What is the impact on the readmission ratio of taking into account readmissions to other hospitals? A cross-sectional study.

OBJECTIVES: Readmissions are used widespread as an indicator of the quality of care within hospitals. Including readmissions to other hospitals might have consequences for hospitals. The aim of our study is to determine the impact of taking into account readmissions to other hospitals on the readmission ratio. DESIGN AND SETTING: We performed a cross-sectional study and used administrative data from 77 Dutch hospitals (2 333 173 admissions) in 2015 and 2016 (97% of all hospitals). We performed logistic regression analyses to calculate 30-day readmission ratios for each hospital (the number of observed admissions divided by the number of expected readmissions based on the case mix of the hospital, multiplied by 100). We then compared two models: one with readmissions only to the same hospital, and another with readmissions to any hospital in the Netherlands. The models were calculated on the hospital level for all in-patients and, in more detail, on the level of medical specialties. MAIN OUTCOME MEASURES: Percentage of readmissions to another hospital, readmission ratios same hospital and any hospital and C-statistic of each model in order to determine the discriminative ability. RESULTS: The overall percentage of readmissions was 10.3%, of which 91.1% were to the same hospital and 8.9% to another hospital. Patients who went to another hospital were younger, more often men and had fewer comorbidities. The readmission ratios for any hospital versus the same hospital were strongly correlated (r=0.91). There were differences between the medical specialties in percentage of readmissions to another hospital and C-statistic. CONCLUSIONS: The overall impact of taking into account readmissions to other hospitals seems to be limited in the Netherlands. However, it does have consequences for some hospitals. It would be interesting to explore what causes this difference for some hospitals and if it is related to the quality of care.

Can differences in length of stay between Dutch university hospitals and other hospitals be explained by patient characteristics? A cross-sectional study.

OBJECTIVES: The indicator unexpectedly long length of stay (UL-LOS) is used to gain insight into quality and safety of care in hospitals. The calculation of UL-LOS takes patients' age, main diagnosis and main procedure into account. University hospitals have relatively more patients with a UL-LOS than other hospitals. Our main research question is whether the high number of patients with a UL-LOS in university hospitals is caused by differences in additional patient characteristics between university hospitals and other hospitals. DESIGN: We performed a cross-sectional study and used administrative data from 1 510 627 clinical admissions in 87 Dutch hospitals. Patients who died in hospital, stayed in hospital for 100 days or longer or whose country of residence was not the Netherlands were excluded from the UL-LOS indicator. We identified which patient groups were treated only in university hospitals or only in other hospitals and which were treated in both hospital types. For these last patient groups, we added supplementary patient characteristics to the current model to determine the effect on the UL-LOS model. RESULTS: Patient groups treated in both hospital types differed in terms of detailed primary diagnosis, socioeconomic status, source of admission, type of admission and amount of Charlson comorbidities. Nevertheless, when adding these characteristics to the current model, university hospitals still have a significantly higher mean UL-LOS score compared with other hospitals (p<0.001). CONCLUSIONS: The difference in UL-LOS scores between both hospital types remains after adding patient characteristics in which both hospital types differ. We conclude that the high UL-LOS scores in university hospitals are not caused by the investigated additional patient characteristics that differ between university and other hospitals. Patients might stay relatively longer in university hospitals due to differences in work processes because of their education and research tasks or financing differences of both hospital types.

Re-admission patterns in England and the Netherlands: a comparison based on administrative data of all hospitals.

