Press release: Study shows that CMIOs see Natural Language Processing (NLP) as critical step in improving patient care and hospital efficiency

November 11 2015

Faced with the challenges of Accountable Care, the triple aim and Meaningful Use, NLP will help CMIOs to improve insights into patient and population health.

CAMBRIDGE, UK and BOSTON, USA - November 10th, 2015 – Chief Medical Informatics Officers (CMIOs) at US healthcare providers see that Accountable Care, the triple aim and Meaningful Use are all creating an unprecedented demand for more insights from patient data. Since much of the key information is locked away in unstructured data, the overwhelming majority of CMIOs believe that rapidly increasing the use of Natural Language Processing (NLP) will be a critical step in accessing this data and thus improving the delivery of patient care. These are the key findings of a recent study prepared by Linguamatics, with support from the American Medical Informatics Association (AMIA).

Linguamatics commissioned their “Assessing the Role of Clinical NLP in the Delivery of Patient Care” report, with the aim of discovering how CMIOs envisage using NLP in new applications that both enhance patient care and improve hospital efficiency. The report succeeded in uncovering four key areas where CMIOs foresee key developments:

• The CMIOs surveyed considered the potential improvement in the quality of care and patient safety, and the resulting reduction in costs that could be generated with predictive models, to be the most important application involving NLP. For example, CMIOs expressed interest in applications such as predicting hospital readmissions or outmigration/patient leakage.

• Improving patient care by providing clinicians with improved real-time access to a richer set of knowledge was also an area that was flagged up by CMIOs. They felt that NLP technology would improve the process of surfacing the right data for decision support, and maximize the value of external sources such as biomedical literature and clinical trials databases.

• The data capture process for disease registries, widely used to support population health studies, is still largely manual and extremely time-consuming. Using NLP for automated information extraction to reduce costs and time required for data curation is an attractive proposition for CMIOs.

• Of particular importance to academic medical and cancer treatment centers is the potential for increasing participation in clinical trials and improving industry collaboration by using NLP to mine unstructured patient data. Benefits would include significant time savings both for chart review and identifying patient cohorts.

Commenting on the study, Simon Beaulah, Director of Healthcare Strategy at Linguamatics said; “This study highlights important areas where healthcare informatics can improve patient care and make cost savings using NLP.  There is a consensus that NLP is essential for managing large and expanding volumes of unstructured patient data, and transforming it into actionable insights to support predictive risk models and analysis of patient populations. The quantity of unstructured data and the need to analyze that data are sure to increase over the coming years, so the importance of NLP across the healthcare sector is set to grow still further.”