State of the art in clinical informatics: evidence and examples.

OBJECTIVE: The field of clinical informatics has expanded substantially in the six decades since its inception. Early research focused on simple demonstrations that health information technology (HIT) such as electronic health records (EHRs), computerized provider order entry (CPOE), and clinical decision support (CDS) systems were feasible and potentially beneficial in clinical practice. METHODS: In this review, we present recent evidence on clinical informatics in the United States covering three themes: 1) clinical informatics systems and interventions for providers, including EHRs, CPOE, CDS, and health information exchange; 2) consumer health informatics systems, including personal health records and web-based and mobile HIT; and 3) methods and governance for clinical informatics, including EHR usability; data mining, text mining, natural language processing, privacy, and security. RESULTS: Substantial progress has been made in demonstrating that various clinical informatics methodologies and applications improve the structure, process, and outcomes of various facets of the healthcare system. CONCLUSION: Over the coming years, much more will be expected from the field. As we move past the "early adopters" in Rogers' diffusion of innovations' curve through the "early majority" and into the "late majority," there will be a crucial need for new research methodologies and clinical applications that have been rigorously demonstrated to work (i.e., to improve health outcomes) in multiple settings with different types of patients and clinicians.

Protecting genomic sequence anonymity with generalization lattices.

OBJECTIVES: Current genomic privacy technologies assume the identity of genomic sequence data is protected if personal information, such as demographics, are obscured, removed, or encrypted. While demographic features can directly compromise an individual's identity, recent research demonstrates such protections are insufficient because sequence data itself is susceptible to re-identification. To counteract this problem, we introduce an algorithm for anonymizing a collection of person-specific DNA sequences. METHODS: The technique is termed DNA lattice anonymization (DNALA), and is based upon the formal privacy protection schema of k -anonymity. Under this model, it is impossible to observe or learn features that distinguish one genetic sequence from k-1 other entries in a collection. To maximize information retained in protected sequences, we incorporate a concept generalization lattice to learn the distance between two residues in a single nucleotide region. The lattice provides the most similar generalized concept for two residues (e.g. adenine and guanine are both purines). RESULTS: The method is tested and evaluated with several publicly available human population datasets ranging in size from 30 to 400 sequences. Our findings imply the anonymization schema is feasible for the protection of sequences privacy. CONCLUSIONS: The DNALA method is the first computational disclosure control technique for general DNA sequences. Given the computational nature of the method, guarantees of anonymity can be formally proven. There is room for improvement and validation, though this research provides the groundwork from which future researchers can construct genomics anonymization schemas tailored to specific datasharing scenarios.

Inferring genotype from clinical phenotype through a knowledge based algorithm.

Genomic information is becoming increasingly useful for studying the origins of disease. Recent studies have focused on discovering new genetic loci and the influence of these loci upon disease. However, it is equally desirable to go in the opposite direction--that is, to infer genotype from the clinical phenotype for increased efficiency of treatment. This paper proposes a methodology for such inference. Our method constructs a simple knowledge-based model without the need of a domain expert and is useful in situations that have very little data and/or no training data. The model relates a disease's symptoms to particular clinical states of the disease. Clinical information is processed using the model, where appropriate weighting of the symptoms is learned from observed diagnoses to subsequently identify the state of the disease presented in hospital visits. This approach applies to any simple genetic disorder that has defined clinical phenotypes. We demonstrate the use of our methods by inferring age of onset and DNA mutations for Huntington's disease patients.