A Learning Health Care System Using Computer-Aided Diagnosis

Abstract

Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.

Keywords: crowdsourcing; decision support systems, clinical; diagnosis support systems; diagnosis, computer-assisted; diagnostic errors; knowledge bases; knowledge management; pattern recognition, automated; structured knowledge representation.

Figure 1

Examples of the view of a set of findings in a patient by a physician and a traditional diagnosis support system (DSS): chest pain and shortness of breath (upper panel), fever and rash (lower panel). Temporal and semantic interrelations between findings are crucial in putting findings in the right clinical context. RMSF, Rocky mountain spotted fever.

Figure 2

An integrated approach to computer-aided diagnosis. The process addresses the 2 questions that lead to likely diagnoses: Left side: How similar is the presentation of the patient to a set of manifestations of a known disease? Right side: What is the likelihood of encountering that disease in a patient like this?

Figure 3

Generating a real-time structured representation of a patient presentation supports a computer-aided diagnostic process (blue arrows) and a learning health care system through knowledge reuse (gray arrows).

Figure 4

Structured patient and disease representation. (A) A simulated view of an electronic health record with admission notes. Key terms are highlighted automatically using a real-time natural language processing engine or marked by the user. (B) Selected terms are then manipulated by the user by means of a touch screen to create a pattern representing key temporal and semantic interrelations between terms in a structured format. This pattern is augmented by automatically extracted relevant clinical data, demographics, and other metadata. (C) Structured patterned representation of the manifestations of Rocky Mountain spotted fever derived from a review article. Applying analytics schemes for assessing patient and disease similarity in context of disease prevalence can inform the generation of a ranked differential diagnosis for the patient in question.