eMeasure Name | All Cause Readmission Index (risk adjusted) | eMeasure Id | F75627CF-933F-4914-ACD8-D1AB60D90BD2 |
Version number | 1 | eMeasure Set Id | C3B72B76-CBD7-4958-8DE7-1590C203F8FC |
Available Date | No information | Measurement Period | January 1, 20xx through December 31, 20xx |
Measure Steward | Iowa Foundation for Medical Care | ||
Endorsed by | National Quality Forum | ||
Description | 30-day Readmission Index for Non-Maternity and Non-Pediatric Discharges | ||
Copyright | |||
Measure scoring | Proportion | ||
Measure type | OUTCOME | ||
Stratification | None |
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Risk Adjustment | None |
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Data Aggregation | |||
Rationale | As an outcome measure for hospital's quality of care during the hospital stay, the discharge process as well as post-discharge care coordination process, readmission has been examined in many different health condition areas such as CHF, Asthma, cardiac or other surgical procedures 1-3. However, the magnitude and impact of 30-day all-cause readmission has not been examined to a great extent. In a study that examined the proportion of Medicare expenditures attributable to readmissions to the hospital, GF Anderson and EP Steinberg 4 found that 22% of hospitalizations of Medicare beneficiaries were followed by a readmission within 60 days of discharge, which accounts for 24 % of inpatient expenditures ($2.5 billion per year) on such readmissions between 1974 and 1977. In the "Report to the Congress: Promoting Greater Efficiency in Medicare - June 2007" 5, Medicare Payment Advisory Commission (MedPAC) reported that readmission rate within 7 days of discharge is 6.2% and 17.6% within 30 days of discharge in Medicare population. Readmission rate among the beneficiaries with co-morbidities such as ESRD is as high as 31.6%. MedPAC estimated that the overall 30-day readmissions account for 15 billion of Medicare spending. PacifiCare has been tracking 30-day readmission rate among legacy PacifiCare's member population (both commercial and Secure Horizons) and found that the magnitude is also significant. (Luthi, J.C. et. al., 2004; Fleisher, L.A. et.al., 2004; Bisgaard, H. et. al., 1999; Anderson, G. et. al., 1984; MedPAC., 2007) |
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Clinical Recommendation Statement | Although not all of the readmissions are avoidable given the complexity of the causes/drivers of readmissions, the potential opportunity for improvement is still substantial. Studies have found that a significant proportion of the readmissions are preventable. In a literature review conducted by BJ Taragin on hospital readmissions 1, the author found that 9% to 48% of all readmissions are preventable as those readmissions are caused by poor care received during the initial index hospitalization or inadequate post-discharge care. MedPAC conducted a study 2 to identify which of the readmissions are potentially preventable by applying 3M software to Medicare claims data in 2005 and found that 84% of 7-day readmissions, 78% of 15-day readmissions and 76% of 30-day readmissions are preventable. If converted into dollars, those potentially preventable readmissions are equivalent to Medicare spending of $5 billion for patients readmitted within 7 days, $8 billion for patients readmitted within 15 days and as high as $12 billion for patients readmitted within 30 days. Given the magnitude of this readmission problem, an even small decrease in the readmission rate can result in substantial savings in health care spending. The purpose of reporting the all-cause readmission measure is to help hospitals and other providers to improve efficiency of care provided in hospital as well as at discharge. Benefits of reduced readmission rate include: 1) Improved efficiency as preventable readmissions cause extra financial burden to purchasers, payers and patients 2) Improved effectiveness of care both in-hospital and post-discharge 3) Improved patient safety For consumers (patients and their families), the improvement in avoiding any unnecessary readmission significantly reduces the anxiety, financial burden and extra time demanded from being re-hospitalized. It also improves patient's safety. For purchases or payers, the improvement of preventable readmissions can significantly reduce their financial cost. (Taragin, M. et. al., 2000; MedPAC, 2007) |
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Improvement notation | Higher O/E ratio score indicates lower quality |
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Measurement duration | 12 month(s) |
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Reference | Luthi, J C, Burnand, B, McClellan, W M, Pitts, S R, Flanders, W D (2004). Is readmission to hospital an indicator of poor process of care for patients with heart failure? Qual Saf Health Care 13: 46-51 |
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Reference | Fleisher, L. A., Pasternak, L. R., Herbert, R., Anderson, G. F. (2004). Inpatient Hospital Admission and Death After Outpatient Surgery in Elderly Patients: Importance of Patient and System Characteristics and Location of Care. Arch Surg 139: 67-72 |
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Reference | Bisgaard, H, Møller, H (1999). Changes in risk of hospital readmission among asthmatic children in Denmark, 1978-9. BMJ 319: 229 - 230. |
