions than did white patients (5 ± 4 versus 9 ± 7; mean difference, -4; 95% CI, -7 to 0; p = 0.032). No other patient characteristics were associated with the number of questions asked. Surgeons only occasionally (29%; 24/84) asked patients if they had questions during the encounter, but when they did, most patients (79%; 19/24) asked questions.

CONCLUSIONS

Limited health literacy is a barrier to effective patient engagement in hand surgery care. In the increasingly tangled health-information environment, it is important to actively involve patients with limited health literacy in the decision-making process by encouraging question-asking, particularly in practice settings where most decisions are preference-sensitive. Instead of assuming that patients understand what they are told, orthopaedic surgeons may take "universal precautions" by assuming that patients do not understand unless proved otherwise.

LEVEL OF EVIDENCE

Level II, therapeutic study.

Evaluation of a care transition program with pharmacist-provided home-based medication review for elderly Singaporeans at high risk of readmissions

OBJECTIVE

This study aimed to determine whether pharmacist-provided home-based medication review (HBMR) can reduce readmissions in the elderly.

DESIGN

Retrospective cohort study.

SETTING

Patient's home.

PARTICIPANTS

Records of patients referred to a care transition program from March 2011 through March 2015 were reviewed. Patients aged 60 years and older taking more than 5 medications and had at least 2 unplanned admissions within 3 months preceding the first home visit were included.

INTERVENTION

Pharmacist-provided HBMR.

MAIN OUTCOME MEASURES

Primary outcome was readmission rate over 6 months after the first home visit. Secondary outcomes included emergency department (ED) visits, outpatient visits and mortality. Drug-related problems (DRPs) were reported for the HBMR group. Multivariate incidence rate ratios (IRR) and hazard ratio (HR) were calculated with adjustments for covariates.

RESULTS

The study included 499 patients (97 HBMR, 402 no HBMR). Pharmacist-provided HBMR reduced readmissions by 26% (IRR = 0.74, 95% CI: 0.59-0.92, P = 0.007), reduced ED visits by 20% (IRR = 0.80, 95% CI: 0.66-0.98, P = 0.030) and increased outpatient visits by 16% (IRR = 1.16, 95% CI: 0.95-1.41, P = 0.150). There were 8 and 44 deaths in the HBMR and no HBMR groups respectively (HR = 0.73, 95% CI: 0.29-1.81, P = 0.492). Pharmacists identified 464 DRPs, with 169 (36.4%) resolved within 1 month after the home visit.

CONCLUSIONS

The study suggests that pharmacist-provided HBMR is effective in reducing readmissions and ED visits in the elderly. More studies in the Asian population are needed to determine its long term benefits and patient's acceptability.

Is pain at discharge a risk factor for unplanned hospital readmission?

INTRODUCTION

Unplanned readmissions are associated with a high cost to health insurances and the incidence of preventable readmissions could be considered as a quality indicator for the initial hospital admission. We aimed to assess the predictive value for unplanned readmission of higher pain scores at discharge of the initial admission as well as of other pain and demographic characteristics. The documentation of significant associations would provide further support for a structured pain management policy.

METHODS

A retrospective analysis of a large single university hospital data-set of 33.122 admissions within a 13-month period allowed for the assessment of the predictive relationship of pain toward unplanned readmission at 7 and at 30 days after discharge through logistic regression, and of other characteristics through linear regression.

RESULTS

Pain scores at discharge of the initial admission were not significantly different (p > 0.05) with or without unplanned readmission and hence have no predictive value on the individual patient level. The prediction of the number of patients for each group, for example the number of patients that will be readmitted (size of the group), shows significance for pain at the moment of discharge (p_initial = 0.000), pain medication (p = 0.0044), and age (p = 0.0017). Pathology (p = 0.6151) and gender (p = 0.7029) have no significant predictive value.

CONCLUSION

Pain as dichotomous variable upon discharge cannot be used as single risk predictor for unplanned readmission. However, the pain score at discharge in combination with the use of pain medication and age is a risk factor for the number of short-term unplanned readmissions.

Prevention and Management of Postoperative Delirium in Elderly Patients Following Elective Spinal Surgery

STUDY DESIGN

This study is a systematic review.

OBJECTIVE

Propose an evidence-based algorithm for prevention, diagnosis, and management of postoperative delirium in geriatric patients undergoing elective spine surgery.

