Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care.

We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which - dose - was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.

Genomics and electronic health record systems.

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

A study of clinically related open source software projects.

Open source software development has recently gained significant interest due to several successful mainstream open source projects. This methodology has been proposed as being similarly viable and beneficial in the clinical application domain as well. However, the clinical software development venue differs significantly from the mainstream software venue. Existing clinical open source projects have not been well characterized nor formally studied so the 'fit' of open source in this domain is largely unknown. In order to better understand the open source movement in the clinical application domain, we undertook a study of existing open source clinical projects. In this study we sought to characterize and classify existing clinical open source projects and to determine metrics for their viability. This study revealed several findings which we believe could guide the healthcare community in its quest for successful open source clinical software projects.

Validation: the new challenge for pathology.

Modern pathologists have been challenged to "validate" mouse models of human cancer. Validation requires matching of morphological attributes of the model to human disease. Computers can assist in the validation process. However, adequate controlled, computer-readable vocabularies that can match terms do not currently exist in mouse pathology. Further, current standard diagnostic terminologies do not include the new concepts discussed here such as pathway pathology and mammary intraepithelial neoplasia. The terminologies must be revised and improved to meet the challenge. Human medicine has traditionally used "guilt-by-association" to validate interpretations of disease. Experimental pathology uses experimental verification exemplified by "test-by-transplantation." Genetically Engineered Mice (GEM) develop unique tumor phenotypes bringing new structural-functional insights and reevaluation of concepts. Novel GEM-related tumors appear in all organ systems but mouse models of human breast cancer are prototypes. For example, mammary tumors induced by Mouse Mammary Tumor Virus (MMTV), chemical, radiation or other carcinogenic stimuli have limited phenotypes. These "spontaneous" or induced mammary tumors have never resembled human breast cancers. GEM tumors created with genes associated with human cancer are strikingly different. GEM tumors have unique histological phenotypes. Depending on the genes, the tumors may: 1) resemble MMTV-induced tumors, 2) display "signature" phenotypes, and 3) mimic human breast cancers. The phenotypes can be placed into structural and functional clusters with shared characteristics leading to the concepts of Pathway Pathology: tumor phenotype reflects the genotype.

Implementing a mission-based reporting system at an academic health center: a method for mission enhancement.

PURPOSE: To describe the utility of school-wide use of mission-based reporting (MBR) for medical school deans and department chairs. METHOD: All faculty members in the University of California, Davis, School of Medicine reported their clinical, creative, teaching, and service activities for 2000-2001 to the MBR system. The authors report on school-wide and department MBR profiles, and profiles by rank and academic series. They validate MBR by comparing individual results with actual merit actions reviewed independently by the school's academic personnel committee. RESULTS: A total of 419 faculty members (85%) completed their MBR reports. The average faculty member spent considerably more than 50 hours per week fulfilling the missions of the school, and full professors and faculty members in academic series supported by state funds were the most productive in investigative and creative work. The teaching load was shared equally by all the academic ranks, although the clinician-scholars taught more than did faculty members in the other series. There was an inverse relationship between clinical load and academic rank, with the majority of the clinical work performed by junior faculty members. MBR results compared favorably with the merit review process, although MBR is not expected to replace the traditional peer review system. CONCLUSION: The creation of these graphic profiles and summaries is a valuable feature of MBR that would not have been possible without such quantitative data. The profiles allow monitoring to ensure that workload conforms to established objectives for individuals, departments, academic ranks and series. Finally, the authors discuss future directions for their MBR system.

jTerm: an open source terminology server.

The consistent semantic transmission of clinical data between systems, and from system to user, is a key requirement for clinical information systems. The use of terminology servers to provide this functionality has been well articulated (1, 2). We describe here an open source terminology server for use in clinical infrastructures.