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将诊断决策支持系统(DXplain™)引入教学医院服务的工作流程可以降低诊断具有挑战性的诊断相关组(DRG)的服务成本。

The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs).

机构信息

Center for Biomedical Informatics, Mount Sinai School of Medicine, One Gustave L. Levy Place, Box 1023, New York, NY 10029, USA.

出版信息

Int J Med Inform. 2010 Nov;79(11):772-7. doi: 10.1016/j.ijmedinf.2010.09.004. Epub 2010 Oct 14.

Abstract

BACKGROUND

In an era of short inpatient stays, residents may overlook relevant elements of the differential diagnosis as they try to evaluate and treat patients. However, if a resident's first principal diagnosis is wrong, the patient's appropriate evaluation and treatment may take longer, cost more, and lead to worse outcomes. A diagnostic decision support system may lead to the generation of a broader differential diagnosis that more often includes the correct diagnosis, permitting a shorter, more effective, and less costly hospital stay.

METHODS

We provided residents on General Medicine services access to DXplain, an established computer-based diagnostic decision support system, for 6 months. We compared charges and cost of service for diagnostically challenging cases seen during the fourth through sixth month of access to DXplain (intervention period) to control cases seen in the 6 months before the system was made available.

RESULTS

564 cases were identified as diagnostically challenging by our criteria during the intervention period along with 1173 cases during the control period. Total charges were $1281 lower (p=.006), Medicare Part A charges $1032 lower (p=0.006) and cost of service $990 lower (p=0.001) per admission in the intervention cases than in control cases.

CONCLUSIONS

Using DXplain on all diagnostically challenging cases might save our medical center over $2,000,000 a year on the General Medicine Services alone. Using clinical diagnostic decision support systems may improve quality and decrease cost substantially at teaching hospitals.

摘要

背景

在住院时间较短的时代,住院医师在评估和治疗患者时可能会忽略鉴别诊断的相关要素。然而,如果住院医师的主要诊断错误,患者的适当评估和治疗可能需要更长的时间,花费更多,导致更差的结果。诊断决策支持系统可能会生成更广泛的鉴别诊断,其中更常包括正确的诊断,从而缩短住院时间,提高治疗效果,降低成本。

方法

我们为普通内科服务的住院医师提供了 6 个月的 DXplain,这是一种成熟的基于计算机的诊断决策支持系统。我们将使用 DXplain 的第四个月至第六个月(干预期)期间遇到的具有诊断挑战性的病例的收费和服务成本与系统提供前 6 个月的对照病例进行比较。

结果

在干预期间,根据我们的标准确定了 564 例具有诊断挑战性的病例,而对照期则有 1173 例。干预组的总费用降低了 1281 美元(p=.006),医疗保险 A 部分费用降低了 1032 美元(p=0.006),服务成本降低了 990 美元(p=0.001)。

结论

在所有具有诊断挑战性的病例中使用 DXplain,仅在普通内科服务方面,我们的医疗中心每年就可能节省 200 多万美元。在教学医院中使用临床诊断决策支持系统可以显著提高质量并大幅降低成本。

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本文引用的文献

1
Role of computerized physician order entry systems in facilitating medication errors.
JAMA. 2005 Mar 9;293(10):1197-203. doi: 10.1001/jama.293.10.1197.
5
DXplain on the Internet.
Proc AMIA Symp. 1998:607-11.
6
Computer training for doctors and students.
BMJ. 1994 Nov 5;309(6963):1234-5. doi: 10.1136/bmj.309.6963.1234c.
7
The use of medical logic modules at LDS hospital.
Comput Biol Med. 1994 Sep;24(5):391-5. doi: 10.1016/0010-4825(94)90007-8.
8
Artificial intelligence.
N Engl J Med. 1980 Jun 26;302(26):1482.
9
The HELP system.
J Med Syst. 1983 Apr;7(2):87-102. doi: 10.1007/BF00995116.
10
An artificial intelligence program to advise physicians regarding antimicrobial therapy.
Comput Biomed Res. 1973 Dec;6(6):544-60. doi: 10.1016/0010-4809(73)90029-3.

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