Utility of 30-Day Readmission Metrics in the Burn Population: Navigating Quality Metric Limitations in Special Populations

Hospital readmission data may be a useful tool in identifying risk factors leading to higher costs of care or poorer overall outcomes. Several studies have emerged utilizing these datasets to examine the trauma and burn population, which have been unable to distinguish planned from unplanned readmissions. The 2014 Nationwide Readmissions Database was queried for 363 burn-specific ICD-9 DX codes and filtered by age and readmission status to capture the adult burn population. Additionally, burn-related excision and grafting procedures were filtered from 25 ICD-9 SG codes to distinguish planned readmissions. A total of 26,719 burn patients were identified with 781 all-cause unscheduled 30-day readmissions. Further filtering by burn-related excision and grafting procedures then identified 468 patients undergoing a burn-related excision and grafting procedure on readmission, reducing the dataset to 313 patients and identifying up to 60% of readmissions as possibly improperly coded planned readmissions. From this dataset, nonoperative management on initial admission was found to have the strongest correlation with readmission (OR 5.00; 3.33–7.14). Notably corrected data, when stratified by annual burn patient admission volume, identified a significant likelihood of readmission (OR 4.57; 2.15–9.70) of centers receiving the lowest annual number of burn patients, which was not identified in the unfiltered dataset. Healthcare performance statistics may be a powerful metric when utilized appropriately; however, these databases must be carefully applied to small and special populations. This study has determined that as many as 60% of burn patient readmissions included in prior studies may be improperly coded planned readmissions.

A Smartphone App to Manage Cirrhotic Ascites Among Outpatients: Feasibility Study

Background Ascites is a common, painful, and serious complication of cirrhosis. Body weight is a reliable proxy for ascites volume; therefore, daily weight monitoring is recommended to optimize ascites management. Objective This study aims to evaluate the feasibility of a smartphone app in facilitating outpatient ascites management. Methods In this feasibility study, patients with cirrhotic ascites requiring active management were identified in both inpatient and outpatient settings. Patients were provided with a Bluetooth-connected scale, which transmitted weight data to a smartphone app and then via the internet to an electronic medical record (EMR). Weights were monitored every weekday. In the event of a weight change of ≥5 lbs in 1 week, patients were called and administered a short symptom questionnaire, and providers received an email alert. The primary outcomes of this study were the percentage of enrolled days during which weight data were successfully transmitted to an EMR and the percentage of weight alerts that prompted responses by the provider. Results In this study, 25 patients were enrolled: 12 (48%) were male, and the mean age was 58 (SD 13; range 35-81) years. A total of 18 (72%) inpatients were enrolled. Weight data were successfully transmitted to an EMR during 71.2% (697/979) of the study enrollment days, with technology issues reported on 16.5% (162/979) of the days. Of a total of 79 weight change alerts fired, 41 (52%) were triggered by weight loss and 38 (48%) were by weight gain. Providers responded in some fashion to 66 (84%) of the weight alerts and intervened in response to 45 (57%) of the alerts, for example, by contacting the patient, scheduling clinic or paracentesis appointments, modifying the diuretic dose, or requesting a laboratory workup. Providers responded equally to weight increase and decrease alerts (P=.87). The staff called patients a mean of 3.7 (SD 3.5) times per patient, and the number of phone calls correlated with technology issues (r=0.60; P=.002). A total of 60% (15/25) of the patients chose to extend their participation beyond 30 days. A total of 17 patient readmissions occurred during the study period, with only 4 (24%) related to ascites. Conclusions We demonstrated the feasibility of a smartphone app to facilitate the management of ascites and reported excellent rates of patient and provider engagement. This innovation could enable early therapeutic intervention, thereby decreasing the burden of morbidity and mortality among patients with cirrhosis.

A Smartphone App to Manage Cirrhotic Ascites Among Outpatients: Feasibility Study (Preprint)

