Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room

被引:0
|
作者
Elsaqa, Mohamed [1 ,2 ]
El Tayeb, Marawan M. [1 ]
Yano, Stephanie [1 ]
Papaconstantinou, Harry T. [1 ]
机构
[1] Baylor Scott & White Med Ctr, Temple, TX 76508 USA
[2] Alexandria Univ, Fac Med, Alexandria, Egypt
关键词
CASE DURATION; PREDICTION;
D O I
10.1097/JHM-D-23-00073
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Goal: Accurate prediction of operating room (OR) time is critical for effective utilization of resources, optimal staffing, and reduced costs. Currently, electronic health record (EHR) systems aid OR scheduling by predicting OR time for a specific surgeon and operation. On many occasions, the predicted OR time is subject to manipulation by surgeons during scheduling. We aimed to address the use of the EHR for OR scheduling and the impact of manipulations on OR time accuracy. Methods: Between April and August 2022, a pilot study was performed in our tertiary center where surgeons in multiple surgical specialties were encouraged toward nonmanipulation for predicted OR time during scheduling. The OR time accuracy within 5 months before trial (Group 1) and within the trial period (Group 2) were compared. Accurate cases were defined as cases with total length (wheels-in to wheels-out) within +/- 30 min or +/- 20% of the scheduled duration if the scheduled time is >= or <150 min, respectively. The study included single and multiple Current Procedural Terminology code procedures, while procedures involving multiple surgical specialties (combo cases) were excluded. Principal Findings: The study included a total of 8,821 operations, 4,243 (Group 1) and 4,578 (Group 2), (p < .001). The percentage of manipulation dropped from 19.8% (Group 1) to 7.6% (Group 2), (p < .001), while scheduling accuracy rose from 41.7% (Group 1) to 47.9% (Group 2), (p = .0001) with a significant reduction of underscheduling percentage (38.7% vs. 31.7%, p = .0001) and without a significant difference in the percentage of overscheduled cases (15% vs. 17%, p = .22). Inaccurate OR hours were reduced by 18% during the trial period (2,383 hr vs. 1,954 hr). Practical Applications: The utilization of EHR systems for predicting OR time and reducing manipulation by surgeons helps improve OR scheduling accuracy and utilization of OR resources.
引用
收藏
页码:132 / 139
页数:8
相关论文
共 6 条
  • [1] Stroke Risk Calculators in the Era of Electronic Health Records Linked to Administrative Databases
    Richards, Adam
    Cheng, Eric M.
    STROKE, 2013, 44 (02) : 564 - 569
  • [2] Procedure prediction from symbolic Electronic Health Records via time intervals analytics
    Moskovitch, Robert
    Polubriaginof, Fernanda
    Weiss, Aviram
    Ryan, Patrick
    Tatonetti, Nicholas
    JOURNAL OF BIOMEDICAL INFORMATICS, 2017, 75 : 70 - 82
  • [3] Learning and diSentangling patient static information from time-series Electronic hEalth Records (STEER)
    Liao, Wei
    Voldman, Joel
    PLOS DIGITAL HEALTH, 2024, 3 (10):
  • [4] Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model
    Yuan Wang
    Yake Wei
    Hao Yang
    Jingwei Li
    Yubo Zhou
    Qin Wu
    BMC Medical Informatics and Decision Making, 20
  • [5] Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model
    Wang, Yuan
    Wei, Yake
    Yang, Hao
    Li, Jingwei
    Zhou, Yubo
    Wu, Qin
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2020, 20 (01)
  • [6] Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data
    Rangarajan, Prashant
    Mody, Sandeep K.
    Marathe, Madhav
    PLOS COMPUTATIONAL BIOLOGY, 2019, 15 (11)