References
- Nuffield Trust. (2018). Emergency readmissions: Trends in
emergency readmissions to hospital in England . Retrieved from
- Billings, J., Blunt, I., Steventon, A., Georghiou, T., Lewis, G., &
Bardsley, M. (2012). Development of a predictive model to identify
inpatients at risk of re-admission within 30 days of discharge
(PARR-30). BMJ Open, 2 (4). doi:10.1136/bmjopen-2012-001667
- Donze, J., Aujesky, D., Williams, D., & Schnipper, J. L. (2013).
Potentially avoidable 30-day hospital readmissions in medical
patients: derivation and validation of a prediction model. JAMA
Intern Med, 173 (8), 632-638. doi:10.1001/jamainternmed.2013.3023
- Donze, J., Lipsitz, S., Bates, D. W., & Schnipper, J. L. (2013).
Causes and patterns of readmissions in patients with common
comorbidities: retrospective cohort study. BMJ, 347 , f7171.
doi:10.1136/bmj.f7171
- Halfon, P., Eggli, Y., Pretre-Rohrbach, I., Meylan, D., Marazzi, A.,
& Burnand, B. (2006). Validation of the potentially avoidable
hospital readmission rate as a routine indicator of the quality of
hospital care. Medical care, 44 (11), 972-981.
doi:10.1097/01.mlr.0000228002.43688.c2
- Hasan, O., Meltzer, D. O., Shaykevich, S. A., Bell, C. M., Kaboli, P.
J., Auerbach, A. D., . . . Schnipper, J. L. (2010). Hospital
readmission in general medicine patients: a prediction model. J
Gen Intern Med, 25 (3), 211-219. doi:10.1007/s11606-009-1196-1
- Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C.,
Freeman, M., & Kripalani, S. (2011). Risk prediction models for
hospital readmission: a systematic review. JAMA : the journal of
the American Medical Association, 306 (15), 1688-1698.
doi:10.1001/jama.2011.1515
- Leppin, A. L., Gionfriddo, M. R., Kessler, M., Brito, J. P., Mair, F.
S., Gallacher, K., . . . Montori, V. M. (2014). Preventing 30-day
hospital readmissions: a systematic review and meta-analysis of
randomized trials. JAMA Intern Med, 174 (7), 1095-1107.
doi:10.1001/jamainternmed.2014.1608
- Zhou, H., Della, P. R., Roberts, P., Goh, L., & Dhaliwal, S. S.
(2016). Utility of models to predict 28-day or 30-day unplanned
hospital readmissions: an updated systematic review. BMJ Open,
6 (6), e011060. doi:10.1136/bmjopen-2016-011060
- van Walraven, C., Dhalla, I. A., Bell, C., Etchells, E., Stiell, I.
G., Zarnke, K., . . . Forster, A. J. (2010). Derivation and validation
of an index to predict early death or unplanned readmission after
discharge from hospital to the community. CMAJ, 182 (6),
551-557. doi:10.1503/cmaj.091117
- Lee, G. A., Freedman, D., Beddoes, P., Lyness, E., Nixon, I., &
Srivastava, V. (2016). Can we predict Acute Medical readmissions using
the BOOST tool? A retrospective case note review. Acute Med,
15 (3), 119-123.
- Goksuluk D, Korkmaz S, Zararsiz G, Karaağaoğlu AE (2016). easyROC: An
Interactive Web-tool for ROC Curve Analysis Using R Language
Environment. The R Journal, 8(2):213-230.
- van der Bruge F (2017) ’Readmission rates: what can we learn from the
Netherlands?’ Nuffield Trust comment, 11 January 2017.
https://www.nuffieldtrust.org.uk/news-item/readmission-rates-what-can-we-learn-from-the-netherlands
[Accessed June 2019].
- IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version
25.0. Armonk, NY: IBM Corp.
- Miller, W. D., Nguyen, K., Vangala, S., & Dowling, E. (2018).
Clinicians can independently predict 30-day hospital readmissions as
well as the LACE index. BMC Health Serv Res, 18 (1), 32.
doi:10.1186/s12913-018-2833-3
- Cotter, P. E., Bhalla, V. K., Wallis, S. J., & Biram, R. W. (2012).
Predicting readmissions: poor performance of the LACE index in an
older UK population. Age Ageing, 41 (6), 784-789.
doi:10.1093/ageing/afs073
- Cancino, R. S., Manasseh, C., Kwong, L., Mitchell, S. E., Martin, J.,
& Jack, B. W. (2017). Project RED Impacts Patient Experience. J
Patient Exp, 4 (4), 185-190. doi:10.1177/2374373517714454
- Jack, B. W., Chetty, V. K., Anthony, D., Greenwald, J. L., Sanchez, G.
M., Johnson, A. E., . . . Culpepper, L. (2009). A reengineered
hospital discharge program to decrease rehospitalization: a randomized
trial. Annals of internal medicine, 150 (3), 178-187.
- Lee, G. A., & Titchener, K. (2017). The Guy’s and St Thomas’s NHS
Foundation Trust @home service: an overview of a new service.London J Prim Care (Abingdon), 9 (2), 18-22.
doi:10.1080/17571472.2016.1211592
- Roberts, S., Moore, L. C., & Jack, B. (2019). Improving discharge
planning using the re-engineered discharge programme. J Nurs
Manag, 27 (3), 609-615. doi:10.1111/jonm.12719
- Lee, K. H., Low, L. L., Allen, J., Barbier, S., Ng, L. B., Ng, M. J.,
. . . Tan, S. Y. (2015). Transitional care for the highest risk
patients: findings of a randomised control study. Int J Integr
Care, 15 , e039.
- Fluitman, K. S., van Galen, L. S., Merten, H., Rombach, S. M.,
Brabrand, M., Cooksley, T., . . . safer@home, c. (2016). Exploring the
preventable causes of unplanned readmissions using root cause
analysis: Coordination of care is the weakest link. Eur J Intern
Med, 30 , 18-24. doi:10.1016/j.ejim.2015.12.021
- Lee, G., Pickstone, N., Facultad, J., & Titchener, K. (2017). The
future of community nursing: Hospital in the Home. Br J
Community Nurs, 22 (4), 174-180. doi:10.12968/bjcn.2017.22.4.174
- Facultad, J., & Lee, G. A. (2019). Patient satisfaction with a
hospital-in-the-home service. Br J Community Nurs, 24 (4),
179-185. doi:10.12968/bjcn.2019.24.4.179
- Pickstone, N., & Lee, G. A. (2019). Does the @home team reduce local
Emergency Department attendances? The experience of one London
service. International emergency nursing .
doi:10.1016/j.ienj.2019.04.003