Predictors of 30-day readmission and association with primary care follow-up plans

Exploring Telehealth to Improve Discharge Outcomes in Children

OBJECTIVES

The inpatient to outpatient transition is critical for patient safety but suffers from lack of standardization and communication. Expanding telehealth use allows unique opportunities to leverage secure video conferencing to streamline communication between families and hospital-based providers (HBPs) after hospital discharge. We conducted a qualitative study to evaluate HBP and caregiver beliefs regarding a proposed telehealth follow-up visit after hospital discharge (THDF).

METHODS

Interviews were conducted with pediatric hospitalists, senior pediatric residents, and caregivers of patients recently hospitalized on the study hospital's pediatric hospitalist service. Authors developed consensus regarding major themes to inform THDF design. These were organized into a conceptual model.

RESULTS

We conducted 23 interviews with 6 hospitalists, 6 senior residents, and 11 caregivers. Three primary themes were identified: (1) Caregivers and HBPs agree THDF would be beneficial for patients and families; however, evidence is not robust enough to solidify provider buy-in. (2) Telehealth should supplement and enhance current discharge practices; it should not serve as a bandage for a broken system. Although a key aspect of THDF is to have the hospitalist provide follow-up care, this should be provided in addition to primary care provider follow-up. (3) HBPs expressed concerns about challenging workflows, competing demands, and inadequate resources, which are potential barriers to widespread adoption.

CONCLUSIONS

THDF leverages expanding telehealth use to provide hospital-based follow-up. While HBPs shared workflow challenges in conducting telehealth, HBPs and caregivers believed potential benefits of THDF outweighed the challenges. This qualitative study will guide implementation of THDF in future studies.

Identifying the Prevalence and Causes of 30-Day Hospital Readmission in Children: A Case Study from a Tertiary Pediatric Hospital

Introduction

The objectives of this study were to determine the prevalence of unplanned readmissions in the pediatric population within 30 days of discharge, identify the possible reasons behind them, and develop a predictive model for unplanned admissions.

Methods

A retrospective chart review study of 25,211 patients was conducted to identify the prevalence of readmissions occurring within 30 days of discharge from the King Abdullah Specialized Children's Hospital (KASCH) in Riyadh, Saudi Arabia, between Jan 1, 2019, and Dec 31, 2021. The data were collected using the BestCare electronic health records system and analyzed using Jamovi statistical software version 1.6.

Results

Among the 25,211 patients admitted to the hospital during the study period, the prevalence of unplanned readmission within 30 days was 1291 (5.12%). Of the 1291 patients, 1.91% had subsequent unplanned readmissions. In 57.8% of the cases, the cause of the first unplanned readmission was related to the cause of the first admission, and in 90.64% of the cases, the cause of the subsequent unplanned readmission was related to the cause of the first unplanned readmission. The most common reason for the first unplanned readmission was postoperative complications (18.75%), whereas pneumonia (10.81%) was the most common reason for subsequent unplanned readmissions. Most patients with subsequent unplanned readmissions were also found to have either isolated central nervous system pathology or chronic complex medical conditions.

Conclusion

Internationally, the rate of unplanned readmissions in pediatric patients has been estimated to be 6.5% within 30 days, which is comparable to the results of our study (5.12%). Most of the causes of first and subsequent unplanned readmission were found to be related to primary admission. The diagnosis/causes of readmission vary depending on the patient's age. A predictive model for pediatric readmission should be established so that preventive measures can be implemented.

The impact of unplanned 30-day readmission as a quality indicator in pediatric surgery

Introduction

Hospital readmission is one of the indicators used to assess quality of service provided in healthcare. Based on accumulated knowledge, risk management teams assess data related to readmissions to find curative solutions for underlying factors. The current article's aim is investigating readmission routes within the workplace in paediatric surgery service during the first 30 days post discharge from Mater Dei Hospital (MDH).

Materials and method

A retrospective study of children's hospital readmissions between October 2017 and November 2019 was performed, strictly before COVID-19 pandemic. Demographics and clinical records including age, gender, pre-existing comorbidities, diagnosis during primary admission and readmission, procedure carried out, ASA grade, length of stay, and outcomes were collected. All children re-admitted under a single paediatric surgical department within 30 days from initial admission to tertiary referral hospital were included. Patients undergoing emergency visitation without subsequent admissions were excluded. Readmissions were classified into cohorts: elective and emergency, depending on the nature of primary admission. Contributing factors and outcomes were compared.

Results

935 surgical admissions (221 elective and 714 emergencies) were registered at MDH over the given period, with an average hospital stay of 3.62 days. Total readmission rate was 1.7% (n = 16). 25% (n = 4) of readmissions were post elective, 75% (n = 12) post emergency admission, with an average stay of 4.37 days and no mortalities. 43.7% (n = 7) were re-admissions post-surgical intervention. Further surgical interventions were necessary in 25% (n = 4) of readmitted patients, the remainder (n = 12) treated conservatively.

Conclusion

Published reports concerning paediatric surgical readmission rates are limited, challenging healthcare systems. Most readmissions area voidable; therefore, healthcare workers must provide adequate strategies tailored to their resources, efficient multidisciplinary approaches with improved communication to decrease morbidity and prevent readmissions.

Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years

IMPORTANCE

Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children.

OBJECTIVE

To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages.

DESIGN, SETTING, AND PARTICIPANTS

This population-based prognostic analysis used electronic health record-derived data from a freestanding children's hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021.

MAIN OUTCOMES AND MEASURES

Three models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges.

RESULTS

The derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort.

CONCLUSIONS AND RELEVANCE

In this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.

Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review

OBJECTIVES

To summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice.

