Errors in little data
Little data documentation can be enabled by technology. For instance, machine learning is a form of artificial intelligence that trains systems to make predictions about certain characteristics of data. While machine learning has proved successful for identifying missing diagnoses, it is of limited use for symptoms and the findings of physical examinations.
Despite technological advances, clinicians’ notes remain the richest source of patient data. These are largely beyond the reach of big data.
Another technology, which supports clinical documentation, is natural language processing (NLP). This identifies key data from clinical notes. However, until the quality of those notes improve, it will be challenging for NLP programmes to procure the most salient information. Continued investment in technical solutions will improve data accuracy, but without fundamental changes in how care is documented, technology will have limited ability to rid data of systematic errors.
Incomplete data
Even if we achieve perfect data accuracy, we’re still faced with the challenge of data fragmentation. Incomplete data are common in clinical practice and reflect the fragmented nature of our healthcare systems. Patients see multiple health professionals who do not communicate optimally.
Incomplete data, like inaccurate data, can also lead to missed or spurious associations that can be wasteful or even harmful to patient care.
Privacy is less challenging
Solutions to address fragmented data are no easier than those to address inaccurate data. For decades policy makers have pursued greater interoperability between electronic clinical systems, but with little success.
Recent initiatives on interoperability of big data primarily focus on moving specific clinical data, such as laboratory test results, between discrete health providers. This does little to ensure that provider organizations have a comprehensive picture of a patient’s care across all care sites.
Privacy advocates are understandably concerned about efforts to aggregate data. However, with adequate de-identification and security safeguards, the risks of aggregation can be minimized and the benefits of better care at lower costs are substantial.
Takeaway
To reap the benefits from big data requires that we understand and effectively address the challenges of little data. This is not easy. But ignoring the challenges is not an option.
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