Running decision support logic retrospectively to determine guideline adherence: a case study with diabetes

PosterWe describe the application of our previously developed analytical infrastructure called RetroGuide to conduct studies on retrospective data using a clinician friendly flowchart paradigm. Introduction Requiring additional clinician?s input in a new decision support system (DSS) is often a major implementation obstacle. Another limitation is the process of fine tuning the exact logic of the new DSS, which is often done in the production environment after piloting an initial version and gathering clinicians? feedback. Our approach was to utilize only currently available Electronic Health Record data (EHR) and beta test the logic and impact of a potential decision support module using only retrospective data. The main objective of this study is to demonstrate the use of a suite of medical informatics tools called RetroGuide [1] for such an effort. As a case study, the problem of blood pressure control in diabetic patients and adherence to the JNC7 hypertension guideline is presented. Methods Intermountain Healthcare?s Enterprise Data Warehouse (EDW) was used as the source of coded lifetime EHRs. RetroGuide was used to create graphical scenarios representing a temporal sequence of events of interest associated with analytical questions. RetroGuide uses a workflow engine to execute the modeled scenario on real retrospective data and produces a series of reports about the scenario execution. Type II diabetic patients with at least 2 available blood pressure measurements and some additional criteria were included in the study (194 subjects total). Results A simplified version of the executable flowchart is shown in Figure 1. The scenario logic included some temporal restrictions on the considered blood pressure values as shown in Figure 1. The variables inside brackets show that the engine can remember the timestamp or value associated with a previously identified event. According to the JNC7 guideline the systolic blood pressure value should always be maintained less than 140 mmHg. RetroGuide output showed that 15.9% of study patients had both values over 140 (31/194; 95% CI 10.4 - 21.4). We investigated 27 additional related clinical questions in a more complex scenario. Discussion: In contrast with a comparable study also investigating blood pressure control in diabetics [2] our methodology enables easy integration of additional temporal restrictions on the considered blood pressure values or other events of interest. Additional advantages of RetroGuide when compared to traditional SQL-based database tools are: (a) a user-friendly flowchart model as a shared logic formalism between the DSS developer and clinicians; and (b) support for extensive ?drill-down? capability into available EHR data via a hierarchy of customizable reports. We have also initiated a controlled study to evaluate the suitability of RetroGuide?s methodology to lower the skill barrier for ?champion? clinicians to run analyses on data in large EDWs. The value of our graphical tool will increase as more detail is stored in EHR, longer time-span is available and its temporal aspects are better

modeling of HEDIS quality measures and prototyping of related decision support rules

PosterWe describe the application of the RetroGuide analytical toolset to quality improvement in osteoporosis and cholesterol management. Our graphical executable scenarios enable user-friendly modeling of temporal processes and retrospective prototyping of decision support on real EHR data. The graphical format is well understood by clinicians and improves the analyst-clinician collaboration

Use of XML Technology for E-resources Management within a Healthcare Enterprise

PosterProvision of access to electronic resources to clinicians is becoming increasingly important. We have created a framework for librarians to manage access to e-resources at enterprise level rather than separately at individual hospital libraries. We present our initial project requirements, implementation details and preliminary results

Chapter 5: RetroGuide Evaluation

Journal ArticleThis chapter presents a RetroGuide (RG) evaluation study which was conducted to assess the flowchart-based modeling approach. This study complements the previously presented case studies in an overall effort to evaluate the RG project. The evaluation was targeted at informatics users with small to moderate analytical experience. Due to RG's key developmental goal to lower the technological barriers for novice users to analyze data stored in an Enterprise Data Warehouse (EDW), this specific target group was deemed most appropriate for the evaluation. A literature review was conducted, looking at how projects similar in nature to RG were evaluated and what study designs and potential measures were used. This review is described in section 5.2. The rest of the chapter describes the design, methodology, results, and discussion of the RG evaluation. This resource structure validation study compared RG with SQL-based tools using a sample of nonexpert users. Using the R G approach, the subjects achieved significantly higher scores in solving analytical tasks, and also scored higher in tasks which required understanding of given analytical solutions. The study demonstrated that most users preferred RG to SQL because RG was easier to learn, it better supported temporal tasks, and it seemed to be a more logical modeling paradigm. Using UTAUT technology acceptance prediction model, the study results suggest that a fully developed, RG-like technology likely would be well accepted by users

Implementation of workflow engine technology to deliver basic clinical decision support functionality

BACKGROUND: Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. RESULTS: We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. CONCLUSIONS: We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform

Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM)

AbstractEfficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM’s initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements.Using a case study approach, we evaluated ODM’s ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard

Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison

Large healthcare datasets of Electronic Health Record data became indispensable in clinical research. Data quality in such datasets recently became a focus of many distributed research networks. Despite the fact that data quality is specific to a given research question, many existing data quality platform prove that general data quality assessment on dataset level (given a spectrum of research questions) is possible and highly requested by researchers. We present comparison of 12 datasets and extension of Achilles Heel data quality software tool with new rules and data characterization measures

The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally