Can the Use of Health Insurance Claim Data Benefit the Risk-Based Supervision of General Practitioner Practices? An Exploratory Study in the Netherlands.

BACKGROUND: The Dutch Health and Youth Care Inspectorate has organized a study investigating whether there are benefits to using claim data in the risk-based supervision of general practitioner (GP) practices. METHODS: We identified and selected signals of risks based on interviews with experts. Next, we selected 3 indicators that could be measured in the claim database. These were: the expected and actual costs of the GP practice; the percentage of reserve antibiotics prescribed; and the percentage of patients undergoing an emergency admission during the weekend. We corrected the scores of the GP practices based on their casemix and identified practices with the most unfavorable scores, 'red flags,' in 2015, or the trend between 2013-2015. Finally, we analysed the data of GP practices already identified as delivering substandard care by the Health and Youth Care Inspectorate and calculated the sensitivity and specificity of using the indicators to identify poor performing GP practices. RESULTS: By combining the 3 indicators, we identified 1 GP practice with 3 red flags and 24 GP practices with 2 red flags. The a priori chance of identifying a GP practice that shows substandard care is 0.3%. Using the indicators, this improved to 1.0%. The sensitivity was 26.7%, the specificity was 92.8%. CONCLUSION: The Dutch Health and Youth Care Inspectorate might use claim data to calculate indicators on costs, the prescribing of reserve antibiotics and emergency admissions during the weekend, when setting priorities for its visits to GP practices. Visiting more GP practices by the Health and Youth Care Inspectorate, and identifying substandard care, is necessary to validate the use of these indicators.

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.

Investigating the Potential Contribution of Patient Rating Sites to Hospital Supervision: Exploratory Results From an Interview Study in the Netherlands.

BACKGROUND: Over the last decades, the patient perspective on health care quality has been unconditionally integrated into quality management. For several years now, patient rating sites have been rapidly gaining attention. These offer a new approach toward hearing the patient's perspective on the quality of health care. OBJECTIVE: The aim of our study was to explore whether and how patient reviews of hospitals, as reported on rating sites, have the potential to contribute to health care inspector's daily supervision of hospital care. METHODS: Given the unexplored nature of the topic, an interview study among hospital inspectors was designed in the Netherlands. We performed 2 rounds of interviews with 10 senior inspectors, addressing their use and their judgment on the relevance of review data from a rating site. RESULTS: All 10 Dutch senior hospital inspectors participated in this research. The inspectors initially showed some reluctance to use the major patient rating site in their daily supervision. This was mainly because of objections such as worries about how representative they are, subjectivity, and doubts about the relevance of patient reviews for supervision. However, confrontation with, and assessment of, negative reviews by the inspectors resulted in 23% of the reviews being deemed relevant for risk identification. Most inspectors were cautiously positive about the contribution of the reviews to their risk identification. CONCLUSIONS: Patient rating sites may be of value to the risk-based supervision of hospital care carried out by the Health Care Inspectorate. Health care inspectors do have several objections against the use of patient rating sites for daily supervision. However, when they are presented with texts of negative reviews from a hospital under their supervision, it appears that most inspectors consider it as an additional source of information to detect poor quality of care. Still, it should always be accompanied and verified by other quality and safety indicators. More research on the value and usability of patient rating sites in daily hospital supervision and other health settings is needed.

Influence of Intensified Supervision by Health Care Inspectorates on Online Patient Ratings of Hospitals: A Multilevel Study of More Than 43,000 Online Ratings.

BACKGROUND: In the Netherlands, hospitals with quality or safety issues are put under intensified supervision by the Dutch Health Care Inspectorate, which involves frequent announced and unannounced site visits and other measures. Patient rating sites are an upcoming phenomenon in health care. Patient reviews might be influenced by perceived quality including the media coverage of health care providers when the health care inspectorate imposes intensified supervision, but no data are available to show how these are related. OBJECTIVE: The aim of this study was to investigate whether and how being under intensified supervision of the health care inspectorate influences online patient ratings of hospitals. METHODS: We performed a longitudinal study using data from the patient rating site Zorgkaart Nederland, from January 1, 2010 to December 31, 2015. We compared data of 7 hospitals under intensified supervision with a control group of 28 hospitals. The dataset contained 43,856 ratings. We performed a multilevel logistic regression analysis to account for clustering of ratings within hospitals. Fixed effects in our analysis were hospital type, time, and the period of intensified supervision. Random effect was the hospital. The outcome variable was the dichotomized rating score. RESULTS: The period of intensified supervision was associated with a low rating score for the hospitals compared with control group hospitals; both 1 year before intensified supervision (odds ratio, OR, 1.67, 95% CI 1.06-2.63) and 1 year after (OR 1.79, 95% CI 1.14-2.81) the differences are significant. For all periods, the odds on a low rating score for hospitals under intensified supervision are higher than for the control group hospitals, corrected for time. Time is also associated with low rating scores, with decreasing ORs over time since 2010. CONCLUSIONS: Hospitals that are confronted with intensified supervision by the health care inspectorate have lower ratings on patient rating sites. The scores are independent of the period: before, during, or just after the intervention by the health care inspectorate. Health care inspectorates might learn from these results because they indicate that the inspectorate identifies the same hospitals as "at risk" as the patients rate as underperformers.

