Management of chronic disease is performed inadequately in the United States in spite of the availability of beneficial, effective therapies. Successful programs to manage patients with these diseases must overcome multiple challenges, including the recognized fragmentation and complexity of the healthcare system, misaligned incentives, a focus on acute problems, and a lack of team-based care. In many successful programs, care is provided in settings or episodes that focus on a single disease. While these programs may allow for streamlined, focused provision of care, comprehensive care for multiple diseases may be more difficult. At Intermountain Healthcare (Intermountain), a generalist model of chronic disease management was formulated to overcome the limitations associated with specialization. In the Intermountain approach, which reflects elements of the Chronic Care Model (CCM), care managers located within multipayer primary care clinics collaborate with physicians, patients, and other members of a primary care team to improve patient outcomes for a variety of conditions. An important part of the intervention is widespread use of an electronic health record (EHR). This EHR provides flexible access to clinical data, individualized decision support designed to encourage best practice for patients with a variety of diseases (including co-occurring ones), and convenient communication between providers. This generalized model is used to treat diverse patients with disparate and coexisting chronic conditions. Early results from the application of this model show improved patient outcomes and improved physician productivity. Success factors, challenges, and obstacles in implementing the model are discussed.

The results support the hypothesis that topic links are more efficient than nonspecific links regarding the time seeking for information. It is unclear whether the statistical difference demonstrated will result in a clinically significant impact. However, the overall results confirm previous evidence that infobuttons are effective at helping clinicians to answer questions at the point of care and demonstrate a modest incremental change in the efficiency of information delivery for routine users of this tool.

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Authors evaluated the impact of computerized alerts on the quality of outpatient laboratory monitoring for transplant patients. For 356 outpatient liver transplant patients managed at LDS Hospital, Salt Lake City, this observational study compared traditional laboratory result reporting, using faxes and printouts, to computerized alerts implemented in 2004. Study alerts within the electronic health record notified clinicians of new results and overdue new orders for creatinine tests and immunosuppression drug levels. After implementing alerts, completeness of reporting increased from 66 to >99 %, as did positive predictive value that a report included new information (from 46 to >99 %). Timeliness of reporting and clinicians' responses improved after implementing alerts (p <0.001): median times for clinicians to receive and complete actions decreased to 9 hours from 33 hours using the prior traditional reporting system. Computerized alerts led to more efficient, complete, and timely management of laboratory information.

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The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a naïve Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.

Provision of query systems which are intuitive for non-experts has been recognized as an important informatics challenge. We developed a prototype of a flowchart-based analytical framework called RetroGuide that enables non-experts to formulate query tasks using a step-based, patient-centered paradigm inspired by workflow technology. We present results of the evaluation of RetroGuide in comparison to Structured Query Language (SQL) in laboratory settings using a mixed method design. We asked 18 human subjects with limited database experience to solve query tasks in RetroGuide and SQL, and quantitatively compared their test scores. A follow-up questionnaire was designed to compare both technologies qualitatively and investigate RetroGuide technology acceptance. The quantitative comparison of test scores showed that the study subjects achieved significantly higher scores using the RetroGuide technology. Qualitative study results indicated that 94% of subjects preferred RetroGuide to SQL because RetroGuide was easier to learn, it better supported temporal tasks, and it seemed to be a more logical modeling paradigm. Additional qualitative evaluation results, based on a technology acceptance model, suggested that a fully developed RetroGuide-like technology would be well accepted by users. Our study is an example of a structure validation study of a prototype query system, results of which provided significant guidance in further development of a novel query paradigm for EHR data. We discuss the strengths and weakness of our study design and results, and their implication for future evaluations of query systems in general.

Our results and methodology can guide the broader medical and informatics communities by informing what and how to continuously monitor EHR impact on quality, productivity, and safety.