SUMMARY OF BACKGROUND DATA

Delirium is associated with longer stays after elective surgery, increased risk of readmission, and $6.9 billion annually in medical costs. Early diagnosis and treatment of delirium can reduce length of stay (LOS), in-hospital morbidity, and health care costs. After spinal surgery, postoperative delirium increases average LOS to >7 days and is diagnosed in 12.5%-24.3% of geriatric patients. Currently, studies for management of postoperative delirium after elective spinal procedures are not available.

METHODS

A literature review was performed for observational studies, randomized controlled trials, and systematic reviews between 1990 and 2015.

RESULTS

Risk factors for delirium after elective spinal surgery include age, functional impairment, preexisting dementia, general anesthesia, surgical duration >3 hours, intraoperative hypercapnia and hypotension, greater blood loss, low hematocrit and albumin, preoperative affective dysfunction, and postoperative sleep disorders. Postoperatively, decreasing the use of methylprednisolone and promoting movement with an appropriate orthosis can reduce delirium incidence (P=0.0091). Polypharmacy is an independent risk factor for delirium (P=0.01) and decreasing use of delirium-inducing medications may reduce incidence. The delirium observation screening scale diagnoses and monitors delirium and is rated by nurses as easier to use than the NEECHAM Confusion Scale (P<0.003). Haloperidol is used widely to treat postoperative delirium. Randomized controlled trials show that adding quetiapine results in delirium resolution an average of 3.5 days faster than haloperidol alone (P=0.001) and decreases agitation and LOS (P=0.02; P=0.05).

CONCLUSIONS

An evidence-based algorithm is proposed to prevent, diagnose, and manage postoperative delirium that can be used clinically for geriatric patients undergoing elective spine surgery. Prevention and diagnosis involve efforts from the anesthesiologist and postoperative clinical care team. Treatment may include a therapeutic regimen of low-dose neuroleptic medications as needed.

LEVEL OF EVIDENCE

Level II.

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.

Patient experiences and predictors in an acute geriatric ward: A cross-sectional study

AIMS AND OBJECTIVES

To investigate older peoples' experiences with acute hospital treatment and care, and variables that may influence these experiences.

BACKGROUND

In the Nordic countries, research on older peoples' experiences with hospital care is sparse.

METHODS

Participants were recruited from an acute geriatric ward in a hospital in Norway during a 1-year period (n = 189). Patient experience was assessed using the Picker Patient Experience Questionnaire, self-rated health by Euro-QoL 5-dimension, three-level version and comorbidity by Charlson comorbidity index.

RESULTS

Responders' median age was 79 years, 48.7% male. Most of the problems were related to continuity and transition, while fewest problems were related to respect for patient preferences. Charlson comorbidity index score and the variables "still working," "in a relationship" and "living alone" negatively influenced patient experience.

CONCLUSIONS

A high amount of problems were identified among patients hospitalised in an acute geriatric ward. Findings from this study may provide important information about issues that need focus to increase the care experiences of older people.

RELEVANCE TO CLINICAL PRACTICE

Results from this study adds to existing knowledge, and emphasize the need for increased focus on discharge and continuity of care processes.

Depression treatment and healthcare expenditures among elderly Medicare beneficiaries with newly diagnosed depression and incident breast, colorectal, or prostate cancer

OBJECTIVES

Depression is associated with high healthcare expenditures, and depression treatment may reduce healthcare expenditures. However, to date, there have not been any studies on the effect of depression treatment on healthcare expenditures among cancer survivors. Therefore, this study examined the association between depression treatment and healthcare expenditures among elderly with depression and incident cancer.

METHODS

The current study used a retrospective longitudinal study design, the linked Surveillance, Epidemiology, and End Results-Medicare database. Elderly (≥66 years) fee-for-service Medicare beneficiaries with newly diagnosed depression and incident breast, colorectal, or prostate cancer (N = 1502) were followed for a period of 12 months after depression diagnosis. Healthcare expenditures were measured every month for a period of 12-month follow-up period. Depression treatment was identified during the 6-month follow-up period. The adjusted associations between depression treatment and healthcare expenditures were analyzed with generalized linear mixed model regressions with gamma distribution and log link after controlling for other factors.

RESULTS

The average 1-year total healthcare expenditures after depression diagnosis were $38 219 for those who did not receive depression treatment; $42 090 for those treated with antidepressants only; $46 913 for those treated with psychotherapy only; and $51 008 for those treated with a combination of antidepressants and psychotherapy. As compared to no depression treatment, those who received antidepressants only, psychotherapy only, or a combination of antidepressants and psychotherapy had higher healthcare expenditures. However, second-year expenditures did not significantly differ among depression treatment categories.

CONCLUSIONS

Among cancer survivors with newly diagnosed depression, depression treatment did not have a significant effect on expenditures in the long term.