Patient Factors Predictive of Hospital Readmissions Within 30 Days.

BACKGROUND: Under the Affordable Care Act, the Congress has mandated that the Centers for Medicare and Medicaid Services reduce payments to hospitals subject to their Inpatient Prospective Payment System that exhibits excess readmissions. Using hospital-coded discharge abstracts, we constructed a readmission measure that accounts for cross-hospital variation that enables hospitals to monitor their entire inpatient populations and evaluate their readmission rates relative to national benchmarks. METHODS: Multivariate logistic regressions are applied to determine which patient factors increase the odds of a readmission within 30 days and by how much. This study uses deidentified discharge abstract data from a database of approximately 15 million inpatient discharges representing 611 acute care hospitals from Premier healthcare alliance over a 2-year period (2008q4-2010q3). The hospitals are geographically diverse and represent large urban academic centers and small rural community hospitals. RESULTS: This study demonstrates that meaningful risk-adjusted readmission rates can be tracked in a dynamic database. The clinical conditions responsible for the index admission were the strongest predictive factor of readmissions, but factors such as age and accompanying comorbid conditions were also important. Socioeconomic factors, such as race, income, and payer status, also showed strong statistical significance in predicting readmissions. CONCLUSIONS: Payment models that are based on stratified comparisons might result in a more equitable payment system while at the same time providing transparency regarding disparities based on these factors. No model, yet available, discriminates potentially modifiable readmissions from those not subject to intervention highlighting the fact that the optimum readmission rate for any given condition is yet to be identified.

Identifying hospital-wide harm: a set of ICD-9-CM-coded conditions associated with increased cost, length of stay, and risk of mortality.

This study identifies an expanded set of hospital-acquired conditions (HACs), using the Present-On-Admission (POA) indicator and secondary diagnoses present on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-coded discharge abstracts and evaluates their association with mortality, length of stay (LOS), and cost. A sample of 500 000 de-identified ICD-9-CM-coded discharge abstracts was randomly drawn from a data set of 11 million. A total of 138 secondary condition clusters were identified as potential inpatient complications (PICs). Regression modeling was used to determine marginal association of each PIC with mortality, LOS, and cost. In all, 16% of hospitalized patients developed 1 or more of these conditions while in the hospital compared with less than 1% of inpatients experiencing HACs defined by the Centers for Medicare and Medicaid Services. Also, 74 PICs were associated with seriously higher mortality rates (5 excess deaths per 1000), significantly LOS (0.4 extra days per discharge), and significantly higher costs (an extra $1000 per discharge).

Developing a pathway for high-value, patient-centered total joint arthroplasty.

BACKGROUND: Total joint arthroplasty (TJA) is one of the most widely performed elective procedures; however, there are wide variations in cost and quality among facilities where the procedure is performed. QUESTIONS/PURPOSES: The purposes of this study were to (1) develop a generalizable clinical care pathway for primary TJA using inputs from clinical, academic, and patient stakeholders; and (2) identify system- and patient-level processes to provide safe, effective, efficient, and patient-centered care for patients undergoing TJA. METHODS: We used a combination of quantitative and qualitative methods to design a care pathway that spans 14 months beginning with the presurgical office visit and concluding 12 months after discharge. We derived care suggestions from interviews with 16 hospitals selected based on readmission rates, cost, and quality (n = 10) and author opinion (n = 6). A 32-member multistakeholder panel refined the pathway during a 1-day workshop. Participants were selected based on leadership in orthopaedic (n = 4) and anesthesia (n = 1) specialty societies; involvement in organizations specializing in safety and high reliability care (n = 3), lean production/consumption of care (n = 3), and patient experience of care (n = 3); membership in an interdisciplinary care team of a hospital selected for interviewing (n = 8); recent receipt of a TJA (n = 1); and participation in the pathway development team (n = 9). RESULTS: The care pathway includes 40 suggested processes to improve care, 37 techniques to reduce waste, and 55 techniques to improve communication. Central themes include standardization and process improvement, interdisciplinary communication and collaboration, and patient/family engagement and education. Selected recommendations include standardizing care protocols and staff roles; aligning information flow with patient and process flow; identifying a role accountable for care delivery and communication; managing patient expectations; and stratifying patients into the most appropriate care level. CONCLUSIONS: We developed a multidisciplinary clinical care pathway for patients undergoing TJA based on principles of high-value care. The pathway is ready for clinical testing and context-specific adaptation. LEVEL OF EVIDENCE: Level V, therapeutic study. See the Instructions for Authors for a complete description of levels of evidence.