Oncology Information System Data - Implications for 'Big Data'

Our Oncology Information Systems (OIS) with local User Documentation manages the medical data explored in this 26 item report of a minimal medical dataset. Over 10 years to 2016, 12906 diagnoses were registered (ICD10: C00-C80), with 18.84% of cases, and 63.9% of data points complete. Two sites were quality assured with high completion rates (H&N - 97.4% [4.26% of total cases], RECTUM - 88.74% [4.06%]). Sites lacking clinician attention varied from poor (eg, LUNG - 23.23% [13.24%]) to largely incomplete (eg, BRAIN - 2.01% [0.38%]).This disappointing medical data completion rate makes its use in a 'Big Data' effort suspect. Data extrapolation is compromised by variable natural history. Extrapolation techniques are unlikely to cope with only 18.84% complete data. Data mining requires input from domain experts. The 4 requirements of Big Data are not evident in oncological data.