BACKGROUND: Examining variation in patterns of re-admissions between countries can be valuable for mutual learning in order to reduce unnecessary re-admissions. The aim of this study was to compare re-admission rates and reasons for re-admissions between England and the Netherlands. METHODS: We used data from 85 Dutch hospitals (1 355 947 admissions) and 451 English hospitals (5 260 227 admissions) in 2014 (96% of all Dutch hospitals and 100% of all English NHS hospitals). Re-admission data from England and the Netherlands were compared for all hospital patients and for specific diagnosis groups: pneumonia, urinary tract infection, chronic obstructive pulmonary disease, coronary atherosclerosis, biliary tract disease, hip fracture and acute myocardial infarction. Re-admissions were categorized using a classification system developed on administrative data. The classification distinguishes between potentially preventable re-admissions and other reasons for re-admission. RESULTS: England had a higher 30-day re-admission rate (adjusted for age and gender) compared to the Netherlands: 11.17% (95% CI 11.14-11.20%) vs. 9.83% (95% CI 9.77-9.88%). The main differences appeared to be in re-admissions for the elderly (England 17.2% vs. the Netherlands 10.0%) and in emergency re-admissions (England 85.3% of all 30-day re-admissions vs. the Netherlands 66.8%). In the Netherlands, however, more emergency re-admissions were classified as potentially preventable compared to England (33.8% vs. 28.8%). CONCLUSIONS: The differences found between England and the Netherlands indicate opportunities to reduce unnecessary re-admissions. For England this concerns more expanded palliative care, integrated social care and reduction of waiting times. In the Netherlands, the use of treatment plans for daily life could be increased.

To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database.

BACKGROUND: It is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmissions on the hospital level after adjusting for case-mix factors. METHODS: We performed multilevel logistic regression analyses with a random intercept for the factor 'hospital' to estimate the variance on the hospital level after adjustment for case-mix variables. We used administrative data from 53 Dutch hospitals from 2010 to 2012 (58% of all Dutch hospitals; 2,577,053 admissions). We calculated models for the top ten diagnosis groups with the highest number of readmissions after an index admission for a surgical procedure. We calculated intraclass correlation coefficients (ICC) per diagnosis group in order to explore the variation in readmissions between hospitals. Furthermore, we determined C-statistics for the models with and without a random effect on the hospital level to determine the discriminative ability. RESULTS: The ICCs on the hospital level ranged from 0.48 to 2.70% per diagnosis group. The C-statistics of the models with a random effect on the hospital level ranged from 0.58 to 0.65 for the different diagnosis groups. The C-statistics of the models that included the hospital level were higher compared to the models without this level. CONCLUSIONS: For some diagnosis groups, a small part of the explained variation in readmissions was found on the hospital level, after adjusting for case-mix variables. However, the C-statistics of the prediction models are moderate, so the discriminative ability is limited. Readmission indicators might be useful for identifying areas for improving quality within hospitals on the level of diagnosis or specialty.

How to identify potentially preventable readmissions by classifying them using a national administrative database.

IMPORTANCE: Hospital readmissions are being used increasingly as an indicator of quality of care. However, it remains difficult to identify potentially preventable readmissions. OBJECTIVES: To evaluate the identification of potentially preventable hospital readmissions by using a classification of readmissions based on administrative data. DESIGN AND SETTING: We classified a random sample of 455 readmissions to a Dutch university hospital in 2014 using administrative data. We compared these results to a classification based on reviewing the medical records of these readmissions to evaluate the accuracy of classification by administrative data. MAIN OUTCOME MEASURES: Frequencies of categories of readmissions based on reviewing records versus those based on administrative data. Cohen's kappa for the agreement between both methods. The sensitivity and specificity of the identification of potentially preventable readmissions with classification by administrative data. RESULTS: Reviewing the medical records of acute readmissions resulted in 28.5% of the records being classified as potentially preventable. With administrative data this was 44.1%. There was slight agreement between both methods: ƙ 0.08 (95% CI: 0.02-0.15, P < 0.05). The sensitivity of the classification of potentially preventable readmissions by administrative data was 63.1% and the specificity was 63.5%. CONCLUSIONS: This explorative study demonstrated differences between categorizing readmissions based on reviewing records compared to using administrative data. Therefore, this tool can only be used in practice with great caution. It is not suitable for penalizing hospitals based on their number of potentially preventable readmissions. However, hospitals might use this classification as a screening tool to identify potentially preventable readmissions more efficiently.

