Halpern NA, Pastores SM, Thaler HT, et al.: Critical care medicine use and cost among Medicare beneficiaries 1995-2000: Major discrepancies between two United States federal Medicare databases
ABSTRACT A comparison of federal Medicare databases to identify critical care medicine (CCM) use, cost discrepancies, and their possible causes.
A 6-yr (1995-2000) retrospective analysis of Medicare hospital and CCM use and cost, comparing the Hospital Cost Report Information System (HCRIS) with Medicare Provider Analysis and Review File (MedPAR) supplemented when necessary by Health Care Information System (HCIS) (identified herein as MedPAR/HCIS).
All nonfederal U.S. hospitals.
Data are presented as days (M = million) and costs ($; B = Billion) for both hospitals and CCM. Between 1995 and 2000, the number of hospital days decreased in both databases: HCRIS (-13.2%; 78M to 67.7M) and MedPAR/HCIS (-14.1%; 82.8M to 71.1M). CCM days decreased in HCRIS (-4.6%; 8.3M to 7.9M). In contrast, CCM days increased in MedPAR/HCIS (7.2%; 13.9M to 14.9M). The discrepancy in CCM days between HCRIS and MedPAR/HCIS increased from 40% (5.6M days) in 1995 to 47% (7M days) in 2000. Two CCM billing codes (intensive care unit and coronary care unit "post/intermediate") used in MedPAR/HCIS were responsible for 73% on average per year, over the study period, for this CCM discrepancy. The use of these two codes progressively increased (44%; 3.9M to 5.6M days) by the end of the study. The cumulative 6-yr discrepancy in CCM days between HCRIS and MedPAR/HCIS (37.3M days) had a calculated cost of $92.3B.
We have identified major, and progressively increasing, discrepancies between two U.S. federal databases tabulating hospital and CCM use and cost for Medicare beneficiaries. Two CCM "post/intermediate" billing codes in MedPAR/HCIS were predominantly responsible for the CCM discrepancy. To accurately assess Medicare CCM use and cost, either HCRIS, or MedPAR/HCIS without the "post/intermediate" codes, should be used.
- SourceAvailable from: Debprakash Patnaik
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- "We excluded beneficiaries covered under certain types of group health organizations with capitated premiums who are not required to file claims. We excluded from critical care " psychiatric critical care " and intermediate or step-down units , but included medical , surgical, cardiac and burn units. Transfers between hospitals are not directly indicated in the claims. "
ABSTRACT: Most Americans are in Intensive Care Units (ICUs) at some point during their lives. There is wide variation in the outcome quality of ICUs and so, thousands of patients who die each year in ICUs may have survived if they were at the appropriate hospital. In spite of a policy agenda from IOM calling for effective transfer of patients to more capable hospitals to improve outcomes, there appear to be substantial inefficiencies in the existing system. In particular, patients recurrently transfer to secondary hospitals rather than to a most-preferred option. We present data mining schemes and significance tests to discover these inefficient cascades. We analyze critical care transfer data in Medicare across nearly 5,000 hospitals in the United States over 10 years and present evidence that these transfers to secondary hospitals repeatedly cascade across multiple transfers, and that some hospitals seem to be involved in many cascades.03/2011; 2011:74-8.
- "organizations with capitated premiums who are not required to file claims. We excluded from critical care " psychiatric critical care " and intermediate or step-down units, but included medical, surgical, cardiac and burn units. "
Conference Paper: Discovering specific cascades in critical care transfer networks.[Show abstract] [Hide abstract]
ABSTRACT: Most Americans will need the services of Intensive Care Units (ICUs) at some point during their lives. There are wide variations between hospitals in the outcome of critical care and, as a result, thousands of patients who die each year in ICUs may have survived if they were at the appropriate hospital. A policy agenda---including an IOM report---calls for effectively transferring patients to more capable hospitals to improve outcomes. But there appear to be substantial inefficiencies in the existing system. In particular, patients recurrently transfer to secondary hospitals rather than to a most-preferred option. Analyzing critical care transfer data across nearly 5,000 hospitals over 10 year in Medicare, we present evidence that these transfers to secondary hospitals repeatedly cascade across multiple transfers, and that specific "hotspot" hospitals appear to be triggers of such cascades. We present data mining schemes to discover inefficient cascades of transfers in this dataset. We also present methods to determine the statistical significance of these discovered cascades. We examine the exemplar case of Michigan, suggesting a possible application to create alerts when multiple, significant cascades occur.ACM International Health Informatics Symposium, IHI 2010, Arlington, VA, USA, November 11 - 12, 2010, Proceedings; 01/2010
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ABSTRACT: Patients requiring prolonged mechanical ventilation (PMV) are a subset of critically ill patients with high resource utilization and poor long-term outcomes. We sought to develop an algorithm for identifying patients receiving PMV, defined as either 14 or 21days of mechanical ventilation, in administrative and claims data. The algorithm was derived in mechanically ventilated patients at an academic medical center (n=1,500) and validated in patients with community-acquired pneumonia in a multi-center clinical registry (n=20,370), with further evaluation in the Pennsylvania discharge database (n=62,383). The final algorithm combined the International Classification of Diseases codes for mechanical ventilation, diagnosis related groups for ventilation and tracheostomy, and intensive care unit length of stay. In the derivation dataset the algorithm was highly sensitive (14days=92.4%; 21days=97.6%) and specific (14days=91.6%, 21days=92.1%). The definition continued to perform well in the validation dataset (14days: sensitivity=87.6%, specificity=88.5%). In both the derivation and validation datasets the negative predictive value was over 95% and positive predictive values ranged from 60% to 70%. In state discharge data the algorithm identified a cohort of patients with high costs and frequent discharge to skilled care facilities. Administrative data can be used to accurately identify populations of patients receiving PMV.Health Services and Outcomes Research Methodology 06/2009; 9(2):117-132. DOI:10.1007/s10742-009-0050-6