Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Is the Readmission Rate a Valid Quality Indicator? A Review of the Evidence

Is the Readmission Rate a Valid Quality Indicator? A Review of the Evidence

  • Claudia Fischer, 
  • Hester F. Lingsma, 
  • Perla J. Marang-van de Mheen, 
  • Dionne S. Kringos, 
  • Niek S. Klazinga, 
  • Ewout W. Steyerberg
PLOS
x

Correction

27 Feb 2015: The PLOS ONE Staff (2015) Correction: Is the Readmission Rate a Valid Quality Indicator? A Review of the Evidence. PLOS ONE 10(2): e0118968. https://doi.org/10.1371/journal.pone.0118968 View correction

Abstract

Introduction

Hospital readmission rates are increasingly used for both quality improvement and cost control. However, the validity of readmission rates as a measure of quality of hospital care is not evident. We aimed to give an overview of the different methodological aspects in the definition and measurement of readmission rates that need to be considered when interpreting readmission rates as a reflection of quality of care.

Methods

We conducted a systematic literature review, using the bibliographic databases Embase, Medline OvidSP, Web-of-Science, Cochrane central and PubMed for the period of January 2001 to May 2013.

Results

The search resulted in 102 included papers. We found that definition of the context in which readmissions are used as a quality indicator is crucial. This context includes the patient group and the specific aspects of care of which the quality is aimed to be assessed. Methodological flaws like unreliable data and insufficient case-mix correction may confound the comparison of readmission rates between hospitals. Another problem occurs when the basic distinction between planned and unplanned readmissions cannot be made. Finally, the multi-faceted nature of quality of care and the correlation between readmissions and other outcomes limit the indicator's validity.

Conclusions

Although readmission rates are a promising quality indicator, several methodological concerns identified in this study need to be addressed, especially when the indicator is intended for accountability or pay for performance. We recommend investing resources in accurate data registration, improved indicator description, and bundling outcome measures to provide a more complete picture of hospital care.

Background

Readmissions cause a high burden to healthcare systems and patients. In the US nearly 20% of Medicare patients are readmitted within 30 days after hospital discharge, associated with an estimated annual cost of $17billion [1]. Readmissions are thought to be related to quality of care, for instance due to postoperative complications. As readmissions vary widely across countries, regions and centers, at least part of them might be avoidable [2][6]. As a consequence, there is a high interest in the readmission rate as an indicator of quality of hospital care. Nevertheless, the actual way this indicator is used in different countries varies widely.

In the US, since 2009 all-cause hospital readmission rates for pneumonia, congestive heart failure, and acute myocardial infarction are publically reported by the Centers for Medicare and Medicaid Services (CMS) [7]. In 2010, the Patient Protection and Affordable Care Act (ACA) introduced the Hospital Readmissions Reduction Program (HRRP), for cost controlling. The program included financial penalties for hospitals having high readmission rates, which will be extended in the coming years [8]. In the UK, readmission rates for specific diseases have been published since 1998 by the National Centre for Health Outcomes Development (NCHOD) to improve quality [9]. It was found that the crude emergency readmission rate had increased from about 8% in 1998 to about 10% in 2006 [9]. In response, the NHS started a new regulation for reimbursement payments in 2011: hospitals receive no reimbursement for emergency readmissions within 30 days of discharge following an elective admission. All other emergency readmissions are reimbursed for only 25% [10]. Since the year 2006 also the Australian government monitors 28-day readmission rates to gain more insight in quality of care [11].

Readmissions are used for different aims, such as cost control or as balancing measure for length of hospital stay or other outcome measures. However, in recent years the focus has primarily been on using it as an easily available measure of the quality of hospital care. Despite its use by policymakers for both quality improvement and cost control, the validity of readmission rates as a measure of quality of hospital care is not evident [12].

However, in order to consider a quality indicator to evaluate care for external purposes it needs to fulfill certain criteria in regards to its reliability and validity. An indicator needs to show relevance, based on its impact on health, its importance for policy and its susceptibility to being influenced by the health care system. The assessment of an indicator needs to be feasible. The data needed to calculate an indicator need to be available, reliable and need to be seen in relation to the burden of reporting. Further, an indicator needs to show scientific soundness [13]. In the case of the readmission rate, this suggests, that readmissions are determined by quality of hospital care, measured by structures and processes. This implies that we are interested in avoidable readmissions.

We aim to give an overview of the different methodological aspects in the definition and measurement of readmission rates that need to be considered when interpreting readmission rates as a reflection of quality of hospital care for external purposes.

Methodology

A systematic computerized literature search was applied in the bibliographic databases Embase, Medline OvidSP, Web-of-Science, Cochrane central and PubMed for the period of 1st January 2001 to 27th May 2013.

With the search terms we aimed to cover quality indicators, quality measurement and readmission. This resulted in the following search strategy, which was adapted for the different bibliographic databases: (‘clinical indicator’/de OR ‘performance measurement system’/exp OR ‘quality control procedures’/de OR ‘quality control’/de OR ‘medical audit’/de OR (((qualit* OR perform* OR safet* OR governance) NEAR/3 (indicat* OR measure* OR assessment* OR control* OR marker* OR metric*)) OR ((clinical OR medical) NEAR/3 (indicator* OR audit*))):ab,ti) AND (‘hospital readmission’/de OR (readmiss* OR rehospital* OR ((re OR return) NEAR/3 (hospital* OR admiss*))):ab,ti).

Studies were included when they were written in English, focused on methodological aspects of readmission rates as a quality indicator for hospital care and full texts were available. We included only studies in major disease fields. Hence, studies focusing on rare diseases, just describing readmission rates over time or using readmissions as outcome measures of interventions were excluded.

Of the references identified in the literature search, titles and abstracts were screened and articles that did not meet the inclusion criteria were excluded. The full text of the remaining potentially eligible articles was reviewed to assess whether they should be included. In case of doubt, the article was discussed among the authors and if necessary, an independent researcher was consulted.

We discuss the methodological aspects that emerged from the literature review that are important for the validity of the readmission rates as an indicator of quality of care.

