A STATISTICAL METHOD FOR MONITORING NON-ACCEPTABLE DIAGN
监测不可接受诊断的统计方法
基本信息
- 批准号:2871176
- 负责人:
- 金额:$ 6.11万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:1998
- 资助国家:美国
- 起止时间:1998-09-30 至 2000-09-29
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The government under Medicare, private insurers, and some managed care
organizations have adopted the (Diagnosis Related Group) DRG system as
a basis for reimbursing hospitals for inpatient stays. The DRG code for
a hospital stay is based on a complicated algorithm that uses patient
medical records for determining health care reimbursement. A recent
audit of the Medicare system estimates that billions of unnecessary
dollars may have been spent due to incorrect coding by providers.
Currently, a finding of incorrect DRG codes results from expensive
audits of samples of patient medical records. As the audit relies on
human capital which requires adequate staffing and coordination with the
hospital, financial adjustments from the audit will lag from the time
of the hospital stay. Also, only a sample of all medical records are
examined, most records are not.
This project will use data already available from one insurer to design
and test a model to predict whether a claim is coded incorrectly.
Estimates from the statistical model will be used as input to the
statistical monitor to determine whether the process (percent of
incorrect DRG codes) has changed. Strategies for application of the
model and monitor will be determined. The effectiveness of the new
system will be compared to that which is currently in use by examining
the rate of incorrect DRG codes and the dollars of unnecessary payments.
An inexpensive statistical control system to monitor the incorrect DRG
coding for all claims would decrease the administrative costs and
increase the precision of monitoring for unnecessary payments. A
statistical model built on electronically available information could
expedite the auditing process and provide adjustments in a more timely
fashion. All claims could be included in such a system and those claims
with a higher chance of being incorrect could be further examined. The
results of the analysis could then be used to improve the predictive
accuracy of the original model. The model and monitor will not be
proprietary and the results of the research will be published in the
open scientific literature.
政府在医疗保险,私人保险公司,和一些管理医疗
组织已经采用了(诊断相关组)DRG系统,
为医院报销住院费用提供依据。DRG代码为
住院时间是基于一个复杂的算法,
用于确定医疗保健报销的医疗记录。 最近的一
对医疗保险系统的审计估计,
由于提供商的编码不正确,可能已花费了10万美元。
目前,发现不正确的DRG代码是由于昂贵的
对患者医疗记录样本的审计。 由于审计依赖于
人力资本,需要有足够的工作人员和协调,
医院的财务调整从审计时间上会滞后
住院时间 此外,只有所有医疗记录的样本
检查,大多数记录都没有。
该项目将使用一家保险公司提供的数据来设计
并测试模型以预测索赔是否被错误地编码。
统计模型的估计值将用作
统计监视器,以确定过程是否(
错误的DRG代码)已更改。 实施战略
将确定模型和监视器。 新的有效性
系统将与目前使用的系统进行比较,
错误DRG代码的比率和不必要支付的美元。
一个廉价的统计控制系统,以监测不正确的DRG
对所有索赔进行编码将减少行政费用,
提高对不必要付款的监测的准确性。 一
建立在电子信息基础上统计模型可以
加快审计过程,并更及时地提供调整
时尚. 所有索赔都可列入这一制度,
有更高的机会是不正确的,可以进一步检查。的
分析结果可用于改善预测
原始模型的准确性。 模型和显示器将不会
专利和研究结果将发表在
开放的科学文献
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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MARJORIE ROSENBERG其他文献
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