DESIGN

Systematic review.

DATA SOURCE

CINAHL, Embase and PubMed up to 7 October 2021.

ELIGIBILITY CRITERIA

English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs related to all-cause, surgical conditions or general medical conditions were included.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, risk factors significant for predicting readmissions and information about performance measures (eg, c-statistic) were extracted. Reporting quality was addressed by the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' (TRIPOD) adherence form. The study quality was assessed by applying six domains of potential biases. Due to expected heterogeneity among the studies, the data were qualitatively synthesised.

RESULTS

Based on 28 studies, 37 predictive models were identified, which could potentially be used for determining individual 30-day UHR risk in paediatrics. The number of study participants ranged from 190 children to 1.4 million encounters. The two most common significant risk factors were comorbidity and (postoperative) length of stay. 23 models showed a c-statistic above 0.7 and are primarily applicable at discharge. The median TRIPOD adherence of the models was 59% (P₂₅-P₇₅, 55%-69%), ranging from a minimum of 33% to a maximum of 81%. Overall, the quality of many studies was moderate to low in all six domains.

CONCLUSION

Predictive models may be useful in identifying paediatric patients at increased risk of readmission. To support the application of predictive models, more attention should be placed on completeness in reporting, particularly for those items that may be relevant for implementation in practice.

Weekend Admissions Associated with Increased Length of Stay for Children Undergoing Cholecystectomy

Background and Objectives:

Prior research shows an association between increased length of stay (LOS) and weekend surgical admissions, but none have looked at this relationship in children undergoing nonelective cholecystectomy for benign noncongenital biliary disease. We investigated whether weekend admissions lead to a longer LOS in this patient population.

Methods:

The Statewide Planning and Research Cooperative System database was queried for children ≤ 17 years undergoing cholecystectomy in New York State between January 1, 2009 and December 31, 2012. Parametric and nonparametric statistical testing was used for univariate analysis; multivariable binary logistic regression and linear regression models were used for multivariable analysis. Statistical significance was < 0.05.

Results:

A total of 1066 pediatric patients underwent nonelective cholecystectomy for gallstone pancreatitis (9.7%) and other benign biliary noncongenital diseases (90.3%), of which 22.1% of all patients were admitted over the weekend. Most cases (97.2%) were treated laparoscopically with an overall 3-day median LOS. Weekend admission was associated with an increased LOS of 4 days as opposed to 3 days during the weekday (p < 0.001). On a multivariable binary logistic regression model controlling for hospital factors, indication for surgery, and comorbidities, weekend admission was associated with 1.92 odds of increased length of stay (adjusted odds ratio of 1.924, 95% confidence interval: 1.386–2.673).

Conclusion:

Weekend admissions were associated with increased LOS and charges for children requiring nonelective cholecystectomy, despite the wide use of laparoscopic surgery.

Targeted Clinical Interventions for Reducing Pediatric Readmissions

BACKGROUND

In this interventional study, we addressed the selection and application of clinical interventions on pediatric patients identified as at risk by a predictive model for readmissions.

METHODS

A predictive model for readmissions was implemented, and a team of providers expanded corresponding clinical interventions for at-risk patients at a freestanding children's hospital. Interventions encompassed social determinants of health, outpatient care, medication reconciliation, inpatient and discharge planning, and postdischarge calls and/or follow-up. Statistical process control charts were used to compare readmission rates for the 3-year period preceding adoption of the model and clinical interventions with those for the 2-year period after adoption of the model and clinical interventions. Potential financial savings were estimated by using national estimates of the cost of pediatric inpatient readmissions.

RESULTS

The 30-day all-cause readmission rates during the periods before and after predictive modeling (and corresponding 95% confidence intervals [CI]) were 12.5% (95% CI: 12.2%-12.8%) and 11.1% (95% CI: 10.8%-11.5%), respectively. More modest but similar improvements were observed for 7-day readmissions. Statistical process control charts indicated nonrandom reductions in readmissions after predictive model adoption. The national estimate of the cost of pediatric readmissions indicates an associated health care savings due to reduced 30-day readmission during the 2-year predictive modeling period at $2 673 264 (95% CI: $2 612 431-$2 735 364).

CONCLUSIONS

A combination of predictive modeling and targeted clinical interventions to improve the management of pediatric patients at high risk for readmission was successful in reducing the rate of readmission and reducing overall health care costs. The continued prioritization of patients with potentially modifiable outcomes is key to improving patient outcomes.

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

Objectives To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning. Results Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ2 17=29.4, P=0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic=0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients' social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary. Conclusions The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models. What is known about the topic? Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions. What does this paper add? This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression. What are the implications for practitioners? The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary.

A 5-year retrospective cohort study of unplanned readmissions in an Australian tertiary paediatric hospital