Results

Our search strategy resulted in 1609 unique references of which titles and abstracts were screened. Based on title and abstract 1189 studies were excluded. Of the remaining 420 articles another 318 were excluded based on full text review (Figure 1). We provide a detailed description of the included studies in the appendix S1, and below we discuss the most important findings.

The context in which readmission rates are used

Prior to using the readmission rate as a measure of quality of care, the context in which the indicator will be used needs to be clearly defined. The rationale for using readmission rates is one aspect of this context. The readmission rate can be used with the primary aim to improve quality of care or rather for reasons of cost control. Next, specification of the clinical processes of which quality of care is assessed is important. Currently, readmission rates are mostly intended to measure quality of care in hospitals. Which implies that the risk of being readmitted is determined by the quality of care delivered during the hospital stay. Yet, literature shows that the conditions after patients' discharge, like the presence of a social network after discharge [14] as well as patients' capacity for managing their own care, influence the likelihood of being readmitted [15], [16]. As a result, hospitals pay attention to improving transitional care [17][24], for instance by patient education to prepare the patient for discharge and to coordinate outpatient follow up [23]. Although such a transition phase may help, the actual post-discharge phase is not really in a hospital's reach anymore. Another example are readmissions in chronic diseases, such as heart failure. These patients are readmitted often because of their comorbidities or because their condition becomes too severe to be treated by the general practitioner, irrespective from the quality of delivered care during their hospital stay [1]. Hence, the quality of care processes captured by readmission rates will often be broader than only in-hospital care [25].

In summary, using readmission rates as a quality measure requires a clear definition of the context, including the rationale of measuring readmissions, the related care processes and the patient groups.

Methodological aspects

Based on the literature we defined several methodological aspects that need to be considered when using the readmission rates as a quality indicator (table 1). These range from fundamental issues like the definition and the effect of competing outcomes, to more practical issues as the possibility to adjust for case-mix and the data reliability. These issues and their effects will be described in the next paragraphs. In the final paragraph we will focus on studies that have specifically tested the validity of readmission rates as a quality indicator.

thumbnail
Table 1. Overview of methodological aspects challenging the validity of readmission rates for benchmarking.

https://doi.org/10.1371/journal.pone.0112282.t001

Indicator definition

Type of readmission.

The definition of readmissions determines the number of readmissions that will be counted (numerator). Planned procedures, such as staged operations, are readmissions that are not determined by quality of care and therefore should not be included in the numerator of the quality indicator [26], [27]. However, this basic distinction is not always made [28]. Hence, capture quality of care related readmissions requires a more specific definition (such as disease specific or emergency readmissions) rather than all-cause readmissions. [29].

A frequently suggested alternative is to count unplanned readmission rates. However, not all unplanned readmissions are a result of poor quality of care as certain complications cannot be avoided. Research has shown that just about 25% of all readmissions are avoidable/preventable. Therefore, ideally, the addition on whether a readmission was avoidable/preventable. Although high variation in overall readmission rate can be observed, this is not the case for the rate of preventable readmissions [2], [29]. Therefore, ideally it is defined, whether a readmission was avoidable/preventable (through proper care delivery) [28], [30] but the judgment on the preventability of a readmission remains subjective [2].

Time window.

The time window after the index admission in which admissions are regarded as readmissions is not consistently defined in the literature. The indicator is generally calculated on basis of readmissions within one month (28 days UK, 31 days USA) regardless of the patient group and condition [28], [31][33]. When choosing a time window, it needs to be considered that a too short time window might miss related readmissions while a large one increases the likelihood of included admissions unrelated to the index admission. For example, in cancer surgery a longer time frame would allow to provide a better overview of actual costs, but it would also include readmissions due to disease progression instead of poor quality of surgery [25]. Clearly, the type of disease the patient was originally treated for is largely influencing the optimal timeframe [32]. Therefore the timeframe for readmissions should be defined per disease.

The effect of competing outcomes

Association with (in-hospital) mortality.

Mortality can be seen as a competing endpoint for readmissions: patients who die will not be readmitted [34], [35]. Therefore patients who died during their hospital stay need to be excluded from the denominator of the readmission rate. Further, hospitals with high 30-day in-hospital mortality rates are not necessarily outliers on the readmission rate as well [36]. Research showed that the link between high readmission rates and mortality rates on hospital level is limited. A “modest” inverse relationship was merely found for heart failure patients, and no relation could be observed for pneumonia and acute myocardial infarction, suggesting that the two indicators measure different aspects of quality of care, which are not strongly related [37]. Therefore different outcome measures, such as the readmission rate and the mortality rate should be brought in relation with each other to gain insight in total hospital performance [36], [37].

Association with length of in hospital stay.

Length of stay is generally decreasing, partly because of efficiency gaining interventions, such as a “just-in-time bed availability system” to increase the bed turnover ratio [38], [39]. Research suggests a link between length of stay and the risk of being readmitted [39][45]. For each day shorter in hospital, a 6% increase in likelihood of readmission was found [40]. Other studies fail to confirm this link [24], [42], [46][50], which might be due to inappropriate adjustment for disease severity [41], [51].

Case mix adjustment

The likelihood that a patient is readmitted is not only affected by quality of care but also by characteristics of the patient. Between-hospital differences in readmission rates may be caused by differences in patient population and therefore readmission rates need to be adjusted for patient characteristics. Although many case-mix adjustment models for readmissions have been developed, there is little consensus on which patient characteristics affect the likelihood of a readmission [27], [52]. Numerous studies, varying in their methodology, geographical characteristics, patient groups and considered variables, find different factors that increase the risk of re-admission. In general, two patient groups seem to be at a high risk of being readmitted: the sickest and poorest patients [2], [20], [51], [53], [54]. However, these factors are often not included as standard variables in case-mix adjustment models, as these models are often based on administrative data and therefore miss detailed clinical information.

