Rios Nancy G, Oldiges Paige E, Lizano Marcela S, Doucet Wadford Danielle S, Quick David L, Martin John, Korvink Michael, Gunn Laura H
School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Healthcare (Basel). 2022 Jul 23;10(8):1368. doi: 10.3390/healthcare10081368.
Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of procedures is limited, with a need for risk-adjusted methods that help identify over- and under-coding using commonly available data, such as administrative claims. Risk-adjusted metrics are needed for quality control and enhancement. We propose a two-step approach to risk adjustment, using a zero-inflated Poisson model, applied to a hip-knee arthroplasty cohort discharged during 2019 ( = 313,477) for patient-level risk adjustment, and a potential additional layer for adjustment based on facility-level characteristics, when desired. A 21.41% reduction in root-mean-square error was achieved upon risk adjustment for patient-level factors alone. Furthermore, we identified facilities that over- and under-code versus industry coding expectations, adjusting for both patient-level and facility-level factors. Excess coding intensity was found to vary across multiple levels: (1) geographically across U.S. Census regional divisions; (2) temporally with marked seasonal components; (3) by facility, with some facilities largely departing from industry standards, even after adjusting for both patient- and facility-level characteristics. Our proposed method is simple to implement, generalizable, it can be used across cohorts with different sets of information available, and it is not limited by the accessibility and sparsity of electronic health records. By identifying potential over- and under-coding of procedures, quality control personnel can explore and assess internal needs for enhancements in their health delivery services and monitor subsequent quality improvements.
手术编码强度的变化，定义为相对于行业（而非临床）标准的手术过度编码，可能导致患者护理质量的差异，并对医疗机构和付款人产生额外影响。关于手术编码强度的文献有限，需要风险调整方法，以帮助使用行政索赔等常用数据识别编码过度和编码不足的情况。质量控制和提升需要风险调整指标。我们提出了一种两步风险调整方法，使用零膨胀泊松模型，应用于2019年出院的髋膝关节置换术队列（n = 313,477）进行患者层面的风险调整，并在需要时基于机构层面特征增加一层潜在的调整。仅对患者层面因素进行风险调整后，均方根误差降低了21.41%。此外，我们识别出了相对于行业编码预期编码过度和编码不足的机构，并对患者层面和机构层面因素进行了调整。发现过度编码强度在多个层面存在差异：（1）按美国人口普查区域划分在地理上存在差异；（2）在时间上存在明显的季节性成分；（3）在机构层面，一些机构即使在对患者和机构层面特征进行调整后，仍大幅偏离行业标准。我们提出的方法易于实施、具有通用性，可以应用于具有不同可用信息集的队列，并且不受电子健康记录的可及性和稀疏性的限制。通过识别手术编码的潜在过度和不足情况，质量控制人员可以探索和评估其医疗服务改进的内部需求，并监测后续的质量改进情况。
