An Assessment of Artificial Intelligence Software Measurement of Physician Performance: Not Quite Ready for Prime Time

G. Sc
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Abstract

With an increase in hospital-based employment of plastic surgery graduates, understanding how hospitals evaluate physician performance is essential, as data could have important career implications. At our hospital (Loma Linda University Medical Center), the software program The CRIMSON Initiative is used to provide data related to physician performance. Upon review of this data, our Plastic Surgery department had a 30 days readmission rate that was higher than the hospital average. We were interested to see how accurate this number actually was, and so we closely examined the 30 days readmission rates for our department for an entire year. Using the CRIMSON Initiative software, two separate major searches were used (by “Attending Physician” and by “Performing Physician”). Searching either way revealed a 30 days readmission rate that was higher for our department than the hospital average. However, after manually sorting through each patient chart, there were cases that were not readmissions due to the patient’s plastic surgery. Once these cases were excluded from the original calculations, it brought our department’s readmission rate down to slightly lower than the hospital average for each search. This discrepancy highlights the margin of error of such automated physician performance programs, and brings to light pitfalls that physicians should be aware of concerning similar programs at their own institutions. As plastic surgery residency graduates are increasingly employed by hospitals, it would behoove them to be cognizant of this issue, and to be empowered to question the data being used to assess their performance.
评估人工智能软件测量医生的表现:还没有完全准备好黄金时间
随着整形外科毕业生在医院就业的增加,了解医院如何评估医生的表现是至关重要的,因为数据可能具有重要的职业含义。在我们医院(洛马林达大学医学中心),软件程序the CRIMSON Initiative被用来提供与医生表现相关的数据。根据这些数据,我们的整形外科有30天的再入院率,高于医院的平均水平。我们很想知道这个数字到底有多准确,所以我们仔细检查了我们部门一整年的30天再入院率。使用CRIMSON Initiative软件,使用了两个独立的主要搜索(“主治医生”和“主治医生”)。无论用哪一种方式搜索,我们部门的30天再入院率都高于医院的平均水平。然而,在手工整理每个患者的病历后,有一些病例由于患者的整形手术而没有再入院。一旦这些病例从最初的计算中排除,我们部门的再入院率就会下降到略低于医院每次搜索的平均水平。这种差异突出了这种自动化医生绩效项目的误差范围,并揭示了医生在自己机构中应该意识到的类似项目的陷阱。随着越来越多的整形外科住院医师毕业生被医院聘用,他们理应认识到这个问题,并有权对用于评估他们表现的数据提出质疑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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