In a review that evaluated 30 validated readmission risk prediction models, the authors concluded that most models had poor predictive ability. Almost all studies had c-statistics less than 0.70 [55], possibly due to missing demographic or clinical variables. In a more recent paper, the prediction model reached a higher predictive ability (c-statistic = 0.80) [41]. The authors concluded however that information on demographics, SES, prior utilization and diagnosis still had restricted predictive power [41]. Thus, current research provides limited guidance on which variables should be included in models to adjust for case-mix [41], [55][57].

Data reliability

Missing readmissions to other institutions.

Not all patients are readmitted to the same center where they had their index admission. This is mainly due to the centralization of complex operations in tertiary centers, such as in oncology [25]. When patients unexpectedly develop complications and are readmitted in their local center, they are not captured when only readmissions to the “same hospital” are counted [25]. Missing these patients leads to an underestimation of the true overall readmission rate.

Coding.

The coding practice within a hospital has an essential impact on the validity of readmission rate as a quality indicator [58]. The way a “planned” procedure is defined is crucial for the comparability between hospitals. Ideally a planned readmission is coded in the registration system, for example, with an additional coding element “staged” at the index admission, which would indicate that a follow-up procedure is planned [59].

Urgent readmissions are sometimes considered as a potential proxy for the relatively subjective ‘avoidable readmissions’, as these are coded, for example an admission through the ER. Although low urgent readmission rates showed to be related to low avoidable readmission rates [60] it was shown that the “avoidability” of urgent readmissions also significantly varied by the time from discharge, with early readmissions being more likely to be avoidable [2], [61].

Other causes for biased comparisons between hospitals are the different and unspecific definitions of the type of readmissions assessed, and variation in coding between hospitals. It is essential who is in charge of the coding process. For example administrative staff at the department or hospital level, the treating clinician, or specialized data coders. The variation in coding practice may affect both the readmission rates and the case-mix variables.

Completeness and accuracy of data source.

Electronic health records and health information exchange networks result in more accurate and complete clinical data [62]. The major information source to calculate the readmission rate is administrative data. The advantage of administrative data is that this data is standard available and patient journeys can be followed (within hospitals) [63]. Nevertheless, one major limitation of administrative data is the data inaccuracy [64], which includes the non-exact or incomplete registration of variables that are not relevant for financial concerns [38], [40], [41]. Research showed that to a certain degree administrative data captures similar information compared to medical records, for example on all-cause readmissions [65][68]. However more specific information, like the identification of unplanned readmissions or index procedure related readmissions, showed to be more difficult to extract [66], [69]. An accurate indication of whether a readmission is a part of treatment or due to a cancelled procedure and not a readmission related to a quality of care problem, would enhance the reliability of the data source [64], [65].

The case-mix adjustment variables that have been investigated so far are most often present in administrative databases. However, clinical information such as disease severity is often lacking limiting case-mix adjustment possibilities. The addition of a unique patient identifier across different databases would enhance the possibility for linking data, such as pharmacy data [70] or clinical data. This would largely improve the possibilities for more precise definitions of readmissions and better case-mix adjustment.

Validity of readmission rates as a quality measure

No gold standard exists on how to assess quality of care. Usually different hospital structures and processes and their relation with patient outcomes are measured. The different definitions and proxies used in studies to quantify quality of hospital care influence whether an association between the readmission rate and ‘quality’ is found. For example, we found studies that relate readmissions to hospital volume, but neither can be regarded as a ‘gold standard’ of hospital quality.

Furthermore, the methodological aspects we discussed have a potential influence on the validity of the readmission rates as a quality indicator. These may contribute to the huge variation in conclusions with regard to the validity of readmission rates found in the literature. Different studies in different patient groups and conditions come to the conclusion that lower quality of hospital care is linked to a higher number of readmissions [71][94]. Especially safety-related events (such as postoperative complications) show a relation with readmissions [71], [95]. Rosen and colleagues, who evaluated the correlation between patient safety indicators and readmissions, showed that patients who experienced a patient safety event had an increased risk of readmission [71]. Nevertheless, there are also studies that are inconclusive [96][101], show an inverse relationship [102], [103] or no relationship at all between readmission rate and in-hospital quality of care [98], [104][113]. Analysis of additionally collected data could help to gain insight into outlier hospitals in order to understand driving mechanisms behind high readmission rates [93].

Discussion

This review aimed to summarize the methodological aspects that need to be considered when using the readmission rate as a measure for quality of hospital care for external purposes. We found that the validity of readmission rates as a quality indicator is influenced by the clinical process that is assessed, the indicator definition, the extend of case-mix correction, the effect of competing outcomes and the data reliability. Ignoring or poorly handling these aspects may lead to a biased estimation of the overall readmission rate and a biased comparison of readmission rates between hospitals. As a result of variance in handling these methodological threats, studies on the validity of readmission rates as a quality indicator reach conflicting conclusions. We conclude that given the limitations of readmission rates, they need to be used with caution as a measure of in-hospital quality, even more when used as a tool for a pay for performance scheme.

Some of the discussed factors concerning the readmission rate could in principal be improved by investing resources in accurate data registry and refinements of indicator description. For instance, by using unique patient identifiers to follow patients across centers. That would help to avoid missing readmissions to other institutions. Another option would be to flag planned admissions, which are a part of the treatment plan or due to cancelled procedures, to measure just the quality of care related readmissions.

Other problems, such as the competing endpoint “mortality” are more complex. Patients who died in hospital need to be excluded from the patient group forming the denominator to calculate the readmission rate, as they are not at risk any more to be readmitted. These deaths are captured in the mortality rate. Therefore it is essential to combine outcome in order to provide a more complete picture of the quality of hospital care.

