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The effect of Massachusetts health reform on 30 day hospital readmissions: retrospective analysis of hospital episode statistics.

ABSTRACT: OBJECTIVES:To analyse changes in overall readmission rates and disparities in such rates, among patients aged 18-64 (those most likely to have been affected by reform), using all payer inpatient discharge databases (hospital episode statistics) from Massachusetts and two control states (New York and New Jersey). DESIGN:Difference in differences analysis to identify the post-reform change, adjusted for secular changes unrelated to reform. SETTING:US hospitals in Massachusetts, New York, and New Jersey. PARTICIPANTS:Adults aged 18-64 admitted for any cause, excluding obstetrical. MAIN OUTCOME MEASURE:Readmissions at 30 days after an index admission. RESULTS:After adjustment for known confounders, including age, sex, comorbidity, hospital ownership, teaching hospital status, and nurse to census ratio, the odds of all cause readmission in Massachusetts was slightly increased compared with control states post-reform (odds ratio 1.02, 95% confidence interval 1.01 to 1.04, P<0.05). Racial and ethnic disparities in all cause readmission rates did not change in Massachusetts compared with control states. In analyses limited to Massachusetts only, there were minimal overall differences in changes in readmission rates between counties with differing baseline uninsurance rates, but black people in counties with the highest uninsurance rates had decreased odds of readmission (0.91, 0.84 to 1.00) compared with black people in counties with lower uninsurance rates. Similarly, white people in counties with the highest uninsurance rates had decreased odds of readmission (0.96, 0.94 to 0.99) compared with white people in counties with lower uninsurance rates. CONCLUSIONS:In the United States, and in Massachusetts in particular, extending health insurance coverage alone seems insufficient to improve readmission rates. Additional efforts are needed to reduce hospital readmissions and disparities in this outcome.

SUBMITTER: Lasser KE

PROVIDER: S-EPMC3970763 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesTo examine the impact of Massachusetts healthcare reform on changes in rates of admission to hospital for ambulatory care sensitive conditions (ACSCs), which are potentially preventable with good access to outpatient medical care, and racial and ethnic disparities in such rates, using complete inpatient discharge data (hospital episode statistics) from Massachusetts and three control states.DesignDifference in differences analysis to identify the change, overall and according to race/ethnicity, adjusted for secular changes unrelated to reform.SettingHospitals in Massachusetts, New York, New Jersey, and Pennsylvania, United States.ParticipantsAdults aged 18-64 (those most likely to have been affected by the reform) admitted for any of 12 ACSCs in the 21 months before and after the period during which reform was implemented (July 2006 to December 2007).Main outcome measuresAdmission rates for a composite of all 12 ACSCs, and subgroup composites of acute and chronic ACSCs.ResultsAfter adjustment for potential confounders, including age, race and ethnicity, sex, and county income, unemployment rate and physician supply, we found no evidence of a change in the admission rate for overall composite ACSC (1.2%, 95% confidence interval -1.6% to 4.1%) or for subgroup composites of acute and chronic ACSCs. Nor did we find a change in disparities in admission rates between black and white people (-1.9%, -8.5% to 5.1%) or white and Hispanic people (2.0%, -7.5% to 12.4%) for overall composite ACSC that existed in Massachusetts before reform. In analyses limited to Massachusetts only, we found no evidence of a change in admission rate for overall composite ACSC between counties with higher and lower rates of uninsurance at baseline (1.4%, -2.3% to 5.3%).ConclusionsMassachusetts reform was not associated with significantly lower overall or racial and ethnic disparities in rates of admission to hospital for ACSCs. In the US, and Massachusetts in particular, additional efforts might be needed to improve access to outpatient care and reduce preventable admissions.

Project description:IntroductionNew financial incentives, such as reduced Medicare reimbursements, have led hospitals to closely monitor their readmission rates and initiate efforts aimed at reducing them. In this context, many surgical departments participate in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), which collects detailed demographic, laboratory, clinical, procedure and perioperative occurrence data. The availability of such data enables the development of data science methods which predict readmissions and, as done in this paper, offer specific recommendations aimed at preventing readmissions.Materials and methodsThis study leverages NSQIP data for 722,101 surgeries to develop predictive and prescriptive models, predicting readmissions and offering real-time, personalized treatment recommendations for surgical patients during their hospital stay, aimed at reducing the risk of a 30-day readmission. We applied a variety of classification methods to predict 30-day readmissions and developed two prescriptive methods to recommend pre-operative blood transfusions to increase the patient's hematocrit with the objective of preventing readmissions. The effect of these interventions was evaluated using several predictive models.ResultsPredictions of 30-day readmissions based on the entire collection of NSQIP variables achieve an out-of-sample accuracy of 87% (Area Under the Curve-AUC). Predictions based only on pre-operative variables have an accuracy of 74% AUC, out-of-sample. Personalized interventions, in the form of pre-operative blood transfusions identified by the prescriptive methods, reduce readmissions by 12%, on average, for patients considered as candidates for pre-operative transfusion (pre-operative hematoctic <30). The prediction accuracy of the proposed models exceeds results in the literature.ConclusionsThis study is among the first to develop a methodology for making specific, data-driven, personalized treatment recommendations to reduce the 30-day readmission rate. The reported predicted reduction in readmissions can lead to more than $20 million in savings in the U.S. annually.

Project description:Identification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit.To externally validate the HOSPITAL score in an international multicenter study to assess its generalizability.International retrospective cohort study of 117?065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded.The HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay.30-day potentially avoidable readmission to the index hospital using the SQLape algorithm.Overall, 117?065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16?992 of 117?065 (14.5%) were followed by a 30-day readmission, and 11?307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n?=?73?031 [62.4%]), intermediate (n?=?27?612 [23.6%]), and high risk (n?=?16?422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson ?2 test P?=?.89).The HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.

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The effect of Massachusetts health reform on 30 day hospital readmissions: retrospective analysis of hospital episode statistics.

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