The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Proportion of hospital readmissions deemed avoidable: a systematic review
Open accessAbstract
BACKGROUND: Readmissions to hospital are increasingly being used as an indicator of quality of care. However, this approach is valid only when we know what proportion of readmissions are avoidable. We conducted a systematic review of studies that measured the proportion of readmissions deemed avoidable. We examined how such readmissions were measured and estimated their prevalence. METHODS: We searched the MEDLINE and EMBASE databases to identify all studies published from 1966 to July 2010 that reviewed hospital readmissions and that specified how many were classified as avoidable. RESULTS: Our search strategy identified 34 studies. Three of the studies used combinations of administrative diagnostic codes to determine whether readmissions were avoidable. Criteria used in the remaining studies were subjective. Most of the studies were conducted at single teaching hospitals, did not consider information from the community or treating physicians, and used only one reviewer to decide whether readmissions were avoidable. The median proportion of readmissions deemed avoidable was 27.1% but varied from 5% to 79%. Three study-level factors (teaching status of hospital, whether all diagnoses or only some were considered, and length of follow-up) were significantly associated with the proportion of admissions deemed to be avoidable and explained some, but not all, of the heterogeneity between the studies. INTERPRETATION: All but three of the studies used subjective criteria to determine whether readmissions were avoidable. Study methods had notable deficits and varied extensively, as did the proportion of readmissions deemed avoidable. The true proportion of hospital readmissions that are potentially avoidable remains unclear.
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Measuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139
Meta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552
Evaluation of diagnostic tests when there is no gold standard. A review of methods
Rutjes, A., Reitsma, J., Coomarasamy, A., Khan, K., & Bossuyt, P. (2007) · Health Technology Assessment · doi:10.3310/hta11500 · Cited by 524
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Donzé, J., Aujesky, D., Williams, D., & Schnipper, J. L. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.3023 · Cited by 553
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Nguyen, O. K., Makam, A. N., Clark, C., Zhang, S., Xie, B., Velasco, F., Amarasingham, R., & Halm, E. A. (2016) · Journal of Hospital Medicine · doi:10.1002/jhm.2568 · Cited by 76
Romagnoli, K. M., Handler, S. M., Ligons, F. M., & Hochheiser, H. (2013) · BMJ Quality & Safety · doi:10.1136/bmjqs-2012-001207 · Cited by 59
Implementation of complex adaptive chronic care: the Patient Journey Record system (PaJR)
Martin, C. M., Vogel, C., Grady, D., Zarabzadeh, A., Hederman, L., Kellett, J., Smith, K., & O’ Shea, B. (2012) · Journal of Evaluation in Clinical Practice · doi:10.1111/j.1365-2753.2012.01880.x · Cited by 48
Metadata
- Authors
- van Walraven, C., Bennett, C., Jennings, A., Austin, P. C., & Forster, A. J.
- Venue
- Canadian Medical Association Journal
- Publisher
- CMA Joule Inc.
- Published
- 2011-03-28
- Volume
- 183
- Issue
- 7
- Pages
- E391-E402
- ISSN
- 0820-3946 (ISSN-L)
- 1488-2329
- OpenAlex
- W2112334854
- Cited by
- 711
- References
- 3
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- gold
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- van Walraven, C., Bennett, C., Jennings, A., Austin, P. C., & Forster, A. J. (2011). Proportion of hospital readmissions deemed avoidable: a systematic review. Canadian Medical Association Journal. https://doi.org/10.1503/cmaj.101860
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