Nevertheless there are theoretical considerations whether a readmission is an indication of bad quality of care. First, a readmission is obviously a more positive outcome than dying. Secondly, if there is for example a chance of six percent that a complication occurs after discharge, it would mean that 100 patients need to be admitted longer, to avoid a complication in six patients [114]. It can be questioned whether by a longer length of hospital stay a complication really can be avoided or only detected at an earlier stage. It is also possible to inform the patient on the risk of developing a complication and decide together how to continue. Furthermore, it needs to be taken into account that readmissions are not always solely determined by quality of hospital care. For certain diseases, like heart failure, the patient's condition is the major driver behind repeated admissions. Patients with low socioeconomic status, elderly and patients with co-morbidities are at high risk of getting readmitted. Therefore case-mix adjustment is essential. Furthermore, the role of facilities outside the hospital and after the 30day time window, like community services, need to be involved in the conceptual framework of readmissions. When aiming to improve quality of care (in and outpatient) increased integration and cooperation between primary and secondary care is needed.

The literature study revealed inconclusive results for some methodological aspects, such as the relation with length of stay, or patient characteristics. The studies we assessed investigated different patient populations and often were based on hospital administrative data. A recent high quality study which was not included in our review investigated surgical readmissions of 479,471 patients from 3004 hospitals. The authors found that higher surgical volume was significantly related with lower composite readmission rates (upper volume quartile 12.7% vs. lower volume quartile 16.8% P<0.0001), and hospitals with the lowest surgical mortality rates had significantly lower readmission rates (lower mortality quartile 13.3% vs. upper mortality quartile 14.2% P<0.0001). But high adherence to surgical process measures was only marginally linked with lower readmission rates (highest quartile vs. lowest quartile, 13.1% vs. 13.6%; P = 0.02), showing that it is still unclear whether low readmission rates are the result of good quality [115].

Furthermore, the risk of getting readmitted is also varying between patient groups and conditions. This supports the idea that outcome measures, like the readmission rate, are not a one size fits all measure. Even if quality of hospital care and the transition phase can potentially be improved, readmissions might be a more applicable measure for certain diseases than for others. For chronic diseases, where planned admissions are part of treatment strategies, readmissions are a less suitable performance measure. At least not until generally used data systems can identify planned admissions with high certainty. It requires clinical knowledge to determine whether (avoidable) readmissions may theoretically represent poor quality of care for specific diseases. Consequently more research is needed to build reliable algorithms to identify avoidable readmissions.

In sum, avoidable readmissions are of high relevance, as they are an adverse event to patients and family and are a high financial burden for healthcare systems. The assessment of the indicator shows difficulties, as the indicator definition is often not explicit enough to identify readmissions related to quality of care (avoidable readmissions). The data used to calculate the indicator is mainly administrative data, which generally includes incomplete and inaccurate data elements and lacks clinical information. Furthermore, in many countries readmissions to other institutions cannot be followed. Readmission rates are influenced also by other factors than quality of hospital care, which include length of stay, (in-hospital) mortality and patient characteristics. The magnitude of influence is partly not know as data is missing to investigate the association (e.g. no post discharge mortality, no clinical characteristics). Further, the scientific evidence of the indicator is limited, as existing research shows conflicting results with regard to the influence of quality of hospital care on the readmission rate (see Appendix S1). This, however, could be related to the prior mentioned methodological aspects that are variously.

Using outcome measures externally to measure and compare hospital performance has consequences. When financial consequences are linked to the outcome, unintended effects could occur. For example, hospitals may try to reduce their readmission to escape the penalty of exceeding the readmission rate by lowering admissions, moving readmissions after the 30-day window, or risk-avoidance in regards to high risk groups. These gaming efforts might reduce the focus on the actual intention: improving quality of hospital care.

A measure used for external purposes should be underpinned with solid evidence for its validity. However, the link between readmissions and the quality of hospital care seems not to be fully explained yet. Still, this does not imply that there is no room for improvement for hospitals in their readmission rate and the indicator could not be useful for internal use. Research should continue to gain insight in the driving mechanisms behind readmissions for the different conditions to improve our understanding how the readmission rate is a part of the quality of hospital care picture. In addition, the readmission rate needs to be brought into relation with other outcome indicators, and hence considered as part of a bundle, to understand all aspects of hospital performance [36].

The methodological aspects we identified need to be considered when using readmission rates as quality indicator. The use of readmission rates for external quality purposes, such as for pay for performance requires strict methodological criteria to avoid confounding. At its current state the rate of readmission does not fulfill the methodological requirements of a reliable and valid indicator. Therefore the indicator should not be used for external purposes. As this is nevertheless currently happening, readmission rates should be interpreted with great caution.

Supporting Information

Appendix S1.

Descriptive information of included studies.

https://doi.org/10.1371/journal.pone.0112282.s002

(EPS)

Author Contributions

Conceived and designed the experiments: CF NK DK HL. Performed the experiments: CF HL. Analyzed the data: CF HL. Contributed reagents/materials/analysis tools: CF HL PM ES DK NK. Wrote the paper: CF HL PM ES DK NK.

