POTENTIALLY AVOIDABLE READMISSIONS

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Aim	Measuring the quality of hospital discharges. A potentially avoidable readmission may be considered as the consequence of an adverse event or a too early discharge.
Definition of the indicator	Number of potentially avoidable readmissions (cases) divided by the number of eligible discharges
Cases	Readmissions related to a condition of the previous hospitalization and not expected as part of a program of phased care, occurring within 30 days after the previous discharge. Cases are identified by a computerized algorithm. This algorithm excludes foreseen readmissions like readmissions for transplantation, delivery, chemo- or radiotherapy, and other specific surgical procedure. Similarly, readmissions for new conditions, unknown during the preceding stay are not considered as avoidable. Readmissions in a different hospital are identified with the anonymous patient linkage code developed by the Swiss office for statistics (BFS/OFS) and included in the numerator.
Eligible discharges	Hospital discharge of an alive patient, followed 30 days, censoring for foreseen readmission. Healthy newborns, candidates for one day surgery, in hospital death and patients living in other countries are excluded.
Comments	Most studies use unplanned readmissions as a proxy of potentially avoidable readmissions ; this is an inadequate approach: some unplanned readmissions may be nevertheless expected at discharge (readmission for delivery or organ transplant of a patient on a transplantation list); many unplanned readmissions are caused by a new problem unrelated to any condition treated during the previous admission; on the contrary some readmissions for a complication, unforeseen at the time of discharge are planned during the follow-up : example is reopening of a surgical site after discharge.
Risk adjustment	Risk categories : based on SQLape® categories : 1. Delivery& cesarian : F-pA, PAR2, PAR3, VAG2 2. Pregnancy : F-pG, F-pP 3. High risk operation : ANP2, BIL3, CER3, COR3 ,CRU4, GVA2, HEP2, INC2, INC4, INT1, INT2, INT3, LIE2, OES2, OES3, ORB2, ORL1, PUL4, REN3, REN4, STO1, STO4, STO5, THO2, TRA3, URE3, VAS5, VES1, VES5 4. Other operations: not listed in other risk categories 5. High risk malignancy: D-mO, D-mS, D-mI, F-mG, F-mO, H-mH, H-mP, O-mO, R-mP, SAN2, S-mL, S-mM, S-mS, T-mS, Z-mZ, 6. Other malignancy: D-mR, F-mM, N-mC, S-mO, U-mU, Z-mC, Z-mM, Z-mR 7. Agranulocytosis : S-dL 8. Nervosa anorexia : P-zA 9. Cerebral diseases: N-bC, N-dC, N-fC, N-oC, N-sC, N-zC 10. Ischemic affections : C+sC, C-sC, C-sV 11. Acute nephropathy : U-fR 12. Biliary disease: H-dP, H-fB, H-iB, H-oB, H-zH 13. Intestine obstruction : D-iP, D-oD, D-dI, D-oI 14. Generalized sepsis or immunodeficiency: S-dI, S+dI, S-iS, S+iS, S-iZ, S+iZ 15. Metabolic disease and cachexia : S-dS, S-fM 16. Hepatitis and cirrhosis : H-dH, H-iH, H+iH, H-iP 17. Inflammatory disease of muscles : L-iO 18. Chronic renal failure : U-dR, U-dT 19. Acute or chronic respiratory : R-dK, R-dP, R-dR, R-fR, R-iB, R-iP, R-zZ 20. Acute cardiac diseases: C-dH, C-fV, C+fV, C-oC, C-oG, C-oV, C-zA, C-zC, C-zS, C-zV 21. Chronic cardiovascular diseases: C-aC, C-dC, C-dG, C-iC, C-sA, C-zG 22. Blood diseases : S-zA, S-fT, S-zT, S-zZ 23. Drug abuse : P-xS 24. Other psychiatric disease: P-fH, P-tD, P-zZ 25. All other diseases : not listed in other categories 26. Low risk operation : ABD2, ABD3, ABD4, ANU*, APP2, ART2, ART3, AUE*, BIL2, BRA*, BUC*, CAP2, COL*, CON2, COX5, CRU2, CRU3, CRY2, CUT1, DIG*, FAC*, GEN*, MAM*, MAN2, MUS*, NAS*, NER*, OCU2, OSS*, OVA*, PAL2, PAR*, PED2, PED3, PEN*, PRO2, REC2, SAL2, SCA*, SIN2, SPI*, STO3, TES2, THR2, URT3, UTE2, UTE3, VAS2, VES3, VES4, VUL2 27. Low risk disease : B-nA, B-nR, B+nZ, B-nI, B-nZ, D-iD, F-zZ, L-tB, L-tJ, L-tL, L-tT, L-tV, L-tZ, N-oR, N-sT, N-tC, O+iO, O-zV, T+iS, T+tT, U-zV, T-tS, T-tT, T-zT, T-zZ, Y-tO, Z-tZ, Z-zM, Z-zR, Z-zX
	These groups are protected by copyright (© Yves Eggli, 2009). More detailed information is given in the user manual. Adjustment model : Poisson regression based on the groups described above, age, gender, previous admission within six month, and elective admission. Reference population: 3’209’670 discharges dataset of 262 Swiss hospitals having data of adequate quality (from December 1^st 2003 to November 30^th 2007).
Scientific validation	The sensitivity and the specificity of the screening algorithm reached both 95% in a random sample of admission readmission pairs drawn in a university hospital, which was used to develop the screening tool. The predictive value of the screen in a random sample of 18 public Swiss hospitals (nearly a thousand medical records of detected admission readmission pairs) showed that it was close to 80 percent in hospitals whose data fulfil the required criteria of quality. SQLape® groups showed a statistically significant higher discrimination power than other patients classification systems (C statistics = 0.72 in the validation set). Reference for more details : ^-^Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand B. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol 2002; 55:573-587. ^-^Halfon P, Eggli Y, Prêtre-Rohrbach I, Meylan D, Marazzi A, Burnand B. Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Medical Care 2006;44(11);972-981.
Implementation	For more details about required data and material, format, software package implementation, click on the following button: Manuel technique (French) Technisches Handbuch (German)

Last update: July 9, 2009