Introduction: Although the characteristics of readmitted patients associated with a family medicine inpatient service have been reported, differing characteristics between groups of patients based on readmission rates have not been studied. The aim of this project was to examine patients with differing rates of readmission. Methods: Patients admitted to a family medicine inpatient service were classified into 1 of 3 groups based on the number of admission and readmissions in a given year. Demographic data and other characteristics of these patients were collected and used in analysis. Descriptive statistics were used to characterize the 3 groups of admissions. Differences in characteristics of groups were compared using Wilcoxon rank sum test for continuous variables and 2 test or Fisher exact test for categoric variables. Multivariate logistic regressions were used for predicting high-frequency readmission. Results: Patients in the high-frequency readmission group more commonly had a psychiatric, substance abuse, and chronic pain diagnosis. The primary discharge diagnoses among the 3 groups were similar. Age-group, Charlson severity index, Morse Fall Scale medication list, and problem list were significant for predicting high frequency of readmission. Annually, patients in the high-frequency readmission group had about an 80% turnover rate. Conclusions: Although this study examined patient care data from only one large academic health center hospital, the results found that patients who experience 3 or more readmissions in a calendar are associated with specific characteristics. In addition, the list of specific individual patients considered to be high utilizers for hospital readmissions was dynamic and significantly changed during 3 consecutive years.
BackgroundDespite increased research using large administrative databases to identify determinants of maternal morbidity and mortality, the extent to which these databases capture obstetric co‐morbidities is unknown.ObjectiveTo evaluate the impact that the time window used to assess obstetric co‐morbidities has on the completeness of ascertainment of those co‐morbidities.MethodsWe conducted a five‐year analysis of inpatient hospitalisations of pregnant women from 2010‐2014 using the Nationwide Readmissions Database. For each woman, using discharge diagnoses, we identified 24 conditions used to create the Obstetric Comorbidity Index. Using various assessment windows for capturing obstetric co‐morbidities, including the delivery hospitalisation only and all weekly windows from 7 to 280 days, we calculated the frequency and rate of each co‐morbidity and the degree of underascertainment of the co‐morbidity. Under each scenario, and for each co‐morbidity, we also calculated the all‐cause, 30‐day readmission rate.ResultsThere were over 3 million delivery hospitalisations from 2010 to 2014 included in this analysis. Compared with a full 280‐day window, assessment of obstetric co‐morbidities using only diagnoses made during the delivery hospitalisation would result in failing to identify over 35% of cases of chronic renal disease, 28.5% cases in which alcohol abuse was documented during pregnancy, and 23.1% of women with pulmonary hypertension. For seven other co‐morbidities, at least 1 in 20 women with that condition would have been missed with exclusive reliance on the delivery hospitalisation for co‐morbidity diagnoses. Not only would reliance on delivery hospitalisations have resulted in missed cases of co‐morbidities, but for many conditions, estimates of readmission rates for women with obstetric co‐morbidities would have been underestimated.ConclusionsAn increasing proportion of maternal and child health research is based on large administrative databases. This study provides data that facilitate the assessment of the degree to which important obstetric co‐morbidities may be underascertained when using these databases.
BACKGROUND:Little is known about the frequency, patterns, and determinants of readmissions among patients initially hospitalized for an ambulatory care-sensitive condition (ACSC). The degree to which hospitalizations in close temporal proximity cluster has also not been studied. Readmission patterns involving clustering likely reflect different underlying determinants than the same number of readmissions more evenly spaced. OBJECTIVE: To characterize readmission rates, patterns, and predictors among patients initially hospitalized with an ACSC. DESIGN: Retrospective analysis of the 2010-2014 Nationwide Readmissions Database. PARTICIPANTS: Non-pregnant patients aged 18-64 years old during initial ACSC hospitalization and who were discharged alive (N = 5,007,820). MAIN MEASURES: Frequency and pattern of 30-day allcause readmissions, grouped as 0, 1, 2+ non-clustered, and 2+ clustered readmissions. KEY RESULTS: Approximately 14% of patients had 1 readmission, 2.4% had 2+ non-clustered readmissions, and 3.3% patients had 2+ clustered readmissions during the 270-day follow-up. A higher Elixhauser Comorbidity Index was associated with increased risk for all readmission groups, namely with adjusted odds ratios (AORs) ranging from 1.12 to 3.34. Compared to patients aged 80 years and older, those in younger age groups had increased risk of 2+ non-clustered and 2+ clustered readmissions (AOR range 1.27-2.49). Patients with chronic versus acute ACSCs had an increased odds ratio of all readmission groups compared to those with 0 readmissions (AOR range 1.37-2.69). CONCLUSIONS: Among patients with 2+ 30-day readmissions, factors were differentially distributed between clustered and non-clustered readmissions. Identifying factors that could predict future readmission patterns can inform primary care in the prevention of readmissions following ACSC-related hospitalizations.
Objective To explore the extent to which the severity of birth defects could be differentiated using severity of illness (SOI) and risk of mortality (ROM) measures available in national discharge databases. Methods Data from the 2012–14 National Inpatient Sample (NIS) was used to identify hospitalizations with one or more major birth defects reported annually to the National Birth Defects Prevention Network using the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD‐9‐CM) diagnosis codes. Each hospitalization also contained a 4‐level SOI and 4‐level ROM classification measure. For each birth defect and for each individual birth defect‐related ICD‐9‐CM code, we calculated mean and median SOI and ROM, the proportion of hospitalizations in each level of SOI and ROM, the inpatient mortality rate, and level of agreement between various existing or derived severity proxies in the NIS and the Texas Birth Defects Registry (TBDR). Results Mean SOI ranged from 1.5 (cleft lip alone) to 3.7 (single ventricle), and mean ROM ranged from 1.1 (cleft lip alone) to 3.9 (anencephaly). As a group, critical congenital heart defects had the highest average number of co‐occurring defects, mean SOI, and ROM, whereas orofacial and genitourinary defects had the lowest SOI and ROM. We found strong levels of agreement between TBDR severity classifications and NIS severity classifications defined using Level 3 or 4 SOI or ROM Level 3 or 4. Conclusions This preliminary investigation demonstrated how severity indices of birth defects could be differentiated and compared to a severity algorithm of an existing surveillance program.
Background: Increases in emergency department (ED) use are contributing to inefficient health care spending and becoming a public health concern. Previous studies have identified characteristics of ED high utilizers aimed at designing interventions to improve efficiency. We aim to expand on these findings in a family medicine outpatient population. Methods: We conducted a retrospective analysis on a population of ED high utilizers, defined as those who had been to the ED 6 or more times in 1 year, including medical and demographic characteristics from 2015 to 2017. Results: Compared with our source population, ED high utilizers were most commonly female, African American, or single and insured by Medicare or Medicaid. They did not have a chronic pain or substance use diagnosis, but more than half had a psychiatric condition. The only demographic characteristic that changed over time was home location from 2015 to 2017 (P < .05). Less than 10% of ED high utilizers were the same over 3 years. Conclusions: Most demographic characteristics did not change over time, whereas individuals did change. Interventions aimed at improving efficiency of ED use should be geared toward unchanging characteristics rather than individuals. The only demographic characteristic that did change significantly was home location that correlated in time with the availability of new EDs providing support for a theory of supply-sensitive ED use.
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