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.

Is the length of stay in hospital correlated with patient satisfaction?

OBJECTIVE: To investigate the correlation between length of stay (LOS) and patient satisfaction on the level of hospital wards. The underlying hypothesis is that good quality of care leads both to shorter LOS and to patients that are more satisfied. DESIGN: We used standardized LOS and standardized patient satisfaction data from seven specialisms: internal medicine, cardiology, pulmonology, neurology, general surgery, orthopaedic surgery and obstetrics and gynaecology in the period 2003-2010. All LOS data were derived from the National Medical Registration and patient satisfaction scores were measured by a questionnaire covering six aspects of care. The LOS data were standardized for the year of discharge, age, primary diagnosis and procedure. Patient satisfaction data were standardized for the year, age, education and health status. SETTING: One hundred and eighty-eight Dutch hospital wards. PARTICIPANTS: The patient satisfaction data were gathered by questionnaires returned by 102 815 patients. INTERVENTION: None. MAIN OUTCOME MEASURE: Pearson correlations and two-tailed significance. between standardized mean LOS and standardized mean patient satisfaction score. RESULTS: We found no correlation between LOS and patient satisfaction in six out of seven specialties. We only found significantly higher patient satisfaction scores in pulmonology for some specific items on hospitals wards with a shorter LOS. These items concerned the reception on the ward, the information provided by nurses on admission, the expertise of the nursing staff, the way information was transferred from one person to another and respect for patients' privacy such as in conversations, and during physical examinations. CONCLUSIONS: We found no evidence that hospital wards with a relatively short mean LOS had higher, or lower, patient satisfaction than hospital wards with a relatively long LOS, with the exception of pulmonology.

Fifty ways to reduce length of stay: an inventory of how hospital staff would reduce the length of stay in their hospital.

PURPOSE AND SETTING: In this study we present a bottom up approach to developing interventions to shorten lengths of stay. Between 1999 and 2009 we applied the approach in 21 Dutch clinical wards in 12 hospitals. We present the complete inventory of all interventions. DESIGN: We organised, on the hospital ward level, structured meetings with the staff in order to first identify barriers to reduce the length of stay and then later to link them to interventions. The key components of the approach were a benchmark with the fifteenth percentile and the use of a matrix, that on one side was arranged along the main phases of the care process--the admission, stay and discharge--and on the other side to the degree to which the length of stay could be shortened by the medical specialists and nurses themselves or by involving others. FINDINGS AND CONCLUSIONS: The matrix consists of a wide variety of interventions that mainly cover what we found in published research. As a bottom up approach is more likely to succeed, we would advise wards that have to reduce length of stay to make the inventory themselves, using appropriate benchmark data, and by using the matrix.

Benchmarking and reducing length of stay in Dutch hospitals.

BACKGROUND: To assess the development of and variation in lengths of stay in Dutch hospitals and to determine the potential reduction in hospital days if all Dutch hospitals would have an average length of stay equal to that of benchmark hospitals. METHODS: The potential reduction was calculated using data obtained from 69 hospitals that participated in the National Medical Registration (LMR). For each hospital, the average length of stay was adjusted for differences in type of admission (clinical or day-care admission) and case mix (age, diagnosis and procedure). We calculated the number of hospital days that theoretically could be saved by (i) counting unnecessary clinical admissions as day cases whenever possible, and (ii) treating all remaining clinical patients with a length of stay equal to the benchmark (15th percentile length of stay hospital). RESULTS: The average (mean) length of stay in Dutch hospitals decreased from 14 days in 1980 to 7 days in 2006. In 2006 more than 80% of all hospitals reached an average length of stay shorter than the 15th percentile hospital in the year 2000. In 2006 the mean length of stay ranged from 5.1 to 8.7 days. If the average length of stay of the 15th percentile hospital in 2006 is identified as the standard that other hospitals can achieve, a 14% reduction of hospital days can be attained. This percentage varied substantially across medical specialties. Extrapolating the potential reduction of hospital days of the 69 hospitals to all 98 Dutch hospitals yielded a total savings of 1.8 million hospital days (2006). The average length of stay in Dutch hospitals if all hospitals were able to treat their patients as the 15th percentile hospital would be 6 days and the number of day cases would increase by 13%. CONCLUSION: Hospitals in the Netherlands vary substantially in case mix adjusted length of stay. Benchmarking--using the method presented--shows the potential for efficiency improvement which can be realized by decreasing inputs (e.g. available beds for inpatient care). Future research should focus on the effect of length of stay reduction programs on outputs such as quality of care.