Clinical evaluation of computer-based respiratory care algorithms

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Human Factors in Critical Care Medical Environments

The purpose of this chapter on human factors in critical care medical environments is to provide a systematic review of the human factors and ergonomics contributions that led to significant improvements in patient safety over the last five decades. The review will focus on issues that contributed to patient injury and fatalities and how human factors and ergonomics can improve performance of providers in critical care. Given the complexity of critical care delivery, a review needs to cover a wide range of subjects. In this review, I take a sociotechnical systems perspective on critical care and discuss the people, their technical and nontechnical skills, the importance of teamwork, technology, and ergonomics in this complex environment. After a description of the importance of a safety climate, the chapter will conclude with a summary on how human factors and ergonomics can improve quality in critical care delivery.

Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review

Background

Greater use of computerized decision support (DS) systems could address continuing safety and quality problems in healthcare, but the healthcare field has struggled to implement DS technology. This study surveys DS experience across multiple non-healthcare disciplines for new insights that are generalizable to healthcare provider decisions. In particular, it sought design principles and lessons learned from the other disciplines that could inform efforts to accelerate the adoption of clinical decision support (CDS).

Methods

Our systematic review drew broadly from non-healthcare databases in the basic sciences, social sciences, humanities, engineering, business, and defense: PsychINFO, BusinessSource Premier, Social Sciences Abstracts, Web of Science, and Defense Technical Information Center. Because our interest was in DS that could apply to clinical decisions, we selected articles that (1) provided a review, overview, discussion of lessons learned, or an evaluation of design or implementation aspects of DS within a non-healthcare discipline and (2) involved an element of human judgment at the individual level, as opposed to decisions that can be fully automated or that are made at the organizational level.

Results

Clinical decisions share some similarities with decisions made by military commanders, business managers, and other leaders: they involve assessing new situations and choosing courses of action with major consequences, under time pressure, and with incomplete information. We identified seven high-level DS system design features from the non-healthcare literature that could be applied to CDS: providing broad, system-level perspectives; customizing interfaces to specific users and roles; making the DS reasoning transparent; presenting data effectively; generating multiple scenarios covering disparate outcomes (e.g., effective; effective with side effects; ineffective); allowing for contingent adaptations; and facilitating collaboration. The article provides examples of each feature. The DS literature also emphasizes the importance of organizational culture and training in implementation success. The literature contrasts “rational-analytic” vs. “naturalistic-intuitive” decision-making styles, but the best approach is often a balanced approach that combines both styles. It is also important for DS systems to enable exploration of multiple assumptions, and incorporation of new information in response to changing circumstances.

Conclusions

Complex, high-level decision-making has common features across disciplines as seemingly disparate as defense, business, and healthcare. National efforts to advance the health information technology agenda through broader CDS adoption could benefit by applying the DS principles identified in this review.

A model-based decision support system for critiquing mechanical ventilation treatments

A computerized system for critiquing mechanical ventilation treatments is presented that can be used as an aide to the intensivist. The presented system is based on the physiological model of the subject's respiratory system. It uses modified versions of previously developed models of adult and neonatal respiratory systems to simulate the effects of different ventilator treatments on the patient's blood gases. The physiological models that have been used for research and teaching purposes by many researchers in the field include lungs, body tissue, and the brain tissue. The lung volume is continuously time-varying and the effects of shunt in the lung, changes in cardiac output and cerebral blood flow, and the arterial transport delays are included in the system. Evaluation tests were done on adult and neonate patients with different diagnoses. In both groups combined, the differences between the arterial partial pressures of CO(2) predicted by the system and the experimental values were 1.86 ± 1.6 mmHg (mean ± SD), and the differences between the predicted arterial hemoglobin oxygen saturation values, S(aO2), and the experimental values measured by using pulse oximetry, S(pO2), were 0.032 ± 0.02 (mean ± SD). The proposed system has the potential to be used alone or in combination with other decision support systems to set ventilation parameters and optimize treatment for patients on mechanical ventilation.

Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems

BACKGROUND

Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems.

OBJECTIVE

To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs.

STUDY DESIGN AND METHODS

We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4).

RESULTS

Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common.

CONCLUSION

We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.

Critiquing treatment and setting ventilatory parameters by using physiological modeling

A modeling system is presented that can be used to predict the effects of ventilatory settings on the blood gases of patients on mechanical ventilation. The system uses a physiological model of the patient that includes lungs, body tissue, and brain tissue compartments. The model includes the effects of changes in the cardiac output and cerebral blood flow and lung mechanical factors. The system has applications in critiquing different treatment options and can be used alone or in combination with decision support systems to set ventilatory parameters and optimize treatment for patients on mechanical ventilation.

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.

