A framework for analyzing data from the electronic health record: verbal orders as a case in point.

BACKGROUND: Investment in health care information technology is resulting in a large amount of data electronically captured during patient care. These databases offer the opportunity to implement ongoing monitoring and analysis of processes with important patient care quality and safety implications to an extent that was previously not feasible with paper-based records. Thus, there is a growing need for analytic frameworks to efficiently support both ongoing monitoring and as-needed periodic detailed analyses to explore particular issues. One patient care process-the use of verbal orders-is used as a case in point to present a framework for analyzing data pulled from electronic health record (EHR) and computerized provider order entry systems. METHODS: Longitudinal and cross-sectional data on verbal orders (VOs) were analyzed at University of Missouri Health Care, Columbia, an academic medical center composed of five specialty hospitals and other care settings. RESULTS: A variety of verbal order analyses were conducted, addressing longitudinal-order patterns, provider-specific patterns, order content and urgency, associated computer-generated alerts, and compliance with institutional policy of a provider cosignature within 48 hours. For example, at the individual prescriber level, in July 2011 there were 14 physicians with 50 or more VOs, with the top 2 having 253 and 233 individual VOs, respectively. CONCLUSIONS: Taking advantage of the automatic data-capture features associated with health information technologies now being incorporated into clinical work flows offers new opportunities to expand the ability to analyze care processes. Health care organizations can now study and statistically model, understand, and improve complex patient care processes.

Improvement in survival and muscle function in an mdx/utrn(-/-) double mutant mouse using a human retinal dystrophin transgene.

Duchenne muscular dystrophy is a progressive muscle disease characterized by increasing muscle weakness and death by the third decade. mdx mice exhibit the underlying muscle disease but appear physically normal with ordinary lifespans, possibly due to compensatory expression of utrophin. In contrast, double mutant mice (mdx/utrn(-/-)), deficient for both dystrophin and utrophin die by approximately 3 months and suffer from severe muscle weakness, growth retardation, and severe spinal curvature. The capacity of human retinal dystrophin (Dp260) to compensate for the missing 427 kDa muscle dystrophin was tested in mdx/utrn(-/-) mice. Functional outcomes were assessed by histology, EMG, MRI, mobility, weight and longevity. MCK-driven transgenic expression of Dp260 in mdx/utrn(-/-) mice converts their disease course from a severe, lethal muscular dystrophy to a viable, mild myopathic phenotype. This finding is relevant to the design of exon-skipping therapeutic strategies since Dp260 lacks dystrophin exons 1-29.

Why is it difficult to implement e-health initiatives? A qualitative study.

BACKGROUND: The use of information and communication technologies in healthcare is seen as essential for high quality and cost-effective healthcare. However, implementation of e-health initiatives has often been problematic, with many failing to demonstrate predicted benefits. This study aimed to explore and understand the experiences of implementers -- the senior managers and other staff charged with implementing e-health initiatives and their assessment of factors which promote or inhibit the successful implementation, embedding, and integration of e-health initiatives. METHODS: We used a case study methodology, using semi-structured interviews with implementers for data collection. Case studies were selected to provide a range of healthcare contexts (primary, secondary, community care), e-health initiatives, and degrees of normalization. The initiatives studied were Picture Archiving and Communication System (PACS) in secondary care, a Community Nurse Information System (CNIS) in community care, and Choose and Book (C&B) across the primary-secondary care interface. Implementers were selected to provide a range of seniority, including chief executive officers, middle managers, and staff with 'on the ground' experience. Interview data were analyzed using a framework derived from Normalization Process Theory (NPT). RESULTS: Twenty-three interviews were completed across the three case studies. There were wide differences in experiences of implementation and embedding across these case studies; these differences were well explained by collective action components of NPT. New technology was most likely to 'normalize' where implementers perceived that it had a positive impact on interactions between professionals and patients and between different professional groups, and fit well with the organisational goals and skill sets of existing staff. However, where implementers perceived problems in one or more of these areas, they also perceived a lower level of normalization. CONCLUSIONS: Implementers had rich understandings of barriers and facilitators to successful implementation of e-health initiatives, and their views should continue to be sought in future research. NPT can be used to explain observed variations in implementation processes, and may be useful in drawing planners' attention to potential problems with a view to addressing them during implementation planning.