BACKGROUND Ascites is a common, painful, and serious complication of cirrhosis. Body weight is a reliable proxy for ascites volume; therefore, daily weight monitoring is recommended to optimize ascites management. OBJECTIVE This study aims to evaluate the feasibility of a smartphone app in facilitating outpatient ascites management. METHODS In this feasibility study, patients with cirrhotic ascites requiring active management were identified in both inpatient and outpatient settings. Patients were provided with a Bluetooth-connected scale, which transmitted weight data to a smartphone app and then via the internet to an electronic medical record (EMR). Weights were monitored every weekday. In the event of a weight change of ≥5 lbs in 1 week, patients were called and administered a short symptom questionnaire, and providers received an email alert. The primary outcomes of this study were the percentage of enrolled days during which weight data were successfully transmitted to an EMR and the percentage of weight alerts that prompted responses by the provider. RESULTS In this study, 25 patients were enrolled: 12 (48%) were male, and the mean age was 58 (SD 13; range 35-81) years. A total of 18 (72%) inpatients were enrolled. Weight data were successfully transmitted to an EMR during 71.2% (697/979) of the study enrollment days, with technology issues reported on 16.5% (162/979) of the days. Of a total of 79 weight change alerts fired, 41 (52%) were triggered by weight loss and 38 (48%) were by weight gain. Providers responded in some fashion to 66 (84%) of the weight alerts and intervened in response to 45 (57%) of the alerts, for example, by contacting the patient, scheduling clinic or paracentesis appointments, modifying the diuretic dose, or requesting a laboratory workup. Providers responded equally to weight increase and decrease alerts (<i>P</i>=.87). The staff called patients a mean of 3.7 (SD 3.5) times per patient, and the number of phone calls correlated with technology issues (<i>r</i>=0.60; <i>P</i>=.002). A total of 60% (15/25) of the patients chose to extend their participation beyond 30 days. A total of 17 patient readmissions occurred during the study period, with only 4 (24%) related to ascites. CONCLUSIONS We demonstrated the feasibility of a smartphone app to facilitate the management of ascites and reported excellent rates of patient and provider engagement. This innovation could enable early therapeutic intervention, thereby decreasing the burden of morbidity and mortality among patients with cirrhosis.

Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.

Predictors of readmission and long length of stay in elders admitted with neurological disorders in a tertiary center: a real-world investigation

ABSTRACT Hospital readmission and long length of stay (LOS) increase morbidity and hospital mortality and are associated with excessive costs to health systems. Objective: This study aimed to identify predictors of hospital readmission and long LOS among elders with neurological disorders (NDs). Methods: Patients ≥ 60 years of age admitted to the hospital between January 1, 2009, and December 31, 2010, with acute NDs, chronic NDs as underpinnings of acute clinical disorders, and neurological complications of other diseases were studied. We analyzed demographic factors, NDs, and comorbidities as independent predictors of readmission and long LOS (≥ 9 days). Logistic regression was performed for multivariate analysis. Results: Overall, 1,154 NDs and 2,679 comorbidities were identified among 798 inpatients aged ≥ 60 years (mean 75.8 ± 9.1). Of the patients, 54.5% were female. Patient readmissions were 251(31%) and 409 patients (51%) had an LOS ≥ 9 days (95% confidence interval 48%–55%). We found no predictors for readmission. Low socioeconomic class (p = 0.001), respiratory disorder (p < 0.001), infection (p < 0.001), genitourinary disorder (p < 0.033), and arterial hypertension (p = 0.002) were predictors of long LOS. Identified risks of long LOS explained 22% of predictors. Conclusions: Identifying risk factors for patient readmission are challenges for neurology teams and health system stakeholders. As low socioeconomic class and four comorbidities, but no NDs, were identified as predictors for long LOS, we recommend studying patient multimorbidity as well as functional and cognitive scores to determine whether they improve the risk model of long LOS in this population.

Readmission and mortality in patients treated by interprofessional student teams at a training ward compared with patients receiving usual care: a retrospective cohort study

ObjectiveThis study aimed to compare the rate of patient readmissions and mortality between care provided at an orthopaedic interprofessional training ward (IPTW) and usual care.DesignRetrospective cohort study.SettingOrthopaedic wards at a level II trauma centre at a Swedish university teaching hospital between 2006 and 2011.ParticipantsTwo cohorts were identified: (1) a control cohort that had not received care at the IPTW, and (2) patients who had been treated for at least 1 day at the IPTW.Main outcome measuresReadmission at 90 days and 1-year mortality.ResultsWe included 4652 controls and 1109 in the IPTW group. The mean age was 63 years, and 58% were women. The groups did not differ in any of the outcomes: the readmission rate in the control and IPTW groups was 13.5% and 14.0%, respectively, while mortality was 5.2% and 5.3%, respectively. This lack of difference remained after adjusting for confounders.ConclusionInterprofessional undergraduate training in patient-based settings can be performed in a level II trauma hospital with satisfactory patient safety.