Trends in hospital and intensive care admissions in the Netherlands attributable to the very elderly in an ageing population.

INTRODUCTION: The Dutch population is ageing and it is unknown how this is affecting trends in the percentage of hospital and intensive care unit (ICU) admissions attributable to patients aged 80 years or older, the very elderly. METHODS: We present data on the percentage of the very elderly in the general population and the percentage of hospital admissions attributable to the very elderly. We subsequently performed a longitudinal cross-sectional study on ICU admissions from hospitals participating in the National Intensive Care Evaluation registry for the period 2005 to 2014. We modeled the percentage of adult ICU admissions and treatment days attributable to the very elderly separately for ICU admissions following cardiac surgery and other reasons. RESULTS: The percentage of Dutch adults aged 80 years and older, increased from 4.5 % in 2005 to 5.4 % in 2014 (p-value < 0.0001) and with this ageing of the population, the percentage of hospital admissions attributable to very elderly increased from 9.0 % in 2005 to 10.6 % in 2014 (p-value < 0.0001). The percentage of ICU admissions following cardiac surgery attributable to the very elderly increased from 6.7 % in 2005 to 11.0 % in 2014 in nine hospitals (p-value < 0.0001), while the percentage of treatment days attributable to this group rose from 8.6 % in 2005 to 11.7 % in 2014 (p-value = 0.0157). In contrast, the percentage of very elderly patients admitted to the ICU for other reasons than following cardiac surgery remained stable at 13.8 % between 2005 and 2014 in 33 hospitals (p-value = 0.1315). The number of treatment days attributable to the very elderly rose from 11,810 in 2005 to 15,234 in 2014 (p-value = 0.0002), but the percentage of ICU treatment days attributable to this group remained stable at 12.0 % (p-value = 0.1429). CONCLUSIONS: As in many European countries the Dutch population is ageing and the percentage of hospital admissions attributable to the very elderly rose between 2005 and 2014. However, the percentage of ICU admissions and treatment days attributable to very elderly remained stable. The percentage of ICU admissions following cardiac surgery attributable to this group increased between 2005 and 2014.

Unexpectedly long hospital stays as an indicator of risk of unsafe care: an exploratory study.

OBJECTIVES: We developed an outcome indicator based on the finding that complications often prolong the patient's hospital stay. A higher percentage of patients with an unexpectedly long length of stay (UL-LOS) compared to the national average may indicate shortcomings in patient safety. We explored the utility of the UL-LOS indicator. SETTING: We used data of 61 Dutch hospitals. In total these hospitals had 1 400 000 clinical discharges in 2011. PARTICIPANTS: The indicator is based on the percentage of patients with a prolonged length of stay of more than 50% of the expected length of stay and calculated among survivors. INTERVENTIONS: No interventions were made. OUTCOME MEASURES: The outcome measures were the variability of the indicator across hospitals, the stability over time, the correlation between the UL-LOS and standardised mortality and the influence on the indicator of hospitals that did have problems discharging their patients to other health services such as nursing homes. RESULTS: In order to compare hospitals properly the expected length of stay was computed based on comparison with benchmark populations. The standardisation was based on patients' age, primary diagnosis and main procedure. The UL-LOS indicator showed considerable variability between the Dutch hospitals: from 8.6% to 20.1% in 2011. The outcomes had relatively small CIs since they were based on large numbers of patients. The stability of the indicator over time was quite high. The indicator had a significant positive correlation with the standardised mortality (r=0.44 (p<0.001)), and no significant correlation with the percentage of patients that was discharged to other facilities than other hospitals and home (r=-0.15 (p>0.05)). CONCLUSIONS: The UL-LOS indicator is a useful addition to other patient safety indicators by revealing variation between hospitals and areas of possible patient safety improvement.

A pilot study on record reviewing with a priori patient selection.

