International Journal for Quality in Health Care 15:57-066 (2003)
© 2003 International Society for Quality in Health Care
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Predictors of inappropriate hospital stay: a clinical case study
1Department of Clinical Epidemiology and Medical Technology Assessment
3Quality Council, University Hospital Maastricht
6Transmural and Diagnostic Center, University Hospital Maastricht, the Netherlands
5Department of Internal Medicine, University of Maastricht
2University of Maastricht
4Dutch Institute for Healthcare Improvement (CBO), Utrecht
Objective. To assess the reasons for inappropriate patient stay (IPS) and to identify possible predictors of this inappropriate stay.
Design. The reasons for IPS were analyzed in a cross-sectional survey at two surgical, one gynecologic and one obstetric ward, using a sample of 610 days of hospital stay by means of the Dutch Appropriateness Evaluation Protocol.
Setting. The study was conducted at the University Hospital Maastricht, a 715-bed hospital with a regional and teaching function, located in the southern part of The Netherlands.
Results. Results indicate that >20% of the hospital stay was inappropriate. The reasons for IPS were statistically significantly related to the patients’ age, the availability of home care and medical specialty. In a predictive model, only the specialty proved to be a predictor of IPS. Most of the IPS occurred during the first days of hospital stay and the days before the patient’s discharge.
Conclusion. A substantial proportion of hospital stay was found to be inappropriate, due to hospital procedures and the inability to refer patients to other care facilities or care providers. Analysis of the causes of IPS provided useful data for improvement actions. Efficient use of hospital resources should be promoted by reducing the delay in interventions and discharge procedures.
Keywords: appropriateness evaluation protocol, Dutch Appropriateness Evaluation Protocol, inappropriate patient stay
Hospital services are the most expensive component of modern health care systems. The total costs of the Dutch health care system over the year 2000 were estimated to be almost $$$33 billion, being 8.2% of the Dutch Gross National Product. Intramural care (including hospital services) accounted for almost half of these costs ($$$15.4 billion, $$$14.8 billion US$ at conversion rate of 12 August 2002) [1]. In order to contain these costs, the Dutch government aims to improve the efficiency of the health care services by reducing the costs of these services and by using the capacity of existing health care facilities as optimally as possible. But as a result of various cost-containing measures, waiting lists for in-patient care have risen and are still rising [1]. Thus, the remaining hospital beds have to be used as efficiently as possible. One way to achieve this is to avoid inappropriate hospital stay or at least to keep it to a minimum. Reducing this inappropriate stay would improve the productivity of the hospital and reduce the waiting lists. Appropriate stay is considered to be: up to standards, effective, efficient and tailored to the patients’ actual needs [2]. In other words inappropriate patient stay (IPS) is neither effective (it serves no clinical purpose) nor efficient (resources cannot be used optimally).
Previous research
Previous studies [3–8] at the University Hospital Maastricht assessed ways to measure the inappropriate hospital stay in the Dutch health care setting, resulting in the Dutch Appropriateness Evaluation Protocol (DAEP) [8]. The DAEP is based on the US Appropriateness Evaluation Protocol (US-AEP) [9–12]. The US-AEP is a widely used tool to assess the appropriateness of the patients’ hospital admission or stay. The DAEP consists of 19 diagnosis-independent and care-related criteria to assess the appropriateness of a patient’s stay. Our previous studies showed that >20% of the hospital stay was inappropriate and that 45.1% of this inappropriate stay was due to (internal) hospital procedures, such as delay in discharge or delay in therapy and diagnostics. Specific causes of these delays, however, have not been assessed. Other international studies [13–24] have also shown that a significant proportion of hospital stay is inappropriate. The DAEP allows us to assess the appropriateness of the patients’ stay, but the causes of IPS are not entirely clear.
Objectives
The objectives of the present follow-up study were:
- to classify the inappropriate stay whether or not it was the result of hospital procedures;
- to assess the average delay in hospital procedures and the causes of these delays;
- to determine possible predictors of these delays;
- to provide possible solutions to these problems.
The present study assessed the appropriateness of the setting, not the appropriateness of care [25]. Neither the appropriateness of the patient’s admission nor that of professional services was assessed in this study.
Materials and methods
Setting
This study was conducted at the University Hospital Maastricht. This 715-bed teaching hospital is located in the southeastern part of The Netherlands. In 1999 the University Hospital Maastricht admitted 22 000 patients and generated 
200 000 days of stay with an average length of stay of 8.9 days (nationwide 9.2 days). The occupancy rate of the University Hospital Maastricht in 1999 was 79.8%.