References

  1. 1. Jencks SF, Williams MV, Coleman EA (2009) Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 360(14): 1418–28.
  2. 2. Van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ (2011) Proportion of hospital readmissions deemed avoidable: A systematic review. CMAJ. 183(7): E391–E402.
  3. 3. Krumholz HM, Merrill AR, Schone EM, Schreiner GC, Chen J, et al. (2009) Patterns of Hospital Performance in Acute Myocardial Infarction and Heart Failure 30-Day Mortality and Readmission. Circulation-Cardiovascular Quality and Outcomes 2(5): 407–13.
  4. 4. Jensen PH, Webster E, Witt J (2009) Hospital type and patient outcomes: an empirical examination using AMI readmission and mortality records. Health Econ 18(12): 1440–60.
  5. 5. Bernheim SM, Grady JN, Lin ZQ, Wang Y, Wang YF, et al. (2010) National Patterns of Risk-Standardized Mortality and Readmission for Acute Myocardial Infarction and Heart Failure Update on Publicly Reported Outcomes Measures Based on the 2010 Release. Circulation-Cardiovascular Quality and Outcomes 3(5): 459–67.
  6. 6. Schiotz M, Price M, Frolich A, Sogaard J, Kristensen JK, et al. (2011) Something is amiss in Denmark: a comparison of preventable hospitalisations and readmissions for chronic medical conditions in the Danish Healthcare system and Kaiser Permanente. BMC Health Serv Res 11: 347.
  7. 7. Axon RN, Williams MV (2011) Hospital readmission as an accountability measure. JAMA. 2 305(5): 504–5.
  8. 8. Desai AS, Stevenson LW (2012) Rehospitalization for heart failure: predict or prevent? Circulation 24 126(4): 501–6.
  9. 9. Department of Health. Emergency readmission rates (2008) London: Department of Health.
  10. 10. Department of Health. The operating framework. 2010. Available: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/151906/dh_122736.pdf.pdf. Accessed 2013 July 8.
  11. 11. Australian Government. Australian Institute of Health and Welfare. National Healthcare Agreement: PI 23-Unplanned hospital readmission rates, 2013 QS. Available: http://meteor.aihw.gov.au/content/index.phtml/itemId/507456. Accessed 2013 July 11.
  12. 12. Gu Q, Koenig L, Faerberg J, Steinberg CR, Vaz C, et al. (2014) The medicare hospital readmissions reduction program: potential unintended consequences for hospitals serving vulnerable populations. Health Serv Res 49(3): 818–37.
  13. 13. Mattke S, Kelley E, Scherer P, Hurst J, Lapetra MLG, et al. (2006) “Health Care Quality Indicators Project Initial Indicators Report”, OECD Health Working Papers. Paris.
  14. 14. Kangovi S, Grande D (2011) Hospital readmissions - Not just a measure of quality. J Am Med Assoc 306(16): 1796–7.
  15. 15. Spector WD, Mutter R, Owens P, Limcangco R (2012) Thirty-day, all-cause readmissions for elderly patients who have an injury-related inpatient stay. Med Care 50(10): 863–9.
  16. 16. Forster AJ, van Walraven C (2012) The use of quality indicators to promote accountability in health care: the good, the bad, and the ugly. Open Med 6(2): e75–9.
  17. 17. Shepperd S, Lannin NA, Clemson LM, McCluskey A, Cameron ID (2013) Discharge planning from hospital to home. Cochrane Database Syst Rev 1: CD000313.
  18. 18. Lambrinou E, Kalogirou F, Lamnisos D, Sourtzi P (2012) Effectiveness of heart failure management programmes with nurse-led discharge planning in reducing re-admissions: A systematic review and meta-analysis. Int J Nurs Stud 49(5): 610–24.
  19. 19. Wong FKY, Ho MM, Yeung S, Tam SK, Chow SK (2011) Effects of a health-social partnership transitional program on hospital readmission: A randomized controlled trial. Soc Sci Med 73(7): 960–9.
  20. 20. Demir E, Chaussalet T, Adeyemi S, Toffa S (2012) Profiling hospitals based on emergency readmission: A multilevel transition modelling approach. Comput Methods Programs Biomed 108(2): 487–99.
  21. 21. Jha AK, Orav EJ, Epstein AM (2009) Public reporting of discharge planning and rates of readmissions. New Engl J Med 361(27): 2637–45.
  22. 22. Benbassat J, Taragin MI (2013) The effect of clinical interventions on hospital readmissions: a meta-review of published meta-analyses. Isr J Health Policy Res 2(1): 1.
  23. 23. Burke RE, Kripalani S, Vasilevskis EE, Schnipper JL (2013) Moving beyond readmission penalties: Creating an ideal process to improve transitional care. J Hosp Med 8(2): 102–9.
  24. 24. Chan FW, Wong FY, Yam CH, Cheung WL, Wong EL, et al. (2011) Risk factors of hospitalization and readmission of patients with COPD in Hong Kong population: analysis of hospital admission records. BMC Health Serv Res 11: 186.
  25. 25. Rochefort MM, Tomlinson JS (2012) Unexpected Readmissions After Major Cancer Surgery. An Evaluation of Readmissions as a Quality-of-Care Indicator. Surg Oncol Clin North Am 21(3): 397–405.
  26. 26. McCormack R, Michels R, Ramos N, Hutzler L, Slover JD, et al. (2013) Thirty-day readmission rates as a measure of quality: causes of readmission after orthopedic surgeries and accuracy of administrative data. J Healthc Manag 58(1): 64–76 discussion -7.
  27. 27. Brooke BS, De Martino RR, Girotti M, Dimick JB, Goodney PP (2012) Developing strategies for predicting and preventing readmissions in vascular surgery. J Vasc Surg 56(2): 556–62.
  28. 28. Fischer C, Anema HA, Klazinga NS (2012) The validity of indicators for assessing quality of care: a review of the European literature on hospital readmission rate. Eur J Public Health 22(4): 484–91.
  29. 29. Van Walraven C, Jennings A, Forster AJ (2012) A meta-analysis of hospital 30-day avoidable readmission rates. J Eval Clin Pract 18(6): 1211–8.
  30. 30. Courtney EDJ, Ankrett S, McCollum PT (2003) 28-Day emergency surgical re-admission rates as a clinical indicator of performance. Ann R Coll Surg Engl 85(2): 75–8.
