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. 2010 Jul 10;28(20):3307-15.
doi: 10.1200/JCO.2009.25.6636. Epub 2010 Jun 7.

Characteristics and predictors of missed opportunities in lung cancer diagnosis: an electronic health record-based study

Hardeep Singh  1 , Kamal Hirani, Himabindu Kadiyala, Olga Rudomiotov, Traber Davis, Myrna M Khan, Terry L Wahls
Affiliations

Characteristics and predictors of missed opportunities in lung cancer diagnosis: an electronic health record-based study

Hardeep Singh et al. J Clin Oncol. .

Abstract

Purpose: Understanding delays in cancer diagnosis requires detailed information about timely recognition and follow-up of signs and symptoms. This information has been difficult to ascertain from paper-based records. We used an integrated electronic health record (EHR) to identify characteristics and predictors of missed opportunities for earlier diagnosis of lung cancer.

Methods: Using a retrospective cohort design, we evaluated 587 patients of primary lung cancer at two tertiary care facilities. Two physicians independently reviewed each case, and disagreements were resolved by consensus. Type I missed opportunities were defined as failure to recognize predefined clinical clues (ie, no documented follow-up) within 7 days. Type II missed opportunities were defined as failure to complete a requested follow-up action within 30 days.

Results: Reviewers identified missed opportunities in 222 (37.8%) of 587 patients. Median time to diagnosis in cases with and without missed opportunities was 132 days and 19 days, respectively (P < .001). Abnormal chest x-ray was the clue most frequently associated with type I missed opportunities (62%). Follow-up on abnormal chest x-ray (odds ratio [OR], 2.07; 95% CI, 1.04 to 4.13) and completion of first needle biopsy (OR, 3.02; 95% CI, 1.76 to 5.18) were associated with type II missed opportunities. Patient adherence contributed to 44% of patients with missed opportunities.

Conclusion: Preventable delays in lung cancer diagnosis arose mostly from failure to recognize documented abnormal imaging results and failure to complete key diagnostic procedures in a timely manner. Potential solutions include EHR-based strategies to improve recognition of abnormal imaging and track patients with suspected cancers.

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Conflict of interest statement

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Figures

Fig 1.
Fig 1.
Study flowchart. IQR, interquartile range.
Fig A1.
Fig A1.
Diagnostic flowchart for lung cancer. CT, computed tomography; PET, positron emission tomography.
See this image and copyright information in PMC

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