Assessment of inappropriate stay
We used the DAEP to assess the appropriateness of stay. The DAEP is a criteria-based decision support tool for determining the medical necessity of hospital admissions and days of care. It consists of sets of generic (diagnosis-independent) and explicit (care-related) criteria. The DAEP consists of 19 criteria for appropriate stay, related to medical and nursing services and a list of reasons for IPS. If none of these criteria can be met the day of stay is considered to be ‘inappropriate’ in an acute care hospital and a list of reasons has to be filled out, indicating why the patient is nevertheless staying in the hospital. For our study, appropriate hospital stay was defined as in-patient stay that requires continuous and active medical, nursing or paramedical treatment, which under existing legislation cannot be provided through extramural care, daycare or outpatient care [8]. The validity and reliability of the DAEP were assessed in previous studies [7,8].
A flow-chart was designed to facilitate an unambiguous classification of the reasons for IPS into the six main categories [8] (see Figure 1). Three of these categories, delay in therapy or diagnostics, delay in receiving test results and delay in discharge procedures, can be controlled by the hospital. The hospital management itself, however, has no or little control over the other three categories: lack of available alternative care facilities, lack of available primary care and difficulties in the patients’ situation at home. As this study focused only on the ‘controllable’ inappropriate stay, an Ishikawa diagram was used for an in-depth analysis of these reasons (see Figure 2).
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Study sampling and data collection
For this study two general surgery wards (providing a total of 81 beds for general, vascular and abdominal surgery), one obstetric ward (10 beds) and one ward providing 10 gynecologic beds and 14 beds for ophthalmologic short-stay patients (total 24 beds) were selected. During a 6-month period, 610 days of hospital stay were assessed by registered nurses (n = 6) of the wards involved. These days of stay were randomly selected by computer program. The stay of each patient, present on the randomly selected days, was reviewed by a concurrent application of the DAEP, assessing the hospital stay during that particular day. Only the selected day of stay was assessed, not the entire hospitalization period. Data reviewed included the date, the patient registration number, gender, age, the availability of professional or informal home care, medical specialty, ward and the individual scores on the DAEP’s criteria or reason list. Also, the general data from the hospital information system (date of admission and discharge, length of stay) were used. To facilitate presentation of the data, the patient’s age was divided into three categories: <30 years, from 30 to 60 years and >60 years of age. The assessment of the index day was done after the medical and nursing information was exchanged and discussed in the team and the treatment plan was specified.
The flow-chart to classify the inappropriate stay was designed with the use of expert opinions from registered nurses on the participating wards. Nineteen versions of this flow-chart were tested before the final version was determined. Agreement in the identification of the reasons for inappropriate stay between nursing staff using the decision tree and nurses not using this decision tree was assessed as a measure for the reliability of the chart. Therefore, 49 days of inappropriate stay were assessed by nurses using the flow-chart (n = 3) and nurses not using this tool (n = 3). To express this agreement, the ‘Kappa’ was calculated, describing the true agreement as a proportion of the potential agreement (after correction for coincidental agreement) [26]. The reliability of classification using a flow-chart was high (Kappa: 86).
Statistical analysis
Descriptive analysis was applied to the data concerning the reasons for inappropriate stay. To avoid bias by patient-related factors and correlated measurements in the same patient (age, gender, availability of home care, medical specialty, ward) in calculating the significance of the data and predicting their value, all assessed days of stay related to the same patient had to be removed.
To avoid bias on gender, the data were also corrected for obstetric and gynecologic patients.
We used the Student’s t-test to test the mean age between groups with and without IPS. The Pearson Chi-squared test was used to test the significance of differences in the rate of appropriateness between groups. The differences in (mean) age between the several types of IPS were analyzed by means of ANOVA. To examine the effect of different factors on IPS, a logistic regression model was estimated. In this model an independent variable found to be statistically significant in a two-way table might turn out to be insignificant when other possible explanatory variables are controlled for. To identify the possible determinants of IPS, all relevant theoretically significant variables were included in the model. The logistic regression model contains the dichotomous variable ‘appropriate stay’ as dependent measure. If the stay was considered appropriate, the variable takes the value of one; otherwise the variable was assigned the value of zero.
