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Evidence-based pharmacotherapy is a central aspect of optimal patient care for many chronic conditions. However, medication non-adherence frequently inhibits the attainment of optimal pharmacotherapy regimens. In this study, we designed, developed, and implemented a multifaceted clinical decision support (CDS) intervention that supports evidence-based pharmacotherapy and enhanced medication adherence through the use of a scalable, claims-driven, and service-oriented approach. The intervention includes a medication management report and a low adherence alert based on thirteen evidence-based pharmacotherapy rules for seven chronic conditions. Reports and alerts are delivered to primary care clinics and care managers that participate in a healthcare information exchange in North Carolina. The resulting system architecture may enable this CDS intervention to be widely disseminated to healthcare networks through an open-source model.

Millions of consumers seek health information on the Internet. Unfortunately, this searching often falls short because of design limitations of many consumer-oriented Web sites. In this paper, we describe an approach that addresses several known barriers to consumer health information seeking. This approach primarily involves maintaining the referential context throughout a consumer's search for information. To maintain referential context, this approach uses multiple levels of hierarchical constructs to organize complex information, and data elements are toggled to minimize the need for scrolling. An information resource based on this approach was implemented for information about smoking using standard Web technologies. The resource was evaluated by 31 diverse consumers through standardized usability instruments. Consumers found the resource to be easy to navigate and to use. We conclude that the approach described in this manuscript could be applied more broadly to facilitate the organization and presentation of consumer health information.

The clinic-based healthcare model does not deliver high quality, cost-effective care to populations of patients. Despite public perception that aggressive investment in information technology will lead to improvements in the safety and quality of healthcare delivery, there is little evidence that health information technology can be used to promote population-based health management. This paper describes the use of a standards-based clinical decision support system to facilitate proactive population health management using data from a regional health information exchange (HIE) network. The initial release of this system was designed to detect ten sentinel health events related to hospitalization, emergency department (ED) utilization, and care coordination in a population of 36,000 individuals. In an analysis of 11,899 continuously enrolled patients from a single county over a six-month period, 2,285 unique patients experienced 7,226 sentinel health events. The most common events were ED utilization for low severity conditions (2,546), two or more missed appointments within a 60-day period (1,728), ED encounters for patients with asthma (1,220), and three or more ED encounters within 90 days (731). Logistic regression analysis identified patients aged 19-64 as the population most likely to have sentinel health events. In addition to presenting data demonstrating the feasibility of population health management in the context of an HIE, this paper also includes lessons learned from the development, implementation, and operational support of the population health management system.

Health information technology evaluators need to distinguish between intervention efficacy as assessed in the ideal circumstances of clinical trials and intervention effectiveness as assessed in the real world circumstances of actual practice. Because current evaluation study designs do not routinely allow for this distinction, we have developed a framework for evaluation of implementation fidelity that considers health information technologies as complex interventions and makes use of common intervention components as defined in the Oxford Implementation Index. We also propose statistical methods for the evaluation of interventions at the system and component level using the Rubin Causal Model. We then describe how to apply this framework to evaluate an ongoing clinical trial of three health information technology interventions currently implemented in a 17,000 patient community-based health network caring for Medicaid beneficiaries in Durham County, North Carolina.

Standards for the economic evaluation of medical technologies were instituted in the mid-1990s, yet little is known about their application in medical information technology studies. In a review of evaluation studies published between 1982 and 2002, we found that the volume and variety of economic evaluations had increased. However, investigators routinely omitted key cost or effectiveness elements in their designs, resulting in publications with incomplete, and potentially biased, economic findings.

Collecting clinical data directly from clinicians is a challenge. Many standard development environments designed to expedite the creation of user interfaces for electronic healthcare applications do not provide acceptable components for satisfying the requirements for collecting and displaying clinical data at the point of care on the tablet computer. Through an iterative design and testing approach using think-aloud sessions in the eye care setting, we were able to identify and resolve several user interface issues. Issues that we discovered and subsequently resolved included checkboxes that were too small to be selectable with a stylus, radio buttons that could not be unselected, and font sizes that were too small to be read at arm's length.

Patient Internet portals that allow patients to access their personal health information are an emerging form of enabling technology. The purported benefits from increasing use of information technology in healthcare, however, may not be universal because of a widening digital divide along racial and socioeconomic lines. In this pilot study, we surveyed 31 Medicaid beneficiaries to ascertain their interest in and projected use of a healthcare patient Internet portal. We found that most Medicaid beneficiaries were very interested in accessing personal health information about themselves or their dependents online. Moreover, ninety percent of respondents reported that they have access to the Internet, and sixty-eight percent of those with Internet access use the Internet once a week or more.

Clinical information collected directly from patients is critical to the practice of medicine. Past efforts to collect this information using computers have had limited utility because these efforts required users to be facile with the information collecting system. This poster describes the development and function of a computer system that uses technology to overcome the limitations of previous computer-based data collection tools by adapting the human-computer interface to fit the skills of the user. The system has been successfully used at two diverse clinical sites.

Clinical practice guidelines (CPGs) have been shown to improve the quality and reduce the cost of health care. Unfortunately, CPGs are underutilized by clinicians in part because they are not available in an easily accessible format at the point of care. To overcome these limitations of availability and accessibility, we have created applications that can present CPGs interactively using handheld devices. These handheld compatible CPG applications were developed by systematically modifying the Java applets generated by our Web-based guideline system.

Computerized medical record systems have met with limited success in the outpatient clinic environment. Given the need for cost-effective collection of clinical outcome data, the desire to gather such data routinely at the point of care is a strong incentive for computerized medical records. Despite this desire for coded data, however, a simultaneous need to capture and preserve individualized clinical narrative still exists. We designed a unique data model that allows the storage and retrieval of both free text and coded data drawn from a user-defined database dictionary.