Objective The aim of this study was to examine the characteristics and prevalence of all-cause unplanned hospital readmissions at a tertiary paediatric hospital in Western Australia from 2010 to 2014. Methods A retrospective cohort descriptive study was conducted. Unplanned hospital readmission was identified using both 28- and 30-day measurements from discharge date of an index hospital admission to the subsequent related unplanned admission date. This allowed international comparison. Results In all, 73132 patients with 134314 discharges were identified. During the 5-year period, 4070 discharges (3.03%) and 3330 patients (4.55%) were identified as 30-day unplanned hospital readmissions. There were minimal differences in the rate of readmissions on Days 28, 29 and 30 (0.2%). More than 50% of readmissions were identified as a 5-day readmission. Nearly all readmissions for croup and epiglottitis occurred by Day 5; those for acute bronchiolitis and obstructive sleep apnoea requiring tonsillectomy and/or adenoidectomy occurred by Day 15 and those for acute appendicitis and abdominal and pelvic pain occurred by Day 30. Conclusion This study highlights the variability in the distribution of time intervals from discharge to readmission among diagnoses, suggesting the commonly used 28- or 30-day readmission measurement requires review. It is crucial to establish an appropriate measurement for specific paediatric conditions related to readmissions for the accurate determination of the prevalence and actual costs associated with readmissions. What is known about this topic? Unplanned hospital readmissions result in inefficient use of health resources. Australia has used 28 days to measure unplanned readmissions. However, the 30-day measurement is commonly used in the literature. Only five Australian studies were identified with a focus on readmissions associated with specific paediatric health conditions. What does this paper add? This is the first known study examining paediatric all-cause unplanned same-hospital readmissions in Western Australia. The study used both 28- and 30-day measures from discharge to unplanned readmission to allow international comparison. More than half the unplanned hospital readmissions occurred between Day 0 and Day 5 following discharge from the index admission. Time intervals from discharge date to readmission date varied for diagnosis-specific readmissions of paediatric patients. What are the implications for practitioners? Targeting the top principal index admission diagnoses identified for paediatric readmissions is critical for improvement in the continuity of discharge care delivery, health resource utilisation and associated costs. Because 52% of unplanned readmissions occurred in the first 5 days, urgent investigation and implementation of prevention strategies are required, especially when the readmission occurs on the date of discharge.

Risk factors associated with 30-day all-cause unplanned hospital readmissions at a tertiary children's hospital in Western Australia

AIM

To identify risk factors associated with 30-day all-cause unplanned hospital readmission at a tertiary children's hospital in Western Australia.

METHODS

An administrative paediatric inpatient dataset was analysed retrospectively. Patients of all ages discharged between 1 January 2010 and 31 December 2014 were included. Demographic and clinical information at the index admission was examined using multivariate logistic regression analysis.

RESULTS

A total of 3330 patients (4.55%) experienced at least one unplanned readmission after discharge. Readmission was more likely to occur in patients who were either older than 16 years (odds ratio (OR) = 1.46; 95% confidence interval (CI) 1.07-1.98), utilising private insurance as an inpatient (OR = 1.16; 95% CI 1.00-1.34), with greater socio-economic advantage (OR = 1.20; 95% CI 1.02-1.41), admitted on Friday (OR = 1.21; 95% CI 1.05-1.39), discharged on Friday/Saturday/Sunday (OR = 1.26, 95% CI 1.10-1.44; OR = 1.34, 95% CI 1.15-1.57; OR = 1.24, 95% CI 1.05-1.47, respectively), with four or more diagnoses at the index admission (OR = 2.41; 95% CI 2.08-2.80) or hospitalised for 15 days or longer (OR = 2.39; 95% CI 1.88-2.98). Area under receiver operating characteristic curve of the predictive model is 0.645.

CONCLUSIONS

A moderate discriminative ability predictive model for 30-day all-cause same hospital readmission was developed. A structured discharge plan is suggested to be commenced from admission to ensure continuity of care for patients identified as being at higher risk of readmission. A recommendation is made that a designated staff member be assigned to co-ordinate the plan, including assessment of patients' and primary carers' readiness for discharge. Further research is required to establish comprehensive paediatric readmission rates by accessing linkage data to capture different hospital readmissions.

Factors Associated with Readmission of Pediatric Patients in a Developing Nation

OBJECTIVES

To determine the incidence of readmission in pediatric patients in a tertiary care hospital in a developing nation and to ascertain factors precipitating readmissions.

METHODS

A prospective study was conducted from February 2016 through January 2017 at a tertiary care hospital. Children between 1 mo to 15 y of age were included if they were readmitted within 60 d of discharge. The risk factors for readmission were determined on the basis of medical record review and a structured questionnaire and the ascribed cause of readmission was grouped into three categories: Patient specific factors, Hospital specific factors and Unrelated/ New illness.

RESULTS

The readmission rate was 3%, out of which 80.66% were found to be causally related to the index admission. Several sociodemographic characteristics i.e. lack of health information like television, lower socioeconomic status, absence of adequate breastfeeding, lower age, migrants were found to be significantly associated with readmission along with other patient specific factors like presence of cardiac disease, presence of comorbid conditions like anemia, malnutrition, and global developmental delay. The most important cause for readmission was determined as patient specific (48.66%) followed by hospital specific (38%) and unknown/unrelated factors (13.33%).

CONCLUSIONS

The progression of the primary illness and social determinants of pediatric readmissions are important contributing risk factors for readmission in developing countries in pediatric patients. Multicentric studies are needed from this region of the world to include different hospital readmissions rate and to address the issue of potential preventability of pediatric readmissions.

Risk factors associated with paediatric unplanned hospital readmissions: a systematic review

OBJECTIVE

To synthesise evidence on risk factors associated with paediatric unplanned hospital readmissions (UHRs).

DESIGN

Systematic review.

DATA SOURCE

CINAHL, EMBASE (Ovid) and MEDLINE from 2000 to 2017.

ELIGIBILITY CRITERIA

Studies published in English with full-text access and focused on paediatric All-cause, Surgical procedure and General medical condition related UHRs were included.

DATA EXTRACTION AND SYNTHESIS

Characteristics of the included studies, examined variables and the statistically significant risk factors were extracted. Two reviewers independently assessed study quality based on six domains of potential bias. Pooling of extracted risk factors was not permitted due to heterogeneity of the included studies. Data were synthesised using content analysis and presented in narrative form.

