Decision support and disease management: a logic engineering approach

Clinical Decision Support Systems (CDSS) for preventive management of COPD patients

BACKGROUND

The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway.

OBJECTIVES

The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow.

METHODS

The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team.

RESULTS

A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms.

CONCLUSIONS

Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.

The five Ws for information visualization with application to healthcare informatics

The Five Ws is a popular concept for information gathering in journalistic reporting. It captures all aspects of a story or incidence: who, when, what, where, and why. We propose a framework composed of a suite of cooperating visual information displays to represent the Five Ws and demonstrate its use within a healthcare informatics application. Here, the who is the patient, the where is the patient's body, and the when, what, why is a reasoning chain which can be interactively sorted and brushed. The patient is represented as a radial sunburst visualization integrated with a stylized body map. This display captures all health conditions of the past and present to serve as a quick overview to the interrogating physician. The reasoning chain is represented as a multistage flow chart, composed of date, symptom, data, diagnosis, treatment, and outcome. Our system seeks to improve the usability of information captured in the electronic medical record (EMR) and we show via multiple examples that our framework can significantly lower the time and effort needed to access the medical patient information required to arrive at a diagnostic conclusion.

@neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

Computerized clinical decision support: a technology to implement and validate evidence based guidelines

Faced with a documented crisis of patients not receiving appropriate care, there is a need to implement and refine evidence-based guidelines (EBGs) to ensure that patients receive the best care available. Although valuable in content, among their deficiencies, EBGs do not provide explicit methods to bring proven therapies to the bedside. Computerized information technology, now an integral part of the US healthcare system at all levels, presents clinicians with information from laboratory, imaging, physiologic monitoring systems, and many other sources. It is imperative that we clinicians use this information technology to improve medical care and efficacy of its delivery. If we do not do this, nonclinicians will use this technology to tell us how to practice medicine. Computerized clinical decision support (CCDS) offers a powerful method to use this information and implement a broad range of EBGs. CCDS is a technology that can be used to develop, implement, and refine computerized protocols for specific processes of care derived from EBGs, including complex care provided in intensive care units. We describe this technology as a desirable option for the trauma community to use information technology and maintain the trauma surgeon/intensivist's essential role in specifying and implementing best care for patients. We describe a process of logical protocol development based on standardized clinical decision making to enable EBGs. The resulting logical process is readily computerized, and, when properly implemented, provides a stable platform for systematic review and study of the process and interventions.

CONCLUSION

: CCDS to implement and refine EBG derived computerized protocols offers a method to decrease variability, test interventions, and validate improved quality of care.

Are we measuring the right end-points? Variables that affect the impact of computerised decision support on patient outcomes: a systematic review

Previous reviews of electronic decision-support systems (EDSS) have often treated them as a single category, and factors that may modify their effectiveness of EDSS have not been examined. The objective was to summarise the evidence associating the use of computerised decision support and improved patient outcomes. PubMed/Medline and the Database of Abstracts were searched for randomised controlled trials (RCT) of EDSS from 1 January 1994 to 31 January 2006. Twenty-four RCT studies from 97 reviewed were selected, eight of them examined systems supporting decisions for patients who presented with an acute illness, and 16 studies enrolled patients with chronic conditions. Overall, 13 (54%) of the studies showed a positive result, and 11 (46%) were with no impact. Critiquing and consultative systems showed the impact in 71% and 47% of studies, respectively. All systems targeting decisions related to acute disease improved patient outcomes compared with 38% of systems focused on the management of chronic conditions (P = 0.005). Provision of EDSS improves prescribing practices and treatment outcomes of patients with acute illnesses; however, EDSS were less effective in primary care. Complex interventions as clinical EDSS may require new metrics of assessment to describe the impact on patient outcomes.

