Janos L. Mathe

Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit*

Objective:To determine whether automated identification with physician notification of the systemic inflammatory response syndrome in medical intensive care unit patients expedites early administration of new antibiotics or improvement of other patient outcomes in patients with sepsis. Design:A prospective randomized, controlled, single center study. Setting:Medical intensive care unit of an academic, tertiary care medical center. Patients:Four hundred forty-two consecutive patients admitted over a 4-month period who met modified systemic inflammatory response syndrome criteria in a medical intensive care unit. Intervention:Patients were randomized to monitoring by an electronic “Listening Application” to detect modified (systemic inflammatory response syndrome) criteria vs. usual care. The listening application notified physicians in real time when modified systemic inflammatory response syndrome criteria were detected, but did not provide management recommendations. Measurements and Main Results:The median time to new antibiotics was similar between the intervention and usual care groups when comparing among all patients (6.0 hr vs. 6.1 hr, p = .95), patients with sepsis (5.3 hr vs. 5.1 hr; p = .90), patients on antibiotics at enrollment (5.2 hr vs. 7.0 hr, p = .27), or patients not on antibiotics at enrollment (5.2 hr vs. 5.1 hr, p = .85). The amount of fluid administered following detection of modified systemic inflammatory response syndrome criteria was similar between groups whether comparing all patients or only patients who were hypotensive at enrollment. Other clinical outcomes including intensive care unit length of stay, hospital length of stay, and mortality were not shown to be different between patients in the intervention and control groups. Conclusions:Realtime alerts of modified systemic inflammatory response syndrome criteria to physicians in one tertiary care medical intensive care unit were feasible and safe but did not influence measured therapeutic interventions for sepsis or significantly alter clinical outcomes.

A Model-Integrated, Guideline-Driven, Clinical Decision-Support System

Using evidence-based guidelines to standardize the care of patients with complex medical problems is a difficult challenge. In acute care settings, such as intensive care units, the inherent problems of stabilizing and improving vital patient parameters is complicated by the division of responsibilities among different healthcare team members. Computerized support for implementing such guidelines has tremendous potential. The use of model-integrated techniques for specifying and implementing guidelines as coordinated asynchronous processes is a promising new methodology for providing advanced clinical decision support. Combined with visual dashboards, which show the status of the implemented guidelines, a new approach to computer-supported care is possible. The Vanderbilt Medical Center is applying these techniques to the management of sepsis.

Model-based design of clinical information systems.

OBJECTIVE The goal of this research is to provide a framework to enable the model-based development, simulation, and deployment of clinical information system prototypes with mechanisms that enforce security and privacy policies. METHODS We developed the Model-Integrated Clinical Information System (MICIS), a software toolkit that is based on model-based design techniques and high-level modeling abstractions to represent complex clinical workflows in a service-oriented architecture paradigm. MICIS translates models into executable constructs, such as web service descriptions, business process execution language procedures, and deployment instructions. MICIS models are enriched with formal security and privacy specifications, which are enforced within the execution environment. RESULTS We successfully validated our design platform by modeling multiple clinical workflows and deploying them onto the execution platform. CONCLUSIONS The model-based approach shows great promise for developing, simulating, and evolving clinical information systems with formal properties and policy restrictions.

Towards the security and privacy analysis of patient portals

Clinical information systems (CIS) significantly influence the quality and efficiency of health care delivery. However, CIS are complex environments that integrate information technologies, human stakeholders, and patient-specific data. Given the sensitivity of patient data, federal regulations require healthcare providers to adopt policy, as well as technology, protections for patient data. Ad hoc system design and implementation of CIS can cause unforeseen and unintended privacy and security breaches. The introduction of model-based design techniques combined with the development of high-level modeling abstractions and analysis methods provide a mechanism to investigate these concerns by conceptually simplifying CIS without losing expressive power. This work introduces the Model-based Design Environment for Clinical Information Systems (MODECIS) - a graphical design environment that assists CIS architects in formalizing CIS systems as well-defined services. MODECIS leverages Service-Oriented Architectures to create realistic system models at an abstract level. By modeling CIS using abstractions, we enable the analysis of legacy architectures, as well as the design and simulation of, future CIS. We present the feasibility of MODECIS via modeling certain functions, such as the authentication process of the MyHealth@Vanderbilt patient portal.

Cancer treatment planning: formal methods to the rescue

This paper describes the ongoing development of ATTENTION, a new kind of clinical decision support system for synthesizing and managing longitudinal treatment plans, such as cancer treatment plans. ATTENTION combines stateof- the-art formal modeling and constraint solving with clinical information systems to synthesize complex cancer treatment plans that are also executable.

Platform-Based Design for Clinical Information Systems

Clinical information systems (CIS) have emerged as a new critical infrastructure that influence affordability and security of health care delivery. Complex and conflicting societal requirements, such as providing control for patients over their personal health information and requiring health organizations to assure the security and privacy of patient-specific information, create significant technical challenges for the design of CIS. This paper presents a novel approach that is based on the principles and tools of model integrated computing (MIC), platform-based design (PBD) and service-oriented architectures (SOA). We present a domain-specific, graphical design environment and show how formal system specifications can be mapped to different service-oriented architecture execution platforms through a set of standard languages, such as WSBPEL and XACML. The model-integrated clinical information systems (MICIS) design environment includes a suite of domain-specific modeling languages capturing essential aspects of CIS design, model transformation tools that map the domain models onto the standard specification languages of SOA platforms and static model analysis tools checking the consistency and wellformedeness of the multiple-view models. The MICIS design tool is tested in modeling the MyHealth@Vanderbilt patient portal of the Vanderbilt University Medical Center.