RESULTS

Thirty-six significant risk factors were extracted from the 44 included studies and presented under three health condition groupings. For All-cause UHRs, ethnicity, comorbidity and type of health insurance were the most frequently cited factors. For Surgical procedure related UHRs, specific surgical procedures, comorbidity, length of stay (LOS), age, the American Society of Anaesthesiologists class, postoperative complications, duration of procedure, type of health insurance and illness severity were cited more frequently. The four most cited risk factors associated with General medical condition related UHRs were comorbidity, age, health service usage prior to the index admission and LOS.

CONCLUSIONS

This systematic review acknowledges the complexity of readmission risk prediction in paediatric populations. This review identified four risk factors across all three health condition groupings, namely comorbidity; public health insurance; longer LOS and patients<12 months or between 13-18 years. The identification of risk factors, however, depended on the variables examined by each of the included studies. Consideration should be taken into account when generalising reported risk factors to other institutions. This review highlights the need to develop a standardised set of measures to capture key hospital discharge variables that predict unplanned readmission among paediatric patients.

Perceived Access to Outpatient Care and Hospital Reutilization Following Acute Respiratory Illnesses

OBJECTIVE

Efforts to decrease hospital revisits often focus on improving access to outpatient follow-up. Our objective was to assess the relationship between perceived access to timely office-based care and subsequent 30-day revisits following hospital discharge for 4 common respiratory illnesses.

METHODS

This was a prospective cohort study of children 2 weeks to 16years admitted to 5 US children's hospitals for asthma, bronchiolitis, croup, or pneumonia between July 2014 and June 2016. Hospital and emergency department (ED) (in the case of croup) admission surveys administered to caregivers included the Consumer Assessments of Healthcare Providers and Systems Timely Access to Care. Access composite scores (range 0-100, with greater scores indicating better access) were linked with 30-day ED revisits and inpatient readmissions from the Pediatric Health Information System. The relationship between access to timely care and repeat utilization was assessed using multivariable logistic regression adjusting for demographics, hospitalization, and home/outpatient factors.

RESULTS

Of the 2438 children enrolled, 2179 (89%) reported an office visit in the previous 6 months. Average access composite score was 52.0 (standard deviation, 36.3). In adjusted analyses, greater access scores were associated with greater odds of 30-day ED revisits (odds ratio [OR] = 1.07; 95% confidence interval [CI], 1.02-1.13)-particularly for croup (OR = 1.17; 95% CI, 1.02-1.36)-but not inpatient readmissions (OR = 1.02; 95% CI, 0.96-1.09).

CONCLUSIONS

Perceived access to timely office-based care was associated with significantly greater odds of subsequent ED revisit. Focusing solely on enhancing timely access to care following discharge for common respiratory illnesses may be insufficient to prevent repeat utilization.

An Electronic Health Record Tool Designed to Improve Pediatric Hospital Discharge has Low Predictive Utility for Readmissions

We developed an electronic health record tool to improve pediatric hospital discharge. This tool flags children with three components that might complicate discharge: home health, polypharmacy (greater than or equal to 6 medications), or nonEnglish speaking caregiver. The tool tallies components and displays them as a composite score of 0-3 points. We describe the tool's development, implementation, and an evaluation of its predictive utility for 30-day unplanned readmissions in 29,542 discharged children. Of these children, 28% had a composite score of 1, 8% a score greater than or equal to 2, and 4% were readmitted. The odds of readmission was significantly higher in children with composite score of 1 versus 0 (odds ratio [OR]: 1.7; 95% CI, 1.5-2) and greater than or equal to 2 versus 0 (OR 4.2; 95% CI 3.6-4.9). The C-statistic for this model was 0.6259. Despite the positive association of the score with readmission, the tool's discriminatory performance is low. Additional research is needed to evaluate its practical benefit for improving the quality of hospital discharge. This study was supported by an institutional Clinical and Operational Effectiveness and Patient Safety Small Grants Program.

Hospital-to-Home Interventions, Use, and Satisfaction: A Meta-analysis

CONTEXT

Hospital-to-home transitions are critical opportunities to promote patient safety and high-quality care. However, such transitions are often fraught with difficulties associated with increased health care use and poor patient satisfaction.

OBJECTIVE

In this review, we determine which pediatric hospital discharge interventions affect subsequent health care use or parental satisfaction compared with usual care.

DATA SOURCES

We searched 7 bibliographic databases and 5 pediatric journals.

STUDY SELECTION

Inclusion criteria were: (1) available in English, (2) focused on children <18 years of age, (3) pediatric data reported separately from adult data, (4) not focused on normal newborns or pregnancy, (5) discharge intervention implemented in the inpatient setting, and (6) outcomes of health care use or caregiver satisfaction. Reviews, case studies, and commentaries were excluded.

DATA EXTRACTION

Two reviewers independently abstracted data using modified Cochrane data collection forms and assessed quality using modified Downs and Black checklists.

RESULTS

Seventy one articles met inclusion criteria. Although most interventions improved satisfaction, interventions variably reduced use. Interventions focused on follow-up care, discharge planning, teach back-based parental education, and contingency planning were associated with reduced use across patient groups. Bundled care coordination and family engagement interventions were associated with lower use in patients with chronic illnesses and neonates.

LIMITATIONS

Variability limited findings and reduced generalizability.

CONCLUSIONS

In this review, we highlight the utility of a pediatric discharge bundle in reducing health care use. Coordinating follow-up, discharge planning, teach back-based parental education, and contingency planning are potential foci for future efforts to improve hospital-to-home transitions.

A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission

OBJECTIVES

To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions.

METHODS

Data consisting of 38 143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (pRI) was 1 of the novel candidate predictors considered. Multivariate model selection was conducted by minimization of Akaike Information Criteria. The area under the receiver operator characteristic curve (AUC) and values for sensitivity, specificity, positive predictive value, relative risk, and accuracy were computed on the remaining 50% of the data.

