Other than privacy, what are the other major ethical challenges in clinical data?
sampling bias, unfair treatment due to biased data,  incomplete clinical stories, and reflection of health disparities.
Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.
The use of notes written by healthcare providers in the clinical settings has long been recognized to be a source of valuable information for clinical practice and medical research. Access to large quantities of clinical reports may help in identifying causes of diseases, establishing diagnoses, detecting side effects of beneficial treatments, and monitoring clinical outcomes BIBREF0 , BIBREF1 , BIBREF2 . The goal of clinical natural language processing (NLP) is to develop and apply computational methods for linguistic analysis and extraction of knowledge from free text reports BIBREF3 , BIBREF4 , BIBREF5 . But while the benefits of clinical NLP and data mining have been universally acknowledged, progress in the development of clinical NLP techniques has been slow. Several contributing factors have been identified, most notably difficult access to data, limited collaboration between researchers from different groups, and little sharing of implementations and trained models BIBREF6 . For comparison, in biomedical NLP, where the working data consist of biomedical research literature, these conditions have been present to a much lesser degree, and the progress has been more rapid BIBREF7 . The main contributing factor to this situation has been the sensitive nature of data, whose processing may in certain situations put patient's privacy at risk. The ethics discussion is gaining momentum in general NLP BIBREF8 . We aim in this paper to gather the ethical challenges that are especially relevant for clinical NLP, and to stimulate discussion about those in the broader NLP community. Although enhancing privacy through restricted data access has been the norm, we do not only discuss the right to privacy, but also draw attention to the social impact and biases emanating from clinical notes and their processing. The challenges we describe here are in large part not unique to clinical NLP, and are applicable to general data science as well.