This case study was made available to MIEC through our partnership with CRICO. It was written by Kristin O’Reilly, RN, BSN, MPH, CRICO

Description

A 38-year-old patient had a delayed diagnosis of breast cancer resulting in an increased tumor size and advanced staging at the time of diagnosis progressing from Stage 1 to Stage 2.

Key Lesson

• Adhering to national standards and internal policies and procedures is imperative to ensuring the timely diagnosis of cancer.

Clinical Sequence

A 38-year-old presented to her primary care provider (PCP) with concern for a palpable breast lump. The PCP noted the presence of the lump but documented that it was “likely a cyst, no imaging needed.”  No follow-up imaging was ordered.

One year later, the patient had a screening mammogram. The mammogram showed a 1.5cm mass, and an ultrasound-guided biopsy was performed. She was diagnosed with Stage 2 breast cancer. She required chemotherapy before undergoing a mastectomy and subsequent radiation. The patient required a 5-year course of Tamoxifen after surgery and had to delay pregnancy for that length of time.

Allegation

The patient alleged that the PCP should have ordered additional diagnostic testing when she presented with a new breast lump per the standard of care, which led to a delay in diagnosis of breast cancer.

Disposition

The case was settled for more than $400K.

Analysis

• Adherence to national guidelines, at the time, recommended additional diagnostic testing and/or a physical assessment to follow up on the new breast lump.
• The delay in diagnosis resulted in a higher staging of cancer, a potential decrease in 5-10 year survival rates, and a delay in the patient’s pursuit of pregnancy.

Discussion Questions

• Are policies and guidelines readily available related to diagnosis algorithms?
• What is the cadence of review and education for staff around these algorithms?
• What processes are in place to reduce missed and delayed diagnoses in your organization?

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Crico’s national database of medical professional liability (MPL) cases is a robust patient safety learning engine, built for making better data-informed decisions that can help save lives. rmf.harvard.edu/ or call 877.763.2742