  31. 31. Rumball-Smith J, Hider P (2009) The validity of readmission rate as a marker of the quality of hospital care, and a recommendation for its definition. New Zealand Med J. 122(1289).
  32. 32. Demir E, Chaussalet TJ, Xie H, Millard PH (2008) Emergency readmission criterion: a technique for determining the emergency readmission time window. IEEE Trans Inf Technol Biomed 12(5): 644–9.
  33. 33. Maurer PP, Ballmer PE (2004) Hospital readmissions - are they predictable and avoidable? Swiss Medical Weekly 134(41–42): 606–11.
  34. 34. Gheorghiade M, Vaduganathan M, Fonarow GC, Bonow RO (2013) Rehospitalization for heart failure: Problems and perspectives. J Am Coll Cardiol 61(4): 391–403.
  35. 35. Grocott MPW (2010) Monitoring surgical outcomes: How and why? Curr Anaesth Crit Care 21(3): 129–36.
  36. 36. Almoudaris AM, Burns EM, Bottle A, Aylin P, Darzi A, et al. (2013) Single measures of performance do not reflect overall institutional quality in colorectal cancer surgery. Gut 62(3): 423–9.
  37. 37. Krumholz HM, Lin Z, Keenan PS, Chen J, Ross JS, et al. (2013) Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA 309(6): 587–93.
  38. 38. Basu A, Howell R, Gopinath D (2010) Clinical performance indicators: Intolerance for variety? Int J Health Care Qual Assur 23(4): 436–49.
  39. 39. Heggestad T (2002) Do hospital length of stay and staffing ratio affect elderly patients' risk of readmission? - A nation-wide study of Norwegian hospitals. Health Services Research 37(3): 647–65.
  40. 40. Kaboli PJ, Go OT, Hockenberry J, Glasgow JM, Johnson SR, et al. (2012) Associations between reduced hospital length of stay and 30-day readmission rate and mortality: 14-year experience in 129 veterans affairs hospitals. Ann Intern Med 157(12): 837–45.
  41. 41. Shulan M, Gao K, Moore CD (2013) Predicting 30-day all-cause hospital readmissions. Health Care Manag Sci 16(2): 167–75.
  42. 42. Kramer AA, Higgins TL, Zimmerman JE (2013) The association between ICU readmission rate and patient outcomes. Crit Care Med 41(1): 24–33.
  43. 43. Ahmed J, Khan S, Lim M, Chandrasekaran TV, MacFie J (2012) Enhanced recovery after surgery protocols - compliance and variations in practice during routine colorectal surgery. Colorectal Disease 14(9): 1045–51.
  44. 44. Dobrzanska L, Newell R (2006) Readmissions: A primary care examination of reasons for readmission of older people and possible readmission risk factors. J Clin Nurs 15(5): 599–606.
  45. 45. Schneider EB, Hyder O, Brooke BS, Efron J, Cameron JL, et al. (2012) Patient readmission and mortality after colorectal surgery for colon cancer: Impact of length of stay relative to other clinical factors. J Am Coll Surg 214(4): 390–8.
  46. 46. Dunlay SM, Weston SA, Killian JM, Bell MR, Jaffe AS, et al. (2012) Thirty-day rehospitalizations after acute myocardial infarction: A cohort study. Ann Intern Med 157(1): 11–8.
  47. 47. Dallal RM, Trang A (2012) Analysis of perioperative outcomes, length of hospital stay, and readmission rate after gastric bypass. Surg Endosc Interv Tech 26(3): 754–8.
  48. 48. Lau ACW, Yam LYC, Poon E (2001) Hospital re-admission in patients with acute exacerbation of chronic obstructive pulmonary disease. Respiratory Medicine 95(11): 876–84.
  49. 49. Kiran RP, Delaney CP, Senagore AJ, Steel M, Garafalo T, et al. (2004) Outcomes and prediction of hospital readmission after intestinal surgery. J Am Coll Surg 198(6): 877–83.
  50. 50. Kociol RD, Liang L, Hernandez AF, Curtis LH, Heidenreich PA, et al. (2013) Are we targeting the right metric for heart failure? Comparison of hospital 30-day readmission rates and total episode of care inpatient days. Am Heart J
  51. 51. Johnson T, Bardhan J, Odwazny R, Harting B, Skarupski K, et al. (2012) Hospital care may not affect the risk of readmission. Qual Manag Health Care 21(2): 68–73.
  52. 52. Van Walraven C, Wong J, Hawken S, Forster AJ (2012) Comparing methods to calculate hospital-specific rates of early death or urgent readmission. CMAJ 184(15): E810–E7.
  53. 53. Khawaja FJ, Shah ND, Lennon RJ, Slusser JP, Alkatib AA, et al. (2012) Factors associated with 30-day readmission rates after percutaneous coronary intervention. Arch Intern Med 172(2): 112–7.
  54. 54. Goldfield NI, McCullough EC, Hughes JS, Tang AM, Eastman B, et al. (2008) Identifying potentially preventable readmissions. Health Care Financ Rev 30(1): 75–91.
  55. 55. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Kripalani S (2011) Readmission risk modeling: A systematic review. J Gen Intern Med 26: S125.
  56. 56. Correll PK, Xuan W, Williamson M, Sundararajan V, Ringland C, et al. (2007) Reattendance at hospital for asthma in two Australian states, 2000–2003. Respirology 12(2): 220–6.
  57. 57. Giamouzis G, Kalogeropoulos A, Georgiopoulou V, Laskar S, Smith AL, et al. (2011) Hospitalization epidemic in patients with heart failure: Risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail 17(1): 54–75.
  58. 58. Cram P, Ibrahim SA, Lu X, Wolf BR (2012) Impact of alternative coding schemes on incidence rates of key complications after total hip arthroplasty: a risk-adjusted analysis of a national data set. Geriatr orthop surg rehabil 3(1): 17–26.
  59. 59. Hannan EL, Zhong Y, Lahey SJ, Culliford AT, Gold JP, et al. (2011) 30-Day readmissions after coronary artery bypass graft surgery in New York State. JACC Cardiovasc Interventions 4(5): 569–76.
  60. 60. van Walraven C, Austin PC, Forster AJ (2012) Urgent readmission rates can be used to infer differences in avoidable readmission rates between hospitals. Journal of Clinical Epidemiology 65(10): 1124–30.