RESULTS

The multivariate logistic regression model of readmission consists of 7 disease diagnosis groups, 4 measures of hospital resource use, 3 measures of disease severity and/or medical complexities, and 2 variables derived from the pRI. Four of the predictors are novel, including history of previous 30-day readmissions within last 6 months (P < .001), planned admissions (P < .001), the discharge pRI score (P < .001), and indicator of whether the maximum pRI occurred during the last 24 hours of hospitalization (P = .005). An AUC of 0.79 (0.77-0.80) was obtained on the independent test data set.

CONCLUSIONS

Our model provides significant performance improvements in the prediction of unplanned 30-day pediatric readmissions with AUC higher than the LACE readmission model and other general unplanned 30-day pediatric readmission models. The model is expected to provide an opportunity to capture 39% of readmissions (at a selected operating point) and may therefore assist clinicians in reducing avoidable readmissions.

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk

BACKGROUND

Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions.

OBJECTIVES

To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses.

MATERIALS AND METHODS

Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed.

RESULTS

C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862.

CONCLUSIONS

Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result.

Adding Social Determinant Data Changes Children's Hospitals' Readmissions Performance

OBJECTIVES

To determine whether social determinants of health (SDH) risk adjustment changes hospital-level performance on the 30-day Pediatric All-Condition Readmission (PACR) measure and improves fit and accuracy of discharge-level models.

STUDY DESIGN

We performed a retrospective cohort study of all hospital discharges meeting criteria for the PACR from 47 hospitals in the Pediatric Health Information database from January to December 2014. We built four nested regression models by sequentially adding risk adjustment factors as follows: chronic condition indicators (CCIs); PACR patient factors (age and sex); electronic health record-derived SDH (race, ethnicity, payer), and zip code-linked SDH (families below poverty level, vacant housing units, adults without a high school diploma, single-parent households, median household income, unemployment rate). For each model, we measured the change in hospitals' readmission decile-rank and assessed model fit and accuracy.

RESULTS

For the 458 686 discharges meeting PACR inclusion criteria, in multivariable models, factors associated with higher discharge-level PACR measure included age <1 year, female sex, 1 of 17 CCIs, higher CCI count, Medicaid insurance, higher median household income, and higher percentage of single-parent households. Adjustment for SDH made small but significant improvements in fit and accuracy of discharge-level PACR models, with larger effect at the hospital level, changing decile-rank for 17 of 47 hospitals.

CONCLUSIONS

We found that risk adjustment for SDH changed hospitals' readmissions rate rank order. Hospital-level changes in relative readmissions performance can have considerable financial implications; thus, for pay for performance measures calculated at the hospital level, and for research associated therewith, our findings support the inclusion of SDH variables in risk adjustment.

Examination of the Patient and Hospitalization Characteristics of 30-Day SCD Readmissions

OBJECTIVES

Sickle cell disease (SCD) is associated with a high level of emergency department and hospital utilization, as well as a high rate of hospital readmissions. At Johns Hopkins Hospital, as at other institutions, SCD accounts for a large proportion of readmissions. Our study examined patient and hospitalization factors involved in readmissions at Johns Hopkins Hospital.

METHODS

Patients at the Johns Hopkins Sickle Cell Center for Adults with a readmission in fiscal year 2011 were compared with an age- and sex-matched sample of clinic patients for comorbidities, complications, and prior utilization. Hospitalizations that were followed by readmissions were compared with those that were not as to admitting service, length of stay, and average daily opioid dose.

RESULTS

Patients with readmissions had more complications and comorbidities and much higher prior utilization than typical clinic patients, whereas hospitalizations that were followed by readmissions had a longer length of stay but similar opioid doses.

CONCLUSIONS

For patients with SCD with a high volume of hospital use, readmissions may be a natural consequence of a high-admission frequency associated with greater disease severity and higher comorbidity.

Discharge Handoff Communication and Pediatric Readmissions

BACKGROUND

Improvement in hospital transitional care has become a major national priority, although the impact on children's postdischarge outcomes is unclear.

OBJECTIVE

To characterize common handoff practices between hospital and primary care providers (PCPs), and test the hypothesis that common handoff practices would be associated with fewer unplanned readmissions.

DESIGN, SETTING, AND PATIENTS

This prospective cohort study enrolled randomly selected pediatric patients during an acute hospitalization at a tertiary children's hospital in 2012-2014.

MEASUREMENTS

Primary care and patient data were abstracted from administrative, caregiver, and PCP questionnaires on admission through 30 days postdischarge. The primary outcome was 30-day unplanned readmission to any hospital. Logistic regression assessed relationships between readmissions and 11 handoff communication practices.

RESULTS

We enrolled 701 children, from which 685 identified PCPs. Complete data were collected from 84% of PCPs. Communication practices varied widely--verbal handoffs occurred rarely (10.7%); PCP notification of admission occurred for 50.8%. Caregiver experience scores, using an adapted Care Transitions Measure-3, were high but were unrelated to readmissions. Thirty-day unplanned readmissions to any hospital were unrelated to most handoff practices. Having PCP follow-up appointments scheduled prior to discharge was associated with more readmissions (adjusted odds ratio, 2.20; 95% confidence interval, 1.08-4.46).

CONCLUSION

Despite their presumed value, common handoff practices between hospital providers and PCPs may not lead to reductions in postdischarge utilization for children. Addressing broader constructs like caregiver self-efficacy or social determinants is likely necessary. Journal of Hospital Medicine 2017;12:29-35.