Knowledge representation and sharing using visual semantic modeling for diagnostic medical image databases

Information technology offers great opportunities for supporting radiologists' expertise in decision support and training. However, this task is challenging due to difficulties in articulating and modeling visual patterns of abnormalities in a computational way. To address these issues, well established approaches to content management and image retrieval have been studied and applied to assist physicians in diagnoses. Unfortunately, most of the studies lack the flexibility of sharing both explicit and tacit knowledge involved in the decision making process, while adapting to each individual's opinion. In this paper, we propose a knowledge repository and exchange framework for diagnostic image databases called "evolutionary system for semantic exchange of information in collaborative environments" (Essence). This framework uses semantic methods to describe visual abnormalities, and offers a solution for tacit knowledge elicitation and exchange in the medical domain. Also, our approach provides a computational and visual mechanism for associating synonymous semantics of visual abnormalities. We conducted several experiments to demonstrate the system's capability of matching synonym terms, and the benefit of using tacit knowledge in improving the meaningfulness of semantic queries.

Computer support for interpreting family histories of breast and ovarian cancer in primary care: comparative study with simulated cases

OBJECTIVES

To evaluate the potential effect of computer support on general practitioners' management of familial breast and ovarian cancer, and to compare the effectiveness of two different types of computer program.

DESIGN

Crossover experiment with balanced block design.

PARTICIPANTS

Of a random sample of 100 general practitioners from Buckinghamshire who were invited, 41 agreed to participate. From these, 36 were selected for a fully balanced study.

INTERVENTIONS

Doctors managed 18 simulated cases: 6 with computerised decision support system Risk Assessment in Genetics (RAGs), 6 with Cyrillic (an established pedigree drawing program designed for clinical geneticists), and 6 with pen and paper.

MAIN OUTCOME MEASURES

Number of appropriate management decisions made (maximum 6), mean time taken to reach a decision, number of pedigrees accurately drawn (maximum 6). Secondary measures were method of support preferred for particular aspects of managing family histories of cancer; importance of specific information on cancer genetics that might be provided by an "ideal computer program."

RESULTS

RAGs resulted in significantly more appropriate management decisions (median 6) than either Cyrillic (median 3) or pen and paper (median 3); median difference between RAGs and Cyrillic 2.5 (95% confidence interval 2.0 to 3.0; P<0.0001). RAGs also resulted in significantly more accurate pedigrees (median 5) than both Cyrillic (median 3.5) and pen and paper (median 2); median difference between RAGs and Cyrillic 1.5 (1.0 to 2.0; P<0.0001). The time taken to use RAGs (median 178 seconds) was 51 seconds longer per case (95% confidence interval 36 to 65; P<0.0001) than pen and paper (median 124 seconds) but was less than Cyrillic (median 203 seconds; difference 23. (5 to 43; P=0.02)). 33 doctors (92% (78% to 98%)) preferred using RAGs overall. The most important elements of an "ideal computer program" for genetic advice in primary care were referral advice, the capacity to create pedigrees, and provision of evidence and explanations to support advice.

CONCLUSIONS

RAGs could enable general practitioners to be more effective gatekeepers to genetics services, empowering them to reassure the majority of patients with a family history of breast and ovarian cancer who are not at increased genetic risk.

The development and evaluation of CADMIUM: a prototype system to assist in the interpretation of mammograms

We have developed CADMIUM, a novel approach for the design of systems to assist in the interpretation of medical images. CADMIUM uses symbolic reasoning to relate information obtained from image processing to the decisions radiologists take. The approach is based on a symbolic decision procedure which has already been used successfully in a variety of nonimaging clinical decision systems. In CADMIUM this decision procedure is extended with models of three generic image interpretation tasks: detection, measurement and classification of image features. The extended procedure is used to construct the lines of reasoning needed in each task and to control the acquisition of information by image processing. CADMIUM has been evaluated as an aid to the differential diagnosis of microcalcifications on mammographic images. Radiographers who had been trained to interpret images performed better when using the advice provided by the system.