Using Analytics to Improve Clinical Coding

ENSURING CLINICAL CODING ACCURACY

Precise clinical coding improves diagnostic recording and accuracy of clinical documentation, helping to ensure patients are treated more effectively. Suboptimal clinical coding can negatively impact payment, limiting the money available for patient care.¹

DESPARATE SYSTEMS ADD TO CODING CHALLENGES

Guy’s and St Thomas’ clinical coders process approximately 380,000 inpatient episodes per year, reviewing documentation in up to six unique sources to improve diagnostic recording and accuracy. Coders manually review data across these systems, including handwritten notes, computerized records, healthcare provider correspondence, clinical worksheets, nursing care pathways, diagnostic test reports, and discharge letters and forms.

The volume of sources, coupled with burdensome manual review processes and limited coding resources, limited coders’ ability to review every source for each patient, resulting in a gap in coding accuracy and potentially jeopardizing appropriate payment for services rendered.

Guy’s and St Thomas’ needed a solution that would allow it to enhance the quality of medical records and the accuracy of its coding without increasing the number of coders or the documentation burden.

DATA-DRIVEN ANALYTICS IMPROVES CODING PROCESS

To enhance its coding process, Guy’s and St Thomas’ chose to leverage the Health Catalyst^®数据操作系统平台。世界杯厄瓜多尔vs塞内加尔波胆预测DOS将跨源系统的数据进行组合和标准化，在单一技术平台上提供可操作的见解，从而实现对数据的有意义使用。世界杯厄瓜多尔vs塞内加尔波胆预测

With improved access to data, the organization identified several opportunities where the data platform could be used to streamline and enhance the coders’ work—including automating the discovery of patients whose diagnostic coding could be improved. Guy’s and St Thomas’ prioritized potential use cases, selecting circumstances with clear clinical criteria that could be easily identified. This prompted the organization to leverage the data platform to extract data and identify patients with missing diagnostic codes for obesity, vitamin D deficiency, and acute kidney injury (AKI)/chronic kidney disease (CKD).

To identify each group of patients, Guy’s and St Thomas’ worked with clinicians to design and approve the decision-making logic. For patients who are obese, the data platform extracts BMI documentation, locating patients with a BMI greater than 30. The BMIs are then matched to various episodes of care. The logic identifies if the patient is missing the diagnostic code for obesity, generating a list of patients for coders who then apply the diagnostic code.

Guy’s and St Thomas’ developed a similar workflow for vitamin D deficiency. The data platform gathers lab results using the decision-making logic approved by clinicians to identify patients with vitamin D deficiency. Coders are provided a list of patients with vitamin D deficiency criteria that do not have the associated diagnostic code, prompting bulk assignment of that code.

更多的标准被用于识别AKI和CKD患者。在与临床医生合作确定了能够准确识别患者的标准后，研究小组选择使用肾小球滤过率、肌酐和白蛋白来识别可能患有AKI或CKD但尚未被诊断为肾脏疾病的患者。然后将AKI或CKD的初步诊断送至临床团队进行验证。一旦验证，编码器分配AKI或CKD诊断代码。

RESULTS

盖伊医院和圣托马斯医院现在可以获得数据和技术，以增强编码人员的工作，自动化数据收集，更好地确定哪些病人的诊断代码可以得到改进。临床医生现在可以根据数据做出决定，以改善患者护理，并评估干预对患者结果的有效性。这些改进正在为信托带来巨大的价值。Results include:

• 仅在一个月内就发现了800名缺失肥胖诊断代码的患者，每年价值150万英镑(190万美元)。
• 250 patients with AKI and/or CKD identified in just one month, representing a value of £100K ($130K) annually.
• 50 patients with vitamin D deficiency identified in just one week, representing a value of £100K ($130K) annually.

“我们可以获得以前从未使用过的数据，这让我们能够提高临床编码的准确性。我们现在有机会利用编码数据来进一步改善实践。”

– Cormac Breen, Chief Clinical Information Officer

WHAT’S NEXT

Guy’s and St Thomas’ will continue to identify opportunities to augment clinical coding, improving diagnostic accuracy. The organization plans to leverage technology to auto-populate diagnostic codes, further improving efficiency and accuracy.

REFERENCES

1. Mirza, S. (2013).An introduction to clinical coding.HSJ.