Thirty-days readmissions in pediatric surgery: The first U.K. experience

INTRODUCTION

The aim of this study was to investigate readmissions within 30days of operation (ReAd) in the setting of a tertiary pediatric surgical practice in the UK.

METHODS

Using Hospital Episode Statistics, cases that were readmitted within 30days of primary operation were identified retrospectively. Demographics including age, gender, preexisting comorbidities, diagnosis on primary admission and the treatment, length of stay, and diagnosis on readmission with treatment, including further surgical intervention, were collected from discharge summaries and hospital notes. Neonates were excluded from this study. Comorbidities, involving one or more systems, were also identified for each case of readmission. ReAds were classified into emergency and elective cohort depending on the nature of the primary operation. Outcomes were compared between these two groups. Data were quoted as median (range) unless indicated otherwise. Data were analyzed using SPSS software Desktop 22.0, using Mann-Whitney U and Chi-Squared tests, with a consideration that a P≤0.05 was significant.

RESULTS

A total of 2378 procedures were performed during the study period. Elective cases, including day cases, accounted for 77% (n=1837) of all cases. The remaining 23% (n=541) were emergency cases. Total unplanned readmission rate within 30days (ReAd) was 2%. Further surgical procedures were required in 38%. Having excluded neonates, the most common primary procedure leading to readmission within 30days was appendicectomy (26%). Overall, the most common cause for readmission within 30days was postoperative infection (30%). The ReAd in emergency cohort was 3.5% in comparison to 1.5% in elective, which was significantly different (P value=0.007).

CONCLUSION

Readmission within thirty days of primary procedure in pediatric surgery has little published data. An efficient discharge planning may play a vital role in preventing unwanted readmission. Elective operations had a significantly lower readmission rate than emergency operations. Having excluded neonates, appendicectomy was found to be the most common operation associated with readmission in the pediatric surgical practice. Although widely used as quality care indicator in adults, more studies are required to validate readmission rate as a quality of care indicator in pediatric surgery practice.

Roles of disease severity and post-discharge outpatient visits as predictors of hospital readmissions

Background

Risks prediction models of 30-day all-cause hospital readmissions are multi-factorial. Severity of illness (SOI) and risk of mortality (ROM) categorized by All Patient Refined Diagnosis Related Groups (APR-DRG) seem to predict hospital readmission but lack large sample validation. Effects of risk reduction interventions including providing post-discharge outpatient visits remain uncertain. We aim to determine the accuracy of using SOI and ROM to predict readmission and further investigate the role of outpatient visits in association with hospital readmission.

Methods

Hospital readmission data were reviewed retrospectively from September 2012 through June 2015. Patient demographics and clinical variables including insurance type, homeless status, substance abuse, psychiatric problems, length of stay, SOI, ROM, ICD-10 diagnoses and medications prescribed at discharge, and prescription ratio at discharge (number of medications prescribed divided by number of ICD-10 diagnoses) were analyzed using logistic regression. Relationships among SOI, type of hospital visits, time between hospital visits, and readmissions were also investigated.

Results

A total of 6011 readmissions occurred from 55,532 index admissions. The adjusted odds ratios of SOI and ROM predicting readmissions were 1.31 (SOI: 95 % CI 1.25–1.38) and 1.09 (ROM: 95 % CI 1.05–1.14) separately. Ninety percent (5381/6011) of patients were readmitted from the Emergency Department (ED) or Urgent Care Center (UCC). Average time interval from index discharge date to ED/UCC visit was 9 days in both the no readmission and readmission groups (p > 0.05). Similar hospital readmission rates were noted during the first 10 days from index discharge regardless of whether post-index discharge patient clinic visits occurred when time-to-event analysis was performed.

Conclusions

SOI and ROM significantly predict hospital readmission risk in general. Most readmissions occurred among patients presenting for ED/UCC visits after index discharge. Simply providing early post-discharge follow-up clinic visits does not seem to prevent hospital readmissions.

A Quality Improvement Collaborative to Improve the Discharge Process for Hospitalized Children

OBJECTIVE

To assess the impact of a quality improvement collaborative on quality and efficiency of pediatric discharges.

METHODS

This was a multicenter quality improvement collaborative including 11 tertiary-care freestanding children's hospitals in the United States, conducted between November 1, 2011 and October 31, 2012. Sites selected interventions from a change package developed by an expert panel. Multiple plan-do-study-act cycles were conducted on patient populations selected by each site. Data on discharge-related care failures, family readiness for discharge, and 72-hour and 30-day readmissions were reported monthly by each site. Surveys of each site were also conducted to evaluate the use of various change strategies.

RESULTS

Most sites addressed discharge planning, quality of discharge instructions, and providing postdischarge support by phone. There was a significant decrease in discharge-related care failures, from 34% in the first project quarter to 21% at the end of the collaborative (P < .05). There was also a significant improvement in family perception of readiness for discharge, from 85% of families reporting the highest rating to 91% (P < .05). There was no improvement in unplanned 72-hour (0.7% vs 1.1%, P = .29) and slight worsening of the 30-day readmission rate (4.5% vs 6.3%, P = .05).

CONCLUSIONS

Institutions that participated in the collaborative had lower rates of discharge-related care failures and improved family readiness for discharge. There was no significant improvement in unplanned readmissions. More studies are needed to evaluate which interventions are most effective and to assess feasibility in non-children's hospital settings.

Identifying risk factors and patterns for unplanned readmission to a general medical service

OBJECTIVE

To identify factors and patterns associated with 7- and 28-day readmission for general medicine patients at a tertiary public hospital.

