Using Routinely Collected Electronic Health Record Data to Predict Readmission and Target Care Coordination.

ABSTRACT: Patients with chronic renal failure (CRF) are at high risk of being readmitted to hospitals within 30 days. Routinely collected electronic health record (EHR) data may enable hospitals to predict CRF readmission and target interventions to increase quality and reduce readmissions. We compared the ability of manually extracted variables to predict readmission compared with EHR-based prediction using multivariate logistic regression on 1 year of admission data from an academic medical center. Categorizing three routinely collected variables (creatinine, B-type natriuretic peptide, and length of stay) increased readmission prediction by 30% compared with paper-based methods as measured by C-statistic (AUC). Marginal effects analysis using the final multivariate model provided patient-specific risk scores from 0% to 44.3%. These findings support the use of routinely collected EHR data for effectively stratifying readmission risk for patients with CRF. Generic readmission risk tools may be evidence-based but are designed for general populations and may not account for unique traits of specific patient populations-such as those with CRF. Routinely collected EHR data are a rapid, more efficient strategy for risk stratifying and strategically targeting care. Earlier risk stratification and reallocation of clinician effort may reduce readmissions. Testing this risk model in additional populations and settings is warranted.

Current Procedural Terminology-based Procedure Categorization Enhances Cost Prediction of Medicare Severity Diagnosis Related Group in Spine Surgery.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: The aim of this study was to analyze how a Current Procedural Terminology (CPT)-based categorization method can predict cost variation in surgical spine procedures. SUMMARY OF BACKGROUND DATA: Neck and back disorders affect a majority of the adult population and account for tens of billions of dollars in health care spending each year. In the era of bundled payments and value-based reimbursement, it is imperative for surgeons to identify sources of cost variability across surgical spine procedures. Historically, this has been accomplished using Medicare Severity Diagnosis Related Group (MS-DRG) codes, but they utilize an overly simplistic categorization of surgical procedures. The specificity and familiarity of the CPT coding structure makes it a better option for categorizing differences in surgical decision making and technique. METHODS: Hospital billing data for patients undergoing a surgical spine procedure requiring an overnight, in-patient stay was retrospectively collected over 4 fiscal years (2012-2016) from a single health care system. Linear regression analysis was performed to assess the correlation between cost variation and: spine-specific MS-DRG codes; a novel CPT-based categorization method; and the combination of MS-DRG codes and CPT-based categorization. RESULTS: There were 5020 surgical procedures were analyzed with respect to 16 different MS-DRG codes and 30 distinct CPT-based surgical categories (CSCs). Linear regression results were: MS-DRG R2 = 0.6545 (P < 0.001); CSC R2 = 0.5709 (P < 0.001); and R2 = 0.744 for the combined MS-DRG and CSC methods (P < 0.05). Median difference between the actual and predicted cost for the combined model was -$261.00, compared with -$727.50 for the CSC model and -$478.70 for the MS-DRG model. CONCLUSION: Addition of the CPT-based categorization method to MS-DRG coding provides an enhanced method to evaluate the association between predicted and actual cost when using linear regression analysis to assess cost variation in spine surgery.Level of Evidence: 3.

Breaking down silos: engaging students to help fix the US health care system.

PROBLEM: The field of health care is becoming a team effort as patient care becomes increasingly complex and multifaceted. Despite the need for multidisciplinary education, there persists a lack of student engagement and collaboration among health care disciplines, which presents a growing concern as students join the workforce. APPROACH: In October 2013, the Emory-Georgia Tech Healthcare Innovation Program organized a student driven symposium entitled "US Healthcare: What's Broken and How to Fix It: The Student Perspective". The symposium engaged students from multiple disciplines to work together in addressing problems associated with US health care delivery. The symposium was organized and carried out by a diverse group of student leaders from local institutions who adopted a multidisciplinary approach throughout the planning process. OUTCOMES: The innovative planning process leading up to the symposium revealed that many of the student-discipline groups lacked an understanding of one another's role in health care, and that students were interested in learning how to work together to leverage each other's profession. The symposium was widely attended and positively received by students and faculty from the Atlanta metropolitan area, and has since helped to promote interdepartmental collaboration and multidisciplinary education across institutions. NEXT STEPS: The student symposium will become an annual event and incorporate broader discipline representation, as well as a patient perspective. Proposals for additional institution-wide, multidisciplinary educational offerings are being addressed with the help of faculty and health care providers across the network. Accordingly, the implementation of student-driven symposia to engage students and stimulate institution-wide changes may be a beneficial and cost-effective means for academic health centers looking to facilitate multidisciplinary health care education.