OBJECTIVES: To investigate whether a priori selection of patient records using unexpectedly long length of stay (UL-LOS) leads to detection of more records with adverse events (AEs) compared to non-UL-LOS. DESIGN: To investigate the opportunities of the UL-LOS, we looked for AEs in all records of patients with colorectal cancer. Within this group, we compared the number of AEs found in records of patients with a UL-LOS with the number found in records of patients who did not have a UL-LOS. SETTING: Our study was done at a general hospital in The Netherlands. The hospital is medium sized with approximately 30 000 admissions on an annual basis. The hospital has two major locations in different cities where both primary and secondary care is provided. PARTICIPANTS: The patient records of 191 patients with colorectal cancer were reviewed. PRIMARY AND SECONDARY OUTCOME MEASURES: Number of triggers and adverse events were the primary outcome measures. RESULTS: In the records of patients with colorectal cancer who had a UL-LOS, 51% of the records contained one or more AEs compared with 9% in the reference group of non-UL-LOS patients. By reviewing only the UL-LOS group with at least one trigger, we found in 84% (43 out of 51) of these records at least one adverse event. CONCLUSIONS: A priori selection of patient records using the UL-LOS indicator appears to be a powerful selection method which could be an effective way for healthcare professionals to identify opportunities to improve patient safety in their day-to-day work.

The dark side of ambiguous discrimination: how state self-esteem moderates emotional and behavioural responses to ambiguous and unambiguous discrimination.

Two experiments examine how experimentally induced differences in state self-esteem moderate emotional and behavioural responses to ambiguous and unambiguous discrimination. Study 1 (N=108) showed that participants who were exposed to ambiguous discrimination report more negative self-directed emotions when they have low compared to high self-esteem. These differences did not emerge when participants were exposed to unambiguous discrimination. Study 2 (N=118) additionally revealed that self-esteem moderated the effect of ambiguous discrimination on self-concern, task performance, and self-stereotyping. Results show that ambiguous discrimination caused participants with low self-esteem to report more negative self-directed emotions, more self-concern, an inferior task performance, and more self-stereotyping, compared to participants in the high self-esteem condition. Emotional and behavioural responses to unambiguous discrimination did not depend on the induced level of self-esteem in these studies.

Patient satisfaction revisited: a multilevel approach.

Patient satisfaction surveys are increasingly used for benchmarking purposes. In the Netherlands, the results of these surveys are reported at the univariate level without taking case mix factors into account. The first objective of the present study was to determine whether differences in patient satisfaction are attributed to the hospital, department or patient characteristics. Our second aim was to investigate which case mix variables could be taken into account when satisfaction surveys are carried out for benchmarking purposes. Patients who either were discharged from eight academic and fourteen general Dutch hospitals or visited the outpatient departments of the same hospitals in 2005 participated in cross-sectional satisfaction surveys. Satisfaction was measured on six dimensions of care and one general dimension. We used multilevel analysis to estimate the proportion of variance in satisfaction scores determined by the hospital and department levels by calculating intra-class correlation coefficients (ICCs). Hospital size, hospital type, population density and response rate are four case mix variables we investigated at the hospital level. We also measured the effects of patient characteristics (gender, age, education, health status, and mother language) on satisfaction. We found ICCs on hospital and department levels ranging from 0% to 4% for all dimensions. This means that only a minor part of the variance in patient satisfaction scores is attributed to the hospital and department levels. Although all patient characteristics had some statistically significant influence on patient satisfaction, age, health status and education appeared to be the most important determinants of patient satisfaction and could be considered for case mix correction. Gender, mother language, hospital type, hospital size, population density and response rate seemed to be less important determinants. The explained variance of the patient and hospital characteristics ranged from 3% to 5% for the different dimensions. Our conclusions are, first, that a substantial part of the variance is on the patient level, while only a minor part of the variance is at the hospital and department levels. Second, patient satisfaction outcomes in the Netherlands can be corrected by the case mix variables age, health status and education.