Objective: The objective of this study was to evaluate the incremental predictive power of electronic medical record (EMR) data, relative to the information available in more easily accessible and standardized insurance claims data. Data and Methods: Using both EMR and Claims data, we predicted outcomes for 118,510 patients with 144,966 hospitalizations in 8 hospitals, using widely used prediction models. We use cross-validation to prevent overfitting and tested predictive performance on separate data that were not used for model training. Main Outcomes: We predict 4 binary outcomes: length of stay (≥7 d), death during the index admission, 30-day readmission, and 1-year mortality. Results: We achieve nearly the same prediction accuracy using both EMR and claims data relative to using claims data alone in predicting 30-day readmissions [area under the receiver operating characteristic curve (AUC): 0.698 vs. 0.711; positive predictive value (PPV) at top 10% of predicted risk: 37.2% vs. 35.7%], and 1-year mortality (AUC: 0.902 vs. 0.912; PPV: 64.6% vs. 57.6%). EMR data, especially from the first 2 days of the index admission, substantially improved prediction of length of stay (AUC: 0.786 vs. 0.837; PPV: 58.9% vs. 55.5%) and inpatient mortality (AUC: 0.897 vs. 0.950; PPV: 24.3% vs. 14.0%). Results were similar for sensitivity, specificity, and negative predictive value across alternative cutoffs and for using alternative types of predictive models. Conclusion: EMR data are useful in predicting short-term outcomes. However, their incremental value for predicting longer-term outcomes is smaller. Therefore, for interventions that are based on long-term predictions, using more broadly available claims data is equally effective.

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We study the sources of high end-of-life spending for cancer patients. Even among patients with similar initial prognoses, spending in the year post diagnosis is over twice as high for those who die within the year than those who survive. Elevated spending on decedents is predominantly driven by higher inpatient spending, particularly low-intensity admissions. However, most such admissions do not result in death, making it difficult to target spending reductions. Furthermore, end-of-life spending is substantially more elevated for younger patients, compared to older patients with similar prognosis. Results highlight sources of high end-of-life spending without revealing any natural “remedies.”

The concentration of healthcare spending at the end of life is widely documented but poorly understood. To gain insight, we focus on patients newly diagnosed with cancer. They display the familiar pattern: even among cancer patients with similar initial prognoses, monthly spending in the year post diagnosis is over twice as high for those who die within the year than those who survive. This elevated spending on decedents is almost entirely driven by higher inpatient spending, particularly low-intensity admissions, which rise as the prognosis deteriorates. However, even for patients with very poor prognoses at the time of admission, most low-intensity admissions do not result in death, making it difficult to target spending reductions. We also find that among patients with the same cancer type and initial prognosis, end-of-life spending is substantially more elevated for younger patients compared to older patients, suggesting that treatment decisions are not exclusively present-focused. Taken together, these results provide a richer understanding of the sources of high end-of-life spending, without revealing any natural "remedies."

We study a large intervention intended to reduce hospital readmission rates in Israel. Since 2012, readmission risk was calculated for patients aged 65 and older, and high-risk patients were flagged to providers upon admission and after discharge. Analyzing 171,541 admissions during 2009-2016, we find that the intervention reduced 30-day readmission rates by 5.9% among patients aged 65-70 relative to patients aged 60-64, who were not targeted by the intervention and for whom no risk-scores were calculated. The largest reduction, 12.3%, was among high-risk patients, though some of it may reflect substitution of attention away from patients with unknown high-risk at the point of care. Post-discharge follow-up encounters were significantly expedited. Estimated effects declined after incentives to reduce readmission rates were discontinued. The evidence demonstrates that informing providers about patient risk in real-time coupled with incentives to reduce readmissions can improve care continuity and reduce hospital readmissions.