In many instances disease diagnosis is more of an art than a science due to the complexity of disease, lack of detailed information on parameters that are indicative of the disease, and lack of sufficient data to apply these parameters to both diagnosis and treatment. Broad-based expansion of electronic health records (EHRs) will produce additional data for improved model development. However many obstacles remain. Patient record content is not broadly available because of privacy concerns and the lack of standardization of EHR formats. If available on a large scale, de-identified medical records can provide a basis for development of disease models by removing privacy concerns. Once comprehensive disease models have been developed that assist in identifying possible diseases and also include parameters that were utilized along with their relative importance, automated analytic methods can be used to indicate the likelihood of the presence of specific diseases. Although the physician will always remain as the final expert, these methods can provide an expanded information set and provide analysis that is too complex for standard methods.

Attempts to automate the medical decision making process have been underway for the at least fifty years, beginning with data-based approaches that relied chiefly on statistically-based methods. Approaches expanded to include knowledge-based systems, both linear and non-linear neural networks, agent-based systems, and hybrid methods. While some of these models produced excellent results none have been used extensively in medical practice. In order to move these methods forward into practical use, a number of obstacles must be overcome, including validation of existing systems on large data sets, development of methods for including new knowledge as it becomes available, construction of a broad range of decision models, and development of non-intrusive methods that allow the physician to use these decision aids in conjunction with, not instead of, his or her own medical knowledge. None of these four requirements will come easily. A cooperative effort among researchers, including practicing MDs, is vital, particularly as more information on diseases and their contributing factors continues to expand resulting in more parameters than the human decision maker can process effectively. In this article some of the basic structures that are necessary to facilitate the use of an automated decision support system are discussed, along with potential methods for overcoming existing barriers.

The percentage of the population who are elderly will significantly increase over the next decade due to the aging of the baby-boom generation, putting additional stress on healthcare. The elderly are at higher risk for many disorders, especially cardiac-related problems and diminishing mental capacity. The impact will be felt in various domains, including additional pressures on existing infrastructure and increasing per capita costs for medical care. A potential approach to alleviate these problems is the implementation of home healthcare using new technologies. With remote interventions, the patient can remain at home, not only reducing costs but also benefiting from a familiar environment and support of family members. Methods are outlined that can contribute to the delivery of home healthcare in the areas of monitoring and support using intelligent agent methodologies. The methods are illustrated in two distinct applications: support and intervention for patients with dementia and remote monitoring of cardiac conditions. The methods outlined can also be adapted for use in other areas.

New technologies in medicine have led to an explosion in the number of parameters that must be considered when diagnosing and treating a patient. Because of this high volume of data it is not possible for the human decision maker to take all information into account in arriving at a decision. Automated methods are needed to effectively evaluate electronic information in many formats and provide summaries to the medical professional. The task is complicated by the complexity of the data and the potential uncertainty of some of the results. In this article complexity and uncertainty in medical data are discussed in terms of both representation and types of analysis. Methods that can address multiple complex data types are illustrated and examples are provided for specific medical problems. These methods are particularly important for automated trend analysis in the personal health record as small errors can be propagated through the complex system resulting in incorrect diagnosis and treatment.

Author(s): Wynden, Rob A; Hudson, Donna L. | Abstract: Within the CTSA (Clinical Translational Sciences Awards) program academic medical centers are tasked with the storage of clinical laboratory data within an Integrated Data Repository (IDR) and the subsequent exposure of that data over grid computing environments for hypothesis generation and cohort selection. Lab data that is collected from multiple machines over long periods of time from many labs and across multiple institutions requires normalization before data sets can be aggregated and compared. However, lab data normalization is difficult when published reference intervals are not always reliable and when the lab data collected is not always normally distributed. This paper sets forth a proposed solution to the challenge of generating derived aggregated normalized views from large, distributed data sets of clinical lab data intended for re-use within clinical translational research.

Many changes have taken place in medicine over the last century. In the first-half of the 20th century physicians were faced with the challenge of making diagnoses with too little information, often resorting to exploratory surgery to confirm the presence or absence of a condition. Due to rapid technological advances during the second-half of the 20th century, and continuing to this day, the position of the physician has now shifted from an information-poor environment to an environment with too much information, often exceeding the limits of human decision-making capabilities. To take full advantage of all available information, a new approach based on refined automated decision support methods is needed to assist the physician in the decision-making process. Medical decision support systems need to evolve from stand-alone systems to cooperative systems in which the physician becomes the decision maker, but relies on the decision support system to sift through information to determine relevant trends. In this paper, a decision support system that combines a number of methodologies for trend analysis is described, along with examples in cardiology. The methods have also been used in applications in neurology as well as cancer diagnosis and prognosis.

Traditional medical decision support systems have not met with widespread use in clinical practice. Issues that have prevented clinical adoption include narrow domain focus, models which are not sufficiently deep, and lack of trust in the system's reliability. While the methodologies may be sound, the acquisition of sufficient medical domain knowledge is often the drawback. In this work, basic methodologies that have been applied in independent decision support systems are modified to function in a new domain in which computer-assisted decision making is focused on analysis of the electronic patient record with the goal of notifying the physician if significant changes have occurred in the state of the patient. The objective is not simply to report individual values that have fallen outside of normal ranges, but to examine the patient record using in-depth diagnostic models and provide three levels of alerts when significant changes occur.