Evaluation of a knowledge-based decision-support system for ventilator therapy management

Evaluation of knowledge-based systems differs from that of conventional systems in terms of verification and validation techniques. Furthermore, evaluating medical decision-support systems is difficult because the field is thus far comparatively unexplored. This paper presents an evaluation of a medical knowledge-based system called VentEx that supports decision-making in the management of ventilator therapy. Real patient data from 1300 hours of patient care involving 12 patients with 6 diagnoses are used to validate the knowledge base. The results range from 4.5% to 15.6% disagreement between the setting recommendations produced by VentEx and a gold standard, and 22.2% disagreement for recommendations for weaning. A comparison between the standard and two physicians showed that VentEx produced advice of the same quality as the physicians.

Intelligent systems in patient monitoring and therapy management. A survey of research projects

Although today's advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel. In addition, storage, reduction, retrieval, processing, and presentation of information are significant challenges. These problems are most severe in critical care environments such as intensive care units (ICUs) and operating room (ORs) where many events are life-threatening and thus require immediate attention and the execution of definitive corrective actions. This article focuses on intelligent monitoring and control (IMC), or the use of artificial intelligence (AI) techniques to alleviate some of the common information management problems encountered in health-care environments. This article presents the findings of a survey of over 30 IMC projects. A major finding of the survey is that although significant advances have been made in introducing AI technology in critical care, successful examples of fielded systems are still few and far between. Widespread acceptance of these systems in critical care environments depends on a number of factors, including fruitful collaborations between clinicians and computer scientists, emphasis on evaluation studies, and easy access to clinical information.

Computer-based physician order entry: the state of the art

Direct computer-based physician order entry has been the subject of debate for over 20 years. Many sites have implemented systems successfully. Others have failed outright or flirted with disaster, incurring substantial delays, cost overruns, and threatened work actions. The rationale for physician order entry includes process improvement, support of cost-conscious decision making, clinical decision support, and optimization of physicians' time. Barriers to physician order entry result from the changes required in practice patterns, roles within the care team, teaching patterns, and institutional policies. Key ingredients for successful implementation include: the system must be fast and easy to use, the user interface must behave consistently in all situations, the institution must have broad and committed involvement and direction by clinicians prior to implementation, the top leadership of the organization must be committed to the project, and a group of problem solvers and users must meet regularly to work out procedural issues. This article reviews the peer-reviewed scientific literature to present the current state of the art of computer-based physician order entry.

Clonal hybrids of the common laboratory fish Fundulus heteroclitus

All-female hybrids of the killifishes Fundulus heteroclitus and Fundulus diaphanus, known from two sites in Nova Scotia, Canada, are shown to reproduce clonally. Isozyme analysis of crosses between female hybrids and male F. heteroclitus reveals that their progeny are genetically identical and show no evidence of recombination or paternal inheritance. Flow cytometric measurement of DNA content shows the hybrids to be diploid, with DNA values intermediate to those of the parental species. Because they are related to F. heteroclitus, a fish used widely as a model organism in experimental biology, the clonal hybrids are potentially valuable for experimental studies requiring subjects with a constant genetic background. In addition, the discovery of unisexuality and cloning in a fish whose reproductive physiology and development are so well characterized provides a unique opportunity to examine the underlying causes of clonal reproduction in vertebrates.

INFORM: development of information management and decision support systems for High Dependency Environments

The long-term aim in the INFORM Project is to develop, evaluate and implement a new generation of Information Systems for hospital High Dependency Environments (HDE-Intensive Care Units, Neonatal Units, Burns Units. Operating and Recovery Rooms, and other specialised areas). The distinguishing feature of the HDE is the very large amount of data that is collected through monitors and paper records about the state of critically ill patients; this has made the role of the staff a technical one in addition to a caring one. The INFORM System will integrate Decision Support with on-line, off-line and observed patient data and, in addition, will incorporate and integrate unit management features. In the Exploratory Phase of the Project, functional requirements have been set out. These are based on four components: conceptual model of the HDE; evaluation of existing HDE Information Systems; development of a novel software architecture using a Knowledge-Based Systems (KBS) methodology, and based on a critical review of KBS applied to the HDE: monitoring of appropriate leading-edge technological developments. The conceptual model has two components: a patient-related information model, and a department-related cost model. The patient-related model is identifying key and difficult areas of decision making. A key aspect of INFORM is integration of clinical Decision Support for these areas into the Information System through a layered software architecture. The lower layers are concerned with monitoring and alarming and the higher levels with patient assessment and therapy planning. The functionality and interconnection of these layers are being determined.

Medical informatics in the intensive care unit: state of the art 1991

Intensive care medicine requires timely, accurate, and integrated patient records to provide the highest quality patient care. Computerized patient records offer the best method to achieve these needs. The expectations of society for medical progress through increased use of computers is growing. For optimal use of computers in the ICU there must be a harmonious collaboration between medical informaticists, physicians, nurses, therapists, and administrators. The future use of computers in ICU care will be evolutionary rather than revolutionary. We are on the frontier of some exciting times in the next decade as computers become commonplace in the clinical care process rather than an unusual event. This paper discusses the progress and challenges of computers in the ICU.