All data analyses were performed using the SPSS-PC software package, version 10.0.
Results
In total we assessed 610 days of stay. After removal of repeated measurements of the same patient, a total of 412 days of hospital stay of individual patients were analyzed further. In this sample, 80 days of stay (19.4%) were found to be inappropriate. There was a significant difference in inappropriate stay between the various wards (P < 0.001) (see Table 1). Table 2 shows the rates of IPS related to the patient’s characteristics. Age, medical specialty and availability of home care were significantly related (0.001 < P < 0.023) to the proportion of IPS, whereas the gender showed no relationship with it (P: 0.168). The rate of IPS was higher in elderly patients, in vascular and general surgery and in cases of lack of available home care.
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Table 3 presents the rates of association of the six main reasons for IPS with a number of patient characteristics. Of these reasons, the unavailability of test results (2.5%) and lack of available primary care (1.3%) were not often found to be related to IPS. Therefore, these two reasons could not be analyzed in detail. Approximately 66% of the observed IPS was related to two factors: delay in discharge (27.5%) and delay in therapeutic or diagnostic procedures (38.8%). Patients waiting for discharge were relatively young (mean age 45.0) and mostly female. In >80% of these patients home care was available. The delay in discharge occurred especially after abdominal surgery (22.7%) and on the obstetric and gynecologic wards (54.5%). Limiting the analysis by excluding obstetric and gynecologic patients, still 80% of these patients were female. However, as said before, gender was not statistically significantly related to the four reasons for IPS. The delay in treatment or diagnostic procedures occurred mainly in general (29.0%) and abdominal (25.8%) surgery.
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A more detailed analysis of the delays in therapeutic or diagnostic procedures is shown in Tables 4 and 5. In defining the potential causes of IPS, the Ishikawa diagram for the ‘controllable’ inappropriate stay showed the ‘five P’s’ that are allegedly the sources of IPS: Patient, Personnel (staff), Planning (procedures), Place (setting) and Products (resources) (Figure 2). Table 4 shows that four ‘P’s’ were found as reasons for IPS: Patients, Personnel, Planning and Place. The delay in therapy or diagnostics and discharge is especially related to ‘Planning’, whereas the IPS due to lack of available alternative care facilities is categorized as caused by ‘Place’. This table also presents the average delay in days for the different reasons for IPS. If the patient had to wait for treatment or diagnostics, this delay was on average 2 (2.2) days. As already said, this delay was mainly related to inefficiencies in the application for or planning of these procedures. In 65.3% of the cases the patient had to wait for an operating room to become available (Table 5). This delay occurs in the first days of the patients’ stay. A further 27.3% of this delay was related to diagnostic procedures. The observed IPS occurred at two moments: the first before therapy or diagnostics (mostly right after the admission) and the second before discharge.
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Predictors for inappropriate patient stay
Univariately age, availability of home care and medical specialty were statistically significantly related to IPS. However, in the multiple logistic regression model, only specialty proved to be statistically significantly related to IPS, whereas age, gender and the availability of home care were not. Table 6 shows the eventual logistic regression results with the results for medical specialty controlled for the age and gender of the patient. The chances for IPS in patients in general surgery are 2.87 times higher than those for patients in obstetrics and gynecology (P: 0.018) and 2.54 times higher than those for patients in abdominal surgery (P: 0.022). Next to these, the chances for IPS of patients in vascular surgery are 3.50 times higher than for patients in obstetrics and gynecology (P: 0.011) and 3.10 times higher than for patients in abdominal surgery (P: 0.006).
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Discussion
Although the overall mean length of stay in the University Hospital Maastricht (8.9 days) is lower than the national mean length of stay (9.2 days [1]), the IPS rate in this study is 20%, which is consistent with the findings in our previous studies [3–8]. This study shows that it is possible to identify predictors for IPS. However, it is still unclear how to proceed. How can we actually reduce IPS? In the search for an efficient care system IPS must be avoided. However, the aim of ‘zero IPS’ is a utopian dream. There will always be factors uncontrollable by the organization, e.g. the lack of available alternative care facilities. According to the data in Table 3, 65% of the IPS (38.8 and 27.5%) could be avoided if corrective measures were implemented. This amounts to 12.9% of the hospital stay in this study. By reducing the IPS more patients could be treated and waiting lists would be decreased, although the workload of the hospital staff and specific hospital costs would probably increase.