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Reference | Anderson G., Steinberg E. Hospital re-admissions in the Medicare population. N Engl J Med 1984;311:1349 |
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Reference | Benbassat J, Taragin M. Hospital readmissions as a measure of quality of health care: advantages and limitations. Arch Intern Med. 2000 Apr 24; 160 (8):1074-81. |
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Reference | Medicare Payment Advisory Commission (MedPAC). Report to the Congress: Promoting Greater Efficiency in Medicare, June 2007. |
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Reference | Barbara J. Daly, Sara L. Douglas, Carol Genet Kelley, Elizabeth O'Toole, and Hugo Montenegro. Trial of a Disease Management Program to Reduce Hospital Readmissions of the Chronically Critically Ill Chest, Aug 2005; 128: 507 - 517. |
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Reference | Weinberger M, Oddone EZ, Henderson WG. Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. New England Journal of Medicine. 1996; 334:1441-7. |
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Reference | Jonás Gonseth , Pilar Guallar-Castillón , José R. Banegas , and Fernando Rodríguez-Artalejo. The effectiveness of disease management programmes in reducing hospital re-admission in older patients with heart failure: a systematic review and meta-analysis of published reports Eur Heart J 25: 1570-1595. |
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Reference | Andrea M. Spehar, etc. Seamless Care: Safe Patient Transitions from Hospital to Home. Advances in Patient Safety vol1:79-98. |
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Definition | Initial Patient Population(s): All patients aged 18 or older who were discharged alive from acute care hospitals. |
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Definition | Denominator(s): Total inpatient discharges from acute care hospitals incurred by the eligible population during the measurement period. |
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Definition | Denominator Exclusion(s): Acute care hospital inpatient discharges associated with maternity, pediatric, and mental health and substance abuse services |
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Definition | Numerator(s): Total acute care hospital inpatient readmissions within 30 days from the index discharges that are identified in the denominator. |
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Definition | Denominator Exception(s): N/A |
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Guidance | The measure provides unadjusted data that would then need to have the following risk adjustment methodology applied: 1. For each inpatient confinement claim, a MS-DRG will be assigned and relative weight will be determined accordingly. The RELATIVE WEIGHT represents the degree of resources consumed to treat the case, which is used to adjust readmission rate across hospitals. 2. Identify index discharges during the measurement period - Measure denominator 3. Identify readmission with 30 days from the index discharge date for each case during the measurement period - Measure numerator 4. Run logistic regression analysis to model readmission probability (0,1) as a function of case mix of the index discharge case ( case mix is represented by MS-DRG Relative Weight) Logic (P_readmission )= a + b * Relative Weight (from MS-DRG grouper) 5. Calculate readmission probability for each index case (expected readmission) Pi = e^(a+bx)/ (1+e^(a+bx)) 6. Calculate expected readmission rate for each hospital Expected_Readmission_HospA = (PA1 + PA2 + PA3 ... PAn)/N_HospA where N_HospA = The total number of discharges in the denominator for the calculation of the observed rate for hospital A 7. Calculate Observed Readmission Rate for each hospital Observed_Readmission_Rate_HospA = Total_Number_of_Readmissions_HospA / Total_Numbers_of_Index_Discharges_HospA 8. Calculate Expected/Observed Readmission Rate Ratio for each hospital O/E_Ratio_Hosp_A = Observed_Rate_HospA/Expected_Rate_Hosp_A Note: Since Electronic Health Record (HER) of a hospital normally does not contain a nationally representative inpatient confinement case sample, there will be large variations in readmission probability of MS-DRG based on each hospital’s inpatient confinement cases. It is recommended that readmission probability of MS-DRG be determined using steps 1- 5 logistic regression modeling process using administrative claims data from a large nationally representative benchmark population such as Medicare FFS beneficiaries for Medicare population. The readmission probability of MS-DRG is then made available in EHRs so a hospital’s readmission rate O/E ratio can be calculated and compared to its peer hospitals. Readmission probability should be determined separately for Medicare, Medicaid, and Commercial population. |
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Supplemental Data Elements | Report "Patient Characteristic: Gender" using "Gender HL7 Value Set (2.16.840.1.113883.1.11.1)"; Report "Patient Characteristic: Race" using "Race CDC Value Set (2.16.840.1.114222.4.11.836)"; Report "Patient Characteristic: Ethnicity" using "Ethnicity CDC Value Set (2.16.840.1.114222.4.11.837)"; Report "Patient Characteristic: Payer" using "Payer Source of Payment Typology Value Set (2.16.840.1.113883.3.221.5)". |
Measure set | CLINICAL QUALITY MEASURE SET 2011-2012 |