METHODS

A retrospective observational study was conducted using an administrative database at a general medicine service in a tertiary public hospital between 1 January 2007 and 31 December 2011. Demographic and clinical factors, as well as readmission patterns, were evaluated for the association with 7- and 28-day readmission.

RESULTS

The study cohort included 13 802 patients and the 28-day readmission rate was 10.9%. In multivariate analysis, longer hospital stay of the index admission (adjusted relative risk (ARR) 1.34), Charlson index ≥ 3 (ARR 1.28), discharge against medical advice (ARR 1.87), active malignancy (ARR 1.83), cardiac failure (ARR 1.48) and incomplete discharge summaries (ARR 1.61) were independently associated with increased risk of 28-day readmission. Patients with diseases of the respiratory system, neurological or genitourinary disease, injury and unclassifiable conditions were likely to be readmitted within 7 days. Patients with circulatory and respiratory disease were likely to be readmitted with the same system diagnosis.

CONCLUSION

Readmission of general medicine patients within 28 days is relatively common and is associated with clinical factors and patterns. Identification of these risk factors and patterns will enable the interventions to reduce potentially preventable readmissions.

Influence of Obesity Diagnosis With Organ Dysfunction, Mortality, and Resource Use Among Children Hospitalized With Infection in the United States

BACKGROUND

Sepsis induces inflammation in response to infection and is a major cause of mortality and hospitalization in children. Obesity induces chronic inflammation leading to many clinical manifestations. Our understanding of the impact of obesity on diseases, such as infection and sepsis, is limited. The objective of this study was to evaluate the association of obesity with organ dysfunction, mortality, duration, and charges during among US children hospitalized with infection.

METHODS

Retrospective study of hospitalizations in children with infection aged 0 to 20 years, using the 2009 Kids' Inpatient Database.

RESULTS

Of 3.4 million hospitalizations, 357 701 were for infection, 5685 of which were reported as obese children. Obese patients had higher rates of organ dysfunction (7.35% vs 5.5%, P < .01), longer hospital stays (4.1 vs 3.5 days, P < .001), and accrued higher charges (US$29 019 vs US$21 200, P < .001). In multivariable analysis, mortality did not differ by obesity status (odds ratio: 0.56, 95% confidence interval: 0.23-1.34), however severity of illness modified the association between obesity status and the other outcomes.

CONCLUSIONS

While there was no difference in in-hospital mortality by obesity diagnosis, variation in organ dysfunction, hospital stay, and hospital charges according to obesity status was mediated by illness severity. Findings from this study have significant implications for targeted approaches to mitigate the burden of obesity on infection and sepsis.

The Medical Home and Hospital Readmissions

BACKGROUND AND OBJECTIVE

Despite considerable attention, little is known about the degree to which primary care medical homes influence early postdischarge utilization. We sought to test the hypothesis that patients with medical homes are less likely to have early postdischarge hospital or emergency department (ED) encounters.

METHODS

This prospective cohort study enrolled randomly selected patients during an acute hospitalization at a children's hospital during 2012 to 2014. Demographic and clinical data were abstracted from administrative sources and caregiver questionnaires on admission through 30 days postdischarge. Medical home experience was assessed by using Maternal and Child Health Bureau definitions. Primary outcomes were 30-day unplanned readmission and 7-day ED visits to any hospital. Logistic regression explored relationships between outcomes and medical home experiences.

RESULTS

We followed 701 patients, 97% with complete data. Thirty-day unplanned readmission and 7-day ED revisit rates were 12.4% and 5.6%, respectively. More than 65% did not have a medical home. In adjusted models, those with medical home component "having a usual source of sick and well care" had fewer readmissions than those without (adjusted odds ratio 0.54, 95% confidence interval 0.30-0.96). Readmissions were higher among those with less parent confidence in avoiding a readmission, subspecialist primary care providers, longer length of index stay, and more hospitalizations in the past year. ED visits were associated with lack of parent confidence but not medical home components.

CONCLUSIONS

Lacking a usual source for care was associated with readmissions. Lack of parent confidence was associated with readmissions and ED visits. This information may be used to target interventions or identify high-risk patients before discharge.

Identifying hospitalized pediatric patients for early discharge planning: a feasibility study

A screening tool utilized by nurses at a critical point in the discharge planning process has the potential to improve caregiver decisions and enhance communication. The Early Screen for Discharge Planning-Child version (ESDP-C) identifies pediatric patients early in their hospital stay who will benefit from early engagement of a discharge planner. This study used a quasi-experimental, non-equivalent comparison group design to evaluate the impact of the ESDP-C on important outcomes related to discharge planning. Findings from the study provide preliminary evidence that the integration of the ESDP-C into the pediatric discharge planning process may be clinically useful.

Outpatient follow-up visits and readmission in medically complex children enrolled in Medicaid

OBJECTIVE

To examine the association between postdischarge outpatient follow-up and 30-day readmissions in Medicaid enrolled children with complex, chronic conditions.

STUDY DESIGN

This was a retrospective cohort analysis of Colorado Medicaid recipients with complex, chronic conditions who were discharged from the hospital between 2006 and 2008. The primary outcome was readmission between 4 and 30 days after index hospital discharge. Using multivariable logistic regression, we examined the association between early postdischarge outpatient visits (≤ 3 days postdischarge) and readmission. We secondarily analyzed the relationship between any outpatient visit from 4 to 29 days of index discharge and readmission.

RESULTS

For the 2415 patients with complex, chronic conditions included in the analysis, the 4- to 30-day readmission rate was 6.3%. The odds of readmission was significantly greater for patients with ≥ 1 outpatient visit ≤ 3 days after discharge compared with patients without a visit ≤ 3 days after discharge (aOR 1.7 [1.1-2.4]). The odds of readmission were significantly lower for patients with ≥ 1 outpatient visit from 4 to 29 days after discharge compared with patients without such visits (aOR 0.5 [0.3-0.7]). Other factors associated with readmission included index hospital length of stay and number of complex, chronic conditions.