A substantial part of the IPS is related to delays in diagnostics and delays in operation room procedures. As most surgical admissions are planned in advance, some of the corresponding diagnostic procedures could be carried out on an outpatient base. Subsequently, the operation could be performed on the day of admission and a great amount of this type of IPS would be avoided. The current situation may be related to the fact that professionals often try to control the environment in which the treatment, including the diagnostics, takes place. During the patients’ stay, IPS usually occurs during two phases of the treatment. These are the transfer of the patient from one department to another, such as from the ward to the operating room and vice versa or from the hospital to the home situation (discharge). There are several possibilities for reducing this type of IPS in clinical practice. To avoid waiting for diagnostics, the criteria for the use of certain diagnostics could be tightened and the subsequent compliance increased. This would mean less but more rational use of these facilities. At this moment non-acute diagnostics are not performed outside ordinary business hours. If diagnostics were performed outside business hours capacity would increase by 60%, as at this moment the existing capacity is only in use in one-third (i.e. 8 hours) of a day. To do so would revolutionize the Dutch health care system. But this is still an emotive subject. Alternatively, additional equipment could be installed, although both these options would also mean an increase in costs.
The causes of the delay in treatment have not been specified in this study. Possible causes for this type of IPS might be: insufficient staff or poor operation room (OR) scheduling. If so, this type of IPS could be decreased by appointing more staff or adjustment of the OR scheduling. Certainly the first option would again mean an increase in costs. This argues for a better overall planning of processes and the use of protocols or clinical pathways [27] for improving the flow of different patient groups. This planning would link the several phases of patient care more efficiently.
Another part of IPS occurs before discharge. The literature shows that many hospitals are struggling with this issue [28–31]. If discharge procedures were dealt with at the moment that the patient has no further need of hospital stay, the patient could be discharged and be treated elsewhere even better. To start planning the discharge procedure even before the patient’s admission could be a useful concept for change, whereas ‘minimal-care facilities’ might be appropriate to provide the needed care.
Patient characteristics might be useful in predicting IPS. As the age of the patient increases, the IPS rates show an upward trend. The availability of home care affects IPS rates in a positive way. And the IPS is related to the medical specialty. If a patient matches one or more of these characteristics (>60 years of age, lack of available home care, vascular or general surgery) extra attention should be paid in avoiding IPS. This attention should focus on the planning of and the compliance with therapeutic, diagnostic or discharge procedures. Furthermore, the availability of other care facilities should be improved, although this is hard for the hospital to control by itself. The ‘minimal-care ward’ mentioned before where patients can recover outside a hospital setting could help to address this problem.
Future research
Several practical suggestions to reduce IPS have been mentioned above. Implementing these suggestions without other efforts to address the waste of quality care will certainly increase hospital costs. Thus, cost-benefit analyses should be made in order to select the best solution(s). Although this study gives some insight into causes and predictors of IPS, several questions remain to be answered. How should we proceed in order to reduce IPS in clinical practice? What are the effects of reducing IPS on the workload of hospital staff? Might the reduction of IPS in one part of the organization result in an increase of IPS in another? Why is there a significant difference in IPS between several wards? Can this difference be explained by the type of patient, e.g. obstetric patients who are relatively young and for whom home care is available? These questions have to be answered in future research.
In this study we focused on those factors for IPS that can be controlled by the hospital. But as seen in this study, patients designated for transfer to other care facilities wait on average >20 days for their transfer. Much profit could be gained by reducing this type of IPS (5% of all days of stay, average length 22 days) and this also requires further research.
Conclusions
The rate of IPS measured in this study (19.4%) was consistent with our previous studies. IPS was caused mainly by delays in diagnosis or treatment (38.8%), delays in discharge procedures (27.5%) and lack of available alternative care facilities (21.3%). The age of the patient, the availability of home care and the medical specialty were statistically proven to be predictors for IPS. Elderly patients (>60 years) show higher rates of IPS. There was a significant difference of IPS between the several medical specialties. Finally, the IPS occurs mostly during the first days after admission and the days before discharge.
We would like to thank F. H. M. Nieman, PhD, for checking the statistical analysis and in particular the logistic regression model.
Address reprint requests to Lambert Panis, Department of Clinical Epidemiology and Medical Technology Assessment, University Hospital Maastricht, PO Box 5800, 6202 AZ Maastricht, the Netherlands. E-mail: bpa{at}groupwise.azm.nl 
Accepted for publication October 9, 2002.
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