  61. 61. Bianco A, Mole A, Nobile CGA, Di Giuseppe G, Pileggi C, et al. (2012) Hospital Readmission Prevalence and Analysis of Those Potentially Avoidable in Southern Italy. Plos One 7 (11)..
  62. 62. Ben-Assuli O, Shabtai I, Leshno M (2013) The impact of EHR and HIE on reducing avoidable admissions: controlling main differential diagnoses. BMC Med Inf Decis Mak 13: 49.
  63. 63. Holt PJE, Poloniecki JD, Hofman D, Hinchliffe RJ, Loftus IM, et al. (2010) Re-interventions, Readmissions and Discharge Destination: Modern Metrics for the Assessment of the Quality of Care. Eur J Vasc Endovasc Surg 39(1): 49–54.
  64. 64. Amin BY, Schairer W, Tu TH, Ames CP, Berven S, et al. (2012) Improving benchmarking in spine surgery: Suggested modifications of the UHC algorithm for calculating readmission rates following spine surgery. Neurosurgery 71(2): E575–E6.
  65. 65. Lindenauer PK, Normand SLT, Drye EE, Lin Z, Goodrich K, et al. (2011) Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia. J Hosp Med 6(3): 142–50.
  66. 66. Sellers MM, Merkow RP, Halverson A, Hinami K, Kelz RR, et al. (2013) Validation of New Readmission Data in the American College of Surgeons National Surgical Quality Improvement Program. Journal of the American College of Surgeons 216(3): 420–7.
  67. 67. Krumholz HM, Wang Y, Mattera JA, Wang YF, Han LF, et al. (2006) An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure. Circulation 113(13): 1693–701.
  68. 68. Wallmann R, Llorca J, Gomez-Acebo I, Ortega AC, Roldan FR, et al. (2013) Prediction of 30-day cardiac-related-emergency-readmissions using simple administrative hospital data. Int J Cardiol 164(2): 193–200.
  69. 69. Adeyemo D, Radley S (2007) Unplanned general surgical re-admissions - How many, which patients and why? Ann R Coll Surg Engl 89(4): 363–7.
  70. 70. Parker JP, McCombs JS, Graddy EA (2003) Can pharmacy data improve prediction of hospital outcomes? Comparisons with a diagnosis-based comorbidity measure. Med Care 41(3): 407–19.
  71. 71. Rosen AK, Loveland S, Shin M, Shwartz M, Hanchate A, et al. (2013) Examining the Impact of the AHRQ Patient Safety Indicators (PSIs) on the Veterans Health Administration: The Case of Readmissions. Med Care 51(1): 37–44.
  72. 72. Mokhtar SA, El. Mahalli AA, Al-Mulla S, Al-Hussaini R (2012) Study of the relation between quality of inpatient care and early readmission for diabetic patients at a hospital in the eastern province of Saudi Arabia. East Mediterr Health J 18(5): 474–9.
  73. 73. Kergoat MJ, Latour J, Lebel P, Leclerc BS, Leduc N, et al. (2012) Quality-of-care processes in geriatric assessment units: principles, practice, and outcomes. J Am Med Dir Assoc 13(5): 459–63.
  74. 74. Auerbach AD, Hilton JF, Maselli J, Pekow PS, Rothberg MB (2009) Shop for quality or volume? Volume, quality, and outcomes of coronary artery bypass surgery. Ann Intern Med 150(10): .
  75. 75. Stukel TA, Alter DA, Schull MJ, Ko DT, Li P (2010) Association between hospital cardiac management and outcomes for acute myocardial infarction patients. Med Care 48(2): 157–65.
  76. 76. Chen JY, Ma Q, Chen H, Yermilov I (2012) New bundled world: quality of care and readmission in diabetes patients. J Diabetes Sci Technol 6(3): 563–71.
  77. 77. Weber RS, Lewis CM, Eastman SD, Hanna EY, Akiwumi O, et al. (2010) Quality and performance indicators in an Academic Department of Head and Neck Surgery. Arch Otolaryngol Head Neck Surg 136(12): 1212–8.
  78. 78. Kent TS, Sachs TE, Callery MP, Vollmer Jr CM (2011) Readmission after major pancreatic resection: A necessary evil? J Am Coll Surg 213(4): 515–23.
  79. 79. Barbieri CE, Lee B, Cookson MS, Bingham J, Clark PE, et al. (2007) Association of procedure volume with radical cystectomy outcomes in a nationwide database. Journal of Urology 178(4): 1418–21.
  80. 80. Chung ES, Guo L, Casey DE Jr, Bartone C, Menon S, et al. (2008) Relationship of a quality measure composite to clinical outcomes for patients with heart failure. Am J Med Qual 23(3): 168–75.
  81. 81. Judge A, Chard J, Learmonth I, Dieppe P (2006) The effects of surgical volumes and training centre status on outcomes following total joint replacement: analysis of the Hospital Episode Statistics for England. J Public Health (Oxf) 28(2): 116–24.
  82. 82. Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R (2011) Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Managed Care 17(1): 41–8.
  83. 83. Youn YJ, Yoo BS, Lee JW, Kim JY, Han SW, et al. (2012) Treatment performance measures affect clinical outcomes in patients with acute systolic heart failure: Report from the Korean Heart Failure Registry. Circ J 76(5): 1151–8.
  84. 84. VanSuch M, Naessens JM, Stroebel RJ, Huddleston JM, Williams AR (2006) Effect of discharge instructions on readmission of hospitalised patients with heart failure: Do all of the Joint Commission on Accreditation of Healthcare Organizations heart failure core measures reflect better care? Qual Saf Health Care 15(6): 414–7.
  85. 85. Showalter JW, Rafferty CM, Swallow NA, DaSilva KO, Chuang CH (2011) Effect of Standardized Electronic Discharge Instructions on Post-Discharge Hospital Utilization. Journal of General Internal Medicine 26(7): 718–23.
  86. 86. Polanczyk CA, Newton C, Dec GW, Di Salvo TG (2001) Quality of care and hospital readmission in congestive heart failure: An explicit review process. J Card Fail 4: 289–98.
  87. 87. Luthi JC, Lund MJ, Sampietro-Colom L, Kleinbaum DG, Ballard DJ, et al. (2003) Readmissions and the quality of care in patients hospitalized with heart failure. International Journal for Quality in Health Care 15(5): 413–21.