CONCLUSIONS

In medically complex children, there is a positive association between early postdischarge outpatient follow-up and readmission. There is an inverse association between later postdischarge outpatient follow-up and readmission. Outpatient follow-up occurring within 4-29 days after discharge may help to prevent 30-day readmissions. Additional research is needed to inform guidelines regarding longer term postdischarge outpatient follow-up in these children.

Feasibility of using a pediatric call center as part of a quality improvement effort to prevent hospital readmission

The primary aim of this intervention was to assess the feasibility of using call center nurses who are experts in telephone triage to conduct post discharge telephone calls, as part of a quality improvement effort to prevent hospital readmission. Families of patients with bronchiolitis were called between 24 and 48 hours after discharge. The calls conducted by the nurses were efficient (average time was 12 minutes), and their assessments helped to identify gaps in inpatient family education. Overall, the project demonstrated the efficacy in readmission prevention by using nurses who staff a call center to conduct post-hospitalization telephone calls.

Summary of STARNet: Seamless Transitions and (Re)admissions Network

The Seamless Transitions and (Re)admissions Network (STARNet) met in December 2012 to synthesize ongoing hospital-to-home transition work, discuss goals, and develop a plan to centralize transition information in the future. STARNet participants consisted of experts in the field of pediatric hospital medicine quality improvement and research, and included physicians and key stakeholders from hospital groups, private payers, as well as representatives from current transition collaboratives. In this report, we (1) review the current knowledge regarding hospital-to-home transitions; (2) outline the challenges of measuring and reducing readmissions; and (3) highlight research gaps and list potential measures for transition quality. STARNet met with the support of the American Academy of Pediatrics' Quality Improvement Innovation Networks and the Section on Hospital Medicine.

Preventing hospitalizations in children with medical complexity: a systematic review

BACKGROUND AND OBJECTIVES

Children with medical complexity (CMC) account for disproportionately high hospital use, and it is unknown if hospitalizations may be prevented. Our objective was to summarize evidence from (1) studies characterizing potentially preventable hospitalizations in CMC and (2) interventions aiming to reduce such hospitalizations.

METHODS

Our data sources include Medline, Cochrane Central Register of Controlled Trials, Web of Science, and Cumulative Index to Nursing and Allied Health Literature databases from their originations, and hand search of article bibliographies. Observational studies (n = 13) characterized potentially preventable hospitalizations, and experimental studies (n = 4) evaluated the efficacy of interventions to reduce them. Data were extracted on patient and family characteristics, medical complexity and preventable hospitalization indicators, hospitalization rates, costs, and days. Results of interventions were summarized by their effect on changes in hospital use.

RESULTS

Preventable hospitalizations were measured in 3 ways: ambulatory care sensitive conditions, readmissions, or investigator-defined criteria. Postsurgical patients, those with neurologic disorders, and those with medical devices had higher preventable hospitalization rates, as did those with public insurance and nonwhite race/ethnicity. Passive smoke exposure, nonadherence to medications, and lack of follow-up after discharge were additional risks. Hospitalizations for ambulatory care sensitive conditions were less common in more complex patients. Patients receiving home visits, care coordination, chronic care-management, and continuity across settings had fewer preventable hospitalizations.

CONCLUSIONS

There were a limited number of published studies. Measures for CMC and preventable hospitalizations were heterogeneous. Risk of bias was moderate due primarily to limited controlled experimental designs. Reductions in hospital use among CMC might be possible. Strategies should target primary drivers of preventable hospitalizations.

The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions

BACKGROUND

Hospital readmission risk prediction remains a motivated area of investigation and operations in light of the hospital readmissions reduction program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance.

OBJECTIVES

To study the effects of varying three factors of model development in the prediction of risk based on health record data: (1) reason for readmission (primary readmission diagnosis); (2) available data and data types (e.g. visit history, laboratory results, etc); (3) cohort selection.

METHODS

Regularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison in cohort selection testing. Calibration by model refitting to outcome prevalence.

RESULTS

Predicting readmission risk across multiple reasons for readmission resulted in ROC areas ranging from 0.92 for readmission for congestive heart failure to 0.71 for syncope and 0.68 for all-cause readmission. Visit history and laboratory tests contributed the most predictive value; contributions varied by readmission diagnosis. Cohort definition affected performance for both parametric and nonparametric algorithms. Compared to all patients, limiting the cohort to patients whose index admission and readmission diagnoses matched resulted in a decrease in average ROC from 0.78 to 0.55 (difference in ROC 0.23, p value 0.01). Calibration plots demonstrate good calibration with low mean squared error.

CONCLUSION

Targeting reason for readmission in risk prediction impacted discriminatory performance. In general, laboratory data and visit history data contributed the most to prediction; data source contributions varied by reason for readmission. Cohort selection had a large impact on model performance, and these results demonstrate the difficulty of comparing results across different studies of predictive risk modeling.

Effective communication with primary care providers

Effective communication requires direct interaction between the hospitalist and the primary care provider using a standardized method of information exchange with the opportunity to ask questions and assign accountability for follow-up roles. The discharge summary is part of the process but does not provide the important aspects of handoff, such as closed loop communication and role assignments. Hospital discharge is a significant safety risk for patients, with more than half of discharged patients experiencing at least one error. Hospitalist and primary care providers need to collaborate to develop a standardized system to communicate about shared patients that meets handoff requirements.