Abstract-The healthcare system is a knowledge driven industry which consists of vast and growing volumes of narrative information obtained from discharge summaries/reports, physicians case notes, pathologists as well as radiologists reports. This information is usually stored in unstructured and non-standardized formats in electronic healthcare systems which make it difficult for the systems to understand the information contents of the narrative information. Thus, the access to valuable and meaningful healthcare information for decision making is a challenge. Nevertheless, Natural Language Processing (NLP) techniques have been used to structure narrative information in healthcare. Thus, NLP techniques have the capability to capture unstructured healthcare information, analyze its grammatical structure, determine the meaning of the information and translate the information so that it can be easily understood by the electronic healthcare systems. Consequently, NLP techniques reduce cost as well as improve the quality of healthcare. It is therefore against this background that this paper reviews the NLP techniques used in healthcare, their applications as well as their limitations.
Fraud, waste and abuse have been a concern in healthcare system due to the exponential increase in the loss of revenue, loss of reputation and goodwill, and a rapid decline in the relationship between healthcare providers and patients. Consequently, fraud, waste and abuse result in a high cost of healthcare services, decreased quality of care, and threat to patients' lives. Its enormous side effects in healthcare have attracted diverse efforts in the healthcare industry, data analytics industry and research communities towards the development of fraud detection methods. Hence, this study examines and analyzes fraud, waste and abuse detection methods used in healthcare, to reveal the strengths and limitations of each approach. Eighty eight literatures obtained from journal articles, conference proceedings and books based on their relevance to the research problem were reviewed. The result of this review revealed that fraud detection methods are difficult to implement in the healthcare system because new fraud patterns are constantly developed to circumvent fraud detection methods. Research in medical fraud assessment is limited due to data limitations as well as privacy and confidentiality concerns.
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