  88. 88. Joynt KE, Orav EJ, Jha AK (2011) The association between hospital volume and processes, outcomes, and costs of care for congestive heart failure. Ann Intern Med 154(2): 94–102.
  89. 89. Horwitz LI, Wang YF, Desai MM, Curry LA, Bradley EH, et al. (2012) Correlations among risk-standardized mortality rates and among risk-standardized readmission rates within hospitals. Journal of Hospital Medicine 7(9): 690–6.
  90. 90. Hernandez AF, Hammill BG, Peterson ED, Yancy CW, Schulman KA, et al. (2010) Relationships between emerging measures of heart failure processes of care and clinical outcomes. Am Heart J 159(3): 406–13.
  91. 91. Bottle A, Aylin P (2009) Application of AHRQ patient safety indicators to English hospital data. Qual Saf Health Care 18(4): 303–8.
  92. 92. Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, et al. (2002) Measuring potentially avoidable hospital readmissions. Journal of Clinical Epidemiology 55(6): 573–87.
  93. 93. Halfon P, Eggli Y, Pretre-Rohrbach I, Meylan D, Marazzi A, et al. (2006) Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care 44(11): 972–81.
  94. 94. Maeda JLK (2010) Evidence-Based Heart Failure Performance Measures and Clinical Outcomes: A Systematic Review. Journal of Cardiac Failure 16(5): 411–8.
  95. 95. Encinosa WE, Hellinger FJ (2008) The impact of medical errors on ninety-day costs and outcomes: An examination of surgical patients. Health Serv Res 43(6): 2067–85.
  96. 96. Shahian DM, Meyer GS, Mort E, Atamian S, Liu X, et al. (2012) Association of National Hospital Quality Measure adherence with long-term mortality and readmissions. BMJ Qual Saf 21(4): 325–36.
  97. 97. Fonarow GC, Abraham WT, Albert NM, Stough WG, Gheorghiade M, et al. (2007) Association between performance measures and clinical outcomes for patients hospitalized with heart failure. J Am Med Assoc 297(1): 61–70.
  98. 98. Stefan MS, Pekow PS, Nsa W, Priya A, Miller LE, et al. (2012) Hospital Performance Measures and 30-day Readmission Rates. J Gen Intern Med. 1–9.
  99. 99. Nathwani D, Williams F, Winter J, Winter J, Ogston S, et al. (2002) Use of indicators to evaluate the quality of community-acquired pneumonia management. Clin Infect Dis 34(3): 318–23.
  100. 100. Morse RB, Hall M, Fieldston ES, McGwire G, Anspacher M, et al. (2011) Hospital-level compliance with asthma care quality measures at children's hospitals and subsequent asthma-related outcomes. J Am Med Assoc 306(13): 1454–60.
  101. 101. Schopfer DW, Whooley MA, Stamos TD (2012) Hospital compliance with performance measures and 30-day outcomes in patients with heart failure. Am Heart J 164(1): 80–6.
  102. 102. Mansi IA, Shi R, Khan M, Huang J, Carden D (2010) Effect of compliance with quality performance measures for heart failure on clinical outcomes in high-risk patients. J Natl Med Assoc 102(10): 898–905.
  103. 103. Marcin JP, Romano PS (2004) Impact of between-hospital volume and within-hospital volume on mortality and readmission rates for trauma patients in California. Crit Care Med 32(7): 1477–83.
  104. 104. Patterson ME, Hernandez AF, Hammill BG, Fonarow GC, Peterson ED, et al. (2010) Process of care performance measures and long-term outcomes in patients hospitalized with heart failure. Med Care 48(3): 210–6.
  105. 105. Roccaforte R, Demers C, Baldassarre F, Teo K, Yusuf S (2005) Effectiveness of comprehensive disease management programmes in improving clinical outcomes in heart failure patients. A meta-analysis. Eur J Heart Fail 7(7): 1133–44.
  106. 106. Gawlas I, Sethi M, Winner M, Epelboym I, Lee JL, et al. (2012) Readmission After Pancreatic Resection is not an Appropriate Measure of Quality. Ann Surg Oncol. 1–7.
  107. 107. Jimenez-Puente A, Garcia-Alegria J, Gomez-Aracena J, Hidalgo-Rojas L, Lorenzo-Nogueiras L, et al. (2004) Readmission rate as an indicator of hospital performance: The case of Spain. Int J Technol Assess Health Care 20(3): 385–91.
  108. 108. Ricciardi MJ, Selzer F, Marroquin OC, Holper EM, Venkitachalam L, et al. (2012) Incidence and predictors of 30-day hospital readmission rate following percutaneous coronary intervention (from the national heart, lung, and blood institute dynamic registry). Am J Cardiol 110(10): 1389–96.
  109. 109. Auerbach AD, Maselli J, Carter J, Pekow PS, Lindenauer PK (2010) The relationship between case volume, care quality, and outcomes of complex cancer surgery. J Am Coll Surg 211(5): 601–8.
  110. 110. Rumball-Smith J, Blakely T, Sarfati D, Hider P (2013) The mismeasurement of quality by readmission rate: How blunt is too blunt an instrument?: A quantitative bias analysis. Med Care 51(5): 418–24.
  111. 111. Luthi JC, Burnand B, McClellan WM, Pitts SR, Flanders WD (2004) Is readmission to hospital an indicator of poor process of care for patients with heart failure? Qual Saf Health Care 13(1): 46–51.
  112. 112. Mayer EK, Bottle A, Aylin P, Darzi AW, Athanasiou T, et al. (2011) The volume-outcome relationship for radical cystectomy in England: An analysis of outcomes other than mortality. BJU Int. 108(8 B):E258–E65.
  113. 113. McConnell KJ, Lindrooth RC, Wholey DR, Maddox TM, Bloom N (2013) Management practices and the quality of care in cardiac units. JAMA Intern Med 173(8): 684–92.
  114. 114. Marang-van de Mheen PJ, Dijs-Elsinga J, Otten W, Versluijs M, Smeets HJ, et al. (2010) The importance of experienced adverse outcomes on patients' future choice of a hospital for surgery. Qual Saf Health Care 19(6): e16.
  115. 115. Tsai TC, Joynt KE, Orav EJ, Gawande AA, Jha AK (2013) Variation in surgical-readmission rates and quality of hospital care. N Engl J Med 369(12): 1134–42.